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"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__A : int = 10
def A_ ( snake_case_ : int ,snake_case_ : int ,snake_case_ : list[int] ,snake_case_ : int ):
'''simple docstring'''
for i in range(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
if array[i] == target:
return i
return -1
def A_ ( snake_case_ : list[int] ,snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : Optional[int] = 0
UpperCamelCase : Tuple = len(SCREAMING_SNAKE_CASE_ )
while left <= right:
if right - left < precision:
return lin_search(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = (left + right) // 3 + 1
UpperCamelCase : Dict = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
UpperCamelCase : Union[str, Any] = one_third - 1
elif array[two_third] < target:
UpperCamelCase : Any = two_third + 1
else:
UpperCamelCase : Optional[Any] = one_third + 1
UpperCamelCase : Any = two_third - 1
else:
return -1
def A_ ( snake_case_ : int ,snake_case_ : int ,snake_case_ : list[int] ,snake_case_ : int ):
'''simple docstring'''
if left < right:
if right - left < precision:
return lin_search(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = (left + right) // 3 + 1
UpperCamelCase : Optional[Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(SCREAMING_SNAKE_CASE_ ,one_third - 1 ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
else:
return rec_ternary_search(one_third + 1 ,two_third - 1 ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
__A : List[Any] = input('''Enter numbers separated by comma:\n''').strip()
__A : int = [int(item.strip()) for item in user_input.split(''',''')]
assert collection == sorted(collection), F"List must be ordered.\n{collection}."
__A : Tuple = int(input('''Enter the number to be found in the list:\n''').strip())
__A : Any = ite_ternary_search(collection, target)
__A : List[Any] = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(F'''Iterative search: {target} found at positions: {resulta}''')
print(F'''Recursive search: {target} found at positions: {resulta}''')
else:
print('''Not found''')
| 363 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A : Optional[Any] = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__A : Any = {'''allegro/herbert-base-cased''': 514}
__A : Optional[Any] = {}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Dict = VOCAB_FILES_NAMES
lowercase : Any = PRETRAINED_VOCAB_FILES_MAP
lowercase : List[str] = PRETRAINED_INIT_CONFIGURATION
lowercase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Union[str, Any] = HerbertTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_="</s>" , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = [self.cls_token_id]
UpperCamelCase : str = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Tuple = [self.sep_token_id]
UpperCamelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Optional[int] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 27 | 0 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
__A : List[str] = logging.getLogger(__name__)
__A : Union[str, Any] = '''pytorch_model.bin'''
@dataclasses.dataclass
class lowerCamelCase :
lowercase : str = dataclasses.field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'} )
lowercase : Optional[str] = dataclasses.field(
default=a__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'} , )
@dataclasses.dataclass
class lowerCamelCase :
lowercase : str = dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'} )
lowercase : str = dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'} )
lowercase : Optional[str] = dataclasses.field(
default=a__ , metadata={'help': 'A csv or a json file containing the validation data.'} )
lowercase : Optional[str] = dataclasses.field(
default=a__ , metadata={'help': 'The name of the task to train on.'} , )
lowercase : Optional[List[str]] = dataclasses.field(
default=a__ , metadata={'help': 'The list of labels for the task.'} )
@dataclasses.dataclass
class lowerCamelCase :
lowercase : str = dataclasses.field(
metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'} )
lowercase : Optional[str] = dataclasses.field(
default='accuracy' , metadata={'help': 'The evaluation metric used for the task.'} )
lowercase : Optional[str] = dataclasses.field(
default='no' , metadata={
'help': 'The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]'
} , )
lowercase : Optional[int] = dataclasses.field(
default=1_0 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
lowercase : Optional[float] = dataclasses.field(
default=0.0 , metadata={
'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
} , )
lowercase : Optional[bool] = dataclasses.field(
default=a__ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'} , )
lowercase : Optional[bool] = dataclasses.field(
default=a__ , metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'} , )
lowercase : Optional[bool] = dataclasses.field(
default=a__ , metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'} , )
lowercase : Optional[float] = dataclasses.field(
default=0.0 , metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'} , )
lowercase : Optional[int] = dataclasses.field(
default=1_0_0 , metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'} , )
lowercase : Optional[int] = dataclasses.field(
default=a__ , metadata={'help': 'Random seed for initialization.'} , )
def A_ ( snake_case_ : Optional[int] ,snake_case_ : Dict ,snake_case_ : Union[str, Any] ,snake_case_ : int ,snake_case_ : Tuple ,snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : int = datasets.concatenate_datasets([infer_input, infer_output] ,axis=1 )
if args.do_filter_by_confidence:
UpperCamelCase : Optional[int] = dataset.filter(lambda snake_case_ : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
UpperCamelCase : List[str] = int(eval_result * len(snake_case__ ) )
print(snake_case__ )
UpperCamelCase : Union[str, Any] = dataset.sort("""probability""" ,reverse=snake_case__ )
UpperCamelCase : int = dataset.select(range(snake_case__ ) )
UpperCamelCase : Any = dataset.remove_columns(["""label""", """probability"""] )
UpperCamelCase : int = dataset.rename_column("""prediction""" ,"""label""" )
UpperCamelCase : Any = dataset.map(lambda snake_case_ : {"label": idalabel[example["label"]]} )
UpperCamelCase : List[Any] = dataset.shuffle(seed=args.seed )
UpperCamelCase : Dict = os.path.join(snake_case__ ,f'train_pseudo.{args.data_file_extension}' )
if args.data_file_extension == "csv":
dataset.to_csv(snake_case__ ,index=snake_case__ )
else:
dataset.to_json(snake_case__ )
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : List[Any] ,snake_case_ : List[str] ,snake_case_ : Tuple ,**snake_case_ : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase : List[str] = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,datefmt="""%m/%d/%Y %H:%M:%S""" ,level=logging.INFO ,)
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
UpperCamelCase : List[str] = STModelArguments(model_name_or_path=snake_case__ )
UpperCamelCase : int = STDataArguments(train_file=snake_case__ ,infer_file=snake_case__ )
UpperCamelCase : Dict = STTrainingArguments(output_dir=snake_case__ )
UpperCamelCase : Optional[Any] = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(snake_case__ ).items():
setattr(snake_case__ ,snake_case__ ,snake_case__ )
for key, value in kwargs.items():
if hasattr(snake_case__ ,snake_case__ ):
setattr(snake_case__ ,snake_case__ ,snake_case__ )
# Sanity checks
UpperCamelCase : int = {}
UpperCamelCase : Optional[int] = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
UpperCamelCase : int = args.train_file
UpperCamelCase : Optional[Any] = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
UpperCamelCase : int = args.eval_file
for key in data_files:
UpperCamelCase : List[str] = data_files[key].split(""".""" )[-1]
assert extension in ["csv", "json"], f'`{key}_file` should be a csv or a json file.'
if args.data_file_extension is None:
UpperCamelCase : List[Any] = extension
else:
assert extension == args.data_file_extension, f'`{key}_file` should be a {args.data_file_extension} file`.'
assert (
args.eval_metric in datasets.list_metrics()
), f'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info("""Creating the initial data directory for self-training...""" )
UpperCamelCase : Dict = f'{args.output_dir}/self-train_iter-{{}}'.format
UpperCamelCase : Any = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir ,exist_ok=snake_case__ )
os.makedirs(snake_case__ ,exist_ok=snake_case__ )
accelerator.wait_for_everyone()
UpperCamelCase : List[Any] = None
UpperCamelCase : Any = None
UpperCamelCase : Tuple = 0
UpperCamelCase : str = False
# Show the progress bar
UpperCamelCase : List[str] = tqdm(range(args.max_selftrain_iterations ) ,disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 ,int(args.max_selftrain_iterations ) ):
UpperCamelCase : List[str] = data_dir_format(snake_case__ )
assert os.path.exists(snake_case__ )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
UpperCamelCase : List[Any] = os.path.join(snake_case__ ,"""stage-1""" )
UpperCamelCase : Optional[int] = {
'accelerator': accelerator,
'model_name_or_path': args.model_name_or_path,
'cache_dir': args.cache_dir,
'do_train': True,
'train_file': data_files['train'] if iteration == 0 else data_files['train_pseudo'],
'do_eval': True if args.eval_file is not None else False,
'eval_file': data_files['eval'],
'do_predict': True,
'infer_file': data_files['infer'],
'task_name': args.task_name,
'label_list': args.label_list,
'output_dir': current_output_dir,
'eval_metric': args.eval_metric,
'evaluation_strategy': args.evaluation_strategy,
'early_stopping_patience': args.early_stopping_patience,
'early_stopping_threshold': args.early_stopping_threshold,
'seed': args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(snake_case__ ,snake_case__ ):
arguments_dict.update({key: value} )
UpperCamelCase : Any = os.path.join(snake_case__ ,"""best-checkpoint""" ,snake_case__ )
if os.path.exists(snake_case__ ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.""" ,snake_case__ ,snake_case__ ,)
else:
logger.info("""***** Running self-training: iteration: %d, stage: 1 *****""" ,snake_case__ )
finetune(**snake_case__ )
accelerator.wait_for_everyone()
assert os.path.exists(snake_case__ )
logger.info("""Self-training job completed: iteration: %d, stage: 1.""" ,snake_case__ )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
UpperCamelCase : Tuple = os.path.join(snake_case__ ,"""best-checkpoint""" )
UpperCamelCase : str = os.path.join(snake_case__ ,"""stage-2""" )
# Update arguments_dict
UpperCamelCase : str = model_path
UpperCamelCase : Tuple = data_files['train']
UpperCamelCase : int = current_output_dir
UpperCamelCase : Dict = os.path.join(snake_case__ ,"""best-checkpoint""" ,snake_case__ )
if os.path.exists(snake_case__ ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.""" ,snake_case__ ,snake_case__ ,)
else:
logger.info("""***** Running self-training: iteration: %d, stage: 2 *****""" ,snake_case__ )
finetune(**snake_case__ )
accelerator.wait_for_everyone()
assert os.path.exists(snake_case__ )
logger.info("""Self-training job completed: iteration: %d, stage: 2.""" ,snake_case__ )
UpperCamelCase : int = iteration
UpperCamelCase : Any = data_dir_format(iteration + 1 )
UpperCamelCase : Optional[int] = AutoConfig.from_pretrained(os.path.join(snake_case__ ,"""best-checkpoint""" ) )
UpperCamelCase : Union[str, Any] = config.idalabel
UpperCamelCase : str = os.path.join(snake_case__ ,"""eval_results_best-checkpoint.json""" )
UpperCamelCase : List[Any] = os.path.join(snake_case__ ,"""test_results_best-checkpoint.json""" )
assert os.path.exists(snake_case__ )
with open(snake_case__ ,"""r""" ) as f:
UpperCamelCase : Tuple = float(json.load(snake_case__ )[args.eval_metric] )
UpperCamelCase : Union[str, Any] = os.path.join(snake_case__ ,"""infer_output_best-checkpoint.csv""" )
assert os.path.exists(snake_case__ )
# Loading the dataset from local csv or json files.
UpperCamelCase : Tuple = load_dataset(args.data_file_extension ,data_files={"""data""": data_files["""infer"""]} )['data']
UpperCamelCase : Optional[Any] = load_dataset("""csv""" ,data_files={"""data""": infer_output_file} )['data']
if accelerator.is_main_process:
os.makedirs(snake_case__ ,exist_ok=snake_case__ )
shutil.copy(snake_case__ ,os.path.join(snake_case__ ,f'eval_results_iter-{iteration}.json' ) )
if os.path.exists(snake_case__ ):
shutil.copy(snake_case__ ,os.path.join(snake_case__ ,f'test_results_iter-{iteration}.json' ) )
create_pseudo_labeled_data(snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ )
accelerator.wait_for_everyone()
UpperCamelCase : Tuple = os.path.join(snake_case__ ,f'train_pseudo.{args.data_file_extension}' )
if args.evaluation_strategy != IntervalStrategy.NO.value:
UpperCamelCase : Optional[Any] = eval_result
if best_iteration is None:
UpperCamelCase : Dict = new_iteration
UpperCamelCase : List[str] = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
UpperCamelCase : Tuple = new_iteration
UpperCamelCase : List[Any] = new_eval_result
UpperCamelCase : str = 0
else:
if new_eval_result == best_eval_result:
UpperCamelCase : Any = new_iteration
UpperCamelCase : Tuple = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
UpperCamelCase : Tuple = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info("""Best iteration: %d""" ,snake_case__ )
logger.info("""Best evaluation result: %s = %f""" ,args.eval_metric ,snake_case__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(snake_case__ ,f'eval_results_iter-{iteration}.json' ) ,os.path.join(snake_case__ ,"""eval_results_best-iteration.json""" ) ,)
else:
# Assume that the last iteration is the best
logger.info("""Best iteration: %d""" ,args.max_selftrain_iterations - 1 )
logger.info("""Best evaluation result: %s = %f""" ,args.eval_metric ,snake_case__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(snake_case__ ,f'eval_results_iter-{args.max_selftrain_iterations - 1}.json' ) ,os.path.join(snake_case__ ,"""eval_results_best-iteration.json""" ) ,)
| 364 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=3.6 ):
UpperCamelCase : Dict = tokenizer
UpperCamelCase : Optional[Any] = tokenizer.bos_token_id
UpperCamelCase : Any = dataset
UpperCamelCase : List[str] = seq_length
UpperCamelCase : Optional[Any] = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
UpperCamelCase : Dict = iter(self.dataset )
UpperCamelCase : Union[str, Any] = True
while more_examples:
UpperCamelCase , UpperCamelCase : Tuple = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(SCREAMING_SNAKE_CASE_ )["""content"""] )
buffer_len += len(buffer[-1] )
except StopIteration:
UpperCamelCase : Dict = False
break
UpperCamelCase : str = tokenizer(SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )["""input_ids"""]
UpperCamelCase : str = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , self.seq_length ):
UpperCamelCase : List[str] = all_token_ids[i : i + self.seq_length]
if len(SCREAMING_SNAKE_CASE_ ) == self.seq_length:
yield torch.tensor(SCREAMING_SNAKE_CASE_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : Dict = {"""streaming""": True}
UpperCamelCase : Optional[int] = load_dataset(args.dataset_name ,split="""train""" ,**snake_case_ )
UpperCamelCase : Optional[int] = ConstantLengthDataset(snake_case_ ,snake_case_ ,seq_length=args.seq_length )
UpperCamelCase : List[Any] = DataLoader(snake_case_ ,batch_size=args.batch_size )
return eval_dataloader
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
model.eval()
UpperCamelCase : Dict = []
for step, batch in enumerate(snake_case_ ):
with torch.no_grad():
UpperCamelCase : List[Any] = model(snake_case_ ,labels=snake_case_ )
UpperCamelCase : Any = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(snake_case_ ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
UpperCamelCase : Dict = torch.mean(torch.cat(snake_case_ ) )
try:
UpperCamelCase : Dict = torch.exp(snake_case_ )
except OverflowError:
UpperCamelCase : Optional[int] = float("""inf""" )
return loss.item(), perplexity.item()
# Setup Accelerator
__A : List[Any] = Accelerator()
# Parse configuration
__A : str = HfArgumentParser(EvaluationArguments)
__A : List[Any] = parser.parse_args()
set_seed(args.seed)
# Logging
__A : Any = logging.getLogger(__name__)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
# Load model and tokenizer
__A : List[Any] = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__A : List[Any] = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
__A : int = create_dataloader(args)
# Prepare everything with our `accelerator`.
__A , __A : Optional[Any] = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
__A , __A : Tuple = evaluate(args)
logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 27 | 0 |
from __future__ import annotations
__A : Optional[Any] = 1.6_0_2_1e-1_9 # units = C
def A_ ( snake_case_ : float ,snake_case_ : float ,snake_case_ : float ,):
'''simple docstring'''
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif conductivity < 0:
raise ValueError("""Conductivity cannot be negative""" )
elif electron_conc < 0:
raise ValueError("""Electron concentration cannot be negative""" )
elif mobility < 0:
raise ValueError("""mobility cannot be negative""" )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 365 |
"""simple docstring"""
import argparse
import os
import re
__A : Any = '''src/transformers'''
# Pattern that looks at the indentation in a line.
__A : Tuple = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : List[Any] = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : List[Any] = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : Any = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : str ,snake_case_ : str="" ,snake_case_ : Any=None ,snake_case_ : Union[str, Any]=None ):
'''simple docstring'''
UpperCamelCase : List[Any] = 0
UpperCamelCase : Optional[int] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Tuple = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : Tuple = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Dict = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Optional[Any] = [lines[index + 1]]
index += 1
else:
UpperCamelCase : str = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
def _inner(snake_case_ : List[str] ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Tuple=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Optional[int] ):
return x
if key is None:
UpperCamelCase : List[str] = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[str] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : Tuple = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : int = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Union[str, Any] = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : Any ):
UpperCamelCase : Union[str, Any] = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : int = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : str = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : Optional[int] = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : int = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Tuple = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : List[Any] = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Union[str, Any] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : List[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : Optional[int] = keys[:-1]
UpperCamelCase : Union[str, Any] = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : Any = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : int=True ):
'''simple docstring'''
with open(snake_case_ ,encoding="""utf-8""" ) as f:
UpperCamelCase : List[str] = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : int = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Dict = main_blocks[block_idx]
UpperCamelCase : Dict = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : List[str] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : Optional[Any] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Optional[Any] = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Any = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : List[Any] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Optional[Any] = _re_direct_key if """_import_structure = {""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Optional[Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Any = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : Union[str, Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[str] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : Optional[int] = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ,encoding="""utf-8""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Optional[int] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : List[Any] = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : Union[str, Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27 | 0 |
"""simple docstring"""
from __future__ import annotations
def A_ ( snake_case_ : Dict ,snake_case_ : Dict ,snake_case_ : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase : Tuple = list(range(len(snake_case_ ) ) )
UpperCamelCase : Dict = [v / w for v, w in zip(snake_case_ ,snake_case_ )]
index.sort(key=lambda snake_case_ : ratio[i] ,reverse=snake_case_ )
UpperCamelCase : float = 0
UpperCamelCase : list[float] = [0] * len(snake_case_ )
for i in index:
if weight[i] <= capacity:
UpperCamelCase : Dict = 1
max_value += value[i]
capacity -= weight[i]
else:
UpperCamelCase : Optional[int] = capacity / weight[i]
max_value += value[i] * capacity / weight[i]
break
return max_value, fractions
if __name__ == "__main__":
import doctest
doctest.testmod()
| 366 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 0 |
"""simple docstring"""
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Optional[int] ,snake_case_ : Tuple ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Optional[int] = multiprocessing.Manager()
UpperCamelCase : Optional[int] = manager.list()
UpperCamelCase : Union[str, Any] = multiprocessing.Process(target=snake_case_ ,args=(check_program, result, timeout) )
p.start()
p.join(timeout=timeout + 1 )
if p.is_alive():
p.kill()
if not result:
result.append("""timed out""" )
return {
"task_id": task_id,
"passed": result[0] == "passed",
"result": result[0],
"completion_id": completion_id,
}
def A_ ( snake_case_ : int ,snake_case_ : Optional[Any] ,snake_case_ : int ):
'''simple docstring'''
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
UpperCamelCase : Dict = shutil.rmtree
UpperCamelCase : List[Any] = os.rmdir
UpperCamelCase : List[str] = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
UpperCamelCase : List[str] = {}
with swallow_io():
with time_limit(snake_case_ ):
exec(snake_case_ ,snake_case_ )
result.append("""passed""" )
except TimeoutException:
result.append("""timed out""" )
except BaseException as e:
result.append(f'failed: {e}' )
# Needed for cleaning up.
UpperCamelCase : List[Any] = rmtree
UpperCamelCase : Tuple = rmdir
UpperCamelCase : List[Any] = chdir
@contextlib.contextmanager
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
def signal_handler(snake_case_ : Optional[Any] ,snake_case_ : int ):
raise TimeoutException("""Timed out!""" )
signal.setitimer(signal.ITIMER_REAL ,snake_case_ )
signal.signal(signal.SIGALRM ,snake_case_ )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL ,0 )
@contextlib.contextmanager
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Tuple = WriteOnlyStringIO()
with contextlib.redirect_stdout(snake_case_ ):
with contextlib.redirect_stderr(snake_case_ ):
with redirect_stdin(snake_case_ ):
yield
@contextlib.contextmanager
def A_ ( ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as dirname:
with chdir(snake_case_ ):
yield dirname
class lowerCamelCase ( UpperCamelCase__ ):
pass
class lowerCamelCase ( io.StringIO ):
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
raise OSError
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
raise OSError
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
raise OSError
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return False
class lowerCamelCase ( contextlib._RedirectStream ): # type: ignore
lowercase : Union[str, Any] = """stdin"""
@contextlib.contextmanager
def A_ ( snake_case_ : str ):
'''simple docstring'''
if root == ".":
yield
return
UpperCamelCase : Any = os.getcwd()
os.chdir(snake_case_ )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(snake_case_ )
def A_ ( snake_case_ : Any=None ):
'''simple docstring'''
if maximum_memory_bytes is not None:
import resource
resource.setrlimit(resource.RLIMIT_AS ,(maximum_memory_bytes, maximum_memory_bytes) )
resource.setrlimit(resource.RLIMIT_DATA ,(maximum_memory_bytes, maximum_memory_bytes) )
if not platform.uname().system == "Darwin":
resource.setrlimit(resource.RLIMIT_STACK ,(maximum_memory_bytes, maximum_memory_bytes) )
faulthandler.disable()
import builtins
UpperCamelCase : int = None
UpperCamelCase : Any = None
import os
UpperCamelCase : Optional[Any] = """1"""
UpperCamelCase : List[str] = None
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Tuple = None
UpperCamelCase : Dict = None
UpperCamelCase : Any = None
UpperCamelCase : List[str] = None
UpperCamelCase : Dict = None
UpperCamelCase : int = None
UpperCamelCase : int = None
UpperCamelCase : Optional[int] = None
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : Tuple = None
UpperCamelCase : Any = None
UpperCamelCase : List[str] = None
UpperCamelCase : int = None
UpperCamelCase : Optional[Any] = None
UpperCamelCase : int = None
UpperCamelCase : Dict = None
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : Any = None
UpperCamelCase : Optional[int] = None
UpperCamelCase : int = None
UpperCamelCase : Any = None
UpperCamelCase : Dict = None
UpperCamelCase : Tuple = None
import shutil
UpperCamelCase : List[str] = None
UpperCamelCase : List[str] = None
UpperCamelCase : str = None
import subprocess
UpperCamelCase : Tuple = None # type: ignore
UpperCamelCase : Optional[int] = None
import sys
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : int = None
UpperCamelCase : Dict = None
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : List[str] = None
| 367 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
__A : Optional[Any] = logging.get_logger(__name__)
def A_ ( snake_case_ : np.ndarray ,snake_case_ : Union[int, Iterable[int]] ,snake_case_ : bool ,snake_case_ : int ):
'''simple docstring'''
def constraint_to_multiple_of(snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ,snake_case_ : List[str]=0 ,snake_case_ : Optional[Any]=None ):
UpperCamelCase : List[str] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
UpperCamelCase : Optional[Any] = math.floor(val / multiple ) * multiple
if x < min_val:
UpperCamelCase : Dict = math.ceil(val / multiple ) * multiple
return x
UpperCamelCase : Any = (output_size, output_size) if isinstance(snake_case_ ,snake_case_ ) else output_size
UpperCamelCase , UpperCamelCase : int = get_image_size(snake_case_ )
UpperCamelCase , UpperCamelCase : Union[str, Any] = output_size
# determine new height and width
UpperCamelCase : List[str] = output_height / input_height
UpperCamelCase : List[str] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
UpperCamelCase : int = scale_width
else:
# fit height
UpperCamelCase : Optional[Any] = scale_height
UpperCamelCase : int = constraint_to_multiple_of(scale_height * input_height ,multiple=snake_case_ )
UpperCamelCase : Union[str, Any] = constraint_to_multiple_of(scale_width * input_width ,multiple=snake_case_ )
return (new_height, new_width)
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = ['pixel_values']
def __init__( self , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 1 / 255 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = size if size is not None else {"""height""": 384, """width""": 384}
UpperCamelCase : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = do_resize
UpperCamelCase : Union[str, Any] = size
UpperCamelCase : Union[str, Any] = keep_aspect_ratio
UpperCamelCase : Any = ensure_multiple_of
UpperCamelCase : List[Any] = resample
UpperCamelCase : str = do_rescale
UpperCamelCase : Optional[Any] = rescale_factor
UpperCamelCase : List[str] = do_normalize
UpperCamelCase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCamelCase : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Tuple = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
UpperCamelCase : Dict = get_resize_output_image_size(
SCREAMING_SNAKE_CASE_ , output_size=(size["""height"""], size["""width"""]) , keep_aspect_ratio=SCREAMING_SNAKE_CASE_ , multiple=SCREAMING_SNAKE_CASE_ , )
return resize(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return normalize(SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCamelCase : List[Any] = size if size is not None else self.size
UpperCamelCase : Dict = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
UpperCamelCase : Optional[int] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
UpperCamelCase : Tuple = resample if resample is not None else self.resample
UpperCamelCase : str = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase : Any = image_mean if image_mean is not None else self.image_mean
UpperCamelCase : List[Any] = image_std if image_std is not None else self.image_std
UpperCamelCase : str = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
UpperCamelCase : Tuple = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
UpperCamelCase : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
UpperCamelCase : int = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
UpperCamelCase : List[str] = [self.normalize(image=SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Any = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Union[str, Any] = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : str = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = target_sizes.numpy()
UpperCamelCase : Dict = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
UpperCamelCase : List[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : List[Any] = logits.argmax(dim=1 )
UpperCamelCase : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 27 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def A_ ( snake_case_ : Optional[int] ,snake_case_ : str ,snake_case_ : Union[str, Any] ,snake_case_ : int ,snake_case_ : Any = None ,snake_case_ : Union[str, Any] = None ,snake_case_ : int = None ,):
'''simple docstring'''
if config_name_or_path is None:
UpperCamelCase : str = 'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'
if generator_tokenizer_name_or_path is None:
UpperCamelCase : int = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
UpperCamelCase : int = question_encoder_name_or_path
UpperCamelCase : Tuple = RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration
# Save model.
UpperCamelCase : Dict = RagConfig.from_pretrained(lowerCAmelCase_ )
UpperCamelCase : int = AutoConfig.from_pretrained(lowerCAmelCase_ )
UpperCamelCase : Union[str, Any] = AutoConfig.from_pretrained(lowerCAmelCase_ )
UpperCamelCase : Union[str, Any] = gen_config
UpperCamelCase : Tuple = question_encoder_config
UpperCamelCase : List[str] = model_class.from_pretrained_question_encoder_generator(
lowerCAmelCase_ ,lowerCAmelCase_ ,config=lowerCAmelCase_ )
rag_model.save_pretrained(lowerCAmelCase_ )
# Sanity check.
model_class.from_pretrained(lowerCAmelCase_ )
# Save tokenizers.
UpperCamelCase : Optional[Any] = AutoTokenizer.from_pretrained(lowerCAmelCase_ )
gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/""" )
UpperCamelCase : Any = AutoTokenizer.from_pretrained(lowerCAmelCase_ )
question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/""" )
if __name__ == "__main__":
__A : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
'''--model_type''',
choices=['''rag_sequence''', '''rag_token'''],
required=True,
type=str,
help='''RAG model type: rag_sequence, rag_token''',
)
parser.add_argument('''--dest''', type=str, required=True, help='''Path to the output checkpoint directory.''')
parser.add_argument('''--generator_name_or_path''', type=str, required=True, help='''Generator model identifier''')
parser.add_argument(
'''--question_encoder_name_or_path''', type=str, required=True, help='''Question encoder model identifier'''
)
parser.add_argument(
'''--generator_tokenizer_name_or_path''',
type=str,
help='''Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``''',
)
parser.add_argument(
'''--question_encoder_tokenizer_name_or_path''',
type=str,
help='''Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``''',
)
parser.add_argument(
'''--config_name_or_path''',
type=str,
help=(
'''Identifier of the model config to use, if not provided, resolves to a base config for a given'''
''' ``model_type``'''
),
)
__A : List[Any] = parser.parse_args()
__A : Optional[int] = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 368 |
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : float = b
if function(snake_case_ ) == 0: # one of the a or b is a root for the function
return a
elif function(snake_case_ ) == 0:
return b
elif (
function(snake_case_ ) * function(snake_case_ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("""could not find root in given interval.""" )
else:
UpperCamelCase : float = start + (end - start) / 2.0
while abs(start - mid ) > 1_0**-7: # until precisely equals to 10^-7
if function(snake_case_ ) == 0:
return mid
elif function(snake_case_ ) * function(snake_case_ ) < 0:
UpperCamelCase : Dict = mid
else:
UpperCamelCase : List[str] = mid
UpperCamelCase : Tuple = start + (end - start) / 2.0
return mid
def A_ ( snake_case_ : float ):
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 27 | 0 |
"""simple docstring"""
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=24 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=6 , SCREAMING_SNAKE_CASE_=37 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=1000 , ):
UpperCamelCase : Any = parent
UpperCamelCase : Any = batch_size
UpperCamelCase : List[Any] = seq_length
UpperCamelCase : Optional[Any] = is_training
UpperCamelCase : List[Any] = use_input_mask
UpperCamelCase : List[Any] = use_token_type_ids
UpperCamelCase : Union[str, Any] = use_labels
UpperCamelCase : Optional[int] = vocab_size
UpperCamelCase : Union[str, Any] = hidden_size
UpperCamelCase : Optional[Any] = num_hidden_layers
UpperCamelCase : Optional[int] = num_attention_heads
UpperCamelCase : List[Any] = intermediate_size
UpperCamelCase : Dict = hidden_act
UpperCamelCase : List[Any] = hidden_dropout_prob
UpperCamelCase : Union[str, Any] = attention_probs_dropout_prob
UpperCamelCase : str = max_position_embeddings
UpperCamelCase : List[str] = type_vocab_size
UpperCamelCase : Optional[Any] = type_sequence_label_size
UpperCamelCase : str = initializer_range
UpperCamelCase : int = num_labels
UpperCamelCase : Union[str, Any] = scope
UpperCamelCase : Any = range_bbox
def a_ ( self ):
UpperCamelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Any = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
UpperCamelCase : List[str] = bbox[i, j, 3]
UpperCamelCase : Union[str, Any] = bbox[i, j, 1]
UpperCamelCase : int = t
if bbox[i, j, 2] < bbox[i, j, 0]:
UpperCamelCase : Dict = bbox[i, j, 2]
UpperCamelCase : List[str] = bbox[i, j, 0]
UpperCamelCase : Optional[int] = t
UpperCamelCase : Optional[int] = None
if self.use_input_mask:
UpperCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
UpperCamelCase : List[Any] = None
if self.use_token_type_ids:
UpperCamelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase : str = None
UpperCamelCase : Union[str, Any] = None
if self.use_labels:
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Dict = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def a_ ( self ):
return LiltConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = LiltModel(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase : Dict = model(UpperCamelCase__ , bbox=UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ )
UpperCamelCase : List[Any] = model(UpperCamelCase__ , bbox=UpperCamelCase__ , token_type_ids=UpperCamelCase__ )
UpperCamelCase : Tuple = model(UpperCamelCase__ , bbox=UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[Any] = self.num_labels
UpperCamelCase : Tuple = LiltForTokenClassification(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase : str = model(
UpperCamelCase__ , bbox=UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = LiltForQuestionAnswering(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCamelCase : List[str] = model(
UpperCamelCase__ , bbox=UpperCamelCase__ , attention_mask=UpperCamelCase__ , token_type_ids=UpperCamelCase__ , start_positions=UpperCamelCase__ , end_positions=UpperCamelCase__ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self ):
UpperCamelCase : str = self.prepare_config_and_inputs()
(
UpperCamelCase
) : Optional[int] = config_and_inputs
UpperCamelCase : List[Any] = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class lowerCamelCase ( __snake_case , __snake_case , __snake_case , unittest.TestCase ):
lowercase : Any = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowercase : Union[str, Any] = (
{
"""feature-extraction""": LiltModel,
"""question-answering""": LiltForQuestionAnswering,
"""text-classification""": LiltForSequenceClassification,
"""token-classification""": LiltForTokenClassification,
"""zero-shot""": LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase : Any = False
lowercase : Tuple = False
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return True
def a_ ( self ):
UpperCamelCase : Dict = LiltModelTester(self )
UpperCamelCase : Optional[int] = ConfigTester(self , config_class=UpperCamelCase__ , hidden_size=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCamelCase : List[Any] = type
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__ )
@slow
def a_ ( self ):
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : Any = LiltModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
@require_torch
@slow
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
UpperCamelCase : Union[str, Any] = LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base""" ).to(UpperCamelCase__ )
UpperCamelCase : Optional[int] = torch.tensor([[1, 2]] , device=UpperCamelCase__ )
UpperCamelCase : int = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=UpperCamelCase__ )
# forward pass
with torch.no_grad():
UpperCamelCase : str = model(input_ids=UpperCamelCase__ , bbox=UpperCamelCase__ )
UpperCamelCase : Optional[int] = torch.Size([1, 2, 768] )
UpperCamelCase : Optional[int] = torch.tensor(
[[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]] , device=UpperCamelCase__ , )
self.assertTrue(outputs.last_hidden_state.shape , UpperCamelCase__ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , UpperCamelCase__ , atol=1e-3 ) )
| 369 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def a_ ( self ):
UpperCamelCase : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
UpperCamelCase : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
UpperCamelCase : Dict = """xvjiarui/stable-diffusion-2-inpainting"""
UpperCamelCase , UpperCamelCase : List[str] = FlaxStableDiffusionInpaintPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = """Face of a yellow cat, high resolution, sitting on a park bench"""
UpperCamelCase : List[str] = jax.random.PRNGKey(0 )
UpperCamelCase : Tuple = 50
UpperCamelCase : Dict = jax.device_count()
UpperCamelCase : Optional[int] = num_samples * [prompt]
UpperCamelCase : int = num_samples * [init_image]
UpperCamelCase : List[Any] = num_samples * [mask_image]
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[Any] = pipeline.prepare_inputs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# shard inputs and rng
UpperCamelCase : Optional[int] = replicate(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = jax.random.split(SCREAMING_SNAKE_CASE_ , jax.device_count() )
UpperCamelCase : str = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = pipeline(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , jit=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = output.images.reshape(SCREAMING_SNAKE_CASE_ , 512 , 512 , 3 )
UpperCamelCase : List[Any] = images[0, 253:256, 253:256, -1]
UpperCamelCase : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase : Dict = jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 27 | 0 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : List[Any] = {
'''snap-research/efficientformer-l1-300''': (
'''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
),
}
class lowerCamelCase ( lowerCamelCase_ ):
lowercase : int = '''efficientformer'''
def __init__( self , SCREAMING_SNAKE_CASE_ = [3, 2, 6, 4] , SCREAMING_SNAKE_CASE_ = [48, 96, 224, 448] , SCREAMING_SNAKE_CASE_ = [True, True, True, True] , SCREAMING_SNAKE_CASE_ = 448 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = 4 , SCREAMING_SNAKE_CASE_ = 7 , SCREAMING_SNAKE_CASE_ = 5 , SCREAMING_SNAKE_CASE_ = 8 , SCREAMING_SNAKE_CASE_ = 4 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 3 , SCREAMING_SNAKE_CASE_ = 3 , SCREAMING_SNAKE_CASE_ = 3 , SCREAMING_SNAKE_CASE_ = 2 , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 1e-5 , SCREAMING_SNAKE_CASE_ = "gelu" , SCREAMING_SNAKE_CASE_ = 0.02 , SCREAMING_SNAKE_CASE_ = 1e-12 , SCREAMING_SNAKE_CASE_ = 224 , SCREAMING_SNAKE_CASE_ = 1e-05 , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**__snake_case )
UpperCamelCase : List[Any] = hidden_act
UpperCamelCase : Optional[int] = hidden_dropout_prob
UpperCamelCase : List[str] = hidden_sizes
UpperCamelCase : Optional[Any] = num_hidden_layers
UpperCamelCase : Optional[Any] = num_attention_heads
UpperCamelCase : List[str] = initializer_range
UpperCamelCase : Dict = layer_norm_eps
UpperCamelCase : Optional[int] = patch_size
UpperCamelCase : Tuple = num_channels
UpperCamelCase : Any = depths
UpperCamelCase : Tuple = mlp_expansion_ratio
UpperCamelCase : int = downsamples
UpperCamelCase : Union[str, Any] = dim
UpperCamelCase : Dict = key_dim
UpperCamelCase : int = attention_ratio
UpperCamelCase : Optional[Any] = resolution
UpperCamelCase : Optional[int] = pool_size
UpperCamelCase : Optional[Any] = downsample_patch_size
UpperCamelCase : Optional[int] = downsample_stride
UpperCamelCase : List[str] = downsample_pad
UpperCamelCase : Optional[int] = drop_path_rate
UpperCamelCase : List[Any] = num_metaad_blocks
UpperCamelCase : Optional[int] = distillation
UpperCamelCase : int = use_layer_scale
UpperCamelCase : str = layer_scale_init_value
UpperCamelCase : str = image_size
UpperCamelCase : Dict = batch_norm_eps
| 370 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def A_ ( ):
'''simple docstring'''
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
UpperCamelCase : Optional[Any] = [1, 2, 3]
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=2 )
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" ,[2, -1] )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : List[Any] = [1, 2]
UpperCamelCase : List[Any] = {"""a""": 1, """b""": 2}
UpperCamelCase : List[str] = {"""a""": [1, 2], """b""": [3, 4]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 1}, """b""": 2}
UpperCamelCase : Any = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
UpperCamelCase : Optional[int] = [2, 3]
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3}
UpperCamelCase : Any = {"""a""": [2, 3], """b""": [4, 5]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 2}, """b""": 3}
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
| 27 | 0 |
"""simple docstring"""
import socket
def A_ ( ):
'''simple docstring'''
UpperCamelCase : List[str] = socket.socket(socket.AF_INET ,socket.SOCK_STREAM )
UpperCamelCase : str = socket.gethostname()
UpperCamelCase : List[str] = 1_2_3_1_2
sock.connect((host, port) )
sock.send(b"""Hello server!""" )
with open("""Received_file""" ,"""wb""" ) as out_file:
print("""File opened""" )
print("""Receiving data...""" )
while True:
UpperCamelCase : Union[str, Any] = sock.recv(1_0_2_4 )
if not data:
break
out_file.write(snake_case_ )
print("""Successfully received the file""" )
sock.close()
print("""Connection closed""" )
if __name__ == "__main__":
main()
| 371 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_="last" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=0 , ):
UpperCamelCase : Union[str, Any] = parent
UpperCamelCase : str = batch_size
UpperCamelCase : int = seq_length
UpperCamelCase : Optional[Any] = is_training
UpperCamelCase : Any = use_input_lengths
UpperCamelCase : Tuple = use_token_type_ids
UpperCamelCase : List[Any] = use_labels
UpperCamelCase : Union[str, Any] = gelu_activation
UpperCamelCase : Dict = sinusoidal_embeddings
UpperCamelCase : Optional[int] = causal
UpperCamelCase : List[Any] = asm
UpperCamelCase : int = n_langs
UpperCamelCase : Optional[Any] = vocab_size
UpperCamelCase : str = n_special
UpperCamelCase : Dict = hidden_size
UpperCamelCase : Union[str, Any] = num_hidden_layers
UpperCamelCase : Optional[Any] = num_attention_heads
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : int = max_position_embeddings
UpperCamelCase : Any = type_sequence_label_size
UpperCamelCase : str = initializer_range
UpperCamelCase : str = num_labels
UpperCamelCase : Union[str, Any] = num_choices
UpperCamelCase : List[str] = summary_type
UpperCamelCase : int = use_proj
UpperCamelCase : List[str] = scope
UpperCamelCase : Dict = bos_token_id
def a_ ( self ):
UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Union[str, Any] = None
if self.use_input_lengths:
UpperCamelCase : str = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
UpperCamelCase : Tuple = None
if self.use_token_type_ids:
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
UpperCamelCase : int = None
UpperCamelCase : Dict = None
UpperCamelCase : str = None
if self.use_labels:
UpperCamelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Dict = ids_tensor([self.batch_size] , 2 ).float()
UpperCamelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : List[str] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def a_ ( self ):
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = XLMModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , lengths=SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[Any] = XLMWithLMHeadModel(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[str] = XLMForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = XLMForQuestionAnswering(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , p_mask=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , )
((UpperCamelCase) , ) : Union[str, Any] = result_with_labels.to_tuple()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
((UpperCamelCase) , ) : Tuple = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = XLMForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = self.num_labels
UpperCamelCase : int = XLMForTokenClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[int] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[Any] = self.num_choices
UpperCamelCase : Tuple = XLMForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Dict = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Optional[Any] = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : int = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : List[Any] = config_and_inputs
UpperCamelCase : Dict = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
lowercase : List[Any] = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
lowercase : Optional[Any] = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("""Fast""" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
UpperCamelCase : Union[str, Any] = super()._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
UpperCamelCase : Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
return inputs_dict
def a_ ( self ):
UpperCamelCase : List[Any] = XLMModelTester(self )
UpperCamelCase : Optional[int] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , emb_dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_attentions in attentions] , [True] * len(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : int = min_length + idx + 1
UpperCamelCase : Tuple = min_length + idx + 1
UpperCamelCase : Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_hidden_states in hidden_states] , [True] * len(SCREAMING_SNAKE_CASE_ ) , )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : List[str] = min_length + idx + 1
UpperCamelCase : Optional[Any] = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) , )
pass
@slow
def a_ ( self ):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : str = XLMModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Dict = XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""" )
model.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.tensor([[14, 447]] , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ ) # the president
UpperCamelCase : List[Any] = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
UpperCamelCase : Optional[int] = model.generate(SCREAMING_SNAKE_CASE_ , do_sample=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , SCREAMING_SNAKE_CASE_ )
| 27 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Optional[Any] = logging.get_logger(__name__)
__A : Tuple = {
'''andreasmadsen/efficient_mlm_m0.40''': (
'''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
),
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = 'roberta-prelayernorm'
def __init__( self , SCREAMING_SNAKE_CASE_=5_0265 , SCREAMING_SNAKE_CASE_=768 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=3072 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=1e-12 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_="absolute" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = vocab_size
UpperCamelCase : List[str] = hidden_size
UpperCamelCase : List[Any] = num_hidden_layers
UpperCamelCase : List[str] = num_attention_heads
UpperCamelCase : Tuple = hidden_act
UpperCamelCase : List[str] = intermediate_size
UpperCamelCase : List[Any] = hidden_dropout_prob
UpperCamelCase : Dict = attention_probs_dropout_prob
UpperCamelCase : Union[str, Any] = max_position_embeddings
UpperCamelCase : Optional[Any] = type_vocab_size
UpperCamelCase : Dict = initializer_range
UpperCamelCase : List[str] = layer_norm_eps
UpperCamelCase : Any = position_embedding_type
UpperCamelCase : List[str] = use_cache
UpperCamelCase : Union[str, Any] = classifier_dropout
class lowerCamelCase ( _UpperCAmelCase ):
@property
def a_ ( self ):
if self.task == "multiple-choice":
UpperCamelCase : Optional[int] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
UpperCamelCase : Any = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 350 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : int = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = [
'''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTBigCodeForSequenceClassification''',
'''GPTBigCodeForTokenClassification''',
'''GPTBigCodeForCausalLM''',
'''GPTBigCodeModel''',
'''GPTBigCodePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : str = logging.get_logger(__name__)
__A : Union[str, Any] = {
'''MIT/ast-finetuned-audioset-10-10-0.4593''': (
'''https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'''
),
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : List[str] = 'audio-spectrogram-transformer'
def __init__( self , SCREAMING_SNAKE_CASE_=768 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=3072 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=1e-12 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=128 , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = hidden_size
UpperCamelCase : Tuple = num_hidden_layers
UpperCamelCase : List[Any] = num_attention_heads
UpperCamelCase : Any = intermediate_size
UpperCamelCase : Optional[int] = hidden_act
UpperCamelCase : List[str] = hidden_dropout_prob
UpperCamelCase : Optional[int] = attention_probs_dropout_prob
UpperCamelCase : Union[str, Any] = initializer_range
UpperCamelCase : Tuple = layer_norm_eps
UpperCamelCase : Tuple = patch_size
UpperCamelCase : Tuple = qkv_bias
UpperCamelCase : Optional[Any] = frequency_stride
UpperCamelCase : Dict = time_stride
UpperCamelCase : Any = max_length
UpperCamelCase : Any = num_mel_bins
| 351 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase ( torch.nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_="sayef/fsner-bert-base-uncased" ):
super(SCREAMING_SNAKE_CASE_ , self ).__init__()
UpperCamelCase : int = AutoModel.from_pretrained(SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.nn.CosineSimilarity(3 , 1e-08 )
UpperCamelCase : Any = torch.nn.Softmax(dim=1 )
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
return self.bert(**SCREAMING_SNAKE_CASE_ ).last_hidden_state
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return token_embeddings.sum(2 , keepdim=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1 ):
return self.softmax(T * self.cos(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = W_supports["""sizes"""].tolist()
UpperCamelCase : List[str] = W_supports["""start_token_id"""].item()
UpperCamelCase : List[Any] = W_supports["""end_token_id"""].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
UpperCamelCase : List[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Tuple = W_supports["""input_ids"""] == start_token_id
UpperCamelCase : Optional[Any] = W_supports["""input_ids"""] == end_token_id
for i, size in enumerate(SCREAMING_SNAKE_CASE_ ):
if i == 0:
UpperCamelCase : int = 0
else:
UpperCamelCase : Optional[int] = support_sizes[i - 1]
UpperCamelCase : Tuple = S[s : s + size][start_token_masks[s : s + size]]
UpperCamelCase : int = S[s : s + size][end_token_masks[s : s + size]]
UpperCamelCase : Dict = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
UpperCamelCase : Tuple = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
UpperCamelCase : List[str] = torch.vstack((p_starts, p_start) )
UpperCamelCase : Optional[Any] = torch.vstack((p_ends, p_end) )
else:
UpperCamelCase : Optional[int] = p_start
UpperCamelCase : Tuple = p_end
return p_starts, p_ends
| 27 | 0 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Optional[Any] = (DDIMParallelScheduler,)
lowercase : Any = (('eta', 0.0), ('num_inference_steps', 5_0))
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""clip_sample""": True,
}
config.update(**SCREAMING_SNAKE_CASE_ )
return config
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = self.scheduler_classes[0]
UpperCamelCase : Tuple = self.get_scheduler_config(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = scheduler_class(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = 10, 0.0
UpperCamelCase : Optional[Any] = self.dummy_model()
UpperCamelCase : Dict = self.dummy_sample_deter
scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
for t in scheduler.timesteps:
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = scheduler.step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).prev_sample
return sample
def a_ ( self ):
for timesteps in [100, 500, 1000]:
self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.scheduler_classes[0]
UpperCamelCase : Tuple = self.get_scheduler_config(steps_offset=1 )
UpperCamelCase : str = scheduler_class(**SCREAMING_SNAKE_CASE_ )
scheduler.set_timesteps(5 )
assert torch.equal(scheduler.timesteps , torch.LongTensor([801, 601, 401, 201, 1] ) )
def a_ ( self ):
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_ , beta_end=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
for timestep_spacing in ["trailing", "leading"]:
self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
for rescale_betas_zero_snr in [True, False]:
self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_ )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(
thresholding=SCREAMING_SNAKE_CASE_ , prediction_type=SCREAMING_SNAKE_CASE_ , sample_max_value=SCREAMING_SNAKE_CASE_ , )
def a_ ( self ):
for t in [1, 10, 49]:
self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 500] ):
self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_ , num_inference_steps=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0] ):
self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_ , eta=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Dict = self.scheduler_classes[0]
UpperCamelCase : Optional[int] = self.get_scheduler_config()
UpperCamelCase : Any = scheduler_class(**SCREAMING_SNAKE_CASE_ )
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(420 , 400 ) - 0.14771 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(980 , 960 ) - 0.32460 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 , 486 ) - 0.00979 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 , 998 ) - 0.02 ) ) < 1e-5
def a_ ( self ):
UpperCamelCase : Dict = self.scheduler_classes[0]
UpperCamelCase : str = self.get_scheduler_config()
UpperCamelCase : List[str] = scheduler_class(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = 10, 0.0
scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.dummy_model()
UpperCamelCase : Optional[int] = self.dummy_sample_deter
UpperCamelCase : Optional[Any] = self.dummy_sample_deter + 0.1
UpperCamelCase : Any = self.dummy_sample_deter - 0.1
UpperCamelCase : Any = samplea.shape[0]
UpperCamelCase : Any = torch.stack([samplea, samplea, samplea] , dim=0 )
UpperCamelCase : int = torch.arange(SCREAMING_SNAKE_CASE_ )[0:3, None].repeat(1 , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
UpperCamelCase : Optional[int] = scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE_ , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Tuple = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )
assert abs(result_sum.item() - 1147.7904 ) < 1e-2
assert abs(result_mean.item() - 0.4982 ) < 1e-3
def a_ ( self ):
UpperCamelCase : Tuple = self.full_loop()
UpperCamelCase : str = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : List[Any] = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )
assert abs(result_sum.item() - 172.0067 ) < 1e-2
assert abs(result_mean.item() - 0.223967 ) < 1e-3
def a_ ( self ):
UpperCamelCase : Tuple = self.full_loop(prediction_type="""v_prediction""" )
UpperCamelCase : Tuple = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Dict = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )
assert abs(result_sum.item() - 52.5302 ) < 1e-2
assert abs(result_mean.item() - 0.0684 ) < 1e-3
def a_ ( self ):
# We specify different beta, so that the first alpha is 0.99
UpperCamelCase : Union[str, Any] = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_ , beta_start=0.01 )
UpperCamelCase : Any = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : str = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )
assert abs(result_sum.item() - 149.8295 ) < 1e-2
assert abs(result_mean.item() - 0.1951 ) < 1e-3
def a_ ( self ):
# We specify different beta, so that the first alpha is 0.99
UpperCamelCase : Optional[Any] = self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE_ , beta_start=0.01 )
UpperCamelCase : Union[str, Any] = torch.sum(torch.abs(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : str = torch.mean(torch.abs(SCREAMING_SNAKE_CASE_ ) )
assert abs(result_sum.item() - 149.0784 ) < 1e-2
assert abs(result_mean.item() - 0.1941 ) < 1e-3
| 352 |
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):
return f'Node({self.data})'
class lowerCamelCase :
def __init__( self ):
UpperCamelCase : Dict = None
def __iter__( self ):
UpperCamelCase : int = self.head
while node:
yield node.data
UpperCamelCase : Union[str, Any] = node.next
def __len__( self ):
return sum(1 for _ in self )
def __repr__( self ):
return "->".join([str(SCREAMING_SNAKE_CASE_ ) for item in self] )
def __getitem__( self , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
UpperCamelCase : List[Any] = self.head
for _ in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = current.next
UpperCamelCase : Optional[Any] = data
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(len(self ) , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(0 , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index <= len(self ):
raise IndexError("""list index out of range""" )
UpperCamelCase : Optional[Any] = Node(SCREAMING_SNAKE_CASE_ )
if self.head is None:
UpperCamelCase : Dict = new_node
elif index == 0:
UpperCamelCase : Any = self.head # link new_node to head
UpperCamelCase : Any = new_node
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : str = temp.next
UpperCamelCase : Any = temp.next
UpperCamelCase : Optional[Any] = new_node
def a_ ( self ): # print every node data
print(self )
def a_ ( self ):
return self.delete_nth(0 )
def a_ ( self ): # delete from tail
return self.delete_nth(len(self ) - 1 )
def a_ ( self , SCREAMING_SNAKE_CASE_ = 0 ):
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("""List index out of range.""" )
UpperCamelCase : Union[str, Any] = self.head # default first node
if index == 0:
UpperCamelCase : Optional[Any] = self.head.next
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : int = temp.next
UpperCamelCase : Optional[Any] = temp.next
UpperCamelCase : Dict = temp.next.next
return delete_node.data
def a_ ( self ):
return self.head is None
def a_ ( self ):
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Union[str, Any] = self.head
while current:
# Store the current node's next node.
UpperCamelCase : Optional[int] = current.next
# Make the current node's next point backwards
UpperCamelCase : Optional[Any] = prev
# Make the previous node be the current node
UpperCamelCase : int = current
# Make the current node the next node (to progress iteration)
UpperCamelCase : Optional[int] = next_node
# Return prev in order to put the head at the end
UpperCamelCase : Optional[int] = prev
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = LinkedList()
assert linked_list.is_empty() is True
assert str(snake_case_ ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(1_0 ):
assert len(snake_case_ ) == i
linked_list.insert_nth(snake_case_ ,i + 1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_1 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(1_1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(0 ,1_2 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 1_0
assert linked_list.delete_tail() == 1_1
assert len(snake_case_ ) == 9
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_0 ) )
assert all(linked_list[i] == i + 1 for i in range(0 ,9 ) ) is True
for i in range(0 ,9 ):
UpperCamelCase : Optional[Any] = -i
assert all(linked_list[i] == -i for i in range(0 ,9 ) ) is True
linked_list.reverse()
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(-8 ,1 ) )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = [
-9,
1_0_0,
Node(7_7_3_4_5_1_1_2 ),
"""dlrow olleH""",
7,
5_5_5_5,
0,
-192.55555,
"""Hello, world!""",
77.9,
Node(1_0 ),
None,
None,
12.20,
]
UpperCamelCase : List[Any] = LinkedList()
for i in test_input:
linked_list.insert_tail(snake_case_ )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(snake_case_ ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
UpperCamelCase : Dict = linked_list.delete_head()
assert result == -9
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
UpperCamelCase : int = linked_list.delete_tail()
assert result == 12.2
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
UpperCamelCase : Optional[Any] = linked_list.delete_nth(1_0 )
assert result is None
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("""Hello again, world!""" ) )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(snake_case_ )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(snake_case_ )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def A_ ( ):
'''simple docstring'''
from doctest import testmod
testmod()
UpperCamelCase : List[Any] = LinkedList()
linked_list.insert_head(input("""Inserting 1st at head """ ).strip() )
linked_list.insert_head(input("""Inserting 2nd at head """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
linked_list.insert_tail(input("""\nInserting 1st at tail """ ).strip() )
linked_list.insert_tail(input("""Inserting 2nd at tail """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nDelete head""" )
linked_list.delete_head()
print("""Delete tail""" )
linked_list.delete_tail()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nReverse linked list""" )
linked_list.reverse()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nString representation of linked list:""" )
print(snake_case_ )
print("""\nReading/changing Node data using indexing:""" )
print(f'Element at Position 1: {linked_list[1]}' )
UpperCamelCase : List[Any] = input("""Enter New Value: """ ).strip()
print("""New list:""" )
print(snake_case_ )
print(f'length of linked_list is : {len(snake_case_ )}' )
if __name__ == "__main__":
main()
| 27 | 0 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
UpperCamelCase : List[Any] = inspect.getfile(accelerate.test_utils )
UpperCamelCase : Union[str, Any] = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ["""scripts""", """external_deps""", """test_metrics.py"""] )
from accelerate.test_utils.scripts.external_deps import test_metrics # noqa: F401
UpperCamelCase : Optional[Any] = test_metrics
@require_cpu
def a_ ( self ):
debug_launcher(self.test_metrics.main , num_processes=1 )
@require_cpu
def a_ ( self ):
debug_launcher(self.test_metrics.main )
@require_single_gpu
def a_ ( self ):
self.test_metrics.main()
@require_multi_gpu
def a_ ( self ):
print(f'Found {torch.cuda.device_count()} devices.' )
UpperCamelCase : Union[str, Any] = ["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(SCREAMING_SNAKE_CASE_ , env=os.environ.copy() )
| 353 |
"""simple docstring"""
import argparse
import os
import re
__A : Dict = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__A : Union[str, Any] = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : Tuple = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : Tuple = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Dict="" ,snake_case_ : Dict=None ,snake_case_ : Any=None ):
'''simple docstring'''
UpperCamelCase : Optional[int] = 0
UpperCamelCase : List[Any] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Optional[Any] = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : int = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Any = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Any = [lines[index + 1]]
index += 1
else:
UpperCamelCase : List[str] = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
def _inner(snake_case_ : Tuple ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : List[Any] ,snake_case_ : Optional[int]=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Dict ):
return x
if key is None:
UpperCamelCase : int = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[Any] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : str = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : List[str] = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Tuple = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : int ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : List[Any] ):
UpperCamelCase : Any = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : Union[str, Any] = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[str] = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : str = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : str = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Dict = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : int = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Any = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[Any] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : Optional[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[Any] = keys[:-1]
UpperCamelCase : int = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Tuple ,snake_case_ : str=True ):
'''simple docstring'''
with open(snake_case_ ,"""r""" ) as f:
UpperCamelCase : int = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : Dict = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Optional[Any] = main_blocks[block_idx]
UpperCamelCase : Optional[int] = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : Union[str, Any] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : List[str] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Dict = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Union[str, Any] = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : Optional[int] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Union[str, Any] = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Union[str, Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Optional[Any] = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : List[Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[Any] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : str = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Any = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Union[str, Any] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : Any = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : str = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27 | 0 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
__A : Any = logging.get_logger(__name__)
__A : Any = [
('''bert.bert''', '''visual_bert'''),
('''bert.cls''', '''cls'''),
('''bert.classifier''', '''cls'''),
('''token_type_embeddings_visual''', '''visual_token_type_embeddings'''),
('''position_embeddings_visual''', '''visual_position_embeddings'''),
('''projection''', '''visual_projection'''),
]
__A : Optional[int] = [
'''nlvr2_coco_pre_trained.th''',
'''nlvr2_fine_tuned.th''',
'''nlvr2_pre_trained.th''',
'''vcr_coco_pre_train.th''',
'''vcr_fine_tune.th''',
'''vcr_pre_train.th''',
'''vqa_coco_pre_trained.th''',
'''vqa_fine_tuned.th''',
'''vqa_pre_trained.th''',
]
def A_ ( snake_case_ : Tuple ):
'''simple docstring'''
UpperCamelCase : Optional[int] = torch.load(snake_case_ ,map_location="""cpu""" )
return sd
def A_ ( snake_case_ : Optional[int] ,snake_case_ : Union[str, Any] ,snake_case_ : int=rename_keys_prefix ):
'''simple docstring'''
UpperCamelCase : List[Any] = OrderedDict()
UpperCamelCase : Union[str, Any] = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
UpperCamelCase : List[str] = key
for name_pair in rename_keys_prefix:
UpperCamelCase : Dict = new_key.replace(name_pair[0] ,name_pair[1] )
UpperCamelCase : Optional[int] = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
UpperCamelCase : List[Any] = new_d["""cls.predictions.bias"""]
return new_d
@torch.no_grad()
def A_ ( snake_case_ : Dict ,snake_case_ : int ):
'''simple docstring'''
assert (
checkpoint_path.split("""/""" )[-1] in ACCEPTABLE_CHECKPOINTS
), f'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'
# Get Config
if "pre" in checkpoint_path:
UpperCamelCase : str = """pretraining"""
if "vcr" in checkpoint_path:
UpperCamelCase : Dict = {"""visual_embedding_dim""": 5_1_2}
elif "vqa_advanced" in checkpoint_path:
UpperCamelCase : Tuple = {"""visual_embedding_dim""": 2_0_4_8}
elif "vqa" in checkpoint_path:
UpperCamelCase : Dict = {"""visual_embedding_dim""": 2_0_4_8}
elif "nlvr" in checkpoint_path:
UpperCamelCase : Union[str, Any] = {"""visual_embedding_dim""": 1_0_2_4}
else:
raise NotImplementedError(f'No implementation found for `{checkpoint_path}`.' )
else:
if "vcr" in checkpoint_path:
UpperCamelCase : Any = {"""visual_embedding_dim""": 5_1_2}
UpperCamelCase : Union[str, Any] = """multichoice"""
elif "vqa_advanced" in checkpoint_path:
UpperCamelCase : Tuple = {"""visual_embedding_dim""": 2_0_4_8}
UpperCamelCase : int = """vqa_advanced"""
elif "vqa" in checkpoint_path:
UpperCamelCase : str = {"""visual_embedding_dim""": 2_0_4_8, """num_labels""": 3_1_2_9}
UpperCamelCase : Optional[Any] = """vqa"""
elif "nlvr" in checkpoint_path:
UpperCamelCase : Tuple = {
"""visual_embedding_dim""": 1_0_2_4,
"""num_labels""": 2,
}
UpperCamelCase : List[str] = """nlvr"""
UpperCamelCase : Any = VisualBertConfig(**snake_case_ )
# Load State Dict
UpperCamelCase : Dict = load_state_dict(snake_case_ )
UpperCamelCase : Dict = get_new_dict(snake_case_ ,snake_case_ )
if model_type == "pretraining":
UpperCamelCase : str = VisualBertForPreTraining(snake_case_ )
elif model_type == "vqa":
UpperCamelCase : int = VisualBertForQuestionAnswering(snake_case_ )
elif model_type == "nlvr":
UpperCamelCase : List[str] = VisualBertForVisualReasoning(snake_case_ )
elif model_type == "multichoice":
UpperCamelCase : List[str] = VisualBertForMultipleChoice(snake_case_ )
model.load_state_dict(snake_case_ )
# Save Checkpoints
Path(snake_case_ ).mkdir(exist_ok=snake_case_ )
model.save_pretrained(snake_case_ )
if __name__ == "__main__":
__A : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''orig_checkpoint_path''', type=str, help='''A path to .th on local filesystem.''')
parser.add_argument('''pytorch_dump_folder_path''', type=str, help='''Path to the output PyTorch model.''')
__A : Dict = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 354 |
"""simple docstring"""
def A_ ( snake_case_ : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(snake_case_ ,(list, tuple) ) or not all(
isinstance(snake_case_ ,snake_case_ ) for number in numbers ):
raise ValueError("""numbers must be an iterable of integers""" )
UpperCamelCase : int = numbers[0]
for i in range(1 ,len(snake_case_ ) ):
# update the maximum and minimum subarray products
UpperCamelCase : List[str] = numbers[i]
if number < 0:
UpperCamelCase , UpperCamelCase : Optional[int] = min_till_now, max_till_now
UpperCamelCase : Dict = max(snake_case_ ,max_till_now * number )
UpperCamelCase : Union[str, Any] = min(snake_case_ ,min_till_now * number )
# update the maximum product found till now
UpperCamelCase : Union[str, Any] = max(snake_case_ ,snake_case_ )
return max_prod
| 27 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : int = '''▁'''
__A : str = {'''vocab_file''': '''sentencepiece.bpe.model'''}
__A : Dict = {
'''vocab_file''': {
'''facebook/mbart-large-en-ro''': (
'''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
),
'''facebook/mbart-large-cc25''': (
'''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
),
}
}
__A : Dict = {
'''facebook/mbart-large-en-ro''': 1024,
'''facebook/mbart-large-cc25''': 1024,
}
# fmt: off
__A : Optional[int] = ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : List[Any] = VOCAB_FILES_NAMES
lowercase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : List[str] = PRETRAINED_VOCAB_FILES_MAP
lowercase : Any = ['input_ids', 'attention_mask']
lowercase : List[int] = []
lowercase : List[int] = []
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ):
# Mask token behave like a normal word, i.e. include the space before it
UpperCamelCase : int = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else mask_token
UpperCamelCase : Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , src_lang=SCREAMING_SNAKE_CASE_ , tgt_lang=SCREAMING_SNAKE_CASE_ , additional_special_tokens=SCREAMING_SNAKE_CASE_ , sp_model_kwargs=self.sp_model_kwargs , **SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : str = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
UpperCamelCase : int = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
UpperCamelCase : str = 1
UpperCamelCase : List[str] = len(self.sp_model )
UpperCamelCase : List[str] = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(SCREAMING_SNAKE_CASE_ )
}
UpperCamelCase : Dict = {v: k for k, v in self.lang_code_to_id.items()}
UpperCamelCase : Union[str, Any] = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
UpperCamelCase : int = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
UpperCamelCase : Tuple = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
UpperCamelCase : List[Any] = src_lang if src_lang is not None else """en_XX"""
UpperCamelCase : str = self.lang_code_to_id[self._src_lang]
UpperCamelCase : Union[str, Any] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self ):
UpperCamelCase : str = self.__dict__.copy()
UpperCamelCase : List[Any] = None
UpperCamelCase : int = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
UpperCamelCase : Any = {}
UpperCamelCase : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def a_ ( self ):
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def a_ ( self ):
return self._src_lang
@src_lang.setter
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = [1] * len(self.prefix_tokens )
UpperCamelCase : Union[str, Any] = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(SCREAMING_SNAKE_CASE_ )) + suffix_ones
return prefix_ones + ([0] * len(SCREAMING_SNAKE_CASE_ )) + ([0] * len(SCREAMING_SNAKE_CASE_ )) + suffix_ones
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Optional[int] = [self.sep_token_id]
UpperCamelCase : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
UpperCamelCase : Any = src_lang
UpperCamelCase : Union[str, Any] = self(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = tgt_lang_id
return inputs
def a_ ( self ):
UpperCamelCase : Union[str, Any] = {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return self.sp_model.encode(SCREAMING_SNAKE_CASE_ , out_type=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCamelCase : List[str] = self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = """""".join(SCREAMING_SNAKE_CASE_ ).replace(SCREAMING_SNAKE_CASE_ , """ """ ).strip()
return out_string
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
UpperCamelCase : List[str] = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE_ )
elif not os.path.isfile(self.vocab_file ):
with open(SCREAMING_SNAKE_CASE_ , """wb""" ) as fi:
UpperCamelCase : Dict = self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE_ )
return (out_vocab_file,)
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = "en_XX" , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "ro_RO" , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : str = src_lang
UpperCamelCase : Optional[int] = tgt_lang
return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
return self.set_src_lang_special_tokens(self.src_lang )
def a_ ( self ):
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = self.lang_code_to_id[src_lang]
UpperCamelCase : Dict = []
UpperCamelCase : List[Any] = [self.eos_token_id, self.cur_lang_code]
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = self.lang_code_to_id[lang]
UpperCamelCase : Union[str, Any] = []
UpperCamelCase : List[str] = [self.eos_token_id, self.cur_lang_code]
| 355 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Any = AudioLDMPipeline
lowercase : Union[str, Any] = TEXT_TO_AUDIO_PARAMS
lowercase : List[str] = TEXT_TO_AUDIO_BATCH_PARAMS
lowercase : Tuple = frozenset(
[
'num_inference_steps',
'num_waveforms_per_prompt',
'generator',
'latents',
'output_type',
'return_dict',
'callback',
'callback_steps',
] )
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : Tuple = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=(32, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Optional[Any] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_one=SCREAMING_SNAKE_CASE_ , )
torch.manual_seed(0 )
UpperCamelCase : Optional[int] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
UpperCamelCase : int = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
UpperCamelCase : Optional[int] = ClapTextModelWithProjection(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 )
UpperCamelCase : Tuple = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=1_6000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : List[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Any = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def a_ ( self ):
UpperCamelCase : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Any = self.get_dummy_components()
UpperCamelCase : int = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Tuple = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[str] = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Optional[int] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : str = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
UpperCamelCase : Tuple = prompt_embeds
# forward
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : List[str] = self.get_dummy_components()
UpperCamelCase : List[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * ["""this is a negative prompt"""]
UpperCamelCase : List[Any] = negative_prompt
UpperCamelCase : str = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : str = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
UpperCamelCase : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[Any] = []
for p in [prompt, negative_prompt]:
UpperCamelCase : int = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Union[str, Any] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
embeds.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase : Tuple = embeds
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Optional[int] = self.get_dummy_components()
UpperCamelCase : List[str] = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = """egg cracking"""
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ , negative_prompt=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Union[str, Any] = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Union[str, Any] = self.get_dummy_components()
UpperCamelCase : Tuple = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
UpperCamelCase : List[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
UpperCamelCase : Dict = 2
UpperCamelCase : List[str] = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
UpperCamelCase : List[str] = 2
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
UpperCamelCase : Any = 2
UpperCamelCase : str = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Tuple = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = audioldm_pipe.vocoder.config.sampling_rate
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe(audio_length_in_s=0.016 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.016
UpperCamelCase : Optional[Any] = audioldm_pipe(audio_length_in_s=0.032 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.032
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Optional[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = ["""hey"""]
UpperCamelCase : Dict = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : str = output.audios.shape
assert audio_shape == (1, 256)
UpperCamelCase : Optional[Any] = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
UpperCamelCase : str = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : List[str] = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def a_ ( self ):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
self._test_inference_batch_single_identical(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def a_ ( self ):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@slow
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="cpu" , SCREAMING_SNAKE_CASE_=torch.floataa , SCREAMING_SNAKE_CASE_=0 ):
UpperCamelCase : str = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = np.random.RandomState(SCREAMING_SNAKE_CASE_ ).standard_normal((1, 8, 128, 16) )
UpperCamelCase : int = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def a_ ( self ):
UpperCamelCase : Optional[int] = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = 25
UpperCamelCase : Optional[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[7_7230:7_7240]
UpperCamelCase : Optional[Any] = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
UpperCamelCase : Any = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def a_ ( self ):
UpperCamelCase : Any = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : Any = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
UpperCamelCase : str = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[2_7780:2_7790]
UpperCamelCase : Tuple = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
UpperCamelCase : Tuple = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 27 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Any = KandinskyImgaImgPipeline
lowercase : Tuple = ['prompt', 'image_embeds', 'negative_image_embeds', 'image']
lowercase : str = [
'prompt',
'negative_prompt',
'image_embeds',
'negative_image_embeds',
'image',
]
lowercase : Union[str, Any] = [
'generator',
'height',
'width',
'strength',
'guidance_scale',
'negative_prompt',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
lowercase : Dict = False
@property
def a_ ( self ):
return 32
@property
def a_ ( self ):
return 32
@property
def a_ ( self ):
return self.time_input_dim
@property
def a_ ( self ):
return self.time_input_dim * 4
@property
def a_ ( self ):
return 100
@property
def a_ ( self ):
UpperCamelCase : List[str] = XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" )
return tokenizer
@property
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : Optional[Any] = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1005 , )
UpperCamelCase : int = MultilingualCLIP(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = text_encoder.eval()
return text_encoder
@property
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : List[Any] = {
"""in_channels""": 4,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """text_image""",
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""encoder_hid_dim""": self.text_embedder_hidden_size,
"""encoder_hid_dim_type""": """text_image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
UpperCamelCase : Optional[int] = UNetaDConditionModel(**SCREAMING_SNAKE_CASE_ )
return model
@property
def a_ ( self ):
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : Any = VQModel(**self.dummy_movq_kwargs )
return model
def a_ ( self ):
UpperCamelCase : Optional[int] = self.dummy_text_encoder
UpperCamelCase : Optional[int] = self.dummy_tokenizer
UpperCamelCase : Union[str, Any] = self.dummy_unet
UpperCamelCase : Optional[Any] = self.dummy_movq
UpperCamelCase : List[str] = {
"""num_train_timesteps""": 1000,
"""beta_schedule""": """linear""",
"""beta_start""": 0.00085,
"""beta_end""": 0.012,
"""clip_sample""": False,
"""set_alpha_to_one""": False,
"""steps_offset""": 0,
"""prediction_type""": """epsilon""",
"""thresholding""": False,
}
UpperCamelCase : Optional[Any] = DDIMScheduler(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = {
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
UpperCamelCase : Any = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(SCREAMING_SNAKE_CASE_ )
# create init_image
UpperCamelCase : Dict = floats_tensor((1, 3, 64, 64) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCamelCase : Optional[int] = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_ ) ).convert("""RGB""" ).resize((256, 256) )
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : int = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : int = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = {
"""prompt""": """horse""",
"""image""": init_image,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 10,
"""guidance_scale""": 7.0,
"""strength""": 0.2,
"""output_type""": """np""",
}
return inputs
def a_ ( self ):
UpperCamelCase : Tuple = """cpu"""
UpperCamelCase : Dict = self.get_dummy_components()
UpperCamelCase : Optional[int] = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Optional[Any] = output.images
UpperCamelCase : int = pipe(
**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
UpperCamelCase : Tuple = image[0, -3:, -3:, -1]
UpperCamelCase : str = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCamelCase : Dict = np.array(
[0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
@slow
@require_torch_gpu
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self ):
UpperCamelCase : List[str] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinsky/kandinsky_img2img_frog.npy""" )
UpperCamelCase : Dict = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
UpperCamelCase : List[Any] = """A red cartoon frog, 4k"""
UpperCamelCase : List[Any] = KandinskyPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = KandinskyImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1""" , torch_dtype=torch.floataa )
UpperCamelCase : List[str] = pipeline.to(SCREAMING_SNAKE_CASE_ )
pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = torch.Generator(device="""cpu""" ).manual_seed(0 )
UpperCamelCase : Optional[int] = pipe_prior(
SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
UpperCamelCase : Optional[int] = pipeline(
SCREAMING_SNAKE_CASE_ , image=SCREAMING_SNAKE_CASE_ , image_embeds=SCREAMING_SNAKE_CASE_ , negative_image_embeds=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type="""np""" , )
UpperCamelCase : str = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 356 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( snake_case_ : Dataset ,snake_case_ : Dict[str, str] ):
'''simple docstring'''
UpperCamelCase : List[str] = args.log_outputs
UpperCamelCase : Tuple = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
UpperCamelCase : List[Any] = load_metric("""wer""" )
UpperCamelCase : Any = load_metric("""cer""" )
# compute metrics
UpperCamelCase : str = wer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
UpperCamelCase : Dict = cer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
# print & log results
UpperCamelCase : Optional[int] = f'WER: {wer_result}\nCER: {cer_result}'
print(snake_case_ )
with open(f'{dataset_id}_eval_results.txt' ,"""w""" ) as f:
f.write(snake_case_ )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
UpperCamelCase : Optional[Any] = f'log_{dataset_id}_predictions.txt'
UpperCamelCase : str = f'log_{dataset_id}_targets.txt'
with open(snake_case_ ,"""w""" ) as p, open(snake_case_ ,"""w""" ) as t:
# mapping function to write output
def write_to_file(snake_case_ : Union[str, Any] ,snake_case_ : Tuple ):
p.write(f'{i}' + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(f'{i}' + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(snake_case_ ,with_indices=snake_case_ )
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Dict = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
UpperCamelCase : str = re.sub(snake_case_ ,"""""" ,text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
UpperCamelCase : List[str] = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
UpperCamelCase : Tuple = """ """.join(text.split(snake_case_ ) )
return text
def A_ ( snake_case_ : str ):
'''simple docstring'''
# load dataset
UpperCamelCase : Union[str, Any] = load_dataset(args.dataset ,args.config ,split=args.split ,use_auth_token=snake_case_ )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
UpperCamelCase : List[Any] = AutoFeatureExtractor.from_pretrained(args.model_id )
UpperCamelCase : Dict = feature_extractor.sampling_rate
# resample audio
UpperCamelCase : Optional[Any] = dataset.cast_column("""audio""" ,Audio(sampling_rate=snake_case_ ) )
# load eval pipeline
if args.device is None:
UpperCamelCase : int = 0 if torch.cuda.is_available() else -1
UpperCamelCase : Union[str, Any] = pipeline("""automatic-speech-recognition""" ,model=args.model_id ,device=args.device )
# map function to decode audio
def map_to_pred(snake_case_ : Union[str, Any] ):
UpperCamelCase : List[Any] = asr(
batch["""audio"""]["""array"""] ,chunk_length_s=args.chunk_length_s ,stride_length_s=args.stride_length_s )
UpperCamelCase : Union[str, Any] = prediction["""text"""]
UpperCamelCase : Optional[Any] = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
UpperCamelCase : Any = dataset.map(snake_case_ ,remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(snake_case_ ,snake_case_ )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
__A : Optional[Any] = parser.parse_args()
main(args)
| 27 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_keras_nlp_available():
from transformers.models.gpta import TFGPTaTokenizer
__A : int = ['''gpt2''']
__A : List[str] = '''gpt2'''
if is_tf_available():
class lowerCamelCase ( tf.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ ):
super().__init__()
UpperCamelCase : List[str] = tokenizer
UpperCamelCase : Union[str, Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = TFGPTaLMHeadModel.from_config(SCREAMING_SNAKE_CASE_ )
@tf.function(input_signature=(tf.TensorSpec((None,) , tf.string , name="""text""" ),) )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[Any] = self.tokenizer(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = tokenized["""input_ids"""].to_tensor()
UpperCamelCase : List[str] = tf.cast(input_ids_dense > 0 , tf.intaa )
# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])
UpperCamelCase : int = self.model(input_ids=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )["""logits"""]
return outputs
@require_tf
@require_keras_nlp
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
super().setUp()
UpperCamelCase : Union[str, Any] = [GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
UpperCamelCase : Optional[Any] = [TFGPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ ) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
UpperCamelCase : Dict = [
"""This is a straightforward English test sentence.""",
"""This one has some weird characters\rto\nsee\r\nif those\u00E9break things.""",
"""Now we're going to add some Chinese: 一 二 三 一二三""",
"""And some much more rare Chinese: 齉 堃 齉堃""",
"""Je vais aussi écrire en français pour tester les accents""",
"""Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
]
UpperCamelCase : Dict = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def a_ ( self ):
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in self.test_sentences:
UpperCamelCase : Dict = tokenizer([test_inputs] , return_tensors="""tf""" )
UpperCamelCase : Any = tf_tokenizer([test_inputs] )
for key in python_outputs.keys():
# convert them to numpy to avoid messing with ragged tensors
UpperCamelCase : List[Any] = python_outputs[key].numpy()
UpperCamelCase : Dict = tf_outputs[key].numpy()
self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )
self.assertTrue(tf.reduce_all(tf.cast(SCREAMING_SNAKE_CASE_ , tf.intaa ) == tf_outputs_values ) )
@slow
def a_ ( self ):
for tf_tokenizer in self.tf_tokenizers:
UpperCamelCase : str = tf.function(SCREAMING_SNAKE_CASE_ )
for test_inputs in self.test_sentences:
UpperCamelCase : int = tf.constant(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = compiled_tokenizer(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = tf_tokenizer(SCREAMING_SNAKE_CASE_ )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def a_ ( self ):
for tf_tokenizer in self.tf_tokenizers:
UpperCamelCase : Optional[Any] = ModelToSave(tokenizer=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = tf.convert_to_tensor([self.test_sentences[0]] )
UpperCamelCase : Union[str, Any] = model.serving(SCREAMING_SNAKE_CASE_ ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
UpperCamelCase : Any = Path(SCREAMING_SNAKE_CASE_ ) / """saved.model"""
tf.saved_model.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , signatures={"""serving_default""": model.serving} )
UpperCamelCase : Optional[Any] = tf.saved_model.load(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = loaded_model.signatures["""serving_default"""](SCREAMING_SNAKE_CASE_ )["""output_0"""]
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertTrue(tf.reduce_all(out == loaded_output ) )
@slow
def a_ ( self ):
for tf_tokenizer in self.tf_tokenizers:
UpperCamelCase : int = tf.convert_to_tensor([self.test_sentences[0]] )
UpperCamelCase : int = tf_tokenizer(SCREAMING_SNAKE_CASE_ ) # Build model with some sample inputs
UpperCamelCase : Tuple = tf_tokenizer.get_config()
UpperCamelCase : Tuple = TFGPTaTokenizer.from_config(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = model_from_config(SCREAMING_SNAKE_CASE_ )
for key in from_config_output.keys():
self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )
@slow
def a_ ( self ):
for tf_tokenizer in self.tf_tokenizers:
# for the test to run
UpperCamelCase : int = 12_3123
for max_length in [3, 5, 1024]:
UpperCamelCase : Optional[int] = tf.convert_to_tensor([self.test_sentences[0]] )
UpperCamelCase : str = tf_tokenizer(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = out["""input_ids"""].numpy().shape[1]
assert out_length == max_length
| 357 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = 'EncodecFeatureExtractor'
lowercase : List[Any] = ('T5Tokenizer', 'T5TokenizerFast')
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.feature_extractor
UpperCamelCase : Any = False
def a_ ( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True ):
return self.tokenizer.get_decoder_prompt_ids(task=SCREAMING_SNAKE_CASE_ , language=SCREAMING_SNAKE_CASE_ , no_timestamps=SCREAMING_SNAKE_CASE_ )
def __call__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = kwargs.pop("""sampling_rate""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = kwargs.pop("""text""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Any = args[0]
UpperCamelCase : str = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if text is not None:
UpperCamelCase : Optional[int] = self.tokenizer(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is not None:
UpperCamelCase : str = self.feature_extractor(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
UpperCamelCase : int = audio_inputs["""input_values"""]
if "padding_mask" in audio_inputs:
UpperCamelCase : Optional[Any] = audio_inputs["""padding_mask"""]
return inputs
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = kwargs.pop("""padding_mask""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Optional[int] = args[0]
UpperCamelCase : Any = args[1:]
if audio_values is not None:
return self._decode_audio(SCREAMING_SNAKE_CASE_ , padding_mask=SCREAMING_SNAKE_CASE_ )
else:
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = to_numpy(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase , UpperCamelCase : int = audio_values.shape
if padding_mask is None:
return list(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = to_numpy(SCREAMING_SNAKE_CASE_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
UpperCamelCase : List[str] = seq_len - padding_mask.shape[-1]
UpperCamelCase : Optional[int] = 1 - self.feature_extractor.padding_value
UpperCamelCase : Any = np.pad(SCREAMING_SNAKE_CASE_ , ((0, 0), (0, difference)) , """constant""" , constant_values=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audio_values.tolist()
for i in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
UpperCamelCase : Optional[Any] = sliced_audio.reshape(SCREAMING_SNAKE_CASE_ , -1 )
return audio_values
| 27 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__A : List[Any] = logging.get_logger(__name__)
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
warnings.warn(
"""The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use DeiTImageProcessor instead.""" , SCREAMING_SNAKE_CASE_ , )
super().__init__(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
| 358 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( snake_case_ : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
UpperCamelCase : Any = BeautifulSoup(requests.get(snake_case_ ).text ,"""html.parser""" )
UpperCamelCase : Optional[int] = soup.findAll("""h1""" )
UpperCamelCase : List[Any] = soup.findAll("""div""" ,{"""class""": """maincounter-number"""} )
keys += soup.findAll("""span""" ,{"""class""": """panel-title"""} )
values += soup.findAll("""div""" ,{"""class""": """number-table-main"""} )
return {key.text.strip(): value.text.strip() for key, value in zip(snake_case_ ,snake_case_ )}
if __name__ == "__main__":
print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
for key, value in world_covidaa_stats().items():
print(F'''{key}\n{value}\n''')
| 27 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
'''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
'''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : List[Any] = 'mobilenet_v2'
def __init__( self , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=224 , SCREAMING_SNAKE_CASE_=1.0 , SCREAMING_SNAKE_CASE_=8 , SCREAMING_SNAKE_CASE_=8 , SCREAMING_SNAKE_CASE_=6 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_="relu6" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=0.8 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=0.001 , SCREAMING_SNAKE_CASE_=255 , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
if depth_multiplier <= 0:
raise ValueError("""depth_multiplier must be greater than zero.""" )
UpperCamelCase : Dict = num_channels
UpperCamelCase : str = image_size
UpperCamelCase : List[str] = depth_multiplier
UpperCamelCase : Dict = depth_divisible_by
UpperCamelCase : int = min_depth
UpperCamelCase : Optional[int] = expand_ratio
UpperCamelCase : Any = output_stride
UpperCamelCase : Tuple = first_layer_is_expansion
UpperCamelCase : Optional[Any] = finegrained_output
UpperCamelCase : Dict = hidden_act
UpperCamelCase : Dict = tf_padding
UpperCamelCase : List[Any] = classifier_dropout_prob
UpperCamelCase : str = initializer_range
UpperCamelCase : Optional[int] = layer_norm_eps
UpperCamelCase : Tuple = semantic_loss_ignore_index
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Optional[Any] = version.parse('1.11' )
@property
def a_ ( self ):
return OrderedDict([("""pixel_values""", {0: """batch"""})] )
@property
def a_ ( self ):
if self.task == "image-classification":
return OrderedDict([("""logits""", {0: """batch"""})] )
else:
return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})] )
@property
def a_ ( self ):
return 1e-4
| 359 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=1 , ):
UpperCamelCase : Tuple = parent
UpperCamelCase : Optional[int] = batch_size
UpperCamelCase : Optional[Any] = seq_length
UpperCamelCase : int = is_training
UpperCamelCase : Union[str, Any] = use_input_mask
UpperCamelCase : Union[str, Any] = use_token_type_ids
UpperCamelCase : Dict = use_labels
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Union[str, Any] = hidden_size
UpperCamelCase : Tuple = num_hidden_layers
UpperCamelCase : Any = num_attention_heads
UpperCamelCase : int = intermediate_size
UpperCamelCase : str = hidden_act
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : List[Any] = max_position_embeddings
UpperCamelCase : Optional[Any] = type_vocab_size
UpperCamelCase : int = type_sequence_label_size
UpperCamelCase : Dict = initializer_range
UpperCamelCase : Dict = num_labels
UpperCamelCase : Tuple = num_choices
UpperCamelCase : Optional[int] = scope
UpperCamelCase : List[Any] = q_groups
UpperCamelCase : Tuple = k_groups
UpperCamelCase : Any = v_groups
UpperCamelCase : List[str] = post_attention_groups
UpperCamelCase : Tuple = intermediate_groups
UpperCamelCase : int = output_groups
def a_ ( self ):
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Tuple = None
if self.use_input_mask:
UpperCamelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Optional[int] = None
UpperCamelCase : List[Any] = None
UpperCamelCase : Dict = None
if self.use_labels:
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : Dict = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ ( self ):
return SqueezeBertConfig(
embedding_size=self.hidden_size , vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = SqueezeBertModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = SqueezeBertForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = SqueezeBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : str = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = self.num_labels
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = self.num_labels
UpperCamelCase : str = SqueezeBertForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = self.num_choices
UpperCamelCase : Tuple = SqueezeBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Union[str, Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) : Optional[int] = config_and_inputs
UpperCamelCase : Optional[int] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
lowercase : Dict = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase : Dict = False
lowercase : str = True
lowercase : str = False
def a_ ( self ):
UpperCamelCase : Any = SqueezeBertModelTester(self )
UpperCamelCase : List[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : Optional[Any] = SqueezeBertModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_sentencepiece
@require_tokenizers
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli""" )
UpperCamelCase : Dict = torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]] )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[Any] = torch.Size((1, 3) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.tensor([[0.6401, -0.0349, -0.6041]] )
self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 27 | 0 |
"""simple docstring"""
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def A_ ( *snake_case_ : Optional[int] ):
'''simple docstring'''
with open(snake_case_ ,"""r""" ) as fh:
fcntl.flock(snake_case_ ,fcntl.LOCK_EX )
try:
print(*snake_case_ )
finally:
fcntl.flock(snake_case_ ,fcntl.LOCK_UN )
__A : Optional[Any] = int(os.environ['''LOCAL_RANK'''])
torch.cuda.set_device(local_rank)
__A : Optional[int] = torch.device('''cuda''', local_rank)
__A : List[str] = socket.gethostname()
__A : Optional[Any] = F'''[{hostname}-{local_rank}]'''
try:
# test distributed
dist.init_process_group('''nccl''')
dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
dist.barrier()
# test cuda is available and can allocate memory
torch.cuda.is_available()
torch.ones(1).cuda(local_rank)
# global rank
__A : List[Any] = dist.get_rank()
__A : List[str] = dist.get_world_size()
printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')
dist.barrier()
if rank == 0:
printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')
except Exception:
printflock(F'''{gpu} is broken''')
raise
| 360 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "geglu" , SCREAMING_SNAKE_CASE_ = None , ):
super().__init__()
UpperCamelCase : int = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=SCREAMING_SNAKE_CASE_ , attention_head_dim=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , dropout=SCREAMING_SNAKE_CASE_ , norm_num_groups=SCREAMING_SNAKE_CASE_ , cross_attention_dim=SCREAMING_SNAKE_CASE_ , attention_bias=SCREAMING_SNAKE_CASE_ , sample_size=SCREAMING_SNAKE_CASE_ , num_vector_embeds=SCREAMING_SNAKE_CASE_ , activation_fn=SCREAMING_SNAKE_CASE_ , num_embeds_ada_norm=SCREAMING_SNAKE_CASE_ , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
UpperCamelCase : Optional[Any] = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
UpperCamelCase : List[Any] = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
UpperCamelCase : int = [1, 0]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_ = True , ):
UpperCamelCase : Dict = hidden_states
UpperCamelCase : Optional[Any] = []
UpperCamelCase : List[Any] = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
UpperCamelCase : Optional[int] = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
UpperCamelCase : str = self.transformer_index_for_condition[i]
UpperCamelCase : Any = self.transformers[transformer_index](
SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ , timestep=SCREAMING_SNAKE_CASE_ , cross_attention_kwargs=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
UpperCamelCase : Any = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
UpperCamelCase : List[str] = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE_ )
| 27 | 0 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def A_ ( snake_case_ : Optional[int] ,snake_case_ : Optional[int] ,snake_case_ : List[Any]=[] ):
'''simple docstring'''
UpperCamelCase : List[str] = size[0] - overlap_pixels * 2
UpperCamelCase : Optional[Any] = size[1] - overlap_pixels * 2
for letter in ["l", "r"]:
if letter in remove_borders:
size_x += overlap_pixels
for letter in ["t", "b"]:
if letter in remove_borders:
size_y += overlap_pixels
UpperCamelCase : List[Any] = np.ones((size_y, size_x) ,dtype=np.uinta ) * 2_5_5
UpperCamelCase : Any = np.pad(snake_case_ ,mode="""linear_ramp""" ,pad_width=snake_case_ ,end_values=0 )
if "l" in remove_borders:
UpperCamelCase : int = mask[:, overlap_pixels : mask.shape[1]]
if "r" in remove_borders:
UpperCamelCase : List[str] = mask[:, 0 : mask.shape[1] - overlap_pixels]
if "t" in remove_borders:
UpperCamelCase : Union[str, Any] = mask[overlap_pixels : mask.shape[0], :]
if "b" in remove_borders:
UpperCamelCase : List[str] = mask[0 : mask.shape[0] - overlap_pixels, :]
return mask
def A_ ( snake_case_ : Tuple ,snake_case_ : Any ,snake_case_ : Optional[Any] ):
'''simple docstring'''
return max(snake_case_ ,min(snake_case_ ,snake_case_ ) )
def A_ ( snake_case_ : [int] ,snake_case_ : [int] ,snake_case_ : [int] ):
'''simple docstring'''
return (
clamp(rect[0] ,min[0] ,max[0] ),
clamp(rect[1] ,min[1] ,max[1] ),
clamp(rect[2] ,min[0] ,max[0] ),
clamp(rect[3] ,min[1] ,max[1] ),
)
def A_ ( snake_case_ : [int] ,snake_case_ : int ,snake_case_ : [int] ):
'''simple docstring'''
UpperCamelCase : int = list(snake_case_ )
rect[0] -= overlap
rect[1] -= overlap
rect[2] += overlap
rect[3] += overlap
UpperCamelCase : List[str] = clamp_rect(snake_case_ ,[0, 0] ,[image_size[0], image_size[1]] )
return rect
def A_ ( snake_case_ : List[Any] ,snake_case_ : Optional[int] ,snake_case_ : Tuple ,snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : int = Image.new("""RGB""" ,(tile.size[0] + original_slice, tile.size[1]) )
result.paste(
original_image.resize((tile.size[0], tile.size[1]) ,Image.BICUBIC ).crop(
(slice_x, 0, slice_x + original_slice, tile.size[1]) ) ,(0, 0) ,)
result.paste(snake_case_ ,(original_slice, 0) )
return result
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : List[str] = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
UpperCamelCase : int = tile.crop(snake_case_ )
return tile
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = n % d
return n - divisor
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 350 , ):
super().__init__(
vae=SCREAMING_SNAKE_CASE_ , text_encoder=SCREAMING_SNAKE_CASE_ , tokenizer=SCREAMING_SNAKE_CASE_ , unet=SCREAMING_SNAKE_CASE_ , low_res_scheduler=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ , max_noise_level=SCREAMING_SNAKE_CASE_ , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
torch.manual_seed(0 )
UpperCamelCase : Dict = (
min(image.size[0] - (tile_size + original_image_slice) , x * tile_size ),
min(image.size[1] - (tile_size + original_image_slice) , y * tile_size ),
min(image.size[0] , (x + 1) * tile_size ),
min(image.size[1] , (y + 1) * tile_size ),
)
UpperCamelCase : Optional[int] = add_overlap_rect(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , image.size )
UpperCamelCase : Optional[int] = image.crop(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
UpperCamelCase : int = translated_slice_x - (original_image_slice / 2)
UpperCamelCase : List[str] = max(0 , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = squeeze_tile(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = to_input.size
UpperCamelCase : List[Any] = to_input.resize((tile_size, tile_size) , Image.BICUBIC )
UpperCamelCase : Optional[int] = super(SCREAMING_SNAKE_CASE_ , self ).__call__(image=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ).images[0]
UpperCamelCase : Optional[Any] = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) , Image.BICUBIC )
UpperCamelCase : int = unsqueeze_tile(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) , Image.BICUBIC )
UpperCamelCase : List[Any] = []
if x == 0:
remove_borders.append("""l""" )
elif crop_rect[2] == image.size[0]:
remove_borders.append("""r""" )
if y == 0:
remove_borders.append("""t""" )
elif crop_rect[3] == image.size[1]:
remove_borders.append("""b""" )
UpperCamelCase : Optional[Any] = Image.fromarray(
make_transparency_mask(
(upscaled_tile.size[0], upscaled_tile.size[1]) , tile_border * 4 , remove_borders=SCREAMING_SNAKE_CASE_ ) , mode="""L""" , )
final_image.paste(
SCREAMING_SNAKE_CASE_ , (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) , SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def __call__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 75 , SCREAMING_SNAKE_CASE_ = 9.0 , SCREAMING_SNAKE_CASE_ = 50 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 128 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = 32 , ):
UpperCamelCase : int = Image.new("""RGB""" , (image.size[0] * 4, image.size[1] * 4) )
UpperCamelCase : Optional[Any] = math.ceil(image.size[0] / tile_size )
UpperCamelCase : Optional[int] = math.ceil(image.size[1] / tile_size )
UpperCamelCase : Tuple = tcx * tcy
UpperCamelCase : Optional[Any] = 0
for y in range(SCREAMING_SNAKE_CASE_ ):
for x in range(SCREAMING_SNAKE_CASE_ ):
self._process_tile(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , prompt=SCREAMING_SNAKE_CASE_ , num_inference_steps=SCREAMING_SNAKE_CASE_ , guidance_scale=SCREAMING_SNAKE_CASE_ , noise_level=SCREAMING_SNAKE_CASE_ , negative_prompt=SCREAMING_SNAKE_CASE_ , num_images_per_prompt=SCREAMING_SNAKE_CASE_ , eta=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , latents=SCREAMING_SNAKE_CASE_ , )
current_count += 1
if callback is not None:
callback({"""progress""": current_count / total_tile_count, """image""": final_image} )
return final_image
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = """stabilityai/stable-diffusion-x4-upscaler"""
UpperCamelCase : Union[str, Any] = StableDiffusionTiledUpscalePipeline.from_pretrained(snake_case_ ,revision="""fp16""" ,torch_dtype=torch.floataa )
UpperCamelCase : Any = pipe.to("""cuda""" )
UpperCamelCase : List[Any] = Image.open("""../../docs/source/imgs/diffusers_library.jpg""" )
def callback(snake_case_ : Any ):
print(f'progress: {obj["progress"]:.4f}' )
obj["image"].save("""diffusers_library_progress.jpg""" )
UpperCamelCase : Optional[int] = pipe(image=snake_case_ ,prompt="""Black font, white background, vector""" ,noise_level=4_0 ,callback=snake_case_ )
final_image.save("""diffusers_library.jpg""" )
if __name__ == "__main__":
main()
| 361 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Optional[int] = 'mvp'
lowercase : Optional[Any] = ['past_key_values']
lowercase : Union[str, Any] = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , SCREAMING_SNAKE_CASE_=5_0267 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=100 , SCREAMING_SNAKE_CASE_=800 , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Dict = max_position_embeddings
UpperCamelCase : Optional[int] = d_model
UpperCamelCase : Optional[Any] = encoder_ffn_dim
UpperCamelCase : Any = encoder_layers
UpperCamelCase : List[Any] = encoder_attention_heads
UpperCamelCase : Optional[Any] = decoder_ffn_dim
UpperCamelCase : Optional[int] = decoder_layers
UpperCamelCase : Dict = decoder_attention_heads
UpperCamelCase : List[str] = dropout
UpperCamelCase : List[str] = attention_dropout
UpperCamelCase : List[Any] = activation_dropout
UpperCamelCase : Dict = activation_function
UpperCamelCase : List[str] = init_std
UpperCamelCase : int = encoder_layerdrop
UpperCamelCase : Dict = decoder_layerdrop
UpperCamelCase : Any = classifier_dropout
UpperCamelCase : Tuple = use_cache
UpperCamelCase : Dict = encoder_layers
UpperCamelCase : Tuple = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase : Optional[Any] = use_prompt
UpperCamelCase : Any = prompt_length
UpperCamelCase : List[Any] = prompt_mid_dim
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , forced_eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""" , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = self.bos_token_id
warnings.warn(
f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
"""The config can simply be saved and uploaded again to be fixed.""" )
| 27 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : int = 'WhisperFeatureExtractor'
lowercase : Dict = 'WhisperTokenizer'
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = self.feature_extractor
UpperCamelCase : Optional[Any] = False
def a_ ( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True ):
return self.tokenizer.get_decoder_prompt_ids(task=SCREAMING_SNAKE_CASE_ , language=SCREAMING_SNAKE_CASE_ , no_timestamps=SCREAMING_SNAKE_CASE_ )
def __call__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = kwargs.pop("""sampling_rate""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = kwargs.pop("""text""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Optional[Any] = args[0]
UpperCamelCase : Dict = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if audio is not None:
UpperCamelCase : Tuple = self.feature_extractor(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if text is not None:
UpperCamelCase : Dict = self.tokenizer(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
UpperCamelCase : Optional[Any] = encodings["""input_ids"""]
return inputs
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="np" ):
return self.tokenizer.get_prompt_ids(SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ )
| 362 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__A : Optional[Any] = 16
__A : str = 32
def A_ ( snake_case_ : Accelerator ,snake_case_ : int = 1_6 ):
'''simple docstring'''
UpperCamelCase : Tuple = AutoTokenizer.from_pretrained("""bert-base-cased""" )
UpperCamelCase : Optional[int] = load_dataset("""glue""" ,"""mrpc""" )
def tokenize_function(snake_case_ : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase : Union[str, Any] = tokenizer(examples["""sentence1"""] ,examples["""sentence2"""] ,truncation=snake_case_ ,max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCamelCase : Optional[Any] = datasets.map(
snake_case_ ,batched=snake_case_ ,remove_columns=["""idx""", """sentence1""", """sentence2"""] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase : str = tokenized_datasets.rename_column("""label""" ,"""labels""" )
def collate_fn(snake_case_ : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCamelCase : Union[str, Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCamelCase : Optional[Any] = 1_6
elif accelerator.mixed_precision != "no":
UpperCamelCase : Any = 8
else:
UpperCamelCase : Optional[Any] = None
return tokenizer.pad(
snake_case_ ,padding="""longest""" ,max_length=snake_case_ ,pad_to_multiple_of=snake_case_ ,return_tensors="""pt""" ,)
# Instantiate dataloaders.
UpperCamelCase : str = DataLoader(
tokenized_datasets["""train"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
UpperCamelCase : Dict = DataLoader(
tokenized_datasets["""validation"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__A : int = mocked_dataloaders # noqa: F811
def A_ ( snake_case_ : Tuple ,snake_case_ : Dict ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,snake_case_ ) == "1":
UpperCamelCase : Union[str, Any] = 2
# New Code #
UpperCamelCase : Dict = int(args.gradient_accumulation_steps )
UpperCamelCase : List[Any] = int(args.local_sgd_steps )
# Initialize accelerator
UpperCamelCase : str = Accelerator(
cpu=args.cpu ,mixed_precision=args.mixed_precision ,gradient_accumulation_steps=snake_case_ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase : Union[str, Any] = config["""lr"""]
UpperCamelCase : int = int(config["""num_epochs"""] )
UpperCamelCase : int = int(config["""seed"""] )
UpperCamelCase : List[Any] = int(config["""batch_size"""] )
UpperCamelCase : Optional[int] = evaluate.load("""glue""" ,"""mrpc""" )
set_seed(snake_case_ )
UpperCamelCase , UpperCamelCase : Dict = get_dataloaders(snake_case_ ,snake_case_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase : Optional[int] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,return_dict=snake_case_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCamelCase : Tuple = model.to(accelerator.device )
# Instantiate optimizer
UpperCamelCase : List[Any] = AdamW(params=model.parameters() ,lr=snake_case_ )
# Instantiate scheduler
UpperCamelCase : str = get_linear_schedule_with_warmup(
optimizer=snake_case_ ,num_warmup_steps=1_0_0 ,num_training_steps=(len(snake_case_ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Any = accelerator.prepare(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
# Now we train the model
for epoch in range(snake_case_ ):
model.train()
with LocalSGD(
accelerator=snake_case_ ,model=snake_case_ ,local_sgd_steps=snake_case_ ,enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(snake_case_ ):
UpperCamelCase : Optional[Any] = model(**snake_case_ )
UpperCamelCase : Optional[int] = output.loss
accelerator.backward(snake_case_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCamelCase : Any = model(**snake_case_ )
UpperCamelCase : Tuple = outputs.logits.argmax(dim=-1 )
UpperCamelCase , UpperCamelCase : int = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=snake_case_ ,references=snake_case_ ,)
UpperCamelCase : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' ,snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" ,type=snake_case_ ,default=snake_case_ ,choices=["""no""", """fp16""", """bf16""", """fp8"""] ,help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" ,)
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" ,type=snake_case_ ,default=1 ,help="""The number of minibatches to be ran before gradients are accumulated.""" ,)
parser.add_argument(
"""--local_sgd_steps""" ,type=snake_case_ ,default=8 ,help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" ,action="""store_true""" ,help="""If passed, will train on the CPU.""" )
UpperCamelCase : Dict = parser.parse_args()
UpperCamelCase : List[Any] = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(snake_case_ ,snake_case_ )
if __name__ == "__main__":
main()
| 27 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Dict = {
'''BAAI/AltCLIP''': '''https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json''',
# See all AltCLIP models at https://huggingface.co/models?filter=altclip
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Optional[int] = 'altclip_text_model'
def __init__( self , SCREAMING_SNAKE_CASE_=25_0002 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=24 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=514 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=1e-05 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_="absolute" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=768 , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = vocab_size
UpperCamelCase : Tuple = hidden_size
UpperCamelCase : Optional[Any] = num_hidden_layers
UpperCamelCase : Tuple = num_attention_heads
UpperCamelCase : Dict = hidden_act
UpperCamelCase : Dict = intermediate_size
UpperCamelCase : str = hidden_dropout_prob
UpperCamelCase : Optional[int] = attention_probs_dropout_prob
UpperCamelCase : Optional[int] = max_position_embeddings
UpperCamelCase : str = type_vocab_size
UpperCamelCase : List[Any] = initializer_range
UpperCamelCase : str = initializer_factor
UpperCamelCase : Optional[int] = layer_norm_eps
UpperCamelCase : Any = position_embedding_type
UpperCamelCase : Union[str, Any] = use_cache
UpperCamelCase : List[str] = project_dim
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : List[Any] = 'altclip_vision_model'
def __init__( self , SCREAMING_SNAKE_CASE_=768 , SCREAMING_SNAKE_CASE_=3072 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=224 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_="quick_gelu" , SCREAMING_SNAKE_CASE_=1e-5 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=1.0 , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = hidden_size
UpperCamelCase : str = intermediate_size
UpperCamelCase : Union[str, Any] = projection_dim
UpperCamelCase : List[Any] = num_hidden_layers
UpperCamelCase : Optional[Any] = num_attention_heads
UpperCamelCase : str = num_channels
UpperCamelCase : List[str] = patch_size
UpperCamelCase : List[str] = image_size
UpperCamelCase : Optional[int] = initializer_range
UpperCamelCase : Optional[int] = initializer_factor
UpperCamelCase : List[Any] = attention_dropout
UpperCamelCase : Union[str, Any] = layer_norm_eps
UpperCamelCase : Dict = hidden_act
@classmethod
def a_ ( cls , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = cls.get_config_dict(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
# get the vision config dict if we are loading from AltCLIPConfig
if config_dict.get("""model_type""" ) == "altclip":
UpperCamelCase : int = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : int = 'altclip'
lowercase : str = True
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=768 , SCREAMING_SNAKE_CASE_=2.6592 , **SCREAMING_SNAKE_CASE_ ):
# If `_config_dict` exist, we use them for the backward compatibility.
# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
# of confusion!).
UpperCamelCase : Dict = kwargs.pop("""text_config_dict""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = kwargs.pop("""vision_config_dict""" , SCREAMING_SNAKE_CASE_ )
super().__init__(**SCREAMING_SNAKE_CASE_ )
# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
if text_config_dict is not None:
if text_config is None:
UpperCamelCase : List[str] = {}
# This is the complete result when using `text_config_dict`.
UpperCamelCase : int = AltCLIPTextConfig(**SCREAMING_SNAKE_CASE_ ).to_dict()
# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
for key, value in _text_config_dict.items():
if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
# If specified in `text_config_dict`
if key in text_config_dict:
UpperCamelCase : List[Any] = (
f'`{key}` is found in both `text_config_dict` and `text_config` but with different values. '
f'The value `text_config_dict["{key}"]` will be used instead.'
)
# If inferred from default argument values (just to be super careful)
else:
UpperCamelCase : Any = (
f'`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '
f'value `text_config["{key}"]` will be overriden.'
)
logger.warning(SCREAMING_SNAKE_CASE_ )
# Update all values in `text_config` with the ones in `_text_config_dict`.
text_config.update(_text_config_dict )
if vision_config_dict is not None:
if vision_config is None:
UpperCamelCase : List[str] = {}
# This is the complete result when using `vision_config_dict`.
UpperCamelCase : Dict = AltCLIPVisionConfig(**SCREAMING_SNAKE_CASE_ ).to_dict()
# convert keys to string instead of integer
if "id2label" in _vision_config_dict:
UpperCamelCase : List[Any] = {
str(SCREAMING_SNAKE_CASE_ ): value for key, value in _vision_config_dict["""id2label"""].items()
}
# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
for key, value in _vision_config_dict.items():
if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
# If specified in `vision_config_dict`
if key in vision_config_dict:
UpperCamelCase : int = (
f'`{key}` is found in both `vision_config_dict` and `vision_config` but with different '
f'values. The value `vision_config_dict["{key}"]` will be used instead.'
)
# If inferred from default argument values (just to be super careful)
else:
UpperCamelCase : List[Any] = (
f'`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '
f'The value `vision_config["{key}"]` will be overriden.'
)
logger.warning(SCREAMING_SNAKE_CASE_ )
# Update all values in `vision_config` with the ones in `_vision_config_dict`.
vision_config.update(_vision_config_dict )
if text_config is None:
UpperCamelCase : int = {}
logger.info("""`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.""" )
if vision_config is None:
UpperCamelCase : str = {}
logger.info("""`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.""" )
UpperCamelCase : str = AltCLIPTextConfig(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = AltCLIPVisionConfig(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = projection_dim
UpperCamelCase : Union[str, Any] = logit_scale_init_value
UpperCamelCase : Union[str, Any] = 1.0
@classmethod
def a_ ( cls , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Union[str, Any] = copy.deepcopy(self.__dict__ )
UpperCamelCase : Dict = self.text_config.to_dict()
UpperCamelCase : Union[str, Any] = self.vision_config.to_dict()
UpperCamelCase : Any = self.__class__.model_type
return output
| 363 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A : Optional[Any] = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__A : Any = {'''allegro/herbert-base-cased''': 514}
__A : Optional[Any] = {}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Dict = VOCAB_FILES_NAMES
lowercase : Any = PRETRAINED_VOCAB_FILES_MAP
lowercase : List[str] = PRETRAINED_INIT_CONFIGURATION
lowercase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Union[str, Any] = HerbertTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_="</s>" , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = [self.cls_token_id]
UpperCamelCase : str = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Tuple = [self.sep_token_id]
UpperCamelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Optional[int] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 27 | 0 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError
from .utils import require_pil
class lowerCamelCase ( _UpperCAmelCase ):
def a_ ( self ):
UpperCamelCase : Optional[int] = pa.array(TypedSequence([1, 2, 3] ) )
self.assertEqual(arr.type , pa.intaa() )
def a_ ( self ):
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = pa.array(TypedSequence([1, 2, 3] ) , type=pa.intaa() )
def a_ ( self ):
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = pa.array(TypedSequence([1, 2, 3] , try_type=Value("""bool""" ) , type=Value("""int64""" ) ) )
def a_ ( self ):
UpperCamelCase : Tuple = pa.array(TypedSequence([1, 2, 3] , type=Value("""int32""" ) ) )
self.assertEqual(arr.type , pa.intaa() )
def a_ ( self ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
UpperCamelCase : int = pa.array(TypedSequence(["""foo""", """bar"""] , type=Value("""int64""" ) ) )
def a_ ( self ):
UpperCamelCase : Dict = pa.array(TypedSequence([1, 2, 3] , try_type=Value("""int32""" ) ) )
self.assertEqual(arr.type , pa.intaa() )
def a_ ( self ):
UpperCamelCase : List[str] = pa.array(TypedSequence(["""foo""", """bar"""] , try_type=Value("""int64""" ) ) )
self.assertEqual(arr.type , pa.string() )
def a_ ( self ):
UpperCamelCase : List[Any] = pa.array(TypedSequence([[[1, 2, 3]]] , type=ArrayaD((1, 3) , """int64""" ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , """int64""" ) )
def a_ ( self ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
UpperCamelCase : Tuple = pa.array(TypedSequence(["""foo""", """bar"""] , type=ArrayaD((1, 3) , """int64""" ) ) )
def a_ ( self ):
UpperCamelCase : List[str] = pa.array(TypedSequence([[[1, 2, 3]]] , try_type=ArrayaD((1, 3) , """int64""" ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , """int64""" ) )
def a_ ( self ):
UpperCamelCase : int = pa.array(TypedSequence(["""foo""", """bar"""] , try_type=ArrayaD((1, 3) , """int64""" ) ) )
self.assertEqual(arr.type , pa.string() )
@require_pil
def a_ ( self ):
import PIL.Image
UpperCamelCase : List[str] = PIL.Image.fromarray(np.arange(10 , dtype=np.uinta ).reshape(2 , 5 ) )
with patch(
"""datasets.arrow_writer.cast_to_python_objects""" , side_effect=SCREAMING_SNAKE_CASE_ ) as mock_cast_to_python_objects:
UpperCamelCase : Optional[int] = pa.array(TypedSequence([{"""path""": None, """bytes""": b"""image_bytes"""}, pil_image] , type=Image() ) )
UpperCamelCase : Tuple = mock_cast_to_python_objects.call_args_list[-1]
self.assertIn("""optimize_list_casting""" , SCREAMING_SNAKE_CASE_ )
self.assertFalse(kwargs["""optimize_list_casting"""] )
def A_ ( snake_case_ : Any ,snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : str = pa.BufferReader(snake_case_ ) if isinstance(snake_case_ ,pa.Buffer ) else pa.memory_map(snake_case_ )
UpperCamelCase : List[Any] = pa.ipc.open_stream(snake_case_ )
UpperCamelCase : pa.Table = f.read_all()
assert len(pa_table.to_batches() ) == expected_num_chunks
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
del pa_table
@pytest.mark.parametrize("""writer_batch_size""" ,[None, 1, 1_0] )
@pytest.mark.parametrize(
"""fields""" ,[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def A_ ( snake_case_ : int ,snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = pa.BufferOutputStream()
UpperCamelCase : str = pa.schema(snake_case_ ) if fields else None
with ArrowWriter(stream=snake_case_ ,schema=snake_case_ ,writer_batch_size=snake_case_ ) as writer:
writer.write({"""col_1""": """foo""", """col_2""": 1} )
writer.write({"""col_1""": """bar""", """col_2""": 2} )
UpperCamelCase : Optional[Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
UpperCamelCase : Union[str, Any] = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
assert writer._schema == pa.schema(snake_case_ ,metadata=writer._schema.metadata )
_check_output(output.getvalue() ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Any = pa.BufferOutputStream()
UpperCamelCase : str = Features({"""labels""": ClassLabel(names=["""neg""", """pos"""] )} )
with ArrowWriter(stream=snake_case_ ,features=snake_case_ ) as writer:
writer.write({"""labels""": 0} )
writer.write({"""labels""": 1} )
UpperCamelCase : Any = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == features.arrow_schema
assert writer._schema.metadata == features.arrow_schema.metadata
UpperCamelCase : Tuple = pa.BufferReader(output.getvalue() )
UpperCamelCase : Union[str, Any] = pa.ipc.open_stream(snake_case_ )
UpperCamelCase : pa.Table = f.read_all()
UpperCamelCase : List[Any] = pa_table.schema
assert pa_table.num_rows == 2
assert schema == features.arrow_schema
assert schema.metadata == features.arrow_schema.metadata
assert features == Features.from_arrow_schema(snake_case_ )
@pytest.mark.parametrize("""writer_batch_size""" ,[None, 1, 1_0] )
def A_ ( snake_case_ : Optional[int] ):
'''simple docstring'''
UpperCamelCase : List[Any] = pa.BufferOutputStream()
with ArrowWriter(
stream=snake_case_ ,writer_batch_size=snake_case_ ,hash_salt="""split_name""" ,check_duplicates=snake_case_ ,) as writer:
with pytest.raises(snake_case_ ):
writer.write({"""col_1""": """foo""", """col_2""": 1} ,key=[1, 2] )
UpperCamelCase : int = writer.finalize()
@pytest.mark.parametrize("""writer_batch_size""" ,[None, 2, 1_0] )
def A_ ( snake_case_ : Optional[int] ):
'''simple docstring'''
UpperCamelCase : Optional[int] = pa.BufferOutputStream()
with ArrowWriter(
stream=snake_case_ ,writer_batch_size=snake_case_ ,hash_salt="""split_name""" ,check_duplicates=snake_case_ ,) as writer:
with pytest.raises(snake_case_ ):
writer.write({"""col_1""": """foo""", """col_2""": 1} ,key=1_0 )
writer.write({"""col_1""": """bar""", """col_2""": 2} ,key=1_0 )
UpperCamelCase : Any = writer.finalize()
@pytest.mark.parametrize("""writer_batch_size""" ,[None, 2, 1_0] )
def A_ ( snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : List[Any] = pa.BufferOutputStream()
with ArrowWriter(
stream=snake_case_ ,writer_batch_size=snake_case_ ,hash_salt="""split_name""" ,check_duplicates=snake_case_ ,) as writer:
writer.write({"""col_1""": """foo""", """col_2""": 1} ,key=1 )
writer.write({"""col_1""": """bar""", """col_2""": 2} ,key=2 )
UpperCamelCase : Dict = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_check_output(output.getvalue() ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize("""writer_batch_size""" ,[None, 1, 1_0] )
@pytest.mark.parametrize(
"""fields""" ,[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def A_ ( snake_case_ : Optional[int] ,snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : Any = pa.BufferOutputStream()
UpperCamelCase : Optional[Any] = pa.schema(snake_case_ ) if fields else None
with ArrowWriter(stream=snake_case_ ,schema=snake_case_ ,writer_batch_size=snake_case_ ) as writer:
writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]} )
writer.write_batch({"""col_1""": [], """col_2""": []} )
UpperCamelCase : Dict = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
UpperCamelCase : str = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
assert writer._schema == pa.schema(snake_case_ ,metadata=writer._schema.metadata )
_check_output(output.getvalue() ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize("""writer_batch_size""" ,[None, 1, 1_0] )
@pytest.mark.parametrize(
"""fields""" ,[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def A_ ( snake_case_ : Any ,snake_case_ : Optional[Any] ):
'''simple docstring'''
UpperCamelCase : Any = pa.BufferOutputStream()
UpperCamelCase : Dict = pa.schema(snake_case_ ) if fields else None
with ArrowWriter(stream=snake_case_ ,schema=snake_case_ ,writer_batch_size=snake_case_ ) as writer:
writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]} ) )
UpperCamelCase : int = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
UpperCamelCase : Union[str, Any] = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
assert writer._schema == pa.schema(snake_case_ ,metadata=writer._schema.metadata )
_check_output(output.getvalue() ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize("""writer_batch_size""" ,[None, 1, 1_0] )
@pytest.mark.parametrize(
"""fields""" ,[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : Optional[Any] ):
'''simple docstring'''
UpperCamelCase : Tuple = pa.BufferOutputStream()
UpperCamelCase : Tuple = pa.schema(snake_case_ ) if fields else None
with ArrowWriter(stream=snake_case_ ,schema=snake_case_ ,writer_batch_size=snake_case_ ) as writer:
writer.write_row(pa.Table.from_pydict({"""col_1""": ["""foo"""], """col_2""": [1]} ) )
writer.write_row(pa.Table.from_pydict({"""col_1""": ["""bar"""], """col_2""": [2]} ) )
UpperCamelCase : Union[str, Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
UpperCamelCase : List[Any] = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
assert writer._schema == pa.schema(snake_case_ ,metadata=writer._schema.metadata )
_check_output(output.getvalue() ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def A_ ( ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase : List[Any] = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
UpperCamelCase : Any = os.path.join(snake_case_ ,"""test.arrow""" )
with ArrowWriter(path=snake_case_ ,schema=pa.schema(snake_case_ ) ) as writer:
writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]} )
UpperCamelCase : List[Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == pa.schema(snake_case_ ,metadata=writer._schema.metadata )
_check_output(snake_case_ ,1 )
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
if pa.types.is_list(snake_case_ ):
return get_base_dtype(arr_type.value_type )
else:
return arr_type
def A_ ( snake_case_ : Optional[int] ,snake_case_ : Dict ):
'''simple docstring'''
if isinstance(lst[0] ,snake_case_ ):
change_first_primitive_element_in_list(lst[0] ,snake_case_ )
else:
UpperCamelCase : List[Any] = value
@pytest.mark.parametrize("""optimized_int_type, expected_dtype""" ,[(None, pa.intaa()), (Value("""int32""" ), pa.intaa())] )
@pytest.mark.parametrize("""sequence""" ,[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def A_ ( snake_case_ : Tuple ,snake_case_ : List[Any] ,snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = pa.array(TypedSequence(snake_case_ ,optimized_int_type=snake_case_ ) )
assert get_base_dtype(arr.type ) == expected_dtype
@pytest.mark.parametrize(
"""col, expected_dtype""" ,[
("""attention_mask""", pa.inta()),
("""special_tokens_mask""", pa.inta()),
("""token_type_ids""", pa.inta()),
("""input_ids""", pa.intaa()),
("""other""", pa.intaa()),
] ,)
@pytest.mark.parametrize("""sequence""" ,[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Union[str, Any] ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : str = pa.array(OptimizedTypedSequence(snake_case_ ,col=snake_case_ ) )
assert get_base_dtype(arr.type ) == expected_dtype
# not in range
if col != "other":
# avoids errors due to in-place modifications
UpperCamelCase : str = copy.deepcopy(snake_case_ )
UpperCamelCase : Tuple = np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
change_first_primitive_element_in_list(snake_case_ ,snake_case_ )
UpperCamelCase : int = pa.array(OptimizedTypedSequence(snake_case_ ,col=snake_case_ ) )
assert get_base_dtype(arr.type ) == pa.intaa()
@pytest.mark.parametrize("""raise_exception""" ,[False, True] )
def A_ ( snake_case_ : Dict ,snake_case_ : Tuple ):
'''simple docstring'''
UpperCamelCase : Tuple = str(tmp_path / """dataset-train.arrow""" )
try:
with ArrowWriter(path=snake_case_ ) as writer:
if raise_exception:
raise pa.lib.ArrowInvalid()
else:
writer.stream.close()
except pa.lib.ArrowInvalid:
pass
finally:
assert writer.stream.closed
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : Dict = """mock://dataset-train.arrow"""
with ArrowWriter(path=snake_case_ ,storage_options=mockfs.storage_options ) as writer:
assert isinstance(writer._fs ,type(snake_case_ ) )
assert writer._fs.storage_options == mockfs.storage_options
writer.write({"""col_1""": """foo""", """col_2""": 1} )
writer.write({"""col_1""": """bar""", """col_2""": 2} )
UpperCamelCase : int = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert mockfs.exists(snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = pa.BufferOutputStream()
with ParquetWriter(stream=snake_case_ ) as writer:
writer.write({"""col_1""": """foo""", """col_2""": 1} )
writer.write({"""col_1""": """bar""", """col_2""": 2} )
UpperCamelCase : List[str] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
UpperCamelCase : List[str] = pa.BufferReader(output.getvalue() )
UpperCamelCase : pa.Table = pq.read_table(snake_case_ )
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize("""embed_local_files""" ,[False, True] )
def A_ ( snake_case_ : str ,snake_case_ : List[Any] ):
'''simple docstring'''
import PIL.Image
UpperCamelCase : Optional[Any] = str(tmp_path / """test_image_rgb.jpg""" )
PIL.Image.fromarray(np.zeros((5, 5) ,dtype=np.uinta ) ).save(snake_case_ ,format="""png""" )
UpperCamelCase : List[str] = pa.BufferOutputStream()
with ParquetWriter(
stream=snake_case_ ,features=Features({"""image""": Image()} ) ,embed_local_files=snake_case_ ) as writer:
writer.write({"""image""": image_path} )
writer.finalize()
UpperCamelCase : Union[str, Any] = pa.BufferReader(output.getvalue() )
UpperCamelCase : pa.Table = pq.read_table(snake_case_ )
UpperCamelCase : Any = pa_table.to_pydict()
if embed_local_files:
assert isinstance(out["""image"""][0]["""path"""] ,snake_case_ )
with open(snake_case_ ,"""rb""" ) as f:
assert out["image"][0]["bytes"] == f.read()
else:
assert out["image"][0]["path"] == image_path
assert out["image"][0]["bytes"] is None
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = pa.schema([pa.field("""col_1""" ,pa.string() ,nullable=snake_case_ )] )
UpperCamelCase : Dict = pa.BufferOutputStream()
with ArrowWriter(stream=snake_case_ ) as writer:
writer._build_writer(inferred_schema=snake_case_ )
assert writer._schema == pa.schema([pa.field("""col_1""" ,pa.string() )] )
| 364 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=3.6 ):
UpperCamelCase : Dict = tokenizer
UpperCamelCase : Optional[Any] = tokenizer.bos_token_id
UpperCamelCase : Any = dataset
UpperCamelCase : List[str] = seq_length
UpperCamelCase : Optional[Any] = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
UpperCamelCase : Dict = iter(self.dataset )
UpperCamelCase : Union[str, Any] = True
while more_examples:
UpperCamelCase , UpperCamelCase : Tuple = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(SCREAMING_SNAKE_CASE_ )["""content"""] )
buffer_len += len(buffer[-1] )
except StopIteration:
UpperCamelCase : Dict = False
break
UpperCamelCase : str = tokenizer(SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )["""input_ids"""]
UpperCamelCase : str = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , self.seq_length ):
UpperCamelCase : List[str] = all_token_ids[i : i + self.seq_length]
if len(SCREAMING_SNAKE_CASE_ ) == self.seq_length:
yield torch.tensor(SCREAMING_SNAKE_CASE_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : Dict = {"""streaming""": True}
UpperCamelCase : Optional[int] = load_dataset(args.dataset_name ,split="""train""" ,**snake_case_ )
UpperCamelCase : Optional[int] = ConstantLengthDataset(snake_case_ ,snake_case_ ,seq_length=args.seq_length )
UpperCamelCase : List[Any] = DataLoader(snake_case_ ,batch_size=args.batch_size )
return eval_dataloader
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
model.eval()
UpperCamelCase : Dict = []
for step, batch in enumerate(snake_case_ ):
with torch.no_grad():
UpperCamelCase : List[Any] = model(snake_case_ ,labels=snake_case_ )
UpperCamelCase : Any = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(snake_case_ ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
UpperCamelCase : Dict = torch.mean(torch.cat(snake_case_ ) )
try:
UpperCamelCase : Dict = torch.exp(snake_case_ )
except OverflowError:
UpperCamelCase : Optional[int] = float("""inf""" )
return loss.item(), perplexity.item()
# Setup Accelerator
__A : List[Any] = Accelerator()
# Parse configuration
__A : str = HfArgumentParser(EvaluationArguments)
__A : List[Any] = parser.parse_args()
set_seed(args.seed)
# Logging
__A : Any = logging.getLogger(__name__)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
# Load model and tokenizer
__A : List[Any] = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__A : List[Any] = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
__A : int = create_dataloader(args)
# Prepare everything with our `accelerator`.
__A , __A : Optional[Any] = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
__A , __A : Tuple = evaluate(args)
logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 27 | 0 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
__A : Optional[Any] = 2048
__A : List[Any] = 4096
__A : str = 42
__A : Optional[int] = os.environ.pop('''PROCESS_TRAIN''', '''false''')
__A : List[Any] = {'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': 4}
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
def choose_first(snake_case_ : Dict ,snake_case_ : str=False ):
assert isinstance(snake_case_ ,snake_case_ )
if len(snake_case_ ) == 1:
UpperCamelCase : Union[str, Any] = answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
UpperCamelCase : Optional[Any] = {k: [a[k]] for k in a}
if len(a["""start_token"""] ) > 0:
break
return a
UpperCamelCase : str = {"""id""": example["""id"""]}
UpperCamelCase : List[str] = example["""annotations"""]
UpperCamelCase : Dict = annotation["""yes_no_answer"""]
if 0 in yes_no_answer or 1 in yes_no_answer:
UpperCamelCase : Optional[int] = ["""yes"""] if 1 in yes_no_answer else ["""no"""]
UpperCamelCase : Optional[int] = []
UpperCamelCase : Optional[Any] = []
UpperCamelCase : Tuple = ["""<cls>"""]
else:
UpperCamelCase : int = ["""short"""]
UpperCamelCase : Union[str, Any] = choose_first(annotation["""short_answers"""] )
if len(out["""start_token"""] ) == 0:
# answer will be long if short is not available
UpperCamelCase : List[str] = ["""long"""]
UpperCamelCase : List[str] = choose_first(annotation["""long_answer"""] ,is_long_answer=snake_case_ )
UpperCamelCase : str = []
answer.update(snake_case_ )
# disregard some samples
if len(answer["""start_token"""] ) > 1 or answer["start_token"] == answer["end_token"]:
UpperCamelCase : str = True
else:
UpperCamelCase : List[Any] = False
UpperCamelCase : Any = ["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""]
if not all(isinstance(answer[k] ,snake_case_ ) for k in cols ):
raise ValueError("""Issue in ID""" ,example["""id"""] )
return answer
def A_ ( snake_case_ : Any ,snake_case_ : Optional[Any]=False ):
'''simple docstring'''
UpperCamelCase : List[str] = _get_single_answer(snake_case_ )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
UpperCamelCase : Optional[int] = example["""document"""]["""tokens"""]
UpperCamelCase : List[Any] = []
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
return {
"context": " ".join(snake_case_ ),
"answer": {
"start_token": -1_0_0, # ignore index in cross-entropy
"end_token": -1_0_0, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
UpperCamelCase : str = ["""start_token""", """end_token"""]
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
UpperCamelCase : Dict = example["""document"""]["""tokens"""]
UpperCamelCase : List[str] = answer["""start_token"""]
UpperCamelCase : Optional[int] = answer["""end_token"""]
UpperCamelCase : Optional[Any] = []
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
UpperCamelCase : Optional[Any] = """ """.join(context[start_token:end_token] )
# checking above code
if assertion:
UpperCamelCase : Optional[Any] = doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
UpperCamelCase : Dict = doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
UpperCamelCase : Optional[Any] = """ """.join([old[i] for i in range(len(snake_case_ ) ) if not is_html[i]] )
if new != old:
print("""ID:""" ,example["""id"""] )
print("""New:""" ,snake_case_ ,end="""\n""" )
print("""Old:""" ,snake_case_ ,end="""\n\n""" )
return {
"context": " ".join(snake_case_ ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def A_ ( snake_case_ : Dict ,snake_case_ : Optional[Any] ,snake_case_ : Tuple=2_0_4_8 ,snake_case_ : str=4_0_9_6 ,snake_case_ : Optional[int]=True ):
'''simple docstring'''
UpperCamelCase : List[Any] = get_context_and_ans(snake_case_ ,assertion=snake_case_ )
UpperCamelCase : Optional[Any] = out["""answer"""]
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
UpperCamelCase : Optional[Any] = tokenizer(example["""question"""]["""text"""] ,out["""context"""] ).input_ids
UpperCamelCase : Dict = input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
UpperCamelCase : List[Any] = []
UpperCamelCase : List[str] = []
UpperCamelCase : Optional[Any] = input_ids[:q_len]
UpperCamelCase : List[str] = range(snake_case_ ,len(snake_case_ ) ,max_length - doc_stride )
for i in doc_start_indices:
UpperCamelCase : str = i + max_length - q_len
UpperCamelCase : Optional[Any] = input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer["""category"""][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-1_0_0] * len(snake_case_ ),
"end_token": [-1_0_0] * len(snake_case_ ),
"category": category,
},
}
UpperCamelCase : int = out["""context"""].split()
UpperCamelCase : Tuple = splitted_context[answer["""end_token"""]]
UpperCamelCase : List[Any] = len(
tokenizer(
""" """.join(splitted_context[: answer["""start_token"""]] ) ,add_special_tokens=snake_case_ ,).input_ids )
UpperCamelCase : List[str] = len(
tokenizer(""" """.join(splitted_context[: answer["""end_token"""]] ) ,add_special_tokens=snake_case_ ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
UpperCamelCase : Any = len(tokenizer(snake_case_ ,add_special_tokens=snake_case_ ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
UpperCamelCase : Tuple = input_ids[answer["""start_token"""] : answer["""end_token"""] + 1] # right & left are inclusive
UpperCamelCase : Any = answer["""start_token"""]
UpperCamelCase : Optional[Any] = answer["""end_token"""]
if assertion:
UpperCamelCase : str = tokenizer.decode(snake_case_ )
if answer["span"] != new:
print("""ISSUE IN TOKENIZATION""" )
print("""OLD:""" ,answer["""span"""] )
print("""NEW:""" ,snake_case_ ,end="""\n\n""" )
if len(snake_case_ ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
UpperCamelCase : Tuple = input_ids[:q_len]
UpperCamelCase : Union[str, Any] = range(snake_case_ ,len(snake_case_ ) ,max_length - doc_stride )
UpperCamelCase : Any = []
UpperCamelCase : str = []
UpperCamelCase : Optional[int] = []
UpperCamelCase : Tuple = [] # null, yes, no, long, short
for i in doc_start_indices:
UpperCamelCase : Tuple = i + max_length - q_len
UpperCamelCase : Optional[Any] = input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
UpperCamelCase : Any = start_token - i + q_len
UpperCamelCase : Any = end_token - i + q_len
answers_category.append(answer["""category"""][0] ) # ["short"] -> "short"
else:
UpperCamelCase : Optional[Any] = -1_0_0
UpperCamelCase : List[Any] = -1_0_0
answers_category.append("""null""" )
UpperCamelCase : Optional[int] = inputs[-1][start_token : end_token + 1]
answers_start_token.append(snake_case_ )
answers_end_token.append(snake_case_ )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print("""ISSUE in strided for ID:""" ,example["""id"""] )
print("""New:""" ,tokenizer.decode(snake_case_ ) )
print("""Old:""" ,tokenizer.decode(snake_case_ ) ,end="""\n\n""" )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : List[str] ,snake_case_ : str=2_0_4_8 ,snake_case_ : List[Any]=4_0_9_6 ,snake_case_ : Any=False ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = get_strided_contexts_and_ans(
snake_case_ ,snake_case_ ,doc_stride=snake_case_ ,max_length=snake_case_ ,assertion=snake_case_ ,)
return example
def A_ ( snake_case_ : Optional[int] ,snake_case_ : Optional[int] ):
'''simple docstring'''
with jsonlines.open(snake_case_ ,"""a""" ) as writer:
for example in tqdm(snake_case_ ,total=len(snake_case_ ) ,desc="""Saving samples ... """ ):
UpperCamelCase : int = example["""labels"""]
for ids, start, end, cat in zip(
example["""input_ids"""] ,labels["""start_token"""] ,labels["""end_token"""] ,labels["""category"""] ,):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
"""input_ids""": ids,
"""start_token""": start,
"""end_token""": end,
"""category""": CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
__A : Union[str, Any] = load_dataset('''natural_questions''')
__A : List[str] = BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')
__A : Dict = data['''train''' if PROCESS_TRAIN == '''true''' else '''validation''']
__A : Tuple = {
'''tokenizer''': tokenizer,
'''doc_stride''': DOC_STRIDE,
'''max_length''': MAX_LENGTH,
'''assertion''': False,
}
__A : List[Any] = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
__A : int = data.remove_columns(['''annotations''', '''document''', '''id''', '''question'''])
print(data)
np.random.seed(SEED)
__A : Union[str, Any] = '''nq-training.jsonl''' if PROCESS_TRAIN == '''true''' else '''nq-validation.jsonl'''
save_to_disk(data, file_name=cache_file_name)
| 365 |
"""simple docstring"""
import argparse
import os
import re
__A : Any = '''src/transformers'''
# Pattern that looks at the indentation in a line.
__A : Tuple = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : List[Any] = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : List[Any] = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : Any = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : str ,snake_case_ : str="" ,snake_case_ : Any=None ,snake_case_ : Union[str, Any]=None ):
'''simple docstring'''
UpperCamelCase : List[Any] = 0
UpperCamelCase : Optional[int] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Tuple = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : Tuple = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Dict = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Optional[Any] = [lines[index + 1]]
index += 1
else:
UpperCamelCase : str = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
def _inner(snake_case_ : List[str] ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Tuple=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Optional[int] ):
return x
if key is None:
UpperCamelCase : List[str] = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[str] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : Tuple = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : int = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Union[str, Any] = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : Any ):
UpperCamelCase : Union[str, Any] = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : int = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : str = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : Optional[int] = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : int = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Tuple = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : List[Any] = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Union[str, Any] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : List[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : Optional[int] = keys[:-1]
UpperCamelCase : Union[str, Any] = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : Any = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : int=True ):
'''simple docstring'''
with open(snake_case_ ,encoding="""utf-8""" ) as f:
UpperCamelCase : List[str] = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : int = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Dict = main_blocks[block_idx]
UpperCamelCase : Dict = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : List[str] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : Optional[Any] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Optional[Any] = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Any = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : List[Any] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Optional[Any] = _re_direct_key if """_import_structure = {""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Optional[Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Any = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : Union[str, Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[str] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : Optional[int] = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ,encoding="""utf-8""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Optional[int] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : List[Any] = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : Union[str, Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27 | 0 |
"""simple docstring"""
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__A : List[Any] = '''.'''
if __name__ == "__main__":
__A : str = os.path.join(REPO_PATH, '''utils/documentation_tests.txt''')
__A : Dict = []
__A : Union[str, Any] = []
with open(doctest_file_path) as fp:
for line in fp:
__A : int = line.strip()
__A : List[str] = os.path.join(REPO_PATH, line)
if not (os.path.isfile(path) or os.path.isdir(path)):
non_existent_paths.append(line)
all_paths.append(path)
if len(non_existent_paths) > 0:
__A : Tuple = '''\n'''.join(non_existent_paths)
raise ValueError(F'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
if all_paths != sorted(all_paths):
raise ValueError('''Files in `utils/documentation_tests.txt` are not in alphabetical order.''')
| 366 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 0 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ ):
super().__init__()
UpperCamelCase : Optional[int] = nn.ModuleList(SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = True , ):
for i, (image, scale, controlnet) in enumerate(zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.nets ) ):
UpperCamelCase : str = controlnet(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , )
# merge samples
if i == 0:
UpperCamelCase : List[str] = down_samples, mid_sample
else:
UpperCamelCase : int = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , ):
UpperCamelCase : Dict = 0
UpperCamelCase : Any = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
SCREAMING_SNAKE_CASE_ , is_main_process=SCREAMING_SNAKE_CASE_ , save_function=SCREAMING_SNAKE_CASE_ , safe_serialization=SCREAMING_SNAKE_CASE_ , variant=SCREAMING_SNAKE_CASE_ , )
idx += 1
UpperCamelCase : str = model_path_to_save + f'_{idx}'
@classmethod
def a_ ( cls , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = 0
UpperCamelCase : Optional[int] = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
UpperCamelCase : Tuple = pretrained_model_path
while os.path.isdir(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = ControlNetModel.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
controlnets.append(SCREAMING_SNAKE_CASE_ )
idx += 1
UpperCamelCase : Tuple = pretrained_model_path + f'_{idx}'
logger.info(f'{len(SCREAMING_SNAKE_CASE_ )} controlnets loaded from {pretrained_model_path}.' )
if len(SCREAMING_SNAKE_CASE_ ) == 0:
raise ValueError(
f'No ControlNets found under {os.path.dirname(SCREAMING_SNAKE_CASE_ )}. Expected at least {pretrained_model_path + "_0"}.' )
return cls(SCREAMING_SNAKE_CASE_ )
| 367 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
__A : Optional[Any] = logging.get_logger(__name__)
def A_ ( snake_case_ : np.ndarray ,snake_case_ : Union[int, Iterable[int]] ,snake_case_ : bool ,snake_case_ : int ):
'''simple docstring'''
def constraint_to_multiple_of(snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ,snake_case_ : List[str]=0 ,snake_case_ : Optional[Any]=None ):
UpperCamelCase : List[str] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
UpperCamelCase : Optional[Any] = math.floor(val / multiple ) * multiple
if x < min_val:
UpperCamelCase : Dict = math.ceil(val / multiple ) * multiple
return x
UpperCamelCase : Any = (output_size, output_size) if isinstance(snake_case_ ,snake_case_ ) else output_size
UpperCamelCase , UpperCamelCase : int = get_image_size(snake_case_ )
UpperCamelCase , UpperCamelCase : Union[str, Any] = output_size
# determine new height and width
UpperCamelCase : List[str] = output_height / input_height
UpperCamelCase : List[str] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
UpperCamelCase : int = scale_width
else:
# fit height
UpperCamelCase : Optional[Any] = scale_height
UpperCamelCase : int = constraint_to_multiple_of(scale_height * input_height ,multiple=snake_case_ )
UpperCamelCase : Union[str, Any] = constraint_to_multiple_of(scale_width * input_width ,multiple=snake_case_ )
return (new_height, new_width)
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = ['pixel_values']
def __init__( self , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 1 / 255 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = size if size is not None else {"""height""": 384, """width""": 384}
UpperCamelCase : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = do_resize
UpperCamelCase : Union[str, Any] = size
UpperCamelCase : Union[str, Any] = keep_aspect_ratio
UpperCamelCase : Any = ensure_multiple_of
UpperCamelCase : List[Any] = resample
UpperCamelCase : str = do_rescale
UpperCamelCase : Optional[Any] = rescale_factor
UpperCamelCase : List[str] = do_normalize
UpperCamelCase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCamelCase : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Tuple = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
UpperCamelCase : Dict = get_resize_output_image_size(
SCREAMING_SNAKE_CASE_ , output_size=(size["""height"""], size["""width"""]) , keep_aspect_ratio=SCREAMING_SNAKE_CASE_ , multiple=SCREAMING_SNAKE_CASE_ , )
return resize(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return normalize(SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCamelCase : List[Any] = size if size is not None else self.size
UpperCamelCase : Dict = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
UpperCamelCase : Optional[int] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
UpperCamelCase : Tuple = resample if resample is not None else self.resample
UpperCamelCase : str = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase : Any = image_mean if image_mean is not None else self.image_mean
UpperCamelCase : List[Any] = image_std if image_std is not None else self.image_std
UpperCamelCase : str = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
UpperCamelCase : Tuple = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
UpperCamelCase : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
UpperCamelCase : int = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
UpperCamelCase : List[str] = [self.normalize(image=SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Any = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Union[str, Any] = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : str = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = target_sizes.numpy()
UpperCamelCase : Dict = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
UpperCamelCase : List[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : List[Any] = logits.argmax(dim=1 )
UpperCamelCase : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 27 | 0 |
"""simple docstring"""
from importlib import import_module
from .logging import get_logger
__A : str = get_logger(__name__)
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ):
UpperCamelCase : str = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith("""__""" ):
setattr(self , SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : List[Any] = module._original_module if isinstance(SCREAMING_SNAKE_CASE_ , _PatchedModuleObj ) else module
class lowerCamelCase :
lowercase : Any = []
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ):
UpperCamelCase : Dict = obj
UpperCamelCase : Any = target
UpperCamelCase : Any = new
UpperCamelCase : Optional[Any] = target.split(""".""" )[0]
UpperCamelCase : int = {}
UpperCamelCase : List[str] = attrs or []
def __enter__( self ):
UpperCamelCase : int = self.target.split(""".""" )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
try:
UpperCamelCase : int = import_module(""".""".join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
UpperCamelCase : Tuple = getattr(self.obj , SCREAMING_SNAKE_CASE_ )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(SCREAMING_SNAKE_CASE_ , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
UpperCamelCase : Optional[Any] = obj_attr
# patch at top level
setattr(self.obj , SCREAMING_SNAKE_CASE_ , _PatchedModuleObj(SCREAMING_SNAKE_CASE_ , attrs=self.attrs ) )
UpperCamelCase : Optional[Any] = getattr(self.obj , SCREAMING_SNAKE_CASE_ )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , _PatchedModuleObj(getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , attrs=self.attrs ) )
UpperCamelCase : Any = getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# finally set the target attribute
setattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
UpperCamelCase : Union[str, Any] = getattr(import_module(""".""".join(SCREAMING_SNAKE_CASE_ ) ) , SCREAMING_SNAKE_CASE_ )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , SCREAMING_SNAKE_CASE_ ) is attr_value:
UpperCamelCase : Union[str, Any] = getattr(self.obj , SCREAMING_SNAKE_CASE_ )
setattr(self.obj , SCREAMING_SNAKE_CASE_ , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
UpperCamelCase : str = globals()["""__builtins__"""][target_attr]
setattr(self.obj , SCREAMING_SNAKE_CASE_ , self.new )
else:
raise RuntimeError(f'Tried to patch attribute {target_attr} instead of a submodule.' )
def __exit__( self , *SCREAMING_SNAKE_CASE_ ):
for attr in list(self.original ):
setattr(self.obj , SCREAMING_SNAKE_CASE_ , self.original.pop(SCREAMING_SNAKE_CASE_ ) )
def a_ ( self ):
self.__enter__()
self._active_patches.append(self )
def a_ ( self ):
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 368 |
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : float = b
if function(snake_case_ ) == 0: # one of the a or b is a root for the function
return a
elif function(snake_case_ ) == 0:
return b
elif (
function(snake_case_ ) * function(snake_case_ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("""could not find root in given interval.""" )
else:
UpperCamelCase : float = start + (end - start) / 2.0
while abs(start - mid ) > 1_0**-7: # until precisely equals to 10^-7
if function(snake_case_ ) == 0:
return mid
elif function(snake_case_ ) * function(snake_case_ ) < 0:
UpperCamelCase : Dict = mid
else:
UpperCamelCase : List[str] = mid
UpperCamelCase : Tuple = start + (end - start) / 2.0
return mid
def A_ ( snake_case_ : float ):
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 27 | 0 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
__A : Optional[Any] = random.Random()
def A_ ( snake_case_ : str ,snake_case_ : List[str]=1.0 ,snake_case_ : Any=None ,snake_case_ : Optional[Any]=None ):
'''simple docstring'''
if rng is None:
UpperCamelCase : Any = global_rng
UpperCamelCase : int = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
class lowerCamelCase ( unittest.TestCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=400 , SCREAMING_SNAKE_CASE_=2000 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=1_6000 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=80 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_="hann_window" , SCREAMING_SNAKE_CASE_=80 , SCREAMING_SNAKE_CASE_=7600 , SCREAMING_SNAKE_CASE_=1e-10 , SCREAMING_SNAKE_CASE_=True , ):
UpperCamelCase : List[Any] = parent
UpperCamelCase : Any = batch_size
UpperCamelCase : List[Any] = min_seq_length
UpperCamelCase : List[str] = max_seq_length
UpperCamelCase : Union[str, Any] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
UpperCamelCase : Tuple = feature_size
UpperCamelCase : int = padding_value
UpperCamelCase : Dict = sampling_rate
UpperCamelCase : Tuple = do_normalize
UpperCamelCase : List[Any] = num_mel_bins
UpperCamelCase : Dict = hop_length
UpperCamelCase : Optional[Any] = win_length
UpperCamelCase : Union[str, Any] = win_function
UpperCamelCase : Optional[int] = fmin
UpperCamelCase : str = fmax
UpperCamelCase : Optional[int] = mel_floor
UpperCamelCase : Any = return_attention_mask
def a_ ( self ):
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"do_normalize": self.do_normalize,
"num_mel_bins": self.num_mel_bins,
"hop_length": self.hop_length,
"win_length": self.win_length,
"win_function": self.win_function,
"fmin": self.fmin,
"fmax": self.fmax,
"mel_floor": self.mel_floor,
"return_attention_mask": self.return_attention_mask,
}
def a_ ( self , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False ):
def _flatten(SCREAMING_SNAKE_CASE_ ):
return list(itertools.chain(*SCREAMING_SNAKE_CASE_ ) )
if equal_length:
UpperCamelCase : Tuple = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
UpperCamelCase : Any = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
UpperCamelCase : Tuple = [np.asarray(SCREAMING_SNAKE_CASE_ ) for x in speech_inputs]
return speech_inputs
def a_ ( self , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False ):
if equal_length:
UpperCamelCase : List[str] = [floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
UpperCamelCase : List[Any] = [
floats_list((x, self.num_mel_bins) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
UpperCamelCase : Tuple = [np.asarray(SCREAMING_SNAKE_CASE_ ) for x in speech_inputs]
return speech_inputs
@require_torch
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : List[Any] = SpeechTaFeatureExtractor
def a_ ( self ):
UpperCamelCase : Optional[Any] = SpeechTaFeatureExtractionTester(self )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.assertTrue(np.all(np.mean(SCREAMING_SNAKE_CASE_ , axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(SCREAMING_SNAKE_CASE_ , axis=0 ) - 1 ) < 1e-3 ) )
def a_ ( self ):
# Tests that all call wrap to encode_plus and batch_encode_plus
UpperCamelCase : List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
UpperCamelCase : Dict = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
UpperCamelCase : Optional[int] = [np.asarray(SCREAMING_SNAKE_CASE_ ) for speech_input in speech_inputs]
# Test not batched input
UpperCamelCase : Optional[Any] = feat_extract(speech_inputs[0] , return_tensors="""np""" ).input_values
UpperCamelCase : Dict = feat_extract(np_speech_inputs[0] , return_tensors="""np""" ).input_values
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 ) )
# Test batched
UpperCamelCase : Optional[Any] = feat_extract(SCREAMING_SNAKE_CASE_ , return_tensors="""np""" ).input_values
UpperCamelCase : Dict = feat_extract(SCREAMING_SNAKE_CASE_ , return_tensors="""np""" ).input_values
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 ) )
def a_ ( self ):
UpperCamelCase : Union[str, Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : Any = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
UpperCamelCase : List[Any] = ["""longest""", """max_length""", """do_not_pad"""]
UpperCamelCase : Union[str, Any] = [None, 1600, None]
for max_length, padding in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = feat_extract(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , return_tensors="""np""" )
UpperCamelCase : Dict = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self.assertTrue(input_values[0][800:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self.assertTrue(input_values[0][1000:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def a_ ( self ):
UpperCamelCase : Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : Optional[Any] = range(800 , 1400 , 200 )
UpperCamelCase : Any = [floats_list((1, x) )[0] for x in lengths]
UpperCamelCase : int = ["""longest""", """max_length""", """do_not_pad"""]
UpperCamelCase : str = [None, 1600, None]
for max_length, padding in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = feat_extract(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def a_ ( self ):
UpperCamelCase : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : Union[str, Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
UpperCamelCase : str = feat_extract(
SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=1000 , padding="""max_length""" , return_tensors="""np""" )
UpperCamelCase : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def a_ ( self ):
UpperCamelCase : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : int = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
UpperCamelCase : str = feat_extract(
SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=1000 , padding="""longest""" , return_tensors="""np""" )
UpperCamelCase : Optional[int] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000) )
UpperCamelCase : Any = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
UpperCamelCase : Dict = feat_extract(
SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=2000 , padding="""longest""" , return_tensors="""np""" )
UpperCamelCase : Any = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200) )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : Dict = np.random.rand(100 ).astype(np.floataa )
UpperCamelCase : Any = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
UpperCamelCase : Any = feature_extractor.pad([{"""input_values""": inputs}] , return_tensors="""np""" )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
UpperCamelCase : int = feature_extractor.pad([{"""input_values""": inputs}] , return_tensors="""pt""" )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def a_ ( self ):
# Tests that all call wrap to encode_plus and batch_encode_plus
UpperCamelCase : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
UpperCamelCase : Any = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
UpperCamelCase : Tuple = [np.asarray(SCREAMING_SNAKE_CASE_ ) for speech_input in speech_inputs]
# Test feature size
UpperCamelCase : Optional[Any] = feature_extractor(audio_target=SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , return_tensors="""np""" ).input_values
self.assertTrue(input_values.ndim == 3 )
self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )
# Test not batched input
UpperCamelCase : List[str] = feature_extractor(speech_inputs[0] , return_tensors="""np""" ).input_values
UpperCamelCase : List[str] = feature_extractor(np_speech_inputs[0] , return_tensors="""np""" ).input_values
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 ) )
# Test batched
UpperCamelCase : int = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors="""np""" ).input_values
UpperCamelCase : Tuple = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors="""np""" ).input_values
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
UpperCamelCase : Optional[Any] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
UpperCamelCase : int = np.asarray(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors="""np""" ).input_values
UpperCamelCase : List[str] = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors="""np""" ).input_values
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 ) )
def a_ ( self ):
UpperCamelCase : Dict = self.feat_extract_tester.prepare_inputs_for_target()
UpperCamelCase : Any = self.feature_extraction_class(**self.feat_extract_dict )
UpperCamelCase : List[str] = feat_extract.model_input_names[0]
UpperCamelCase : Optional[int] = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(SCREAMING_SNAKE_CASE_ ) == len(SCREAMING_SNAKE_CASE_ ) for x, y in zip(SCREAMING_SNAKE_CASE_ , processed_features[input_name] ) ) )
UpperCamelCase : str = self.feat_extract_tester.prepare_inputs_for_target(equal_length=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = BatchFeature({input_name: speech_inputs} , tensor_type="""np""" )
UpperCamelCase : str = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
UpperCamelCase : Union[str, Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def a_ ( self ):
UpperCamelCase : Optional[int] = self.feat_extract_tester.prepare_inputs_for_target(equal_length=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
UpperCamelCase : List[Any] = feat_extract.model_input_names[0]
UpperCamelCase : Any = BatchFeature({input_name: speech_inputs} , tensor_type="""pt""" )
UpperCamelCase : Dict = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
UpperCamelCase : str = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def a_ ( self ):
UpperCamelCase : Any = self.feature_extraction_class(**self.feat_extract_dict )
UpperCamelCase : int = self.feat_extract_tester.prepare_inputs_for_target()
UpperCamelCase : str = feat_extract.model_input_names[0]
UpperCamelCase : str = BatchFeature({input_name: speech_inputs} )
UpperCamelCase : Optional[int] = feat_extract.num_mel_bins # hack!
UpperCamelCase : int = feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding="""longest""" , return_tensors="""np""" )[input_name]
UpperCamelCase : Dict = feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding="""longest""" , return_tensors="""pt""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def a_ ( self ):
UpperCamelCase : List[Any] = self.feat_extract_dict
UpperCamelCase : List[Any] = True
UpperCamelCase : Optional[Any] = self.feature_extraction_class(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_target()
UpperCamelCase : str = [len(SCREAMING_SNAKE_CASE_ ) for x in speech_inputs]
UpperCamelCase : str = feat_extract.model_input_names[0]
UpperCamelCase : List[Any] = BatchFeature({input_name: speech_inputs} )
UpperCamelCase : Tuple = feat_extract.num_mel_bins # hack!
UpperCamelCase : Optional[int] = feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding="""longest""" , return_tensors="""np""" )
self.assertIn("""attention_mask""" , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.feat_extract_dict
UpperCamelCase : Union[str, Any] = True
UpperCamelCase : List[str] = self.feature_extraction_class(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = self.feat_extract_tester.prepare_inputs_for_target()
UpperCamelCase : Any = [len(SCREAMING_SNAKE_CASE_ ) for x in speech_inputs]
UpperCamelCase : int = feat_extract.model_input_names[0]
UpperCamelCase : Any = BatchFeature({input_name: speech_inputs} )
UpperCamelCase : str = min(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = feat_extract.num_mel_bins # hack!
UpperCamelCase : Optional[Any] = feat_extract.pad(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""np""" )
self.assertIn("""attention_mask""" , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
from datasets import load_dataset
UpperCamelCase : List[str] = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
# automatic decoding with librispeech
UpperCamelCase : Union[str, Any] = ds.sort("""id""" ).select(range(SCREAMING_SNAKE_CASE_ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def a_ ( self ):
# fmt: off
UpperCamelCase : int = torch.tensor(
[2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03] )
# fmt: on
UpperCamelCase : List[Any] = self._load_datasamples(1 )
UpperCamelCase : int = SpeechTaFeatureExtractor()
UpperCamelCase : str = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" ).input_values
self.assertEquals(input_values.shape , (1, 9_3680) )
self.assertTrue(torch.allclose(input_values[0, :30] , SCREAMING_SNAKE_CASE_ , atol=1e-6 ) )
def a_ ( self ):
# fmt: off
UpperCamelCase : Dict = torch.tensor(
[-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
-3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
-3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
-3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998] )
# fmt: on
UpperCamelCase : Tuple = self._load_datasamples(1 )
UpperCamelCase : Optional[Any] = SpeechTaFeatureExtractor()
UpperCamelCase : Optional[int] = feature_extractor(audio_target=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" ).input_values
self.assertEquals(input_values.shape , (1, 366, 80) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 369 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def a_ ( self ):
UpperCamelCase : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
UpperCamelCase : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
UpperCamelCase : Dict = """xvjiarui/stable-diffusion-2-inpainting"""
UpperCamelCase , UpperCamelCase : List[str] = FlaxStableDiffusionInpaintPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = """Face of a yellow cat, high resolution, sitting on a park bench"""
UpperCamelCase : List[str] = jax.random.PRNGKey(0 )
UpperCamelCase : Tuple = 50
UpperCamelCase : Dict = jax.device_count()
UpperCamelCase : Optional[int] = num_samples * [prompt]
UpperCamelCase : int = num_samples * [init_image]
UpperCamelCase : List[Any] = num_samples * [mask_image]
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[Any] = pipeline.prepare_inputs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# shard inputs and rng
UpperCamelCase : Optional[int] = replicate(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = jax.random.split(SCREAMING_SNAKE_CASE_ , jax.device_count() )
UpperCamelCase : str = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = pipeline(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , jit=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = output.images.reshape(SCREAMING_SNAKE_CASE_ , 512 , 512 , 3 )
UpperCamelCase : List[Any] = images[0, 253:256, 253:256, -1]
UpperCamelCase : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase : Dict = jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 27 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[List[np.ndarray], torch.FloatTensor]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 370 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def A_ ( ):
'''simple docstring'''
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
UpperCamelCase : Optional[Any] = [1, 2, 3]
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=2 )
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" ,[2, -1] )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : List[Any] = [1, 2]
UpperCamelCase : List[Any] = {"""a""": 1, """b""": 2}
UpperCamelCase : List[str] = {"""a""": [1, 2], """b""": [3, 4]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 1}, """b""": 2}
UpperCamelCase : Any = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
UpperCamelCase : Optional[int] = [2, 3]
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3}
UpperCamelCase : Any = {"""a""": [2, 3], """b""": [4, 5]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 2}, """b""": 3}
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
| 27 | 0 |
"""simple docstring"""
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : Dict=False ):
'''simple docstring'''
try:
UpperCamelCase : Optional[int] = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
UpperCamelCase : Any = default
else:
# KEY is set, convert it to True or False.
try:
UpperCamelCase : str = strtobool(snake_case_ )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(f'If set, {key} must be yes or no.' )
return _value
__A : int = parse_flag_from_env('''RUN_SLOW''', default=False)
def A_ ( snake_case_ : Tuple ):
'''simple docstring'''
return unittest.skip("""Test was skipped""" )(snake_case_ )
def A_ ( snake_case_ : Optional[int] ):
'''simple docstring'''
return unittest.skipUnless(_run_slow_tests ,"""test is slow""" )(snake_case_ )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
return unittest.skipUnless(not torch.cuda.is_available() ,"""test requires only a CPU""" )(snake_case_ )
def A_ ( snake_case_ : Tuple ):
'''simple docstring'''
return unittest.skipUnless(torch.cuda.is_available() ,"""test requires a GPU""" )(snake_case_ )
def A_ ( snake_case_ : str ):
'''simple docstring'''
return unittest.skipUnless(is_xpu_available() ,"""test requires a XPU""" )(snake_case_ )
def A_ ( snake_case_ : Tuple ):
'''simple docstring'''
return unittest.skipUnless(is_mps_available() ,"""test requires a `mps` backend support in `torch`""" )(snake_case_ )
def A_ ( snake_case_ : Tuple ):
'''simple docstring'''
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() ,"""test requires the Hugging Face suite""" )(snake_case_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
return unittest.skipUnless(is_bnb_available() ,"""test requires the bitsandbytes library""" )(snake_case_ )
def A_ ( snake_case_ : Any ):
'''simple docstring'''
return unittest.skipUnless(is_tpu_available() ,"""test requires TPU""" )(snake_case_ )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() == 1 ,"""test requires a GPU""" )(snake_case_ )
def A_ ( snake_case_ : str ):
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() == 1 ,"""test requires a XPU""" )(snake_case_ )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() > 1 ,"""test requires multiple GPUs""" )(snake_case_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() > 1 ,"""test requires multiple XPUs""" )(snake_case_ )
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
return unittest.skipUnless(is_safetensors_available() ,"""test requires safetensors""" )(snake_case_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
return unittest.skipUnless(is_deepspeed_available() ,"""test requires DeepSpeed""" )(snake_case_ )
def A_ ( snake_case_ : Optional[int] ):
'''simple docstring'''
return unittest.skipUnless(is_torch_version(""">=""" ,"""1.12.0""" ) ,"""test requires torch version >= 1.12.0""" )(snake_case_ )
def A_ ( snake_case_ : Tuple=None ,snake_case_ : List[str]=None ):
'''simple docstring'''
if test_case is None:
return partial(snake_case_ ,version=snake_case_ )
return unittest.skipUnless(is_torch_version(""">=""" ,snake_case_ ) ,f'test requires torch version >= {version}' )(snake_case_ )
def A_ ( snake_case_ : Tuple ):
'''simple docstring'''
return unittest.skipUnless(is_tensorboard_available() ,"""test requires Tensorboard""" )(snake_case_ )
def A_ ( snake_case_ : Union[str, Any] ):
'''simple docstring'''
return unittest.skipUnless(is_wandb_available() ,"""test requires wandb""" )(snake_case_ )
def A_ ( snake_case_ : Tuple ):
'''simple docstring'''
return unittest.skipUnless(is_comet_ml_available() ,"""test requires comet_ml""" )(snake_case_ )
__A : int = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def A_ ( snake_case_ : Tuple ):
'''simple docstring'''
return unittest.skipUnless(
_atleast_one_tracker_available ,"""test requires at least one tracker to be available and for `comet_ml` to not be installed""" ,)(snake_case_ )
class lowerCamelCase ( unittest.TestCase ):
lowercase : Optional[Any] = True
@classmethod
def a_ ( cls ):
UpperCamelCase : Optional[Any] = tempfile.mkdtemp()
@classmethod
def a_ ( cls ):
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def a_ ( self ):
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob("""**/*""" ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = mocks if isinstance(SCREAMING_SNAKE_CASE_ , (tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def A_ ( snake_case_ : Tuple ):
'''simple docstring'''
UpperCamelCase : List[str] = AcceleratorState()
UpperCamelCase : Dict = tensor[None].clone().to(state.device )
UpperCamelCase : Optional[Any] = gather(snake_case_ ).cpu()
UpperCamelCase : Tuple = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] ,snake_case_ ):
return False
return True
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = returncode
UpperCamelCase : List[str] = stdout
UpperCamelCase : Union[str, Any] = stderr
async def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : List[str] ):
'''simple docstring'''
while True:
UpperCamelCase : Any = await stream.readline()
if line:
callback(snake_case_ )
else:
break
async def A_ ( snake_case_ : List[str] ,snake_case_ : Tuple=None ,snake_case_ : int=None ,snake_case_ : str=None ,snake_case_ : Dict=False ,snake_case_ : List[str]=False ):
'''simple docstring'''
if echo:
print("""\nRunning: """ ,""" """.join(snake_case_ ) )
UpperCamelCase : Any = await asyncio.create_subprocess_exec(
cmd[0] ,*cmd[1:] ,stdin=snake_case_ ,stdout=asyncio.subprocess.PIPE ,stderr=asyncio.subprocess.PIPE ,env=snake_case_ ,)
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
UpperCamelCase : List[Any] = []
UpperCamelCase : List[str] = []
def tee(snake_case_ : List[Any] ,snake_case_ : Tuple ,snake_case_ : int ,snake_case_ : str="" ):
UpperCamelCase : Tuple = line.decode("""utf-8""" ).rstrip()
sink.append(snake_case_ )
if not quiet:
print(snake_case_ ,snake_case_ ,file=snake_case_ )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout ,lambda snake_case_ : tee(snake_case_ ,snake_case_ ,sys.stdout ,label="""stdout:""" ) ) ),
asyncio.create_task(_read_stream(p.stderr ,lambda snake_case_ : tee(snake_case_ ,snake_case_ ,sys.stderr ,label="""stderr:""" ) ) ),
] ,timeout=snake_case_ ,)
return _RunOutput(await p.wait() ,snake_case_ ,snake_case_ )
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : Optional[Any]=None ,snake_case_ : Dict=None ,snake_case_ : Any=1_8_0 ,snake_case_ : List[str]=False ,snake_case_ : Optional[Any]=True ):
'''simple docstring'''
UpperCamelCase : List[str] = asyncio.get_event_loop()
UpperCamelCase : Dict = loop.run_until_complete(
_stream_subprocess(snake_case_ ,env=snake_case_ ,stdin=snake_case_ ,timeout=snake_case_ ,quiet=snake_case_ ,echo=snake_case_ ) )
UpperCamelCase : int = """ """.join(snake_case_ )
if result.returncode > 0:
UpperCamelCase : List[Any] = """\n""".join(result.stderr )
raise RuntimeError(
f'\'{cmd_str}\' failed with returncode {result.returncode}\n\n'
f'The combined stderr from workers follows:\n{stderr}' )
return result
class lowerCamelCase ( _UpperCAmelCase ):
pass
def A_ ( snake_case_ : List[str] ,snake_case_ : Optional[int]=False ):
'''simple docstring'''
try:
UpperCamelCase : Dict = subprocess.check_output(snake_case_ ,stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(snake_case_ ,"""decode""" ):
UpperCamelCase : Optional[Any] = output.decode("""utf-8""" )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
f'Command `{" ".join(snake_case_ )}` failed with the following error:\n\n{e.output.decode()}' ) from e
| 371 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_="last" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=0 , ):
UpperCamelCase : Union[str, Any] = parent
UpperCamelCase : str = batch_size
UpperCamelCase : int = seq_length
UpperCamelCase : Optional[Any] = is_training
UpperCamelCase : Any = use_input_lengths
UpperCamelCase : Tuple = use_token_type_ids
UpperCamelCase : List[Any] = use_labels
UpperCamelCase : Union[str, Any] = gelu_activation
UpperCamelCase : Dict = sinusoidal_embeddings
UpperCamelCase : Optional[int] = causal
UpperCamelCase : List[Any] = asm
UpperCamelCase : int = n_langs
UpperCamelCase : Optional[Any] = vocab_size
UpperCamelCase : str = n_special
UpperCamelCase : Dict = hidden_size
UpperCamelCase : Union[str, Any] = num_hidden_layers
UpperCamelCase : Optional[Any] = num_attention_heads
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : int = max_position_embeddings
UpperCamelCase : Any = type_sequence_label_size
UpperCamelCase : str = initializer_range
UpperCamelCase : str = num_labels
UpperCamelCase : Union[str, Any] = num_choices
UpperCamelCase : List[str] = summary_type
UpperCamelCase : int = use_proj
UpperCamelCase : List[str] = scope
UpperCamelCase : Dict = bos_token_id
def a_ ( self ):
UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Union[str, Any] = None
if self.use_input_lengths:
UpperCamelCase : str = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
UpperCamelCase : Tuple = None
if self.use_token_type_ids:
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
UpperCamelCase : int = None
UpperCamelCase : Dict = None
UpperCamelCase : str = None
if self.use_labels:
UpperCamelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Dict = ids_tensor([self.batch_size] , 2 ).float()
UpperCamelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : List[str] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def a_ ( self ):
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = XLMModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , lengths=SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[Any] = XLMWithLMHeadModel(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[str] = XLMForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = XLMForQuestionAnswering(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , p_mask=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , )
((UpperCamelCase) , ) : Union[str, Any] = result_with_labels.to_tuple()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
((UpperCamelCase) , ) : Tuple = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = XLMForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = self.num_labels
UpperCamelCase : int = XLMForTokenClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[int] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[Any] = self.num_choices
UpperCamelCase : Tuple = XLMForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Dict = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Optional[Any] = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : int = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : List[Any] = config_and_inputs
UpperCamelCase : Dict = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
lowercase : List[Any] = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
lowercase : Optional[Any] = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("""Fast""" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
UpperCamelCase : Union[str, Any] = super()._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
UpperCamelCase : Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
return inputs_dict
def a_ ( self ):
UpperCamelCase : List[Any] = XLMModelTester(self )
UpperCamelCase : Optional[int] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , emb_dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_attentions in attentions] , [True] * len(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : int = min_length + idx + 1
UpperCamelCase : Tuple = min_length + idx + 1
UpperCamelCase : Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_hidden_states in hidden_states] , [True] * len(SCREAMING_SNAKE_CASE_ ) , )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : List[str] = min_length + idx + 1
UpperCamelCase : Optional[Any] = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) , )
pass
@slow
def a_ ( self ):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : str = XLMModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Dict = XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""" )
model.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.tensor([[14, 447]] , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ ) # the president
UpperCamelCase : List[Any] = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
UpperCamelCase : Optional[int] = model.generate(SCREAMING_SNAKE_CASE_ , do_sample=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , SCREAMING_SNAKE_CASE_ )
| 27 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : Dict = {
'''configuration_pix2struct''': [
'''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Pix2StructConfig''',
'''Pix2StructTextConfig''',
'''Pix2StructVisionConfig''',
],
'''processing_pix2struct''': ['''Pix2StructProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : int = ['''Pix2StructImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = [
'''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Pix2StructPreTrainedModel''',
'''Pix2StructForConditionalGeneration''',
'''Pix2StructVisionModel''',
'''Pix2StructTextModel''',
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
__A : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 350 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : int = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = [
'''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTBigCodeForSequenceClassification''',
'''GPTBigCodeForTokenClassification''',
'''GPTBigCodeForCausalLM''',
'''GPTBigCodeModel''',
'''GPTBigCodePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : Tuple = {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
),
'''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
),
'''distilbert-base-uncased-finetuned-sst-2-english''': (
'''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
),
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Dict = 'distilbert'
lowercase : str = {
'hidden_size': 'dim',
'num_attention_heads': 'n_heads',
'num_hidden_layers': 'n_layers',
}
def __init__( self , SCREAMING_SNAKE_CASE_=3_0522 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=6 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=768 , SCREAMING_SNAKE_CASE_=4 * 768 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.2 , SCREAMING_SNAKE_CASE_=0 , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[Any] = vocab_size
UpperCamelCase : Optional[Any] = max_position_embeddings
UpperCamelCase : List[str] = sinusoidal_pos_embds
UpperCamelCase : str = n_layers
UpperCamelCase : int = n_heads
UpperCamelCase : int = dim
UpperCamelCase : List[str] = hidden_dim
UpperCamelCase : Optional[int] = dropout
UpperCamelCase : int = attention_dropout
UpperCamelCase : Dict = activation
UpperCamelCase : Any = initializer_range
UpperCamelCase : int = qa_dropout
UpperCamelCase : Optional[int] = seq_classif_dropout
super().__init__(**SCREAMING_SNAKE_CASE_ , pad_token_id=SCREAMING_SNAKE_CASE_ )
class lowerCamelCase ( _UpperCAmelCase ):
@property
def a_ ( self ):
if self.task == "multiple-choice":
UpperCamelCase : Tuple = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
UpperCamelCase : Optional[int] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 351 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase ( torch.nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_="sayef/fsner-bert-base-uncased" ):
super(SCREAMING_SNAKE_CASE_ , self ).__init__()
UpperCamelCase : int = AutoModel.from_pretrained(SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.nn.CosineSimilarity(3 , 1e-08 )
UpperCamelCase : Any = torch.nn.Softmax(dim=1 )
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
return self.bert(**SCREAMING_SNAKE_CASE_ ).last_hidden_state
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return token_embeddings.sum(2 , keepdim=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1 ):
return self.softmax(T * self.cos(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = W_supports["""sizes"""].tolist()
UpperCamelCase : List[str] = W_supports["""start_token_id"""].item()
UpperCamelCase : List[Any] = W_supports["""end_token_id"""].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
UpperCamelCase : List[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Tuple = W_supports["""input_ids"""] == start_token_id
UpperCamelCase : Optional[Any] = W_supports["""input_ids"""] == end_token_id
for i, size in enumerate(SCREAMING_SNAKE_CASE_ ):
if i == 0:
UpperCamelCase : int = 0
else:
UpperCamelCase : Optional[int] = support_sizes[i - 1]
UpperCamelCase : Tuple = S[s : s + size][start_token_masks[s : s + size]]
UpperCamelCase : int = S[s : s + size][end_token_masks[s : s + size]]
UpperCamelCase : Dict = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
UpperCamelCase : Tuple = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
UpperCamelCase : List[str] = torch.vstack((p_starts, p_start) )
UpperCamelCase : Optional[Any] = torch.vstack((p_ends, p_end) )
else:
UpperCamelCase : Optional[int] = p_start
UpperCamelCase : Tuple = p_end
return p_starts, p_ends
| 27 | 0 |
"""simple docstring"""
def A_ ( snake_case_ : List[Any] ,snake_case_ : Any ,snake_case_ : Optional[Any] ,snake_case_ : Dict=None ):
'''simple docstring'''
UpperCamelCase : str = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
UpperCamelCase : Any = True, True
UpperCamelCase : Optional[Any] = dfs(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
return path
def A_ ( snake_case_ : List[Any] ,snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : int = 0
UpperCamelCase : Any = -1
for i in range(snake_case_ ):
if i not in graph.keys():
continue
if len(graph[i] ) % 2 == 1:
odd_degree_nodes += 1
UpperCamelCase : int = i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def A_ ( snake_case_ : int ,snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : int = [[False for _ in range(max_node + 1 )] for _ in range(max_node + 1 )]
UpperCamelCase : int = check_circuit_or_path(snake_case_ ,snake_case_ )
if check == 3:
print("""graph is not Eulerian""" )
print("""no path""" )
return
UpperCamelCase : str = 1
if check == 2:
UpperCamelCase : Optional[Any] = odd_node
print("""graph has a Euler path""" )
if check == 1:
print("""graph has a Euler cycle""" )
UpperCamelCase : Tuple = dfs(snake_case_ ,snake_case_ ,snake_case_ )
print(snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Optional[int] = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
UpperCamelCase : int = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
UpperCamelCase : Union[str, Any] = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
UpperCamelCase : List[Any] = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
UpperCamelCase : int = {
1: [],
2: []
# all degree is zero
}
UpperCamelCase : Any = 1_0
check_euler(snake_case_ ,snake_case_ )
check_euler(snake_case_ ,snake_case_ )
check_euler(snake_case_ ,snake_case_ )
check_euler(snake_case_ ,snake_case_ )
check_euler(snake_case_ ,snake_case_ )
if __name__ == "__main__":
main()
| 352 |
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):
return f'Node({self.data})'
class lowerCamelCase :
def __init__( self ):
UpperCamelCase : Dict = None
def __iter__( self ):
UpperCamelCase : int = self.head
while node:
yield node.data
UpperCamelCase : Union[str, Any] = node.next
def __len__( self ):
return sum(1 for _ in self )
def __repr__( self ):
return "->".join([str(SCREAMING_SNAKE_CASE_ ) for item in self] )
def __getitem__( self , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
UpperCamelCase : List[Any] = self.head
for _ in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = current.next
UpperCamelCase : Optional[Any] = data
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(len(self ) , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(0 , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index <= len(self ):
raise IndexError("""list index out of range""" )
UpperCamelCase : Optional[Any] = Node(SCREAMING_SNAKE_CASE_ )
if self.head is None:
UpperCamelCase : Dict = new_node
elif index == 0:
UpperCamelCase : Any = self.head # link new_node to head
UpperCamelCase : Any = new_node
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : str = temp.next
UpperCamelCase : Any = temp.next
UpperCamelCase : Optional[Any] = new_node
def a_ ( self ): # print every node data
print(self )
def a_ ( self ):
return self.delete_nth(0 )
def a_ ( self ): # delete from tail
return self.delete_nth(len(self ) - 1 )
def a_ ( self , SCREAMING_SNAKE_CASE_ = 0 ):
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("""List index out of range.""" )
UpperCamelCase : Union[str, Any] = self.head # default first node
if index == 0:
UpperCamelCase : Optional[Any] = self.head.next
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : int = temp.next
UpperCamelCase : Optional[Any] = temp.next
UpperCamelCase : Dict = temp.next.next
return delete_node.data
def a_ ( self ):
return self.head is None
def a_ ( self ):
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Union[str, Any] = self.head
while current:
# Store the current node's next node.
UpperCamelCase : Optional[int] = current.next
# Make the current node's next point backwards
UpperCamelCase : Optional[Any] = prev
# Make the previous node be the current node
UpperCamelCase : int = current
# Make the current node the next node (to progress iteration)
UpperCamelCase : Optional[int] = next_node
# Return prev in order to put the head at the end
UpperCamelCase : Optional[int] = prev
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = LinkedList()
assert linked_list.is_empty() is True
assert str(snake_case_ ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(1_0 ):
assert len(snake_case_ ) == i
linked_list.insert_nth(snake_case_ ,i + 1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_1 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(1_1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(0 ,1_2 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 1_0
assert linked_list.delete_tail() == 1_1
assert len(snake_case_ ) == 9
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_0 ) )
assert all(linked_list[i] == i + 1 for i in range(0 ,9 ) ) is True
for i in range(0 ,9 ):
UpperCamelCase : Optional[Any] = -i
assert all(linked_list[i] == -i for i in range(0 ,9 ) ) is True
linked_list.reverse()
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(-8 ,1 ) )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = [
-9,
1_0_0,
Node(7_7_3_4_5_1_1_2 ),
"""dlrow olleH""",
7,
5_5_5_5,
0,
-192.55555,
"""Hello, world!""",
77.9,
Node(1_0 ),
None,
None,
12.20,
]
UpperCamelCase : List[Any] = LinkedList()
for i in test_input:
linked_list.insert_tail(snake_case_ )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(snake_case_ ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
UpperCamelCase : Dict = linked_list.delete_head()
assert result == -9
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
UpperCamelCase : int = linked_list.delete_tail()
assert result == 12.2
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
UpperCamelCase : Optional[Any] = linked_list.delete_nth(1_0 )
assert result is None
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("""Hello again, world!""" ) )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(snake_case_ )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(snake_case_ )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def A_ ( ):
'''simple docstring'''
from doctest import testmod
testmod()
UpperCamelCase : List[Any] = LinkedList()
linked_list.insert_head(input("""Inserting 1st at head """ ).strip() )
linked_list.insert_head(input("""Inserting 2nd at head """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
linked_list.insert_tail(input("""\nInserting 1st at tail """ ).strip() )
linked_list.insert_tail(input("""Inserting 2nd at tail """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nDelete head""" )
linked_list.delete_head()
print("""Delete tail""" )
linked_list.delete_tail()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nReverse linked list""" )
linked_list.reverse()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nString representation of linked list:""" )
print(snake_case_ )
print("""\nReading/changing Node data using indexing:""" )
print(f'Element at Position 1: {linked_list[1]}' )
UpperCamelCase : List[Any] = input("""Enter New Value: """ ).strip()
print("""New list:""" )
print(snake_case_ )
print(f'length of linked_list is : {len(snake_case_ )}' )
if __name__ == "__main__":
main()
| 27 | 0 |
"""simple docstring"""
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
__A : Optional[int] = datasets.logging.get_logger(__name__)
__A : Tuple = '''\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Text Generation},
author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
booktitle={ACL},
year={2020},
url={https://arxiv.org/abs/2004.04696}
}
'''
__A : Dict = '''\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).
See the project\'s README at https://github.com/google-research/bleurt#readme for more information.
'''
__A : Any = '''
BLEURT score.
Args:
`predictions` (list of str): prediction/candidate sentences
`references` (list of str): reference sentences
`checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.
Returns:
\'scores\': List of scores.
Examples:
>>> predictions = ["hello there", "general kenobi"]
>>> references = ["hello there", "general kenobi"]
>>> bleurt = datasets.load_metric("bleurt")
>>> results = bleurt.compute(predictions=predictions, references=references)
>>> print([round(v, 2) for v in results["scores"]])
[1.03, 1.04]
'''
__A : List[str] = {
'''bleurt-tiny-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip''',
'''bleurt-tiny-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip''',
'''bleurt-base-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip''',
'''bleurt-base-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip''',
'''bleurt-large-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip''',
'''bleurt-large-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip''',
'''BLEURT-20-D3''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip''',
'''BLEURT-20-D6''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip''',
'''BLEURT-20-D12''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip''',
'''BLEURT-20''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip''',
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase ( datasets.Metric ):
def a_ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""https://github.com/google-research/bleurt""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/google-research/bleurt"""] , reference_urls=["""https://github.com/google-research/bleurt""", """https://arxiv.org/abs/2004.04696"""] , )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
# check that config name specifies a valid BLEURT model
if self.config_name == "default":
logger.warning(
"""Using default BLEURT-Base checkpoint for sequence maximum length 128. """
"""You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').""" )
UpperCamelCase : Dict = """bleurt-base-128"""
if self.config_name.lower() in CHECKPOINT_URLS:
UpperCamelCase : int = self.config_name.lower()
elif self.config_name.upper() in CHECKPOINT_URLS:
UpperCamelCase : Tuple = self.config_name.upper()
else:
raise KeyError(
f'{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}' )
# download the model checkpoint specified by self.config_name and set up the scorer
UpperCamelCase : Any = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
UpperCamelCase : Optional[int] = score.BleurtScorer(os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : int = self.scorer.score(references=SCREAMING_SNAKE_CASE_ , candidates=SCREAMING_SNAKE_CASE_ )
return {"scores": scores}
| 353 |
"""simple docstring"""
import argparse
import os
import re
__A : Dict = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__A : Union[str, Any] = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : Tuple = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : Tuple = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Dict="" ,snake_case_ : Dict=None ,snake_case_ : Any=None ):
'''simple docstring'''
UpperCamelCase : Optional[int] = 0
UpperCamelCase : List[Any] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Optional[Any] = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : int = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Any = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Any = [lines[index + 1]]
index += 1
else:
UpperCamelCase : List[str] = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
def _inner(snake_case_ : Tuple ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : List[Any] ,snake_case_ : Optional[int]=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Dict ):
return x
if key is None:
UpperCamelCase : int = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[Any] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : str = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : List[str] = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Tuple = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : int ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : List[Any] ):
UpperCamelCase : Any = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : Union[str, Any] = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[str] = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : str = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : str = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Dict = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : int = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Any = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[Any] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : Optional[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[Any] = keys[:-1]
UpperCamelCase : int = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Tuple ,snake_case_ : str=True ):
'''simple docstring'''
with open(snake_case_ ,"""r""" ) as f:
UpperCamelCase : int = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : Dict = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Optional[Any] = main_blocks[block_idx]
UpperCamelCase : Optional[int] = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : Union[str, Any] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : List[str] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Dict = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Union[str, Any] = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : Optional[int] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Union[str, Any] = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Union[str, Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Optional[Any] = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : List[Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[Any] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : str = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Any = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Union[str, Any] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : Any = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : str = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__A : Dict = logging.get_logger(__name__)
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = ['input_values', 'padding_mask']
def __init__( self , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 2_4000 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(feature_size=SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , padding_value=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = chunk_length_s
UpperCamelCase : List[Any] = overlap
@property
def a_ ( self ):
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def a_ ( self ):
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
def __call__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , ):
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
f' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
f' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
if padding and truncation:
raise ValueError("""Both padding and truncation were set. Make sure you only set one.""" )
elif padding is None:
# by default let's pad the inputs
UpperCamelCase : Optional[int] = True
UpperCamelCase : Union[str, Any] = bool(
isinstance(SCREAMING_SNAKE_CASE_ , (list, tuple) ) and (isinstance(raw_audio[0] , (np.ndarray, tuple, list) )) )
if is_batched:
UpperCamelCase : List[Any] = [np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ):
UpperCamelCase : Optional[int] = np.asarray(SCREAMING_SNAKE_CASE_ , dtype=np.floataa )
elif isinstance(SCREAMING_SNAKE_CASE_ , np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
UpperCamelCase : Any = raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
UpperCamelCase : Any = [np.asarray(SCREAMING_SNAKE_CASE_ ).T]
# verify inputs are valid
for idx, example in enumerate(SCREAMING_SNAKE_CASE_ ):
if example.ndim > 2:
raise ValueError(f'Expected input shape (channels, length) but got shape {example.shape}' )
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(f'Expected mono audio but example has {example.shape[-1]} channels' )
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(f'Expected stereo audio but example has {example.shape[-1]} channels' )
UpperCamelCase : List[Any] = None
UpperCamelCase : Dict = BatchFeature({"""input_values""": raw_audio} )
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
UpperCamelCase : Tuple = min(array.shape[0] for array in raw_audio )
UpperCamelCase : List[str] = int(np.floor(max_length / self.chunk_stride ) )
UpperCamelCase : str = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
UpperCamelCase : int = max(array.shape[0] for array in raw_audio )
UpperCamelCase : str = int(np.ceil(max_length / self.chunk_stride ) )
UpperCamelCase : Optional[int] = (nb_step - 1) * self.chunk_stride + self.chunk_length
UpperCamelCase : int = """max_length"""
else:
UpperCamelCase : List[str] = input_values
# normal padding on batch
if padded_inputs is None:
UpperCamelCase : List[Any] = self.pad(
SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , )
if padding:
UpperCamelCase : int = padded_inputs.pop("""attention_mask""" )
UpperCamelCase : Optional[Any] = []
for example in padded_inputs.pop("""input_values""" ):
if self.feature_size == 1:
UpperCamelCase : Union[str, Any] = example[..., None]
input_values.append(example.T )
UpperCamelCase : Dict = input_values
if return_tensors is not None:
UpperCamelCase : Any = padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_ )
return padded_inputs
| 354 |
"""simple docstring"""
def A_ ( snake_case_ : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(snake_case_ ,(list, tuple) ) or not all(
isinstance(snake_case_ ,snake_case_ ) for number in numbers ):
raise ValueError("""numbers must be an iterable of integers""" )
UpperCamelCase : int = numbers[0]
for i in range(1 ,len(snake_case_ ) ):
# update the maximum and minimum subarray products
UpperCamelCase : List[str] = numbers[i]
if number < 0:
UpperCamelCase , UpperCamelCase : Optional[int] = min_till_now, max_till_now
UpperCamelCase : Dict = max(snake_case_ ,max_till_now * number )
UpperCamelCase : Union[str, Any] = min(snake_case_ ,min_till_now * number )
# update the maximum product found till now
UpperCamelCase : Union[str, Any] = max(snake_case_ ,snake_case_ )
return max_prod
| 27 | 0 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
__A : int = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
super().__init__()
self.register_modules(
vae=SCREAMING_SNAKE_CASE_ , text_encoder=SCREAMING_SNAKE_CASE_ , tokenizer=SCREAMING_SNAKE_CASE_ , unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ , )
def a_ ( self , SCREAMING_SNAKE_CASE_ = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
UpperCamelCase : Union[str, Any] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
self.enable_attention_slicing(SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def __call__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 512 , SCREAMING_SNAKE_CASE_ = 50 , SCREAMING_SNAKE_CASE_ = 7.5 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "pil" , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = 1
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : int = len(SCREAMING_SNAKE_CASE_ )
else:
raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_ )}' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.' )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or callback_steps <= 0)
):
raise ValueError(
f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
f' {type(SCREAMING_SNAKE_CASE_ )}.' )
# get prompt text embeddings
UpperCamelCase : Optional[Any] = self.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=self.tokenizer.model_max_length , return_tensors="""pt""" , )
UpperCamelCase : Optional[Any] = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
UpperCamelCase : Tuple = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
"""The following part of your input was truncated because CLIP can only handle sequences up to"""
f' {self.tokenizer.model_max_length} tokens: {removed_text}' )
UpperCamelCase : str = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
UpperCamelCase : Union[str, Any] = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
UpperCamelCase : Optional[Any] = text_embeddings.shape
UpperCamelCase : Optional[Any] = text_embeddings.repeat(1 , SCREAMING_SNAKE_CASE_ , 1 )
UpperCamelCase : Optional[Any] = text_embeddings.view(bs_embed * num_images_per_prompt , SCREAMING_SNAKE_CASE_ , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
UpperCamelCase : Dict = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
UpperCamelCase : List[str]
if negative_prompt is None:
UpperCamelCase : Any = [""""""]
elif type(SCREAMING_SNAKE_CASE_ ) is not type(SCREAMING_SNAKE_CASE_ ):
raise TypeError(
f'`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_ )} !='
f' {type(SCREAMING_SNAKE_CASE_ )}.' )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[Any] = [negative_prompt]
elif batch_size != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
f'`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE_ )}, but `prompt`:'
f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
""" the batch size of `prompt`.""" )
else:
UpperCamelCase : str = negative_prompt
UpperCamelCase : Tuple = text_input_ids.shape[-1]
UpperCamelCase : Dict = self.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Tuple = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
UpperCamelCase : Optional[Any] = uncond_embeddings.shape[1]
UpperCamelCase : str = uncond_embeddings.repeat(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , 1 )
UpperCamelCase : str = uncond_embeddings.view(batch_size * num_images_per_prompt , SCREAMING_SNAKE_CASE_ , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCamelCase : Optional[int] = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
UpperCamelCase : Union[str, Any] = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
UpperCamelCase : Optional[Any] = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
UpperCamelCase : List[str] = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
UpperCamelCase : Any = torch.randn(
SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device="""cpu""" , dtype=SCREAMING_SNAKE_CASE_ ).to(self.device )
UpperCamelCase : Tuple = torch.randn(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device="""cpu""" , dtype=SCREAMING_SNAKE_CASE_ ).to(
self.device )
else:
UpperCamelCase : Any = torch.randn(
SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.randn(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
else:
if latents_reference.shape != latents_shape:
raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
UpperCamelCase : Tuple = latents_reference.to(self.device )
UpperCamelCase : Union[str, Any] = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
UpperCamelCase : str = (latents_shape[3] - latents_shape_reference[3]) // 2
UpperCamelCase : Union[str, Any] = (latents_shape[2] - latents_shape_reference[2]) // 2
UpperCamelCase : Tuple = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
UpperCamelCase : Optional[int] = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
UpperCamelCase : int = 0 if dx < 0 else dx
UpperCamelCase : Union[str, Any] = 0 if dy < 0 else dy
UpperCamelCase : List[Any] = max(-dx , 0 )
UpperCamelCase : Union[str, Any] = max(-dy , 0 )
# import pdb
# pdb.set_trace()
UpperCamelCase : List[str] = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
UpperCamelCase : List[Any] = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
UpperCamelCase : Any = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
UpperCamelCase : Dict = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
UpperCamelCase : Any = {}
if accepts_eta:
UpperCamelCase : List[Any] = eta
for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_ ) ):
# expand the latents if we are doing classifier free guidance
UpperCamelCase : str = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCamelCase : Tuple = self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# predict the noise residual
UpperCamelCase : int = self.unet(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ ).sample
# perform guidance
if do_classifier_free_guidance:
UpperCamelCase : Tuple = noise_pred.chunk(2 )
UpperCamelCase : Tuple = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
UpperCamelCase : Dict = self.scheduler.step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = 1 / 0.18215 * latents
UpperCamelCase : Dict = self.vae.decode(SCREAMING_SNAKE_CASE_ ).sample
UpperCamelCase : Optional[int] = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
UpperCamelCase : int = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
UpperCamelCase : Any = self.feature_extractor(self.numpy_to_pil(SCREAMING_SNAKE_CASE_ ) , return_tensors="""pt""" ).to(
self.device )
UpperCamelCase : Optional[int] = self.safety_checker(
images=SCREAMING_SNAKE_CASE_ , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
UpperCamelCase : List[str] = None
if output_type == "pil":
UpperCamelCase : List[Any] = self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE_ , nsfw_content_detected=SCREAMING_SNAKE_CASE_ )
| 355 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Any = AudioLDMPipeline
lowercase : Union[str, Any] = TEXT_TO_AUDIO_PARAMS
lowercase : List[str] = TEXT_TO_AUDIO_BATCH_PARAMS
lowercase : Tuple = frozenset(
[
'num_inference_steps',
'num_waveforms_per_prompt',
'generator',
'latents',
'output_type',
'return_dict',
'callback',
'callback_steps',
] )
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : Tuple = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=(32, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Optional[Any] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_one=SCREAMING_SNAKE_CASE_ , )
torch.manual_seed(0 )
UpperCamelCase : Optional[int] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
UpperCamelCase : int = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
UpperCamelCase : Optional[int] = ClapTextModelWithProjection(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 )
UpperCamelCase : Tuple = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=1_6000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : List[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Any = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def a_ ( self ):
UpperCamelCase : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Any = self.get_dummy_components()
UpperCamelCase : int = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Tuple = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[str] = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Optional[int] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : str = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
UpperCamelCase : Tuple = prompt_embeds
# forward
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : List[str] = self.get_dummy_components()
UpperCamelCase : List[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * ["""this is a negative prompt"""]
UpperCamelCase : List[Any] = negative_prompt
UpperCamelCase : str = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : str = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
UpperCamelCase : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[Any] = []
for p in [prompt, negative_prompt]:
UpperCamelCase : int = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Union[str, Any] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
embeds.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase : Tuple = embeds
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Optional[int] = self.get_dummy_components()
UpperCamelCase : List[str] = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = """egg cracking"""
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ , negative_prompt=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Union[str, Any] = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Union[str, Any] = self.get_dummy_components()
UpperCamelCase : Tuple = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
UpperCamelCase : List[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
UpperCamelCase : Dict = 2
UpperCamelCase : List[str] = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
UpperCamelCase : List[str] = 2
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
UpperCamelCase : Any = 2
UpperCamelCase : str = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Tuple = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = audioldm_pipe.vocoder.config.sampling_rate
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe(audio_length_in_s=0.016 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.016
UpperCamelCase : Optional[Any] = audioldm_pipe(audio_length_in_s=0.032 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.032
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Optional[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = ["""hey"""]
UpperCamelCase : Dict = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : str = output.audios.shape
assert audio_shape == (1, 256)
UpperCamelCase : Optional[Any] = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
UpperCamelCase : str = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : List[str] = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def a_ ( self ):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
self._test_inference_batch_single_identical(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def a_ ( self ):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@slow
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="cpu" , SCREAMING_SNAKE_CASE_=torch.floataa , SCREAMING_SNAKE_CASE_=0 ):
UpperCamelCase : str = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = np.random.RandomState(SCREAMING_SNAKE_CASE_ ).standard_normal((1, 8, 128, 16) )
UpperCamelCase : int = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def a_ ( self ):
UpperCamelCase : Optional[int] = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = 25
UpperCamelCase : Optional[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[7_7230:7_7240]
UpperCamelCase : Optional[Any] = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
UpperCamelCase : Any = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def a_ ( self ):
UpperCamelCase : Any = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : Any = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
UpperCamelCase : str = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[2_7780:2_7790]
UpperCamelCase : Tuple = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
UpperCamelCase : Tuple = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 27 | 0 |
"""simple docstring"""
def A_ ( snake_case_ : str ):
'''simple docstring'''
if not all(char in """01""" for char in bin_string ):
raise ValueError("""Non-binary value was passed to the function""" )
if not bin_string:
raise ValueError("""Empty string was passed to the function""" )
UpperCamelCase : Any = """"""
while len(snake_case_ ) % 3 != 0:
UpperCamelCase : List[str] = """0""" + bin_string
UpperCamelCase : Tuple = [
bin_string[index : index + 3]
for index in range(len(snake_case_ ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
UpperCamelCase : int = 0
for index, val in enumerate(snake_case_ ):
oct_val += int(2 ** (2 - index) * int(snake_case_ ) )
oct_string += str(snake_case_ )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 356 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( snake_case_ : Dataset ,snake_case_ : Dict[str, str] ):
'''simple docstring'''
UpperCamelCase : List[str] = args.log_outputs
UpperCamelCase : Tuple = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
UpperCamelCase : List[Any] = load_metric("""wer""" )
UpperCamelCase : Any = load_metric("""cer""" )
# compute metrics
UpperCamelCase : str = wer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
UpperCamelCase : Dict = cer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
# print & log results
UpperCamelCase : Optional[int] = f'WER: {wer_result}\nCER: {cer_result}'
print(snake_case_ )
with open(f'{dataset_id}_eval_results.txt' ,"""w""" ) as f:
f.write(snake_case_ )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
UpperCamelCase : Optional[Any] = f'log_{dataset_id}_predictions.txt'
UpperCamelCase : str = f'log_{dataset_id}_targets.txt'
with open(snake_case_ ,"""w""" ) as p, open(snake_case_ ,"""w""" ) as t:
# mapping function to write output
def write_to_file(snake_case_ : Union[str, Any] ,snake_case_ : Tuple ):
p.write(f'{i}' + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(f'{i}' + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(snake_case_ ,with_indices=snake_case_ )
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Dict = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
UpperCamelCase : str = re.sub(snake_case_ ,"""""" ,text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
UpperCamelCase : List[str] = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
UpperCamelCase : Tuple = """ """.join(text.split(snake_case_ ) )
return text
def A_ ( snake_case_ : str ):
'''simple docstring'''
# load dataset
UpperCamelCase : Union[str, Any] = load_dataset(args.dataset ,args.config ,split=args.split ,use_auth_token=snake_case_ )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
UpperCamelCase : List[Any] = AutoFeatureExtractor.from_pretrained(args.model_id )
UpperCamelCase : Dict = feature_extractor.sampling_rate
# resample audio
UpperCamelCase : Optional[Any] = dataset.cast_column("""audio""" ,Audio(sampling_rate=snake_case_ ) )
# load eval pipeline
if args.device is None:
UpperCamelCase : int = 0 if torch.cuda.is_available() else -1
UpperCamelCase : Union[str, Any] = pipeline("""automatic-speech-recognition""" ,model=args.model_id ,device=args.device )
# map function to decode audio
def map_to_pred(snake_case_ : Union[str, Any] ):
UpperCamelCase : List[Any] = asr(
batch["""audio"""]["""array"""] ,chunk_length_s=args.chunk_length_s ,stride_length_s=args.stride_length_s )
UpperCamelCase : Union[str, Any] = prediction["""text"""]
UpperCamelCase : Optional[Any] = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
UpperCamelCase : Any = dataset.map(snake_case_ ,remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(snake_case_ ,snake_case_ )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
__A : Optional[Any] = parser.parse_args()
main(args)
| 27 | 0 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__A : Optional[Any] = pd.read_csv('''sample_data.csv''', header=None)
__A : List[Any] = df.shape[:1][0]
# If you're using some other dataset input the target column
__A : Dict = df.iloc[:, 1:2]
__A : List[str] = actual_data.values.reshape(len_data, 1)
__A : List[str] = MinMaxScaler().fit_transform(actual_data)
__A : List[str] = 10
__A : Optional[int] = 5
__A : Optional[int] = 20
__A : Optional[Any] = len_data - periods * look_back
__A : int = actual_data[:division]
__A : int = actual_data[division - look_back :]
__A : Dict = [], []
__A : Tuple = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
__A : List[Any] = np.array(train_x)
__A : str = np.array(test_x)
__A : Dict = np.array([list(i.ravel()) for i in train_y])
__A : int = np.array([list(i.ravel()) for i in test_y])
__A : Optional[Any] = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss='''mean_squared_error''', optimizer='''adam''')
__A : Union[str, Any] = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
__A : Union[str, Any] = model.predict(x_test)
| 357 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = 'EncodecFeatureExtractor'
lowercase : List[Any] = ('T5Tokenizer', 'T5TokenizerFast')
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.feature_extractor
UpperCamelCase : Any = False
def a_ ( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True ):
return self.tokenizer.get_decoder_prompt_ids(task=SCREAMING_SNAKE_CASE_ , language=SCREAMING_SNAKE_CASE_ , no_timestamps=SCREAMING_SNAKE_CASE_ )
def __call__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = kwargs.pop("""sampling_rate""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = kwargs.pop("""text""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Any = args[0]
UpperCamelCase : str = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if text is not None:
UpperCamelCase : Optional[int] = self.tokenizer(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is not None:
UpperCamelCase : str = self.feature_extractor(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
UpperCamelCase : int = audio_inputs["""input_values"""]
if "padding_mask" in audio_inputs:
UpperCamelCase : Optional[Any] = audio_inputs["""padding_mask"""]
return inputs
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = kwargs.pop("""padding_mask""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Optional[int] = args[0]
UpperCamelCase : Any = args[1:]
if audio_values is not None:
return self._decode_audio(SCREAMING_SNAKE_CASE_ , padding_mask=SCREAMING_SNAKE_CASE_ )
else:
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = to_numpy(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase , UpperCamelCase : int = audio_values.shape
if padding_mask is None:
return list(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = to_numpy(SCREAMING_SNAKE_CASE_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
UpperCamelCase : List[str] = seq_len - padding_mask.shape[-1]
UpperCamelCase : Optional[int] = 1 - self.feature_extractor.padding_value
UpperCamelCase : Any = np.pad(SCREAMING_SNAKE_CASE_ , ((0, 0), (0, difference)) , """constant""" , constant_values=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audio_values.tolist()
for i in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
UpperCamelCase : Optional[Any] = sliced_audio.reshape(SCREAMING_SNAKE_CASE_ , -1 )
return audio_values
| 27 | 0 |
"""simple docstring"""
__A : dict[tuple[int, int, int], int] = {}
def A_ ( snake_case_ : int ,snake_case_ : int ,snake_case_ : int ):
'''simple docstring'''
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
UpperCamelCase : str = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
UpperCamelCase : Dict = _calculate(days - 1 ,snake_case_ ,late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
UpperCamelCase : Dict = _calculate(days - 1 ,absent + 1 ,0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
UpperCamelCase : Any = _calculate(days - 1 ,snake_case_ ,0 )
UpperCamelCase : str = state_late + state_absent + state_ontime
UpperCamelCase : int = prizestrings
return prizestrings
def A_ ( snake_case_ : int = 3_0 ):
'''simple docstring'''
return _calculate(snake_case_ ,absent=0 ,late=0 )
if __name__ == "__main__":
print(solution())
| 358 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( snake_case_ : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
UpperCamelCase : Any = BeautifulSoup(requests.get(snake_case_ ).text ,"""html.parser""" )
UpperCamelCase : Optional[int] = soup.findAll("""h1""" )
UpperCamelCase : List[Any] = soup.findAll("""div""" ,{"""class""": """maincounter-number"""} )
keys += soup.findAll("""span""" ,{"""class""": """panel-title"""} )
values += soup.findAll("""div""" ,{"""class""": """number-table-main"""} )
return {key.text.strip(): value.text.strip() for key, value in zip(snake_case_ ,snake_case_ )}
if __name__ == "__main__":
print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
for key, value in world_covidaa_stats().items():
print(F'''{key}\n{value}\n''')
| 27 | 0 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
UpperCamelCase : Optional[Any] = """laion/clap-htsat-unfused"""
UpperCamelCase : Tuple = tempfile.mkdtemp()
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
return RobertaTokenizer.from_pretrained(self.checkpoint , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
shutil.rmtree(self.tmpdirname )
def a_ ( self ):
UpperCamelCase : Any = self.get_tokenizer()
UpperCamelCase : Any = self.get_feature_extractor()
UpperCamelCase : List[Any] = ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase : Optional[Any] = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , SCREAMING_SNAKE_CASE_ )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Dict = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase : Union[str, Any] = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
UpperCamelCase : Any = self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE_ , padding_value=1.0 )
UpperCamelCase : str = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=SCREAMING_SNAKE_CASE_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , SCREAMING_SNAKE_CASE_ )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : str = self.get_feature_extractor()
UpperCamelCase : int = self.get_tokenizer()
UpperCamelCase : Optional[int] = ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = floats_list((3, 1000) )
UpperCamelCase : Optional[Any] = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors="""np""" )
UpperCamelCase : Union[str, Any] = processor(audios=SCREAMING_SNAKE_CASE_ , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def a_ ( self ):
UpperCamelCase : List[Any] = self.get_feature_extractor()
UpperCamelCase : Optional[int] = self.get_tokenizer()
UpperCamelCase : int = ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = """This is a test string"""
UpperCamelCase : Dict = processor(text=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = tokenizer(SCREAMING_SNAKE_CASE_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.get_feature_extractor()
UpperCamelCase : List[Any] = self.get_tokenizer()
UpperCamelCase : Any = ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCamelCase : List[str] = processor.batch_decode(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.get_feature_extractor()
UpperCamelCase : Dict = self.get_tokenizer()
UpperCamelCase : Optional[int] = ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg="""`processor` and `feature_extractor` model input names do not match""" , )
| 359 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=1 , ):
UpperCamelCase : Tuple = parent
UpperCamelCase : Optional[int] = batch_size
UpperCamelCase : Optional[Any] = seq_length
UpperCamelCase : int = is_training
UpperCamelCase : Union[str, Any] = use_input_mask
UpperCamelCase : Union[str, Any] = use_token_type_ids
UpperCamelCase : Dict = use_labels
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Union[str, Any] = hidden_size
UpperCamelCase : Tuple = num_hidden_layers
UpperCamelCase : Any = num_attention_heads
UpperCamelCase : int = intermediate_size
UpperCamelCase : str = hidden_act
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : List[Any] = max_position_embeddings
UpperCamelCase : Optional[Any] = type_vocab_size
UpperCamelCase : int = type_sequence_label_size
UpperCamelCase : Dict = initializer_range
UpperCamelCase : Dict = num_labels
UpperCamelCase : Tuple = num_choices
UpperCamelCase : Optional[int] = scope
UpperCamelCase : List[Any] = q_groups
UpperCamelCase : Tuple = k_groups
UpperCamelCase : Any = v_groups
UpperCamelCase : List[str] = post_attention_groups
UpperCamelCase : Tuple = intermediate_groups
UpperCamelCase : int = output_groups
def a_ ( self ):
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Tuple = None
if self.use_input_mask:
UpperCamelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Optional[int] = None
UpperCamelCase : List[Any] = None
UpperCamelCase : Dict = None
if self.use_labels:
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : Dict = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ ( self ):
return SqueezeBertConfig(
embedding_size=self.hidden_size , vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = SqueezeBertModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = SqueezeBertForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = SqueezeBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : str = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = self.num_labels
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = self.num_labels
UpperCamelCase : str = SqueezeBertForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = self.num_choices
UpperCamelCase : Tuple = SqueezeBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Union[str, Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) : Optional[int] = config_and_inputs
UpperCamelCase : Optional[int] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
lowercase : Dict = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase : Dict = False
lowercase : str = True
lowercase : str = False
def a_ ( self ):
UpperCamelCase : Any = SqueezeBertModelTester(self )
UpperCamelCase : List[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : Optional[Any] = SqueezeBertModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_sentencepiece
@require_tokenizers
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli""" )
UpperCamelCase : Dict = torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]] )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[Any] = torch.Size((1, 3) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.tensor([[0.6401, -0.0349, -0.6041]] )
self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 27 | 0 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
__A : Optional[int] = sys.version_info >= (3, 10)
def A_ ( snake_case_ : Optional[Any]=None ,snake_case_ : List[Any]=None ):
'''simple docstring'''
return field(default_factory=lambda: default ,metadata=snake_case_ )
@dataclass
class lowerCamelCase :
lowercase : int
lowercase : float
lowercase : str
lowercase : bool
@dataclass
class lowerCamelCase :
lowercase : int = 4_2
lowercase : str = field(default='toto' , metadata={'help': 'help message'} )
@dataclass
class lowerCamelCase :
lowercase : bool = False
lowercase : bool = True
lowercase : Optional[bool] = None
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Any = 'titi'
lowercase : Optional[Any] = 'toto'
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Any = 'titi'
lowercase : Optional[Any] = 'toto'
lowercase : int = 4_2
@dataclass
class lowerCamelCase :
lowercase : BasicEnum = "toto"
def a_ ( self ):
UpperCamelCase : str = BasicEnum(self.foo )
@dataclass
class lowerCamelCase :
lowercase : MixedTypeEnum = "toto"
def a_ ( self ):
UpperCamelCase : Dict = MixedTypeEnum(self.foo )
@dataclass
class lowerCamelCase :
lowercase : Optional[int] = None
lowercase : Optional[float] = field(default=_UpperCAmelCase , metadata={'help': 'help message'} )
lowercase : Optional[str] = None
lowercase : Optional[List[str]] = list_field(default=[] )
lowercase : Optional[List[int]] = list_field(default=[] )
@dataclass
class lowerCamelCase :
lowercase : List[int] = list_field(default=[] )
lowercase : List[int] = list_field(default=[1, 2, 3] )
lowercase : List[str] = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
lowercase : List[float] = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class lowerCamelCase :
lowercase : List[int] = field()
lowercase : str = field()
lowercase : BasicEnum = field()
def a_ ( self ):
UpperCamelCase : Dict = BasicEnum(self.required_enum )
@dataclass
class lowerCamelCase :
lowercase : int
lowercase : "BasicEnum" = field()
lowercase : "Optional[bool]" = None
lowercase : "str" = field(default='toto' , metadata={'help': 'help message'} )
lowercase : "List[str]" = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
if is_python_no_less_than_3_10:
@dataclass
class lowerCamelCase :
lowercase : bool = False
lowercase : bool = True
lowercase : bool | None = None
@dataclass
class lowerCamelCase :
lowercase : int | None = None
lowercase : float | None = field(default=_UpperCAmelCase , metadata={'help': 'help message'} )
lowercase : str | None = None
lowercase : list[str] | None = list_field(default=[] )
lowercase : list[int] | None = list_field(default=[] )
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
UpperCamelCase : List[str] = {k: v for k, v in vars(SCREAMING_SNAKE_CASE_ ).items() if k != """container"""}
UpperCamelCase : int = {k: v for k, v in vars(SCREAMING_SNAKE_CASE_ ).items() if k != """container"""}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get("""choices""" , SCREAMING_SNAKE_CASE_ ) and yy.get("""choices""" , SCREAMING_SNAKE_CASE_ ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx["""type"""](SCREAMING_SNAKE_CASE_ ) , yy["""type"""](SCREAMING_SNAKE_CASE_ ) )
del xx["type"], yy["type"]
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : int = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--bar""" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--baz""" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--flag""" , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ , const=SCREAMING_SNAKE_CASE_ , nargs="""?""" )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = ["""--foo""", """1""", """--baz""", """quux""", """--bar""", """0.5"""]
(UpperCamelCase ) : Optional[Any] = parser.parse_args_into_dataclasses(SCREAMING_SNAKE_CASE_ , look_for_args_file=SCREAMING_SNAKE_CASE_ )
self.assertFalse(example.flag )
def a_ ( self ):
UpperCamelCase : Tuple = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = argparse.ArgumentParser()
expected.add_argument("""--foo""" , default=42 , type=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--baz""" , default="""toto""" , type=SCREAMING_SNAKE_CASE_ , help="""help message""" )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ , const=SCREAMING_SNAKE_CASE_ , nargs="""?""" )
expected.add_argument("""--baz""" , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ , const=SCREAMING_SNAKE_CASE_ , nargs="""?""" )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument("""--no_baz""" , action="""store_false""" , default=SCREAMING_SNAKE_CASE_ , dest="""baz""" )
expected.add_argument("""--opt""" , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(SCREAMING_SNAKE_CASE_ )
for dataclass_type in dataclass_types:
UpperCamelCase : Optional[Any] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = parser.parse_args([] )
self.assertEqual(SCREAMING_SNAKE_CASE_ , Namespace(foo=SCREAMING_SNAKE_CASE_ , baz=SCREAMING_SNAKE_CASE_ , opt=SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Tuple = parser.parse_args(["""--foo""", """--no_baz"""] )
self.assertEqual(SCREAMING_SNAKE_CASE_ , Namespace(foo=SCREAMING_SNAKE_CASE_ , baz=SCREAMING_SNAKE_CASE_ , opt=SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Optional[int] = parser.parse_args(["""--foo""", """--baz"""] )
self.assertEqual(SCREAMING_SNAKE_CASE_ , Namespace(foo=SCREAMING_SNAKE_CASE_ , baz=SCREAMING_SNAKE_CASE_ , opt=SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Union[str, Any] = parser.parse_args(["""--foo""", """True""", """--baz""", """True""", """--opt""", """True"""] )
self.assertEqual(SCREAMING_SNAKE_CASE_ , Namespace(foo=SCREAMING_SNAKE_CASE_ , baz=SCREAMING_SNAKE_CASE_ , opt=SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Optional[int] = parser.parse_args(["""--foo""", """False""", """--baz""", """False""", """--opt""", """False"""] )
self.assertEqual(SCREAMING_SNAKE_CASE_ , Namespace(foo=SCREAMING_SNAKE_CASE_ , baz=SCREAMING_SNAKE_CASE_ , opt=SCREAMING_SNAKE_CASE_ ) )
def a_ ( self ):
UpperCamelCase : Optional[int] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = argparse.ArgumentParser()
expected.add_argument(
"""--foo""" , default="""toto""" , choices=["""titi""", """toto""", 42] , type=make_choice_type_function(["""titi""", """toto""", 42] ) , )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = parser.parse_args([] )
self.assertEqual(args.foo , """toto""" )
UpperCamelCase : Optional[Any] = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
UpperCamelCase : int = parser.parse_args(["""--foo""", """titi"""] )
self.assertEqual(args.foo , """titi""" )
UpperCamelCase : Optional[int] = parser.parse_args_into_dataclasses(["""--foo""", """titi"""] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
UpperCamelCase : str = parser.parse_args(["""--foo""", """42"""] )
self.assertEqual(args.foo , 42 )
UpperCamelCase : Optional[int] = parser.parse_args_into_dataclasses(["""--foo""", """42"""] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def a_ ( self ):
@dataclass
class lowerCamelCase :
lowercase : Literal["titi", "toto", 4_2] = "toto"
UpperCamelCase : Optional[Any] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = argparse.ArgumentParser()
expected.add_argument(
"""--foo""" , default="""toto""" , choices=("""titi""", """toto""", 42) , type=make_choice_type_function(["""titi""", """toto""", 42] ) , )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = parser.parse_args([] )
self.assertEqual(args.foo , """toto""" )
UpperCamelCase : Optional[int] = parser.parse_args(["""--foo""", """titi"""] )
self.assertEqual(args.foo , """titi""" )
UpperCamelCase : int = parser.parse_args(["""--foo""", """42"""] )
self.assertEqual(args.foo , 42 )
def a_ ( self ):
UpperCamelCase : str = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = argparse.ArgumentParser()
expected.add_argument("""--foo_int""" , nargs="""+""" , default=[] , type=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--bar_int""" , nargs="""+""" , default=[1, 2, 3] , type=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--foo_str""" , nargs="""+""" , default=["""Hallo""", """Bonjour""", """Hello"""] , type=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--foo_float""" , nargs="""+""" , default=[0.1, 0.2, 0.3] , type=SCREAMING_SNAKE_CASE_ )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = parser.parse_args([] )
self.assertEqual(
SCREAMING_SNAKE_CASE_ , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=["""Hallo""", """Bonjour""", """Hello"""] , foo_float=[0.1, 0.2, 0.3] ) , )
UpperCamelCase : Optional[Any] = parser.parse_args("""--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7""".split() )
self.assertEqual(SCREAMING_SNAKE_CASE_ , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=["""a""", """b""", """c"""] , foo_float=[0.1, 0.7] ) )
def a_ ( self ):
UpperCamelCase : Dict = argparse.ArgumentParser()
expected.add_argument("""--foo""" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--bar""" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , help="""help message""" )
expected.add_argument("""--baz""" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--ces""" , nargs="""+""" , default=[] , type=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--des""" , nargs="""+""" , default=[] , type=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(SCREAMING_SNAKE_CASE_ )
for dataclass_type in dataclass_types:
UpperCamelCase : List[Any] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = parser.parse_args([] )
self.assertEqual(SCREAMING_SNAKE_CASE_ , Namespace(foo=SCREAMING_SNAKE_CASE_ , bar=SCREAMING_SNAKE_CASE_ , baz=SCREAMING_SNAKE_CASE_ , ces=[] , des=[] ) )
UpperCamelCase : int = parser.parse_args("""--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3""".split() )
self.assertEqual(SCREAMING_SNAKE_CASE_ , Namespace(foo=12 , bar=3.14 , baz="""42""" , ces=["""a""", """b""", """c"""] , des=[1, 2, 3] ) )
def a_ ( self ):
UpperCamelCase : List[Any] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = argparse.ArgumentParser()
expected.add_argument("""--required_list""" , nargs="""+""" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--required_str""" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ )
expected.add_argument(
"""--required_enum""" , type=make_choice_type_function(["""titi""", """toto"""] ) , choices=["""titi""", """toto"""] , required=SCREAMING_SNAKE_CASE_ , )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ )
expected.add_argument(
"""--required_enum""" , type=make_choice_type_function(["""titi""", """toto"""] ) , choices=["""titi""", """toto"""] , required=SCREAMING_SNAKE_CASE_ , )
expected.add_argument("""--opt""" , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--baz""" , default="""toto""" , type=SCREAMING_SNAKE_CASE_ , help="""help message""" )
expected.add_argument("""--foo_str""" , nargs="""+""" , default=["""Hallo""", """Bonjour""", """Hello"""] , type=SCREAMING_SNAKE_CASE_ )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[int] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
UpperCamelCase : Optional[Any] = parser.parse_dict(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[int] = BasicExample(**SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
"""extra""": 42,
}
self.assertRaises(SCREAMING_SNAKE_CASE_ , parser.parse_dict , SCREAMING_SNAKE_CASE_ , allow_extra_keys=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase : List[Any] = os.path.join(SCREAMING_SNAKE_CASE_ , """temp_json""" )
os.mkdir(SCREAMING_SNAKE_CASE_ )
with open(temp_local_path + """.json""" , """w+""" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = parser.parse_yaml_file(Path(temp_local_path + """.json""" ) )[0]
UpperCamelCase : Optional[Any] = BasicExample(**SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase : List[str] = os.path.join(SCREAMING_SNAKE_CASE_ , """temp_yaml""" )
os.mkdir(SCREAMING_SNAKE_CASE_ )
with open(temp_local_path + """.yaml""" , """w+""" ) as f:
yaml.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = parser.parse_yaml_file(Path(temp_local_path + """.yaml""" ) )[0]
UpperCamelCase : str = BasicExample(**SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
| 360 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "geglu" , SCREAMING_SNAKE_CASE_ = None , ):
super().__init__()
UpperCamelCase : int = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=SCREAMING_SNAKE_CASE_ , attention_head_dim=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , dropout=SCREAMING_SNAKE_CASE_ , norm_num_groups=SCREAMING_SNAKE_CASE_ , cross_attention_dim=SCREAMING_SNAKE_CASE_ , attention_bias=SCREAMING_SNAKE_CASE_ , sample_size=SCREAMING_SNAKE_CASE_ , num_vector_embeds=SCREAMING_SNAKE_CASE_ , activation_fn=SCREAMING_SNAKE_CASE_ , num_embeds_ada_norm=SCREAMING_SNAKE_CASE_ , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
UpperCamelCase : Optional[Any] = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
UpperCamelCase : List[Any] = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
UpperCamelCase : int = [1, 0]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_ = True , ):
UpperCamelCase : Dict = hidden_states
UpperCamelCase : Optional[Any] = []
UpperCamelCase : List[Any] = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
UpperCamelCase : Optional[int] = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
UpperCamelCase : str = self.transformer_index_for_condition[i]
UpperCamelCase : Any = self.transformers[transformer_index](
SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ , timestep=SCREAMING_SNAKE_CASE_ , cross_attention_kwargs=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
UpperCamelCase : Any = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
UpperCamelCase : List[str] = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE_ )
| 27 | 0 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=3.6 ):
UpperCamelCase : Dict = tokenizer
UpperCamelCase : Optional[Any] = tokenizer.bos_token_id
UpperCamelCase : Any = dataset
UpperCamelCase : List[str] = seq_length
UpperCamelCase : Optional[Any] = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
UpperCamelCase : Dict = iter(self.dataset )
UpperCamelCase : Union[str, Any] = True
while more_examples:
UpperCamelCase : Tuple = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(SCREAMING_SNAKE_CASE_ )["""content"""] )
buffer_len += len(buffer[-1] )
except StopIteration:
UpperCamelCase : Dict = False
break
UpperCamelCase : str = tokenizer(SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )["""input_ids"""]
UpperCamelCase : str = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , self.seq_length ):
UpperCamelCase : List[str] = all_token_ids[i : i + self.seq_length]
if len(SCREAMING_SNAKE_CASE_ ) == self.seq_length:
yield torch.tensor(SCREAMING_SNAKE_CASE_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : Dict = {"""streaming""": True}
UpperCamelCase : Optional[int] = load_dataset(args.dataset_name ,split="""train""" ,**snake_case_ )
UpperCamelCase : Optional[int] = ConstantLengthDataset(snake_case_ ,snake_case_ ,seq_length=args.seq_length )
UpperCamelCase : List[Any] = DataLoader(snake_case_ ,batch_size=args.batch_size )
return eval_dataloader
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
model.eval()
UpperCamelCase : Dict = []
for step, batch in enumerate(snake_case_ ):
with torch.no_grad():
UpperCamelCase : List[Any] = model(snake_case_ ,labels=snake_case_ )
UpperCamelCase : Any = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(snake_case_ ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
UpperCamelCase : Dict = torch.mean(torch.cat(snake_case_ ) )
try:
UpperCamelCase : Dict = torch.exp(snake_case_ )
except OverflowError:
UpperCamelCase : Optional[int] = float("""inf""" )
return loss.item(), perplexity.item()
# Setup Accelerator
__A : List[Any] = Accelerator()
# Parse configuration
__A : str = HfArgumentParser(EvaluationArguments)
__A : List[Any] = parser.parse_args()
set_seed(args.seed)
# Logging
__A : Any = logging.getLogger(__name__)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
# Load model and tokenizer
__A : List[Any] = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__A : List[Any] = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
__A : int = create_dataloader(args)
# Prepare everything with our `accelerator`.
__A : Optional[Any] = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
__A : Tuple = evaluate(args)
logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 361 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Optional[int] = 'mvp'
lowercase : Optional[Any] = ['past_key_values']
lowercase : Union[str, Any] = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , SCREAMING_SNAKE_CASE_=5_0267 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=100 , SCREAMING_SNAKE_CASE_=800 , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Dict = max_position_embeddings
UpperCamelCase : Optional[int] = d_model
UpperCamelCase : Optional[Any] = encoder_ffn_dim
UpperCamelCase : Any = encoder_layers
UpperCamelCase : List[Any] = encoder_attention_heads
UpperCamelCase : Optional[Any] = decoder_ffn_dim
UpperCamelCase : Optional[int] = decoder_layers
UpperCamelCase : Dict = decoder_attention_heads
UpperCamelCase : List[str] = dropout
UpperCamelCase : List[str] = attention_dropout
UpperCamelCase : List[Any] = activation_dropout
UpperCamelCase : Dict = activation_function
UpperCamelCase : List[str] = init_std
UpperCamelCase : int = encoder_layerdrop
UpperCamelCase : Dict = decoder_layerdrop
UpperCamelCase : Any = classifier_dropout
UpperCamelCase : Tuple = use_cache
UpperCamelCase : Dict = encoder_layers
UpperCamelCase : Tuple = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase : Optional[Any] = use_prompt
UpperCamelCase : Any = prompt_length
UpperCamelCase : List[Any] = prompt_mid_dim
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , forced_eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""" , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = self.bos_token_id
warnings.warn(
f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
"""The config can simply be saved and uploaded again to be fixed.""" )
| 27 | 0 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = 'EncodecFeatureExtractor'
lowercase : List[Any] = ('T5Tokenizer', 'T5TokenizerFast')
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.feature_extractor
UpperCamelCase : Any = False
def a_ ( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True ):
return self.tokenizer.get_decoder_prompt_ids(task=SCREAMING_SNAKE_CASE_ , language=SCREAMING_SNAKE_CASE_ , no_timestamps=SCREAMING_SNAKE_CASE_ )
def __call__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = kwargs.pop("""sampling_rate""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = kwargs.pop("""text""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Any = args[0]
UpperCamelCase : str = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if text is not None:
UpperCamelCase : Optional[int] = self.tokenizer(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is not None:
UpperCamelCase : str = self.feature_extractor(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
UpperCamelCase : int = audio_inputs["""input_values"""]
if "padding_mask" in audio_inputs:
UpperCamelCase : Optional[Any] = audio_inputs["""padding_mask"""]
return inputs
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = kwargs.pop("""padding_mask""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Optional[int] = args[0]
UpperCamelCase : Any = args[1:]
if audio_values is not None:
return self._decode_audio(SCREAMING_SNAKE_CASE_ , padding_mask=SCREAMING_SNAKE_CASE_ )
else:
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = to_numpy(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = audio_values.shape
if padding_mask is None:
return list(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = to_numpy(SCREAMING_SNAKE_CASE_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
UpperCamelCase : List[str] = seq_len - padding_mask.shape[-1]
UpperCamelCase : Optional[int] = 1 - self.feature_extractor.padding_value
UpperCamelCase : Any = np.pad(SCREAMING_SNAKE_CASE_ , ((0, 0), (0, difference)) , """constant""" , constant_values=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audio_values.tolist()
for i in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
UpperCamelCase : Optional[Any] = sliced_audio.reshape(SCREAMING_SNAKE_CASE_ , -1 )
return audio_values
| 362 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__A : Optional[Any] = 16
__A : str = 32
def A_ ( snake_case_ : Accelerator ,snake_case_ : int = 1_6 ):
'''simple docstring'''
UpperCamelCase : Tuple = AutoTokenizer.from_pretrained("""bert-base-cased""" )
UpperCamelCase : Optional[int] = load_dataset("""glue""" ,"""mrpc""" )
def tokenize_function(snake_case_ : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase : Union[str, Any] = tokenizer(examples["""sentence1"""] ,examples["""sentence2"""] ,truncation=snake_case_ ,max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCamelCase : Optional[Any] = datasets.map(
snake_case_ ,batched=snake_case_ ,remove_columns=["""idx""", """sentence1""", """sentence2"""] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase : str = tokenized_datasets.rename_column("""label""" ,"""labels""" )
def collate_fn(snake_case_ : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCamelCase : Union[str, Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCamelCase : Optional[Any] = 1_6
elif accelerator.mixed_precision != "no":
UpperCamelCase : Any = 8
else:
UpperCamelCase : Optional[Any] = None
return tokenizer.pad(
snake_case_ ,padding="""longest""" ,max_length=snake_case_ ,pad_to_multiple_of=snake_case_ ,return_tensors="""pt""" ,)
# Instantiate dataloaders.
UpperCamelCase : str = DataLoader(
tokenized_datasets["""train"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
UpperCamelCase : Dict = DataLoader(
tokenized_datasets["""validation"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__A : int = mocked_dataloaders # noqa: F811
def A_ ( snake_case_ : Tuple ,snake_case_ : Dict ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,snake_case_ ) == "1":
UpperCamelCase : Union[str, Any] = 2
# New Code #
UpperCamelCase : Dict = int(args.gradient_accumulation_steps )
UpperCamelCase : List[Any] = int(args.local_sgd_steps )
# Initialize accelerator
UpperCamelCase : str = Accelerator(
cpu=args.cpu ,mixed_precision=args.mixed_precision ,gradient_accumulation_steps=snake_case_ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase : Union[str, Any] = config["""lr"""]
UpperCamelCase : int = int(config["""num_epochs"""] )
UpperCamelCase : int = int(config["""seed"""] )
UpperCamelCase : List[Any] = int(config["""batch_size"""] )
UpperCamelCase : Optional[int] = evaluate.load("""glue""" ,"""mrpc""" )
set_seed(snake_case_ )
UpperCamelCase , UpperCamelCase : Dict = get_dataloaders(snake_case_ ,snake_case_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase : Optional[int] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,return_dict=snake_case_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCamelCase : Tuple = model.to(accelerator.device )
# Instantiate optimizer
UpperCamelCase : List[Any] = AdamW(params=model.parameters() ,lr=snake_case_ )
# Instantiate scheduler
UpperCamelCase : str = get_linear_schedule_with_warmup(
optimizer=snake_case_ ,num_warmup_steps=1_0_0 ,num_training_steps=(len(snake_case_ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Any = accelerator.prepare(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
# Now we train the model
for epoch in range(snake_case_ ):
model.train()
with LocalSGD(
accelerator=snake_case_ ,model=snake_case_ ,local_sgd_steps=snake_case_ ,enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(snake_case_ ):
UpperCamelCase : Optional[Any] = model(**snake_case_ )
UpperCamelCase : Optional[int] = output.loss
accelerator.backward(snake_case_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCamelCase : Any = model(**snake_case_ )
UpperCamelCase : Tuple = outputs.logits.argmax(dim=-1 )
UpperCamelCase , UpperCamelCase : int = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=snake_case_ ,references=snake_case_ ,)
UpperCamelCase : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' ,snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" ,type=snake_case_ ,default=snake_case_ ,choices=["""no""", """fp16""", """bf16""", """fp8"""] ,help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" ,)
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" ,type=snake_case_ ,default=1 ,help="""The number of minibatches to be ran before gradients are accumulated.""" ,)
parser.add_argument(
"""--local_sgd_steps""" ,type=snake_case_ ,default=8 ,help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" ,action="""store_true""" ,help="""If passed, will train on the CPU.""" )
UpperCamelCase : Dict = parser.parse_args()
UpperCamelCase : List[Any] = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(snake_case_ ,snake_case_ )
if __name__ == "__main__":
main()
| 27 | 0 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Optional[Any] ,snake_case_ : int ,snake_case_ : str ):
'''simple docstring'''
if isinstance(snake_case_ ,snake_case_ ):
UpperCamelCase : Any = np.full((len(snake_case_ ), sequence_length, 2) ,snake_case_ )
else:
UpperCamelCase : Dict = np.full((len(snake_case_ ), sequence_length) ,snake_case_ )
for i, tensor in enumerate(snake_case_ ):
if padding_side == "right":
if isinstance(snake_case_ ,snake_case_ ):
UpperCamelCase : str = tensor[:sequence_length]
else:
UpperCamelCase : Any = tensor[:sequence_length]
else:
if isinstance(snake_case_ ,snake_case_ ):
UpperCamelCase : int = tensor[:sequence_length]
else:
UpperCamelCase : str = tensor[:sequence_length]
return out_tensor.tolist()
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
UpperCamelCase : Optional[int] = ord(snake_case_ )
if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
return True
UpperCamelCase : Optional[Any] = unicodedata.category(snake_case_ )
if cat.startswith("""P""" ):
return True
return False
@dataclass
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : PreTrainedTokenizerBase
lowercase : Union[bool, str, PaddingStrategy] = True
lowercase : Optional[int] = None
lowercase : Optional[int] = None
lowercase : int = -1_0_0
lowercase : str = "pt"
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
import torch
UpperCamelCase : str = """label""" if """label""" in features[0].keys() else """labels"""
UpperCamelCase : Optional[int] = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
UpperCamelCase : Union[str, Any] = self.tokenizer.pad(
SCREAMING_SNAKE_CASE_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="""pt""" if labels is None else None , )
if labels is None:
return batch
UpperCamelCase : Tuple = torch.tensor(batch["""entity_ids"""] ).shape[1]
UpperCamelCase : Any = self.tokenizer.padding_side
if padding_side == "right":
UpperCamelCase : str = [
list(SCREAMING_SNAKE_CASE_ ) + [self.label_pad_token_id] * (sequence_length - len(SCREAMING_SNAKE_CASE_ )) for label in labels
]
else:
UpperCamelCase : List[Any] = [
[self.label_pad_token_id] * (sequence_length - len(SCREAMING_SNAKE_CASE_ )) + list(SCREAMING_SNAKE_CASE_ ) for label in labels
]
UpperCamelCase : List[Any] = [feature["""ner_tags"""] for feature in features]
UpperCamelCase : List[Any] = padding_tensor(SCREAMING_SNAKE_CASE_ , -1 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = [feature["""original_entity_spans"""] for feature in features]
UpperCamelCase : Dict = padding_tensor(SCREAMING_SNAKE_CASE_ , (-1, -1) , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = {k: torch.tensor(SCREAMING_SNAKE_CASE_ , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 363 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A : Optional[Any] = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__A : Any = {'''allegro/herbert-base-cased''': 514}
__A : Optional[Any] = {}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Dict = VOCAB_FILES_NAMES
lowercase : Any = PRETRAINED_VOCAB_FILES_MAP
lowercase : List[str] = PRETRAINED_INIT_CONFIGURATION
lowercase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Union[str, Any] = HerbertTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_="</s>" , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = [self.cls_token_id]
UpperCamelCase : str = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Tuple = [self.sep_token_id]
UpperCamelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Optional[int] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 27 | 0 |
"""simple docstring"""
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def A_ ( snake_case_ : Union[str, Any] ):
'''simple docstring'''
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def A_ ( snake_case_ : Tuple ):
'''simple docstring'''
UpperCamelCase : int = create_tensor(snake_case_ )
UpperCamelCase : str = gather(snake_case_ )
assert gathered_tensor.tolist() == list(range(1 ,state.num_processes**2 + 1 ) )
def A_ ( snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : int = [state.process_index]
UpperCamelCase : Optional[int] = gather_object(snake_case_ )
assert len(snake_case_ ) == state.num_processes, f'{gathered_obj}, {len(snake_case_ )} != {state.num_processes}'
assert gathered_obj == list(range(state.num_processes ) ), f'{gathered_obj} != {list(range(state.num_processes ) )}'
def A_ ( snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : List[Any] = create_tensor(snake_case_ )
UpperCamelCase : Tuple = broadcast(snake_case_ )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 ,state.num_processes + 1 ) )
def A_ ( snake_case_ : Optional[int] ):
'''simple docstring'''
# We need to pad the tensor with one more element if we are the main process
# to ensure that we can pad
if state.is_main_process:
UpperCamelCase : Tuple = torch.arange(state.num_processes + 1 ).to(state.device )
else:
UpperCamelCase : Union[str, Any] = torch.arange(state.num_processes ).to(state.device )
UpperCamelCase : Dict = pad_across_processes(snake_case_ )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 ,state.num_processes ) ) + [0]
def A_ ( snake_case_ : str ):
'''simple docstring'''
# For now runs on only two processes
if state.num_processes != 2:
return
UpperCamelCase : Tuple = create_tensor(snake_case_ )
UpperCamelCase : Dict = reduce(snake_case_ ,"""sum""" )
UpperCamelCase : Dict = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(snake_case_ ,snake_case_ ), f'{reduced_tensor} != {truth_tensor}'
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
# For now runs on only two processes
if state.num_processes != 2:
return
UpperCamelCase : Optional[Any] = create_tensor(snake_case_ )
UpperCamelCase : int = reduce(snake_case_ ,"""mean""" )
UpperCamelCase : List[Any] = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(snake_case_ ,snake_case_ ), f'{reduced_tensor} != {truth_tensor}'
def A_ ( snake_case_ : str ):
'''simple docstring'''
main()
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Any = PartialState()
state.print(f'State: {state}' )
state.print("""testing gather""" )
test_gather(snake_case_ )
state.print("""testing gather_object""" )
test_gather_object(snake_case_ )
state.print("""testing broadcast""" )
test_broadcast(snake_case_ )
state.print("""testing pad_across_processes""" )
test_pad_across_processes(snake_case_ )
state.print("""testing reduce_sum""" )
test_reduce_sum(snake_case_ )
state.print("""testing reduce_mean""" )
test_reduce_mean(snake_case_ )
if __name__ == "__main__":
main()
| 364 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=3.6 ):
UpperCamelCase : Dict = tokenizer
UpperCamelCase : Optional[Any] = tokenizer.bos_token_id
UpperCamelCase : Any = dataset
UpperCamelCase : List[str] = seq_length
UpperCamelCase : Optional[Any] = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
UpperCamelCase : Dict = iter(self.dataset )
UpperCamelCase : Union[str, Any] = True
while more_examples:
UpperCamelCase , UpperCamelCase : Tuple = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(SCREAMING_SNAKE_CASE_ )["""content"""] )
buffer_len += len(buffer[-1] )
except StopIteration:
UpperCamelCase : Dict = False
break
UpperCamelCase : str = tokenizer(SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )["""input_ids"""]
UpperCamelCase : str = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , self.seq_length ):
UpperCamelCase : List[str] = all_token_ids[i : i + self.seq_length]
if len(SCREAMING_SNAKE_CASE_ ) == self.seq_length:
yield torch.tensor(SCREAMING_SNAKE_CASE_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : Dict = {"""streaming""": True}
UpperCamelCase : Optional[int] = load_dataset(args.dataset_name ,split="""train""" ,**snake_case_ )
UpperCamelCase : Optional[int] = ConstantLengthDataset(snake_case_ ,snake_case_ ,seq_length=args.seq_length )
UpperCamelCase : List[Any] = DataLoader(snake_case_ ,batch_size=args.batch_size )
return eval_dataloader
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
model.eval()
UpperCamelCase : Dict = []
for step, batch in enumerate(snake_case_ ):
with torch.no_grad():
UpperCamelCase : List[Any] = model(snake_case_ ,labels=snake_case_ )
UpperCamelCase : Any = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(snake_case_ ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
UpperCamelCase : Dict = torch.mean(torch.cat(snake_case_ ) )
try:
UpperCamelCase : Dict = torch.exp(snake_case_ )
except OverflowError:
UpperCamelCase : Optional[int] = float("""inf""" )
return loss.item(), perplexity.item()
# Setup Accelerator
__A : List[Any] = Accelerator()
# Parse configuration
__A : str = HfArgumentParser(EvaluationArguments)
__A : List[Any] = parser.parse_args()
set_seed(args.seed)
# Logging
__A : Any = logging.getLogger(__name__)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
# Load model and tokenizer
__A : List[Any] = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__A : List[Any] = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
__A : int = create_dataloader(args)
# Prepare everything with our `accelerator`.
__A , __A : Optional[Any] = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
__A , __A : Tuple = evaluate(args)
logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 27 | 0 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def A_ ( snake_case_ : Tuple ,snake_case_ : Union[str, Any] ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : List[str] = AlbertConfig.from_json_file(snake_case_ )
print(f'Building PyTorch model from configuration: {config}' )
UpperCamelCase : int = AlbertForPreTraining(snake_case_ )
# Load weights from tf checkpoint
load_tf_weights_in_albert(snake_case_ ,snake_case_ ,snake_case_ )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() ,snake_case_ )
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--albert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained ALBERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__A : str = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 365 |
"""simple docstring"""
import argparse
import os
import re
__A : Any = '''src/transformers'''
# Pattern that looks at the indentation in a line.
__A : Tuple = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : List[Any] = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : List[Any] = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : Any = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : str ,snake_case_ : str="" ,snake_case_ : Any=None ,snake_case_ : Union[str, Any]=None ):
'''simple docstring'''
UpperCamelCase : List[Any] = 0
UpperCamelCase : Optional[int] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Tuple = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : Tuple = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Dict = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Optional[Any] = [lines[index + 1]]
index += 1
else:
UpperCamelCase : str = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
def _inner(snake_case_ : List[str] ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Tuple=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Optional[int] ):
return x
if key is None:
UpperCamelCase : List[str] = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[str] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : Tuple = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : int = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Union[str, Any] = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : Any ):
UpperCamelCase : Union[str, Any] = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : int = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : str = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : Optional[int] = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : int = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Tuple = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : List[Any] = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Union[str, Any] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : List[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : Optional[int] = keys[:-1]
UpperCamelCase : Union[str, Any] = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : Any = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : int=True ):
'''simple docstring'''
with open(snake_case_ ,encoding="""utf-8""" ) as f:
UpperCamelCase : List[str] = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : int = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Dict = main_blocks[block_idx]
UpperCamelCase : Dict = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : List[str] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : Optional[Any] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Optional[Any] = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Any = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : List[Any] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Optional[Any] = _re_direct_key if """_import_structure = {""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Optional[Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Any = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : Union[str, Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[str] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : Optional[int] = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ,encoding="""utf-8""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Optional[int] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : List[Any] = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : Union[str, Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
__A : List[Any] = '''platform'''
import jax
import jax.numpy as jnp
from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
shift_tokens_right,
)
def A_ ( snake_case_ : str ,snake_case_ : Optional[Any] ,snake_case_ : Union[str, Any]=None ,snake_case_ : List[str]=None ,snake_case_ : List[str]=None ,snake_case_ : Tuple=None ,snake_case_ : str=None ,snake_case_ : Any=None ,):
'''simple docstring'''
if attention_mask is None:
UpperCamelCase : List[str] = np.where(input_ids != config.pad_token_id ,1 ,0 )
if decoder_attention_mask is None:
UpperCamelCase : List[Any] = np.where(decoder_input_ids != config.pad_token_id ,1 ,0 )
if head_mask is None:
UpperCamelCase : int = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCamelCase : int = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCamelCase : int = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=0.02 , ):
UpperCamelCase : Tuple = parent
UpperCamelCase : Dict = batch_size
UpperCamelCase : int = seq_length
UpperCamelCase : Any = is_training
UpperCamelCase : int = use_labels
UpperCamelCase : str = vocab_size
UpperCamelCase : str = hidden_size
UpperCamelCase : Optional[int] = num_hidden_layers
UpperCamelCase : Union[str, Any] = num_attention_heads
UpperCamelCase : str = intermediate_size
UpperCamelCase : Optional[int] = hidden_act
UpperCamelCase : List[str] = hidden_dropout_prob
UpperCamelCase : List[str] = attention_probs_dropout_prob
UpperCamelCase : int = max_position_embeddings
UpperCamelCase : int = eos_token_id
UpperCamelCase : Any = pad_token_id
UpperCamelCase : int = bos_token_id
UpperCamelCase : Any = initializer_range
def a_ ( self ):
UpperCamelCase : Optional[int] = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
UpperCamelCase : Optional[int] = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
UpperCamelCase : Tuple = shift_tokens_right(SCREAMING_SNAKE_CASE_ , 1 , 2 )
UpperCamelCase : int = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Union[str, Any] = prepare_blenderbot_inputs_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return config, inputs_dict
def a_ ( self ):
UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
return config, inputs_dict
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = 20
UpperCamelCase : Tuple = model_class_name(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = model.encode(inputs_dict["""input_ids"""] )
UpperCamelCase : Any = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
UpperCamelCase : Dict = model.init_cache(decoder_input_ids.shape[0] , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""" )
UpperCamelCase : List[str] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCamelCase : Union[str, Any] = model.decode(
decoder_input_ids[:, :-1] , SCREAMING_SNAKE_CASE_ , decoder_attention_mask=SCREAMING_SNAKE_CASE_ , past_key_values=SCREAMING_SNAKE_CASE_ , decoder_position_ids=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Optional[int] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" )
UpperCamelCase : Any = model.decode(
decoder_input_ids[:, -1:] , SCREAMING_SNAKE_CASE_ , decoder_attention_mask=SCREAMING_SNAKE_CASE_ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : str = model.decode(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'Max diff is {diff}' )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = 20
UpperCamelCase : int = model_class_name(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model.encode(inputs_dict["""input_ids"""] )
UpperCamelCase : Dict = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
UpperCamelCase : List[Any] = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
UpperCamelCase : Optional[Any] = model.init_cache(decoder_input_ids.shape[0] , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCamelCase : Optional[Any] = model.decode(
decoder_input_ids[:, :-1] , SCREAMING_SNAKE_CASE_ , decoder_attention_mask=SCREAMING_SNAKE_CASE_ , past_key_values=SCREAMING_SNAKE_CASE_ , decoder_position_ids=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" )
UpperCamelCase : List[str] = model.decode(
decoder_input_ids[:, -1:] , SCREAMING_SNAKE_CASE_ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=SCREAMING_SNAKE_CASE_ , decoder_position_ids=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = model.decode(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , decoder_attention_mask=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'Max diff is {diff}' )
@require_flax
class lowerCamelCase ( unittest.TestCase ):
lowercase : int = 9_9
def a_ ( self ):
UpperCamelCase : Dict = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
UpperCamelCase : List[str] = input_ids.shape[0]
UpperCamelCase : int = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def a_ ( self ):
UpperCamelCase : Tuple = self._get_config_and_data()
UpperCamelCase : Union[str, Any] = FlaxBlenderbotSmallForConditionalGeneration(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = lm_model(input_ids=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
UpperCamelCase : Tuple = FlaxBlenderbotSmallForConditionalGeneration(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
UpperCamelCase : Dict = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
UpperCamelCase : Dict = lm_model(input_ids=SCREAMING_SNAKE_CASE_ , decoder_input_ids=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
UpperCamelCase : Optional[Any] = shift_tokens_right(SCREAMING_SNAKE_CASE_ , 1 , 2 )
UpperCamelCase : Dict = np.equal(SCREAMING_SNAKE_CASE_ , 1 ).astype(np.floataa ).sum()
UpperCamelCase : Tuple = np.equal(SCREAMING_SNAKE_CASE_ , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(SCREAMING_SNAKE_CASE_ , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase , _UpperCAmelCase ):
lowercase : Union[str, Any] = True
lowercase : List[str] = (
(
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallForConditionalGeneration,
)
if is_flax_available()
else ()
)
lowercase : int = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()
def a_ ( self ):
UpperCamelCase : List[str] = FlaxBlenderbotSmallModelTester(self )
def a_ ( self ):
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCamelCase : str = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = model_class(SCREAMING_SNAKE_CASE_ )
@jax.jit
def encode_jitted(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ ):
return model.encode(input_ids=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )
with self.subTest("""JIT Enabled""" ):
UpperCamelCase : Union[str, Any] = encode_jitted(**SCREAMING_SNAKE_CASE_ ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
UpperCamelCase : Optional[Any] = encode_jitted(**SCREAMING_SNAKE_CASE_ ).to_tuple()
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , len(SCREAMING_SNAKE_CASE_ ) )
for jitted_output, output in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertEqual(jitted_output.shape , output.shape )
def a_ ( self ):
UpperCamelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCamelCase : List[Any] = model_class(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""] )
UpperCamelCase : Union[str, Any] = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return model.decode(
decoder_input_ids=SCREAMING_SNAKE_CASE_ , decoder_attention_mask=SCREAMING_SNAKE_CASE_ , encoder_outputs=SCREAMING_SNAKE_CASE_ , )
with self.subTest("""JIT Enabled""" ):
UpperCamelCase : Dict = decode_jitted(**SCREAMING_SNAKE_CASE_ ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
UpperCamelCase : List[Any] = decode_jitted(**SCREAMING_SNAKE_CASE_ ).to_tuple()
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , len(SCREAMING_SNAKE_CASE_ ) )
for jitted_output, output in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def a_ ( self ):
for model_class_name in self.all_model_classes:
UpperCamelCase : Optional[int] = model_class_name.from_pretrained("""facebook/blenderbot_small-90M""" )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
UpperCamelCase : Union[str, Any] = np.ones((1, 1) ) * model.config.eos_token_id
UpperCamelCase : Optional[int] = model(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
| 366 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 0 |
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : float = b
if function(snake_case_ ) == 0: # one of the a or b is a root for the function
return a
elif function(snake_case_ ) == 0:
return b
elif (
function(snake_case_ ) * function(snake_case_ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("""could not find root in given interval.""" )
else:
UpperCamelCase : float = start + (end - start) / 2.0
while abs(start - mid ) > 1_0**-7: # until precisely equals to 10^-7
if function(snake_case_ ) == 0:
return mid
elif function(snake_case_ ) * function(snake_case_ ) < 0:
UpperCamelCase : Dict = mid
else:
UpperCamelCase : List[str] = mid
UpperCamelCase : Tuple = start + (end - start) / 2.0
return mid
def A_ ( snake_case_ : float ):
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 367 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
__A : Optional[Any] = logging.get_logger(__name__)
def A_ ( snake_case_ : np.ndarray ,snake_case_ : Union[int, Iterable[int]] ,snake_case_ : bool ,snake_case_ : int ):
'''simple docstring'''
def constraint_to_multiple_of(snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ,snake_case_ : List[str]=0 ,snake_case_ : Optional[Any]=None ):
UpperCamelCase : List[str] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
UpperCamelCase : Optional[Any] = math.floor(val / multiple ) * multiple
if x < min_val:
UpperCamelCase : Dict = math.ceil(val / multiple ) * multiple
return x
UpperCamelCase : Any = (output_size, output_size) if isinstance(snake_case_ ,snake_case_ ) else output_size
UpperCamelCase , UpperCamelCase : int = get_image_size(snake_case_ )
UpperCamelCase , UpperCamelCase : Union[str, Any] = output_size
# determine new height and width
UpperCamelCase : List[str] = output_height / input_height
UpperCamelCase : List[str] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
UpperCamelCase : int = scale_width
else:
# fit height
UpperCamelCase : Optional[Any] = scale_height
UpperCamelCase : int = constraint_to_multiple_of(scale_height * input_height ,multiple=snake_case_ )
UpperCamelCase : Union[str, Any] = constraint_to_multiple_of(scale_width * input_width ,multiple=snake_case_ )
return (new_height, new_width)
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = ['pixel_values']
def __init__( self , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 1 / 255 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = size if size is not None else {"""height""": 384, """width""": 384}
UpperCamelCase : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = do_resize
UpperCamelCase : Union[str, Any] = size
UpperCamelCase : Union[str, Any] = keep_aspect_ratio
UpperCamelCase : Any = ensure_multiple_of
UpperCamelCase : List[Any] = resample
UpperCamelCase : str = do_rescale
UpperCamelCase : Optional[Any] = rescale_factor
UpperCamelCase : List[str] = do_normalize
UpperCamelCase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCamelCase : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Tuple = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
UpperCamelCase : Dict = get_resize_output_image_size(
SCREAMING_SNAKE_CASE_ , output_size=(size["""height"""], size["""width"""]) , keep_aspect_ratio=SCREAMING_SNAKE_CASE_ , multiple=SCREAMING_SNAKE_CASE_ , )
return resize(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return normalize(SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCamelCase : List[Any] = size if size is not None else self.size
UpperCamelCase : Dict = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
UpperCamelCase : Optional[int] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
UpperCamelCase : Tuple = resample if resample is not None else self.resample
UpperCamelCase : str = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase : Any = image_mean if image_mean is not None else self.image_mean
UpperCamelCase : List[Any] = image_std if image_std is not None else self.image_std
UpperCamelCase : str = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
UpperCamelCase : Tuple = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
UpperCamelCase : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
UpperCamelCase : int = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
UpperCamelCase : List[str] = [self.normalize(image=SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Any = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Union[str, Any] = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : str = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = target_sizes.numpy()
UpperCamelCase : Dict = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
UpperCamelCase : List[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : List[Any] = logits.argmax(dim=1 )
UpperCamelCase : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 27 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : List[str] = {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json''',
'''google/bigbird-roberta-large''': '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json''',
'''google/bigbird-base-trivia-itc''': '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json''',
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = 'big_bird'
def __init__( self , SCREAMING_SNAKE_CASE_=5_0358 , SCREAMING_SNAKE_CASE_=768 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=3072 , SCREAMING_SNAKE_CASE_="gelu_new" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=1e-12 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=66 , SCREAMING_SNAKE_CASE_="block_sparse" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , sep_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : List[Any] = vocab_size
UpperCamelCase : Optional[int] = max_position_embeddings
UpperCamelCase : List[Any] = hidden_size
UpperCamelCase : Any = num_hidden_layers
UpperCamelCase : List[Any] = num_attention_heads
UpperCamelCase : Any = intermediate_size
UpperCamelCase : Dict = hidden_act
UpperCamelCase : Any = hidden_dropout_prob
UpperCamelCase : int = attention_probs_dropout_prob
UpperCamelCase : List[str] = initializer_range
UpperCamelCase : str = type_vocab_size
UpperCamelCase : str = layer_norm_eps
UpperCamelCase : Dict = use_cache
UpperCamelCase : List[Any] = rescale_embeddings
UpperCamelCase : List[Any] = attention_type
UpperCamelCase : List[Any] = use_bias
UpperCamelCase : List[Any] = block_size
UpperCamelCase : Any = num_random_blocks
UpperCamelCase : List[str] = classifier_dropout
class lowerCamelCase ( _UpperCAmelCase ):
@property
def a_ ( self ):
if self.task == "multiple-choice":
UpperCamelCase : Dict = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
UpperCamelCase : Dict = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 368 |
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : float = b
if function(snake_case_ ) == 0: # one of the a or b is a root for the function
return a
elif function(snake_case_ ) == 0:
return b
elif (
function(snake_case_ ) * function(snake_case_ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("""could not find root in given interval.""" )
else:
UpperCamelCase : float = start + (end - start) / 2.0
while abs(start - mid ) > 1_0**-7: # until precisely equals to 10^-7
if function(snake_case_ ) == 0:
return mid
elif function(snake_case_ ) * function(snake_case_ ) < 0:
UpperCamelCase : Dict = mid
else:
UpperCamelCase : List[str] = mid
UpperCamelCase : Tuple = start + (end - start) / 2.0
return mid
def A_ ( snake_case_ : float ):
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 27 | 0 |
"""simple docstring"""
import argparse
import os
import re
__A : Dict = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__A : Union[str, Any] = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : Tuple = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : Tuple = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Dict="" ,snake_case_ : Dict=None ,snake_case_ : Any=None ):
'''simple docstring'''
UpperCamelCase : Optional[int] = 0
UpperCamelCase : List[Any] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Optional[Any] = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : int = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Any = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Any = [lines[index + 1]]
index += 1
else:
UpperCamelCase : List[str] = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
def _inner(snake_case_ : Tuple ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : List[Any] ,snake_case_ : Optional[int]=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Dict ):
return x
if key is None:
UpperCamelCase : int = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[Any] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : str = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : List[str] = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Tuple = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : int ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : List[Any] ):
UpperCamelCase : Any = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : Union[str, Any] = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[str] = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : str = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : str = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Dict = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : int = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Any = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[Any] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : Optional[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[Any] = keys[:-1]
UpperCamelCase : int = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Tuple ,snake_case_ : str=True ):
'''simple docstring'''
with open(snake_case_ ,"""r""" ) as f:
UpperCamelCase : int = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : Dict = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Optional[Any] = main_blocks[block_idx]
UpperCamelCase : Optional[int] = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : Union[str, Any] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : List[str] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Dict = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Union[str, Any] = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : Optional[int] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Union[str, Any] = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Union[str, Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Optional[Any] = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : List[Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[Any] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : str = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Any = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Union[str, Any] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : Any = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : str = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 369 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def a_ ( self ):
UpperCamelCase : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
UpperCamelCase : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
UpperCamelCase : Dict = """xvjiarui/stable-diffusion-2-inpainting"""
UpperCamelCase , UpperCamelCase : List[str] = FlaxStableDiffusionInpaintPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = """Face of a yellow cat, high resolution, sitting on a park bench"""
UpperCamelCase : List[str] = jax.random.PRNGKey(0 )
UpperCamelCase : Tuple = 50
UpperCamelCase : Dict = jax.device_count()
UpperCamelCase : Optional[int] = num_samples * [prompt]
UpperCamelCase : int = num_samples * [init_image]
UpperCamelCase : List[Any] = num_samples * [mask_image]
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[Any] = pipeline.prepare_inputs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# shard inputs and rng
UpperCamelCase : Optional[int] = replicate(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = jax.random.split(SCREAMING_SNAKE_CASE_ , jax.device_count() )
UpperCamelCase : str = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = pipeline(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , jit=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = output.images.reshape(SCREAMING_SNAKE_CASE_ , 512 , 512 , 3 )
UpperCamelCase : List[Any] = images[0, 253:256, 253:256, -1]
UpperCamelCase : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase : Dict = jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 27 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : List[str] = StableDiffusionDiffEditPipeline
lowercase : List[Any] = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
lowercase : str = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
lowercase : List[Any] = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowercase : Union[str, Any] = frozenset([] )
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : List[str] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Union[str, Any] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_one=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Dict = DDIMInverseScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_zero=SCREAMING_SNAKE_CASE_ , )
torch.manual_seed(0 )
UpperCamelCase : Any = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
UpperCamelCase : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""gelu""" , projection_dim=512 , )
UpperCamelCase : List[str] = CLIPTextModel(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
UpperCamelCase : Any = {
"""unet""": unet,
"""scheduler""": scheduler,
"""inverse_scheduler""": inverse_scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
UpperCamelCase : str = floats_tensor((1, 16, 16) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : List[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Dict = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = {
"""prompt""": """a dog and a newt""",
"""mask_image""": mask,
"""image_latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
UpperCamelCase : Optional[int] = floats_tensor((1, 3, 32, 32) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCamelCase : Optional[int] = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_ ) ).convert("""RGB""" )
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : Optional[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : str = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = {
"""image""": image,
"""source_prompt""": """a cat and a frog""",
"""target_prompt""": """a dog and a newt""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""num_maps_per_mask""": 2,
"""mask_encode_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
UpperCamelCase : Any = floats_tensor((1, 3, 32, 32) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCamelCase : List[Any] = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_ ) ).convert("""RGB""" )
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : Any = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Optional[Any] = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = {
"""image""": image,
"""prompt""": """a cat and a frog""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""decode_latents""": True,
"""output_type""": """numpy""",
}
return inputs
def a_ ( self ):
if not hasattr(self.pipeline_class , """_optional_components""" ):
return
UpperCamelCase : Optional[Any] = self.get_dummy_components()
UpperCamelCase : str = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = pipe(**SCREAMING_SNAKE_CASE_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = self.pipeline_class.from_pretrained(SCREAMING_SNAKE_CASE_ )
pipe_loaded.to(SCREAMING_SNAKE_CASE_ )
pipe_loaded.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) is None , f'`{optional_component}` did not stay set to None after loading.' , )
UpperCamelCase : Optional[int] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = pipe_loaded(**SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Dict = np.abs(output - output_loaded ).max()
self.assertLess(SCREAMING_SNAKE_CASE_ , 1e-4 )
def a_ ( self ):
UpperCamelCase : Dict = """cpu"""
UpperCamelCase : Dict = self.get_dummy_components()
UpperCamelCase : Dict = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = self.get_dummy_mask_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = pipe.generate_mask(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
UpperCamelCase : List[Any] = np.array([0] * 9 )
UpperCamelCase : List[Any] = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(SCREAMING_SNAKE_CASE_ , 1e-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def a_ ( self ):
UpperCamelCase : Dict = """cpu"""
UpperCamelCase : Tuple = self.get_dummy_components()
UpperCamelCase : Any = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.get_dummy_inversion_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = pipe.invert(**SCREAMING_SNAKE_CASE_ ).images
UpperCamelCase : str = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
UpperCamelCase : Union[str, Any] = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799] , )
UpperCamelCase : Dict = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(SCREAMING_SNAKE_CASE_ , 1e-3 )
def a_ ( self ):
super().test_inference_batch_single_identical(expected_max_diff=5e-3 )
def a_ ( self ):
UpperCamelCase : str = """cpu"""
UpperCamelCase : int = self.get_dummy_components()
UpperCamelCase : str = {"""beta_start""": 0.00085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
UpperCamelCase : List[Any] = DPMSolverMultistepScheduler(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = DPMSolverMultistepInverseScheduler(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = self.get_dummy_inversion_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = pipe.invert(**SCREAMING_SNAKE_CASE_ ).images
UpperCamelCase : Optional[Any] = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
UpperCamelCase : Tuple = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799] , )
UpperCamelCase : Optional[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(SCREAMING_SNAKE_CASE_ , 1e-3 )
@require_torch_gpu
@slow
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def a_ ( cls ):
UpperCamelCase : Any = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png""" )
UpperCamelCase : List[str] = raw_image.convert("""RGB""" ).resize((768, 768) )
UpperCamelCase : Optional[int] = raw_image
def a_ ( self ):
UpperCamelCase : List[Any] = torch.manual_seed(0 )
UpperCamelCase : List[Any] = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=SCREAMING_SNAKE_CASE_ , torch_dtype=torch.floataa )
UpperCamelCase : List[Any] = DDIMScheduler.from_config(pipe.scheduler.config )
UpperCamelCase : List[Any] = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = """a bowl of fruit"""
UpperCamelCase : int = """a bowl of pears"""
UpperCamelCase : Tuple = pipe.generate_mask(
image=self.raw_image , source_prompt=SCREAMING_SNAKE_CASE_ , target_prompt=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = pipe.invert(
prompt=SCREAMING_SNAKE_CASE_ , image=self.raw_image , inpaint_strength=0.7 , generator=SCREAMING_SNAKE_CASE_ ).latents
UpperCamelCase : Optional[int] = pipe(
prompt=SCREAMING_SNAKE_CASE_ , mask_image=SCREAMING_SNAKE_CASE_ , image_latents=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , negative_prompt=SCREAMING_SNAKE_CASE_ , inpaint_strength=0.7 , output_type="""numpy""" , ).images[0]
UpperCamelCase : Dict = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5e-1
def a_ ( self ):
UpperCamelCase : Union[str, Any] = torch.manual_seed(0 )
UpperCamelCase : int = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=SCREAMING_SNAKE_CASE_ , torch_dtype=torch.floataa )
UpperCamelCase : Optional[int] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
UpperCamelCase : Dict = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = """a bowl of fruit"""
UpperCamelCase : List[Any] = """a bowl of pears"""
UpperCamelCase : List[str] = pipe.generate_mask(
image=self.raw_image , source_prompt=SCREAMING_SNAKE_CASE_ , target_prompt=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Dict = pipe.invert(
prompt=SCREAMING_SNAKE_CASE_ , image=self.raw_image , inpaint_strength=0.7 , generator=SCREAMING_SNAKE_CASE_ , num_inference_steps=25 , ).latents
UpperCamelCase : Any = pipe(
prompt=SCREAMING_SNAKE_CASE_ , mask_image=SCREAMING_SNAKE_CASE_ , image_latents=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , negative_prompt=SCREAMING_SNAKE_CASE_ , inpaint_strength=0.7 , num_inference_steps=25 , output_type="""numpy""" , ).images[0]
UpperCamelCase : Any = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5e-1
| 370 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def A_ ( ):
'''simple docstring'''
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
UpperCamelCase : Optional[Any] = [1, 2, 3]
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=2 )
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" ,[2, -1] )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : List[Any] = [1, 2]
UpperCamelCase : List[Any] = {"""a""": 1, """b""": 2}
UpperCamelCase : List[str] = {"""a""": [1, 2], """b""": [3, 4]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 1}, """b""": 2}
UpperCamelCase : Any = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
UpperCamelCase : Optional[int] = [2, 3]
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3}
UpperCamelCase : Any = {"""a""": [2, 3], """b""": [4, 5]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 2}, """b""": 3}
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
| 27 | 0 |
"""simple docstring"""
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
__A : List[Any] = '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))
print('''Googling.....''')
__A : Optional[Any] = F'''https://www.google.com/search?q={query}&num=100'''
__A : Optional[int] = requests.get(
url,
headers={'''User-Agent''': str(UserAgent().random)},
)
try:
__A : Optional[int] = (
BeautifulSoup(res.text, '''html.parser''')
.find('''div''', attrs={'''class''': '''yuRUbf'''})
.find('''a''')
.get('''href''')
)
except AttributeError:
__A : str = parse_qs(
BeautifulSoup(res.text, '''html.parser''')
.find('''div''', attrs={'''class''': '''kCrYT'''})
.find('''a''')
.get('''href''')
)['''url'''][0]
webbrowser.open(link)
| 371 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_="last" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=0 , ):
UpperCamelCase : Union[str, Any] = parent
UpperCamelCase : str = batch_size
UpperCamelCase : int = seq_length
UpperCamelCase : Optional[Any] = is_training
UpperCamelCase : Any = use_input_lengths
UpperCamelCase : Tuple = use_token_type_ids
UpperCamelCase : List[Any] = use_labels
UpperCamelCase : Union[str, Any] = gelu_activation
UpperCamelCase : Dict = sinusoidal_embeddings
UpperCamelCase : Optional[int] = causal
UpperCamelCase : List[Any] = asm
UpperCamelCase : int = n_langs
UpperCamelCase : Optional[Any] = vocab_size
UpperCamelCase : str = n_special
UpperCamelCase : Dict = hidden_size
UpperCamelCase : Union[str, Any] = num_hidden_layers
UpperCamelCase : Optional[Any] = num_attention_heads
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : int = max_position_embeddings
UpperCamelCase : Any = type_sequence_label_size
UpperCamelCase : str = initializer_range
UpperCamelCase : str = num_labels
UpperCamelCase : Union[str, Any] = num_choices
UpperCamelCase : List[str] = summary_type
UpperCamelCase : int = use_proj
UpperCamelCase : List[str] = scope
UpperCamelCase : Dict = bos_token_id
def a_ ( self ):
UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Union[str, Any] = None
if self.use_input_lengths:
UpperCamelCase : str = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
UpperCamelCase : Tuple = None
if self.use_token_type_ids:
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
UpperCamelCase : int = None
UpperCamelCase : Dict = None
UpperCamelCase : str = None
if self.use_labels:
UpperCamelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Dict = ids_tensor([self.batch_size] , 2 ).float()
UpperCamelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : List[str] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def a_ ( self ):
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = XLMModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , lengths=SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[Any] = XLMWithLMHeadModel(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[str] = XLMForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = XLMForQuestionAnswering(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , p_mask=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , )
((UpperCamelCase) , ) : Union[str, Any] = result_with_labels.to_tuple()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
((UpperCamelCase) , ) : Tuple = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = XLMForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = self.num_labels
UpperCamelCase : int = XLMForTokenClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[int] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[Any] = self.num_choices
UpperCamelCase : Tuple = XLMForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Dict = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Optional[Any] = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : int = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : List[Any] = config_and_inputs
UpperCamelCase : Dict = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
lowercase : List[Any] = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
lowercase : Optional[Any] = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("""Fast""" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
UpperCamelCase : Union[str, Any] = super()._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
UpperCamelCase : Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
return inputs_dict
def a_ ( self ):
UpperCamelCase : List[Any] = XLMModelTester(self )
UpperCamelCase : Optional[int] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , emb_dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_attentions in attentions] , [True] * len(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : int = min_length + idx + 1
UpperCamelCase : Tuple = min_length + idx + 1
UpperCamelCase : Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_hidden_states in hidden_states] , [True] * len(SCREAMING_SNAKE_CASE_ ) , )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : List[str] = min_length + idx + 1
UpperCamelCase : Optional[Any] = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) , )
pass
@slow
def a_ ( self ):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : str = XLMModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Dict = XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""" )
model.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.tensor([[14, 447]] , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ ) # the president
UpperCamelCase : List[Any] = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
UpperCamelCase : Optional[int] = model.generate(SCREAMING_SNAKE_CASE_ , do_sample=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , SCREAMING_SNAKE_CASE_ )
| 27 | 0 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : Tuple = tmp_path / """file.csv"""
UpperCamelCase : Union[str, Any] = textwrap.dedent(
"""\
header1,header2
1,2
10,20
""" )
with open(snake_case_ ,"""w""" ) as f:
f.write(snake_case_ )
return str(snake_case_ )
@pytest.fixture
def A_ ( snake_case_ : Tuple ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = tmp_path / """malformed_file.csv"""
UpperCamelCase : int = textwrap.dedent(
"""\
header1,header2
1,2
10,20,
""" )
with open(snake_case_ ,"""w""" ) as f:
f.write(snake_case_ )
return str(snake_case_ )
@pytest.fixture
def A_ ( snake_case_ : List[str] ,snake_case_ : Tuple ):
'''simple docstring'''
UpperCamelCase : Tuple = tmp_path / """csv_with_image.csv"""
UpperCamelCase : List[str] = textwrap.dedent(
f'\\n image\n {image_file}\n ' )
with open(snake_case_ ,"""w""" ) as f:
f.write(snake_case_ )
return str(snake_case_ )
@pytest.fixture
def A_ ( snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : List[Any] = tmp_path / """csv_with_label.csv"""
UpperCamelCase : Optional[Any] = textwrap.dedent(
"""\
label
good
bad
good
""" )
with open(snake_case_ ,"""w""" ) as f:
f.write(snake_case_ )
return str(snake_case_ )
@pytest.fixture
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : List[Any] = tmp_path / """csv_with_int_list.csv"""
UpperCamelCase : int = textwrap.dedent(
"""\
int_list
1 2 3
4 5 6
7 8 9
""" )
with open(snake_case_ ,"""w""" ) as f:
f.write(snake_case_ )
return str(snake_case_ )
def A_ ( snake_case_ : Dict ,snake_case_ : Optional[Any] ,snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : Tuple = Csv()
UpperCamelCase : Optional[Any] = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(snake_case_ ,match="""Error tokenizing data""" ):
for _ in generator:
pass
assert any(
record.levelname == """ERROR"""
and """Failed to read file""" in record.message
and os.path.basename(snake_case_ ) in record.message
for record in caplog.records )
@require_pil
def A_ ( snake_case_ : Tuple ):
'''simple docstring'''
with open(snake_case_ ,encoding="""utf-8""" ) as f:
UpperCamelCase : Tuple = f.read().splitlines()[1]
UpperCamelCase : List[str] = Csv(encoding="""utf-8""" ,features=Features({"""image""": Image()} ) )
UpperCamelCase : Any = csv._generate_tables([[csv_file_with_image]] )
UpperCamelCase : List[str] = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""image""" ).type == Image()()
UpperCamelCase : List[str] = pa_table.to_pydict()["""image"""]
assert generated_content == [{"path": image_file, "bytes": None}]
def A_ ( snake_case_ : Optional[int] ):
'''simple docstring'''
with open(snake_case_ ,encoding="""utf-8""" ) as f:
UpperCamelCase : List[str] = f.read().splitlines()[1:]
UpperCamelCase : Optional[int] = Csv(encoding="""utf-8""" ,features=Features({"""label""": ClassLabel(names=["""good""", """bad"""] )} ) )
UpperCamelCase : Any = csv._generate_tables([[csv_file_with_label]] )
UpperCamelCase : Union[str, Any] = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""label""" ).type == ClassLabel(names=["""good""", """bad"""] )()
UpperCamelCase : Tuple = pa_table.to_pydict()["""label"""]
assert generated_content == [ClassLabel(names=["""good""", """bad"""] ).straint(snake_case_ ) for label in labels]
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : Any = Csv(encoding="""utf-8""" ,sep=""",""" ,converters={"""int_list""": lambda snake_case_ : [int(snake_case_ ) for i in x.split()]} )
UpperCamelCase : Dict = csv._generate_tables([[csv_file_with_int_list]] )
UpperCamelCase : Union[str, Any] = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field("""int_list""" ).type )
UpperCamelCase : Union[str, Any] = pa_table.to_pydict()["""int_list"""]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 350 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : int = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = [
'''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTBigCodeForSequenceClassification''',
'''GPTBigCodeForTokenClassification''',
'''GPTBigCodeForCausalLM''',
'''GPTBigCodeModel''',
'''GPTBigCodePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27 | 0 |
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Dict = 9, 1_4 # noqa: F841
UpperCamelCase : Union[str, Any] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 1_4],
[3, 4, 9],
[5, 4, 1_0],
[1, 7, 1_1],
]
UpperCamelCase : List[Any] = defaultdict(snake_case_ )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
UpperCamelCase : Tuple = mst(snake_case_ )
UpperCamelCase : Tuple = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
UpperCamelCase : Any = tuple(answer[:2] )
UpperCamelCase : Optional[int] = tuple(edge[::-1] )
assert edge in result or reverse in result
| 351 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase ( torch.nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_="sayef/fsner-bert-base-uncased" ):
super(SCREAMING_SNAKE_CASE_ , self ).__init__()
UpperCamelCase : int = AutoModel.from_pretrained(SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.nn.CosineSimilarity(3 , 1e-08 )
UpperCamelCase : Any = torch.nn.Softmax(dim=1 )
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
return self.bert(**SCREAMING_SNAKE_CASE_ ).last_hidden_state
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return token_embeddings.sum(2 , keepdim=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1 ):
return self.softmax(T * self.cos(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = W_supports["""sizes"""].tolist()
UpperCamelCase : List[str] = W_supports["""start_token_id"""].item()
UpperCamelCase : List[Any] = W_supports["""end_token_id"""].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
UpperCamelCase : List[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Tuple = W_supports["""input_ids"""] == start_token_id
UpperCamelCase : Optional[Any] = W_supports["""input_ids"""] == end_token_id
for i, size in enumerate(SCREAMING_SNAKE_CASE_ ):
if i == 0:
UpperCamelCase : int = 0
else:
UpperCamelCase : Optional[int] = support_sizes[i - 1]
UpperCamelCase : Tuple = S[s : s + size][start_token_masks[s : s + size]]
UpperCamelCase : int = S[s : s + size][end_token_masks[s : s + size]]
UpperCamelCase : Dict = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
UpperCamelCase : Tuple = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
UpperCamelCase : List[str] = torch.vstack((p_starts, p_start) )
UpperCamelCase : Optional[Any] = torch.vstack((p_ends, p_end) )
else:
UpperCamelCase : Optional[int] = p_start
UpperCamelCase : Tuple = p_end
return p_starts, p_ends
| 27 | 0 |
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=8 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=36 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , ):
UpperCamelCase : List[str] = parent
UpperCamelCase : Tuple = batch_size
UpperCamelCase : Tuple = seq_length
UpperCamelCase : str = is_training
UpperCamelCase : List[Any] = use_input_mask
UpperCamelCase : Optional[Any] = use_token_type_ids
UpperCamelCase : Any = use_labels
UpperCamelCase : Tuple = vocab_size
UpperCamelCase : Union[str, Any] = hidden_size
UpperCamelCase : str = num_hidden_layers
UpperCamelCase : Optional[Any] = num_attention_heads
UpperCamelCase : str = intermediate_size
UpperCamelCase : Optional[Any] = hidden_act
UpperCamelCase : Union[str, Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : List[Any] = max_position_embeddings
UpperCamelCase : Tuple = type_vocab_size
UpperCamelCase : Any = type_sequence_label_size
UpperCamelCase : Optional[Any] = initializer_range
UpperCamelCase : Optional[int] = num_labels
UpperCamelCase : Optional[Any] = num_choices
UpperCamelCase : Any = scope
def a_ ( self ):
UpperCamelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Optional[int] = None
if self.use_input_mask:
UpperCamelCase : str = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : List[Any] = None
if self.use_token_type_ids:
UpperCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase : List[Any] = None
UpperCamelCase : List[str] = None
UpperCamelCase : Optional[Any] = None
if self.use_labels:
UpperCamelCase : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : int = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : Optional[int] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ ( self ):
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=SCREAMING_SNAKE_CASE_ , initializer_range=self.initializer_range , )
def a_ ( self ):
UpperCamelCase : List[str] = self.get_config()
UpperCamelCase : int = 300
return config
def a_ ( self ):
(
UpperCamelCase
) : Tuple = self.prepare_config_and_inputs()
UpperCamelCase : str = True
UpperCamelCase : Any = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCamelCase : int = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = MraModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[Any] = True
UpperCamelCase : int = MraModel(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Tuple = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ , encoder_attention_mask=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = MraForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = MraForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Any = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = self.num_labels
UpperCamelCase : Optional[int] = MraForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = self.num_labels
UpperCamelCase : int = MraForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[Any] = self.num_choices
UpperCamelCase : Union[str, Any] = MraForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Optional[int] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : List[str] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : str = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : Any = self.prepare_config_and_inputs()
(
UpperCamelCase
) : Any = config_and_inputs
UpperCamelCase : Union[str, Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Tuple = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
lowercase : List[str] = False
lowercase : List[Any] = False
lowercase : List[Any] = False
lowercase : Optional[int] = False
lowercase : List[Any] = ()
def a_ ( self ):
UpperCamelCase : List[str] = MraModelTester(self )
UpperCamelCase : str = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCamelCase : Optional[int] = type
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : Dict = MraModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@unittest.skip(reason="""MRA does not output attentions""" )
def a_ ( self ):
return
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : List[str] = MraModel.from_pretrained("""uw-madison/mra-base-512-4""" )
UpperCamelCase : int = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : str = torch.Size((1, 256, 768) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.tensor(
[[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
@slow
def a_ ( self ):
UpperCamelCase : Union[str, Any] = MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4""" )
UpperCamelCase : int = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[Any] = 5_0265
UpperCamelCase : Optional[int] = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = torch.tensor(
[[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
@slow
def a_ ( self ):
UpperCamelCase : Optional[Any] = MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3""" )
UpperCamelCase : List[str] = torch.arange(4096 ).unsqueeze(0 )
with torch.no_grad():
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[Any] = 5_0265
UpperCamelCase : Any = torch.Size((1, 4096, vocab_size) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = torch.tensor(
[[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 352 |
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):
return f'Node({self.data})'
class lowerCamelCase :
def __init__( self ):
UpperCamelCase : Dict = None
def __iter__( self ):
UpperCamelCase : int = self.head
while node:
yield node.data
UpperCamelCase : Union[str, Any] = node.next
def __len__( self ):
return sum(1 for _ in self )
def __repr__( self ):
return "->".join([str(SCREAMING_SNAKE_CASE_ ) for item in self] )
def __getitem__( self , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
UpperCamelCase : List[Any] = self.head
for _ in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = current.next
UpperCamelCase : Optional[Any] = data
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(len(self ) , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(0 , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index <= len(self ):
raise IndexError("""list index out of range""" )
UpperCamelCase : Optional[Any] = Node(SCREAMING_SNAKE_CASE_ )
if self.head is None:
UpperCamelCase : Dict = new_node
elif index == 0:
UpperCamelCase : Any = self.head # link new_node to head
UpperCamelCase : Any = new_node
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : str = temp.next
UpperCamelCase : Any = temp.next
UpperCamelCase : Optional[Any] = new_node
def a_ ( self ): # print every node data
print(self )
def a_ ( self ):
return self.delete_nth(0 )
def a_ ( self ): # delete from tail
return self.delete_nth(len(self ) - 1 )
def a_ ( self , SCREAMING_SNAKE_CASE_ = 0 ):
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("""List index out of range.""" )
UpperCamelCase : Union[str, Any] = self.head # default first node
if index == 0:
UpperCamelCase : Optional[Any] = self.head.next
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : int = temp.next
UpperCamelCase : Optional[Any] = temp.next
UpperCamelCase : Dict = temp.next.next
return delete_node.data
def a_ ( self ):
return self.head is None
def a_ ( self ):
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Union[str, Any] = self.head
while current:
# Store the current node's next node.
UpperCamelCase : Optional[int] = current.next
# Make the current node's next point backwards
UpperCamelCase : Optional[Any] = prev
# Make the previous node be the current node
UpperCamelCase : int = current
# Make the current node the next node (to progress iteration)
UpperCamelCase : Optional[int] = next_node
# Return prev in order to put the head at the end
UpperCamelCase : Optional[int] = prev
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = LinkedList()
assert linked_list.is_empty() is True
assert str(snake_case_ ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(1_0 ):
assert len(snake_case_ ) == i
linked_list.insert_nth(snake_case_ ,i + 1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_1 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(1_1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(0 ,1_2 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 1_0
assert linked_list.delete_tail() == 1_1
assert len(snake_case_ ) == 9
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_0 ) )
assert all(linked_list[i] == i + 1 for i in range(0 ,9 ) ) is True
for i in range(0 ,9 ):
UpperCamelCase : Optional[Any] = -i
assert all(linked_list[i] == -i for i in range(0 ,9 ) ) is True
linked_list.reverse()
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(-8 ,1 ) )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = [
-9,
1_0_0,
Node(7_7_3_4_5_1_1_2 ),
"""dlrow olleH""",
7,
5_5_5_5,
0,
-192.55555,
"""Hello, world!""",
77.9,
Node(1_0 ),
None,
None,
12.20,
]
UpperCamelCase : List[Any] = LinkedList()
for i in test_input:
linked_list.insert_tail(snake_case_ )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(snake_case_ ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
UpperCamelCase : Dict = linked_list.delete_head()
assert result == -9
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
UpperCamelCase : int = linked_list.delete_tail()
assert result == 12.2
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
UpperCamelCase : Optional[Any] = linked_list.delete_nth(1_0 )
assert result is None
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("""Hello again, world!""" ) )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(snake_case_ )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(snake_case_ )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def A_ ( ):
'''simple docstring'''
from doctest import testmod
testmod()
UpperCamelCase : List[Any] = LinkedList()
linked_list.insert_head(input("""Inserting 1st at head """ ).strip() )
linked_list.insert_head(input("""Inserting 2nd at head """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
linked_list.insert_tail(input("""\nInserting 1st at tail """ ).strip() )
linked_list.insert_tail(input("""Inserting 2nd at tail """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nDelete head""" )
linked_list.delete_head()
print("""Delete tail""" )
linked_list.delete_tail()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nReverse linked list""" )
linked_list.reverse()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nString representation of linked list:""" )
print(snake_case_ )
print("""\nReading/changing Node data using indexing:""" )
print(f'Element at Position 1: {linked_list[1]}' )
UpperCamelCase : List[Any] = input("""Enter New Value: """ ).strip()
print("""New list:""" )
print(snake_case_ )
print(f'length of linked_list is : {len(snake_case_ )}' )
if __name__ == "__main__":
main()
| 27 | 0 |
"""simple docstring"""
import math
class lowerCamelCase :
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = 0.0
UpperCamelCase : Dict = 0.0
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
da += math.pow((sample[i] - weights[0][i]) , 2 )
da += math.pow((sample[i] - weights[1][i]) , 2 )
return 0 if da > da else 1
return 0
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Tuple = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
UpperCamelCase : List[Any] = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
UpperCamelCase : Optional[Any] = SelfOrganizingMap()
UpperCamelCase : Tuple = 3
UpperCamelCase : int = 0.5
for _ in range(snake_case_ ):
for j in range(len(snake_case_ ) ):
# training sample
UpperCamelCase : int = training_samples[j]
# Compute the winning vector
UpperCamelCase : List[Any] = self_organizing_map.get_winner(snake_case_ ,snake_case_ )
# Update the winning vector
UpperCamelCase : str = self_organizing_map.update(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
# classify test sample
UpperCamelCase : List[Any] = [0, 0, 0, 1]
UpperCamelCase : Optional[Any] = self_organizing_map.get_winner(snake_case_ ,snake_case_ )
# results
print(f'Clusters that the test sample belongs to : {winner}' )
print(f'Weights that have been trained : {weights}' )
# running the main() function
if __name__ == "__main__":
main()
| 353 |
"""simple docstring"""
import argparse
import os
import re
__A : Dict = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__A : Union[str, Any] = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : Tuple = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : Tuple = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Dict="" ,snake_case_ : Dict=None ,snake_case_ : Any=None ):
'''simple docstring'''
UpperCamelCase : Optional[int] = 0
UpperCamelCase : List[Any] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Optional[Any] = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : int = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Any = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Any = [lines[index + 1]]
index += 1
else:
UpperCamelCase : List[str] = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
def _inner(snake_case_ : Tuple ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : List[Any] ,snake_case_ : Optional[int]=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Dict ):
return x
if key is None:
UpperCamelCase : int = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[Any] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : str = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : List[str] = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Tuple = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : int ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : List[Any] ):
UpperCamelCase : Any = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : Union[str, Any] = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[str] = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : str = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : str = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Dict = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : int = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Any = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[Any] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : Optional[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[Any] = keys[:-1]
UpperCamelCase : int = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Tuple ,snake_case_ : str=True ):
'''simple docstring'''
with open(snake_case_ ,"""r""" ) as f:
UpperCamelCase : int = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : Dict = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Optional[Any] = main_blocks[block_idx]
UpperCamelCase : Optional[int] = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : Union[str, Any] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : List[str] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Dict = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Union[str, Any] = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : Optional[int] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Union[str, Any] = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Union[str, Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Optional[Any] = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : List[Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[Any] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : str = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Any = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Union[str, Any] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : Any = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : str = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27 | 0 |
"""simple docstring"""
import numpy as np
def A_ ( snake_case_ : np.array ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 354 |
"""simple docstring"""
def A_ ( snake_case_ : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(snake_case_ ,(list, tuple) ) or not all(
isinstance(snake_case_ ,snake_case_ ) for number in numbers ):
raise ValueError("""numbers must be an iterable of integers""" )
UpperCamelCase : int = numbers[0]
for i in range(1 ,len(snake_case_ ) ):
# update the maximum and minimum subarray products
UpperCamelCase : List[str] = numbers[i]
if number < 0:
UpperCamelCase , UpperCamelCase : Optional[int] = min_till_now, max_till_now
UpperCamelCase : Dict = max(snake_case_ ,max_till_now * number )
UpperCamelCase : Union[str, Any] = min(snake_case_ ,min_till_now * number )
# update the maximum product found till now
UpperCamelCase : Union[str, Any] = max(snake_case_ ,snake_case_ )
return max_prod
| 27 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__A : int = logging.get_logger(__name__)
__A : Optional[int] = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
__A : Optional[int] = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : Dict ,snake_case_ : Tuple ,snake_case_ : Optional[int] ,snake_case_ : List[Any] ):
'''simple docstring'''
for attribute in key.split(""".""" ):
UpperCamelCase : str = getattr(snake_case_ ,snake_case_ )
if weight_type is not None:
UpperCamelCase : Dict = getattr(snake_case_ ,snake_case_ ).shape
else:
UpperCamelCase : List[str] = hf_pointer.shape
assert hf_shape == value.shape, (
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}'
)
if weight_type == "weight":
UpperCamelCase : int = value
elif weight_type == "weight_g":
UpperCamelCase : str = value
elif weight_type == "weight_v":
UpperCamelCase : Union[str, Any] = value
elif weight_type == "bias":
UpperCamelCase : Union[str, Any] = value
else:
UpperCamelCase : int = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def A_ ( snake_case_ : List[str] ,snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = []
UpperCamelCase : str = fairseq_model.state_dict()
UpperCamelCase : List[str] = hf_model.feature_extractor
# if encoder has different dim to decoder -> use proj_weight
UpperCamelCase : str = None
for name, value in fairseq_dict.items():
UpperCamelCase : Optional[Any] = False
if "conv_layers" in name:
load_conv_layer(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,hf_model.config.feat_extract_norm == """group""" ,)
UpperCamelCase : List[str] = True
elif name.split(""".""" )[0] == "proj":
UpperCamelCase : Optional[Any] = fairseq_model.proj
UpperCamelCase : Optional[Any] = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
UpperCamelCase : Optional[int] = True
if "*" in mapped_key:
UpperCamelCase : Optional[Any] = name.split(snake_case_ )[0].split(""".""" )[-2]
UpperCamelCase : List[str] = mapped_key.replace("""*""" ,snake_case_ )
if "weight_g" in name:
UpperCamelCase : str = """weight_g"""
elif "weight_v" in name:
UpperCamelCase : Optional[int] = """weight_v"""
elif "bias" in name:
UpperCamelCase : List[str] = """bias"""
elif "weight" in name:
UpperCamelCase : List[str] = """weight"""
else:
UpperCamelCase : int = None
set_recursively(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
continue
if not is_used:
unused_weights.append(snake_case_ )
logger.warning(f'Unused weights: {unused_weights}' )
return proj_weight
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Dict ,snake_case_ : List[str] ,snake_case_ : Any ,snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : List[str] = full_name.split("""conv_layers.""" )[-1]
UpperCamelCase : int = name.split(""".""" )
UpperCamelCase : Any = int(items[0] )
UpperCamelCase : Union[str, Any] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
)
UpperCamelCase : List[Any] = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
)
UpperCamelCase : List[str] = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
" found."
)
UpperCamelCase : Optional[int] = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
)
UpperCamelCase : Tuple = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(snake_case_ )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : str = emb.weight.shape
UpperCamelCase : Union[str, Any] = nn.Linear(snake_case_ ,snake_case_ ,bias=snake_case_ )
UpperCamelCase : List[str] = emb.weight.data
return lin_layer
def A_ ( snake_case_ : Optional[int] ):
'''simple docstring'''
with open(snake_case_ ,"""r""" ,encoding="""utf-8""" ) as f:
UpperCamelCase : int = f.readlines()
UpperCamelCase : List[Any] = [line.split(""" """ )[0] for line in lines]
UpperCamelCase : int = len(snake_case_ )
UpperCamelCase : List[str] = {
"""<s>""": 0,
"""<pad>""": 1,
"""</s>""": 2,
"""<unk>""": 3,
}
vocab_dict.update(dict(zip(snake_case_ ,range(4 ,num_words + 4 ) ) ) )
return vocab_dict
@torch.no_grad()
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : List[str] ,snake_case_ : Tuple ,snake_case_ : Any ,snake_case_ : Any ,snake_case_ : Optional[Any] ,snake_case_ : Optional[Any] ,):
'''simple docstring'''
UpperCamelCase : List[Any] = WavaVecaConfig.from_pretrained(snake_case_ )
UpperCamelCase : Tuple = SpeechaTextaConfig.from_pretrained(
snake_case_ ,vocab_size=snake_case_ ,decoder_layers=snake_case_ ,do_stable_layer_norm=snake_case_ )
UpperCamelCase : List[str] = WavaVecaFeatureExtractor(
feature_size=1 ,sampling_rate=1_6_0_0_0 ,padding_value=0 ,do_normalize=snake_case_ ,return_attention_mask=snake_case_ ,)
UpperCamelCase : Union[str, Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
UpperCamelCase : Any = model[0].eval()
# set weights for wav2vec2 encoder
UpperCamelCase : Optional[int] = WavaVecaModel(snake_case_ )
UpperCamelCase : List[str] = recursively_load_weights_wavaveca(model.encoder ,snake_case_ )
UpperCamelCase : List[Any] = SpeechaTextaForCausalLM(snake_case_ )
UpperCamelCase : Tuple = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,strict=snake_case_ )
# set output linear layer
unexpected_keys.remove("""embed_out""" )
UpperCamelCase : Any = nn.Parameter(model.decoder.embed_out.detach() )
# layer norm is init to identity matrix so leaving it is fine
logger.warning(f'The following keys are missing when loading the decoder weights: {missing_keys}' )
logger.warning(f'The following keys are unexpected when loading the decoder weights: {unexpected_keys}' )
UpperCamelCase : Tuple = SpeechEncoderDecoderModel(encoder=snake_case_ ,decoder=snake_case_ )
UpperCamelCase : Dict = False
# add projection layer
UpperCamelCase : Optional[Any] = nn.Parameter(projection_layer.weight )
UpperCamelCase : int = nn.Parameter(projection_layer.bias )
UpperCamelCase : Optional[Any] = create_vocab_dict(snake_case_ )
with open(os.path.join(snake_case_ ,"""vocab.json""" ) ,"""w""" ) as fp:
json.dump(snake_case_ ,snake_case_ )
UpperCamelCase : List[str] = SpeechaTextaTokenizer(os.path.join(snake_case_ ,"""vocab.json""" ) )
tokenizer.save_pretrained(snake_case_ )
UpperCamelCase : Dict = hf_wavavec.config.to_dict()
UpperCamelCase : str = tokenizer.pad_token_id
UpperCamelCase : List[str] = tokenizer.bos_token_id
UpperCamelCase : List[Any] = tokenizer.eos_token_id
UpperCamelCase : int = """speech_to_text_2"""
UpperCamelCase : Any = """wav2vec2"""
UpperCamelCase : str = SpeechEncoderDecoderConfig.from_dict(snake_case_ )
hf_wavavec.save_pretrained(snake_case_ )
feature_extractor.save_pretrained(snake_case_ )
if __name__ == "__main__":
__A : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument(
'''--encoder_config_path''',
default='''facebook/wav2vec2-large-lv60''',
type=str,
help='''Path to hf encoder wav2vec2 checkpoint config''',
)
parser.add_argument(
'''--decoder_config_path''',
default='''facebook/s2t-small-mustc-en-fr-st''',
type=str,
help='''Path to hf decoder s2t checkpoint config''',
)
parser.add_argument('''--vocab_size''', default=10224, type=int, help='''Vocab size of decoder''')
parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')
__A : List[str] = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
vocab_size=args.vocab_size,
num_decoder_layers=args.num_decoder_layers,
)
| 355 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Any = AudioLDMPipeline
lowercase : Union[str, Any] = TEXT_TO_AUDIO_PARAMS
lowercase : List[str] = TEXT_TO_AUDIO_BATCH_PARAMS
lowercase : Tuple = frozenset(
[
'num_inference_steps',
'num_waveforms_per_prompt',
'generator',
'latents',
'output_type',
'return_dict',
'callback',
'callback_steps',
] )
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : Tuple = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=(32, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Optional[Any] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_one=SCREAMING_SNAKE_CASE_ , )
torch.manual_seed(0 )
UpperCamelCase : Optional[int] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
UpperCamelCase : int = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
UpperCamelCase : Optional[int] = ClapTextModelWithProjection(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 )
UpperCamelCase : Tuple = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=1_6000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : List[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Any = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def a_ ( self ):
UpperCamelCase : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Any = self.get_dummy_components()
UpperCamelCase : int = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Tuple = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[str] = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Optional[int] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : str = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
UpperCamelCase : Tuple = prompt_embeds
# forward
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : List[str] = self.get_dummy_components()
UpperCamelCase : List[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * ["""this is a negative prompt"""]
UpperCamelCase : List[Any] = negative_prompt
UpperCamelCase : str = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : str = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
UpperCamelCase : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[Any] = []
for p in [prompt, negative_prompt]:
UpperCamelCase : int = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Union[str, Any] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
embeds.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase : Tuple = embeds
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Optional[int] = self.get_dummy_components()
UpperCamelCase : List[str] = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = """egg cracking"""
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ , negative_prompt=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Union[str, Any] = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Union[str, Any] = self.get_dummy_components()
UpperCamelCase : Tuple = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
UpperCamelCase : List[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
UpperCamelCase : Dict = 2
UpperCamelCase : List[str] = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
UpperCamelCase : List[str] = 2
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
UpperCamelCase : Any = 2
UpperCamelCase : str = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Tuple = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = audioldm_pipe.vocoder.config.sampling_rate
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe(audio_length_in_s=0.016 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.016
UpperCamelCase : Optional[Any] = audioldm_pipe(audio_length_in_s=0.032 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.032
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Optional[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = ["""hey"""]
UpperCamelCase : Dict = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : str = output.audios.shape
assert audio_shape == (1, 256)
UpperCamelCase : Optional[Any] = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
UpperCamelCase : str = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : List[str] = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def a_ ( self ):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
self._test_inference_batch_single_identical(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def a_ ( self ):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@slow
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="cpu" , SCREAMING_SNAKE_CASE_=torch.floataa , SCREAMING_SNAKE_CASE_=0 ):
UpperCamelCase : str = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = np.random.RandomState(SCREAMING_SNAKE_CASE_ ).standard_normal((1, 8, 128, 16) )
UpperCamelCase : int = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def a_ ( self ):
UpperCamelCase : Optional[int] = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = 25
UpperCamelCase : Optional[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[7_7230:7_7240]
UpperCamelCase : Optional[Any] = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
UpperCamelCase : Any = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def a_ ( self ):
UpperCamelCase : Any = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : Any = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
UpperCamelCase : str = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[2_7780:2_7790]
UpperCamelCase : Tuple = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
UpperCamelCase : Tuple = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 27 | 0 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def A_ ( ):
'''simple docstring'''
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
UpperCamelCase : Optional[Any] = [1, 2, 3]
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=2 )
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" ,[2, -1] )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : List[Any] = [1, 2]
UpperCamelCase : List[Any] = {"""a""": 1, """b""": 2}
UpperCamelCase : List[str] = {"""a""": [1, 2], """b""": [3, 4]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 1}, """b""": 2}
UpperCamelCase : Any = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
UpperCamelCase : Optional[int] = [2, 3]
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3}
UpperCamelCase : Any = {"""a""": [2, 3], """b""": [4, 5]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 2}, """b""": 3}
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
| 356 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( snake_case_ : Dataset ,snake_case_ : Dict[str, str] ):
'''simple docstring'''
UpperCamelCase : List[str] = args.log_outputs
UpperCamelCase : Tuple = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
UpperCamelCase : List[Any] = load_metric("""wer""" )
UpperCamelCase : Any = load_metric("""cer""" )
# compute metrics
UpperCamelCase : str = wer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
UpperCamelCase : Dict = cer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
# print & log results
UpperCamelCase : Optional[int] = f'WER: {wer_result}\nCER: {cer_result}'
print(snake_case_ )
with open(f'{dataset_id}_eval_results.txt' ,"""w""" ) as f:
f.write(snake_case_ )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
UpperCamelCase : Optional[Any] = f'log_{dataset_id}_predictions.txt'
UpperCamelCase : str = f'log_{dataset_id}_targets.txt'
with open(snake_case_ ,"""w""" ) as p, open(snake_case_ ,"""w""" ) as t:
# mapping function to write output
def write_to_file(snake_case_ : Union[str, Any] ,snake_case_ : Tuple ):
p.write(f'{i}' + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(f'{i}' + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(snake_case_ ,with_indices=snake_case_ )
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Dict = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
UpperCamelCase : str = re.sub(snake_case_ ,"""""" ,text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
UpperCamelCase : List[str] = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
UpperCamelCase : Tuple = """ """.join(text.split(snake_case_ ) )
return text
def A_ ( snake_case_ : str ):
'''simple docstring'''
# load dataset
UpperCamelCase : Union[str, Any] = load_dataset(args.dataset ,args.config ,split=args.split ,use_auth_token=snake_case_ )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
UpperCamelCase : List[Any] = AutoFeatureExtractor.from_pretrained(args.model_id )
UpperCamelCase : Dict = feature_extractor.sampling_rate
# resample audio
UpperCamelCase : Optional[Any] = dataset.cast_column("""audio""" ,Audio(sampling_rate=snake_case_ ) )
# load eval pipeline
if args.device is None:
UpperCamelCase : int = 0 if torch.cuda.is_available() else -1
UpperCamelCase : Union[str, Any] = pipeline("""automatic-speech-recognition""" ,model=args.model_id ,device=args.device )
# map function to decode audio
def map_to_pred(snake_case_ : Union[str, Any] ):
UpperCamelCase : List[Any] = asr(
batch["""audio"""]["""array"""] ,chunk_length_s=args.chunk_length_s ,stride_length_s=args.stride_length_s )
UpperCamelCase : Union[str, Any] = prediction["""text"""]
UpperCamelCase : Optional[Any] = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
UpperCamelCase : Any = dataset.map(snake_case_ ,remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(snake_case_ ,snake_case_ )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
__A : Optional[Any] = parser.parse_args()
main(args)
| 27 | 0 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
__A : Optional[int] = logging.get_logger(__name__)
class lowerCamelCase :
lowercase : List[str] = None
@experimental
def A_ ( snake_case_ : Dict ,snake_case_ : Tuple ,snake_case_ : Any ,snake_case_ : str ,snake_case_ : Union[str, Any] ,snake_case_ : List[Any] ,snake_case_ : Optional[int] ):
'''simple docstring'''
if ParallelBackendConfig.backend_name is None:
return _map_with_multiprocessing_pool(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
return _map_with_joblib(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
def A_ ( snake_case_ : List[str] ,snake_case_ : str ,snake_case_ : str ,snake_case_ : List[str] ,snake_case_ : Optional[int] ,snake_case_ : Optional[Any] ,snake_case_ : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase : Optional[int] = num_proc if num_proc <= len(snake_case_ ) else len(snake_case_ )
UpperCamelCase : List[Any] = [] # We organize the splits ourselve (contiguous splits)
for index in range(snake_case_ ):
UpperCamelCase : Union[str, Any] = len(snake_case_ ) // num_proc
UpperCamelCase : Optional[Any] = len(snake_case_ ) % num_proc
UpperCamelCase : str = div * index + min(snake_case_ ,snake_case_ )
UpperCamelCase : int = start + div + (1 if index < mod else 0)
split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) )
if len(snake_case_ ) != sum(len(i[1] ) for i in split_kwds ):
raise ValueError(
f'Error dividing inputs iterable among processes. '
f'Total number of objects {len(snake_case_ )}, '
f'length: {sum(len(i[1] ) for i in split_kwds )}' )
logger.info(
f'Spawning {num_proc} processes for {len(snake_case_ )} objects in slices of {[len(i[1] ) for i in split_kwds]}' )
UpperCamelCase : Optional[int] = None, None
if not disable_tqdm:
UpperCamelCase : List[str] = (RLock(),), tqdm.set_lock
with Pool(snake_case_ ,initargs=snake_case_ ,initializer=snake_case_ ) as pool:
UpperCamelCase : Optional[int] = pool.map(snake_case_ ,snake_case_ )
logger.info(f'Finished {num_proc} processes' )
UpperCamelCase : Optional[int] = [obj for proc_res in mapped for obj in proc_res]
logger.info(f'Unpacked {len(snake_case_ )} objects' )
return mapped
def A_ ( snake_case_ : Tuple ,snake_case_ : int ,snake_case_ : str ,snake_case_ : List[Any] ,snake_case_ : Any ,snake_case_ : Tuple ,snake_case_ : int ):
'''simple docstring'''
import joblib
with joblib.parallel_backend(ParallelBackendConfig.backend_name ,n_jobs=snake_case_ ):
return joblib.Parallel()(
joblib.delayed(snake_case_ )((function, obj, types, None, True, None) ) for obj in iterable )
@experimental
@contextlib.contextmanager
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : List[str] = backend_name
if backend_name == "spark":
from joblibspark import register_spark
register_spark()
# TODO: call create_cache_and_write_probe if "download" in steps
# TODO: raise NotImplementedError when Dataset.map etc is called
try:
yield
finally:
UpperCamelCase : List[Any] = None
| 357 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = 'EncodecFeatureExtractor'
lowercase : List[Any] = ('T5Tokenizer', 'T5TokenizerFast')
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.feature_extractor
UpperCamelCase : Any = False
def a_ ( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True ):
return self.tokenizer.get_decoder_prompt_ids(task=SCREAMING_SNAKE_CASE_ , language=SCREAMING_SNAKE_CASE_ , no_timestamps=SCREAMING_SNAKE_CASE_ )
def __call__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = kwargs.pop("""sampling_rate""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = kwargs.pop("""text""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Any = args[0]
UpperCamelCase : str = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if text is not None:
UpperCamelCase : Optional[int] = self.tokenizer(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is not None:
UpperCamelCase : str = self.feature_extractor(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
UpperCamelCase : int = audio_inputs["""input_values"""]
if "padding_mask" in audio_inputs:
UpperCamelCase : Optional[Any] = audio_inputs["""padding_mask"""]
return inputs
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = kwargs.pop("""padding_mask""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Optional[int] = args[0]
UpperCamelCase : Any = args[1:]
if audio_values is not None:
return self._decode_audio(SCREAMING_SNAKE_CASE_ , padding_mask=SCREAMING_SNAKE_CASE_ )
else:
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = to_numpy(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase , UpperCamelCase : int = audio_values.shape
if padding_mask is None:
return list(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = to_numpy(SCREAMING_SNAKE_CASE_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
UpperCamelCase : List[str] = seq_len - padding_mask.shape[-1]
UpperCamelCase : Optional[int] = 1 - self.feature_extractor.padding_value
UpperCamelCase : Any = np.pad(SCREAMING_SNAKE_CASE_ , ((0, 0), (0, difference)) , """constant""" , constant_values=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audio_values.tolist()
for i in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
UpperCamelCase : Optional[Any] = sliced_audio.reshape(SCREAMING_SNAKE_CASE_ , -1 )
return audio_values
| 27 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.distilbert.modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertModel,
)
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[str] = parent
UpperCamelCase : Tuple = 13
UpperCamelCase : Optional[Any] = 7
UpperCamelCase : List[Any] = True
UpperCamelCase : List[str] = True
UpperCamelCase : str = False
UpperCamelCase : Dict = True
UpperCamelCase : Tuple = 99
UpperCamelCase : int = 32
UpperCamelCase : Any = 2
UpperCamelCase : Union[str, Any] = 4
UpperCamelCase : Optional[int] = 37
UpperCamelCase : Any = """gelu"""
UpperCamelCase : Any = 0.1
UpperCamelCase : Any = 0.1
UpperCamelCase : Optional[int] = 512
UpperCamelCase : List[Any] = 16
UpperCamelCase : Any = 2
UpperCamelCase : List[Any] = 0.02
UpperCamelCase : List[str] = 3
UpperCamelCase : int = 4
UpperCamelCase : str = None
def a_ ( self ):
UpperCamelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Any = None
if self.use_input_mask:
UpperCamelCase : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Tuple = None
UpperCamelCase : List[str] = None
UpperCamelCase : Any = None
if self.use_labels:
UpperCamelCase : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : str = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : List[Any] = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = TFDistilBertModel(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = {"""input_ids""": input_ids, """attention_mask""": input_mask}
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = [input_ids, input_mask]
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = TFDistilBertForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = {"""input_ids""": input_ids, """attention_mask""": input_mask}
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = TFDistilBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
}
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = self.num_labels
UpperCamelCase : Tuple = TFDistilBertForSequenceClassification(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = {"""input_ids""": input_ids, """attention_mask""": input_mask}
UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = self.num_choices
UpperCamelCase : Tuple = TFDistilBertForMultipleChoice(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_ , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase : Tuple = tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_ , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase : Union[str, Any] = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
}
UpperCamelCase : str = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = self.num_labels
UpperCamelCase : str = TFDistilBertForTokenClassification(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self ):
UpperCamelCase : int = self.prepare_config_and_inputs()
(UpperCamelCase) : int = config_and_inputs
UpperCamelCase : str = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Tuple = (
(
TFDistilBertModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertForMultipleChoice,
)
if is_tf_available()
else None
)
lowercase : Tuple = (
{
'feature-extraction': TFDistilBertModel,
'fill-mask': TFDistilBertForMaskedLM,
'question-answering': TFDistilBertForQuestionAnswering,
'text-classification': TFDistilBertForSequenceClassification,
'token-classification': TFDistilBertForTokenClassification,
'zero-shot': TFDistilBertForSequenceClassification,
}
if is_tf_available()
else {}
)
lowercase : Any = False
lowercase : int = False
def a_ ( self ):
UpperCamelCase : Any = TFDistilBertModelTester(self )
UpperCamelCase : Optional[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1] ):
UpperCamelCase : Optional[int] = TFDistilBertModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_tf
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : List[str] = TFDistilBertModel.from_pretrained("""distilbert-base-uncased""" )
UpperCamelCase : Union[str, Any] = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : List[Any] = [1, 6, 768]
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = tf.constant(
[
[
[0.19261885, -0.13732955, 0.4119799],
[0.22150156, -0.07422661, 0.39037204],
[0.22756018, -0.0896414, 0.3701467],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 )
| 358 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( snake_case_ : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
UpperCamelCase : Any = BeautifulSoup(requests.get(snake_case_ ).text ,"""html.parser""" )
UpperCamelCase : Optional[int] = soup.findAll("""h1""" )
UpperCamelCase : List[Any] = soup.findAll("""div""" ,{"""class""": """maincounter-number"""} )
keys += soup.findAll("""span""" ,{"""class""": """panel-title"""} )
values += soup.findAll("""div""" ,{"""class""": """number-table-main"""} )
return {key.text.strip(): value.text.strip() for key, value in zip(snake_case_ ,snake_case_ )}
if __name__ == "__main__":
print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
for key, value in world_covidaa_stats().items():
print(F'''{key}\n{value}\n''')
| 27 | 0 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__A : Tuple = '''src/diffusers'''
# Matches is_xxx_available()
__A : List[str] = re.compile(R'''is\_([a-z_]*)_available\(\)''')
# Matches from xxx import bla
__A : Union[str, Any] = re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
__A : List[str] = '''
{0} = None
'''
__A : Dict = '''
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, {1})
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, {1})
'''
__A : List[str] = '''
def {0}(*args, **kwargs):
requires_backends({0}, {1})
'''
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : Any = _re_backend.findall(snake_case_ )
if len(snake_case_ ) == 0:
return None
return "_and_".join(snake_case_ )
def A_ ( ):
'''simple docstring'''
with open(os.path.join(snake_case_ ,"""__init__.py""" ) ,"""r""" ,encoding="""utf-8""" ,newline="""\n""" ) as f:
UpperCamelCase : Tuple = f.readlines()
# Get to the point we do the actual imports for type checking
UpperCamelCase : Dict = 0
UpperCamelCase : Dict = {}
# Go through the end of the file
while line_index < len(snake_case_ ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
UpperCamelCase : Dict = find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith("""else:""" ):
line_index += 1
line_index += 1
UpperCamelCase : List[str] = []
# Until we unindent, add backend objects to the list
while line_index < len(snake_case_ ) and len(lines[line_index] ) > 1:
UpperCamelCase : List[Any] = lines[line_index]
UpperCamelCase : Dict = _re_single_line_import.search(snake_case_ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 8 ):
objects.append(line[8:-2] )
line_index += 1
if len(snake_case_ ) > 0:
UpperCamelCase : Dict = objects
else:
line_index += 1
return backend_specific_objects
def A_ ( snake_case_ : Tuple ,snake_case_ : str ):
'''simple docstring'''
if name.isupper():
return DUMMY_CONSTANT.format(snake_case_ )
elif name.islower():
return DUMMY_FUNCTION.format(snake_case_ ,snake_case_ )
else:
return DUMMY_CLASS.format(snake_case_ ,snake_case_ )
def A_ ( snake_case_ : Union[str, Any]=None ):
'''simple docstring'''
if backend_specific_objects is None:
UpperCamelCase : Optional[Any] = read_init()
# For special correspondence backend to module name as used in the function requires_modulename
UpperCamelCase : str = {}
for backend, objects in backend_specific_objects.items():
UpperCamelCase : Optional[int] = """[""" + """, """.join(f'"{b}"' for b in backend.split("""_and_""" ) ) + """]"""
UpperCamelCase : Tuple = """# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(snake_case_ ,snake_case_ ) for o in objects] )
UpperCamelCase : str = dummy_file
return dummy_files
def A_ ( snake_case_ : Tuple=False ):
'''simple docstring'''
UpperCamelCase : int = create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
UpperCamelCase : Any = {"""torch""": """pt"""}
# Locate actual dummy modules and read their content.
UpperCamelCase : Any = os.path.join(snake_case_ ,"""utils""" )
UpperCamelCase : str = {
backend: os.path.join(snake_case_ ,f'dummy_{short_names.get(snake_case_ ,snake_case_ )}_objects.py' )
for backend in dummy_files.keys()
}
UpperCamelCase : Dict = {}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(snake_case_ ):
with open(snake_case_ ,"""r""" ,encoding="""utf-8""" ,newline="""\n""" ) as f:
UpperCamelCase : int = f.read()
else:
UpperCamelCase : str = """"""
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
f'Updating diffusers.utils.dummy_{short_names.get(snake_case_ ,snake_case_ )}_objects.py as the main '
"""__init__ has new objects.""" )
with open(dummy_file_paths[backend] ,"""w""" ,encoding="""utf-8""" ,newline="""\n""" ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
"""The main __init__ has objects that are not present in """
f'diffusers.utils.dummy_{short_names.get(snake_case_ ,snake_case_ )}_objects.py. Run `make fix-copies` '
"""to fix this.""" )
if __name__ == "__main__":
__A : Dict = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
__A : Tuple = parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 359 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=1 , ):
UpperCamelCase : Tuple = parent
UpperCamelCase : Optional[int] = batch_size
UpperCamelCase : Optional[Any] = seq_length
UpperCamelCase : int = is_training
UpperCamelCase : Union[str, Any] = use_input_mask
UpperCamelCase : Union[str, Any] = use_token_type_ids
UpperCamelCase : Dict = use_labels
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Union[str, Any] = hidden_size
UpperCamelCase : Tuple = num_hidden_layers
UpperCamelCase : Any = num_attention_heads
UpperCamelCase : int = intermediate_size
UpperCamelCase : str = hidden_act
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : List[Any] = max_position_embeddings
UpperCamelCase : Optional[Any] = type_vocab_size
UpperCamelCase : int = type_sequence_label_size
UpperCamelCase : Dict = initializer_range
UpperCamelCase : Dict = num_labels
UpperCamelCase : Tuple = num_choices
UpperCamelCase : Optional[int] = scope
UpperCamelCase : List[Any] = q_groups
UpperCamelCase : Tuple = k_groups
UpperCamelCase : Any = v_groups
UpperCamelCase : List[str] = post_attention_groups
UpperCamelCase : Tuple = intermediate_groups
UpperCamelCase : int = output_groups
def a_ ( self ):
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Tuple = None
if self.use_input_mask:
UpperCamelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Optional[int] = None
UpperCamelCase : List[Any] = None
UpperCamelCase : Dict = None
if self.use_labels:
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : Dict = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ ( self ):
return SqueezeBertConfig(
embedding_size=self.hidden_size , vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = SqueezeBertModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = SqueezeBertForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = SqueezeBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : str = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = self.num_labels
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = self.num_labels
UpperCamelCase : str = SqueezeBertForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = self.num_choices
UpperCamelCase : Tuple = SqueezeBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Union[str, Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) : Optional[int] = config_and_inputs
UpperCamelCase : Optional[int] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
lowercase : Dict = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase : Dict = False
lowercase : str = True
lowercase : str = False
def a_ ( self ):
UpperCamelCase : Any = SqueezeBertModelTester(self )
UpperCamelCase : List[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : Optional[Any] = SqueezeBertModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_sentencepiece
@require_tokenizers
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli""" )
UpperCamelCase : Dict = torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]] )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[Any] = torch.Size((1, 3) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.tensor([[0.6401, -0.0349, -0.6041]] )
self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 27 | 0 |
"""simple docstring"""
import math
import os
import sys
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = """"""
try:
with open(snake_case_ ,"""rb""" ) as binary_file:
UpperCamelCase : Any = binary_file.read()
for dat in data:
UpperCamelCase : int = f'{dat:08b}'
result += curr_byte
return result
except OSError:
print("""File not accessible""" )
sys.exit()
def A_ ( snake_case_ : dict[str, str] ,snake_case_ : str ,snake_case_ : int ,snake_case_ : str ):
'''simple docstring'''
lexicon.pop(snake_case_ )
UpperCamelCase : Union[str, Any] = last_match_id
if math.loga(snake_case_ ).is_integer():
for curr_key in lexicon:
UpperCamelCase : Any = """0""" + lexicon[curr_key]
UpperCamelCase : int = bin(snake_case_ )[2:]
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : str = {"""0""": """0""", """1""": """1"""}
UpperCamelCase : Dict = """""", """"""
UpperCamelCase : Optional[int] = len(snake_case_ )
for i in range(len(snake_case_ ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
UpperCamelCase : Tuple = lexicon[curr_string]
result += last_match_id
add_key_to_lexicon(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
index += 1
UpperCamelCase : Any = """"""
while curr_string != "" and curr_string not in lexicon:
curr_string += "0"
if curr_string != "":
UpperCamelCase : Dict = lexicon[curr_string]
result += last_match_id
return result
def A_ ( snake_case_ : str ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : str = os.path.getsize(snake_case_ )
UpperCamelCase : Union[str, Any] = bin(snake_case_ )[2:]
UpperCamelCase : List[str] = len(snake_case_ )
return "0" * (length_length - 1) + file_length_binary + compressed
def A_ ( snake_case_ : str ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : int = 8
try:
with open(snake_case_ ,"""wb""" ) as opened_file:
UpperCamelCase : Dict = [
to_write[i : i + byte_length]
for i in range(0 ,len(snake_case_ ) ,snake_case_ )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append("""10000000""" )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array:
opened_file.write(int(snake_case_ ,2 ).to_bytes(1 ,byteorder="""big""" ) )
except OSError:
print("""File not accessible""" )
sys.exit()
def A_ ( snake_case_ : str ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : List[str] = read_file_binary(snake_case_ )
UpperCamelCase : Optional[int] = compress_data(snake_case_ )
UpperCamelCase : Any = add_file_length(snake_case_ ,snake_case_ )
write_file_binary(snake_case_ ,snake_case_ )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 360 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "geglu" , SCREAMING_SNAKE_CASE_ = None , ):
super().__init__()
UpperCamelCase : int = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=SCREAMING_SNAKE_CASE_ , attention_head_dim=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , dropout=SCREAMING_SNAKE_CASE_ , norm_num_groups=SCREAMING_SNAKE_CASE_ , cross_attention_dim=SCREAMING_SNAKE_CASE_ , attention_bias=SCREAMING_SNAKE_CASE_ , sample_size=SCREAMING_SNAKE_CASE_ , num_vector_embeds=SCREAMING_SNAKE_CASE_ , activation_fn=SCREAMING_SNAKE_CASE_ , num_embeds_ada_norm=SCREAMING_SNAKE_CASE_ , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
UpperCamelCase : Optional[Any] = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
UpperCamelCase : List[Any] = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
UpperCamelCase : int = [1, 0]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_ = True , ):
UpperCamelCase : Dict = hidden_states
UpperCamelCase : Optional[Any] = []
UpperCamelCase : List[Any] = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
UpperCamelCase : Optional[int] = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
UpperCamelCase : str = self.transformer_index_for_condition[i]
UpperCamelCase : Any = self.transformers[transformer_index](
SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ , timestep=SCREAMING_SNAKE_CASE_ , cross_attention_kwargs=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
UpperCamelCase : Any = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
UpperCamelCase : List[str] = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE_ )
| 27 | 0 |
def A_ ( snake_case_ : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(snake_case_ ,(list, tuple) ) or not all(
isinstance(snake_case_ ,snake_case_ ) for number in numbers ):
raise ValueError("""numbers must be an iterable of integers""" )
UpperCamelCase : int = numbers[0]
for i in range(1 ,len(snake_case_ ) ):
# update the maximum and minimum subarray products
UpperCamelCase : List[str] = numbers[i]
if number < 0:
UpperCamelCase : Optional[int] = min_till_now, max_till_now
UpperCamelCase : Dict = max(snake_case_ ,max_till_now * number )
UpperCamelCase : Union[str, Any] = min(snake_case_ ,min_till_now * number )
# update the maximum product found till now
UpperCamelCase : Union[str, Any] = max(snake_case_ ,snake_case_ )
return max_prod
| 361 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Optional[int] = 'mvp'
lowercase : Optional[Any] = ['past_key_values']
lowercase : Union[str, Any] = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , SCREAMING_SNAKE_CASE_=5_0267 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=100 , SCREAMING_SNAKE_CASE_=800 , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Dict = max_position_embeddings
UpperCamelCase : Optional[int] = d_model
UpperCamelCase : Optional[Any] = encoder_ffn_dim
UpperCamelCase : Any = encoder_layers
UpperCamelCase : List[Any] = encoder_attention_heads
UpperCamelCase : Optional[Any] = decoder_ffn_dim
UpperCamelCase : Optional[int] = decoder_layers
UpperCamelCase : Dict = decoder_attention_heads
UpperCamelCase : List[str] = dropout
UpperCamelCase : List[str] = attention_dropout
UpperCamelCase : List[Any] = activation_dropout
UpperCamelCase : Dict = activation_function
UpperCamelCase : List[str] = init_std
UpperCamelCase : int = encoder_layerdrop
UpperCamelCase : Dict = decoder_layerdrop
UpperCamelCase : Any = classifier_dropout
UpperCamelCase : Tuple = use_cache
UpperCamelCase : Dict = encoder_layers
UpperCamelCase : Tuple = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase : Optional[Any] = use_prompt
UpperCamelCase : Any = prompt_length
UpperCamelCase : List[Any] = prompt_mid_dim
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , forced_eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""" , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = self.bos_token_id
warnings.warn(
f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
"""The config can simply be saved and uploaded again to be fixed.""" )
| 27 | 0 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = [], []
while len(snake_case_ ) > 1:
UpperCamelCase : Optional[Any] = min(snake_case_ ), max(snake_case_ )
start.append(snake_case_ )
end.append(snake_case_ )
collection.remove(snake_case_ )
collection.remove(snake_case_ )
end.reverse()
return start + collection + end
if __name__ == "__main__":
__A : Union[str, Any] = input('''Enter numbers separated by a comma:\n''').strip()
__A : Union[str, Any] = [int(item) for item in user_input.split(''',''')]
print(*merge_sort(unsorted), sep=''',''')
| 362 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__A : Optional[Any] = 16
__A : str = 32
def A_ ( snake_case_ : Accelerator ,snake_case_ : int = 1_6 ):
'''simple docstring'''
UpperCamelCase : Tuple = AutoTokenizer.from_pretrained("""bert-base-cased""" )
UpperCamelCase : Optional[int] = load_dataset("""glue""" ,"""mrpc""" )
def tokenize_function(snake_case_ : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase : Union[str, Any] = tokenizer(examples["""sentence1"""] ,examples["""sentence2"""] ,truncation=snake_case_ ,max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCamelCase : Optional[Any] = datasets.map(
snake_case_ ,batched=snake_case_ ,remove_columns=["""idx""", """sentence1""", """sentence2"""] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase : str = tokenized_datasets.rename_column("""label""" ,"""labels""" )
def collate_fn(snake_case_ : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCamelCase : Union[str, Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCamelCase : Optional[Any] = 1_6
elif accelerator.mixed_precision != "no":
UpperCamelCase : Any = 8
else:
UpperCamelCase : Optional[Any] = None
return tokenizer.pad(
snake_case_ ,padding="""longest""" ,max_length=snake_case_ ,pad_to_multiple_of=snake_case_ ,return_tensors="""pt""" ,)
# Instantiate dataloaders.
UpperCamelCase : str = DataLoader(
tokenized_datasets["""train"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
UpperCamelCase : Dict = DataLoader(
tokenized_datasets["""validation"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__A : int = mocked_dataloaders # noqa: F811
def A_ ( snake_case_ : Tuple ,snake_case_ : Dict ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,snake_case_ ) == "1":
UpperCamelCase : Union[str, Any] = 2
# New Code #
UpperCamelCase : Dict = int(args.gradient_accumulation_steps )
UpperCamelCase : List[Any] = int(args.local_sgd_steps )
# Initialize accelerator
UpperCamelCase : str = Accelerator(
cpu=args.cpu ,mixed_precision=args.mixed_precision ,gradient_accumulation_steps=snake_case_ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase : Union[str, Any] = config["""lr"""]
UpperCamelCase : int = int(config["""num_epochs"""] )
UpperCamelCase : int = int(config["""seed"""] )
UpperCamelCase : List[Any] = int(config["""batch_size"""] )
UpperCamelCase : Optional[int] = evaluate.load("""glue""" ,"""mrpc""" )
set_seed(snake_case_ )
UpperCamelCase , UpperCamelCase : Dict = get_dataloaders(snake_case_ ,snake_case_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase : Optional[int] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,return_dict=snake_case_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCamelCase : Tuple = model.to(accelerator.device )
# Instantiate optimizer
UpperCamelCase : List[Any] = AdamW(params=model.parameters() ,lr=snake_case_ )
# Instantiate scheduler
UpperCamelCase : str = get_linear_schedule_with_warmup(
optimizer=snake_case_ ,num_warmup_steps=1_0_0 ,num_training_steps=(len(snake_case_ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Any = accelerator.prepare(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
# Now we train the model
for epoch in range(snake_case_ ):
model.train()
with LocalSGD(
accelerator=snake_case_ ,model=snake_case_ ,local_sgd_steps=snake_case_ ,enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(snake_case_ ):
UpperCamelCase : Optional[Any] = model(**snake_case_ )
UpperCamelCase : Optional[int] = output.loss
accelerator.backward(snake_case_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCamelCase : Any = model(**snake_case_ )
UpperCamelCase : Tuple = outputs.logits.argmax(dim=-1 )
UpperCamelCase , UpperCamelCase : int = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=snake_case_ ,references=snake_case_ ,)
UpperCamelCase : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' ,snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" ,type=snake_case_ ,default=snake_case_ ,choices=["""no""", """fp16""", """bf16""", """fp8"""] ,help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" ,)
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" ,type=snake_case_ ,default=1 ,help="""The number of minibatches to be ran before gradients are accumulated.""" ,)
parser.add_argument(
"""--local_sgd_steps""" ,type=snake_case_ ,default=8 ,help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" ,action="""store_true""" ,help="""If passed, will train on the CPU.""" )
UpperCamelCase : Dict = parser.parse_args()
UpperCamelCase : List[Any] = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(snake_case_ ,snake_case_ )
if __name__ == "__main__":
main()
| 27 | 0 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
__A : int = logging.getLogger()
def A_ ( snake_case_ : Path ,snake_case_ : list ):
'''simple docstring'''
UpperCamelCase : int = """\n""".join(snake_case_ )
Path(snake_case_ ).open("""w""" ).writelines(snake_case_ )
__A : Tuple = '''patrickvonplaten/t5-tiny-random'''
__A : List[str] = '''sshleifer/bart-tiny-random'''
__A : Union[str, Any] = '''sshleifer/tiny-mbart'''
__A : Any = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class lowerCamelCase ( _UpperCAmelCase ):
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = Path(self.get_auto_remove_tmp_dir() ) / """utest_input.source"""
UpperCamelCase : List[Any] = input_file_name.parent / """utest_output.txt"""
assert not output_file_name.exists()
UpperCamelCase : Optional[int] = [""" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."""]
_dump_articles(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = str(Path(self.get_auto_remove_tmp_dir() ) / """scores.json""" )
UpperCamelCase : Optional[int] = """translation_en_to_de""" if model == T5_TINY else """summarization"""
UpperCamelCase : str = f'\n run_eval_search.py\n {model}\n {input_file_name}\n {output_file_name}\n --score_path {score_path}\n --task {task}\n --num_beams 2\n --length_penalty 2.0\n '.split()
with patch.object(SCREAMING_SNAKE_CASE_ , """argv""" , SCREAMING_SNAKE_CASE_ ):
run_generate()
assert Path(SCREAMING_SNAKE_CASE_ ).exists()
# os.remove(Path(output_file_name))
def a_ ( self ):
self.run_eval_tester(SCREAMING_SNAKE_CASE_ )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.run_eval_tester(SCREAMING_SNAKE_CASE_ )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : int = Path(self.get_auto_remove_tmp_dir() ) / """utest_input.source"""
UpperCamelCase : str = input_file_name.parent / """utest_output.txt"""
assert not output_file_name.exists()
UpperCamelCase : Dict = {
"""en""": ["""Machine learning is great, isn't it?""", """I like to eat bananas""", """Tomorrow is another great day!"""],
"""de""": [
"""Maschinelles Lernen ist großartig, oder?""",
"""Ich esse gerne Bananen""",
"""Morgen ist wieder ein toller Tag!""",
],
}
UpperCamelCase : List[Any] = Path(self.get_auto_remove_tmp_dir() )
UpperCamelCase : Tuple = str(tmp_dir / """scores.json""" )
UpperCamelCase : Optional[int] = str(tmp_dir / """val.target""" )
_dump_articles(SCREAMING_SNAKE_CASE_ , text["""en"""] )
_dump_articles(SCREAMING_SNAKE_CASE_ , text["""de"""] )
UpperCamelCase : Dict = """translation_en_to_de""" if model == T5_TINY else """summarization"""
UpperCamelCase : str = f'\n run_eval_search.py\n {model}\n {str(SCREAMING_SNAKE_CASE_ )}\n {str(SCREAMING_SNAKE_CASE_ )}\n --score_path {score_path}\n --reference_path {reference_path}\n --task {task}\n '.split()
testargs.extend(["""--search""", """num_beams=1:2 length_penalty=0.9:1.0"""] )
with patch.object(SCREAMING_SNAKE_CASE_ , """argv""" , SCREAMING_SNAKE_CASE_ ):
with CaptureStdout() as cs:
run_search()
UpperCamelCase : Tuple = [""" num_beams | length_penalty""", model, """Best score args"""]
UpperCamelCase : int = ["""Info"""]
if "translation" in task:
expected_strings.append("""bleu""" )
else:
expected_strings.extend(SCREAMING_SNAKE_CASE_ )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(SCREAMING_SNAKE_CASE_ ).exists()
os.remove(Path(SCREAMING_SNAKE_CASE_ ) )
| 363 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A : Optional[Any] = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__A : Any = {'''allegro/herbert-base-cased''': 514}
__A : Optional[Any] = {}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Dict = VOCAB_FILES_NAMES
lowercase : Any = PRETRAINED_VOCAB_FILES_MAP
lowercase : List[str] = PRETRAINED_INIT_CONFIGURATION
lowercase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Union[str, Any] = HerbertTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_="</s>" , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = [self.cls_token_id]
UpperCamelCase : str = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Tuple = [self.sep_token_id]
UpperCamelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Optional[int] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 27 | 0 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 364 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=3.6 ):
UpperCamelCase : Dict = tokenizer
UpperCamelCase : Optional[Any] = tokenizer.bos_token_id
UpperCamelCase : Any = dataset
UpperCamelCase : List[str] = seq_length
UpperCamelCase : Optional[Any] = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
UpperCamelCase : Dict = iter(self.dataset )
UpperCamelCase : Union[str, Any] = True
while more_examples:
UpperCamelCase , UpperCamelCase : Tuple = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(SCREAMING_SNAKE_CASE_ )["""content"""] )
buffer_len += len(buffer[-1] )
except StopIteration:
UpperCamelCase : Dict = False
break
UpperCamelCase : str = tokenizer(SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )["""input_ids"""]
UpperCamelCase : str = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , self.seq_length ):
UpperCamelCase : List[str] = all_token_ids[i : i + self.seq_length]
if len(SCREAMING_SNAKE_CASE_ ) == self.seq_length:
yield torch.tensor(SCREAMING_SNAKE_CASE_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : Dict = {"""streaming""": True}
UpperCamelCase : Optional[int] = load_dataset(args.dataset_name ,split="""train""" ,**snake_case_ )
UpperCamelCase : Optional[int] = ConstantLengthDataset(snake_case_ ,snake_case_ ,seq_length=args.seq_length )
UpperCamelCase : List[Any] = DataLoader(snake_case_ ,batch_size=args.batch_size )
return eval_dataloader
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
model.eval()
UpperCamelCase : Dict = []
for step, batch in enumerate(snake_case_ ):
with torch.no_grad():
UpperCamelCase : List[Any] = model(snake_case_ ,labels=snake_case_ )
UpperCamelCase : Any = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(snake_case_ ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
UpperCamelCase : Dict = torch.mean(torch.cat(snake_case_ ) )
try:
UpperCamelCase : Dict = torch.exp(snake_case_ )
except OverflowError:
UpperCamelCase : Optional[int] = float("""inf""" )
return loss.item(), perplexity.item()
# Setup Accelerator
__A : List[Any] = Accelerator()
# Parse configuration
__A : str = HfArgumentParser(EvaluationArguments)
__A : List[Any] = parser.parse_args()
set_seed(args.seed)
# Logging
__A : Any = logging.getLogger(__name__)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
# Load model and tokenizer
__A : List[Any] = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__A : List[Any] = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
__A : int = create_dataloader(args)
# Prepare everything with our `accelerator`.
__A , __A : Optional[Any] = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
__A , __A : Tuple = evaluate(args)
logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 27 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
| 365 |
"""simple docstring"""
import argparse
import os
import re
__A : Any = '''src/transformers'''
# Pattern that looks at the indentation in a line.
__A : Tuple = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : List[Any] = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : List[Any] = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : Any = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : str ,snake_case_ : str="" ,snake_case_ : Any=None ,snake_case_ : Union[str, Any]=None ):
'''simple docstring'''
UpperCamelCase : List[Any] = 0
UpperCamelCase : Optional[int] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Tuple = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : Tuple = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Dict = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Optional[Any] = [lines[index + 1]]
index += 1
else:
UpperCamelCase : str = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
def _inner(snake_case_ : List[str] ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Tuple=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Optional[int] ):
return x
if key is None:
UpperCamelCase : List[str] = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[str] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : Tuple = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : int = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Union[str, Any] = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : Any ):
UpperCamelCase : Union[str, Any] = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : int = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : str = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : Optional[int] = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : int = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Tuple = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : List[Any] = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Union[str, Any] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : List[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : Optional[int] = keys[:-1]
UpperCamelCase : Union[str, Any] = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : Any = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : int=True ):
'''simple docstring'''
with open(snake_case_ ,encoding="""utf-8""" ) as f:
UpperCamelCase : List[str] = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : int = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Dict = main_blocks[block_idx]
UpperCamelCase : Dict = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : List[str] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : Optional[Any] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Optional[Any] = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Any = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : List[Any] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Optional[Any] = _re_direct_key if """_import_structure = {""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Optional[Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Any = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : Union[str, Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[str] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : Optional[int] = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ,encoding="""utf-8""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Optional[int] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : List[Any] = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : Union[str, Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["""bs"""] , model_result["""ss"""] ):
UpperCamelCase : int = model_result["""result"""][batch_size][sequence_length]
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = """sshleifer/tiny-gpt2"""
UpperCamelCase : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=SCREAMING_SNAKE_CASE_ , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : int = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a_ ( self ):
UpperCamelCase : Dict = """sgugger/tiny-distilbert-classification"""
UpperCamelCase : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , only_pretrain_model=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : List[Any] = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a_ ( self ):
UpperCamelCase : Optional[Any] = """sshleifer/tiny-gpt2"""
UpperCamelCase : Optional[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : str = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a_ ( self ):
UpperCamelCase : List[Any] = """sshleifer/tiny-gpt2"""
UpperCamelCase : Union[str, Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=SCREAMING_SNAKE_CASE_ , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Optional[int] = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ , [config] )
UpperCamelCase : Dict = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a_ ( self ):
UpperCamelCase : Any = """sshleifer/tiny-gpt2"""
UpperCamelCase : List[Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Union[str, Any] = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ , [config] )
UpperCamelCase : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a_ ( self ):
UpperCamelCase : Any = """sshleifer/tiny-gpt2"""
UpperCamelCase : Tuple = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Dict = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def a_ ( self ):
UpperCamelCase : str = """sshleifer/tiny-gpt2"""
UpperCamelCase : Dict = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ , [config] )
UpperCamelCase : Optional[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def a_ ( self ):
UpperCamelCase : Tuple = """patrickvonplaten/t5-tiny-random"""
UpperCamelCase : Any = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Dict = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ , configs=[config] )
UpperCamelCase : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , """Cannot do xla on CPU.""" )
def a_ ( self ):
UpperCamelCase : Tuple = """sshleifer/tiny-gpt2"""
UpperCamelCase : List[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=SCREAMING_SNAKE_CASE_ , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , use_xla=SCREAMING_SNAKE_CASE_ , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : List[str] = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def a_ ( self ):
UpperCamelCase : str = """sshleifer/tiny-gpt2"""
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=SCREAMING_SNAKE_CASE_ , save_to_csv=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE_ , """inf_time.csv""" ) , inference_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE_ , """inf_mem.csv""" ) , env_info_csv_file=os.path.join(SCREAMING_SNAKE_CASE_ , """env.csv""" ) , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
benchmark.run()
self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ , """inf_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ , """inf_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ , """env.csv""" ) ).exists() )
def a_ ( self ):
UpperCamelCase : List[str] = """sshleifer/tiny-gpt2"""
def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE_ ):
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """sequential""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """cumulative""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """current""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """total""" ) )
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase : Tuple = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=SCREAMING_SNAKE_CASE_ , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(SCREAMING_SNAKE_CASE_ , """log.txt""" ) , log_print=SCREAMING_SNAKE_CASE_ , trace_memory_line_by_line=SCREAMING_SNAKE_CASE_ , eager_mode=SCREAMING_SNAKE_CASE_ , multi_process=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : List[str] = TensorFlowBenchmark(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE_ , """log.txt""" ) ).exists() )
| 366 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 0 |
"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForWholeWordMask,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
__A : str = logging.getLogger(__name__)
__A : List[Any] = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__A : Tuple = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class lowerCamelCase :
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={
'help': (
'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
)
} , )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_UpperCAmelCase )} , )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={
'help': (
'Override some existing default config settings when a model is trained from scratch. Example: '
'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
)
} , )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
lowercase : bool = field(
default=_UpperCAmelCase , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
lowercase : str = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
lowercase : bool = field(
default=_UpperCAmelCase , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
def a_ ( self ):
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"""--config_overrides can't be used in combination with --config_name or --model_name_or_path""" )
@dataclass
class lowerCamelCase :
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
lowercase : Optional[str] = field(default=_UpperCAmelCase , metadata={'help': 'The input training data file (a text file).'} )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'} , )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'} , )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'} , )
lowercase : bool = field(
default=_UpperCAmelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
lowercase : Optional[int] = field(
default=5 , metadata={
'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
} , )
lowercase : Optional[int] = field(
default=_UpperCAmelCase , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated. Default to the max input length of the model.'
)
} , )
lowercase : Optional[int] = field(
default=_UpperCAmelCase , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
lowercase : float = field(
default=0.15 , metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'} )
lowercase : bool = field(
default=_UpperCAmelCase , metadata={
'help': (
'Whether to pad all samples to `max_seq_length`. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
)
} , )
def a_ ( self ):
if self.train_file is not None:
UpperCamelCase : List[Any] = self.train_file.split(""".""" )[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
UpperCamelCase : str = self.validation_file.split(""".""" )[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def A_ ( snake_case_ : List[str] ,snake_case_ : Tuple ):
'''simple docstring'''
with open(snake_case_ ,"""r""" ,encoding="""utf-8""" ) as f:
UpperCamelCase : Optional[int] = [json.loads(snake_case_ ) for line in f.read().splitlines() if (len(snake_case_ ) > 0 and not line.isspace())]
assert len(snake_case_ ) == len(snake_case_ )
UpperCamelCase : Tuple = {c: dataset[c] for c in dataset.column_names}
UpperCamelCase : int = refs
return Dataset.from_dict(snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : List[str] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCamelCase : Optional[int] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCamelCase : Dict = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
UpperCamelCase : int = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCamelCase : List[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. '
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,datefmt="""%m/%d/%Y %H:%M:%S""" ,handlers=[logging.StreamHandler(sys.stdout )] ,)
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ f'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" ,snake_case_ )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
UpperCamelCase : List[str] = load_dataset(data_args.dataset_name ,data_args.dataset_config_name )
if "validation" not in datasets.keys():
UpperCamelCase : List[str] = load_dataset(
data_args.dataset_name ,data_args.dataset_config_name ,split=f'train[:{data_args.validation_split_percentage}%]' ,)
UpperCamelCase : List[str] = load_dataset(
data_args.dataset_name ,data_args.dataset_config_name ,split=f'train[{data_args.validation_split_percentage}%:]' ,)
else:
UpperCamelCase : List[str] = {}
if data_args.train_file is not None:
UpperCamelCase : Any = data_args.train_file
if data_args.validation_file is not None:
UpperCamelCase : Dict = data_args.validation_file
UpperCamelCase : Any = data_args.train_file.split(""".""" )[-1]
if extension == "txt":
UpperCamelCase : int = """text"""
UpperCamelCase : List[Any] = load_dataset(snake_case_ ,data_files=snake_case_ )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase : Dict = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name:
UpperCamelCase : Tuple = AutoConfig.from_pretrained(model_args.config_name ,**snake_case_ )
elif model_args.model_name_or_path:
UpperCamelCase : Any = AutoConfig.from_pretrained(model_args.model_name_or_path ,**snake_case_ )
else:
UpperCamelCase : str = CONFIG_MAPPING[model_args.model_type]()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(f'Overriding config: {model_args.config_overrides}' )
config.update_from_string(model_args.config_overrides )
logger.info(f'New config: {config}' )
UpperCamelCase : Dict = {
"""cache_dir""": model_args.cache_dir,
"""use_fast""": model_args.use_fast_tokenizer,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
UpperCamelCase : int = AutoTokenizer.from_pretrained(model_args.tokenizer_name ,**snake_case_ )
elif model_args.model_name_or_path:
UpperCamelCase : Optional[Any] = AutoTokenizer.from_pretrained(model_args.model_name_or_path ,**snake_case_ )
else:
raise ValueError(
"""You are instantiating a new tokenizer from scratch. This is not supported by this script."""
"""You can do it from another script, save it, and load it from here, using --tokenizer_name.""" )
if model_args.model_name_or_path:
UpperCamelCase : Dict = AutoModelForMaskedLM.from_pretrained(
model_args.model_name_or_path ,from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) ,config=snake_case_ ,cache_dir=model_args.cache_dir ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,)
else:
logger.info("""Training new model from scratch""" )
UpperCamelCase : Dict = AutoModelForMaskedLM.from_config(snake_case_ )
model.resize_token_embeddings(len(snake_case_ ) )
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
UpperCamelCase : Tuple = datasets["""train"""].column_names
else:
UpperCamelCase : Any = datasets["""validation"""].column_names
UpperCamelCase : str = """text""" if """text""" in column_names else column_names[0]
UpperCamelCase : Optional[Any] = """max_length""" if data_args.pad_to_max_length else False
def tokenize_function(snake_case_ : Optional[int] ):
# Remove empty lines
UpperCamelCase : Optional[Any] = [line for line in examples["""text"""] if len(snake_case_ ) > 0 and not line.isspace()]
return tokenizer(examples["""text"""] ,padding=snake_case_ ,truncation=snake_case_ ,max_length=data_args.max_seq_length )
UpperCamelCase : List[str] = datasets.map(
snake_case_ ,batched=snake_case_ ,num_proc=data_args.preprocessing_num_workers ,remove_columns=[text_column_name] ,load_from_cache_file=not data_args.overwrite_cache ,)
# Add the chinese references if provided
if data_args.train_ref_file is not None:
UpperCamelCase : List[Any] = add_chinese_references(tokenized_datasets["""train"""] ,data_args.train_ref_file )
if data_args.validation_ref_file is not None:
UpperCamelCase : Any = add_chinese_references(
tokenized_datasets["""validation"""] ,data_args.validation_ref_file )
# If we have ref files, need to avoid it removed by trainer
UpperCamelCase : List[Any] = data_args.train_ref_file or data_args.validation_ref_file
if has_ref:
UpperCamelCase : List[str] = False
# Data collator
# This one will take care of randomly masking the tokens.
UpperCamelCase : Dict = DataCollatorForWholeWordMask(tokenizer=snake_case_ ,mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
UpperCamelCase : Any = Trainer(
model=snake_case_ ,args=snake_case_ ,train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None ,eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None ,tokenizer=snake_case_ ,data_collator=snake_case_ ,)
# Training
if training_args.do_train:
if last_checkpoint is not None:
UpperCamelCase : List[Any] = last_checkpoint
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
UpperCamelCase : Optional[Any] = model_args.model_name_or_path
else:
UpperCamelCase : int = None
UpperCamelCase : Any = trainer.train(resume_from_checkpoint=snake_case_ )
trainer.save_model() # Saves the tokenizer too for easy upload
UpperCamelCase : List[str] = os.path.join(training_args.output_dir ,"""train_results.txt""" )
if trainer.is_world_process_zero():
with open(snake_case_ ,"""w""" ) as writer:
logger.info("""***** Train results *****""" )
for key, value in sorted(train_result.metrics.items() ):
logger.info(f' {key} = {value}' )
writer.write(f'{key} = {value}\n' )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir ,"""trainer_state.json""" ) )
# Evaluation
UpperCamelCase : str = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
UpperCamelCase : List[str] = trainer.evaluate()
UpperCamelCase : List[str] = math.exp(eval_output["""eval_loss"""] )
UpperCamelCase : Any = perplexity
UpperCamelCase : Optional[int] = os.path.join(training_args.output_dir ,"""eval_results_mlm_wwm.txt""" )
if trainer.is_world_process_zero():
with open(snake_case_ ,"""w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key, value in sorted(results.items() ):
logger.info(f' {key} = {value}' )
writer.write(f'{key} = {value}\n' )
return results
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 367 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
__A : Optional[Any] = logging.get_logger(__name__)
def A_ ( snake_case_ : np.ndarray ,snake_case_ : Union[int, Iterable[int]] ,snake_case_ : bool ,snake_case_ : int ):
'''simple docstring'''
def constraint_to_multiple_of(snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ,snake_case_ : List[str]=0 ,snake_case_ : Optional[Any]=None ):
UpperCamelCase : List[str] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
UpperCamelCase : Optional[Any] = math.floor(val / multiple ) * multiple
if x < min_val:
UpperCamelCase : Dict = math.ceil(val / multiple ) * multiple
return x
UpperCamelCase : Any = (output_size, output_size) if isinstance(snake_case_ ,snake_case_ ) else output_size
UpperCamelCase , UpperCamelCase : int = get_image_size(snake_case_ )
UpperCamelCase , UpperCamelCase : Union[str, Any] = output_size
# determine new height and width
UpperCamelCase : List[str] = output_height / input_height
UpperCamelCase : List[str] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
UpperCamelCase : int = scale_width
else:
# fit height
UpperCamelCase : Optional[Any] = scale_height
UpperCamelCase : int = constraint_to_multiple_of(scale_height * input_height ,multiple=snake_case_ )
UpperCamelCase : Union[str, Any] = constraint_to_multiple_of(scale_width * input_width ,multiple=snake_case_ )
return (new_height, new_width)
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = ['pixel_values']
def __init__( self , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 1 / 255 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = size if size is not None else {"""height""": 384, """width""": 384}
UpperCamelCase : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = do_resize
UpperCamelCase : Union[str, Any] = size
UpperCamelCase : Union[str, Any] = keep_aspect_ratio
UpperCamelCase : Any = ensure_multiple_of
UpperCamelCase : List[Any] = resample
UpperCamelCase : str = do_rescale
UpperCamelCase : Optional[Any] = rescale_factor
UpperCamelCase : List[str] = do_normalize
UpperCamelCase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCamelCase : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Tuple = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
UpperCamelCase : Dict = get_resize_output_image_size(
SCREAMING_SNAKE_CASE_ , output_size=(size["""height"""], size["""width"""]) , keep_aspect_ratio=SCREAMING_SNAKE_CASE_ , multiple=SCREAMING_SNAKE_CASE_ , )
return resize(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return normalize(SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCamelCase : List[Any] = size if size is not None else self.size
UpperCamelCase : Dict = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
UpperCamelCase : Optional[int] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
UpperCamelCase : Tuple = resample if resample is not None else self.resample
UpperCamelCase : str = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase : Any = image_mean if image_mean is not None else self.image_mean
UpperCamelCase : List[Any] = image_std if image_std is not None else self.image_std
UpperCamelCase : str = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
UpperCamelCase : Tuple = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
UpperCamelCase : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
UpperCamelCase : int = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
UpperCamelCase : List[str] = [self.normalize(image=SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Any = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Union[str, Any] = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : str = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = target_sizes.numpy()
UpperCamelCase : Dict = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
UpperCamelCase : List[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : List[Any] = logits.argmax(dim=1 )
UpperCamelCase : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 27 | 0 |
"""simple docstring"""
from __future__ import annotations
def A_ ( snake_case_ : list[list[int]] ):
'''simple docstring'''
UpperCamelCase : str = len(snake_case_ )
# We need to create solution object to save path.
UpperCamelCase : List[Any] = [[0 for _ in range(snake_case_ )] for _ in range(snake_case_ )]
UpperCamelCase : Any = run_maze(snake_case_ ,0 ,0 ,snake_case_ )
if solved:
print("""\n""".join(str(snake_case_ ) for row in solutions ) )
else:
print("""No solution exists!""" )
return solved
def A_ ( snake_case_ : list[list[int]] ,snake_case_ : int ,snake_case_ : int ,snake_case_ : list[list[int]] ):
'''simple docstring'''
UpperCamelCase : str = len(snake_case_ )
# Final check point.
if i == j == (size - 1):
UpperCamelCase : Optional[Any] = 1
return True
UpperCamelCase : Optional[int] = (not i < 0) and (not j < 0) # Check lower bounds
UpperCamelCase : List[Any] = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
UpperCamelCase : List[str] = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
UpperCamelCase : Dict = 1
# check for directions
if (
run_maze(snake_case_ ,i + 1 ,snake_case_ ,snake_case_ )
or run_maze(snake_case_ ,snake_case_ ,j + 1 ,snake_case_ )
or run_maze(snake_case_ ,i - 1 ,snake_case_ ,snake_case_ )
or run_maze(snake_case_ ,snake_case_ ,j - 1 ,snake_case_ )
):
return True
UpperCamelCase : Optional[Any] = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 368 |
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : float = b
if function(snake_case_ ) == 0: # one of the a or b is a root for the function
return a
elif function(snake_case_ ) == 0:
return b
elif (
function(snake_case_ ) * function(snake_case_ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("""could not find root in given interval.""" )
else:
UpperCamelCase : float = start + (end - start) / 2.0
while abs(start - mid ) > 1_0**-7: # until precisely equals to 10^-7
if function(snake_case_ ) == 0:
return mid
elif function(snake_case_ ) * function(snake_case_ ) < 0:
UpperCamelCase : Dict = mid
else:
UpperCamelCase : List[str] = mid
UpperCamelCase : Tuple = start + (end - start) / 2.0
return mid
def A_ ( snake_case_ : float ):
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 27 | 0 |
"""simple docstring"""
def A_ ( snake_case_ : int ,snake_case_ : int ,snake_case_ : list[list[int]] ):
'''simple docstring'''
def update_area_of_max_square(snake_case_ : int ,snake_case_ : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
UpperCamelCase : Tuple = update_area_of_max_square(snake_case_ ,col + 1 )
UpperCamelCase : Optional[int] = update_area_of_max_square(row + 1 ,col + 1 )
UpperCamelCase : List[str] = update_area_of_max_square(row + 1 ,snake_case_ )
if mat[row][col]:
UpperCamelCase : Dict = 1 + min([right, diagonal, down] )
UpperCamelCase : int = max(largest_square_area[0] ,snake_case_ )
return sub_problem_sol
else:
return 0
UpperCamelCase : Dict = [0]
update_area_of_max_square(0 ,0 )
return largest_square_area[0]
def A_ ( snake_case_ : int ,snake_case_ : int ,snake_case_ : list[list[int]] ):
'''simple docstring'''
def update_area_of_max_square_using_dp_array(
snake_case_ : int ,snake_case_ : int ,snake_case_ : list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
UpperCamelCase : Dict = update_area_of_max_square_using_dp_array(snake_case_ ,col + 1 ,snake_case_ )
UpperCamelCase : Union[str, Any] = update_area_of_max_square_using_dp_array(row + 1 ,col + 1 ,snake_case_ )
UpperCamelCase : Dict = update_area_of_max_square_using_dp_array(row + 1 ,snake_case_ ,snake_case_ )
if mat[row][col]:
UpperCamelCase : Optional[Any] = 1 + min([right, diagonal, down] )
UpperCamelCase : List[str] = max(largest_square_area[0] ,snake_case_ )
UpperCamelCase : Optional[Any] = sub_problem_sol
return sub_problem_sol
else:
return 0
UpperCamelCase : Tuple = [0]
UpperCamelCase : str = [[-1] * cols for _ in range(snake_case_ )]
update_area_of_max_square_using_dp_array(0 ,0 ,snake_case_ )
return largest_square_area[0]
def A_ ( snake_case_ : int ,snake_case_ : int ,snake_case_ : list[list[int]] ):
'''simple docstring'''
UpperCamelCase : Any = [[0] * (cols + 1) for _ in range(rows + 1 )]
UpperCamelCase : Union[str, Any] = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
UpperCamelCase : int = dp_array[row][col + 1]
UpperCamelCase : Union[str, Any] = dp_array[row + 1][col + 1]
UpperCamelCase : List[str] = dp_array[row + 1][col]
if mat[row][col] == 1:
UpperCamelCase : List[Any] = 1 + min(snake_case_ ,snake_case_ ,snake_case_ )
UpperCamelCase : Optional[int] = max(dp_array[row][col] ,snake_case_ )
else:
UpperCamelCase : Optional[int] = 0
return largest_square_area
def A_ ( snake_case_ : int ,snake_case_ : int ,snake_case_ : list[list[int]] ):
'''simple docstring'''
UpperCamelCase : Dict = [0] * (cols + 1)
UpperCamelCase : Union[str, Any] = [0] * (cols + 1)
UpperCamelCase : Tuple = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
UpperCamelCase : int = current_row[col + 1]
UpperCamelCase : int = next_row[col + 1]
UpperCamelCase : Union[str, Any] = next_row[col]
if mat[row][col] == 1:
UpperCamelCase : Dict = 1 + min(snake_case_ ,snake_case_ ,snake_case_ )
UpperCamelCase : List[str] = max(current_row[col] ,snake_case_ )
else:
UpperCamelCase : List[Any] = 0
UpperCamelCase : Optional[int] = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 369 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def a_ ( self ):
UpperCamelCase : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
UpperCamelCase : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
UpperCamelCase : Dict = """xvjiarui/stable-diffusion-2-inpainting"""
UpperCamelCase , UpperCamelCase : List[str] = FlaxStableDiffusionInpaintPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = """Face of a yellow cat, high resolution, sitting on a park bench"""
UpperCamelCase : List[str] = jax.random.PRNGKey(0 )
UpperCamelCase : Tuple = 50
UpperCamelCase : Dict = jax.device_count()
UpperCamelCase : Optional[int] = num_samples * [prompt]
UpperCamelCase : int = num_samples * [init_image]
UpperCamelCase : List[Any] = num_samples * [mask_image]
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[Any] = pipeline.prepare_inputs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# shard inputs and rng
UpperCamelCase : Optional[int] = replicate(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = jax.random.split(SCREAMING_SNAKE_CASE_ , jax.device_count() )
UpperCamelCase : str = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = pipeline(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , jit=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = output.images.reshape(SCREAMING_SNAKE_CASE_ , 512 , 512 , 3 )
UpperCamelCase : List[Any] = images[0, 253:256, 253:256, -1]
UpperCamelCase : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase : Dict = jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 27 | 0 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( snake_case_ : Dataset ,snake_case_ : Dict[str, str] ):
'''simple docstring'''
UpperCamelCase : List[str] = args.log_outputs
UpperCamelCase : Tuple = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
UpperCamelCase : List[Any] = load_metric("""wer""" )
UpperCamelCase : Any = load_metric("""cer""" )
# compute metrics
UpperCamelCase : str = wer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
UpperCamelCase : Dict = cer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
# print & log results
UpperCamelCase : Optional[int] = f'WER: {wer_result}\nCER: {cer_result}'
print(snake_case_ )
with open(f'{dataset_id}_eval_results.txt' ,"""w""" ) as f:
f.write(snake_case_ )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
UpperCamelCase : Optional[Any] = f'log_{dataset_id}_predictions.txt'
UpperCamelCase : str = f'log_{dataset_id}_targets.txt'
with open(snake_case_ ,"""w""" ) as p, open(snake_case_ ,"""w""" ) as t:
# mapping function to write output
def write_to_file(snake_case_ : Union[str, Any] ,snake_case_ : Tuple ):
p.write(f'{i}' + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(f'{i}' + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(snake_case_ ,with_indices=snake_case_ )
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Dict = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
UpperCamelCase : str = re.sub(snake_case_ ,"""""" ,text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
UpperCamelCase : List[str] = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
UpperCamelCase : Tuple = """ """.join(text.split(snake_case_ ) )
return text
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = load_dataset(args.dataset ,args.config ,split=args.split ,use_auth_token=snake_case_ )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
UpperCamelCase : List[Any] = AutoFeatureExtractor.from_pretrained(args.model_id )
UpperCamelCase : Dict = feature_extractor.sampling_rate
# resample audio
UpperCamelCase : Optional[Any] = dataset.cast_column("""audio""" ,Audio(sampling_rate=snake_case_ ) )
# load eval pipeline
if args.device is None:
UpperCamelCase : int = 0 if torch.cuda.is_available() else -1
UpperCamelCase : Union[str, Any] = pipeline("""automatic-speech-recognition""" ,model=args.model_id ,device=args.device )
# map function to decode audio
def map_to_pred(snake_case_ : Union[str, Any] ):
UpperCamelCase : List[Any] = asr(
batch["""audio"""]["""array"""] ,chunk_length_s=args.chunk_length_s ,stride_length_s=args.stride_length_s )
UpperCamelCase : Union[str, Any] = prediction["""text"""]
UpperCamelCase : Optional[Any] = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
UpperCamelCase : Any = dataset.map(snake_case_ ,remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(snake_case_ ,snake_case_ )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
__A : Optional[Any] = parser.parse_args()
main(args)
| 370 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def A_ ( ):
'''simple docstring'''
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
UpperCamelCase : Optional[Any] = [1, 2, 3]
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=2 )
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" ,[2, -1] )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : List[Any] = [1, 2]
UpperCamelCase : List[Any] = {"""a""": 1, """b""": 2}
UpperCamelCase : List[str] = {"""a""": [1, 2], """b""": [3, 4]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 1}, """b""": 2}
UpperCamelCase : Any = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
UpperCamelCase : Optional[int] = [2, 3]
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3}
UpperCamelCase : Any = {"""a""": [2, 3], """b""": [4, 5]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 2}, """b""": 3}
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
| 27 | 0 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Union[str, Any]="shi-labs/oneformer_demo" ):
'''simple docstring'''
with open(hf_hub_download(snake_case_ ,snake_case_ ,repo_type="""dataset""" ) ,"""r""" ) as f:
UpperCamelCase : Optional[int] = json.load(snake_case_ )
UpperCamelCase : Any = {}
UpperCamelCase : Optional[int] = []
UpperCamelCase : int = []
for key, info in class_info.items():
UpperCamelCase : Optional[int] = info["""name"""]
class_names.append(info["""name"""] )
if info["isthing"]:
thing_ids.append(int(snake_case_ ) )
UpperCamelCase : Any = thing_ids
UpperCamelCase : Any = class_names
return metadata
class lowerCamelCase ( unittest.TestCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=30 , SCREAMING_SNAKE_CASE_=400 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5] , SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5] , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=255 , SCREAMING_SNAKE_CASE_="shi-labs/oneformer_demo" , SCREAMING_SNAKE_CASE_="ade20k_panoptic.json" , SCREAMING_SNAKE_CASE_=10 , ):
UpperCamelCase : Optional[Any] = parent
UpperCamelCase : List[str] = batch_size
UpperCamelCase : List[Any] = num_channels
UpperCamelCase : Any = min_resolution
UpperCamelCase : List[str] = max_resolution
UpperCamelCase : Tuple = do_resize
UpperCamelCase : Optional[int] = {"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
UpperCamelCase : str = do_normalize
UpperCamelCase : Tuple = image_mean
UpperCamelCase : str = image_std
UpperCamelCase : Optional[int] = class_info_file
UpperCamelCase : Dict = prepare_metadata(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = num_text
UpperCamelCase : Optional[int] = repo_path
# for the post_process_functions
UpperCamelCase : str = 2
UpperCamelCase : Union[str, Any] = 10
UpperCamelCase : List[Any] = 10
UpperCamelCase : Dict = 3
UpperCamelCase : str = 4
UpperCamelCase : Any = num_labels
UpperCamelCase : Dict = do_reduce_labels
UpperCamelCase : Union[str, Any] = ignore_index
def a_ ( self ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
if not batched:
UpperCamelCase : int = image_inputs[0]
if isinstance(SCREAMING_SNAKE_CASE_ , Image.Image ):
UpperCamelCase : Union[str, Any] = image.size
else:
UpperCamelCase : List[str] = image.shape[1], image.shape[2]
if w < h:
UpperCamelCase : Tuple = int(self.size["""shortest_edge"""] * h / w )
UpperCamelCase : str = self.size["""shortest_edge"""]
elif w > h:
UpperCamelCase : Tuple = self.size["""shortest_edge"""]
UpperCamelCase : Union[str, Any] = int(self.size["""shortest_edge"""] * w / h )
else:
UpperCamelCase : List[Any] = self.size["""shortest_edge"""]
UpperCamelCase : str = self.size["""shortest_edge"""]
else:
UpperCamelCase : int = []
for image in image_inputs:
UpperCamelCase : Dict = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCamelCase : List[Any] = max(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : item[0] )[0]
UpperCamelCase : int = max(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : item[1] )[1]
return expected_height, expected_width
def a_ ( self ):
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , )
@require_torch
@require_vision
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Union[str, Any] = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
lowercase : str = image_processing_class
def a_ ( self ):
UpperCamelCase : Optional[Any] = OneFormerImageProcessorTester(self )
@property
def a_ ( self ):
return self.image_processing_tester.prepare_image_processor_dict()
def a_ ( self ):
UpperCamelCase : List[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """image_mean""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """image_std""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_normalize""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_resize""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """size""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """ignore_index""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """class_info_file""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """num_text""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """repo_path""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """metadata""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_reduce_labels""" ) )
def a_ ( self ):
pass
def a_ ( self ):
# Initialize image_processor
UpperCamelCase : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase : int = prepare_image_inputs(self.image_processing_tester , equal_resolution=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , Image.Image )
# Test not batched input
UpperCamelCase : List[str] = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
UpperCamelCase : str = self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase : Tuple = self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = image_processor(
SCREAMING_SNAKE_CASE_ , ["""semantic"""] * len(SCREAMING_SNAKE_CASE_ ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def a_ ( self ):
# Initialize image_processor
UpperCamelCase : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase : List[Any] = prepare_image_inputs(self.image_processing_tester , equal_resolution=SCREAMING_SNAKE_CASE_ , numpify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , np.ndarray )
# Test not batched input
UpperCamelCase : Optional[Any] = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
UpperCamelCase : Any = self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase : Any = self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = image_processor(
SCREAMING_SNAKE_CASE_ , ["""semantic"""] * len(SCREAMING_SNAKE_CASE_ ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def a_ ( self ):
# Initialize image_processor
UpperCamelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase : Dict = prepare_image_inputs(self.image_processing_tester , equal_resolution=SCREAMING_SNAKE_CASE_ , torchify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , torch.Tensor )
# Test not batched input
UpperCamelCase : Optional[int] = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
UpperCamelCase : Optional[Any] = self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase : Optional[Any] = self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = image_processor(
SCREAMING_SNAKE_CASE_ , ["""semantic"""] * len(SCREAMING_SNAKE_CASE_ ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def a_ ( self , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_="np" ):
UpperCamelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
UpperCamelCase : Union[str, Any] = self.image_processing_tester.num_labels
UpperCamelCase : str = None
UpperCamelCase : int = None
UpperCamelCase : Optional[Any] = prepare_image_inputs(self.image_processing_tester , equal_resolution=SCREAMING_SNAKE_CASE_ )
if with_segmentation_maps:
UpperCamelCase : Any = num_labels
if is_instance_map:
UpperCamelCase : Tuple = list(range(SCREAMING_SNAKE_CASE_ ) ) * 2
UpperCamelCase : List[str] = dict(enumerate(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Optional[Any] = [
np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
UpperCamelCase : List[Any] = [Image.fromarray(SCREAMING_SNAKE_CASE_ ) for annotation in annotations]
UpperCamelCase : List[Any] = image_processor(
SCREAMING_SNAKE_CASE_ , ["""semantic"""] * len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , instance_id_to_semantic_id=SCREAMING_SNAKE_CASE_ , pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE_ , )
return inputs
def a_ ( self ):
pass
def a_ ( self ):
def common(SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=None ):
UpperCamelCase : Dict = self.comm_get_image_processor_inputs(
with_segmentation_maps=SCREAMING_SNAKE_CASE_ , is_instance_map=SCREAMING_SNAKE_CASE_ , segmentation_type=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = inputs["""mask_labels"""]
UpperCamelCase : Any = inputs["""class_labels"""]
UpperCamelCase : Any = inputs["""pixel_values"""]
UpperCamelCase : Union[str, Any] = inputs["""text_inputs"""]
# check the batch_size
for mask_label, class_label, text_input in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertEqual(mask_label.shape[0] , class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , self.image_processing_tester.num_text )
common()
common(is_instance_map=SCREAMING_SNAKE_CASE_ )
common(is_instance_map=SCREAMING_SNAKE_CASE_ , segmentation_type="""pil""" )
common(is_instance_map=SCREAMING_SNAKE_CASE_ , segmentation_type="""pil""" )
def a_ ( self ):
UpperCamelCase : int = np.zeros((20, 50) )
UpperCamelCase : Dict = 1
UpperCamelCase : List[Any] = 1
UpperCamelCase : Optional[int] = 1
UpperCamelCase : Optional[int] = binary_mask_to_rle(SCREAMING_SNAKE_CASE_ )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 4 )
self.assertEqual(rle[0] , 21 )
self.assertEqual(rle[1] , 45 )
def a_ ( self ):
UpperCamelCase : Dict = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
UpperCamelCase : List[str] = self.image_processing_tester.get_fake_oneformer_outputs()
UpperCamelCase : int = fature_extractor.post_process_semantic_segmentation(SCREAMING_SNAKE_CASE_ )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape , (
self.image_processing_tester.height,
self.image_processing_tester.width,
) , )
UpperCamelCase : Optional[Any] = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
UpperCamelCase : Union[str, Any] = fature_extractor.post_process_semantic_segmentation(SCREAMING_SNAKE_CASE_ , target_sizes=SCREAMING_SNAKE_CASE_ )
self.assertEqual(segmentation[0].shape , target_sizes[0] )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
UpperCamelCase : List[str] = self.image_processing_tester.get_fake_oneformer_outputs()
UpperCamelCase : Any = image_processor.post_process_instance_segmentation(SCREAMING_SNAKE_CASE_ , threshold=0 )
self.assertTrue(len(SCREAMING_SNAKE_CASE_ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("""segmentation""" in el )
self.assertTrue("""segments_info""" in el )
self.assertEqual(type(el["""segments_info"""] ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(
el["""segmentation"""].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
def a_ ( self ):
UpperCamelCase : List[Any] = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
UpperCamelCase : Union[str, Any] = self.image_processing_tester.get_fake_oneformer_outputs()
UpperCamelCase : Dict = image_processor.post_process_panoptic_segmentation(SCREAMING_SNAKE_CASE_ , threshold=0 )
self.assertTrue(len(SCREAMING_SNAKE_CASE_ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("""segmentation""" in el )
self.assertTrue("""segments_info""" in el )
self.assertEqual(type(el["""segments_info"""] ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(
el["""segmentation"""].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
| 371 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_="last" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=0 , ):
UpperCamelCase : Union[str, Any] = parent
UpperCamelCase : str = batch_size
UpperCamelCase : int = seq_length
UpperCamelCase : Optional[Any] = is_training
UpperCamelCase : Any = use_input_lengths
UpperCamelCase : Tuple = use_token_type_ids
UpperCamelCase : List[Any] = use_labels
UpperCamelCase : Union[str, Any] = gelu_activation
UpperCamelCase : Dict = sinusoidal_embeddings
UpperCamelCase : Optional[int] = causal
UpperCamelCase : List[Any] = asm
UpperCamelCase : int = n_langs
UpperCamelCase : Optional[Any] = vocab_size
UpperCamelCase : str = n_special
UpperCamelCase : Dict = hidden_size
UpperCamelCase : Union[str, Any] = num_hidden_layers
UpperCamelCase : Optional[Any] = num_attention_heads
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : int = max_position_embeddings
UpperCamelCase : Any = type_sequence_label_size
UpperCamelCase : str = initializer_range
UpperCamelCase : str = num_labels
UpperCamelCase : Union[str, Any] = num_choices
UpperCamelCase : List[str] = summary_type
UpperCamelCase : int = use_proj
UpperCamelCase : List[str] = scope
UpperCamelCase : Dict = bos_token_id
def a_ ( self ):
UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Union[str, Any] = None
if self.use_input_lengths:
UpperCamelCase : str = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
UpperCamelCase : Tuple = None
if self.use_token_type_ids:
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
UpperCamelCase : int = None
UpperCamelCase : Dict = None
UpperCamelCase : str = None
if self.use_labels:
UpperCamelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Dict = ids_tensor([self.batch_size] , 2 ).float()
UpperCamelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : List[str] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def a_ ( self ):
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = XLMModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , lengths=SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[Any] = XLMWithLMHeadModel(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[str] = XLMForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = XLMForQuestionAnswering(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , p_mask=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , )
((UpperCamelCase) , ) : Union[str, Any] = result_with_labels.to_tuple()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
((UpperCamelCase) , ) : Tuple = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = XLMForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = self.num_labels
UpperCamelCase : int = XLMForTokenClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[int] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[Any] = self.num_choices
UpperCamelCase : Tuple = XLMForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Dict = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Optional[Any] = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : int = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : List[Any] = config_and_inputs
UpperCamelCase : Dict = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
lowercase : List[Any] = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
lowercase : Optional[Any] = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("""Fast""" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
UpperCamelCase : Union[str, Any] = super()._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
UpperCamelCase : Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
return inputs_dict
def a_ ( self ):
UpperCamelCase : List[Any] = XLMModelTester(self )
UpperCamelCase : Optional[int] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , emb_dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_attentions in attentions] , [True] * len(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : int = min_length + idx + 1
UpperCamelCase : Tuple = min_length + idx + 1
UpperCamelCase : Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_hidden_states in hidden_states] , [True] * len(SCREAMING_SNAKE_CASE_ ) , )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : List[str] = min_length + idx + 1
UpperCamelCase : Optional[Any] = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) , )
pass
@slow
def a_ ( self ):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : str = XLMModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Dict = XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""" )
model.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.tensor([[14, 447]] , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ ) # the president
UpperCamelCase : List[Any] = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
UpperCamelCase : Optional[int] = model.generate(SCREAMING_SNAKE_CASE_ , do_sample=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , SCREAMING_SNAKE_CASE_ )
| 27 | 0 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def A_ ( snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : int = int(number**0.5 )
return number == sq * sq
def A_ ( snake_case_ : int ,snake_case_ : int ,snake_case_ : int ,snake_case_ : int ,snake_case_ : int ,snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
UpperCamelCase : int = x_den * y_den * z_den
UpperCamelCase : int = gcd(snake_case_ ,snake_case_ )
top //= hcf
bottom //= hcf
return top, bottom
def A_ ( snake_case_ : int = 3_5 ):
'''simple docstring'''
UpperCamelCase : set = set()
UpperCamelCase : int
UpperCamelCase : Fraction = Fraction(0 )
UpperCamelCase : tuple[int, int]
for x_num in range(1 ,order + 1 ):
for x_den in range(x_num + 1 ,order + 1 ):
for y_num in range(1 ,order + 1 ):
for y_den in range(y_num + 1 ,order + 1 ):
# n=1
UpperCamelCase : Union[str, Any] = x_num * y_den + x_den * y_num
UpperCamelCase : Any = x_den * y_den
UpperCamelCase : List[str] = gcd(snake_case_ ,snake_case_ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase : Union[str, Any] = add_three(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
unique_s.add(snake_case_ )
# n=2
UpperCamelCase : str = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
UpperCamelCase : int = x_den * x_den * y_den * y_den
if is_sq(snake_case_ ) and is_sq(snake_case_ ):
UpperCamelCase : Optional[Any] = int(sqrt(snake_case_ ) )
UpperCamelCase : int = int(sqrt(snake_case_ ) )
UpperCamelCase : Optional[int] = gcd(snake_case_ ,snake_case_ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase : Union[str, Any] = add_three(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
unique_s.add(snake_case_ )
# n=-1
UpperCamelCase : Optional[Any] = x_num * y_num
UpperCamelCase : Any = x_den * y_num + x_num * y_den
UpperCamelCase : Optional[Any] = gcd(snake_case_ ,snake_case_ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase : List[str] = add_three(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
unique_s.add(snake_case_ )
# n=2
UpperCamelCase : Union[str, Any] = x_num * x_num * y_num * y_num
UpperCamelCase : List[Any] = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(snake_case_ ) and is_sq(snake_case_ ):
UpperCamelCase : List[str] = int(sqrt(snake_case_ ) )
UpperCamelCase : List[str] = int(sqrt(snake_case_ ) )
UpperCamelCase : str = gcd(snake_case_ ,snake_case_ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase : Optional[Any] = add_three(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
unique_s.add(snake_case_ )
for num, den in unique_s:
total += Fraction(snake_case_ ,snake_case_ )
return total.denominator + total.numerator
if __name__ == "__main__":
print(F'''{solution() = }''')
| 350 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : int = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = [
'''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTBigCodeForSequenceClassification''',
'''GPTBigCodeForTokenClassification''',
'''GPTBigCodeForCausalLM''',
'''GPTBigCodeModel''',
'''GPTBigCodePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Optional[Any] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : int = [
'''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InformerForPrediction''',
'''InformerModel''',
'''InformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 351 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase ( torch.nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_="sayef/fsner-bert-base-uncased" ):
super(SCREAMING_SNAKE_CASE_ , self ).__init__()
UpperCamelCase : int = AutoModel.from_pretrained(SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.nn.CosineSimilarity(3 , 1e-08 )
UpperCamelCase : Any = torch.nn.Softmax(dim=1 )
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
return self.bert(**SCREAMING_SNAKE_CASE_ ).last_hidden_state
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return token_embeddings.sum(2 , keepdim=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1 ):
return self.softmax(T * self.cos(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = W_supports["""sizes"""].tolist()
UpperCamelCase : List[str] = W_supports["""start_token_id"""].item()
UpperCamelCase : List[Any] = W_supports["""end_token_id"""].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
UpperCamelCase : List[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Tuple = W_supports["""input_ids"""] == start_token_id
UpperCamelCase : Optional[Any] = W_supports["""input_ids"""] == end_token_id
for i, size in enumerate(SCREAMING_SNAKE_CASE_ ):
if i == 0:
UpperCamelCase : int = 0
else:
UpperCamelCase : Optional[int] = support_sizes[i - 1]
UpperCamelCase : Tuple = S[s : s + size][start_token_masks[s : s + size]]
UpperCamelCase : int = S[s : s + size][end_token_masks[s : s + size]]
UpperCamelCase : Dict = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
UpperCamelCase : Tuple = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
UpperCamelCase : List[str] = torch.vstack((p_starts, p_start) )
UpperCamelCase : Optional[Any] = torch.vstack((p_ends, p_end) )
else:
UpperCamelCase : Optional[int] = p_start
UpperCamelCase : Tuple = p_end
return p_starts, p_ends
| 27 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
__A : List[str] = logging.get_logger(__name__)
def A_ ( snake_case_ : Any ,snake_case_ : int ,snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : str = UniSpeechSatForSequenceClassification.from_pretrained(snake_case_ ,config=snake_case_ )
UpperCamelCase : List[Any] = downstream_dict["""projector.weight"""]
UpperCamelCase : Tuple = downstream_dict["""projector.bias"""]
UpperCamelCase : List[str] = downstream_dict["""model.post_net.linear.weight"""]
UpperCamelCase : int = downstream_dict["""model.post_net.linear.bias"""]
return model
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Tuple ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = UniSpeechSatForAudioFrameClassification.from_pretrained(snake_case_ ,config=snake_case_ )
UpperCamelCase : str = downstream_dict["""model.linear.weight"""]
UpperCamelCase : int = downstream_dict["""model.linear.bias"""]
return model
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : Dict ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = UniSpeechSatForXVector.from_pretrained(snake_case_ ,config=snake_case_ )
UpperCamelCase : Union[str, Any] = downstream_dict["""connector.weight"""]
UpperCamelCase : Optional[Any] = downstream_dict["""connector.bias"""]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
UpperCamelCase : Optional[int] = downstream_dict[
f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
]
UpperCamelCase : int = downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']
UpperCamelCase : Optional[Any] = downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
UpperCamelCase : List[Any] = downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
UpperCamelCase : Union[str, Any] = downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
UpperCamelCase : str = downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
UpperCamelCase : Optional[int] = downstream_dict["""objective.W"""]
return model
@torch.no_grad()
def A_ ( snake_case_ : Optional[int] ,snake_case_ : Dict ,snake_case_ : Optional[int] ,snake_case_ : Optional[Any] ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = torch.load(snake_case_ ,map_location="""cpu""" )
UpperCamelCase : List[str] = checkpoint["""Downstream"""]
UpperCamelCase : List[Any] = UniSpeechSatConfig.from_pretrained(snake_case_ )
UpperCamelCase : List[Any] = WavaVecaFeatureExtractor.from_pretrained(
snake_case_ ,return_attention_mask=snake_case_ ,do_normalize=snake_case_ )
UpperCamelCase : str = hf_config.architectures[0]
if arch.endswith("""ForSequenceClassification""" ):
UpperCamelCase : List[Any] = convert_classification(snake_case_ ,snake_case_ ,snake_case_ )
elif arch.endswith("""ForAudioFrameClassification""" ):
UpperCamelCase : int = convert_diarization(snake_case_ ,snake_case_ ,snake_case_ )
elif arch.endswith("""ForXVector""" ):
UpperCamelCase : List[str] = convert_xvector(snake_case_ ,snake_case_ ,snake_case_ )
else:
raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}' )
if hf_config.use_weighted_layer_sum:
UpperCamelCase : str = checkpoint["""Featurizer"""]["""weights"""]
hf_feature_extractor.save_pretrained(snake_case_ )
hf_model.save_pretrained(snake_case_ )
if __name__ == "__main__":
__A : str = argparse.ArgumentParser()
parser.add_argument(
'''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
)
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
__A : Tuple = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 352 |
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):
return f'Node({self.data})'
class lowerCamelCase :
def __init__( self ):
UpperCamelCase : Dict = None
def __iter__( self ):
UpperCamelCase : int = self.head
while node:
yield node.data
UpperCamelCase : Union[str, Any] = node.next
def __len__( self ):
return sum(1 for _ in self )
def __repr__( self ):
return "->".join([str(SCREAMING_SNAKE_CASE_ ) for item in self] )
def __getitem__( self , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
UpperCamelCase : List[Any] = self.head
for _ in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = current.next
UpperCamelCase : Optional[Any] = data
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(len(self ) , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(0 , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index <= len(self ):
raise IndexError("""list index out of range""" )
UpperCamelCase : Optional[Any] = Node(SCREAMING_SNAKE_CASE_ )
if self.head is None:
UpperCamelCase : Dict = new_node
elif index == 0:
UpperCamelCase : Any = self.head # link new_node to head
UpperCamelCase : Any = new_node
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : str = temp.next
UpperCamelCase : Any = temp.next
UpperCamelCase : Optional[Any] = new_node
def a_ ( self ): # print every node data
print(self )
def a_ ( self ):
return self.delete_nth(0 )
def a_ ( self ): # delete from tail
return self.delete_nth(len(self ) - 1 )
def a_ ( self , SCREAMING_SNAKE_CASE_ = 0 ):
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("""List index out of range.""" )
UpperCamelCase : Union[str, Any] = self.head # default first node
if index == 0:
UpperCamelCase : Optional[Any] = self.head.next
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : int = temp.next
UpperCamelCase : Optional[Any] = temp.next
UpperCamelCase : Dict = temp.next.next
return delete_node.data
def a_ ( self ):
return self.head is None
def a_ ( self ):
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Union[str, Any] = self.head
while current:
# Store the current node's next node.
UpperCamelCase : Optional[int] = current.next
# Make the current node's next point backwards
UpperCamelCase : Optional[Any] = prev
# Make the previous node be the current node
UpperCamelCase : int = current
# Make the current node the next node (to progress iteration)
UpperCamelCase : Optional[int] = next_node
# Return prev in order to put the head at the end
UpperCamelCase : Optional[int] = prev
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = LinkedList()
assert linked_list.is_empty() is True
assert str(snake_case_ ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(1_0 ):
assert len(snake_case_ ) == i
linked_list.insert_nth(snake_case_ ,i + 1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_1 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(1_1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(0 ,1_2 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 1_0
assert linked_list.delete_tail() == 1_1
assert len(snake_case_ ) == 9
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_0 ) )
assert all(linked_list[i] == i + 1 for i in range(0 ,9 ) ) is True
for i in range(0 ,9 ):
UpperCamelCase : Optional[Any] = -i
assert all(linked_list[i] == -i for i in range(0 ,9 ) ) is True
linked_list.reverse()
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(-8 ,1 ) )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = [
-9,
1_0_0,
Node(7_7_3_4_5_1_1_2 ),
"""dlrow olleH""",
7,
5_5_5_5,
0,
-192.55555,
"""Hello, world!""",
77.9,
Node(1_0 ),
None,
None,
12.20,
]
UpperCamelCase : List[Any] = LinkedList()
for i in test_input:
linked_list.insert_tail(snake_case_ )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(snake_case_ ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
UpperCamelCase : Dict = linked_list.delete_head()
assert result == -9
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
UpperCamelCase : int = linked_list.delete_tail()
assert result == 12.2
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
UpperCamelCase : Optional[Any] = linked_list.delete_nth(1_0 )
assert result is None
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("""Hello again, world!""" ) )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(snake_case_ )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(snake_case_ )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def A_ ( ):
'''simple docstring'''
from doctest import testmod
testmod()
UpperCamelCase : List[Any] = LinkedList()
linked_list.insert_head(input("""Inserting 1st at head """ ).strip() )
linked_list.insert_head(input("""Inserting 2nd at head """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
linked_list.insert_tail(input("""\nInserting 1st at tail """ ).strip() )
linked_list.insert_tail(input("""Inserting 2nd at tail """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nDelete head""" )
linked_list.delete_head()
print("""Delete tail""" )
linked_list.delete_tail()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nReverse linked list""" )
linked_list.reverse()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nString representation of linked list:""" )
print(snake_case_ )
print("""\nReading/changing Node data using indexing:""" )
print(f'Element at Position 1: {linked_list[1]}' )
UpperCamelCase : List[Any] = input("""Enter New Value: """ ).strip()
print("""New list:""" )
print(snake_case_ )
print(f'length of linked_list is : {len(snake_case_ )}' )
if __name__ == "__main__":
main()
| 27 | 0 |
"""simple docstring"""
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
__A : str = {
'''Acehnese Arabic''': '''ace_Arab''',
'''Acehnese Latin''': '''ace_Latn''',
'''Mesopotamian Arabic''': '''acm_Arab''',
'''Ta\'izzi-Adeni Arabic''': '''acq_Arab''',
'''Tunisian Arabic''': '''aeb_Arab''',
'''Afrikaans''': '''afr_Latn''',
'''South Levantine Arabic''': '''ajp_Arab''',
'''Akan''': '''aka_Latn''',
'''Amharic''': '''amh_Ethi''',
'''North Levantine Arabic''': '''apc_Arab''',
'''Modern Standard Arabic''': '''arb_Arab''',
'''Modern Standard Arabic Romanized''': '''arb_Latn''',
'''Najdi Arabic''': '''ars_Arab''',
'''Moroccan Arabic''': '''ary_Arab''',
'''Egyptian Arabic''': '''arz_Arab''',
'''Assamese''': '''asm_Beng''',
'''Asturian''': '''ast_Latn''',
'''Awadhi''': '''awa_Deva''',
'''Central Aymara''': '''ayr_Latn''',
'''South Azerbaijani''': '''azb_Arab''',
'''North Azerbaijani''': '''azj_Latn''',
'''Bashkir''': '''bak_Cyrl''',
'''Bambara''': '''bam_Latn''',
'''Balinese''': '''ban_Latn''',
'''Belarusian''': '''bel_Cyrl''',
'''Bemba''': '''bem_Latn''',
'''Bengali''': '''ben_Beng''',
'''Bhojpuri''': '''bho_Deva''',
'''Banjar Arabic''': '''bjn_Arab''',
'''Banjar Latin''': '''bjn_Latn''',
'''Standard Tibetan''': '''bod_Tibt''',
'''Bosnian''': '''bos_Latn''',
'''Buginese''': '''bug_Latn''',
'''Bulgarian''': '''bul_Cyrl''',
'''Catalan''': '''cat_Latn''',
'''Cebuano''': '''ceb_Latn''',
'''Czech''': '''ces_Latn''',
'''Chokwe''': '''cjk_Latn''',
'''Central Kurdish''': '''ckb_Arab''',
'''Crimean Tatar''': '''crh_Latn''',
'''Welsh''': '''cym_Latn''',
'''Danish''': '''dan_Latn''',
'''German''': '''deu_Latn''',
'''Southwestern Dinka''': '''dik_Latn''',
'''Dyula''': '''dyu_Latn''',
'''Dzongkha''': '''dzo_Tibt''',
'''Greek''': '''ell_Grek''',
'''English''': '''eng_Latn''',
'''Esperanto''': '''epo_Latn''',
'''Estonian''': '''est_Latn''',
'''Basque''': '''eus_Latn''',
'''Ewe''': '''ewe_Latn''',
'''Faroese''': '''fao_Latn''',
'''Fijian''': '''fij_Latn''',
'''Finnish''': '''fin_Latn''',
'''Fon''': '''fon_Latn''',
'''French''': '''fra_Latn''',
'''Friulian''': '''fur_Latn''',
'''Nigerian Fulfulde''': '''fuv_Latn''',
'''Scottish Gaelic''': '''gla_Latn''',
'''Irish''': '''gle_Latn''',
'''Galician''': '''glg_Latn''',
'''Guarani''': '''grn_Latn''',
'''Gujarati''': '''guj_Gujr''',
'''Haitian Creole''': '''hat_Latn''',
'''Hausa''': '''hau_Latn''',
'''Hebrew''': '''heb_Hebr''',
'''Hindi''': '''hin_Deva''',
'''Chhattisgarhi''': '''hne_Deva''',
'''Croatian''': '''hrv_Latn''',
'''Hungarian''': '''hun_Latn''',
'''Armenian''': '''hye_Armn''',
'''Igbo''': '''ibo_Latn''',
'''Ilocano''': '''ilo_Latn''',
'''Indonesian''': '''ind_Latn''',
'''Icelandic''': '''isl_Latn''',
'''Italian''': '''ita_Latn''',
'''Javanese''': '''jav_Latn''',
'''Japanese''': '''jpn_Jpan''',
'''Kabyle''': '''kab_Latn''',
'''Jingpho''': '''kac_Latn''',
'''Kamba''': '''kam_Latn''',
'''Kannada''': '''kan_Knda''',
'''Kashmiri Arabic''': '''kas_Arab''',
'''Kashmiri Devanagari''': '''kas_Deva''',
'''Georgian''': '''kat_Geor''',
'''Central Kanuri Arabic''': '''knc_Arab''',
'''Central Kanuri Latin''': '''knc_Latn''',
'''Kazakh''': '''kaz_Cyrl''',
'''Kabiyè''': '''kbp_Latn''',
'''Kabuverdianu''': '''kea_Latn''',
'''Khmer''': '''khm_Khmr''',
'''Kikuyu''': '''kik_Latn''',
'''Kinyarwanda''': '''kin_Latn''',
'''Kyrgyz''': '''kir_Cyrl''',
'''Kimbundu''': '''kmb_Latn''',
'''Northern Kurdish''': '''kmr_Latn''',
'''Kikongo''': '''kon_Latn''',
'''Korean''': '''kor_Hang''',
'''Lao''': '''lao_Laoo''',
'''Ligurian''': '''lij_Latn''',
'''Limburgish''': '''lim_Latn''',
'''Lingala''': '''lin_Latn''',
'''Lithuanian''': '''lit_Latn''',
'''Lombard''': '''lmo_Latn''',
'''Latgalian''': '''ltg_Latn''',
'''Luxembourgish''': '''ltz_Latn''',
'''Luba-Kasai''': '''lua_Latn''',
'''Ganda''': '''lug_Latn''',
'''Luo''': '''luo_Latn''',
'''Mizo''': '''lus_Latn''',
'''Standard Latvian''': '''lvs_Latn''',
'''Magahi''': '''mag_Deva''',
'''Maithili''': '''mai_Deva''',
'''Malayalam''': '''mal_Mlym''',
'''Marathi''': '''mar_Deva''',
'''Minangkabau Arabic ''': '''min_Arab''',
'''Minangkabau Latin''': '''min_Latn''',
'''Macedonian''': '''mkd_Cyrl''',
'''Plateau Malagasy''': '''plt_Latn''',
'''Maltese''': '''mlt_Latn''',
'''Meitei Bengali''': '''mni_Beng''',
'''Halh Mongolian''': '''khk_Cyrl''',
'''Mossi''': '''mos_Latn''',
'''Maori''': '''mri_Latn''',
'''Burmese''': '''mya_Mymr''',
'''Dutch''': '''nld_Latn''',
'''Norwegian Nynorsk''': '''nno_Latn''',
'''Norwegian Bokmål''': '''nob_Latn''',
'''Nepali''': '''npi_Deva''',
'''Northern Sotho''': '''nso_Latn''',
'''Nuer''': '''nus_Latn''',
'''Nyanja''': '''nya_Latn''',
'''Occitan''': '''oci_Latn''',
'''West Central Oromo''': '''gaz_Latn''',
'''Odia''': '''ory_Orya''',
'''Pangasinan''': '''pag_Latn''',
'''Eastern Panjabi''': '''pan_Guru''',
'''Papiamento''': '''pap_Latn''',
'''Western Persian''': '''pes_Arab''',
'''Polish''': '''pol_Latn''',
'''Portuguese''': '''por_Latn''',
'''Dari''': '''prs_Arab''',
'''Southern Pashto''': '''pbt_Arab''',
'''Ayacucho Quechua''': '''quy_Latn''',
'''Romanian''': '''ron_Latn''',
'''Rundi''': '''run_Latn''',
'''Russian''': '''rus_Cyrl''',
'''Sango''': '''sag_Latn''',
'''Sanskrit''': '''san_Deva''',
'''Santali''': '''sat_Olck''',
'''Sicilian''': '''scn_Latn''',
'''Shan''': '''shn_Mymr''',
'''Sinhala''': '''sin_Sinh''',
'''Slovak''': '''slk_Latn''',
'''Slovenian''': '''slv_Latn''',
'''Samoan''': '''smo_Latn''',
'''Shona''': '''sna_Latn''',
'''Sindhi''': '''snd_Arab''',
'''Somali''': '''som_Latn''',
'''Southern Sotho''': '''sot_Latn''',
'''Spanish''': '''spa_Latn''',
'''Tosk Albanian''': '''als_Latn''',
'''Sardinian''': '''srd_Latn''',
'''Serbian''': '''srp_Cyrl''',
'''Swati''': '''ssw_Latn''',
'''Sundanese''': '''sun_Latn''',
'''Swedish''': '''swe_Latn''',
'''Swahili''': '''swh_Latn''',
'''Silesian''': '''szl_Latn''',
'''Tamil''': '''tam_Taml''',
'''Tatar''': '''tat_Cyrl''',
'''Telugu''': '''tel_Telu''',
'''Tajik''': '''tgk_Cyrl''',
'''Tagalog''': '''tgl_Latn''',
'''Thai''': '''tha_Thai''',
'''Tigrinya''': '''tir_Ethi''',
'''Tamasheq Latin''': '''taq_Latn''',
'''Tamasheq Tifinagh''': '''taq_Tfng''',
'''Tok Pisin''': '''tpi_Latn''',
'''Tswana''': '''tsn_Latn''',
'''Tsonga''': '''tso_Latn''',
'''Turkmen''': '''tuk_Latn''',
'''Tumbuka''': '''tum_Latn''',
'''Turkish''': '''tur_Latn''',
'''Twi''': '''twi_Latn''',
'''Central Atlas Tamazight''': '''tzm_Tfng''',
'''Uyghur''': '''uig_Arab''',
'''Ukrainian''': '''ukr_Cyrl''',
'''Umbundu''': '''umb_Latn''',
'''Urdu''': '''urd_Arab''',
'''Northern Uzbek''': '''uzn_Latn''',
'''Venetian''': '''vec_Latn''',
'''Vietnamese''': '''vie_Latn''',
'''Waray''': '''war_Latn''',
'''Wolof''': '''wol_Latn''',
'''Xhosa''': '''xho_Latn''',
'''Eastern Yiddish''': '''ydd_Hebr''',
'''Yoruba''': '''yor_Latn''',
'''Yue Chinese''': '''yue_Hant''',
'''Chinese Simplified''': '''zho_Hans''',
'''Chinese Traditional''': '''zho_Hant''',
'''Standard Malay''': '''zsm_Latn''',
'''Zulu''': '''zul_Latn''',
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : List[str] = 'facebook/nllb-200-distilled-600M'
lowercase : Union[str, Any] = (
'This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '
'be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '
'which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '
'plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'
)
lowercase : List[str] = 'translator'
lowercase : Union[str, Any] = AutoTokenizer
lowercase : List[str] = AutoModelForSeqaSeqLM
lowercase : List[str] = LANGUAGE_CODES
lowercase : int = ['text', 'text', 'text']
lowercase : List[str] = ['text']
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if src_lang not in self.lang_to_code:
raise ValueError(f'{src_lang} is not a supported language.' )
if tgt_lang not in self.lang_to_code:
raise ValueError(f'{tgt_lang} is not a supported language.' )
UpperCamelCase : str = self.lang_to_code[src_lang]
UpperCamelCase : Optional[int] = self.lang_to_code[tgt_lang]
return self.pre_processor._build_translation_inputs(
SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , src_lang=SCREAMING_SNAKE_CASE_ , tgt_lang=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return self.model.generate(**SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return self.post_processor.decode(outputs[0].tolist() , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
| 353 |
"""simple docstring"""
import argparse
import os
import re
__A : Dict = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__A : Union[str, Any] = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : Tuple = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : Tuple = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Dict="" ,snake_case_ : Dict=None ,snake_case_ : Any=None ):
'''simple docstring'''
UpperCamelCase : Optional[int] = 0
UpperCamelCase : List[Any] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Optional[Any] = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : int = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Any = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Any = [lines[index + 1]]
index += 1
else:
UpperCamelCase : List[str] = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
def _inner(snake_case_ : Tuple ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : List[Any] ,snake_case_ : Optional[int]=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Dict ):
return x
if key is None:
UpperCamelCase : int = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[Any] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : str = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : List[str] = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Tuple = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : int ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : List[Any] ):
UpperCamelCase : Any = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : Union[str, Any] = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[str] = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : str = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : str = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Dict = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : int = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Any = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[Any] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : Optional[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[Any] = keys[:-1]
UpperCamelCase : int = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Tuple ,snake_case_ : str=True ):
'''simple docstring'''
with open(snake_case_ ,"""r""" ) as f:
UpperCamelCase : int = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : Dict = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Optional[Any] = main_blocks[block_idx]
UpperCamelCase : Optional[int] = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : Union[str, Any] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : List[str] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Dict = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Union[str, Any] = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : Optional[int] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Union[str, Any] = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Union[str, Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Optional[Any] = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : List[Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[Any] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : str = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Any = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Union[str, Any] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : Any = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : str = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27 | 0 |
"""simple docstring"""
from collections import defaultdict
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
UpperCamelCase : int = [
[-1 for i in range(total + 1 )] for j in range(2 ** len(SCREAMING_SNAKE_CASE_ ) )
]
UpperCamelCase : Optional[int] = defaultdict(SCREAMING_SNAKE_CASE_ ) # stores the list of persons for each task
# final_mask is used to check if all persons are included by setting all bits
# to 1
UpperCamelCase : Dict = (1 << len(SCREAMING_SNAKE_CASE_ )) - 1
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
# if mask == self.finalmask all persons are distributed tasks, return 1
if mask == self.final_mask:
return 1
# if not everyone gets the task and no more tasks are available, return 0
if task_no > self.total_tasks:
return 0
# if case already considered
if self.dp[mask][task_no] != -1:
return self.dp[mask][task_no]
# Number of ways when we don't this task in the arrangement
UpperCamelCase : str = self.count_ways_until(SCREAMING_SNAKE_CASE_ , task_no + 1 )
# now assign the tasks one by one to all possible persons and recursively
# assign for the remaining tasks.
if task_no in self.task:
for p in self.task[task_no]:
# if p is already given a task
if mask & (1 << p):
continue
# assign this task to p and change the mask value. And recursively
# assign tasks with the new mask value.
total_ways_util += self.count_ways_until(mask | (1 << p) , task_no + 1 )
# save the value.
UpperCamelCase : List[str] = total_ways_util
return self.dp[mask][task_no]
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
# Store the list of persons for each task
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
for j in task_performed[i]:
self.task[j].append(SCREAMING_SNAKE_CASE_ )
# call the function to fill the DP table, final answer is stored in dp[0][1]
return self.count_ways_until(0 , 1 )
if __name__ == "__main__":
__A : int = 5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
__A : Optional[int] = [[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
)
| 354 |
"""simple docstring"""
def A_ ( snake_case_ : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(snake_case_ ,(list, tuple) ) or not all(
isinstance(snake_case_ ,snake_case_ ) for number in numbers ):
raise ValueError("""numbers must be an iterable of integers""" )
UpperCamelCase : int = numbers[0]
for i in range(1 ,len(snake_case_ ) ):
# update the maximum and minimum subarray products
UpperCamelCase : List[str] = numbers[i]
if number < 0:
UpperCamelCase , UpperCamelCase : Optional[int] = min_till_now, max_till_now
UpperCamelCase : Dict = max(snake_case_ ,max_till_now * number )
UpperCamelCase : Union[str, Any] = min(snake_case_ ,min_till_now * number )
# update the maximum product found till now
UpperCamelCase : Union[str, Any] = max(snake_case_ ,snake_case_ )
return max_prod
| 27 | 0 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
UpperCamelCase : Optional[int] = torch.nn.Linear(10 , 10 )
UpperCamelCase : Tuple = torch.optim.SGD(model.parameters() , 0.1 )
UpperCamelCase : str = Accelerator()
UpperCamelCase : Union[str, Any] = accelerator.prepare(SCREAMING_SNAKE_CASE_ )
try:
pickle.loads(pickle.dumps(SCREAMING_SNAKE_CASE_ ) )
except Exception as e:
self.fail(f'Accelerated optimizer pickling failed with {e}' )
AcceleratorState._reset_state()
| 355 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Any = AudioLDMPipeline
lowercase : Union[str, Any] = TEXT_TO_AUDIO_PARAMS
lowercase : List[str] = TEXT_TO_AUDIO_BATCH_PARAMS
lowercase : Tuple = frozenset(
[
'num_inference_steps',
'num_waveforms_per_prompt',
'generator',
'latents',
'output_type',
'return_dict',
'callback',
'callback_steps',
] )
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : Tuple = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=(32, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Optional[Any] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_one=SCREAMING_SNAKE_CASE_ , )
torch.manual_seed(0 )
UpperCamelCase : Optional[int] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
UpperCamelCase : int = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
UpperCamelCase : Optional[int] = ClapTextModelWithProjection(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 )
UpperCamelCase : Tuple = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=1_6000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : List[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Any = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def a_ ( self ):
UpperCamelCase : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Any = self.get_dummy_components()
UpperCamelCase : int = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Tuple = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[str] = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Optional[int] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : str = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
UpperCamelCase : Tuple = prompt_embeds
# forward
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : List[str] = self.get_dummy_components()
UpperCamelCase : List[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * ["""this is a negative prompt"""]
UpperCamelCase : List[Any] = negative_prompt
UpperCamelCase : str = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : str = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
UpperCamelCase : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[Any] = []
for p in [prompt, negative_prompt]:
UpperCamelCase : int = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Union[str, Any] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
embeds.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase : Tuple = embeds
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Optional[int] = self.get_dummy_components()
UpperCamelCase : List[str] = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = """egg cracking"""
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ , negative_prompt=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Union[str, Any] = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Union[str, Any] = self.get_dummy_components()
UpperCamelCase : Tuple = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
UpperCamelCase : List[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
UpperCamelCase : Dict = 2
UpperCamelCase : List[str] = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
UpperCamelCase : List[str] = 2
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
UpperCamelCase : Any = 2
UpperCamelCase : str = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Tuple = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = audioldm_pipe.vocoder.config.sampling_rate
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe(audio_length_in_s=0.016 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.016
UpperCamelCase : Optional[Any] = audioldm_pipe(audio_length_in_s=0.032 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.032
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Optional[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = ["""hey"""]
UpperCamelCase : Dict = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : str = output.audios.shape
assert audio_shape == (1, 256)
UpperCamelCase : Optional[Any] = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
UpperCamelCase : str = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : List[str] = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def a_ ( self ):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
self._test_inference_batch_single_identical(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def a_ ( self ):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@slow
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="cpu" , SCREAMING_SNAKE_CASE_=torch.floataa , SCREAMING_SNAKE_CASE_=0 ):
UpperCamelCase : str = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = np.random.RandomState(SCREAMING_SNAKE_CASE_ ).standard_normal((1, 8, 128, 16) )
UpperCamelCase : int = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def a_ ( self ):
UpperCamelCase : Optional[int] = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = 25
UpperCamelCase : Optional[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[7_7230:7_7240]
UpperCamelCase : Optional[Any] = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
UpperCamelCase : Any = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def a_ ( self ):
UpperCamelCase : Any = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : Any = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
UpperCamelCase : str = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[2_7780:2_7790]
UpperCamelCase : Tuple = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
UpperCamelCase : Tuple = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 27 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Tuple = LayoutLMTokenizer
lowercase : List[str] = LayoutLMTokenizerFast
lowercase : List[str] = True
lowercase : str = True
def a_ ( self ):
super().setUp()
UpperCamelCase : Union[str, Any] = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
UpperCamelCase : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = """UNwant\u00E9d,running"""
UpperCamelCase : Dict = """unwanted, running"""
return input_text, output_text
def a_ ( self ):
UpperCamelCase : int = self.tokenizer_class(self.vocab_file )
UpperCamelCase : Any = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [7, 4, 5, 10, 8, 9] )
def a_ ( self ):
pass
| 356 |
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( snake_case_ : Dataset ,snake_case_ : Dict[str, str] ):
'''simple docstring'''
UpperCamelCase : List[str] = args.log_outputs
UpperCamelCase : Tuple = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
UpperCamelCase : List[Any] = load_metric("""wer""" )
UpperCamelCase : Any = load_metric("""cer""" )
# compute metrics
UpperCamelCase : str = wer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
UpperCamelCase : Dict = cer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
# print & log results
UpperCamelCase : Optional[int] = f'WER: {wer_result}\nCER: {cer_result}'
print(snake_case_ )
with open(f'{dataset_id}_eval_results.txt' ,"""w""" ) as f:
f.write(snake_case_ )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
UpperCamelCase : Optional[Any] = f'log_{dataset_id}_predictions.txt'
UpperCamelCase : str = f'log_{dataset_id}_targets.txt'
with open(snake_case_ ,"""w""" ) as p, open(snake_case_ ,"""w""" ) as t:
# mapping function to write output
def write_to_file(snake_case_ : Union[str, Any] ,snake_case_ : Tuple ):
p.write(f'{i}' + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(f'{i}' + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(snake_case_ ,with_indices=snake_case_ )
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Dict = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
UpperCamelCase : str = re.sub(snake_case_ ,"""""" ,text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
UpperCamelCase : List[str] = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
UpperCamelCase : Tuple = """ """.join(text.split(snake_case_ ) )
return text
def A_ ( snake_case_ : str ):
'''simple docstring'''
# load dataset
UpperCamelCase : Union[str, Any] = load_dataset(args.dataset ,args.config ,split=args.split ,use_auth_token=snake_case_ )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
UpperCamelCase : List[Any] = AutoFeatureExtractor.from_pretrained(args.model_id )
UpperCamelCase : Dict = feature_extractor.sampling_rate
# resample audio
UpperCamelCase : Optional[Any] = dataset.cast_column("""audio""" ,Audio(sampling_rate=snake_case_ ) )
# load eval pipeline
if args.device is None:
UpperCamelCase : int = 0 if torch.cuda.is_available() else -1
UpperCamelCase : Union[str, Any] = pipeline("""automatic-speech-recognition""" ,model=args.model_id ,device=args.device )
# map function to decode audio
def map_to_pred(snake_case_ : Union[str, Any] ):
UpperCamelCase : List[Any] = asr(
batch["""audio"""]["""array"""] ,chunk_length_s=args.chunk_length_s ,stride_length_s=args.stride_length_s )
UpperCamelCase : Union[str, Any] = prediction["""text"""]
UpperCamelCase : Optional[Any] = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
UpperCamelCase : Any = dataset.map(snake_case_ ,remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(snake_case_ ,snake_case_ )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
__A : Optional[Any] = parser.parse_args()
main(args)
| 27 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A : int = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[Any] = [
'''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GraphormerForGraphClassification''',
'''GraphormerModel''',
'''GraphormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_graphormer import (
GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
GraphormerForGraphClassification,
GraphormerModel,
GraphormerPreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 357 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = 'EncodecFeatureExtractor'
lowercase : List[Any] = ('T5Tokenizer', 'T5TokenizerFast')
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.feature_extractor
UpperCamelCase : Any = False
def a_ ( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True ):
return self.tokenizer.get_decoder_prompt_ids(task=SCREAMING_SNAKE_CASE_ , language=SCREAMING_SNAKE_CASE_ , no_timestamps=SCREAMING_SNAKE_CASE_ )
def __call__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = kwargs.pop("""sampling_rate""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = kwargs.pop("""text""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Any = args[0]
UpperCamelCase : str = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if text is not None:
UpperCamelCase : Optional[int] = self.tokenizer(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is not None:
UpperCamelCase : str = self.feature_extractor(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
UpperCamelCase : int = audio_inputs["""input_values"""]
if "padding_mask" in audio_inputs:
UpperCamelCase : Optional[Any] = audio_inputs["""padding_mask"""]
return inputs
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = kwargs.pop("""padding_mask""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Optional[int] = args[0]
UpperCamelCase : Any = args[1:]
if audio_values is not None:
return self._decode_audio(SCREAMING_SNAKE_CASE_ , padding_mask=SCREAMING_SNAKE_CASE_ )
else:
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = to_numpy(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase , UpperCamelCase : int = audio_values.shape
if padding_mask is None:
return list(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = to_numpy(SCREAMING_SNAKE_CASE_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
UpperCamelCase : List[str] = seq_len - padding_mask.shape[-1]
UpperCamelCase : Optional[int] = 1 - self.feature_extractor.padding_value
UpperCamelCase : Any = np.pad(SCREAMING_SNAKE_CASE_ , ((0, 0), (0, difference)) , """constant""" , constant_values=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audio_values.tolist()
for i in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
UpperCamelCase : Optional[Any] = sliced_audio.reshape(SCREAMING_SNAKE_CASE_ , -1 )
return audio_values
| 27 | 0 |
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):
return f'Node({self.data})'
class lowerCamelCase :
def __init__( self ):
UpperCamelCase : Dict = None
def __iter__( self ):
UpperCamelCase : int = self.head
while node:
yield node.data
UpperCamelCase : Union[str, Any] = node.next
def __len__( self ):
return sum(1 for _ in self )
def __repr__( self ):
return "->".join([str(SCREAMING_SNAKE_CASE_ ) for item in self] )
def __getitem__( self , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
UpperCamelCase : List[Any] = self.head
for _ in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = current.next
UpperCamelCase : Optional[Any] = data
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(len(self ) , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(0 , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index <= len(self ):
raise IndexError("""list index out of range""" )
UpperCamelCase : Optional[Any] = Node(SCREAMING_SNAKE_CASE_ )
if self.head is None:
UpperCamelCase : Dict = new_node
elif index == 0:
UpperCamelCase : Any = self.head # link new_node to head
UpperCamelCase : Any = new_node
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : str = temp.next
UpperCamelCase : Any = temp.next
UpperCamelCase : Optional[Any] = new_node
def a_ ( self ): # print every node data
print(self )
def a_ ( self ):
return self.delete_nth(0 )
def a_ ( self ): # delete from tail
return self.delete_nth(len(self ) - 1 )
def a_ ( self , SCREAMING_SNAKE_CASE_ = 0 ):
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("""List index out of range.""" )
UpperCamelCase : Union[str, Any] = self.head # default first node
if index == 0:
UpperCamelCase : Optional[Any] = self.head.next
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : int = temp.next
UpperCamelCase : Optional[Any] = temp.next
UpperCamelCase : Dict = temp.next.next
return delete_node.data
def a_ ( self ):
return self.head is None
def a_ ( self ):
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Union[str, Any] = self.head
while current:
# Store the current node's next node.
UpperCamelCase : Optional[int] = current.next
# Make the current node's next point backwards
UpperCamelCase : Optional[Any] = prev
# Make the previous node be the current node
UpperCamelCase : int = current
# Make the current node the next node (to progress iteration)
UpperCamelCase : Optional[int] = next_node
# Return prev in order to put the head at the end
UpperCamelCase : Optional[int] = prev
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = LinkedList()
assert linked_list.is_empty() is True
assert str(snake_case_ ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(1_0 ):
assert len(snake_case_ ) == i
linked_list.insert_nth(snake_case_ ,i + 1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_1 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(1_1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(0 ,1_2 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 1_0
assert linked_list.delete_tail() == 1_1
assert len(snake_case_ ) == 9
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_0 ) )
assert all(linked_list[i] == i + 1 for i in range(0 ,9 ) ) is True
for i in range(0 ,9 ):
UpperCamelCase : Optional[Any] = -i
assert all(linked_list[i] == -i for i in range(0 ,9 ) ) is True
linked_list.reverse()
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(-8 ,1 ) )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = [
-9,
1_0_0,
Node(7_7_3_4_5_1_1_2 ),
"""dlrow olleH""",
7,
5_5_5_5,
0,
-192.55555,
"""Hello, world!""",
77.9,
Node(1_0 ),
None,
None,
12.20,
]
UpperCamelCase : List[Any] = LinkedList()
for i in test_input:
linked_list.insert_tail(snake_case_ )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(snake_case_ ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
UpperCamelCase : Dict = linked_list.delete_head()
assert result == -9
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
UpperCamelCase : int = linked_list.delete_tail()
assert result == 12.2
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
UpperCamelCase : Optional[Any] = linked_list.delete_nth(1_0 )
assert result is None
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("""Hello again, world!""" ) )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(snake_case_ )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(snake_case_ )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def A_ ( ):
'''simple docstring'''
from doctest import testmod
testmod()
UpperCamelCase : List[Any] = LinkedList()
linked_list.insert_head(input("""Inserting 1st at head """ ).strip() )
linked_list.insert_head(input("""Inserting 2nd at head """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
linked_list.insert_tail(input("""\nInserting 1st at tail """ ).strip() )
linked_list.insert_tail(input("""Inserting 2nd at tail """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nDelete head""" )
linked_list.delete_head()
print("""Delete tail""" )
linked_list.delete_tail()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nReverse linked list""" )
linked_list.reverse()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nString representation of linked list:""" )
print(snake_case_ )
print("""\nReading/changing Node data using indexing:""" )
print(f'Element at Position 1: {linked_list[1]}' )
UpperCamelCase : List[Any] = input("""Enter New Value: """ ).strip()
print("""New list:""" )
print(snake_case_ )
print(f'length of linked_list is : {len(snake_case_ )}' )
if __name__ == "__main__":
main()
| 358 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( snake_case_ : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
UpperCamelCase : Any = BeautifulSoup(requests.get(snake_case_ ).text ,"""html.parser""" )
UpperCamelCase : Optional[int] = soup.findAll("""h1""" )
UpperCamelCase : List[Any] = soup.findAll("""div""" ,{"""class""": """maincounter-number"""} )
keys += soup.findAll("""span""" ,{"""class""": """panel-title"""} )
values += soup.findAll("""div""" ,{"""class""": """number-table-main"""} )
return {key.text.strip(): value.text.strip() for key, value in zip(snake_case_ ,snake_case_ )}
if __name__ == "__main__":
print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
for key, value in world_covidaa_stats().items():
print(F'''{key}\n{value}\n''')
| 27 | 0 |
from __future__ import annotations
import math
import random
from typing import Any
class lowerCamelCase :
def __init__( self ):
UpperCamelCase : list[Any] = []
UpperCamelCase : int = 0
UpperCamelCase : int = 0
def a_ ( self ):
return self.head == self.tail
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.data.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.tail + 1
def a_ ( self ):
UpperCamelCase : List[str] = self.data[self.head]
UpperCamelCase : Optional[Any] = self.head + 1
return ret
def a_ ( self ):
return self.tail - self.head
def a_ ( self ):
print(self.data )
print("""**************""" )
print(self.data[self.head : self.tail] )
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : int = data
UpperCamelCase : MyNode | None = None
UpperCamelCase : MyNode | None = None
UpperCamelCase : int = 1
def a_ ( self ):
return self.data
def a_ ( self ):
return self.left
def a_ ( self ):
return self.right
def a_ ( self ):
return self.height
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = data
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = node
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = node
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = height
def A_ ( snake_case_ : MyNode | None ):
'''simple docstring'''
if node is None:
return 0
return node.get_height()
def A_ ( snake_case_ : int ,snake_case_ : int ):
'''simple docstring'''
if a > b:
return a
return b
def A_ ( snake_case_ : MyNode ):
'''simple docstring'''
print("""left rotation node:""" ,node.get_data() )
UpperCamelCase : Optional[int] = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(snake_case_ )
UpperCamelCase : int = my_max(get_height(node.get_right() ) ,get_height(node.get_left() ) ) + 1
node.set_height(snake_case_ )
UpperCamelCase : List[Any] = my_max(get_height(ret.get_right() ) ,get_height(ret.get_left() ) ) + 1
ret.set_height(snake_case_ )
return ret
def A_ ( snake_case_ : MyNode ):
'''simple docstring'''
print("""right rotation node:""" ,node.get_data() )
UpperCamelCase : Tuple = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(snake_case_ )
UpperCamelCase : Union[str, Any] = my_max(get_height(node.get_right() ) ,get_height(node.get_left() ) ) + 1
node.set_height(snake_case_ )
UpperCamelCase : Optional[int] = my_max(get_height(ret.get_right() ) ,get_height(ret.get_left() ) ) + 1
ret.set_height(snake_case_ )
return ret
def A_ ( snake_case_ : MyNode ):
'''simple docstring'''
UpperCamelCase : Any = node.get_left()
assert left_child is not None
node.set_left(left_rotation(snake_case_ ) )
return right_rotation(snake_case_ )
def A_ ( snake_case_ : MyNode ):
'''simple docstring'''
UpperCamelCase : List[Any] = node.get_right()
assert right_child is not None
node.set_right(right_rotation(snake_case_ ) )
return left_rotation(snake_case_ )
def A_ ( snake_case_ : MyNode | None ,snake_case_ : Any ):
'''simple docstring'''
if node is None:
return MyNode(snake_case_ )
if data < node.get_data():
node.set_left(insert_node(node.get_left() ,snake_case_ ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
UpperCamelCase : Any = node.get_left()
assert left_child is not None
if (
data < left_child.get_data()
): # new node is the left child of the left child
UpperCamelCase : Optional[int] = right_rotation(snake_case_ )
else:
UpperCamelCase : str = lr_rotation(snake_case_ )
else:
node.set_right(insert_node(node.get_right() ,snake_case_ ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
UpperCamelCase : int = node.get_right()
assert right_child is not None
if data < right_child.get_data():
UpperCamelCase : str = rl_rotation(snake_case_ )
else:
UpperCamelCase : Union[str, Any] = left_rotation(snake_case_ )
UpperCamelCase : str = my_max(get_height(node.get_right() ) ,get_height(node.get_left() ) ) + 1
node.set_height(snake_case_ )
return node
def A_ ( snake_case_ : MyNode ):
'''simple docstring'''
while True:
UpperCamelCase : List[str] = root.get_right()
if right_child is None:
break
UpperCamelCase : int = right_child
return root.get_data()
def A_ ( snake_case_ : MyNode ):
'''simple docstring'''
while True:
UpperCamelCase : str = root.get_left()
if left_child is None:
break
UpperCamelCase : str = left_child
return root.get_data()
def A_ ( snake_case_ : MyNode ,snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : Any = root.get_left()
UpperCamelCase : str = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
UpperCamelCase : Tuple = get_left_most(snake_case_ )
root.set_data(snake_case_ )
root.set_right(del_node(snake_case_ ,snake_case_ ) )
elif left_child is not None:
UpperCamelCase : List[str] = left_child
elif right_child is not None:
UpperCamelCase : Optional[Any] = right_child
else:
return None
elif root.get_data() > data:
if left_child is None:
print("""No such data""" )
return root
else:
root.set_left(del_node(snake_case_ ,snake_case_ ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(snake_case_ ,snake_case_ ) )
if get_height(snake_case_ ) - get_height(snake_case_ ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
UpperCamelCase : str = left_rotation(snake_case_ )
else:
UpperCamelCase : List[Any] = rl_rotation(snake_case_ )
elif get_height(snake_case_ ) - get_height(snake_case_ ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
UpperCamelCase : Optional[Any] = right_rotation(snake_case_ )
else:
UpperCamelCase : str = lr_rotation(snake_case_ )
UpperCamelCase : Any = my_max(get_height(root.get_right() ) ,get_height(root.get_left() ) ) + 1
root.set_height(snake_case_ )
return root
class lowerCamelCase :
def __init__( self ):
UpperCamelCase : MyNode | None = None
def a_ ( self ):
return get_height(self.root )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
print("""insert:""" + str(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Any = insert_node(self.root , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
print("""delete:""" + str(SCREAMING_SNAKE_CASE_ ) )
if self.root is None:
print("""Tree is empty!""" )
return
UpperCamelCase : Tuple = del_node(self.root , SCREAMING_SNAKE_CASE_ )
def __str__( self , ): # a level traversale, gives a more intuitive look on the tree
UpperCamelCase : Union[str, Any] = """"""
UpperCamelCase : Any = MyQueue()
q.push(self.root )
UpperCamelCase : str = self.get_height()
if layer == 0:
return output
UpperCamelCase : Any = 0
while not q.is_empty():
UpperCamelCase : Tuple = q.pop()
UpperCamelCase : List[str] = """ """ * int(math.pow(2 , layer - 1 ) )
output += space
if node is None:
output += "*"
q.push(SCREAMING_SNAKE_CASE_ )
q.push(SCREAMING_SNAKE_CASE_ )
else:
output += str(node.get_data() )
q.push(node.get_left() )
q.push(node.get_right() )
output += space
UpperCamelCase : str = cnt + 1
for i in range(100 ):
if cnt == math.pow(2 , SCREAMING_SNAKE_CASE_ ) - 1:
UpperCamelCase : Tuple = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def A_ ( ):
'''simple docstring'''
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
__A : str = AVLtree()
__A : List[str] = list(range(10))
random.shuffle(lst)
for i in lst:
t.insert(i)
print(str(t))
random.shuffle(lst)
for i in lst:
t.del_node(i)
print(str(t))
| 359 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=1 , ):
UpperCamelCase : Tuple = parent
UpperCamelCase : Optional[int] = batch_size
UpperCamelCase : Optional[Any] = seq_length
UpperCamelCase : int = is_training
UpperCamelCase : Union[str, Any] = use_input_mask
UpperCamelCase : Union[str, Any] = use_token_type_ids
UpperCamelCase : Dict = use_labels
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Union[str, Any] = hidden_size
UpperCamelCase : Tuple = num_hidden_layers
UpperCamelCase : Any = num_attention_heads
UpperCamelCase : int = intermediate_size
UpperCamelCase : str = hidden_act
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : List[Any] = max_position_embeddings
UpperCamelCase : Optional[Any] = type_vocab_size
UpperCamelCase : int = type_sequence_label_size
UpperCamelCase : Dict = initializer_range
UpperCamelCase : Dict = num_labels
UpperCamelCase : Tuple = num_choices
UpperCamelCase : Optional[int] = scope
UpperCamelCase : List[Any] = q_groups
UpperCamelCase : Tuple = k_groups
UpperCamelCase : Any = v_groups
UpperCamelCase : List[str] = post_attention_groups
UpperCamelCase : Tuple = intermediate_groups
UpperCamelCase : int = output_groups
def a_ ( self ):
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Tuple = None
if self.use_input_mask:
UpperCamelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Optional[int] = None
UpperCamelCase : List[Any] = None
UpperCamelCase : Dict = None
if self.use_labels:
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : Dict = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ ( self ):
return SqueezeBertConfig(
embedding_size=self.hidden_size , vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = SqueezeBertModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = SqueezeBertForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = SqueezeBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : str = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = self.num_labels
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = self.num_labels
UpperCamelCase : str = SqueezeBertForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = self.num_choices
UpperCamelCase : Tuple = SqueezeBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Union[str, Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) : Optional[int] = config_and_inputs
UpperCamelCase : Optional[int] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
lowercase : Dict = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase : Dict = False
lowercase : str = True
lowercase : str = False
def a_ ( self ):
UpperCamelCase : Any = SqueezeBertModelTester(self )
UpperCamelCase : List[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : Optional[Any] = SqueezeBertModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_sentencepiece
@require_tokenizers
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli""" )
UpperCamelCase : Dict = torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]] )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[Any] = torch.Size((1, 3) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.tensor([[0.6401, -0.0349, -0.6041]] )
self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 27 | 0 |
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def A_ ( snake_case_ : Callable ):
'''simple docstring'''
@wraps(snake_case_ )
def _inner_fn(*snake_case_ : Dict ,**snake_case_ : int ):
warnings.warn(
(f'\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.') ,snake_case_ ,)
return fn(*snake_case_ ,**snake_case_ )
return _inner_fn
| 360 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "geglu" , SCREAMING_SNAKE_CASE_ = None , ):
super().__init__()
UpperCamelCase : int = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=SCREAMING_SNAKE_CASE_ , attention_head_dim=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , dropout=SCREAMING_SNAKE_CASE_ , norm_num_groups=SCREAMING_SNAKE_CASE_ , cross_attention_dim=SCREAMING_SNAKE_CASE_ , attention_bias=SCREAMING_SNAKE_CASE_ , sample_size=SCREAMING_SNAKE_CASE_ , num_vector_embeds=SCREAMING_SNAKE_CASE_ , activation_fn=SCREAMING_SNAKE_CASE_ , num_embeds_ada_norm=SCREAMING_SNAKE_CASE_ , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
UpperCamelCase : Optional[Any] = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
UpperCamelCase : List[Any] = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
UpperCamelCase : int = [1, 0]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_ = True , ):
UpperCamelCase : Dict = hidden_states
UpperCamelCase : Optional[Any] = []
UpperCamelCase : List[Any] = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
UpperCamelCase : Optional[int] = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
UpperCamelCase : str = self.transformer_index_for_condition[i]
UpperCamelCase : Any = self.transformers[transformer_index](
SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ , timestep=SCREAMING_SNAKE_CASE_ , cross_attention_kwargs=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
UpperCamelCase : Any = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
UpperCamelCase : List[str] = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE_ )
| 27 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
__A : Dict = '''\
Text data.
Second line of data.'''
__A : Optional[Any] = '''file'''
@pytest.fixture(scope="""session""" )
def A_ ( snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : str = tmp_path_factory.mktemp("""data""" ) / (FILE_PATH + """.zstd""")
UpperCamelCase : List[Any] = bytes(snake_case_ ,"""utf-8""" )
with zstd.open(snake_case_ ,"""wb""" ) as f:
f.write(snake_case_ )
return path
@pytest.fixture
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir ,snake_case_ ) ,"""w""" ) as f:
f.write(snake_case_ )
return FILE_PATH
@pytest.mark.parametrize("""compression_format""" ,["""gzip""", """xz""", """zstd"""] )
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ,snake_case_ : Optional[int] ,snake_case_ : List[Any] ,snake_case_ : str ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = {"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_path}
UpperCamelCase : str = input_paths[compression_format]
UpperCamelCase : Union[str, Any] = tmp_path / """cache"""
UpperCamelCase : List[Any] = DownloadConfig(cache_dir=snake_case_ ,extract_compressed_file=snake_case_ )
UpperCamelCase : Tuple = cached_path(snake_case_ ,download_config=snake_case_ )
with open(snake_case_ ) as f:
UpperCamelCase : Dict = f.read()
with open(snake_case_ ) as f:
UpperCamelCase : Optional[int] = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize("""default_extracted""" ,[True, False] )
@pytest.mark.parametrize("""default_cache_dir""" ,[True, False] )
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : List[Any] ,snake_case_ : Optional[int] ,snake_case_ : Dict ,snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = """custom_cache"""
UpperCamelCase : str = """custom_extracted_dir"""
UpperCamelCase : Optional[Any] = tmp_path / """custom_extracted_path"""
if default_extracted:
UpperCamelCase : Optional[Any] = ("""downloads""" if default_cache_dir else custom_cache_dir, """extracted""")
else:
monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_DIR""" ,snake_case_ )
monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH""" ,str(snake_case_ ) )
UpperCamelCase : str = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
UpperCamelCase : int = xz_file
UpperCamelCase : List[str] = (
DownloadConfig(extract_compressed_file=snake_case_ )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir ,extract_compressed_file=snake_case_ )
)
UpperCamelCase : List[Any] = cached_path(snake_case_ ,download_config=snake_case_ )
assert Path(snake_case_ ).parent.parts[-2:] == expected
def A_ ( snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : Tuple = str(Path(snake_case_ ).resolve() )
assert cached_path(snake_case_ ) == text_file
# relative path
UpperCamelCase : str = str(Path(snake_case_ ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(snake_case_ ) == text_file
def A_ ( snake_case_ : Tuple ):
'''simple docstring'''
UpperCamelCase : Any = str(tmp_path.resolve() / """__missing_file__.txt""" )
with pytest.raises(snake_case_ ):
cached_path(snake_case_ )
# relative path
UpperCamelCase : Any = """./__missing_file__.txt"""
with pytest.raises(snake_case_ ):
cached_path(snake_case_ )
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : str = get_from_cache(f'tmp://{tmpfs_file}' )
with open(snake_case_ ) as f:
UpperCamelCase : Any = f.read()
assert output_file_content == FILE_CONTENT
@patch("""datasets.config.HF_DATASETS_OFFLINE""" ,snake_case_ )
def A_ ( ):
'''simple docstring'''
with pytest.raises(snake_case_ ):
cached_path("""https://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" ,snake_case_ )
def A_ ( snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : Any = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(snake_case_ ):
http_get("""https://huggingface.co""" ,temp_file=snake_case_ )
with pytest.raises(snake_case_ ):
http_head("""https://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" ,snake_case_ )
def A_ ( snake_case_ : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase : List[Any] = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(snake_case_ ):
ftp_get("""ftp://huggingface.co""" ,temp_file=snake_case_ )
with pytest.raises(snake_case_ ):
ftp_head("""ftp://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" ,snake_case_ )
def A_ ( snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : Any = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(snake_case_ ):
fsspec_get("""s3://huggingface.co""" ,temp_file=snake_case_ )
with pytest.raises(snake_case_ ):
fsspec_head("""s3://huggingface.co""" )
| 361 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Optional[int] = 'mvp'
lowercase : Optional[Any] = ['past_key_values']
lowercase : Union[str, Any] = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , SCREAMING_SNAKE_CASE_=5_0267 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=100 , SCREAMING_SNAKE_CASE_=800 , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Dict = max_position_embeddings
UpperCamelCase : Optional[int] = d_model
UpperCamelCase : Optional[Any] = encoder_ffn_dim
UpperCamelCase : Any = encoder_layers
UpperCamelCase : List[Any] = encoder_attention_heads
UpperCamelCase : Optional[Any] = decoder_ffn_dim
UpperCamelCase : Optional[int] = decoder_layers
UpperCamelCase : Dict = decoder_attention_heads
UpperCamelCase : List[str] = dropout
UpperCamelCase : List[str] = attention_dropout
UpperCamelCase : List[Any] = activation_dropout
UpperCamelCase : Dict = activation_function
UpperCamelCase : List[str] = init_std
UpperCamelCase : int = encoder_layerdrop
UpperCamelCase : Dict = decoder_layerdrop
UpperCamelCase : Any = classifier_dropout
UpperCamelCase : Tuple = use_cache
UpperCamelCase : Dict = encoder_layers
UpperCamelCase : Tuple = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase : Optional[Any] = use_prompt
UpperCamelCase : Any = prompt_length
UpperCamelCase : List[Any] = prompt_mid_dim
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , forced_eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""" , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = self.bos_token_id
warnings.warn(
f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
"""The config can simply be saved and uploaded again to be fixed.""" )
| 27 | 0 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
__A : Union[str, Any] = {
'''return_dict''': False,
'''output_hidden_states''': True,
'''output_attentions''': True,
'''torchscript''': True,
'''torch_dtype''': '''float16''',
'''use_bfloat16''': True,
'''tf_legacy_loss''': True,
'''pruned_heads''': {'''a''': 1},
'''tie_word_embeddings''': False,
'''is_decoder''': True,
'''cross_attention_hidden_size''': 128,
'''add_cross_attention''': True,
'''tie_encoder_decoder''': True,
'''max_length''': 50,
'''min_length''': 3,
'''do_sample''': True,
'''early_stopping''': True,
'''num_beams''': 3,
'''num_beam_groups''': 3,
'''diversity_penalty''': 0.5,
'''temperature''': 2.0,
'''top_k''': 10,
'''top_p''': 0.7,
'''typical_p''': 0.2,
'''repetition_penalty''': 0.8,
'''length_penalty''': 0.8,
'''no_repeat_ngram_size''': 5,
'''encoder_no_repeat_ngram_size''': 5,
'''bad_words_ids''': [1, 2, 3],
'''num_return_sequences''': 3,
'''chunk_size_feed_forward''': 5,
'''output_scores''': True,
'''return_dict_in_generate''': True,
'''forced_bos_token_id''': 2,
'''forced_eos_token_id''': 3,
'''remove_invalid_values''': True,
'''architectures''': ['''BertModel'''],
'''finetuning_task''': '''translation''',
'''id2label''': {0: '''label'''},
'''label2id''': {'''label''': '''0'''},
'''tokenizer_class''': '''BertTokenizerFast''',
'''prefix''': '''prefix''',
'''bos_token_id''': 6,
'''pad_token_id''': 7,
'''eos_token_id''': 8,
'''sep_token_id''': 9,
'''decoder_start_token_id''': 10,
'''exponential_decay_length_penalty''': (5, 1.01),
'''suppress_tokens''': [0, 1],
'''begin_suppress_tokens''': 2,
'''task_specific_params''': {'''translation''': '''some_params'''},
'''problem_type''': '''regression''',
}
@is_staging_test
class lowerCamelCase ( unittest.TestCase ):
@classmethod
def a_ ( cls ):
UpperCamelCase : List[Any] = TOKEN
HfFolder.save_token(SCREAMING_SNAKE_CASE_ )
@classmethod
def a_ ( cls ):
try:
delete_repo(token=cls._token , repo_id="""test-config""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-config-org""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""test-dynamic-config""" )
except HTTPError:
pass
def a_ ( self ):
UpperCamelCase : Any = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub("""test-config""" , use_auth_token=self._token )
UpperCamelCase : str = BertConfig.from_pretrained(f'{USER}/test-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""test-config""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(SCREAMING_SNAKE_CASE_ , repo_id="""test-config""" , push_to_hub=SCREAMING_SNAKE_CASE_ , use_auth_token=self._token )
UpperCamelCase : Optional[int] = BertConfig.from_pretrained(f'{USER}/test-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self ):
UpperCamelCase : int = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub("""valid_org/test-config-org""" , use_auth_token=self._token )
UpperCamelCase : Optional[int] = BertConfig.from_pretrained("""valid_org/test-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-config-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
SCREAMING_SNAKE_CASE_ , repo_id="""valid_org/test-config-org""" , push_to_hub=SCREAMING_SNAKE_CASE_ , use_auth_token=self._token )
UpperCamelCase : Union[str, Any] = BertConfig.from_pretrained("""valid_org/test-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self ):
CustomConfig.register_for_auto_class()
UpperCamelCase : Any = CustomConfig(attribute=42 )
config.push_to_hub("""test-dynamic-config""" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {"""AutoConfig""": """custom_configuration.CustomConfig"""} )
UpperCamelCase : Dict = AutoConfig.from_pretrained(f'{USER}/test-dynamic-config' , trust_remote_code=SCREAMING_SNAKE_CASE_ )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , """CustomConfig""" )
self.assertEqual(new_config.attribute , 42 )
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
UpperCamelCase : str = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
UpperCamelCase : Dict = c.n_embd + 1 # int
UpperCamelCase : Dict = c.resid_pdrop + 1.0 # float
UpperCamelCase : Union[str, Any] = not c.scale_attn_weights # bool
UpperCamelCase : Tuple = c.summary_type + """foo""" # str
c.update_from_string(
f'n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}' )
self.assertEqual(SCREAMING_SNAKE_CASE_ , c.n_embd , """mismatch for key: n_embd""" )
self.assertEqual(SCREAMING_SNAKE_CASE_ , c.resid_pdrop , """mismatch for key: resid_pdrop""" )
self.assertEqual(SCREAMING_SNAKE_CASE_ , c.scale_attn_weights , """mismatch for key: scale_attn_weights""" )
self.assertEqual(SCREAMING_SNAKE_CASE_ , c.summary_type , """mismatch for key: summary_type""" )
def a_ ( self ):
UpperCamelCase : Union[str, Any] = PretrainedConfig()
UpperCamelCase : int = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , ["""is_encoder_decoder""", """_name_or_path""", """_commit_hash""", """transformers_version"""] )
UpperCamelCase : Tuple = [key for key, value in config_common_kwargs.items() if value == getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )]
if len(SCREAMING_SNAKE_CASE_ ) > 0:
raise ValueError(
"""The following keys are set with the default values in"""
""" `test_configuration_common.config_common_kwargs` pick another value for them:"""
f' {", ".join(SCREAMING_SNAKE_CASE_ )}.' )
def a_ ( self ):
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
# config is in subfolder, the following should not work without specifying the subfolder
UpperCamelCase : List[Any] = BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert-subfolder""" )
UpperCamelCase : Dict = BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert-subfolder""" , subfolder="""bert""" )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
# A mock response for an HTTP head request to emulate server down
UpperCamelCase : str = mock.Mock()
UpperCamelCase : Union[str, Any] = 500
UpperCamelCase : Optional[int] = {}
UpperCamelCase : List[Any] = HTTPError
UpperCamelCase : Union[str, Any] = {}
# Download this model to make sure it's in the cache.
UpperCamelCase : Optional[Any] = BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("""requests.Session.request""" , return_value=SCREAMING_SNAKE_CASE_ ) as mock_head:
UpperCamelCase : Tuple = BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
# This check we did call the fake head request
mock_head.assert_called()
def a_ ( self ):
# This test is for deprecated behavior and can be removed in v5
UpperCamelCase : int = BertConfig.from_pretrained(
"""https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json""" )
def a_ ( self ):
UpperCamelCase : Union[str, Any] = AutoConfig.from_pretrained("""bert-base-cased""" )
UpperCamelCase : Optional[Any] = ["""config.4.0.0.json"""]
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = 2
json.dump(configuration.to_dict() , open(os.path.join(SCREAMING_SNAKE_CASE_ , """config.4.0.0.json""" ) , """w""" ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
UpperCamelCase : Union[str, Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
UpperCamelCase : List[Any] = ["""config.42.0.0.json"""]
UpperCamelCase : Optional[int] = 768
configuration.save_pretrained(SCREAMING_SNAKE_CASE_ )
shutil.move(os.path.join(SCREAMING_SNAKE_CASE_ , """config.4.0.0.json""" ) , os.path.join(SCREAMING_SNAKE_CASE_ , """config.42.0.0.json""" ) )
UpperCamelCase : List[str] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertEqual(new_configuration.hidden_size , 768 )
def a_ ( self ):
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
UpperCamelCase : List[Any] = """hf-internal-testing/test-two-configs"""
import transformers as new_transformers
UpperCamelCase : Optional[Any] = """v4.0.0"""
UpperCamelCase : Union[str, Any] = new_transformers.models.auto.AutoConfig.from_pretrained(
SCREAMING_SNAKE_CASE_ , return_unused_kwargs=SCREAMING_SNAKE_CASE_ )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(SCREAMING_SNAKE_CASE_ , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
UpperCamelCase : Dict = """v3.0.0"""
UpperCamelCase : int = old_transformers.models.auto.AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertEqual(old_configuration.hidden_size , 768 )
| 362 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__A : Optional[Any] = 16
__A : str = 32
def A_ ( snake_case_ : Accelerator ,snake_case_ : int = 1_6 ):
'''simple docstring'''
UpperCamelCase : Tuple = AutoTokenizer.from_pretrained("""bert-base-cased""" )
UpperCamelCase : Optional[int] = load_dataset("""glue""" ,"""mrpc""" )
def tokenize_function(snake_case_ : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase : Union[str, Any] = tokenizer(examples["""sentence1"""] ,examples["""sentence2"""] ,truncation=snake_case_ ,max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCamelCase : Optional[Any] = datasets.map(
snake_case_ ,batched=snake_case_ ,remove_columns=["""idx""", """sentence1""", """sentence2"""] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase : str = tokenized_datasets.rename_column("""label""" ,"""labels""" )
def collate_fn(snake_case_ : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCamelCase : Union[str, Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCamelCase : Optional[Any] = 1_6
elif accelerator.mixed_precision != "no":
UpperCamelCase : Any = 8
else:
UpperCamelCase : Optional[Any] = None
return tokenizer.pad(
snake_case_ ,padding="""longest""" ,max_length=snake_case_ ,pad_to_multiple_of=snake_case_ ,return_tensors="""pt""" ,)
# Instantiate dataloaders.
UpperCamelCase : str = DataLoader(
tokenized_datasets["""train"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
UpperCamelCase : Dict = DataLoader(
tokenized_datasets["""validation"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__A : int = mocked_dataloaders # noqa: F811
def A_ ( snake_case_ : Tuple ,snake_case_ : Dict ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,snake_case_ ) == "1":
UpperCamelCase : Union[str, Any] = 2
# New Code #
UpperCamelCase : Dict = int(args.gradient_accumulation_steps )
UpperCamelCase : List[Any] = int(args.local_sgd_steps )
# Initialize accelerator
UpperCamelCase : str = Accelerator(
cpu=args.cpu ,mixed_precision=args.mixed_precision ,gradient_accumulation_steps=snake_case_ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase : Union[str, Any] = config["""lr"""]
UpperCamelCase : int = int(config["""num_epochs"""] )
UpperCamelCase : int = int(config["""seed"""] )
UpperCamelCase : List[Any] = int(config["""batch_size"""] )
UpperCamelCase : Optional[int] = evaluate.load("""glue""" ,"""mrpc""" )
set_seed(snake_case_ )
UpperCamelCase , UpperCamelCase : Dict = get_dataloaders(snake_case_ ,snake_case_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase : Optional[int] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,return_dict=snake_case_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCamelCase : Tuple = model.to(accelerator.device )
# Instantiate optimizer
UpperCamelCase : List[Any] = AdamW(params=model.parameters() ,lr=snake_case_ )
# Instantiate scheduler
UpperCamelCase : str = get_linear_schedule_with_warmup(
optimizer=snake_case_ ,num_warmup_steps=1_0_0 ,num_training_steps=(len(snake_case_ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Any = accelerator.prepare(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
# Now we train the model
for epoch in range(snake_case_ ):
model.train()
with LocalSGD(
accelerator=snake_case_ ,model=snake_case_ ,local_sgd_steps=snake_case_ ,enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(snake_case_ ):
UpperCamelCase : Optional[Any] = model(**snake_case_ )
UpperCamelCase : Optional[int] = output.loss
accelerator.backward(snake_case_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCamelCase : Any = model(**snake_case_ )
UpperCamelCase : Tuple = outputs.logits.argmax(dim=-1 )
UpperCamelCase , UpperCamelCase : int = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=snake_case_ ,references=snake_case_ ,)
UpperCamelCase : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' ,snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" ,type=snake_case_ ,default=snake_case_ ,choices=["""no""", """fp16""", """bf16""", """fp8"""] ,help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" ,)
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" ,type=snake_case_ ,default=1 ,help="""The number of minibatches to be ran before gradients are accumulated.""" ,)
parser.add_argument(
"""--local_sgd_steps""" ,type=snake_case_ ,default=8 ,help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" ,action="""store_true""" ,help="""If passed, will train on the CPU.""" )
UpperCamelCase : Dict = parser.parse_args()
UpperCamelCase : List[Any] = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(snake_case_ ,snake_case_ )
if __name__ == "__main__":
main()
| 27 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCamelCase :
lowercase : int
lowercase : int
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : list[list[Edge]] = [[] for _ in range(SCREAMING_SNAKE_CASE_ )]
UpperCamelCase : str = size
def __getitem__( self , SCREAMING_SNAKE_CASE_ ):
return iter(self._graph[vertex] )
@property
def a_ ( self ):
return self._size
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if weight not in (0, 1):
raise ValueError("""Edge weight must be either 0 or 1.""" )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("""Vertex indexes must be in [0; size).""" )
self._graph[from_vertex].append(Edge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = deque([start_vertex] )
UpperCamelCase : list[int | None] = [None] * self.size
UpperCamelCase : str = 0
while queue:
UpperCamelCase : str = queue.popleft()
UpperCamelCase : Optional[Any] = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
UpperCamelCase : Dict = current_distance + edge.weight
UpperCamelCase : Dict = distances[edge.destination_vertex]
if (
isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
and new_distance >= dest_vertex_distance
):
continue
UpperCamelCase : int = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("""No path from start_vertex to finish_vertex.""" )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 363 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A : Optional[Any] = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__A : Any = {'''allegro/herbert-base-cased''': 514}
__A : Optional[Any] = {}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Dict = VOCAB_FILES_NAMES
lowercase : Any = PRETRAINED_VOCAB_FILES_MAP
lowercase : List[str] = PRETRAINED_INIT_CONFIGURATION
lowercase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Union[str, Any] = HerbertTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_="</s>" , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = [self.cls_token_id]
UpperCamelCase : str = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Tuple = [self.sep_token_id]
UpperCamelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Optional[int] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 27 | 0 |
"""simple docstring"""
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__A : Optional[int] = Mapping[str, np.ndarray]
__A : Optional[Any] = Mapping[str, Any] # Is a nested dict.
__A : List[Any] = 0.01
@dataclasses.dataclass(frozen=_UpperCAmelCase )
class lowerCamelCase :
lowercase : np.ndarray # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
lowercase : np.ndarray # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
lowercase : np.ndarray # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
lowercase : np.ndarray # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
lowercase : np.ndarray # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
lowercase : Optional[np.ndarray] = None
# Optional remark about the protein. Included as a comment in output PDB
# files
lowercase : Optional[str] = None
# Templates used to generate this protein (prediction-only)
lowercase : Optional[Sequence[str]] = None
# Chain corresponding to each parent
lowercase : Optional[Sequence[int]] = None
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = R"""(\[[A-Z]+\]\n)"""
UpperCamelCase : List[str] = [tag.strip() for tag in re.split(snake_case_ ,snake_case_ ) if len(snake_case_ ) > 0]
UpperCamelCase : Iterator[Tuple[str, List[str]]] = zip(tags[0::2] ,[l.split("""\n""" ) for l in tags[1::2]] )
UpperCamelCase : List[str] = ["N", "CA", "C"]
UpperCamelCase : List[str] = None
UpperCamelCase : Optional[int] = None
UpperCamelCase : Any = None
for g in groups:
if "[PRIMARY]" == g[0]:
UpperCamelCase : str = g[1][0].strip()
for i in range(len(snake_case_ ) ):
if seq[i] not in residue_constants.restypes:
UpperCamelCase : Dict = """X""" # FIXME: strings are immutable
UpperCamelCase : str = np.array(
[residue_constants.restype_order.get(snake_case_ ,residue_constants.restype_num ) for res_symbol in seq] )
elif "[TERTIARY]" == g[0]:
UpperCamelCase : List[List[float]] = []
for axis in range(3 ):
tertiary.append(list(map(snake_case_ ,g[1][axis].split() ) ) )
UpperCamelCase : Dict = np.array(snake_case_ )
UpperCamelCase : Tuple = np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
for i, atom in enumerate(snake_case_ ):
UpperCamelCase : str = np.transpose(tertiary_np[:, i::3] )
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
UpperCamelCase : Any = np.array(list(map({"""-""": 0, """+""": 1}.get ,g[1][0].strip() ) ) )
UpperCamelCase : Tuple = np.zeros(
(
len(snake_case_ ),
residue_constants.atom_type_num,
) ).astype(np.floataa )
for i, atom in enumerate(snake_case_ ):
UpperCamelCase : List[Any] = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=snake_case_ ,atom_mask=snake_case_ ,aatype=snake_case_ ,residue_index=np.arange(len(snake_case_ ) ) ,b_factors=snake_case_ ,)
def A_ ( snake_case_ : Protein ,snake_case_ : int = 0 ):
'''simple docstring'''
UpperCamelCase : List[str] = []
UpperCamelCase : Dict = prot.remark
if remark is not None:
pdb_headers.append(f'REMARK {remark}' )
UpperCamelCase : Optional[int] = prot.parents
UpperCamelCase : List[Any] = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
UpperCamelCase : List[Any] = [p for i, p in zip(snake_case_ ,snake_case_ ) if i == chain_id]
if parents is None or len(snake_case_ ) == 0:
UpperCamelCase : Any = ["""N/A"""]
pdb_headers.append(f'PARENT {" ".join(snake_case_ )}' )
return pdb_headers
def A_ ( snake_case_ : Protein ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : List[str] = []
UpperCamelCase : Dict = pdb_str.split("""\n""" )
UpperCamelCase : Optional[int] = prot.remark
if remark is not None:
out_pdb_lines.append(f'REMARK {remark}' )
UpperCamelCase : List[List[str]]
if prot.parents is not None and len(prot.parents ) > 0:
UpperCamelCase : Optional[int] = []
if prot.parents_chain_index is not None:
UpperCamelCase : Dict[str, List[str]] = {}
for p, i in zip(prot.parents ,prot.parents_chain_index ):
parent_dict.setdefault(str(snake_case_ ) ,[] )
parent_dict[str(snake_case_ )].append(snake_case_ )
UpperCamelCase : str = max([int(snake_case_ ) for chain_idx in parent_dict] )
for i in range(max_idx + 1 ):
UpperCamelCase : Optional[int] = parent_dict.get(str(snake_case_ ) ,["""N/A"""] )
parents_per_chain.append(snake_case_ )
else:
parents_per_chain.append(list(prot.parents ) )
else:
UpperCamelCase : List[str] = [["""N/A"""]]
def make_parent_line(snake_case_ : Sequence[str] ) -> str:
return f'PARENT {" ".join(snake_case_ )}'
out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )
UpperCamelCase : Any = 0
for i, l in enumerate(snake_case_ ):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(snake_case_ )
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(snake_case_ ):
UpperCamelCase : str = parents_per_chain[chain_counter]
else:
UpperCamelCase : Tuple = ["""N/A"""]
out_pdb_lines.append(make_parent_line(snake_case_ ) )
return "\n".join(snake_case_ )
def A_ ( snake_case_ : Protein ):
'''simple docstring'''
UpperCamelCase : Dict = residue_constants.restypes + ["""X"""]
def res_atoa(snake_case_ : int ) -> str:
return residue_constants.restype_atoa.get(restypes[r] ,"""UNK""" )
UpperCamelCase : Dict = residue_constants.atom_types
UpperCamelCase : List[str] = []
UpperCamelCase : int = prot.atom_mask
UpperCamelCase : str = prot.aatype
UpperCamelCase : Tuple = prot.atom_positions
UpperCamelCase : List[str] = prot.residue_index.astype(np.intaa )
UpperCamelCase : int = prot.b_factors
UpperCamelCase : Union[str, Any] = prot.chain_index
if np.any(aatype > residue_constants.restype_num ):
raise ValueError("""Invalid aatypes.""" )
UpperCamelCase : int = get_pdb_headers(snake_case_ )
if len(snake_case_ ) > 0:
pdb_lines.extend(snake_case_ )
UpperCamelCase : Dict = aatype.shape[0]
UpperCamelCase : int = 1
UpperCamelCase : Optional[int] = 0
UpperCamelCase : Union[str, Any] = string.ascii_uppercase
UpperCamelCase : List[Any] = None
# Add all atom sites.
for i in range(snake_case_ ):
UpperCamelCase : Any = res_atoa(aatype[i] )
for atom_name, pos, mask, b_factor in zip(snake_case_ ,atom_positions[i] ,atom_mask[i] ,b_factors[i] ):
if mask < 0.5:
continue
UpperCamelCase : Any = """ATOM"""
UpperCamelCase : Union[str, Any] = atom_name if len(snake_case_ ) == 4 else f' {atom_name}'
UpperCamelCase : int = """"""
UpperCamelCase : Optional[int] = """"""
UpperCamelCase : int = 1.00
UpperCamelCase : List[str] = atom_name[0] # Protein supports only C, N, O, S, this works.
UpperCamelCase : Optional[int] = """"""
UpperCamelCase : List[Any] = """A"""
if chain_index is not None:
UpperCamelCase : str = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
UpperCamelCase : List[str] = (
f'{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'
f'{res_name_a:>3} {chain_tag:>1}'
f'{residue_index[i]:>4}{insertion_code:>1} '
f'{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'
f'{occupancy:>6.2f}{b_factor:>6.2f} '
f'{element:>2}{charge:>2}'
)
pdb_lines.append(snake_case_ )
atom_index += 1
UpperCamelCase : List[str] = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
UpperCamelCase : int = True
UpperCamelCase : Dict = chain_index[i + 1]
if should_terminate:
# Close the chain.
UpperCamelCase : Optional[Any] = """TER"""
UpperCamelCase : Union[str, Any] = (
f'{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}'
)
pdb_lines.append(snake_case_ )
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(snake_case_ ,snake_case_ ) )
pdb_lines.append("""END""" )
pdb_lines.append("""""" )
return "\n".join(snake_case_ )
def A_ ( snake_case_ : Protein ):
'''simple docstring'''
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def A_ ( snake_case_ : FeatureDict ,snake_case_ : ModelOutput ,snake_case_ : Optional[np.ndarray] = None ,snake_case_ : Optional[np.ndarray] = None ,snake_case_ : Optional[str] = None ,snake_case_ : Optional[Sequence[str]] = None ,snake_case_ : Optional[Sequence[int]] = None ,):
'''simple docstring'''
return Protein(
aatype=features["""aatype"""] ,atom_positions=result["""final_atom_positions"""] ,atom_mask=result["""final_atom_mask"""] ,residue_index=features["""residue_index"""] + 1 ,b_factors=b_factors if b_factors is not None else np.zeros_like(result["""final_atom_mask"""] ) ,chain_index=snake_case_ ,remark=snake_case_ ,parents=snake_case_ ,parents_chain_index=snake_case_ ,)
| 364 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=3.6 ):
UpperCamelCase : Dict = tokenizer
UpperCamelCase : Optional[Any] = tokenizer.bos_token_id
UpperCamelCase : Any = dataset
UpperCamelCase : List[str] = seq_length
UpperCamelCase : Optional[Any] = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
UpperCamelCase : Dict = iter(self.dataset )
UpperCamelCase : Union[str, Any] = True
while more_examples:
UpperCamelCase , UpperCamelCase : Tuple = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(SCREAMING_SNAKE_CASE_ )["""content"""] )
buffer_len += len(buffer[-1] )
except StopIteration:
UpperCamelCase : Dict = False
break
UpperCamelCase : str = tokenizer(SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )["""input_ids"""]
UpperCamelCase : str = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , self.seq_length ):
UpperCamelCase : List[str] = all_token_ids[i : i + self.seq_length]
if len(SCREAMING_SNAKE_CASE_ ) == self.seq_length:
yield torch.tensor(SCREAMING_SNAKE_CASE_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : Dict = {"""streaming""": True}
UpperCamelCase : Optional[int] = load_dataset(args.dataset_name ,split="""train""" ,**snake_case_ )
UpperCamelCase : Optional[int] = ConstantLengthDataset(snake_case_ ,snake_case_ ,seq_length=args.seq_length )
UpperCamelCase : List[Any] = DataLoader(snake_case_ ,batch_size=args.batch_size )
return eval_dataloader
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
model.eval()
UpperCamelCase : Dict = []
for step, batch in enumerate(snake_case_ ):
with torch.no_grad():
UpperCamelCase : List[Any] = model(snake_case_ ,labels=snake_case_ )
UpperCamelCase : Any = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(snake_case_ ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
UpperCamelCase : Dict = torch.mean(torch.cat(snake_case_ ) )
try:
UpperCamelCase : Dict = torch.exp(snake_case_ )
except OverflowError:
UpperCamelCase : Optional[int] = float("""inf""" )
return loss.item(), perplexity.item()
# Setup Accelerator
__A : List[Any] = Accelerator()
# Parse configuration
__A : str = HfArgumentParser(EvaluationArguments)
__A : List[Any] = parser.parse_args()
set_seed(args.seed)
# Logging
__A : Any = logging.getLogger(__name__)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
# Load model and tokenizer
__A : List[Any] = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__A : List[Any] = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
__A : int = create_dataloader(args)
# Prepare everything with our `accelerator`.
__A , __A : Optional[Any] = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
__A , __A : Tuple = evaluate(args)
logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 27 | 0 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Tuple = BertTokenizer
lowercase : int = BertTokenizerFast
lowercase : Any = True
lowercase : List[str] = True
lowercase : List[Any] = filter_non_english
def a_ ( self ):
super().setUp()
UpperCamelCase : List[str] = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
UpperCamelCase : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = """UNwant\u00E9d,running"""
UpperCamelCase : Tuple = """unwanted, running"""
return input_text, output_text
def a_ ( self ):
UpperCamelCase : List[Any] = self.tokenizer_class(self.vocab_file )
UpperCamelCase : List[str] = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [9, 6, 7, 12, 10, 11] )
def a_ ( self ):
if not self.test_rust_tokenizer:
return
UpperCamelCase : Any = self.get_tokenizer()
UpperCamelCase : Dict = self.get_rust_tokenizer()
UpperCamelCase : List[str] = """UNwant\u00E9d,running"""
UpperCamelCase : Union[str, Any] = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = self.get_rust_tokenizer()
UpperCamelCase : Tuple = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# With lower casing
UpperCamelCase : List[str] = self.get_tokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = self.get_rust_tokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = """UNwant\u00E9d,running"""
UpperCamelCase : List[str] = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = self.get_rust_tokenizer()
UpperCamelCase : Any = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )
def a_ ( self ):
UpperCamelCase : Any = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def a_ ( self ):
UpperCamelCase : Any = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] )
def a_ ( self ):
UpperCamelCase : Optional[Any] = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def a_ ( self ):
UpperCamelCase : Union[str, Any] = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def a_ ( self ):
UpperCamelCase : Tuple = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def a_ ( self ):
UpperCamelCase : Any = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def a_ ( self ):
UpperCamelCase : Any = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def a_ ( self ):
UpperCamelCase : List[Any] = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , never_split=["""[UNK]"""] )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )
def a_ ( self ):
UpperCamelCase : Any = BasicTokenizer()
UpperCamelCase : Any = """a\n'll !!to?'d of, can't."""
UpperCamelCase : Dict = ["""a""", """'""", """ll""", """!""", """!""", """to""", """?""", """'""", """d""", """of""", """,""", """can""", """'""", """t""", """."""]
self.assertListEqual(tokenizer.tokenize(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
UpperCamelCase : int = {}
for i, token in enumerate(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = i
UpperCamelCase : Dict = WordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE_ , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] )
def a_ ( self ):
self.assertTrue(_is_whitespace(""" """ ) )
self.assertTrue(_is_whitespace("""\t""" ) )
self.assertTrue(_is_whitespace("""\r""" ) )
self.assertTrue(_is_whitespace("""\n""" ) )
self.assertTrue(_is_whitespace("""\u00A0""" ) )
self.assertFalse(_is_whitespace("""A""" ) )
self.assertFalse(_is_whitespace("""-""" ) )
def a_ ( self ):
self.assertTrue(_is_control("""\u0005""" ) )
self.assertFalse(_is_control("""A""" ) )
self.assertFalse(_is_control(""" """ ) )
self.assertFalse(_is_control("""\t""" ) )
self.assertFalse(_is_control("""\r""" ) )
def a_ ( self ):
self.assertTrue(_is_punctuation("""-""" ) )
self.assertTrue(_is_punctuation("""$""" ) )
self.assertTrue(_is_punctuation("""`""" ) )
self.assertTrue(_is_punctuation(""".""" ) )
self.assertFalse(_is_punctuation("""A""" ) )
self.assertFalse(_is_punctuation(""" """ ) )
def a_ ( self ):
UpperCamelCase : str = self.get_tokenizer()
UpperCamelCase : Dict = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(SCREAMING_SNAKE_CASE_ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
self.assertListEqual(
[rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
@slow
def a_ ( self ):
UpperCamelCase : Union[str, Any] = self.tokenizer_class.from_pretrained("""bert-base-uncased""" )
UpperCamelCase : List[Any] = tokenizer.encode("""sequence builders""" , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = tokenizer.encode("""multi-sequence build""" , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert encoded_sentence == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_a + [102]
def a_ ( self ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
UpperCamelCase : Optional[Any] = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
UpperCamelCase : Any = tokenizer_r.encode_plus(
SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , return_token_type_ids=SCREAMING_SNAKE_CASE_ , return_offsets_mapping=SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : List[Any] = tokenizer_r.do_lower_case if hasattr(SCREAMING_SNAKE_CASE_ , """do_lower_case""" ) else False
UpperCamelCase : Dict = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), """A"""),
((1, 2), ""","""),
((3, 5), """na"""),
((5, 6), """##ï"""),
((6, 8), """##ve"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """Allen"""),
((21, 23), """##NL"""),
((23, 24), """##P"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), """a"""),
((1, 2), ""","""),
((3, 8), """naive"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """allen"""),
((21, 23), """##nl"""),
((23, 24), """##p"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["""offset_mapping"""] )
def a_ ( self ):
UpperCamelCase : Union[str, Any] = ["""的""", """人""", """有"""]
UpperCamelCase : Tuple = """""".join(SCREAMING_SNAKE_CASE_ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
UpperCamelCase : List[Any] = True
UpperCamelCase : Any = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = tokenizer_p.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = tokenizer_r.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = False
UpperCamelCase : str = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = tokenizer_r.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = tokenizer_p.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
# it is expected that only the first Chinese character is not preceded by "##".
UpperCamelCase : Tuple = [
f'##{token}' if idx != 0 else token for idx, token in enumerate(SCREAMING_SNAKE_CASE_ )
]
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 365 |
"""simple docstring"""
import argparse
import os
import re
__A : Any = '''src/transformers'''
# Pattern that looks at the indentation in a line.
__A : Tuple = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : List[Any] = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : List[Any] = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : Any = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : str ,snake_case_ : str="" ,snake_case_ : Any=None ,snake_case_ : Union[str, Any]=None ):
'''simple docstring'''
UpperCamelCase : List[Any] = 0
UpperCamelCase : Optional[int] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Tuple = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : Tuple = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Dict = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Optional[Any] = [lines[index + 1]]
index += 1
else:
UpperCamelCase : str = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
def _inner(snake_case_ : List[str] ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Tuple=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Optional[int] ):
return x
if key is None:
UpperCamelCase : List[str] = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[str] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : Tuple = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : int = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Union[str, Any] = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : Any ):
UpperCamelCase : Union[str, Any] = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : int = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : str = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : Optional[int] = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : int = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Tuple = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : List[Any] = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Union[str, Any] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : List[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : Optional[int] = keys[:-1]
UpperCamelCase : Union[str, Any] = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : Any = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : int=True ):
'''simple docstring'''
with open(snake_case_ ,encoding="""utf-8""" ) as f:
UpperCamelCase : List[str] = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : int = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Dict = main_blocks[block_idx]
UpperCamelCase : Dict = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : List[str] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : Optional[Any] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Optional[Any] = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Any = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : List[Any] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Optional[Any] = _re_direct_key if """_import_structure = {""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Optional[Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Any = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : Union[str, Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[str] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : Optional[int] = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ,encoding="""utf-8""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Optional[int] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : List[Any] = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : Union[str, Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27 | 0 |
"""simple docstring"""
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = 'naver-clova-ix/donut-base-finetuned-docvqa'
lowercase : str = (
'This is a tool that answers a question about an document (pdf). It takes an input named `document` which '
'should be the document containing the information, as well as a `question` that is the question about the '
'document. It returns a text that contains the answer to the question.'
)
lowercase : Dict = 'document_qa'
lowercase : Optional[int] = AutoProcessor
lowercase : List[Any] = VisionEncoderDecoderModel
lowercase : Any = ['image', 'text']
lowercase : Any = ['text']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
if not is_vision_available():
raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool.""" )
super().__init__(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : int = """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
UpperCamelCase : int = task_prompt.replace("""{user_input}""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.pre_processor.tokenizer(
SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" ).input_ids
UpperCamelCase : List[Any] = self.pre_processor(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return self.model.generate(
inputs["""pixel_values"""].to(self.device ) , decoder_input_ids=inputs["""decoder_input_ids"""].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=SCREAMING_SNAKE_CASE_ , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=SCREAMING_SNAKE_CASE_ , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=SCREAMING_SNAKE_CASE_ , ).sequences
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = self.pre_processor.batch_decode(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Dict = sequence.replace(self.pre_processor.tokenizer.eos_token , """""" )
UpperCamelCase : Union[str, Any] = sequence.replace(self.pre_processor.tokenizer.pad_token , """""" )
UpperCamelCase : int = re.sub(r"""<.*?>""" , """""" , SCREAMING_SNAKE_CASE_ , count=1 ).strip() # remove first task start token
UpperCamelCase : Optional[Any] = self.pre_processor.tokenajson(SCREAMING_SNAKE_CASE_ )
return sequence["answer"]
| 366 |
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__A : Any = logging.get_logger(__name__)
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = ['pixel_values']
def __init__( self , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 1 / 255 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = True , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = size if size is not None else {"""height""": 384, """width""": 384}
UpperCamelCase : Any = get_size_dict(SCREAMING_SNAKE_CASE_ , default_to_square=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = do_resize
UpperCamelCase : Optional[int] = size
UpperCamelCase : List[str] = resample
UpperCamelCase : str = do_rescale
UpperCamelCase : Any = rescale_factor
UpperCamelCase : Tuple = do_normalize
UpperCamelCase : List[str] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
UpperCamelCase : str = image_std if image_std is not None else OPENAI_CLIP_STD
UpperCamelCase : Any = do_convert_rgb
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = get_size_dict(SCREAMING_SNAKE_CASE_ , default_to_square=SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}' )
UpperCamelCase : Any = (size["""height"""], size["""width"""])
return resize(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return normalize(SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Any = do_resize if do_resize is not None else self.do_resize
UpperCamelCase : str = resample if resample is not None else self.resample
UpperCamelCase : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase : int = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase : List[str] = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase : Any = image_mean if image_mean is not None else self.image_mean
UpperCamelCase : Any = image_std if image_std is not None else self.image_std
UpperCamelCase : Optional[Any] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
UpperCamelCase : Optional[int] = size if size is not None else self.size
UpperCamelCase : Any = get_size_dict(SCREAMING_SNAKE_CASE_ , default_to_square=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
UpperCamelCase : Optional[Any] = [convert_to_rgb(SCREAMING_SNAKE_CASE_ ) for image in images]
# All transformations expect numpy arrays.
UpperCamelCase : Any = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
UpperCamelCase : Optional[int] = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
UpperCamelCase : str = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
UpperCamelCase : int = [self.normalize(image=SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Optional[Any] = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : List[str] = BatchFeature(data={"""pixel_values""": images} , tensor_type=SCREAMING_SNAKE_CASE_ )
return encoded_outputs
| 367 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
__A : Optional[Any] = logging.get_logger(__name__)
def A_ ( snake_case_ : np.ndarray ,snake_case_ : Union[int, Iterable[int]] ,snake_case_ : bool ,snake_case_ : int ):
'''simple docstring'''
def constraint_to_multiple_of(snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ,snake_case_ : List[str]=0 ,snake_case_ : Optional[Any]=None ):
UpperCamelCase : List[str] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
UpperCamelCase : Optional[Any] = math.floor(val / multiple ) * multiple
if x < min_val:
UpperCamelCase : Dict = math.ceil(val / multiple ) * multiple
return x
UpperCamelCase : Any = (output_size, output_size) if isinstance(snake_case_ ,snake_case_ ) else output_size
UpperCamelCase , UpperCamelCase : int = get_image_size(snake_case_ )
UpperCamelCase , UpperCamelCase : Union[str, Any] = output_size
# determine new height and width
UpperCamelCase : List[str] = output_height / input_height
UpperCamelCase : List[str] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
UpperCamelCase : int = scale_width
else:
# fit height
UpperCamelCase : Optional[Any] = scale_height
UpperCamelCase : int = constraint_to_multiple_of(scale_height * input_height ,multiple=snake_case_ )
UpperCamelCase : Union[str, Any] = constraint_to_multiple_of(scale_width * input_width ,multiple=snake_case_ )
return (new_height, new_width)
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = ['pixel_values']
def __init__( self , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 1 / 255 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = size if size is not None else {"""height""": 384, """width""": 384}
UpperCamelCase : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = do_resize
UpperCamelCase : Union[str, Any] = size
UpperCamelCase : Union[str, Any] = keep_aspect_ratio
UpperCamelCase : Any = ensure_multiple_of
UpperCamelCase : List[Any] = resample
UpperCamelCase : str = do_rescale
UpperCamelCase : Optional[Any] = rescale_factor
UpperCamelCase : List[str] = do_normalize
UpperCamelCase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCamelCase : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Tuple = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
UpperCamelCase : Dict = get_resize_output_image_size(
SCREAMING_SNAKE_CASE_ , output_size=(size["""height"""], size["""width"""]) , keep_aspect_ratio=SCREAMING_SNAKE_CASE_ , multiple=SCREAMING_SNAKE_CASE_ , )
return resize(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return normalize(SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCamelCase : List[Any] = size if size is not None else self.size
UpperCamelCase : Dict = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
UpperCamelCase : Optional[int] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
UpperCamelCase : Tuple = resample if resample is not None else self.resample
UpperCamelCase : str = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase : Any = image_mean if image_mean is not None else self.image_mean
UpperCamelCase : List[Any] = image_std if image_std is not None else self.image_std
UpperCamelCase : str = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
UpperCamelCase : Tuple = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
UpperCamelCase : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
UpperCamelCase : int = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
UpperCamelCase : List[str] = [self.normalize(image=SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Any = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Union[str, Any] = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : str = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = target_sizes.numpy()
UpperCamelCase : Dict = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
UpperCamelCase : List[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : List[Any] = logits.argmax(dim=1 )
UpperCamelCase : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 27 | 0 |
"""simple docstring"""
import requests
__A : List[Any] = '''''' # <-- Put your OpenWeatherMap appid here!
__A : Tuple = '''https://api.openweathermap.org/data/2.5/'''
def A_ ( snake_case_ : str = "Chicago" ,snake_case_ : str = APPID ):
'''simple docstring'''
return requests.get(URL_BASE + """weather""" ,params=locals() ).json()
def A_ ( snake_case_ : str = "Kolkata, India" ,snake_case_ : str = APPID ):
'''simple docstring'''
return requests.get(URL_BASE + """forecast""" ,params=locals() ).json()
def A_ ( snake_case_ : float = 55.68 ,snake_case_ : float = 12.57 ,snake_case_ : str = APPID ):
'''simple docstring'''
return requests.get(URL_BASE + """onecall""" ,params=locals() ).json()
if __name__ == "__main__":
from pprint import pprint
while True:
__A : Optional[int] = input('''Enter a location:''').strip()
if location:
pprint(current_weather(location))
else:
break
| 368 |
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : float = b
if function(snake_case_ ) == 0: # one of the a or b is a root for the function
return a
elif function(snake_case_ ) == 0:
return b
elif (
function(snake_case_ ) * function(snake_case_ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("""could not find root in given interval.""" )
else:
UpperCamelCase : float = start + (end - start) / 2.0
while abs(start - mid ) > 1_0**-7: # until precisely equals to 10^-7
if function(snake_case_ ) == 0:
return mid
elif function(snake_case_ ) * function(snake_case_ ) < 0:
UpperCamelCase : Dict = mid
else:
UpperCamelCase : List[str] = mid
UpperCamelCase : Tuple = start + (end - start) / 2.0
return mid
def A_ ( snake_case_ : float ):
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 27 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A : Union[str, Any] = logging.get_logger(__name__)
__A : int = {
'''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase ):
lowercase : Optional[int] = 'bit'
lowercase : Any = ['preactivation', 'bottleneck']
lowercase : Any = ['SAME', 'VALID']
def __init__( self , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_=[256, 512, 1024, 2048] , SCREAMING_SNAKE_CASE_=[3, 4, 6, 3] , SCREAMING_SNAKE_CASE_="preactivation" , SCREAMING_SNAKE_CASE_="relu" , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
if layer_type not in self.layer_types:
raise ValueError(f'layer_type={layer_type} is not one of {",".join(self.layer_types )}' )
if global_padding is not None:
if global_padding.upper() in self.supported_padding:
UpperCamelCase : str = global_padding.upper()
else:
raise ValueError(f'Padding strategy {global_padding} not supported' )
UpperCamelCase : Optional[int] = num_channels
UpperCamelCase : str = embedding_size
UpperCamelCase : List[Any] = hidden_sizes
UpperCamelCase : Any = depths
UpperCamelCase : Optional[int] = layer_type
UpperCamelCase : List[str] = hidden_act
UpperCamelCase : Any = global_padding
UpperCamelCase : Dict = num_groups
UpperCamelCase : Optional[int] = drop_path_rate
UpperCamelCase : Dict = embedding_dynamic_padding
UpperCamelCase : str = output_stride
UpperCamelCase : str = width_factor
UpperCamelCase : int = ["""stem"""] + [f'stage{idx}' for idx in range(1 , len(SCREAMING_SNAKE_CASE_ ) + 1 )]
UpperCamelCase : List[str] = get_aligned_output_features_output_indices(
out_features=SCREAMING_SNAKE_CASE_ , out_indices=SCREAMING_SNAKE_CASE_ , stage_names=self.stage_names )
| 369 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def a_ ( self ):
UpperCamelCase : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
UpperCamelCase : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
UpperCamelCase : Dict = """xvjiarui/stable-diffusion-2-inpainting"""
UpperCamelCase , UpperCamelCase : List[str] = FlaxStableDiffusionInpaintPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = """Face of a yellow cat, high resolution, sitting on a park bench"""
UpperCamelCase : List[str] = jax.random.PRNGKey(0 )
UpperCamelCase : Tuple = 50
UpperCamelCase : Dict = jax.device_count()
UpperCamelCase : Optional[int] = num_samples * [prompt]
UpperCamelCase : int = num_samples * [init_image]
UpperCamelCase : List[Any] = num_samples * [mask_image]
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[Any] = pipeline.prepare_inputs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# shard inputs and rng
UpperCamelCase : Optional[int] = replicate(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = jax.random.split(SCREAMING_SNAKE_CASE_ , jax.device_count() )
UpperCamelCase : str = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = pipeline(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , jit=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = output.images.reshape(SCREAMING_SNAKE_CASE_ , 512 , 512 , 3 )
UpperCamelCase : List[Any] = images[0, 253:256, 253:256, -1]
UpperCamelCase : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase : Dict = jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 27 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=32 * 8 , SCREAMING_SNAKE_CASE_=32 * 8 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=64 , ):
UpperCamelCase : Union[str, Any] = parent
UpperCamelCase : str = batch_size
UpperCamelCase : str = is_training
UpperCamelCase : List[str] = use_auxiliary_loss
UpperCamelCase : List[str] = num_queries
UpperCamelCase : str = num_channels
UpperCamelCase : Optional[Any] = min_size
UpperCamelCase : Optional[int] = max_size
UpperCamelCase : Any = num_labels
UpperCamelCase : Optional[int] = hidden_dim
UpperCamelCase : str = hidden_dim
def a_ ( self ):
UpperCamelCase : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.ones([self.batch_size, self.min_size, self.max_size] , device=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=SCREAMING_SNAKE_CASE_ ) > 0.5
).float()
UpperCamelCase : Optional[Any] = (torch.rand((self.batch_size, self.num_labels) , device=SCREAMING_SNAKE_CASE_ ) > 0.5).long()
UpperCamelCase : Union[str, Any] = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def a_ ( self ):
UpperCamelCase : str = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
UpperCamelCase : int = self.num_queries
UpperCamelCase : int = self.num_labels
UpperCamelCase : Union[str, Any] = [1, 1, 1, 1]
UpperCamelCase : Any = self.num_channels
UpperCamelCase : Union[str, Any] = 64
UpperCamelCase : Optional[int] = 128
UpperCamelCase : Optional[Any] = self.hidden_dim
UpperCamelCase : List[str] = self.hidden_dim
UpperCamelCase : Any = self.hidden_dim
return config
def a_ ( self ):
UpperCamelCase : List[Any] = self.prepare_config_and_inputs()
UpperCamelCase : Optional[Any] = {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = output.encoder_hidden_states
UpperCamelCase : List[str] = output.pixel_decoder_hidden_states
UpperCamelCase : Dict = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_ ) , config.decoder_layers )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
with torch.no_grad():
UpperCamelCase : Dict = MaskaFormerModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : str = model(pixel_values=SCREAMING_SNAKE_CASE_ , pixel_mask=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
def comm_check_on_output(SCREAMING_SNAKE_CASE_ ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
UpperCamelCase : Optional[Any] = model(pixel_values=SCREAMING_SNAKE_CASE_ , pixel_mask=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ )
comm_check_on_output(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model(
pixel_values=SCREAMING_SNAKE_CASE_ , pixel_mask=SCREAMING_SNAKE_CASE_ , mask_labels=SCREAMING_SNAKE_CASE_ , class_labels=SCREAMING_SNAKE_CASE_ )
comm_check_on_output(SCREAMING_SNAKE_CASE_ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : int = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
lowercase : List[Any] = {'feature-extraction': MaskaFormerModel} if is_torch_available() else {}
lowercase : List[Any] = False
lowercase : Any = False
lowercase : int = False
lowercase : Any = False
def a_ ( self ):
UpperCamelCase : Dict = MaskaFormerModelTester(self )
UpperCamelCase : List[str] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , has_text_modality=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE_ )
@unittest.skip(reason="""Mask2Former does not use inputs_embeds""" )
def a_ ( self ):
pass
@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""" )
def a_ ( self ):
pass
@unittest.skip(reason="""Mask2Former is not a generative model""" )
def a_ ( self ):
pass
@unittest.skip(reason="""Mask2Former does not use token embeddings""" )
def a_ ( self ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def a_ ( self ):
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def a_ ( self ):
pass
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase : int = model_class(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase : int = [*signature.parameters.keys()]
UpperCamelCase : List[Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
UpperCamelCase : str = MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Dict = (self.model_tester.min_size,) * 2
UpperCamelCase : Any = {
"""pixel_values""": torch.randn((2, 3, *size) , device=SCREAMING_SNAKE_CASE_ ),
"""mask_labels""": torch.randn((2, 10, *size) , device=SCREAMING_SNAKE_CASE_ ),
"""class_labels""": torch.zeros(2 , 10 , device=SCREAMING_SNAKE_CASE_ ).long(),
}
UpperCamelCase : Optional[Any] = self.model_tester.get_config()
UpperCamelCase : List[str] = MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model(**SCREAMING_SNAKE_CASE_ )
self.assertTrue(outputs.loss is not None )
def a_ ( self ):
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase : List[str] = model_class(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = model(**SCREAMING_SNAKE_CASE_ , output_attentions=SCREAMING_SNAKE_CASE_ )
self.assertTrue(outputs.attentions is not None )
def a_ ( self ):
if not self.model_tester.is_training:
return
UpperCamelCase : int = self.all_model_classes[1]
UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
UpperCamelCase : Optional[int] = model_class(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.train()
UpperCamelCase : str = model(SCREAMING_SNAKE_CASE_ , mask_labels=SCREAMING_SNAKE_CASE_ , class_labels=SCREAMING_SNAKE_CASE_ ).loss
loss.backward()
def a_ ( self ):
UpperCamelCase : Tuple = self.all_model_classes[1]
UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
UpperCamelCase : Optional[int] = True
UpperCamelCase : int = True
UpperCamelCase : Any = model_class(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
model.train()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , mask_labels=SCREAMING_SNAKE_CASE_ , class_labels=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
UpperCamelCase : List[str] = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
UpperCamelCase : List[str] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
UpperCamelCase : Dict = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A : List[str] = 1e-4
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class lowerCamelCase ( unittest.TestCase ):
@cached_property
def a_ ( self ):
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def a_ ( self ):
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def a_ ( self ):
UpperCamelCase : Optional[int] = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.default_image_processor
UpperCamelCase : str = prepare_img()
UpperCamelCase : Tuple = image_processor(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(SCREAMING_SNAKE_CASE_ , (1, 3, 384, 384) )
with torch.no_grad():
UpperCamelCase : Union[str, Any] = model(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = torch.tensor(
[[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Union[str, Any] = torch.tensor(
[[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : List[Any] = torch.tensor(
[[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ ) )
def a_ ( self ):
UpperCamelCase : Union[str, Any] = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(SCREAMING_SNAKE_CASE_ ).eval()
UpperCamelCase : List[str] = self.default_image_processor
UpperCamelCase : List[Any] = prepare_img()
UpperCamelCase : List[str] = image_processor(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(SCREAMING_SNAKE_CASE_ , (1, 3, 384, 384) )
with torch.no_grad():
UpperCamelCase : Union[str, Any] = model(**SCREAMING_SNAKE_CASE_ )
# masks_queries_logits
UpperCamelCase : str = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
UpperCamelCase : Dict = [
[-8.7839, -9.0056, -8.8121],
[-7.4104, -7.0313, -6.5401],
[-6.6105, -6.3427, -6.4675],
]
UpperCamelCase : Dict = torch.tensor(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ ) )
# class_queries_logits
UpperCamelCase : Tuple = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
UpperCamelCase : Dict = torch.tensor(
[
[1.8324, -8.0835, -4.1922],
[0.8450, -9.0050, -3.6053],
[0.3045, -7.7293, -3.0275],
] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ ) )
def a_ ( self ):
UpperCamelCase : Dict = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(SCREAMING_SNAKE_CASE_ ).eval()
UpperCamelCase : Union[str, Any] = self.default_image_processor
UpperCamelCase : Union[str, Any] = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="""pt""" , )
UpperCamelCase : Optional[int] = inputs["""pixel_values"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = [el.to(SCREAMING_SNAKE_CASE_ ) for el in inputs["""mask_labels"""]]
UpperCamelCase : Tuple = [el.to(SCREAMING_SNAKE_CASE_ ) for el in inputs["""class_labels"""]]
with torch.no_grad():
UpperCamelCase : Dict = model(**SCREAMING_SNAKE_CASE_ )
self.assertTrue(outputs.loss is not None )
| 370 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def A_ ( ):
'''simple docstring'''
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
UpperCamelCase : Optional[Any] = [1, 2, 3]
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=2 )
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" ,[2, -1] )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : List[Any] = [1, 2]
UpperCamelCase : List[Any] = {"""a""": 1, """b""": 2}
UpperCamelCase : List[str] = {"""a""": [1, 2], """b""": [3, 4]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 1}, """b""": 2}
UpperCamelCase : Any = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
UpperCamelCase : Optional[int] = [2, 3]
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3}
UpperCamelCase : Any = {"""a""": [2, 3], """b""": [4, 5]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 2}, """b""": 3}
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
| 27 | 0 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=36 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=37 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=6 , SCREAMING_SNAKE_CASE_=6 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=1000 , ):
UpperCamelCase : Union[str, Any] = parent
UpperCamelCase : Optional[Any] = batch_size
UpperCamelCase : List[str] = num_channels
UpperCamelCase : Optional[Any] = image_size
UpperCamelCase : Tuple = patch_size
UpperCamelCase : Union[str, Any] = is_training
UpperCamelCase : Dict = use_input_mask
UpperCamelCase : Optional[Any] = use_token_type_ids
UpperCamelCase : Union[str, Any] = use_labels
UpperCamelCase : Optional[Any] = vocab_size
UpperCamelCase : str = hidden_size
UpperCamelCase : str = num_hidden_layers
UpperCamelCase : Optional[int] = num_attention_heads
UpperCamelCase : List[Any] = intermediate_size
UpperCamelCase : Any = hidden_act
UpperCamelCase : Dict = hidden_dropout_prob
UpperCamelCase : Tuple = attention_probs_dropout_prob
UpperCamelCase : Tuple = max_position_embeddings
UpperCamelCase : int = type_vocab_size
UpperCamelCase : Tuple = type_sequence_label_size
UpperCamelCase : Any = initializer_range
UpperCamelCase : List[str] = coordinate_size
UpperCamelCase : int = shape_size
UpperCamelCase : Tuple = num_labels
UpperCamelCase : Union[str, Any] = num_choices
UpperCamelCase : Optional[Any] = scope
UpperCamelCase : Tuple = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
UpperCamelCase : Dict = text_seq_length
UpperCamelCase : Optional[int] = (image_size // patch_size) ** 2 + 1
UpperCamelCase : int = self.text_seq_length + self.image_seq_length
def a_ ( self ):
UpperCamelCase : Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
UpperCamelCase : Union[str, Any] = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
UpperCamelCase : Optional[Any] = bbox.numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
UpperCamelCase : Optional[Any] = bbox[i, j, 3]
UpperCamelCase : Optional[Any] = bbox[i, j, 1]
UpperCamelCase : Dict = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
UpperCamelCase : Union[str, Any] = bbox[i, j, 2]
UpperCamelCase : List[Any] = bbox[i, j, 0]
UpperCamelCase : Dict = tmp_coordinate
UpperCamelCase : Optional[int] = tf.constant(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase : List[Any] = None
if self.use_input_mask:
UpperCamelCase : Optional[int] = random_attention_mask([self.batch_size, self.text_seq_length] )
UpperCamelCase : int = None
if self.use_token_type_ids:
UpperCamelCase : Optional[int] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
UpperCamelCase : List[Any] = None
UpperCamelCase : List[Any] = None
if self.use_labels:
UpperCamelCase : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Union[str, Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
UpperCamelCase : Optional[Any] = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[Any] = TFLayoutLMvaModel(config=SCREAMING_SNAKE_CASE_ )
# text + image
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , pixel_values=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = model(
SCREAMING_SNAKE_CASE_ , bbox=SCREAMING_SNAKE_CASE_ , pixel_values=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Optional[int] = model(SCREAMING_SNAKE_CASE_ , bbox=SCREAMING_SNAKE_CASE_ , pixel_values=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
UpperCamelCase : Dict = model({"""pixel_values""": pixel_values} , training=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = self.num_labels
UpperCamelCase : List[Any] = TFLayoutLMvaForSequenceClassification(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = model(
SCREAMING_SNAKE_CASE_ , bbox=SCREAMING_SNAKE_CASE_ , pixel_values=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = self.num_labels
UpperCamelCase : Optional[Any] = TFLayoutLMvaForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = model(
SCREAMING_SNAKE_CASE_ , bbox=SCREAMING_SNAKE_CASE_ , pixel_values=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = 2
UpperCamelCase : int = TFLayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = model(
SCREAMING_SNAKE_CASE_ , bbox=SCREAMING_SNAKE_CASE_ , pixel_values=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self ):
UpperCamelCase : Dict = self.prepare_config_and_inputs()
(UpperCamelCase) : Tuple = config_and_inputs
UpperCamelCase : List[Any] = {
"""input_ids""": input_ids,
"""bbox""": bbox,
"""pixel_values""": pixel_values,
"""token_type_ids""": token_type_ids,
"""attention_mask""": input_mask,
}
return config, inputs_dict
@require_tf
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : List[Any] = (
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
lowercase : Union[str, Any] = (
{'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
if is_tf_available()
else {}
)
lowercase : int = False
lowercase : List[str] = False
lowercase : int = False
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return True
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
UpperCamelCase : Any = copy.deepcopy(SCREAMING_SNAKE_CASE_ )
if model_class in get_values(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = {
k: tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_ , 1 ) , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(SCREAMING_SNAKE_CASE_ , tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = tf.ones(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
UpperCamelCase : Union[str, Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=tf.intaa )
return inputs_dict
def a_ ( self ):
UpperCamelCase : Any = TFLayoutLMvaModelTester(self )
UpperCamelCase : Tuple = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase : Optional[int] = model_class(SCREAMING_SNAKE_CASE_ )
if getattr(SCREAMING_SNAKE_CASE_ , """hf_compute_loss""" , SCREAMING_SNAKE_CASE_ ):
# The number of elements in the loss should be the same as the number of elements in the label
UpperCamelCase : List[Any] = self._prepare_for_class(inputs_dict.copy() , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=SCREAMING_SNAKE_CASE_ )[0]
]
UpperCamelCase : str = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
UpperCamelCase : int = self._prepare_for_class(inputs_dict.copy() , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = prepared_for_class.pop("""input_ids""" )
UpperCamelCase : Optional[int] = model(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
UpperCamelCase : Optional[Any] = self._prepare_for_class(inputs_dict.copy() , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = prepared_for_class.pop("""input_ids""" )
if "labels" in prepared_for_class:
UpperCamelCase : Optional[Any] = prepared_for_class["""labels"""].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
UpperCamelCase : Optional[Any] = -100
UpperCamelCase : Any = tf.convert_to_tensor(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
UpperCamelCase : str = self._prepare_for_class(inputs_dict.copy() , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
UpperCamelCase : Any = self._prepare_for_class(inputs_dict.copy() , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
# Get keys that were added with the _prepare_for_class function
UpperCamelCase : Tuple = prepared_for_class.keys() - inputs_dict.keys()
UpperCamelCase : Optional[Any] = inspect.signature(model.call ).parameters
UpperCamelCase : int = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
UpperCamelCase : List[Any] = {0: """input_ids"""}
for label_key in label_keys:
UpperCamelCase : List[str] = signature_names.index(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = label_key
UpperCamelCase : List[str] = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
UpperCamelCase : Tuple = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
UpperCamelCase : Dict = prepared_for_class[value]
UpperCamelCase : Tuple = tuple(SCREAMING_SNAKE_CASE_ )
# Send to model
UpperCamelCase : str = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def a_ ( self ):
(
UpperCamelCase
) : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
(
UpperCamelCase
) : List[str] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCamelCase : str = type
self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
(
UpperCamelCase
) : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
(
UpperCamelCase
) : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
(
UpperCamelCase
) : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : int = TFLayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
class lowerCamelCase ( unittest.TestCase ):
@cached_property
def a_ ( self ):
return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE_ ) if is_vision_available() else None
@slow
def a_ ( self ):
UpperCamelCase : Any = TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base""" )
UpperCamelCase : Optional[int] = self.default_image_processor
UpperCamelCase : str = prepare_img()
UpperCamelCase : List[str] = image_processor(images=SCREAMING_SNAKE_CASE_ , return_tensors="""tf""" ).pixel_values
UpperCamelCase : List[Any] = tf.constant([[1, 2]] )
UpperCamelCase : Dict = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) , axis=0 )
# forward pass
UpperCamelCase : Union[str, Any] = model(input_ids=SCREAMING_SNAKE_CASE_ , bbox=SCREAMING_SNAKE_CASE_ , pixel_values=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
# verify the logits
UpperCamelCase : Union[str, Any] = (1, 199, 768)
self.assertEqual(outputs.last_hidden_state.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = tf.constant(
[[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 371 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_="last" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=0 , ):
UpperCamelCase : Union[str, Any] = parent
UpperCamelCase : str = batch_size
UpperCamelCase : int = seq_length
UpperCamelCase : Optional[Any] = is_training
UpperCamelCase : Any = use_input_lengths
UpperCamelCase : Tuple = use_token_type_ids
UpperCamelCase : List[Any] = use_labels
UpperCamelCase : Union[str, Any] = gelu_activation
UpperCamelCase : Dict = sinusoidal_embeddings
UpperCamelCase : Optional[int] = causal
UpperCamelCase : List[Any] = asm
UpperCamelCase : int = n_langs
UpperCamelCase : Optional[Any] = vocab_size
UpperCamelCase : str = n_special
UpperCamelCase : Dict = hidden_size
UpperCamelCase : Union[str, Any] = num_hidden_layers
UpperCamelCase : Optional[Any] = num_attention_heads
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : int = max_position_embeddings
UpperCamelCase : Any = type_sequence_label_size
UpperCamelCase : str = initializer_range
UpperCamelCase : str = num_labels
UpperCamelCase : Union[str, Any] = num_choices
UpperCamelCase : List[str] = summary_type
UpperCamelCase : int = use_proj
UpperCamelCase : List[str] = scope
UpperCamelCase : Dict = bos_token_id
def a_ ( self ):
UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Union[str, Any] = None
if self.use_input_lengths:
UpperCamelCase : str = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
UpperCamelCase : Tuple = None
if self.use_token_type_ids:
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
UpperCamelCase : int = None
UpperCamelCase : Dict = None
UpperCamelCase : str = None
if self.use_labels:
UpperCamelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Dict = ids_tensor([self.batch_size] , 2 ).float()
UpperCamelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : List[str] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def a_ ( self ):
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = XLMModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , lengths=SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[Any] = XLMWithLMHeadModel(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[str] = XLMForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = XLMForQuestionAnswering(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , p_mask=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , )
((UpperCamelCase) , ) : Union[str, Any] = result_with_labels.to_tuple()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
((UpperCamelCase) , ) : Tuple = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = XLMForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = self.num_labels
UpperCamelCase : int = XLMForTokenClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[int] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[Any] = self.num_choices
UpperCamelCase : Tuple = XLMForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Dict = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Optional[Any] = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : int = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : List[Any] = config_and_inputs
UpperCamelCase : Dict = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
lowercase : List[Any] = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
lowercase : Optional[Any] = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("""Fast""" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
UpperCamelCase : Union[str, Any] = super()._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
UpperCamelCase : Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
return inputs_dict
def a_ ( self ):
UpperCamelCase : List[Any] = XLMModelTester(self )
UpperCamelCase : Optional[int] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , emb_dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_attentions in attentions] , [True] * len(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : int = min_length + idx + 1
UpperCamelCase : Tuple = min_length + idx + 1
UpperCamelCase : Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_hidden_states in hidden_states] , [True] * len(SCREAMING_SNAKE_CASE_ ) , )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : List[str] = min_length + idx + 1
UpperCamelCase : Optional[Any] = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) , )
pass
@slow
def a_ ( self ):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : str = XLMModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Dict = XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""" )
model.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.tensor([[14, 447]] , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ ) # the president
UpperCamelCase : List[Any] = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
UpperCamelCase : Optional[int] = model.generate(SCREAMING_SNAKE_CASE_ , do_sample=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , SCREAMING_SNAKE_CASE_ )
| 27 | 0 |
from __future__ import annotations
import time
import numpy as np
__A : List[Any] = [8, 5, 9, 7]
__A : Union[str, Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__A : str = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = claim_vector
UpperCamelCase : Dict = allocated_resources_table
UpperCamelCase : Tuple = maximum_claim_table
def a_ ( self ):
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def a_ ( self ):
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def a_ ( self ):
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(SCREAMING_SNAKE_CASE_ ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def a_ ( self ):
return {self.__need().index(SCREAMING_SNAKE_CASE_ ): i for i in self.__need()}
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = self.__need()
UpperCamelCase : Any = self.__allocated_resources_table
UpperCamelCase : Tuple = self.__available_resources()
UpperCamelCase : Union[str, Any] = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("""_""" * 50 + """\n""" )
while need_list:
UpperCamelCase : Optional[Any] = False
for each_need in need_list:
UpperCamelCase : Any = True
for index, need in enumerate(SCREAMING_SNAKE_CASE_ ):
if need > available_resources[index]:
UpperCamelCase : Any = False
break
if execution:
UpperCamelCase : Dict = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
UpperCamelCase : Tuple = original_need_index
print(f'Process {process_number + 1} is executing.' )
# remove the process run from stack
need_list.remove(SCREAMING_SNAKE_CASE_ )
# update available/freed resources stack
UpperCamelCase : Dict = np.array(SCREAMING_SNAKE_CASE_ ) + np.array(
alloc_resources_table[process_number] )
print(
"""Updated available resource stack for processes: """
+ """ """.join([str(SCREAMING_SNAKE_CASE_ ) for x in available_resources] ) )
break
if safe:
print("""The process is in a safe state.\n""" )
else:
print("""System in unsafe state. Aborting...\n""" )
break
def a_ ( self ):
print(""" """ * 9 + """Allocated Resource Table""" )
for item in self.__allocated_resources_table:
print(
f'P{self.__allocated_resources_table.index(SCREAMING_SNAKE_CASE_ ) + 1}'
+ """ """.join(f'{it:>8}' for it in item )
+ """\n""" )
print(""" """ * 9 + """System Resource Table""" )
for item in self.__maximum_claim_table:
print(
f'P{self.__maximum_claim_table.index(SCREAMING_SNAKE_CASE_ ) + 1}'
+ """ """.join(f'{it:>8}' for it in item )
+ """\n""" )
print(
"""Current Usage by Active Processes: """
+ """ """.join(str(SCREAMING_SNAKE_CASE_ ) for x in self.__claim_vector ) )
print(
"""Initial Available Resources: """
+ """ """.join(str(SCREAMING_SNAKE_CASE_ ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 350 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : int = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = [
'''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTBigCodeForSequenceClassification''',
'''GPTBigCodeForTokenClassification''',
'''GPTBigCodeForCausalLM''',
'''GPTBigCodeModel''',
'''GPTBigCodePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27 | 0 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( snake_case_ : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
UpperCamelCase : Any = BeautifulSoup(requests.get(snake_case_ ).text ,"""html.parser""" )
UpperCamelCase : Optional[int] = soup.findAll("""h1""" )
UpperCamelCase : List[Any] = soup.findAll("""div""" ,{"""class""": """maincounter-number"""} )
keys += soup.findAll("""span""" ,{"""class""": """panel-title"""} )
values += soup.findAll("""div""" ,{"""class""": """number-table-main"""} )
return {key.text.strip(): value.text.strip() for key, value in zip(snake_case_ ,snake_case_ )}
if __name__ == "__main__":
print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
for key, value in world_covidaa_stats().items():
print(F'''{key}\n{value}\n''')
| 351 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase ( torch.nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_="sayef/fsner-bert-base-uncased" ):
super(SCREAMING_SNAKE_CASE_ , self ).__init__()
UpperCamelCase : int = AutoModel.from_pretrained(SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.nn.CosineSimilarity(3 , 1e-08 )
UpperCamelCase : Any = torch.nn.Softmax(dim=1 )
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
return self.bert(**SCREAMING_SNAKE_CASE_ ).last_hidden_state
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return token_embeddings.sum(2 , keepdim=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1 ):
return self.softmax(T * self.cos(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = W_supports["""sizes"""].tolist()
UpperCamelCase : List[str] = W_supports["""start_token_id"""].item()
UpperCamelCase : List[Any] = W_supports["""end_token_id"""].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
UpperCamelCase : List[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Tuple = W_supports["""input_ids"""] == start_token_id
UpperCamelCase : Optional[Any] = W_supports["""input_ids"""] == end_token_id
for i, size in enumerate(SCREAMING_SNAKE_CASE_ ):
if i == 0:
UpperCamelCase : int = 0
else:
UpperCamelCase : Optional[int] = support_sizes[i - 1]
UpperCamelCase : Tuple = S[s : s + size][start_token_masks[s : s + size]]
UpperCamelCase : int = S[s : s + size][end_token_masks[s : s + size]]
UpperCamelCase : Dict = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
UpperCamelCase : Tuple = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
UpperCamelCase : List[str] = torch.vstack((p_starts, p_start) )
UpperCamelCase : Optional[Any] = torch.vstack((p_ends, p_end) )
else:
UpperCamelCase : Optional[int] = p_start
UpperCamelCase : Tuple = p_end
return p_starts, p_ends
| 27 | 0 |
"""simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Any = CpmAntTokenizer
lowercase : Optional[Any] = False
def a_ ( self ):
super().setUp()
UpperCamelCase : Union[str, Any] = [
"""<d>""",
"""</d>""",
"""<s>""",
"""</s>""",
"""</_>""",
"""<unk>""",
"""<pad>""",
"""</n>""",
"""我""",
"""是""",
"""C""",
"""P""",
"""M""",
"""A""",
"""n""",
"""t""",
]
UpperCamelCase : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
@tooslow
def a_ ( self ):
UpperCamelCase : Optional[int] = CpmAntTokenizer.from_pretrained("""openbmb/cpm-ant-10b""" )
UpperCamelCase : Union[str, Any] = """今天天气真好！"""
UpperCamelCase : int = ["""今天""", """天气""", """真""", """好""", """！"""]
UpperCamelCase : Optional[Any] = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = """今天天气真好！"""
UpperCamelCase : List[Any] = [tokenizer.bos_token] + tokens
UpperCamelCase : Dict = [6, 9802, 1_4962, 2082, 831, 244]
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 352 |
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):
return f'Node({self.data})'
class lowerCamelCase :
def __init__( self ):
UpperCamelCase : Dict = None
def __iter__( self ):
UpperCamelCase : int = self.head
while node:
yield node.data
UpperCamelCase : Union[str, Any] = node.next
def __len__( self ):
return sum(1 for _ in self )
def __repr__( self ):
return "->".join([str(SCREAMING_SNAKE_CASE_ ) for item in self] )
def __getitem__( self , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
UpperCamelCase : List[Any] = self.head
for _ in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = current.next
UpperCamelCase : Optional[Any] = data
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(len(self ) , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(0 , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index <= len(self ):
raise IndexError("""list index out of range""" )
UpperCamelCase : Optional[Any] = Node(SCREAMING_SNAKE_CASE_ )
if self.head is None:
UpperCamelCase : Dict = new_node
elif index == 0:
UpperCamelCase : Any = self.head # link new_node to head
UpperCamelCase : Any = new_node
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : str = temp.next
UpperCamelCase : Any = temp.next
UpperCamelCase : Optional[Any] = new_node
def a_ ( self ): # print every node data
print(self )
def a_ ( self ):
return self.delete_nth(0 )
def a_ ( self ): # delete from tail
return self.delete_nth(len(self ) - 1 )
def a_ ( self , SCREAMING_SNAKE_CASE_ = 0 ):
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("""List index out of range.""" )
UpperCamelCase : Union[str, Any] = self.head # default first node
if index == 0:
UpperCamelCase : Optional[Any] = self.head.next
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : int = temp.next
UpperCamelCase : Optional[Any] = temp.next
UpperCamelCase : Dict = temp.next.next
return delete_node.data
def a_ ( self ):
return self.head is None
def a_ ( self ):
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Union[str, Any] = self.head
while current:
# Store the current node's next node.
UpperCamelCase : Optional[int] = current.next
# Make the current node's next point backwards
UpperCamelCase : Optional[Any] = prev
# Make the previous node be the current node
UpperCamelCase : int = current
# Make the current node the next node (to progress iteration)
UpperCamelCase : Optional[int] = next_node
# Return prev in order to put the head at the end
UpperCamelCase : Optional[int] = prev
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = LinkedList()
assert linked_list.is_empty() is True
assert str(snake_case_ ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(1_0 ):
assert len(snake_case_ ) == i
linked_list.insert_nth(snake_case_ ,i + 1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_1 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(1_1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(0 ,1_2 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 1_0
assert linked_list.delete_tail() == 1_1
assert len(snake_case_ ) == 9
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_0 ) )
assert all(linked_list[i] == i + 1 for i in range(0 ,9 ) ) is True
for i in range(0 ,9 ):
UpperCamelCase : Optional[Any] = -i
assert all(linked_list[i] == -i for i in range(0 ,9 ) ) is True
linked_list.reverse()
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(-8 ,1 ) )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = [
-9,
1_0_0,
Node(7_7_3_4_5_1_1_2 ),
"""dlrow olleH""",
7,
5_5_5_5,
0,
-192.55555,
"""Hello, world!""",
77.9,
Node(1_0 ),
None,
None,
12.20,
]
UpperCamelCase : List[Any] = LinkedList()
for i in test_input:
linked_list.insert_tail(snake_case_ )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(snake_case_ ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
UpperCamelCase : Dict = linked_list.delete_head()
assert result == -9
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
UpperCamelCase : int = linked_list.delete_tail()
assert result == 12.2
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
UpperCamelCase : Optional[Any] = linked_list.delete_nth(1_0 )
assert result is None
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("""Hello again, world!""" ) )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(snake_case_ )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(snake_case_ )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def A_ ( ):
'''simple docstring'''
from doctest import testmod
testmod()
UpperCamelCase : List[Any] = LinkedList()
linked_list.insert_head(input("""Inserting 1st at head """ ).strip() )
linked_list.insert_head(input("""Inserting 2nd at head """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
linked_list.insert_tail(input("""\nInserting 1st at tail """ ).strip() )
linked_list.insert_tail(input("""Inserting 2nd at tail """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nDelete head""" )
linked_list.delete_head()
print("""Delete tail""" )
linked_list.delete_tail()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nReverse linked list""" )
linked_list.reverse()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nString representation of linked list:""" )
print(snake_case_ )
print("""\nReading/changing Node data using indexing:""" )
print(f'Element at Position 1: {linked_list[1]}' )
UpperCamelCase : List[Any] = input("""Enter New Value: """ ).strip()
print("""New list:""" )
print(snake_case_ )
print(f'length of linked_list is : {len(snake_case_ )}' )
if __name__ == "__main__":
main()
| 27 | 0 |
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