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'''simple docstring'''
lowerCamelCase : int = {}
def _lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : int ) -> int:
"""simple docstring"""
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
_SCREAMING_SNAKE_CASE =(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
_SCREAMING_SNAKE_CASE =_calculate(days - 1 , _UpperCAmelCase , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
_SCREAMING_SNAKE_CASE =_calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
_SCREAMING_SNAKE_CASE =_calculate(days - 1 , _UpperCAmelCase , 0 )
_SCREAMING_SNAKE_CASE =state_late + state_absent + state_ontime
_SCREAMING_SNAKE_CASE =prizestrings
return prizestrings
def _lowerCAmelCase ( _UpperCamelCase : int = 30 ) -> int:
"""simple docstring"""
return _calculate(_UpperCAmelCase , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 47 |
import os
import sys
import unittest
UpperCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCAmelCase__ = os.path.join(git_repo_path, "src", "diffusers")
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = find_backend(' if not is_torch_available():')
self.assertEqual(A , 'torch')
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
_UpperCAmelCase = find_backend(' if not (is_torch_available() and is_transformers_available()):')
self.assertEqual(A , 'torch_and_transformers')
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
_UpperCAmelCase = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):')
self.assertEqual(A , 'torch_and_transformers_and_onnx')
def _lowerCamelCase ( self : int) -> Dict:
"""simple docstring"""
_UpperCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , A)
self.assertIn('torch_and_transformers' , A)
self.assertIn('flax_and_transformers' , A)
self.assertIn('torch_and_transformers_and_onnx' , A)
# Likewise, we can't assert on the exact content of a key
self.assertIn('UNet2DModel' , objects['torch'])
self.assertIn('FlaxUNet2DConditionModel' , objects['flax'])
self.assertIn('StableDiffusionPipeline' , objects['torch_and_transformers'])
self.assertIn('FlaxStableDiffusionPipeline' , objects['flax_and_transformers'])
self.assertIn('LMSDiscreteScheduler' , objects['torch_and_scipy'])
self.assertIn('OnnxStableDiffusionPipeline' , objects['torch_and_transformers_and_onnx'])
def _lowerCamelCase ( self : Union[str, Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = create_dummy_object('CONSTANT' , '\'torch\'')
self.assertEqual(A , '\nCONSTANT = None\n')
_UpperCAmelCase = create_dummy_object('function' , '\'torch\'')
self.assertEqual(
A , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n')
_UpperCAmelCase = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n'
_UpperCAmelCase = create_dummy_object('FakeClass' , '\'torch\'')
self.assertEqual(A , A)
def _lowerCamelCase ( self : Dict) -> int:
"""simple docstring"""
_UpperCAmelCase = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n'
_UpperCAmelCase = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']})
self.assertEqual(dummy_files['torch'] , A)
| 339 | 0 |
"""simple docstring"""
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
__lowercase : Any = k_size // 2
__lowercase ,__lowercase : int = mgrid[0 - center : k_size - center, 0 - center : k_size - center]
__lowercase : int = 1 / (2 * pi * sigma) * exp(-(square(_UpperCAmelCase ) + square(_UpperCAmelCase )) / (2 * square(_UpperCAmelCase )) )
return g
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
__lowercase ,__lowercase : Any = image.shape[0], image.shape[1]
# dst image height and width
__lowercase : Optional[int] = height - k_size + 1
__lowercase : Union[str, Any] = width - k_size + 1
# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
__lowercase : List[str] = zeros((dst_height * dst_width, k_size * k_size) )
__lowercase : Any = 0
for i, j in product(range(_UpperCAmelCase ) , range(_UpperCAmelCase ) ):
__lowercase : Optional[Any] = ravel(image[i : i + k_size, j : j + k_size] )
__lowercase : Union[str, Any] = window
row += 1
# turn the kernel into shape(k*k, 1)
__lowercase : int = gen_gaussian_kernel(_UpperCAmelCase , _UpperCAmelCase )
__lowercase : Union[str, Any] = ravel(_UpperCAmelCase )
# reshape and get the dst image
__lowercase : Optional[int] = dot(_UpperCAmelCase , _UpperCAmelCase ).reshape(_UpperCAmelCase , _UpperCAmelCase ).astype(_UpperCAmelCase )
return dst
if __name__ == "__main__":
# read original image
a_ = imread(r'../image_data/lena.jpg')
# turn image in gray scale value
a_ = cvtColor(img, COLOR_BGR2GRAY)
# get values with two different mask size
a_ = gaussian_filter(gray, 3, sigma=1)
a_ = gaussian_filter(gray, 5, sigma=0.8)
# show result images
imshow('gaussian filter with 3x3 mask', gaussianaxa)
imshow('gaussian filter with 5x5 mask', gaussianaxa)
waitKey()
| 249 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
UpperCAmelCase__ = logging.getLogger(__name__)
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} , )
UpperCamelCase = field(
default=1_0_2_4 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
UpperCamelCase = field(
default=A , 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.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the training data.'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
UpperCamelCase = field(default=A , metadata={'''help''': '''A csv or a json file containing the test data.'''} )
def _lowerCamelCase ( self : str) -> List[Any]:
"""simple docstring"""
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.')
else:
_UpperCAmelCase = self.train_file.split('.')[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
_UpperCAmelCase = self.validation_file.split('.')[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default=A , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCamelCase = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
def A ( ) -> Optional[int]:
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCAmelCase = 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 , _UpperCAmelCase , _UpperCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_args_into_dataclasses()
# 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 )] , )
_UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(_UpperCAmelCase )
datasets.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
_UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase = 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 and training_args.resume_from_checkpoint is 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.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. 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 = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
_UpperCAmelCase = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
_UpperCAmelCase = data_args.train_file.split('.' )[-1]
_UpperCAmelCase = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
_UpperCAmelCase = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(F"load a local file for {key}: {data_files[key]}" )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
_UpperCAmelCase = load_dataset('csv' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
_UpperCAmelCase = load_dataset('json' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
_UpperCAmelCase = raw_datasets['train'].features['label'].names
_UpperCAmelCase = len(_UpperCAmelCase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
_UpperCAmelCase = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , 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 , add_prefix_space=_UpperCAmelCase , )
_UpperCAmelCase = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
_UpperCAmelCase = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
_UpperCAmelCase = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
_UpperCAmelCase = {'Refused': 0, 'Entailed': 1}
_UpperCAmelCase = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
F"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." )
_UpperCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(_UpperCAmelCase : Union[str, Any] ):
# Tokenize the texts
def _convert_table_text_to_pandas(_UpperCAmelCase : Dict ):
_UpperCAmelCase = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
_UpperCAmelCase = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
_UpperCAmelCase = examples['statement']
_UpperCAmelCase = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
_UpperCAmelCase = tokenizer(_UpperCAmelCase , _UpperCAmelCase , padding=_UpperCAmelCase , max_length=_UpperCAmelCase , truncation=_UpperCAmelCase )
_UpperCAmelCase = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
_UpperCAmelCase = raw_datasets.map(
_UpperCAmelCase , batched=_UpperCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
_UpperCAmelCase = raw_datasets['train']
if data_args.max_train_samples is not None:
_UpperCAmelCase = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
_UpperCAmelCase = raw_datasets['validation']
if data_args.max_eval_samples is not None:
_UpperCAmelCase = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
_UpperCAmelCase = raw_datasets['test']
if data_args.max_predict_samples is not None:
_UpperCAmelCase = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_UpperCAmelCase ) ) , 3 ):
logger.info(F"Sample {index} of the training set: {train_dataset[index]}." )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_UpperCAmelCase : EvalPrediction ):
_UpperCAmelCase = p.predictions[0] if isinstance(p.predictions , _UpperCAmelCase ) else p.predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
_UpperCAmelCase = default_data_collator
elif training_args.fpaa:
_UpperCAmelCase = DataCollatorWithPadding(_UpperCAmelCase , pad_to_multiple_of=8 )
else:
_UpperCAmelCase = None
# Initialize our Trainer
_UpperCAmelCase = Trainer(
model=_UpperCAmelCase , args=_UpperCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_UpperCAmelCase , tokenizer=_UpperCAmelCase , data_collator=_UpperCAmelCase , )
# Training
if training_args.do_train:
_UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase = last_checkpoint
_UpperCAmelCase = trainer.train(resume_from_checkpoint=_UpperCAmelCase )
_UpperCAmelCase = train_result.metrics
_UpperCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCAmelCase )
)
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _UpperCAmelCase )
trainer.save_metrics('train' , _UpperCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
_UpperCAmelCase = trainer.evaluate(eval_dataset=_UpperCAmelCase )
_UpperCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCAmelCase )
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.log_metrics('eval' , _UpperCAmelCase )
trainer.save_metrics('eval' , _UpperCAmelCase )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
_UpperCAmelCase = predict_dataset.remove_columns('label' )
_UpperCAmelCase = trainer.predict(_UpperCAmelCase , metric_key_prefix='predict' ).predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
_UpperCAmelCase = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(_UpperCAmelCase , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(_UpperCAmelCase ):
_UpperCAmelCase = label_list[item]
writer.write(F"{index}\t{item}\n" )
_UpperCAmelCase = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**_UpperCAmelCase )
else:
trainer.create_model_card(**_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 339 | 0 |
"""simple docstring"""
a = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
a = [{'type': 'code', 'content': INSTALL_CONTENT}]
a = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 155 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def A ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict ) -> Any:
'''simple docstring'''
_UpperCAmelCase = multiprocessing.Manager()
_UpperCAmelCase = manager.list()
_UpperCAmelCase = multiprocessing.Process(target=_UpperCAmelCase , 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 ( _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
_UpperCAmelCase = shutil.rmtree
_UpperCAmelCase = os.rmdir
_UpperCAmelCase = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
_UpperCAmelCase = {}
with swallow_io():
with time_limit(_UpperCAmelCase ):
exec(_UpperCAmelCase , _UpperCAmelCase )
result.append('passed' )
except TimeoutException:
result.append('timed out' )
except BaseException as e:
result.append(F"failed: {e}" )
# Needed for cleaning up.
_UpperCAmelCase = rmtree
_UpperCAmelCase = rmdir
_UpperCAmelCase = chdir
@contextlib.contextmanager
def A ( _UpperCAmelCase : Union[str, Any] ) -> Any:
'''simple docstring'''
def signal_handler(_UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict ):
raise TimeoutException('Timed out!' )
signal.setitimer(signal.ITIMER_REAL , _UpperCAmelCase )
signal.signal(signal.SIGALRM , _UpperCAmelCase )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def A ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = WriteOnlyStringIO()
with contextlib.redirect_stdout(_UpperCAmelCase ):
with contextlib.redirect_stderr(_UpperCAmelCase ):
with redirect_stdin(_UpperCAmelCase ):
yield
@contextlib.contextmanager
def A ( ) -> Any:
'''simple docstring'''
with tempfile.TemporaryDirectory() as dirname:
with chdir(_UpperCAmelCase ):
yield dirname
class __lowerCAmelCase ( A ):
pass
class __lowerCAmelCase ( io.StringIO ):
def _lowerCamelCase ( self : Tuple , *A : str , **A : Any) -> Any:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : List[str] , *A : Optional[Any] , **A : Optional[Any]) -> Optional[int]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : str , *A : List[str] , **A : List[Any]) -> Union[str, Any]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : Union[str, Any] , *A : Optional[Any] , **A : List[str]) -> Optional[int]:
"""simple docstring"""
return False
class __lowerCAmelCase ( contextlib._RedirectStream ): # type: ignore
UpperCamelCase = '''stdin'''
@contextlib.contextmanager
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
if root == ".":
yield
return
_UpperCAmelCase = os.getcwd()
os.chdir(_UpperCAmelCase )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str]=None ) -> Any:
'''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 = None
_UpperCAmelCase = None
import os
_UpperCAmelCase = '1'
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import shutil
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import subprocess
_UpperCAmelCase = None # type: ignore
_UpperCAmelCase = None
import sys
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
| 339 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case_ ( __A ):
__A : Optional[Any] = "ClapFeatureExtractor"
__A : Optional[Any] = ("RobertaTokenizer", "RobertaTokenizerFast")
def __init__( self : Optional[int] , lowercase_ : Any , lowercase_ : Optional[Any] ) -> Any:
super().__init__(lowercase_ , lowercase_ )
def __call__( self : List[Any] , lowercase_ : List[Any]=None , lowercase_ : Optional[int]=None , lowercase_ : Any=None , **lowercase_ : Any ) -> Optional[Any]:
lowercase__ : Any = kwargs.pop("sampling_rate" , lowercase_ )
if text is None and audios is None:
raise ValueError("You have to specify either text or audios. Both cannot be none." )
if text is not None:
lowercase__ : str = self.tokenizer(lowercase_ , return_tensors=lowercase_ , **lowercase_ )
if audios is not None:
lowercase__ : Dict = self.feature_extractor(
lowercase_ , sampling_rate=lowercase_ , return_tensors=lowercase_ , **lowercase_ )
if text is not None and audios is not None:
lowercase__ : Dict = audio_features.input_features
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowercase_ ) , tensor_type=lowercase_ )
def __UpperCamelCase ( self : int , *lowercase_ : Union[str, Any] , **lowercase_ : Dict ) -> Union[str, Any]:
return self.tokenizer.batch_decode(*lowercase_ , **lowercase_ )
def __UpperCamelCase ( self : Optional[Any] , *lowercase_ : Optional[int] , **lowercase_ : Tuple ) -> Union[str, Any]:
return self.tokenizer.decode(*lowercase_ , **lowercase_ )
@property
def __UpperCamelCase ( self : Optional[Any] ) -> Tuple:
lowercase__ : Optional[Any] = self.tokenizer.model_input_names
lowercase__ : Any = self.feature_extractor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )
| 87 |
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 ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any]=False ) -> str:
'''simple docstring'''
try:
_UpperCAmelCase = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_UpperCAmelCase = default
else:
# KEY is set, convert it to True or False.
try:
_UpperCAmelCase = strtobool(_UpperCAmelCase )
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
UpperCAmelCase__ = parse_flag_from_env("RUN_SLOW", default=False)
def A ( _UpperCAmelCase : List[str] ) -> List[str]:
'''simple docstring'''
return unittest.skip('Test was skipped' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> str:
'''simple docstring'''
return unittest.skipUnless(_run_slow_tests , 'test is slow' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> str:
'''simple docstring'''
return unittest.skipUnless(not torch.cuda.is_available() , 'test requires only a CPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.is_available() , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_xpu_available() , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> List[str]:
'''simple docstring'''
return unittest.skipUnless(is_mps_available() , 'test requires a `mps` backend support in `torch`' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , 'test requires the Hugging Face suite' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : str ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_bnb_available() , 'test requires the bitsandbytes library' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_tpu_available() , 'test requires TPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> str:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() == 1 , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> int:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() == 1 , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() > 1 , 'test requires multiple GPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> Any:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() > 1 , 'test requires multiple XPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_safetensors_available() , 'test requires safetensors' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(is_deepspeed_available() , 'test requires DeepSpeed' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_torch_version('>=' , '1.12.0' ) , 'test requires torch version >= 1.12.0' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any=None , _UpperCAmelCase : List[Any]=None ) -> Dict:
'''simple docstring'''
if test_case is None:
return partial(_UpperCAmelCase , version=_UpperCAmelCase )
return unittest.skipUnless(is_torch_version('>=' , _UpperCAmelCase ) , F"test requires torch version >= {version}" )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> int:
'''simple docstring'''
return unittest.skipUnless(is_tensorboard_available() , 'test requires Tensorboard' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return unittest.skipUnless(is_wandb_available() , 'test requires wandb' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_comet_ml_available() , 'test requires comet_ml' )(_UpperCAmelCase )
UpperCAmelCase__ = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def A ( _UpperCAmelCase : List[str] ) -> Any:
'''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' , )(_UpperCAmelCase )
class __lowerCAmelCase ( unittest.TestCase ):
UpperCamelCase = True
@classmethod
def _lowerCamelCase ( cls : List[Any]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = tempfile.mkdtemp()
@classmethod
def _lowerCamelCase ( cls : Union[str, Any]) -> str:
"""simple docstring"""
if os.path.exists(cls.tmpdir):
shutil.rmtree(cls.tmpdir)
def _lowerCamelCase ( self : List[str]) -> List[Any]:
"""simple docstring"""
if self.clear_on_setup:
for path in Path(self.tmpdir).glob('**/*'):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(A)
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Dict) -> Tuple:
"""simple docstring"""
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[int] , A : Union[mock.Mock, List[mock.Mock]]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = mocks if isinstance(A , (tuple, list)) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop)
def A ( _UpperCAmelCase : List[Any] ) -> int:
'''simple docstring'''
_UpperCAmelCase = AcceleratorState()
_UpperCAmelCase = tensor[None].clone().to(state.device )
_UpperCAmelCase = gather(_UpperCAmelCase ).cpu()
_UpperCAmelCase = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , _UpperCAmelCase ):
return False
return True
class __lowerCAmelCase :
def __init__( self : Optional[Any] , A : Union[str, Any] , A : Optional[int] , A : str) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = returncode
_UpperCAmelCase = stdout
_UpperCAmelCase = stderr
async def A ( _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
while True:
_UpperCAmelCase = await stream.readline()
if line:
callback(_UpperCAmelCase )
else:
break
async def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=None , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Union[str, Any]=False ) -> _RunOutput:
'''simple docstring'''
if echo:
print('\nRunning: ' , ' '.join(_UpperCAmelCase ) )
_UpperCAmelCase = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=_UpperCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_UpperCAmelCase , )
# 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 = []
_UpperCAmelCase = []
def tee(_UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : str="" ):
_UpperCAmelCase = line.decode('utf-8' ).rstrip()
sink.append(_UpperCAmelCase )
if not quiet:
print(_UpperCAmelCase , _UpperCAmelCase , file=_UpperCAmelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stdout , label='stdout:' ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stderr , label='stderr:' ) ) ),
] , timeout=_UpperCAmelCase , )
return _RunOutput(await p.wait() , _UpperCAmelCase , _UpperCAmelCase )
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Dict=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=180 , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : List[Any]=True ) -> _RunOutput:
'''simple docstring'''
_UpperCAmelCase = asyncio.get_event_loop()
_UpperCAmelCase = loop.run_until_complete(
_stream_subprocess(_UpperCAmelCase , env=_UpperCAmelCase , stdin=_UpperCAmelCase , timeout=_UpperCAmelCase , quiet=_UpperCAmelCase , echo=_UpperCAmelCase ) )
_UpperCAmelCase = ' '.join(_UpperCAmelCase )
if result.returncode > 0:
_UpperCAmelCase = '\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 ( A ):
pass
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : str=False ) -> Tuple:
'''simple docstring'''
try:
_UpperCAmelCase = subprocess.check_output(_UpperCAmelCase , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(_UpperCAmelCase , 'decode' ):
_UpperCAmelCase = output.decode('utf-8' )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F"Command `{' '.join(_UpperCAmelCase )}` failed with the following error:\n\n{e.output.decode()}" ) from e
| 339 | 0 |
'''simple docstring'''
from functools import reduce
_lowerCAmelCase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def __lowerCAmelCase ( snake_case__ = N ):
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda snake_case__ , snake_case__ : str(int(_UpperCAmelCase ) * int(_UpperCAmelCase ) ) , n[i : i + 13] ) )
for i in range(len(_UpperCAmelCase ) - 12 ) )
if __name__ == "__main__":
print(f'{solution() = }')
| 298 |
from __future__ import annotations
UpperCAmelCase__ = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
UpperCAmelCase__ = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def A ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
'''simple docstring'''
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def A ( _UpperCAmelCase : Matrix ) -> Matrix | None:
'''simple docstring'''
if location := find_empty_location(_UpperCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
_UpperCAmelCase = 0
return None
def A ( _UpperCAmelCase : Matrix ) -> None:
'''simple docstring'''
for row in grid:
for cell in row:
print(_UpperCAmelCase , end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("\nExample grid:\n" + "=" * 20)
print_solution(example_grid)
print("\nExample grid solution:")
UpperCAmelCase__ = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("Cannot find a solution.")
| 339 | 0 |
import sys
lowerCAmelCase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def _lowerCamelCase( lowercase__ = N ) -> int:
'''simple docstring'''
__lowercase= -sys.maxsize - 1
for i in range(len(_UpperCAmelCase ) - 1_2 ):
__lowercase= 1
for j in range(1_3 ):
product *= int(n[i + j] )
if product > largest_product:
__lowercase= product
return largest_product
if __name__ == "__main__":
print(F'{solution() = }')
| 295 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
UpperCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
UpperCAmelCase__ = "\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n"
UpperCAmelCase__ = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"
UpperCAmelCase__ = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n 'meteor': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric('meteor')\n >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results[\"meteor\"], 4))\n 0.6944\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : List[Any]) -> List[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence'),
'references': datasets.Value('string' , id='sequence'),
}) , codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'] , reference_urls=[
'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
'https://en.wikipedia.org/wiki/METEOR',
] , )
def _lowerCamelCase ( self : Optional[Any] , A : List[str]) -> List[Any]:
"""simple docstring"""
import nltk
nltk.download('wordnet')
if NLTK_VERSION >= version.Version('3.6.5'):
nltk.download('punkt')
if NLTK_VERSION >= version.Version('3.6.6'):
nltk.download('omw-1.4')
def _lowerCamelCase ( self : Optional[Any] , A : Tuple , A : Optional[int] , A : List[Any]=0.9 , A : Optional[Any]=3 , A : Optional[int]=0.5) -> Any:
"""simple docstring"""
if NLTK_VERSION >= version.Version('3.6.5'):
_UpperCAmelCase = [
meteor_score.single_meteor_score(
word_tokenize(A) , word_tokenize(A) , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
else:
_UpperCAmelCase = [
meteor_score.single_meteor_score(A , A , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
return {"meteor": np.mean(A)}
| 339 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
lowerCAmelCase_ : str = logging.get_logger(__name__)
lowerCAmelCase_ : Tuple = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
lowerCAmelCase_ : Optional[int] = {
'vocab_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
),
'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'squeezebert/squeezebert-uncased': (
'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli': (
'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
),
'squeezebert/squeezebert-mnli-headless': (
'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
),
},
}
lowerCAmelCase_ : Optional[Any] = {
'squeezebert/squeezebert-uncased': 5_12,
'squeezebert/squeezebert-mnli': 5_12,
'squeezebert/squeezebert-mnli-headless': 5_12,
}
lowerCAmelCase_ : Dict = {
'squeezebert/squeezebert-uncased': {'do_lower_case': True},
'squeezebert/squeezebert-mnli': {'do_lower_case': True},
'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
__a =VOCAB_FILES_NAMES
__a =PRETRAINED_VOCAB_FILES_MAP
__a =PRETRAINED_INIT_CONFIGURATION
__a =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__a =SqueezeBertTokenizer
def __init__( self : List[str] , __a : Dict=None , __a : int=None , __a : List[str]=True , __a : Any="[UNK]" , __a : int="[SEP]" , __a : List[str]="[PAD]" , __a : Union[str, Any]="[CLS]" , __a : Optional[Any]="[MASK]" , __a : Union[str, Any]=True , __a : Union[str, Any]=None , **__a : str , ):
super().__init__(
__a , tokenizer_file=__a , do_lower_case=__a , unk_token=__a , sep_token=__a , pad_token=__a , cls_token=__a , mask_token=__a , tokenize_chinese_chars=__a , strip_accents=__a , **__a , )
_a = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , __a ) != do_lower_case
or normalizer_state.get("strip_accents" , __a ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , __a ) != tokenize_chinese_chars
):
_a = getattr(__a , normalizer_state.pop("type" ) )
_a = do_lower_case
_a = strip_accents
_a = tokenize_chinese_chars
_a = normalizer_class(**__a )
_a = do_lower_case
def UpperCamelCase__ ( self : List[str] , __a : Any , __a : List[Any]=None ):
_a = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase__ ( self : List[Any] , __a : List[int] , __a : Optional[List[int]] = None ):
_a = [self.sep_token_id]
_a = [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 UpperCamelCase__ ( self : Optional[Any] , __a : str , __a : Optional[str] = None ):
_a = self._tokenizer.model.save(__a , name=__a )
return tuple(__a )
| 63 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
UpperCAmelCase__ = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
"large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
_UpperCAmelCase = ['layers', 'blocks']
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase , _UpperCAmelCase )
UpperCAmelCase__ = {
"blocks": "layers",
"mlp.0": "fc1",
"mlp.2": "fc2",
"mlp_ln": "final_layer_norm",
".attn.query": ".self_attn.q_proj",
".attn.key": ".self_attn.k_proj",
".attn.value": ".self_attn.v_proj",
".attn_ln": ".self_attn_layer_norm",
".attn.out": ".self_attn.out_proj",
".cross_attn.query": ".encoder_attn.q_proj",
".cross_attn.key": ".encoder_attn.k_proj",
".cross_attn.value": ".encoder_attn.v_proj",
".cross_attn_ln": ".encoder_attn_layer_norm",
".cross_attn.out": ".encoder_attn.out_proj",
"decoder.ln.": "decoder.layer_norm.",
"encoder.ln.": "encoder.layer_norm.",
"token_embedding": "embed_tokens",
"encoder.positional_embedding": "encoder.embed_positions.weight",
"decoder.positional_embedding": "decoder.embed_positions.weight",
"ln_post": "layer_norm",
}
def A ( _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = list(s_dict.keys() )
for key in keys:
_UpperCAmelCase = key
for k, v in WHISPER_MAPPING.items():
if k in key:
_UpperCAmelCase = new_key.replace(_UpperCAmelCase , _UpperCAmelCase )
print(F"{key} -> {new_key}" )
_UpperCAmelCase = s_dict.pop(_UpperCAmelCase )
return s_dict
def A ( _UpperCAmelCase : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = emb.weight.shape
_UpperCAmelCase = nn.Linear(_UpperCAmelCase , _UpperCAmelCase , bias=_UpperCAmelCase )
_UpperCAmelCase = emb.weight.data
return lin_layer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> bytes:
'''simple docstring'''
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
_UpperCAmelCase = os.path.basename(_UpperCAmelCase )
_UpperCAmelCase = url.split('/' )[-2]
_UpperCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if os.path.exists(_UpperCAmelCase ) and not os.path.isfile(_UpperCAmelCase ):
raise RuntimeError(F"{download_target} exists and is not a regular file" )
if os.path.isfile(_UpperCAmelCase ):
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(F"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" )
with urllib.request.urlopen(_UpperCAmelCase ) as source, open(_UpperCAmelCase , 'wb' ) as output:
with tqdm(
total=int(source.info().get('Content-Length' ) ) , ncols=80 , unit='iB' , unit_scale=_UpperCAmelCase , unit_divisor=1_024 ) as loop:
while True:
_UpperCAmelCase = source.read(8_192 )
if not buffer:
break
output.write(_UpperCAmelCase )
loop.update(len(_UpperCAmelCase ) )
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() != expected_shaaaa:
raise RuntimeError(
'Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.' )
return model_bytes
def A ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
if ".pt" not in checkpoint_path:
_UpperCAmelCase = _download(_MODELS[checkpoint_path] )
else:
_UpperCAmelCase = torch.load(_UpperCAmelCase , map_location='cpu' )
_UpperCAmelCase = original_checkpoint['dims']
_UpperCAmelCase = original_checkpoint['model_state_dict']
_UpperCAmelCase = state_dict['decoder.token_embedding.weight']
remove_ignore_keys_(_UpperCAmelCase )
rename_keys(_UpperCAmelCase )
_UpperCAmelCase = True
_UpperCAmelCase = state_dict['decoder.layers.0.fc1.weight'].shape[0]
_UpperCAmelCase = WhisperConfig(
vocab_size=dimensions['n_vocab'] , encoder_ffn_dim=_UpperCAmelCase , decoder_ffn_dim=_UpperCAmelCase , num_mel_bins=dimensions['n_mels'] , d_model=dimensions['n_audio_state'] , max_target_positions=dimensions['n_text_ctx'] , encoder_layers=dimensions['n_audio_layer'] , encoder_attention_heads=dimensions['n_audio_head'] , decoder_layers=dimensions['n_text_layer'] , decoder_attention_heads=dimensions['n_text_state'] , max_source_positions=dimensions['n_audio_ctx'] , )
_UpperCAmelCase = WhisperForConditionalGeneration(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = model.model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0 and not set(_UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'
F" but all the following weights are missing {missing}" )
if tie_embeds:
_UpperCAmelCase = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
_UpperCAmelCase = proj_out_weights
model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# # Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCAmelCase__ = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 339 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
__magic_name__ = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
__magic_name__ = TaTokenizerFast
__magic_name__ = {"configuration_mt5": ["MT5Config", "MT5OnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
"MT5EncoderModel",
"MT5ForConditionalGeneration",
"MT5ForQuestionAnswering",
"MT5Model",
"MT5PreTrainedModel",
"MT5Stack",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
__magic_name__ = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
module_spec=__spec__,
)
| 100 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase__ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
UpperCamelCase = None
UpperCamelCase = None
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilder ):
UpperCamelCase = datasets.Audio()
UpperCamelCase = '''audio'''
UpperCamelCase = AudioFolderConfig
UpperCamelCase = 42 # definition at the bottom of the script
UpperCamelCase = AudioClassification(audio_column='''audio''' , label_column='''label''' )
UpperCAmelCase__ = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
UpperCAmelCase__ = AUDIO_EXTENSIONS
| 339 | 0 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : dict , __UpperCamelCase : str ) -> set[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = set(_UpperCAmelCase ), [start]
while stack:
SCREAMING_SNAKE_CASE__ = stack.pop()
explored.add(_UpperCAmelCase )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(_UpperCAmelCase )
return explored
__lowerCamelCase : str = {
'''A''': ['''B''', '''C''', '''D'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F'''],
'''D''': ['''B''', '''D'''],
'''E''': ['''B''', '''F'''],
'''F''': ['''C''', '''E''', '''G'''],
'''G''': ['''F'''],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, '''A'''))
| 219 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
def __init__( self : int) -> str:
"""simple docstring"""
_UpperCAmelCase = []
def _lowerCamelCase ( self : Any , A : List[str]) -> int:
"""simple docstring"""
return self.node_position[vertex]
def _lowerCamelCase ( self : Optional[Any] , A : Optional[int] , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = pos
def _lowerCamelCase ( self : Tuple , A : Tuple , A : Dict , A : List[str] , A : Optional[Any]) -> Dict:
"""simple docstring"""
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_UpperCAmelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_UpperCAmelCase = 2 * start + 1
else:
_UpperCAmelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_UpperCAmelCase , _UpperCAmelCase = heap[smallest_child], positions[smallest_child]
_UpperCAmelCase , _UpperCAmelCase = (
heap[start],
positions[start],
)
_UpperCAmelCase , _UpperCAmelCase = temp, tempa
_UpperCAmelCase = self.get_position(positions[smallest_child])
self.set_position(
positions[smallest_child] , self.get_position(positions[start]))
self.set_position(positions[start] , A)
self.top_to_bottom(A , A , A , A)
def _lowerCamelCase ( self : Optional[int] , A : str , A : Optional[Any] , A : Optional[int] , A : str) -> Any:
"""simple docstring"""
_UpperCAmelCase = position[index]
while index != 0:
_UpperCAmelCase = int((index - 2) / 2) if index % 2 == 0 else int((index - 1) / 2)
if val < heap[parent]:
_UpperCAmelCase = heap[parent]
_UpperCAmelCase = position[parent]
self.set_position(position[parent] , A)
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , A)
break
_UpperCAmelCase = parent
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , 0)
def _lowerCamelCase ( self : Union[str, Any] , A : Optional[int] , A : Tuple) -> str:
"""simple docstring"""
_UpperCAmelCase = len(A) // 2 - 1
for i in range(A , -1 , -1):
self.top_to_bottom(A , A , len(A) , A)
def _lowerCamelCase ( self : Optional[int] , A : int , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = positions[0]
_UpperCAmelCase = sys.maxsize
self.top_to_bottom(A , 0 , len(A) , A)
return temp
def A ( _UpperCAmelCase : int ) -> Any:
'''simple docstring'''
_UpperCAmelCase = Heap()
_UpperCAmelCase = [0] * len(_UpperCAmelCase )
_UpperCAmelCase = [-1] * len(_UpperCAmelCase ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_UpperCAmelCase = [] # Heap of Distance of vertices from their neighboring vertex
_UpperCAmelCase = []
for vertex in range(len(_UpperCAmelCase ) ):
distance_tv.append(sys.maxsize )
positions.append(_UpperCAmelCase )
heap.node_position.append(_UpperCAmelCase )
_UpperCAmelCase = []
_UpperCAmelCase = 1
_UpperCAmelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_UpperCAmelCase = 0
_UpperCAmelCase = distance
heap.heapify(_UpperCAmelCase , _UpperCAmelCase )
for _ in range(1 , len(_UpperCAmelCase ) ):
_UpperCAmelCase = heap.delete_minimum(_UpperCAmelCase , _UpperCAmelCase )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_UpperCAmelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(_UpperCAmelCase )]
):
_UpperCAmelCase = distance
heap.bottom_to_top(
_UpperCAmelCase , heap.get_position(_UpperCAmelCase ) , _UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
UpperCAmelCase__ = int(input("Enter number of edges: ").strip())
UpperCAmelCase__ = defaultdict(list)
for _ in range(edges_number):
UpperCAmelCase__ = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 339 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ : Optional[Any] = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Union[str, Any] = [
'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'AdaptiveEmbedding',
'TransfoXLForSequenceClassification',
'TransfoXLLMHeadModel',
'TransfoXLModel',
'TransfoXLPreTrainedModel',
'load_tf_weights_in_transfo_xl',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Tuple = [
'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFAdaptiveEmbedding',
'TFTransfoXLForSequenceClassification',
'TFTransfoXLLMHeadModel',
'TFTransfoXLMainLayer',
'TFTransfoXLModel',
'TFTransfoXLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 200 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int]=5 ) -> List[Any]:
'''simple docstring'''
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('<mask>' ) == 1
_UpperCAmelCase = torch.tensor(tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) ).unsqueeze(0 ) # Batch size 1
_UpperCAmelCase = model(_UpperCAmelCase )[0] # The last hidden-state is the first element of the output tuple
_UpperCAmelCase = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
_UpperCAmelCase = logits[0, masked_index, :]
_UpperCAmelCase = logits.softmax(dim=0 )
_UpperCAmelCase , _UpperCAmelCase = prob.topk(k=_UpperCAmelCase , dim=0 )
_UpperCAmelCase = ' '.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(_UpperCAmelCase ) )] )
_UpperCAmelCase = tokenizer.mask_token
_UpperCAmelCase = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ' ) ):
_UpperCAmelCase = predicted_token_bpe.replace('\u2581' , ' ' )
if " {0}".format(_UpperCAmelCase ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(' {0}'.format(_UpperCAmelCase ) , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(_UpperCAmelCase , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
UpperCAmelCase__ = CamembertTokenizer.from_pretrained("camembert-base")
UpperCAmelCase__ = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
UpperCAmelCase__ = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 339 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class __A ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] =StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4' )
__UpperCamelCase : int =sd_pipe.to(lowerCamelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
sd_pipe.set_scheduler('sample_euler' )
__UpperCamelCase : List[str] ='A painting of a squirrel eating a burger'
__UpperCamelCase : List[str] =torch.manual_seed(0 )
__UpperCamelCase : Any =sd_pipe([prompt] , generator=lowerCamelCase__ , guidance_scale=9.0 , num_inference_steps=20 , output_type='np' )
__UpperCamelCase : Optional[Any] =output.images
__UpperCamelCase : int =image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__UpperCamelCase : Union[str, Any] =np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Tuple =StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
__UpperCamelCase : Union[str, Any] =sd_pipe.to(lowerCamelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
sd_pipe.set_scheduler('sample_euler' )
__UpperCamelCase : List[str] ='A painting of a squirrel eating a burger'
__UpperCamelCase : List[Any] =torch.manual_seed(0 )
__UpperCamelCase : Optional[Any] =sd_pipe([prompt] , generator=lowerCamelCase__ , guidance_scale=9.0 , num_inference_steps=20 , output_type='np' )
__UpperCamelCase : Optional[Any] =output.images
__UpperCamelCase : Dict =image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__UpperCamelCase : int =np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-1
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[Any] =StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
__UpperCamelCase : List[str] =sd_pipe.to(lowerCamelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
sd_pipe.set_scheduler('sample_dpmpp_2m' )
__UpperCamelCase : List[str] ='A painting of a squirrel eating a burger'
__UpperCamelCase : List[str] =torch.manual_seed(0 )
__UpperCamelCase : List[Any] =sd_pipe(
[prompt] , generator=lowerCamelCase__ , guidance_scale=7.5 , num_inference_steps=15 , output_type='np' , use_karras_sigmas=lowerCamelCase__ , )
__UpperCamelCase : Dict =output.images
__UpperCamelCase : Optional[Any] =image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__UpperCamelCase : Union[str, Any] =np.array(
[0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 71 |
import math
import unittest
def A ( _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Union[str, Any]:
"""simple docstring"""
self.assertTrue(is_prime(2))
self.assertTrue(is_prime(3))
self.assertTrue(is_prime(5))
self.assertTrue(is_prime(7))
self.assertTrue(is_prime(11))
self.assertTrue(is_prime(13))
self.assertTrue(is_prime(17))
self.assertTrue(is_prime(19))
self.assertTrue(is_prime(23))
self.assertTrue(is_prime(29))
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
with self.assertRaises(A):
is_prime(-19)
self.assertFalse(
is_prime(0) , 'Zero doesn\'t have any positive factors, primes must have exactly two.' , )
self.assertFalse(
is_prime(1) , 'One only has 1 positive factor, primes must have exactly two.' , )
self.assertFalse(is_prime(2 * 2))
self.assertFalse(is_prime(2 * 3))
self.assertFalse(is_prime(3 * 3))
self.assertFalse(is_prime(3 * 5))
self.assertFalse(is_prime(3 * 5 * 7))
if __name__ == "__main__":
unittest.main()
| 339 | 0 |
'''simple docstring'''
from graphs.minimum_spanning_tree_kruskal import kruskal
def _lowerCAmelCase ( ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =9
_SCREAMING_SNAKE_CASE =[
[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, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
_SCREAMING_SNAKE_CASE =kruskal(_UpperCAmelCase , _UpperCAmelCase )
_SCREAMING_SNAKE_CASE =[
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(_UpperCAmelCase ) == sorted(_UpperCAmelCase )
| 47 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase__ = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n"
UpperCAmelCase__ = "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"
UpperCAmelCase__ = "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n 'google_bleu': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.4\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : str) -> MetricInfo:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('string' , id='token') , id='sequence'),
'references': datasets.Sequence(
datasets.Sequence(datasets.Value('string' , id='token') , id='sequence') , id='references'),
}) , )
def _lowerCamelCase ( self : Union[str, Any] , A : List[List[List[str]]] , A : List[List[str]] , A : int = 1 , A : int = 4 , ) -> Dict[str, float]:
"""simple docstring"""
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=A , hypotheses=A , min_len=A , max_len=A)
}
| 339 | 0 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
__lowercase ,__lowercase : str = coefficient_matrix.shape
__lowercase ,__lowercase : Union[str, Any] = constant_matrix.shape
if rowsa != colsa:
__lowercase : Optional[int] = f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
raise ValueError(_UpperCAmelCase )
if colsa != 1:
__lowercase : str = f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
raise ValueError(_UpperCAmelCase )
if rowsa != rowsa:
__lowercase : Optional[int] = (
'''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
)
raise ValueError(_UpperCAmelCase )
if len(_UpperCAmelCase ) != rowsa:
__lowercase : Dict = (
'''Number of initial values must be equal to number of rows in coefficient '''
f"""matrix but received {len(_UpperCAmelCase )} and {rowsa}"""
)
raise ValueError(_UpperCAmelCase )
if iterations <= 0:
raise ValueError('''Iterations must be at least 1''' )
__lowercase : Dict = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
__lowercase ,__lowercase : Optional[int] = table.shape
strictly_diagonally_dominant(_UpperCAmelCase )
# Iterates the whole matrix for given number of times
for _ in range(_UpperCAmelCase ):
__lowercase : Optional[Any] = []
for row in range(_UpperCAmelCase ):
__lowercase : Union[str, Any] = 0
for col in range(_UpperCAmelCase ):
if col == row:
__lowercase : Optional[Any] = table[row][col]
elif col == cols - 1:
__lowercase : Dict = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
__lowercase : List[Any] = (temp + val) / denom
new_val.append(_UpperCAmelCase )
__lowercase : List[Any] = new_val
return [float(_UpperCAmelCase ) for i in new_val]
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase ,__lowercase : Optional[int] = table.shape
__lowercase : List[str] = True
for i in range(0 , _UpperCAmelCase ):
__lowercase : List[Any] = 0
for j in range(0 , cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError('''Coefficient matrix is not strictly diagonally dominant''' )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 249 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
UpperCAmelCase__ = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
UpperCAmelCase__ = TaTokenizerFast
UpperCAmelCase__ = {"configuration_mt5": ["MT5Config", "MT5OnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"MT5EncoderModel",
"MT5ForConditionalGeneration",
"MT5ForQuestionAnswering",
"MT5Model",
"MT5PreTrainedModel",
"MT5Stack",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
UpperCAmelCase__ = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
module_spec=__spec__,
)
| 339 | 0 |
"""simple docstring"""
from collections.abc import Generator
from math import sin
def lowercase (snake_case__ : bytes ) -> bytes:
'''simple docstring'''
if len(_UpperCAmelCase ) != 32:
raise ValueError("""Input must be of length 32""" )
lowerCAmelCase = B""""""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def lowercase (snake_case__ : int ) -> bytes:
'''simple docstring'''
if i < 0:
raise ValueError("""Input must be non-negative""" )
lowerCAmelCase = format(_UpperCAmelCase , """08x""" )[-8:]
lowerCAmelCase = B""""""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8""" )
return little_endian_hex
def lowercase (snake_case__ : bytes ) -> bytes:
'''simple docstring'''
lowerCAmelCase = B""""""
for char in message:
bit_string += format(_UpperCAmelCase , """08b""" ).encode("""utf-8""" )
lowerCAmelCase = format(len(_UpperCAmelCase ) , """064b""" ).encode("""utf-8""" )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(_UpperCAmelCase ) % 512 != 448:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )
return bit_string
def lowercase (snake_case__ : bytes ) -> Generator[list[int], None, None]:
'''simple docstring'''
if len(_UpperCAmelCase ) % 512 != 0:
raise ValueError("""Input must have length that\'s a multiple of 512""" )
for pos in range(0 , len(_UpperCAmelCase ) , 512 ):
lowerCAmelCase = bit_string[pos : pos + 512]
lowerCAmelCase = []
for i in range(0 , 512 , 32 ):
block_words.append(int(to_little_endian(block[i : i + 32] ) , 2 ) )
yield block_words
def lowercase (snake_case__ : int ) -> int:
'''simple docstring'''
if i < 0:
raise ValueError("""Input must be non-negative""" )
lowerCAmelCase = format(_UpperCAmelCase , """032b""" )
lowerCAmelCase = """"""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(_UpperCAmelCase , 2 )
def lowercase (snake_case__ : int , snake_case__ : int ) -> int:
'''simple docstring'''
return (a + b) % 2**32
def lowercase (snake_case__ : int , snake_case__ : int ) -> int:
'''simple docstring'''
if i < 0:
raise ValueError("""Input must be non-negative""" )
if shift < 0:
raise ValueError("""Shift must be non-negative""" )
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def lowercase (snake_case__ : bytes ) -> bytes:
'''simple docstring'''
lowerCAmelCase = preprocess(_UpperCAmelCase )
lowerCAmelCase = [int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]
# Starting states
lowerCAmelCase = 0x67_452_301
lowerCAmelCase = 0xef_cda_b89
lowerCAmelCase = 0x98_bad_cfe
lowerCAmelCase = 0x10_325_476
lowerCAmelCase = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(_UpperCAmelCase ):
lowerCAmelCase = aa
lowerCAmelCase = ba
lowerCAmelCase = ca
lowerCAmelCase = da
# Hash current chunk
for i in range(64 ):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
lowerCAmelCase = d ^ (b & (c ^ d))
lowerCAmelCase = i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
lowerCAmelCase = c ^ (d & (b ^ c))
lowerCAmelCase = (5 * i + 1) % 16
elif i <= 47:
lowerCAmelCase = b ^ c ^ d
lowerCAmelCase = (3 * i + 5) % 16
else:
lowerCAmelCase = c ^ (b | not_aa(_UpperCAmelCase ))
lowerCAmelCase = (7 * i) % 16
lowerCAmelCase = (f + a + added_consts[i] + block_words[g]) % 2**32
lowerCAmelCase = d
lowerCAmelCase = c
lowerCAmelCase = b
lowerCAmelCase = sum_aa(_UpperCAmelCase , left_rotate_aa(_UpperCAmelCase , shift_amounts[i] ) )
# Add hashed chunk to running total
lowerCAmelCase = sum_aa(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase = sum_aa(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase = sum_aa(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase = sum_aa(_UpperCAmelCase , _UpperCAmelCase )
lowerCAmelCase = reformat_hex(_UpperCAmelCase ) + reformat_hex(_UpperCAmelCase ) + reformat_hex(_UpperCAmelCase ) + reformat_hex(_UpperCAmelCase )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 155 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class __lowerCAmelCase ( A ):
UpperCamelCase = '''open-llama'''
def __init__( self : str , A : List[Any]=10_00_00 , A : Tuple=40_96 , A : Tuple=1_10_08 , A : List[str]=32 , A : Tuple=32 , A : Optional[Any]="silu" , A : int=20_48 , A : Optional[Any]=0.0_2 , A : Dict=1E-6 , A : Optional[Any]=True , A : List[Any]=0 , A : Dict=1 , A : int=2 , A : Dict=False , A : Optional[int]=True , A : List[Any]=0.1 , A : str=0.1 , A : Dict=True , A : Optional[Any]=True , A : Dict=None , **A : Union[str, Any] , ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = vocab_size
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = hidden_size
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = initializer_range
_UpperCAmelCase = rms_norm_eps
_UpperCAmelCase = use_cache
_UpperCAmelCase = kwargs.pop(
'use_memorry_efficient_attention' , A)
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_dropout_prob
_UpperCAmelCase = use_stable_embedding
_UpperCAmelCase = shared_input_output_embedding
_UpperCAmelCase = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , tie_word_embeddings=A , **A , )
def _lowerCamelCase ( self : List[str]) -> Union[str, Any]:
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , A) or len(self.rope_scaling) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F"got {self.rope_scaling}")
_UpperCAmelCase = self.rope_scaling.get('type' , A)
_UpperCAmelCase = self.rope_scaling.get('factor' , A)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}")
if rope_scaling_factor is None or not isinstance(A , A) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
| 339 | 0 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPTaConfig,
GPTaLMHeadModel,
GPTaTokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
UpperCamelCase = {
'''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
'''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
'''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
'''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def lowercase_ ( _lowerCamelCase : Optional[Any]):
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts)
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config)
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights)
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def lowercase_ ( _lowerCamelCase : Dict , _lowerCamelCase : Union[str, Any]):
if args.student_type == "roberta":
lowercase__ : List[Any] = False
elif args.student_type == "gpt2":
lowercase__ : str = False
def lowercase_ ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : int):
if args.student_type == "roberta":
lowercase__ : List[Any] = False
def lowercase_ ( ):
lowercase__ : Any = argparse.ArgumentParser(description="Training")
parser.add_argument("--force" , action="store_true" , help="Overwrite dump_path if it already exists.")
parser.add_argument(
"--dump_path" , type=_UpperCAmelCase , required=_UpperCAmelCase , help="The output directory (log, checkpoints, parameters, etc.)")
parser.add_argument(
"--data_file" , type=_UpperCAmelCase , required=_UpperCAmelCase , help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence." , )
parser.add_argument(
"--student_type" , type=_UpperCAmelCase , choices=["distilbert", "roberta", "gpt2"] , required=_UpperCAmelCase , help="The student type (DistilBERT, RoBERTa)." , )
parser.add_argument("--student_config" , type=_UpperCAmelCase , required=_UpperCAmelCase , help="Path to the student configuration.")
parser.add_argument(
"--student_pretrained_weights" , default=_UpperCAmelCase , type=_UpperCAmelCase , help="Load student initialization checkpoint.")
parser.add_argument(
"--teacher_type" , choices=["bert", "roberta", "gpt2"] , required=_UpperCAmelCase , help="Teacher type (BERT, RoBERTa).")
parser.add_argument("--teacher_name" , type=_UpperCAmelCase , required=_UpperCAmelCase , help="The teacher model.")
parser.add_argument("--temperature" , default=2.0 , type=_UpperCAmelCase , help="Temperature for the softmax temperature.")
parser.add_argument(
"--alpha_ce" , default=0.5 , type=_UpperCAmelCase , help="Linear weight for the distillation loss. Must be >=0.")
parser.add_argument(
"--alpha_mlm" , default=0.0 , type=_UpperCAmelCase , help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag." , )
parser.add_argument("--alpha_clm" , default=0.5 , type=_UpperCAmelCase , help="Linear weight for the CLM loss. Must be >=0.")
parser.add_argument("--alpha_mse" , default=0.0 , type=_UpperCAmelCase , help="Linear weight of the MSE loss. Must be >=0.")
parser.add_argument(
"--alpha_cos" , default=0.0 , type=_UpperCAmelCase , help="Linear weight of the cosine embedding loss. Must be >=0.")
parser.add_argument(
"--mlm" , action="store_true" , help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.")
parser.add_argument(
"--mlm_mask_prop" , default=0.15 , type=_UpperCAmelCase , help="Proportion of tokens for which we need to make a prediction." , )
parser.add_argument("--word_mask" , default=0.8 , type=_UpperCAmelCase , help="Proportion of tokens to mask out.")
parser.add_argument("--word_keep" , default=0.1 , type=_UpperCAmelCase , help="Proportion of tokens to keep.")
parser.add_argument("--word_rand" , default=0.1 , type=_UpperCAmelCase , help="Proportion of tokens to randomly replace.")
parser.add_argument(
"--mlm_smoothing" , default=0.7 , type=_UpperCAmelCase , help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)." , )
parser.add_argument("--token_counts" , type=_UpperCAmelCase , help="The token counts in the data_file for MLM.")
parser.add_argument(
"--restrict_ce_to_mask" , action="store_true" , help="If true, compute the distillation loss only the [MLM] prediction distribution." , )
parser.add_argument(
"--freeze_pos_embs" , action="store_true" , help="Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only." , )
parser.add_argument(
"--freeze_token_type_embds" , action="store_true" , help="Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only." , )
parser.add_argument("--n_epoch" , type=_UpperCAmelCase , default=3 , help="Number of pass on the whole dataset.")
parser.add_argument("--batch_size" , type=_UpperCAmelCase , default=5 , help="Batch size (for each process).")
parser.add_argument(
"--group_by_size" , action="store_false" , help="If true, group sequences that have similar length into the same batch. Default is true." , )
parser.add_argument(
"--gradient_accumulation_steps" , type=_UpperCAmelCase , default=50 , help="Gradient accumulation for larger training batches." , )
parser.add_argument("--warmup_prop" , default=0.05 , type=_UpperCAmelCase , help="Linear warmup proportion.")
parser.add_argument("--weight_decay" , default=0.0 , type=_UpperCAmelCase , help="Weight decay if we apply some.")
parser.add_argument("--learning_rate" , default=5E-4 , type=_UpperCAmelCase , help="The initial learning rate for Adam.")
parser.add_argument("--adam_epsilon" , default=1E-6 , type=_UpperCAmelCase , help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm" , default=5.0 , type=_UpperCAmelCase , help="Max gradient norm.")
parser.add_argument("--initializer_range" , default=0.02 , type=_UpperCAmelCase , help="Random initialization range.")
parser.add_argument(
"--fp16" , action="store_true" , help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit" , )
parser.add_argument(
"--fp16_opt_level" , type=_UpperCAmelCase , default="O1" , help=(
"For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']."
"See details at https://nvidia.github.io/apex/amp.html"
) , )
parser.add_argument("--n_gpu" , type=_UpperCAmelCase , default=1 , help="Number of GPUs in the node.")
parser.add_argument("--local_rank" , type=_UpperCAmelCase , default=-1 , help="Distributed training - Local rank")
parser.add_argument("--seed" , type=_UpperCAmelCase , default=56 , help="Random seed")
parser.add_argument("--log_interval" , type=_UpperCAmelCase , default=500 , help="Tensorboard logging interval.")
parser.add_argument("--checkpoint_interval" , type=_UpperCAmelCase , default=4000 , help="Checkpoint interval.")
lowercase__ : List[Any] = parser.parse_args()
sanity_checks(_UpperCAmelCase)
# ARGS #
init_gpu_params(_UpperCAmelCase)
set_seed(_UpperCAmelCase)
if args.is_master:
if os.path.exists(args.dump_path):
if not args.force:
raise ValueError(
f'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
" itUse `--force` if you want to overwrite it")
else:
shutil.rmtree(args.dump_path)
if not os.path.exists(args.dump_path):
os.makedirs(args.dump_path)
logger.info(f'''Experiment will be dumped and logged in {args.dump_path}''')
# SAVE PARAMS #
logger.info(f'''Param: {args}''')
with open(os.path.join(args.dump_path , "parameters.json") , "w") as f:
json.dump(vars(_UpperCAmelCase) , _UpperCAmelCase , indent=4)
git_log(args.dump_path)
lowercase__ , lowercase__ , lowercase__ : int = MODEL_CLASSES[args.student_type]
lowercase__ , lowercase__ , lowercase__ : str = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
lowercase__ : Union[str, Any] = teacher_tokenizer_class.from_pretrained(args.teacher_name)
lowercase__ : List[Any] = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
lowercase__ : Optional[int] = tokenizer.all_special_tokens.index(_UpperCAmelCase)
lowercase__ : Any = tokenizer.all_special_ids[idx]
logger.info(f'''Special tokens {special_tok_ids}''')
lowercase__ : int = special_tok_ids
lowercase__ : int = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(f'''Loading data from {args.data_file}''')
with open(args.data_file , "rb") as fp:
lowercase__ : Union[str, Any] = pickle.load(_UpperCAmelCase)
if args.mlm:
logger.info(f'''Loading token counts from {args.token_counts} (already pre-computed)''')
with open(args.token_counts , "rb") as fp:
lowercase__ : Dict = pickle.load(_UpperCAmelCase)
lowercase__ : str = np.maximum(_UpperCAmelCase , 1) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
lowercase__ : Tuple = 0.0 # do not predict special tokens
lowercase__ : Optional[int] = torch.from_numpy(_UpperCAmelCase)
else:
lowercase__ : List[str] = None
lowercase__ : Optional[int] = LmSeqsDataset(params=_UpperCAmelCase , data=_UpperCAmelCase)
logger.info("Data loader created.")
# STUDENT #
logger.info(f'''Loading student config from {args.student_config}''')
lowercase__ : Optional[int] = student_config_class.from_pretrained(args.student_config)
lowercase__ : Optional[Any] = True
if args.student_pretrained_weights is not None:
logger.info(f'''Loading pretrained weights from {args.student_pretrained_weights}''')
lowercase__ : Any = student_model_class.from_pretrained(args.student_pretrained_weights , config=_UpperCAmelCase)
else:
lowercase__ : Tuple = student_model_class(_UpperCAmelCase)
if args.n_gpu > 0:
student.to(f'''cuda:{args.local_rank}''')
logger.info("Student loaded.")
# TEACHER #
lowercase__ : Tuple = teacher_model_class.from_pretrained(args.teacher_name , output_hidden_states=_UpperCAmelCase)
if args.n_gpu > 0:
teacher.to(f'''cuda:{args.local_rank}''')
logger.info(f'''Teacher loaded from {args.teacher_name}.''')
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(_UpperCAmelCase , _UpperCAmelCase)
if args.freeze_token_type_embds:
freeze_token_type_embeddings(_UpperCAmelCase , _UpperCAmelCase)
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
lowercase__ : Any = Distiller(
params=_UpperCAmelCase , dataset=_UpperCAmelCase , token_probs=_UpperCAmelCase , student=_UpperCAmelCase , teacher=_UpperCAmelCase)
distiller.train()
logger.info("Let\'s go get some drinks.")
if __name__ == "__main__":
main()
| 87 |
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = credit_card_number
_UpperCAmelCase = 0
_UpperCAmelCase = len(_UpperCAmelCase ) - 2
for i in range(_UpperCAmelCase , -1 , -2 ):
# double the value of every second digit
_UpperCAmelCase = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
_UpperCAmelCase = cc_number[:i] + str(_UpperCAmelCase ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(_UpperCAmelCase ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = F"{credit_card_number} is an invalid credit card number because"
if not credit_card_number.isdigit():
print(F"{error_message} it has nonnumerical characters." )
return False
if not 13 <= len(_UpperCAmelCase ) <= 16:
print(F"{error_message} of its length." )
return False
if not validate_initial_digits(_UpperCAmelCase ):
print(F"{error_message} of its first two digits." )
return False
if not luhn_validation(_UpperCAmelCase ):
print(F"{error_message} it fails the Luhn check." )
return False
print(F"{credit_card_number} is a valid credit card number." )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 339 | 0 |
'''simple docstring'''
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load
try:
import torch
_lowerCAmelCase = True
except ImportError:
_lowerCAmelCase = False
try:
from torch.hub import _get_torch_home
_lowerCAmelCase = _get_torch_home()
except ImportError:
_lowerCAmelCase = os.path.expanduser(
os.getenv('''TORCH_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''torch'''))
)
_lowerCAmelCase = os.path.join(torch_cache_home, '''transformers''')
_lowerCAmelCase = '''https://cdn.huggingface.co'''
_lowerCAmelCase = '''https://s3.amazonaws.com/models.huggingface.co/bert'''
_lowerCAmelCase = '''/'''.join(str(Path(__file__).resolve()).split('''/''')[:-1])
_lowerCAmelCase = os.path.join(PATH, '''config.yaml''')
_lowerCAmelCase = os.path.join(PATH, '''attributes.txt''')
_lowerCAmelCase = os.path.join(PATH, '''objects.txt''')
_lowerCAmelCase = os.getenv('''PYTORCH_PRETRAINED_BERT_CACHE''', default_cache_path)
_lowerCAmelCase = os.getenv('''PYTORCH_TRANSFORMERS_CACHE''', PYTORCH_PRETRAINED_BERT_CACHE)
_lowerCAmelCase = os.getenv('''TRANSFORMERS_CACHE''', PYTORCH_TRANSFORMERS_CACHE)
_lowerCAmelCase = '''pytorch_model.bin'''
_lowerCAmelCase = '''config.yaml'''
def __lowerCAmelCase ( snake_case__=OBJECTS , snake_case__=ATTRIBUTES ):
__UpperCamelCase : str = []
with open(_UpperCAmelCase ) as f:
for object in f.readlines():
vg_classes.append(object.split("," )[0].lower().strip() )
__UpperCamelCase : str = []
with open(_UpperCAmelCase ) as f:
for object in f.readlines():
vg_attrs.append(object.split("," )[0].lower().strip() )
return vg_classes, vg_attrs
def __lowerCAmelCase ( snake_case__ ):
__UpperCamelCase : Any = OrderedDict()
with open(_UpperCAmelCase , "rb" ) as f:
__UpperCamelCase : List[str] = pkl.load(_UpperCAmelCase )["model"]
for k in copy.deepcopy(list(ckp.keys() ) ):
__UpperCamelCase : Dict = ckp.pop(_UpperCAmelCase )
if isinstance(_UpperCAmelCase , np.ndarray ):
__UpperCamelCase : Tuple = torch.tensor(_UpperCAmelCase )
else:
assert isinstance(_UpperCAmelCase , torch.tensor ), type(_UpperCAmelCase )
__UpperCamelCase : Optional[Any] = v
return r
class A :
'''simple docstring'''
A = {}
def __init__(self , _UpperCAmelCase , _UpperCAmelCase = "root" , _UpperCAmelCase=0 ) -> Tuple:
__UpperCamelCase : Optional[int] = name
__UpperCamelCase : Any = level
__UpperCamelCase : List[Any] = {}
for k, v in dictionary.items():
if v is None:
raise ValueError()
__UpperCamelCase : int = copy.deepcopy(_UpperCAmelCase )
__UpperCamelCase : str = copy.deepcopy(_UpperCAmelCase )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
__UpperCamelCase : Any = Config(_UpperCAmelCase , name=_UpperCAmelCase , level=level + 1 )
__UpperCamelCase : Any = v
setattr(self , _UpperCAmelCase , _UpperCAmelCase )
__UpperCamelCase : Dict = d
def __repr__(self ) -> Optional[int]:
return str(list((self._pointer.keys()) ) )
def __setattr__(self , _UpperCAmelCase , _UpperCAmelCase ) -> Tuple:
__UpperCamelCase : Any = val
__UpperCamelCase : Any = val
__UpperCamelCase : Optional[Any] = key.split("." )
__UpperCamelCase : int = len(_UpperCAmelCase ) - 1
__UpperCamelCase : List[str] = self._pointer
if len(_UpperCAmelCase ) > 1:
for i, l in enumerate(_UpperCAmelCase ):
if hasattr(self , _UpperCAmelCase ) and isinstance(getattr(self , _UpperCAmelCase ) , _UpperCAmelCase ):
setattr(getattr(self , _UpperCAmelCase ) , ".".join(levels[i:] ) , _UpperCAmelCase )
if l == last_level:
__UpperCamelCase : List[Any] = val
else:
__UpperCamelCase : Union[str, Any] = pointer[l]
def a_ (self ) -> Tuple:
return self._pointer
def a_ (self , _UpperCAmelCase , _UpperCAmelCase ) -> str:
with open(f"{file_name}" , "w" ) as stream:
dump(_UpperCAmelCase , _UpperCAmelCase )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase ) -> str:
with open(f"{file_name}" , "w" ) as stream:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
@staticmethod
def a_ (_UpperCAmelCase ) -> int:
with open(_UpperCAmelCase ) as stream:
__UpperCamelCase : int = load(_UpperCAmelCase , Loader=_UpperCAmelCase )
return data
def __str__(self ) -> str:
__UpperCamelCase : Union[str, Any] = " "
if self._name != "root":
__UpperCamelCase : List[str] = f"{t * (self._level-1)}{self._name}:\n"
else:
__UpperCamelCase : List[str] = ""
__UpperCamelCase : Any = self._level
for i, (k, v) in enumerate(self._pointer.items() ):
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
r += f"{t * (self._level)}{v}\n"
self._level += 1
else:
r += f"{t * (self._level)}{k}: {v} ({type(_UpperCAmelCase ).__name__})\n"
__UpperCamelCase : int = level
return r[:-1]
@classmethod
def a_ (cls , _UpperCAmelCase , **_UpperCAmelCase ) -> List[str]:
__UpperCamelCase , __UpperCamelCase : Optional[Any] = cls.get_config_dict(_UpperCAmelCase , **_UpperCAmelCase )
return cls(_UpperCAmelCase )
@classmethod
def a_ (cls , _UpperCAmelCase , **_UpperCAmelCase ) -> Dict:
__UpperCamelCase : Tuple = kwargs.pop("cache_dir" , _UpperCAmelCase )
__UpperCamelCase : Optional[int] = kwargs.pop("force_download" , _UpperCAmelCase )
__UpperCamelCase : Optional[int] = kwargs.pop("resume_download" , _UpperCAmelCase )
__UpperCamelCase : List[Any] = kwargs.pop("proxies" , _UpperCAmelCase )
__UpperCamelCase : List[str] = kwargs.pop("local_files_only" , _UpperCAmelCase )
if os.path.isdir(_UpperCAmelCase ):
__UpperCamelCase : Optional[int] = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
elif os.path.isfile(_UpperCAmelCase ) or is_remote_url(_UpperCAmelCase ):
__UpperCamelCase : str = pretrained_model_name_or_path
else:
__UpperCamelCase : int = hf_bucket_url(_UpperCAmelCase , filename=_UpperCAmelCase , use_cdn=_UpperCAmelCase )
try:
# Load from URL or cache if already cached
__UpperCamelCase : List[str] = cached_path(
_UpperCAmelCase , cache_dir=_UpperCAmelCase , force_download=_UpperCAmelCase , proxies=_UpperCAmelCase , resume_download=_UpperCAmelCase , local_files_only=_UpperCAmelCase , )
# Load config dict
if resolved_config_file is None:
raise EnvironmentError
__UpperCamelCase : List[Any] = Config.load_yaml(_UpperCAmelCase )
except EnvironmentError:
__UpperCamelCase : Any = "Can\'t load config for"
raise EnvironmentError(_UpperCAmelCase )
if resolved_config_file == config_file:
print("loading configuration file from path" )
else:
print("loading configuration file cache" )
return Config.load_yaml(_UpperCAmelCase ), kwargs
def __lowerCAmelCase ( snake_case__ ):
__UpperCamelCase : Optional[Any] = torch.load("dump.pt" , map_location=in_tensor.device )
__UpperCamelCase : List[str] = in_tensor.numpy()
__UpperCamelCase : Union[str, Any] = out_tensor.numpy()[0]
print(na.shape , na[0, 0, :5] )
print(na.shape , na[0, 0, :5] )
assert np.allclose(_UpperCAmelCase , _UpperCAmelCase , rtol=0.01 , atol=0.1 ), (
F"{sum([1 for x in np.isclose(_UpperCAmelCase , _UpperCAmelCase , rtol=0.01 , atol=0.1 ).flatten() if x is False] )/len(na.flatten() )*100:.4f} %"
" element-wise mismatch"
)
raise Exception("tensors are all good" )
# Hugging face functions below
def __lowerCAmelCase ( snake_case__ ):
__UpperCamelCase : Any = urlparse(_UpperCAmelCase )
return parsed.scheme in ("http", "https")
def __lowerCAmelCase ( snake_case__ , snake_case__ , snake_case__=True ):
__UpperCamelCase : Dict = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
__UpperCamelCase : List[str] = "/" not in model_id
if legacy_format:
return F"{endpoint}/{model_id}-{filename}"
else:
return F"{endpoint}/{model_id}/{filename}"
def __lowerCAmelCase ( snake_case__ , snake_case__ , snake_case__=None , snake_case__=0 , snake_case__=None , ):
__UpperCamelCase : Optional[Any] = "python/{}".format(sys.version.split()[0] )
if _torch_available:
ua += "; torch/{}".format(torch.__version__ )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
ua += "; " + "; ".join("{}/{}".format(_UpperCAmelCase , _UpperCAmelCase ) for k, v in user_agent.items() )
elif isinstance(_UpperCAmelCase , _UpperCAmelCase ):
ua += "; " + user_agent
__UpperCamelCase : Union[str, Any] = {"user-agent": ua}
if resume_size > 0:
__UpperCamelCase : List[str] = "bytes=%d-" % (resume_size,)
__UpperCamelCase : Union[str, Any] = requests.get(_UpperCAmelCase , stream=_UpperCAmelCase , proxies=_UpperCAmelCase , headers=_UpperCAmelCase )
if response.status_code == 416: # Range not satisfiable
return
__UpperCamelCase : Union[str, Any] = response.headers.get("Content-Length" )
__UpperCamelCase : Any = resume_size + int(_UpperCAmelCase ) if content_length is not None else None
__UpperCamelCase : Any = tqdm(
unit="B" , unit_scale=_UpperCAmelCase , total=_UpperCAmelCase , initial=_UpperCAmelCase , desc="Downloading" , )
for chunk in response.iter_content(chunk_size=1_024 ):
if chunk: # filter out keep-alive new chunks
progress.update(len(_UpperCAmelCase ) )
temp_file.write(_UpperCAmelCase )
progress.close()
def __lowerCAmelCase ( snake_case__ , snake_case__=None , snake_case__=False , snake_case__=None , snake_case__=10 , snake_case__=False , snake_case__=None , snake_case__=False , ):
if cache_dir is None:
__UpperCamelCase : int = TRANSFORMERS_CACHE
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
__UpperCamelCase : int = str(_UpperCAmelCase )
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
__UpperCamelCase : List[str] = None
if not local_files_only:
try:
__UpperCamelCase : Tuple = requests.head(_UpperCAmelCase , allow_redirects=_UpperCAmelCase , proxies=_UpperCAmelCase , timeout=_UpperCAmelCase )
if response.status_code == 200:
__UpperCamelCase : List[Any] = response.headers.get("ETag" )
except (EnvironmentError, requests.exceptions.Timeout):
# etag is already None
pass
__UpperCamelCase : Optional[int] = url_to_filename(_UpperCAmelCase , _UpperCAmelCase )
# get cache path to put the file
__UpperCamelCase : Any = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
# try to get the last downloaded one
if etag is None:
if os.path.exists(_UpperCAmelCase ):
return cache_path
else:
__UpperCamelCase : Union[str, Any] = [
file
for file in fnmatch.filter(os.listdir(_UpperCAmelCase ) , filename + ".*" )
if not file.endswith(".json" ) and not file.endswith(".lock" )
]
if len(_UpperCAmelCase ) > 0:
return os.path.join(_UpperCAmelCase , matching_files[-1] )
else:
# If files cannot be found and local_files_only=True,
# the models might've been found if local_files_only=False
# Notify the user about that
if local_files_only:
raise ValueError(
"Cannot find the requested files in the cached path and outgoing traffic has been"
" disabled. To enable model look-ups and downloads online, set \'local_files_only\'"
" to False." )
return None
# From now on, etag is not None.
if os.path.exists(_UpperCAmelCase ) and not force_download:
return cache_path
# Prevent parallel downloads of the same file with a lock.
__UpperCamelCase : List[str] = cache_path + ".lock"
with FileLock(_UpperCAmelCase ):
# If the download just completed while the lock was activated.
if os.path.exists(_UpperCAmelCase ) and not force_download:
# Even if returning early like here, the lock will be released.
return cache_path
if resume_download:
__UpperCamelCase : Optional[Any] = cache_path + ".incomplete"
@contextmanager
def _resumable_file_manager():
with open(_UpperCAmelCase , "a+b" ) as f:
yield f
__UpperCamelCase : Any = _resumable_file_manager
if os.path.exists(_UpperCAmelCase ):
__UpperCamelCase : Tuple = os.stat(_UpperCAmelCase ).st_size
else:
__UpperCamelCase : int = 0
else:
__UpperCamelCase : Tuple = partial(tempfile.NamedTemporaryFile , dir=_UpperCAmelCase , delete=_UpperCAmelCase )
__UpperCamelCase : List[str] = 0
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with temp_file_manager() as temp_file:
print(
"%s not found in cache or force_download set to True, downloading to %s" , _UpperCAmelCase , temp_file.name , )
http_get(
_UpperCAmelCase , _UpperCAmelCase , proxies=_UpperCAmelCase , resume_size=_UpperCAmelCase , user_agent=_UpperCAmelCase , )
os.replace(temp_file.name , _UpperCAmelCase )
__UpperCamelCase : Optional[int] = {"url": url, "etag": etag}
__UpperCamelCase : List[Any] = cache_path + ".json"
with open(_UpperCAmelCase , "w" ) as meta_file:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
return cache_path
def __lowerCAmelCase ( snake_case__ , snake_case__=None ):
__UpperCamelCase : int = url.encode("utf-8" )
__UpperCamelCase : str = shaaaa(_UpperCAmelCase )
__UpperCamelCase : List[Any] = url_hash.hexdigest()
if etag:
__UpperCamelCase : List[Any] = etag.encode("utf-8" )
__UpperCamelCase : int = shaaaa(_UpperCAmelCase )
filename += "." + etag_hash.hexdigest()
if url.endswith(".h5" ):
filename += ".h5"
return filename
def __lowerCAmelCase ( snake_case__ , snake_case__=None , snake_case__=False , snake_case__=None , snake_case__=False , snake_case__=None , snake_case__=False , snake_case__=False , snake_case__=False , ):
if cache_dir is None:
__UpperCamelCase : Tuple = TRANSFORMERS_CACHE
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
__UpperCamelCase : Dict = str(_UpperCAmelCase )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
__UpperCamelCase : Optional[int] = str(_UpperCAmelCase )
if is_remote_url(_UpperCAmelCase ):
# URL, so get it from the cache (downloading if necessary)
__UpperCamelCase : Dict = get_from_cache(
_UpperCAmelCase , cache_dir=_UpperCAmelCase , force_download=_UpperCAmelCase , proxies=_UpperCAmelCase , resume_download=_UpperCAmelCase , user_agent=_UpperCAmelCase , local_files_only=_UpperCAmelCase , )
elif os.path.exists(_UpperCAmelCase ):
# File, and it exists.
__UpperCamelCase : Dict = url_or_filename
elif urlparse(_UpperCAmelCase ).scheme == "":
# File, but it doesn't exist.
raise EnvironmentError("file {} not found".format(_UpperCAmelCase ) )
else:
# Something unknown
raise ValueError("unable to parse {} as a URL or as a local path".format(_UpperCAmelCase ) )
if extract_compressed_file:
if not is_zipfile(_UpperCAmelCase ) and not tarfile.is_tarfile(_UpperCAmelCase ):
return output_path
# Path where we extract compressed archives
# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
__UpperCamelCase , __UpperCamelCase : List[str] = os.path.split(_UpperCAmelCase )
__UpperCamelCase : Optional[int] = output_file.replace("." , "-" ) + "-extracted"
__UpperCamelCase : Tuple = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if os.path.isdir(_UpperCAmelCase ) and os.listdir(_UpperCAmelCase ) and not force_extract:
return output_path_extracted
# Prevent parallel extractions
__UpperCamelCase : Dict = output_path + ".lock"
with FileLock(_UpperCAmelCase ):
shutil.rmtree(_UpperCAmelCase , ignore_errors=_UpperCAmelCase )
os.makedirs(_UpperCAmelCase )
if is_zipfile(_UpperCAmelCase ):
with ZipFile(_UpperCAmelCase , "r" ) as zip_file:
zip_file.extractall(_UpperCAmelCase )
zip_file.close()
elif tarfile.is_tarfile(_UpperCAmelCase ):
__UpperCamelCase : List[Any] = tarfile.open(_UpperCAmelCase )
tar_file.extractall(_UpperCAmelCase )
tar_file.close()
else:
raise EnvironmentError("Archive format of {} could not be identified".format(_UpperCAmelCase ) )
return output_path_extracted
return output_path
def __lowerCAmelCase ( snake_case__ , snake_case__="," ):
assert isinstance(_UpperCAmelCase , _UpperCAmelCase )
if os.path.isfile(_UpperCAmelCase ):
with open(_UpperCAmelCase ) as f:
__UpperCamelCase : Union[str, Any] = eval(f.read() )
else:
__UpperCamelCase : Dict = requests.get(_UpperCAmelCase )
try:
__UpperCamelCase : Tuple = requests.json()
except Exception:
__UpperCamelCase : List[str] = req.content.decode()
assert data is not None, "could not connect"
try:
__UpperCamelCase : Dict = eval(_UpperCAmelCase )
except Exception:
__UpperCamelCase : Dict = data.split("\n" )
req.close()
return data
def __lowerCAmelCase ( snake_case__ ):
__UpperCamelCase : Optional[int] = requests.get(_UpperCAmelCase )
__UpperCamelCase : Any = np.array(Image.open(BytesIO(response.content ) ) )
return img
def __lowerCAmelCase ( snake_case__ ):
__UpperCamelCase : str = url.split("/" )[-1]
if fn not in os.listdir(os.getcwd() ):
wget.download(_UpperCAmelCase )
with open(_UpperCAmelCase , "rb" ) as stream:
__UpperCamelCase : List[Any] = pkl.load(_UpperCAmelCase )
__UpperCamelCase : Tuple = weights.pop("model" )
__UpperCamelCase : int = {}
for k, v in model.items():
__UpperCamelCase : Any = torch.from_numpy(_UpperCAmelCase )
if "running_var" in k:
__UpperCamelCase : int = torch.tensor([0] )
__UpperCamelCase : Tuple = k.replace("running_var" , "num_batches_tracked" )
__UpperCamelCase : Dict = zero
return new
def __lowerCAmelCase ( ):
print(F"{os.path.abspath(os.path.join(_UpperCAmelCase , os.pardir ) )}/demo.ipynb" )
def __lowerCAmelCase ( snake_case__ , snake_case__="RGB" ):
assert isinstance(_UpperCAmelCase , _UpperCAmelCase )
if os.path.isfile(_UpperCAmelCase ):
__UpperCamelCase : List[Any] = cva.imread(_UpperCAmelCase )
else:
__UpperCamelCase : int = get_image_from_url(_UpperCAmelCase )
assert img is not None, F"could not connect to: {im}"
__UpperCamelCase : Optional[Any] = cva.cvtColor(_UpperCAmelCase , cva.COLOR_BGR2RGB )
if input_format == "RGB":
__UpperCamelCase : str = img[:, :, ::-1]
return img
def __lowerCAmelCase ( snake_case__ , snake_case__=1 ):
return (images[i : i + batch] for i in range(0 , len(_UpperCAmelCase ) , _UpperCAmelCase ))
| 298 |
from functools import reduce
UpperCAmelCase__ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def A ( _UpperCAmelCase : str = N ) -> int:
'''simple docstring'''
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda _UpperCAmelCase , _UpperCAmelCase : str(int(_UpperCAmelCase ) * int(_UpperCAmelCase ) ) , n[i : i + 13] ) )
for i in range(len(_UpperCAmelCase ) - 12 ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 0 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
lowerCAmelCase = {
'''n_samples''': 6_4,
'''horizon''': 3_2,
'''num_inference_steps''': 2_0,
'''n_guide_steps''': 2, # can set to 0 for faster sampling, does not use value network
'''scale_grad_by_std''': True,
'''scale''': 0.1,
'''eta''': 0.0,
'''t_grad_cutoff''': 2,
'''device''': '''cpu''',
}
if __name__ == "__main__":
lowerCAmelCase = '''hopper-medium-v2'''
lowerCAmelCase = gym.make(env_name)
lowerCAmelCase = ValueGuidedRLPipeline.from_pretrained(
'''bglick13/hopper-medium-v2-value-function-hor32''',
env=env,
)
env.seed(0)
lowerCAmelCase = env.reset()
lowerCAmelCase = 0
lowerCAmelCase = 0
lowerCAmelCase = 1_0_0_0
lowerCAmelCase = [obs.copy()]
try:
for t in tqdm.tqdm(range(T)):
# call the policy
lowerCAmelCase = pipeline(obs, planning_horizon=3_2)
# execute action in environment
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = env.step(denorm_actions)
lowerCAmelCase = env.get_normalized_score(total_reward)
# update return
total_reward += reward
total_score += score
print(
F'Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'
F' {total_score}'
)
# save observations for rendering
rollout.append(next_observation.copy())
lowerCAmelCase = next_observation
except KeyboardInterrupt:
pass
print(F'Total reward: {total_reward}')
| 295 |
from __future__ import annotations
from collections.abc import Callable
UpperCAmelCase__ = list[list[float | int]]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : Matrix ) -> Matrix:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(size + 1 )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for row in range(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = matrix[row][col]
_UpperCAmelCase = vector[row][0]
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while row < size and col < size:
# pivoting
_UpperCAmelCase = max((abs(augmented[rowa][col] ), rowa) for rowa in range(_UpperCAmelCase , _UpperCAmelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
_UpperCAmelCase , _UpperCAmelCase = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , _UpperCAmelCase ):
_UpperCAmelCase = augmented[rowa][col] / augmented[row][col]
_UpperCAmelCase = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , _UpperCAmelCase ):
for row in range(_UpperCAmelCase ):
_UpperCAmelCase = augmented[row][col] / augmented[col][col]
for cola in range(_UpperCAmelCase , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(_UpperCAmelCase )
]
def A ( _UpperCAmelCase : list[int] ) -> Callable[[int], int]:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(_UpperCAmelCase )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = [[0] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for x_val, y_val in enumerate(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = (x_val + 1) ** (size - col - 1)
_UpperCAmelCase = y_val
_UpperCAmelCase = solve(_UpperCAmelCase , _UpperCAmelCase )
def interpolated_func(_UpperCAmelCase : int ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(_UpperCAmelCase ) )
return interpolated_func
def A ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def A ( _UpperCAmelCase : Callable[[int], int] = question_function , _UpperCAmelCase : int = 10 ) -> int:
'''simple docstring'''
_UpperCAmelCase = [func(_UpperCAmelCase ) for x_val in range(1 , order + 1 )]
_UpperCAmelCase = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
_UpperCAmelCase = 0
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for poly in polynomials:
_UpperCAmelCase = 1
while func(_UpperCAmelCase ) == poly(_UpperCAmelCase ):
x_val += 1
ret += poly(_UpperCAmelCase )
return ret
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 0 |
'''simple docstring'''
import math
def _lowerCamelCase ( lowercase : int ) -> list[int]:
_a = []
_a = 2
_a = int(math.sqrt(_UpperCAmelCase ) ) # Size of every segment
_a = [True] * (end + 1)
_a = []
while start <= end:
if temp[start] is True:
in_prime.append(_UpperCAmelCase )
for i in range(start * start , end + 1 , _UpperCAmelCase ):
_a = False
start += 1
prime += in_prime
_a = end + 1
_a = min(2 * end , _UpperCAmelCase )
while low <= n:
_a = [True] * (high - low + 1)
for each in in_prime:
_a = math.floor(low / each ) * each
if t < low:
t += each
for j in range(_UpperCAmelCase , high + 1 , _UpperCAmelCase ):
_a = False
for j in range(len(_UpperCAmelCase ) ):
if temp[j] is True:
prime.append(j + low )
_a = high + 1
_a = min(high + end , _UpperCAmelCase )
return prime
print(sieve(10**6))
| 63 |
from __future__ import annotations
def A ( _UpperCAmelCase : list[int] ) -> bool:
'''simple docstring'''
return len(set(_UpperCAmelCase ) ) == len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 0 |
"""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
########################################################################
# This is a fully working simple example to use Accelerate
# and perform 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
#
########################################################################
__magic_name__ = 16
__magic_name__ = 32
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ = 16 ):
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("""bert-base-cased""" )
__SCREAMING_SNAKE_CASE = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(UpperCamelCase_ ):
# max_length=None => use the model max length (it's actually the default)
__SCREAMING_SNAKE_CASE = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=_UpperCAmelCase , max_length=_UpperCAmelCase )
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():
__SCREAMING_SNAKE_CASE = datasets.map(
_UpperCAmelCase , batched=_UpperCAmelCase , 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
__SCREAMING_SNAKE_CASE = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCamelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__SCREAMING_SNAKE_CASE = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__SCREAMING_SNAKE_CASE = 16
elif accelerator.mixed_precision != "no":
__SCREAMING_SNAKE_CASE = 8
else:
__SCREAMING_SNAKE_CASE = None
return tokenizer.pad(
_UpperCAmelCase , padding="""longest""" , max_length=_UpperCAmelCase , pad_to_multiple_of=_UpperCAmelCase , return_tensors="""pt""" , )
# Instantiate dataloaders.
__SCREAMING_SNAKE_CASE = DataLoader(
tokenized_datasets["""train"""] , shuffle=_UpperCAmelCase , collate_fn=_UpperCAmelCase , batch_size=_UpperCAmelCase )
__SCREAMING_SNAKE_CASE = DataLoader(
tokenized_datasets["""validation"""] , shuffle=_UpperCAmelCase , collate_fn=_UpperCAmelCase , batch_size=_UpperCAmelCase )
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
__magic_name__ = mocked_dataloaders # noqa: F811
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , _UpperCAmelCase ) == "1":
__SCREAMING_SNAKE_CASE = 2
# New Code #
__SCREAMING_SNAKE_CASE = int(args.gradient_accumulation_steps )
# Initialize accelerator
__SCREAMING_SNAKE_CASE = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=_UpperCAmelCase )
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
"""Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__SCREAMING_SNAKE_CASE = config["""lr"""]
__SCREAMING_SNAKE_CASE = int(config["""num_epochs"""] )
__SCREAMING_SNAKE_CASE = int(config["""seed"""] )
__SCREAMING_SNAKE_CASE = int(config["""batch_size"""] )
__SCREAMING_SNAKE_CASE = evaluate.load("""glue""" , """mrpc""" )
set_seed(_UpperCAmelCase )
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = get_dataloaders(_UpperCAmelCase , _UpperCAmelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__SCREAMING_SNAKE_CASE = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=_UpperCAmelCase )
# 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).
__SCREAMING_SNAKE_CASE = model.to(accelerator.device )
# Instantiate optimizer
__SCREAMING_SNAKE_CASE = AdamW(params=model.parameters() , lr=_UpperCAmelCase )
# Instantiate scheduler
__SCREAMING_SNAKE_CASE = get_linear_schedule_with_warmup(
optimizer=_UpperCAmelCase , num_warmup_steps=100 , num_training_steps=(len(_UpperCAmelCase ) * 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.
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = accelerator.prepare(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# Now we train the model
for epoch in range(_UpperCAmelCase ):
model.train()
for step, batch in enumerate(_UpperCAmelCase ):
# 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(_UpperCAmelCase ):
__SCREAMING_SNAKE_CASE = model(**_UpperCAmelCase )
__SCREAMING_SNAKE_CASE = output.loss
accelerator.backward(_UpperCAmelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_UpperCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__SCREAMING_SNAKE_CASE = model(**_UpperCAmelCase )
__SCREAMING_SNAKE_CASE = outputs.logits.argmax(dim=-1 )
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=_UpperCAmelCase , references=_UpperCAmelCase , )
__SCREAMING_SNAKE_CASE = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"epoch {epoch}:" , _UpperCAmelCase )
def _lowerCAmelCase ( ):
__SCREAMING_SNAKE_CASE = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=_UpperCAmelCase , default=_UpperCAmelCase , 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=_UpperCAmelCase , default=1 , help="""The number of minibatches to be ran before gradients are accumulated.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
__SCREAMING_SNAKE_CASE = parser.parse_args()
__SCREAMING_SNAKE_CASE = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(_UpperCAmelCase , _UpperCAmelCase )
if __name__ == "__main__":
main()
| 100 |
import os
UpperCAmelCase__ = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def A ( _UpperCAmelCase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(_UpperCAmelCase ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def A ( _UpperCAmelCase : int ) -> str:
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1_000
numerals += m_count * "M"
num %= 1_000
_UpperCAmelCase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
_UpperCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def A ( _UpperCAmelCase : str = "/p089_roman.txt" ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(_UpperCAmelCase ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(_UpperCAmelCase )
_UpperCAmelCase = generate_roman_numerals(_UpperCAmelCase )
savings += len(_UpperCAmelCase ) - len(_UpperCAmelCase )
return savings
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 0 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
__lowerCamelCase : int = '''tiny-wmt19-en-ru'''
# Build
# borrowed from a test
__lowerCamelCase : List[Any] = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''w</w>''',
'''r</w>''',
'''t</w>''',
'''lo''',
'''low''',
'''er</w>''',
'''low</w>''',
'''lowest</w>''',
'''newer</w>''',
'''wider</w>''',
'''<unk>''',
]
__lowerCamelCase : Union[str, Any] = dict(zip(vocab, range(len(vocab))))
__lowerCamelCase : str = ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']
with tempfile.TemporaryDirectory() as tmpdirname:
__lowerCamelCase : int = Path(tmpdirname)
__lowerCamelCase : Union[str, Any] = build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
__lowerCamelCase : Any = build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
__lowerCamelCase : Optional[int] = build_dir / VOCAB_FILES_NAMES['''merges_file''']
with open(src_vocab_file, '''w''') as fp:
fp.write(json.dumps(vocab_tokens))
with open(tgt_vocab_file, '''w''') as fp:
fp.write(json.dumps(vocab_tokens))
with open(merges_file, '''w''') as fp:
fp.write('''\n'''.join(merges))
__lowerCamelCase : Union[str, Any] = FSMTTokenizer(
langs=['''en''', '''ru'''],
src_vocab_size=len(vocab),
tgt_vocab_size=len(vocab),
src_vocab_file=src_vocab_file,
tgt_vocab_file=tgt_vocab_file,
merges_file=merges_file,
)
__lowerCamelCase : Union[str, Any] = FSMTConfig(
langs=['''ru''', '''en'''],
src_vocab_size=1000,
tgt_vocab_size=1000,
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
__lowerCamelCase : int = FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")
# Test
__lowerCamelCase : List[Any] = tokenizer(['''Making tiny model'''], return_tensors='''pt''')
__lowerCamelCase : Union[str, Any] = tiny_model(**batch)
print('''test output:''', len(outputs.logits[0]))
# Save
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F"""Generated {mname_tiny}""")
# Upload
# transformers-cli upload tiny-wmt19-en-ru
| 219 |
import requests
from bsa import BeautifulSoup
def A ( _UpperCAmelCase : str , _UpperCAmelCase : dict ) -> str:
'''simple docstring'''
_UpperCAmelCase = BeautifulSoup(requests.get(_UpperCAmelCase , params=_UpperCAmelCase ).content , 'html.parser' )
_UpperCAmelCase = soup.find('div' , attrs={'class': 'gs_ri'} )
_UpperCAmelCase = div.find('div' , attrs={'class': 'gs_fl'} ).find_all('a' )
return anchors[2].get_text()
if __name__ == "__main__":
UpperCAmelCase__ = {
"title": (
"Precisely geometry controlled microsupercapacitors for ultrahigh areal "
"capacitance, volumetric capacitance, and energy density"
),
"journal": "Chem. Mater.",
"volume": 30,
"pages": "3979-3990",
"year": 2018,
"hl": "en",
}
print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
| 339 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , __snake_case , __snake_case=13 , __snake_case=7 , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=True , __snake_case=99 , __snake_case=32 , __snake_case=5 , __snake_case=4 , __snake_case=37 , __snake_case="gelu" , __snake_case=0.1 , __snake_case=0.1 , __snake_case=512 , __snake_case=16 , __snake_case=2 , __snake_case=0.02 , __snake_case=4 , ):
_SCREAMING_SNAKE_CASE : Any = parent
_SCREAMING_SNAKE_CASE : List[Any] = batch_size
_SCREAMING_SNAKE_CASE : Optional[Any] = seq_length
_SCREAMING_SNAKE_CASE : Tuple = is_training
_SCREAMING_SNAKE_CASE : List[Any] = use_attention_mask
_SCREAMING_SNAKE_CASE : Optional[int] = use_token_type_ids
_SCREAMING_SNAKE_CASE : Optional[Any] = use_labels
_SCREAMING_SNAKE_CASE : Dict = vocab_size
_SCREAMING_SNAKE_CASE : Dict = hidden_size
_SCREAMING_SNAKE_CASE : Dict = num_hidden_layers
_SCREAMING_SNAKE_CASE : int = num_attention_heads
_SCREAMING_SNAKE_CASE : Union[str, Any] = intermediate_size
_SCREAMING_SNAKE_CASE : List[Any] = hidden_act
_SCREAMING_SNAKE_CASE : Dict = hidden_dropout_prob
_SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE : Dict = max_position_embeddings
_SCREAMING_SNAKE_CASE : Optional[Any] = type_vocab_size
_SCREAMING_SNAKE_CASE : Optional[int] = type_sequence_label_size
_SCREAMING_SNAKE_CASE : Any = initializer_range
_SCREAMING_SNAKE_CASE : Any = num_choices
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE : Tuple = None
if self.use_attention_mask:
_SCREAMING_SNAKE_CASE : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
_SCREAMING_SNAKE_CASE : List[str] = None
if self.use_token_type_ids:
_SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_SCREAMING_SNAKE_CASE : Optional[int] = RoFormerConfig(
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=__snake_case , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[Any] = config_and_inputs
_SCREAMING_SNAKE_CASE : str = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class lowercase__ ( _snake_case , unittest.TestCase ):
'''simple docstring'''
A_ : Optional[Any] = True
A_ : Union[str, Any] = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Any = FlaxRoFormerModelTester(self )
@slow
def UpperCAmelCase_ ( self ):
for model_class_name in self.all_model_classes:
_SCREAMING_SNAKE_CASE : int = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=__snake_case )
_SCREAMING_SNAKE_CASE : Union[str, Any] = model(np.ones((1, 1) ) )
self.assertIsNotNone(__snake_case )
@require_flax
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Tuple = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
_SCREAMING_SNAKE_CASE : str = jnp.array([[0, 1, 2, 3, 4, 5]] )
_SCREAMING_SNAKE_CASE : Dict = model(__snake_case )[0]
_SCREAMING_SNAKE_CASE : Union[str, Any] = 5_0000
_SCREAMING_SNAKE_CASE : Any = (1, 6, vocab_size)
self.assertEqual(output.shape , __snake_case )
_SCREAMING_SNAKE_CASE : Dict = jnp.array(
[[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , __snake_case , atol=1e-4 ) )
| 200 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self : Optional[Any] , A : Dict , A : Union[str, Any]=13 , A : Dict=7 , A : Dict=True , A : Tuple=True , A : Union[str, Any]=True , A : int=True , A : Optional[int]=99 , A : List[str]=32 , A : List[Any]=5 , A : int=4 , A : Any=37 , A : Optional[int]="gelu" , A : Optional[Any]=0.1 , A : Any=0.1 , A : Union[str, Any]=5_12 , A : int=16 , A : List[str]=2 , A : Union[str, Any]=0.0_2 , A : Union[str, Any]=4 , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_attention_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_choices
def _lowerCamelCase ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCAmelCase = None
if self.use_attention_mask:
_UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length])
_UpperCAmelCase = None
if self.use_token_type_ids:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
_UpperCAmelCase = RoFormerConfig(
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=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _lowerCamelCase ( self : List[Any]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class __lowerCAmelCase ( A , unittest.TestCase ):
UpperCamelCase = True
UpperCamelCase = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
_UpperCAmelCase = FlaxRoFormerModelTester(self)
@slow
def _lowerCamelCase ( self : List[Any]) -> Dict:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_UpperCAmelCase = model_class_name.from_pretrained('junnyu/roformer_chinese_small' , from_pt=A)
_UpperCAmelCase = model(np.ones((1, 1)))
self.assertIsNotNone(A)
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def _lowerCamelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base')
_UpperCAmelCase = jnp.array([[0, 1, 2, 3, 4, 5]])
_UpperCAmelCase = model(A)[0]
_UpperCAmelCase = 5_00_00
_UpperCAmelCase = (1, 6, vocab_size)
self.assertEqual(output.shape , A)
_UpperCAmelCase = jnp.array(
[[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]])
self.assertTrue(jnp.allclose(output[:, :3, :3] , A , atol=1E-4))
| 339 | 0 |
A_ :int = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
logging,
)
try:
if not is_onnx_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_onnx_objects import * # noqa F403
else:
from .pipelines import OnnxRuntimeModel
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import * # noqa F403
else:
from .models import (
AutoencoderKL,
ControlNetModel,
ModelMixin,
PriorTransformer,
TaFilmDecoder,
TransformeraDModel,
UNetaDModel,
UNetaDConditionModel,
UNetaDModel,
UNetaDConditionModel,
VQModel,
)
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipelines import (
AudioPipelineOutput,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
DiffusionPipeline,
DiTPipeline,
ImagePipelineOutput,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
CMStochasticIterativeScheduler,
DDIMInverseScheduler,
DDIMParallelScheduler,
DDIMScheduler,
DDPMParallelScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
KDPMaAncestralDiscreteScheduler,
KDPMaDiscreteScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
UnCLIPScheduler,
UniPCMultistepScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .schedulers import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .schedulers import DPMSolverSDEScheduler
try:
if not (is_torch_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
AltDiffusionImgaImgPipeline,
AltDiffusionPipeline,
AudioLDMPipeline,
CycleDiffusionPipeline,
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
ImageTextPipelineOutput,
KandinskyImgaImgPipeline,
KandinskyInpaintPipeline,
KandinskyPipeline,
KandinskyPriorPipeline,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaControlnetPipeline,
KandinskyVaaImgaImgPipeline,
KandinskyVaaInpaintPipeline,
KandinskyVaaPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
KandinskyVaaPriorPipeline,
LDMTextToImagePipeline,
PaintByExamplePipeline,
SemanticStableDiffusionPipeline,
ShapEImgaImgPipeline,
ShapEPipeline,
StableDiffusionAttendAndExcitePipeline,
StableDiffusionControlNetImgaImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImgaImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionInstructPixaPixPipeline,
StableDiffusionLatentUpscalePipeline,
StableDiffusionLDMaDPipeline,
StableDiffusionModelEditingPipeline,
StableDiffusionPanoramaPipeline,
StableDiffusionParadigmsPipeline,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionPixaPixZeroPipeline,
StableDiffusionSAGPipeline,
StableDiffusionUpscalePipeline,
StableUnCLIPImgaImgPipeline,
StableUnCLIPPipeline,
TextToVideoSDPipeline,
TextToVideoZeroPipeline,
UnCLIPImageVariationPipeline,
UnCLIPPipeline,
UniDiffuserModel,
UniDiffuserPipeline,
UniDiffuserTextDecoder,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VideoToVideoSDPipeline,
VQDiffusionPipeline,
)
try:
if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403
else:
from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipelines import StableDiffusionKDiffusionPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
else:
from .pipelines import (
OnnxStableDiffusionImgaImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
OnnxStableDiffusionUpscalePipeline,
StableDiffusionOnnxPipeline,
)
try:
if not (is_torch_available() and is_librosa_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_librosa_objects import * # noqa F403
else:
from .pipelines import AudioDiffusionPipeline, Mel
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .pipelines import SpectrogramDiffusionPipeline
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_objects import * # noqa F403
else:
from .models.controlnet_flax import FlaxControlNetModel
from .models.modeling_flax_utils import FlaxModelMixin
from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipelines import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
try:
if not (is_flax_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
FlaxStableDiffusionControlNetPipeline,
FlaxStableDiffusionImgaImgPipeline,
FlaxStableDiffusionInpaintPipeline,
FlaxStableDiffusionPipeline,
)
try:
if not (is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_note_seq_objects import * # noqa F403
else:
from .pipelines import MidiProcessor
| 71 |
UpperCAmelCase__ = {}
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> 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 = (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 = _calculate(days - 1 , _UpperCAmelCase , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
_UpperCAmelCase = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
_UpperCAmelCase = _calculate(days - 1 , _UpperCAmelCase , 0 )
_UpperCAmelCase = state_late + state_absent + state_ontime
_UpperCAmelCase = prizestrings
return prizestrings
def A ( _UpperCAmelCase : int = 30 ) -> int:
'''simple docstring'''
return _calculate(_UpperCAmelCase , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 339 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class A__ ( unittest.TestCase ):
def A ( self : str ) -> str:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =tempfile.mkdtemp()
_SCREAMING_SNAKE_CASE =[
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
_SCREAMING_SNAKE_CASE =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] ) )
_SCREAMING_SNAKE_CASE ={
'do_resize': True,
'size': 20,
'do_center_crop': True,
'crop_size': 18,
'do_normalize': True,
'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
}
_SCREAMING_SNAKE_CASE =os.path.join(self.tmpdirname , _a )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(_a , _a )
def A ( self : List[str] , **_a : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **_a )
def A ( self : Optional[Any] , **_a : str ) -> List[str]:
'''simple docstring'''
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def A ( self : List[str] , **_a : Union[str, Any] ) -> str:
'''simple docstring'''
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_a )
def A ( self : Tuple ) -> str:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A ( self : int ) -> Union[str, Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =[np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
_SCREAMING_SNAKE_CASE =[Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.get_tokenizer()
_SCREAMING_SNAKE_CASE =self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE =self.get_image_processor()
_SCREAMING_SNAKE_CASE =AlignProcessor(tokenizer=_a , image_processor=_a )
processor_slow.save_pretrained(self.tmpdirname )
_SCREAMING_SNAKE_CASE =AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_a )
_SCREAMING_SNAKE_CASE =AlignProcessor(tokenizer=_a , image_processor=_a )
processor_fast.save_pretrained(self.tmpdirname )
_SCREAMING_SNAKE_CASE =AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _a )
self.assertIsInstance(processor_fast.tokenizer , _a )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _a )
self.assertIsInstance(processor_fast.image_processor , _a )
def A ( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_SCREAMING_SNAKE_CASE =self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
_SCREAMING_SNAKE_CASE =self.get_image_processor(do_normalize=_a , padding_value=1.0 )
_SCREAMING_SNAKE_CASE =AlignProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=_a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _a )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def A ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.get_image_processor()
_SCREAMING_SNAKE_CASE =self.get_tokenizer()
_SCREAMING_SNAKE_CASE =AlignProcessor(tokenizer=_a , image_processor=_a )
_SCREAMING_SNAKE_CASE =self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE =image_processor(_a , return_tensors='np' )
_SCREAMING_SNAKE_CASE =processor(images=_a , return_tensors='np' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def A ( self : List[Any] ) -> Dict:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.get_image_processor()
_SCREAMING_SNAKE_CASE =self.get_tokenizer()
_SCREAMING_SNAKE_CASE =AlignProcessor(tokenizer=_a , image_processor=_a )
_SCREAMING_SNAKE_CASE ='lower newer'
_SCREAMING_SNAKE_CASE =processor(text=_a )
_SCREAMING_SNAKE_CASE =tokenizer(_a , padding='max_length' , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A ( self : Any ) -> Tuple:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.get_image_processor()
_SCREAMING_SNAKE_CASE =self.get_tokenizer()
_SCREAMING_SNAKE_CASE =AlignProcessor(tokenizer=_a , image_processor=_a )
_SCREAMING_SNAKE_CASE ='lower newer'
_SCREAMING_SNAKE_CASE =self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE =processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def A ( self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.get_image_processor()
_SCREAMING_SNAKE_CASE =self.get_tokenizer()
_SCREAMING_SNAKE_CASE =AlignProcessor(tokenizer=_a , image_processor=_a )
_SCREAMING_SNAKE_CASE =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_SCREAMING_SNAKE_CASE =processor.batch_decode(_a )
_SCREAMING_SNAKE_CASE =tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
def A ( self : Dict ) -> str:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.get_image_processor()
_SCREAMING_SNAKE_CASE =self.get_tokenizer()
_SCREAMING_SNAKE_CASE =AlignProcessor(tokenizer=_a , image_processor=_a )
_SCREAMING_SNAKE_CASE ='lower newer'
_SCREAMING_SNAKE_CASE =self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE =processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 47 |
import os
import sys
import unittest
UpperCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCAmelCase__ = os.path.join(git_repo_path, "src", "diffusers")
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = find_backend(' if not is_torch_available():')
self.assertEqual(A , 'torch')
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
_UpperCAmelCase = find_backend(' if not (is_torch_available() and is_transformers_available()):')
self.assertEqual(A , 'torch_and_transformers')
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
_UpperCAmelCase = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):')
self.assertEqual(A , 'torch_and_transformers_and_onnx')
def _lowerCamelCase ( self : int) -> Dict:
"""simple docstring"""
_UpperCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , A)
self.assertIn('torch_and_transformers' , A)
self.assertIn('flax_and_transformers' , A)
self.assertIn('torch_and_transformers_and_onnx' , A)
# Likewise, we can't assert on the exact content of a key
self.assertIn('UNet2DModel' , objects['torch'])
self.assertIn('FlaxUNet2DConditionModel' , objects['flax'])
self.assertIn('StableDiffusionPipeline' , objects['torch_and_transformers'])
self.assertIn('FlaxStableDiffusionPipeline' , objects['flax_and_transformers'])
self.assertIn('LMSDiscreteScheduler' , objects['torch_and_scipy'])
self.assertIn('OnnxStableDiffusionPipeline' , objects['torch_and_transformers_and_onnx'])
def _lowerCamelCase ( self : Union[str, Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = create_dummy_object('CONSTANT' , '\'torch\'')
self.assertEqual(A , '\nCONSTANT = None\n')
_UpperCAmelCase = create_dummy_object('function' , '\'torch\'')
self.assertEqual(
A , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n')
_UpperCAmelCase = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n'
_UpperCAmelCase = create_dummy_object('FakeClass' , '\'torch\'')
self.assertEqual(A , A)
def _lowerCamelCase ( self : Dict) -> int:
"""simple docstring"""
_UpperCAmelCase = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n'
_UpperCAmelCase = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']})
self.assertEqual(dummy_files['torch'] , A)
| 339 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase ):
if not numbers:
return 0
if not isinstance(_UpperCAmelCase , (list, tuple) ) or not all(
isinstance(_UpperCAmelCase , _UpperCAmelCase ) for number in numbers ):
raise ValueError('''numbers must be an iterable of integers''' )
__lowercase : List[str] = numbers[0]
for i in range(1 , len(_UpperCAmelCase ) ):
# update the maximum and minimum subarray products
__lowercase : Union[str, Any] = numbers[i]
if number < 0:
__lowercase ,__lowercase : Optional[int] = min_till_now, max_till_now
__lowercase : Optional[int] = max(_UpperCAmelCase , max_till_now * number )
__lowercase : Tuple = min(_UpperCAmelCase , min_till_now * number )
# update the maximum product found till now
__lowercase : List[Any] = max(_UpperCAmelCase , _UpperCAmelCase )
return max_prod
| 249 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
UpperCAmelCase__ = logging.getLogger(__name__)
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} , )
UpperCamelCase = field(
default=1_0_2_4 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
UpperCamelCase = field(
default=A , 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.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the training data.'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
UpperCamelCase = field(default=A , metadata={'''help''': '''A csv or a json file containing the test data.'''} )
def _lowerCamelCase ( self : str) -> List[Any]:
"""simple docstring"""
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.')
else:
_UpperCAmelCase = self.train_file.split('.')[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
_UpperCAmelCase = self.validation_file.split('.')[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default=A , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCamelCase = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
def A ( ) -> Optional[int]:
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCAmelCase = 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 , _UpperCAmelCase , _UpperCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_args_into_dataclasses()
# 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 )] , )
_UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(_UpperCAmelCase )
datasets.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
_UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase = 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 and training_args.resume_from_checkpoint is 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.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. 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 = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
_UpperCAmelCase = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
_UpperCAmelCase = data_args.train_file.split('.' )[-1]
_UpperCAmelCase = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
_UpperCAmelCase = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(F"load a local file for {key}: {data_files[key]}" )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
_UpperCAmelCase = load_dataset('csv' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
_UpperCAmelCase = load_dataset('json' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
_UpperCAmelCase = raw_datasets['train'].features['label'].names
_UpperCAmelCase = len(_UpperCAmelCase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
_UpperCAmelCase = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , 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 , add_prefix_space=_UpperCAmelCase , )
_UpperCAmelCase = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
_UpperCAmelCase = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
_UpperCAmelCase = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
_UpperCAmelCase = {'Refused': 0, 'Entailed': 1}
_UpperCAmelCase = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
F"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." )
_UpperCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(_UpperCAmelCase : Union[str, Any] ):
# Tokenize the texts
def _convert_table_text_to_pandas(_UpperCAmelCase : Dict ):
_UpperCAmelCase = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
_UpperCAmelCase = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
_UpperCAmelCase = examples['statement']
_UpperCAmelCase = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
_UpperCAmelCase = tokenizer(_UpperCAmelCase , _UpperCAmelCase , padding=_UpperCAmelCase , max_length=_UpperCAmelCase , truncation=_UpperCAmelCase )
_UpperCAmelCase = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
_UpperCAmelCase = raw_datasets.map(
_UpperCAmelCase , batched=_UpperCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
_UpperCAmelCase = raw_datasets['train']
if data_args.max_train_samples is not None:
_UpperCAmelCase = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
_UpperCAmelCase = raw_datasets['validation']
if data_args.max_eval_samples is not None:
_UpperCAmelCase = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
_UpperCAmelCase = raw_datasets['test']
if data_args.max_predict_samples is not None:
_UpperCAmelCase = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_UpperCAmelCase ) ) , 3 ):
logger.info(F"Sample {index} of the training set: {train_dataset[index]}." )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_UpperCAmelCase : EvalPrediction ):
_UpperCAmelCase = p.predictions[0] if isinstance(p.predictions , _UpperCAmelCase ) else p.predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
_UpperCAmelCase = default_data_collator
elif training_args.fpaa:
_UpperCAmelCase = DataCollatorWithPadding(_UpperCAmelCase , pad_to_multiple_of=8 )
else:
_UpperCAmelCase = None
# Initialize our Trainer
_UpperCAmelCase = Trainer(
model=_UpperCAmelCase , args=_UpperCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_UpperCAmelCase , tokenizer=_UpperCAmelCase , data_collator=_UpperCAmelCase , )
# Training
if training_args.do_train:
_UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase = last_checkpoint
_UpperCAmelCase = trainer.train(resume_from_checkpoint=_UpperCAmelCase )
_UpperCAmelCase = train_result.metrics
_UpperCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCAmelCase )
)
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _UpperCAmelCase )
trainer.save_metrics('train' , _UpperCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
_UpperCAmelCase = trainer.evaluate(eval_dataset=_UpperCAmelCase )
_UpperCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCAmelCase )
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.log_metrics('eval' , _UpperCAmelCase )
trainer.save_metrics('eval' , _UpperCAmelCase )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
_UpperCAmelCase = predict_dataset.remove_columns('label' )
_UpperCAmelCase = trainer.predict(_UpperCAmelCase , metric_key_prefix='predict' ).predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
_UpperCAmelCase = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(_UpperCAmelCase , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(_UpperCAmelCase ):
_UpperCAmelCase = label_list[item]
writer.write(F"{index}\t{item}\n" )
_UpperCAmelCase = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**_UpperCAmelCase )
else:
trainer.create_model_card(**_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 339 | 0 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
_a = 42
_a = 42
_a = 0.0
_a = 1
_a = 1
_a = True
_a = False
_a = False
_a = False
_a = jnp.floataa
def __lowercase ( self : Union[str, Any] ):
lowerCAmelCase = []
lowerCAmelCase = []
for i in range(self.num_layers ):
lowerCAmelCase = self.in_channels if i == 0 else self.out_channels
lowerCAmelCase = FlaxResnetBlockaD(
in_channels=lowerCAmelCase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(lowerCAmelCase )
lowerCAmelCase = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(lowerCAmelCase )
lowerCAmelCase = resnets
lowerCAmelCase = attentions
if self.add_downsample:
lowerCAmelCase = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : str , lowerCAmelCase : List[Any] , lowerCAmelCase : str , lowerCAmelCase : int , lowerCAmelCase : Optional[int]=True ):
lowerCAmelCase = ()
for resnet, attn in zip(self.resnets , self.attentions ):
lowerCAmelCase = resnet(lowerCAmelCase , lowerCAmelCase , deterministic=lowerCAmelCase )
lowerCAmelCase = attn(lowerCAmelCase , lowerCAmelCase , deterministic=lowerCAmelCase )
output_states += (hidden_states,)
if self.add_downsample:
lowerCAmelCase = self.downsamplers_a(lowerCAmelCase )
output_states += (hidden_states,)
return hidden_states, output_states
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
_a = 42
_a = 42
_a = 0.0
_a = 1
_a = True
_a = jnp.floataa
def __lowercase ( self : List[str] ):
lowerCAmelCase = []
for i in range(self.num_layers ):
lowerCAmelCase = self.in_channels if i == 0 else self.out_channels
lowerCAmelCase = FlaxResnetBlockaD(
in_channels=lowerCAmelCase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(lowerCAmelCase )
lowerCAmelCase = resnets
if self.add_downsample:
lowerCAmelCase = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : List[Any] , lowerCAmelCase : Tuple , lowerCAmelCase : Dict , lowerCAmelCase : Optional[int]=True ):
lowerCAmelCase = ()
for resnet in self.resnets:
lowerCAmelCase = resnet(lowerCAmelCase , lowerCAmelCase , deterministic=lowerCAmelCase )
output_states += (hidden_states,)
if self.add_downsample:
lowerCAmelCase = self.downsamplers_a(lowerCAmelCase )
output_states += (hidden_states,)
return hidden_states, output_states
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
_a = 42
_a = 42
_a = 42
_a = 0.0
_a = 1
_a = 1
_a = True
_a = False
_a = False
_a = False
_a = jnp.floataa
def __lowercase ( self : Tuple ):
lowerCAmelCase = []
lowerCAmelCase = []
for i in range(self.num_layers ):
lowerCAmelCase = self.in_channels if (i == self.num_layers - 1) else self.out_channels
lowerCAmelCase = self.prev_output_channel if i == 0 else self.out_channels
lowerCAmelCase = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(lowerCAmelCase )
lowerCAmelCase = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(lowerCAmelCase )
lowerCAmelCase = resnets
lowerCAmelCase = attentions
if self.add_upsample:
lowerCAmelCase = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : List[str] , lowerCAmelCase : Tuple , lowerCAmelCase : List[Any] , lowerCAmelCase : List[Any] , lowerCAmelCase : Tuple , lowerCAmelCase : Optional[int]=True ):
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
lowerCAmelCase = res_hidden_states_tuple[-1]
lowerCAmelCase = res_hidden_states_tuple[:-1]
lowerCAmelCase = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
lowerCAmelCase = resnet(lowerCAmelCase , lowerCAmelCase , deterministic=lowerCAmelCase )
lowerCAmelCase = attn(lowerCAmelCase , lowerCAmelCase , deterministic=lowerCAmelCase )
if self.add_upsample:
lowerCAmelCase = self.upsamplers_a(lowerCAmelCase )
return hidden_states
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
_a = 42
_a = 42
_a = 42
_a = 0.0
_a = 1
_a = True
_a = jnp.floataa
def __lowercase ( self : Union[str, Any] ):
lowerCAmelCase = []
for i in range(self.num_layers ):
lowerCAmelCase = self.in_channels if (i == self.num_layers - 1) else self.out_channels
lowerCAmelCase = self.prev_output_channel if i == 0 else self.out_channels
lowerCAmelCase = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(lowerCAmelCase )
lowerCAmelCase = resnets
if self.add_upsample:
lowerCAmelCase = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Any , lowerCAmelCase : Tuple , lowerCAmelCase : Any , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Any=True ):
for resnet in self.resnets:
# pop res hidden states
lowerCAmelCase = res_hidden_states_tuple[-1]
lowerCAmelCase = res_hidden_states_tuple[:-1]
lowerCAmelCase = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
lowerCAmelCase = resnet(lowerCAmelCase , lowerCAmelCase , deterministic=lowerCAmelCase )
if self.add_upsample:
lowerCAmelCase = self.upsamplers_a(lowerCAmelCase )
return hidden_states
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
_a = 42
_a = 0.0
_a = 1
_a = 1
_a = False
_a = False
_a = jnp.floataa
def __lowercase ( self : List[Any] ):
lowerCAmelCase = [
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
lowerCAmelCase = []
for _ in range(self.num_layers ):
lowerCAmelCase = FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(lowerCAmelCase )
lowerCAmelCase = FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(lowerCAmelCase )
lowerCAmelCase = resnets
lowerCAmelCase = attentions
def __call__( self : str , lowerCAmelCase : List[str] , lowerCAmelCase : Optional[int] , lowerCAmelCase : Optional[int] , lowerCAmelCase : Tuple=True ):
lowerCAmelCase = self.resnets[0](lowerCAmelCase , lowerCAmelCase )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
lowerCAmelCase = attn(lowerCAmelCase , lowerCAmelCase , deterministic=lowerCAmelCase )
lowerCAmelCase = resnet(lowerCAmelCase , lowerCAmelCase , deterministic=lowerCAmelCase )
return hidden_states
| 155 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def A ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict ) -> Any:
'''simple docstring'''
_UpperCAmelCase = multiprocessing.Manager()
_UpperCAmelCase = manager.list()
_UpperCAmelCase = multiprocessing.Process(target=_UpperCAmelCase , 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 ( _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
_UpperCAmelCase = shutil.rmtree
_UpperCAmelCase = os.rmdir
_UpperCAmelCase = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
_UpperCAmelCase = {}
with swallow_io():
with time_limit(_UpperCAmelCase ):
exec(_UpperCAmelCase , _UpperCAmelCase )
result.append('passed' )
except TimeoutException:
result.append('timed out' )
except BaseException as e:
result.append(F"failed: {e}" )
# Needed for cleaning up.
_UpperCAmelCase = rmtree
_UpperCAmelCase = rmdir
_UpperCAmelCase = chdir
@contextlib.contextmanager
def A ( _UpperCAmelCase : Union[str, Any] ) -> Any:
'''simple docstring'''
def signal_handler(_UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict ):
raise TimeoutException('Timed out!' )
signal.setitimer(signal.ITIMER_REAL , _UpperCAmelCase )
signal.signal(signal.SIGALRM , _UpperCAmelCase )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def A ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = WriteOnlyStringIO()
with contextlib.redirect_stdout(_UpperCAmelCase ):
with contextlib.redirect_stderr(_UpperCAmelCase ):
with redirect_stdin(_UpperCAmelCase ):
yield
@contextlib.contextmanager
def A ( ) -> Any:
'''simple docstring'''
with tempfile.TemporaryDirectory() as dirname:
with chdir(_UpperCAmelCase ):
yield dirname
class __lowerCAmelCase ( A ):
pass
class __lowerCAmelCase ( io.StringIO ):
def _lowerCamelCase ( self : Tuple , *A : str , **A : Any) -> Any:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : List[str] , *A : Optional[Any] , **A : Optional[Any]) -> Optional[int]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : str , *A : List[str] , **A : List[Any]) -> Union[str, Any]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : Union[str, Any] , *A : Optional[Any] , **A : List[str]) -> Optional[int]:
"""simple docstring"""
return False
class __lowerCAmelCase ( contextlib._RedirectStream ): # type: ignore
UpperCamelCase = '''stdin'''
@contextlib.contextmanager
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
if root == ".":
yield
return
_UpperCAmelCase = os.getcwd()
os.chdir(_UpperCAmelCase )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str]=None ) -> Any:
'''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 = None
_UpperCAmelCase = None
import os
_UpperCAmelCase = '1'
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import shutil
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import subprocess
_UpperCAmelCase = None # type: ignore
_UpperCAmelCase = None
import sys
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
| 339 | 0 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowercase_ ( _lowerCamelCase : str = "isbn/0140328726"):
lowercase__ : Optional[Any] = olid.strip().strip("/") # Remove leading/trailing whitespace & slashes
if new_olid.count("/") != 1:
lowercase__ : Optional[Any] = f'''{olid} is not a valid Open Library olid'''
raise ValueError(_UpperCAmelCase)
return requests.get(f'''https://openlibrary.org/{new_olid}.json''').json()
def lowercase_ ( _lowerCamelCase : dict):
lowercase__ : List[Any] = {
"title": "Title",
"publish_date": "Publish date",
"authors": "Authors",
"number_of_pages": "Number of pages:",
"first_sentence": "First sentence",
"isbn_10": "ISBN (10)",
"isbn_13": "ISBN (13)",
}
lowercase__ : Tuple = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
lowercase__ : Optional[int] = [
get_openlibrary_data(author["key"])["name"] for author in data["Authors"]
]
lowercase__ : Any = data["First sentence"]["value"]
for key, value in data.items():
if isinstance(_UpperCAmelCase , _UpperCAmelCase):
lowercase__ : Any = ", ".join(_UpperCAmelCase)
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
UpperCamelCase = input('''\nEnter the ISBN code to search (or \'quit\' to stop): ''').strip()
if isbn.lower() in ("", "q", "quit", "exit", "stop"):
break
if len(isbn) not in (10, 13) or not isbn.isdigit():
print(f"Sorry, {isbn} is not a valid ISBN. Please, input a valid ISBN.")
continue
print(f"\nSearching Open Library for ISBN: {isbn}...\n")
try:
UpperCamelCase = summarize_book(get_openlibrary_data(f"isbn/{isbn}"))
print('''\n'''.join(f"{key}: {value}" for key, value in book_summary.items()))
except JSONDecodeError: # Workaround for requests.exceptions.RequestException:
print(f"Sorry, there are no results for ISBN: {isbn}.")
| 87 |
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 ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any]=False ) -> str:
'''simple docstring'''
try:
_UpperCAmelCase = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_UpperCAmelCase = default
else:
# KEY is set, convert it to True or False.
try:
_UpperCAmelCase = strtobool(_UpperCAmelCase )
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
UpperCAmelCase__ = parse_flag_from_env("RUN_SLOW", default=False)
def A ( _UpperCAmelCase : List[str] ) -> List[str]:
'''simple docstring'''
return unittest.skip('Test was skipped' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> str:
'''simple docstring'''
return unittest.skipUnless(_run_slow_tests , 'test is slow' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> str:
'''simple docstring'''
return unittest.skipUnless(not torch.cuda.is_available() , 'test requires only a CPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.is_available() , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_xpu_available() , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> List[str]:
'''simple docstring'''
return unittest.skipUnless(is_mps_available() , 'test requires a `mps` backend support in `torch`' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , 'test requires the Hugging Face suite' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : str ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_bnb_available() , 'test requires the bitsandbytes library' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_tpu_available() , 'test requires TPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> str:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() == 1 , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> int:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() == 1 , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() > 1 , 'test requires multiple GPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> Any:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() > 1 , 'test requires multiple XPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_safetensors_available() , 'test requires safetensors' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(is_deepspeed_available() , 'test requires DeepSpeed' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_torch_version('>=' , '1.12.0' ) , 'test requires torch version >= 1.12.0' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any=None , _UpperCAmelCase : List[Any]=None ) -> Dict:
'''simple docstring'''
if test_case is None:
return partial(_UpperCAmelCase , version=_UpperCAmelCase )
return unittest.skipUnless(is_torch_version('>=' , _UpperCAmelCase ) , F"test requires torch version >= {version}" )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> int:
'''simple docstring'''
return unittest.skipUnless(is_tensorboard_available() , 'test requires Tensorboard' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return unittest.skipUnless(is_wandb_available() , 'test requires wandb' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_comet_ml_available() , 'test requires comet_ml' )(_UpperCAmelCase )
UpperCAmelCase__ = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def A ( _UpperCAmelCase : List[str] ) -> Any:
'''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' , )(_UpperCAmelCase )
class __lowerCAmelCase ( unittest.TestCase ):
UpperCamelCase = True
@classmethod
def _lowerCamelCase ( cls : List[Any]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = tempfile.mkdtemp()
@classmethod
def _lowerCamelCase ( cls : Union[str, Any]) -> str:
"""simple docstring"""
if os.path.exists(cls.tmpdir):
shutil.rmtree(cls.tmpdir)
def _lowerCamelCase ( self : List[str]) -> List[Any]:
"""simple docstring"""
if self.clear_on_setup:
for path in Path(self.tmpdir).glob('**/*'):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(A)
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Dict) -> Tuple:
"""simple docstring"""
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[int] , A : Union[mock.Mock, List[mock.Mock]]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = mocks if isinstance(A , (tuple, list)) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop)
def A ( _UpperCAmelCase : List[Any] ) -> int:
'''simple docstring'''
_UpperCAmelCase = AcceleratorState()
_UpperCAmelCase = tensor[None].clone().to(state.device )
_UpperCAmelCase = gather(_UpperCAmelCase ).cpu()
_UpperCAmelCase = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , _UpperCAmelCase ):
return False
return True
class __lowerCAmelCase :
def __init__( self : Optional[Any] , A : Union[str, Any] , A : Optional[int] , A : str) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = returncode
_UpperCAmelCase = stdout
_UpperCAmelCase = stderr
async def A ( _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
while True:
_UpperCAmelCase = await stream.readline()
if line:
callback(_UpperCAmelCase )
else:
break
async def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=None , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Union[str, Any]=False ) -> _RunOutput:
'''simple docstring'''
if echo:
print('\nRunning: ' , ' '.join(_UpperCAmelCase ) )
_UpperCAmelCase = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=_UpperCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_UpperCAmelCase , )
# 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 = []
_UpperCAmelCase = []
def tee(_UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : str="" ):
_UpperCAmelCase = line.decode('utf-8' ).rstrip()
sink.append(_UpperCAmelCase )
if not quiet:
print(_UpperCAmelCase , _UpperCAmelCase , file=_UpperCAmelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stdout , label='stdout:' ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stderr , label='stderr:' ) ) ),
] , timeout=_UpperCAmelCase , )
return _RunOutput(await p.wait() , _UpperCAmelCase , _UpperCAmelCase )
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Dict=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=180 , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : List[Any]=True ) -> _RunOutput:
'''simple docstring'''
_UpperCAmelCase = asyncio.get_event_loop()
_UpperCAmelCase = loop.run_until_complete(
_stream_subprocess(_UpperCAmelCase , env=_UpperCAmelCase , stdin=_UpperCAmelCase , timeout=_UpperCAmelCase , quiet=_UpperCAmelCase , echo=_UpperCAmelCase ) )
_UpperCAmelCase = ' '.join(_UpperCAmelCase )
if result.returncode > 0:
_UpperCAmelCase = '\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 ( A ):
pass
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : str=False ) -> Tuple:
'''simple docstring'''
try:
_UpperCAmelCase = subprocess.check_output(_UpperCAmelCase , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(_UpperCAmelCase , 'decode' ):
_UpperCAmelCase = output.decode('utf-8' )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F"Command `{' '.join(_UpperCAmelCase )}` failed with the following error:\n\n{e.output.decode()}" ) from e
| 339 | 0 |
'''simple docstring'''
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_lowerCAmelCase = collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_lowerCAmelCase = '''https://storage.googleapis.com/cvdf-datasets/mnist/'''
def __lowerCAmelCase ( snake_case__ ):
__UpperCamelCase : int = numpy.dtype(numpy.uintaa ).newbyteorder(">" )
return numpy.frombuffer(bytestream.read(4 ) , dtype=_UpperCAmelCase )[0]
@deprecated(_UpperCAmelCase , "Please use tf.data to implement this functionality." )
def __lowerCAmelCase ( snake_case__ ):
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=_UpperCAmelCase ) as bytestream:
__UpperCamelCase : Union[str, Any] = _readaa(_UpperCAmelCase )
if magic != 2_051:
raise ValueError(
"Invalid magic number %d in MNIST image file: %s" % (magic, f.name) )
__UpperCamelCase : Union[str, Any] = _readaa(_UpperCAmelCase )
__UpperCamelCase : List[Any] = _readaa(_UpperCAmelCase )
__UpperCamelCase : Optional[int] = _readaa(_UpperCAmelCase )
__UpperCamelCase : List[Any] = bytestream.read(rows * cols * num_images )
__UpperCamelCase : int = numpy.frombuffer(_UpperCAmelCase , dtype=numpy.uinta )
__UpperCamelCase : List[Any] = data.reshape(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , 1 )
return data
@deprecated(_UpperCAmelCase , "Please use tf.one_hot on tensors." )
def __lowerCAmelCase ( snake_case__ , snake_case__ ):
__UpperCamelCase : List[Any] = labels_dense.shape[0]
__UpperCamelCase : Optional[int] = numpy.arange(_UpperCAmelCase ) * num_classes
__UpperCamelCase : Union[str, Any] = numpy.zeros((num_labels, num_classes) )
__UpperCamelCase : Dict = 1
return labels_one_hot
@deprecated(_UpperCAmelCase , "Please use tf.data to implement this functionality." )
def __lowerCAmelCase ( snake_case__ , snake_case__=False , snake_case__=10 ):
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=_UpperCAmelCase ) as bytestream:
__UpperCamelCase : Any = _readaa(_UpperCAmelCase )
if magic != 2_049:
raise ValueError(
"Invalid magic number %d in MNIST label file: %s" % (magic, f.name) )
__UpperCamelCase : Union[str, Any] = _readaa(_UpperCAmelCase )
__UpperCamelCase : List[Any] = bytestream.read(_UpperCAmelCase )
__UpperCamelCase : Optional[int] = numpy.frombuffer(_UpperCAmelCase , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(_UpperCAmelCase , _UpperCAmelCase )
return labels
class A :
'''simple docstring'''
@deprecated(
_UpperCAmelCase , "Please use alternatives such as official/mnist/_DataSet.py"
" from tensorflow/models." , )
def __init__(self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=False , _UpperCAmelCase=False , _UpperCAmelCase=dtypes.floataa , _UpperCAmelCase=True , _UpperCAmelCase=None , ) -> List[str]:
__UpperCamelCase , __UpperCamelCase : str = random_seed.get_seed(_UpperCAmelCase )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
__UpperCamelCase : Union[str, Any] = dtypes.as_dtype(_UpperCAmelCase ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype )
if fake_data:
__UpperCamelCase : Any = 1_0_0_0_0
__UpperCamelCase : List[Any] = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), f"images.shape: {images.shape} labels.shape: {labels.shape}"
__UpperCamelCase : List[str] = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
__UpperCamelCase : Tuple = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
__UpperCamelCase : str = images.astype(numpy.floataa )
__UpperCamelCase : Any = numpy.multiply(_UpperCAmelCase , 1.0 / 2_5_5.0 )
__UpperCamelCase : str = images
__UpperCamelCase : Tuple = labels
__UpperCamelCase : int = 0
__UpperCamelCase : Union[str, Any] = 0
@property
def a_ (self ) -> Optional[Any]:
return self._images
@property
def a_ (self ) -> List[str]:
return self._labels
@property
def a_ (self ) -> List[Any]:
return self._num_examples
@property
def a_ (self ) -> Tuple:
return self._epochs_completed
def a_ (self , _UpperCAmelCase , _UpperCAmelCase=False , _UpperCAmelCase=True ) -> Optional[int]:
if fake_data:
__UpperCamelCase : List[Any] = [1] * 7_8_4
__UpperCamelCase : Any = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(_UpperCAmelCase )],
[fake_label for _ in range(_UpperCAmelCase )],
)
__UpperCamelCase : Dict = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
__UpperCamelCase : Tuple = numpy.arange(self._num_examples )
numpy.random.shuffle(_UpperCAmelCase )
__UpperCamelCase : Optional[int] = self.images[perma]
__UpperCamelCase : Optional[int] = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
__UpperCamelCase : List[Any] = self._num_examples - start
__UpperCamelCase : List[Any] = self._images[start : self._num_examples]
__UpperCamelCase : List[Any] = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
__UpperCamelCase : List[str] = numpy.arange(self._num_examples )
numpy.random.shuffle(_UpperCAmelCase )
__UpperCamelCase : List[str] = self.images[perm]
__UpperCamelCase : int = self.labels[perm]
# Start next epoch
__UpperCamelCase : Any = 0
__UpperCamelCase : str = batch_size - rest_num_examples
__UpperCamelCase : List[str] = self._index_in_epoch
__UpperCamelCase : List[Any] = self._images[start:end]
__UpperCamelCase : Optional[Any] = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
__UpperCamelCase : Any = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(_UpperCAmelCase , "Please write your own downloading logic." )
def __lowerCAmelCase ( snake_case__ , snake_case__ , snake_case__ ):
if not gfile.Exists(_UpperCAmelCase ):
gfile.MakeDirs(_UpperCAmelCase )
__UpperCamelCase : Dict = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if not gfile.Exists(_UpperCAmelCase ):
urllib.request.urlretrieve(_UpperCAmelCase , _UpperCAmelCase ) # noqa: S310
with gfile.GFile(_UpperCAmelCase ) as f:
__UpperCamelCase : str = f.size()
print("Successfully downloaded" , _UpperCAmelCase , _UpperCAmelCase , "bytes." )
return filepath
@deprecated(
_UpperCAmelCase , "Please use alternatives such as:" " tensorflow_datasets.load(\'mnist\')" )
def __lowerCAmelCase ( snake_case__ , snake_case__=False , snake_case__=False , snake_case__=dtypes.floataa , snake_case__=True , snake_case__=5_000 , snake_case__=None , snake_case__=DEFAULT_SOURCE_URL , ):
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=_UpperCAmelCase , one_hot=_UpperCAmelCase , dtype=_UpperCAmelCase , seed=_UpperCAmelCase )
__UpperCamelCase : str = fake()
__UpperCamelCase : Any = fake()
__UpperCamelCase : Optional[int] = fake()
return _Datasets(train=_UpperCAmelCase , validation=_UpperCAmelCase , test=_UpperCAmelCase )
if not source_url: # empty string check
__UpperCamelCase : Tuple = DEFAULT_SOURCE_URL
__UpperCamelCase : Any = "train-images-idx3-ubyte.gz"
__UpperCamelCase : List[Any] = "train-labels-idx1-ubyte.gz"
__UpperCamelCase : Dict = "t10k-images-idx3-ubyte.gz"
__UpperCamelCase : Dict = "t10k-labels-idx1-ubyte.gz"
__UpperCamelCase : Union[str, Any] = _maybe_download(
_UpperCAmelCase , _UpperCAmelCase , source_url + train_images_file )
with gfile.Open(_UpperCAmelCase , "rb" ) as f:
__UpperCamelCase : str = _extract_images(_UpperCAmelCase )
__UpperCamelCase : List[Any] = _maybe_download(
_UpperCAmelCase , _UpperCAmelCase , source_url + train_labels_file )
with gfile.Open(_UpperCAmelCase , "rb" ) as f:
__UpperCamelCase : Dict = _extract_labels(_UpperCAmelCase , one_hot=_UpperCAmelCase )
__UpperCamelCase : Any = _maybe_download(
_UpperCAmelCase , _UpperCAmelCase , source_url + test_images_file )
with gfile.Open(_UpperCAmelCase , "rb" ) as f:
__UpperCamelCase : Any = _extract_images(_UpperCAmelCase )
__UpperCamelCase : int = _maybe_download(
_UpperCAmelCase , _UpperCAmelCase , source_url + test_labels_file )
with gfile.Open(_UpperCAmelCase , "rb" ) as f:
__UpperCamelCase : Tuple = _extract_labels(_UpperCAmelCase , one_hot=_UpperCAmelCase )
if not 0 <= validation_size <= len(_UpperCAmelCase ):
__UpperCamelCase : Optional[Any] = (
"Validation size should be between 0 and "
F"{len(_UpperCAmelCase )}. Received: {validation_size}."
)
raise ValueError(_UpperCAmelCase )
__UpperCamelCase : Optional[Any] = train_images[:validation_size]
__UpperCamelCase : Dict = train_labels[:validation_size]
__UpperCamelCase : Union[str, Any] = train_images[validation_size:]
__UpperCamelCase : str = train_labels[validation_size:]
__UpperCamelCase : Any = {"dtype": dtype, "reshape": reshape, "seed": seed}
__UpperCamelCase : Optional[Any] = _DataSet(_UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase )
__UpperCamelCase : Dict = _DataSet(_UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase )
__UpperCamelCase : List[str] = _DataSet(_UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase )
return _Datasets(train=_UpperCAmelCase , validation=_UpperCAmelCase , test=_UpperCAmelCase )
| 298 |
from __future__ import annotations
UpperCAmelCase__ = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
UpperCAmelCase__ = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def A ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
'''simple docstring'''
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def A ( _UpperCAmelCase : Matrix ) -> Matrix | None:
'''simple docstring'''
if location := find_empty_location(_UpperCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
_UpperCAmelCase = 0
return None
def A ( _UpperCAmelCase : Matrix ) -> None:
'''simple docstring'''
for row in grid:
for cell in row:
print(_UpperCAmelCase , end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("\nExample grid:\n" + "=" * 20)
print_solution(example_grid)
print("\nExample grid solution:")
UpperCAmelCase__ = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("Cannot find a solution.")
| 339 | 0 |
from __future__ import annotations
def _lowerCamelCase( lowercase__ ) -> list[int]:
'''simple docstring'''
__lowercase= [True] * limit
__lowercase= False
__lowercase= False
__lowercase= True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ):
__lowercase= i * 2
while index < limit:
__lowercase= False
__lowercase= index + i
__lowercase= [2]
for i in range(3 , _UpperCAmelCase , 2 ):
if is_prime[i]:
primes.append(_UpperCAmelCase )
return primes
def _lowerCamelCase( lowercase__ = 1_0_0_0_0_0_0 ) -> int:
'''simple docstring'''
__lowercase= prime_sieve(_UpperCAmelCase )
__lowercase= 0
__lowercase= 0
for i in range(len(_UpperCAmelCase ) ):
for j in range(i + length , len(_UpperCAmelCase ) ):
__lowercase= sum(primes[i:j] )
if sol >= ceiling:
break
if sol in primes:
__lowercase= j - i
__lowercase= sol
return largest
if __name__ == "__main__":
print(F'{solution() = }')
| 295 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
UpperCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
UpperCAmelCase__ = "\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n"
UpperCAmelCase__ = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"
UpperCAmelCase__ = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n 'meteor': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric('meteor')\n >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results[\"meteor\"], 4))\n 0.6944\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : List[Any]) -> List[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence'),
'references': datasets.Value('string' , id='sequence'),
}) , codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'] , reference_urls=[
'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
'https://en.wikipedia.org/wiki/METEOR',
] , )
def _lowerCamelCase ( self : Optional[Any] , A : List[str]) -> List[Any]:
"""simple docstring"""
import nltk
nltk.download('wordnet')
if NLTK_VERSION >= version.Version('3.6.5'):
nltk.download('punkt')
if NLTK_VERSION >= version.Version('3.6.6'):
nltk.download('omw-1.4')
def _lowerCamelCase ( self : Optional[Any] , A : Tuple , A : Optional[int] , A : List[Any]=0.9 , A : Optional[Any]=3 , A : Optional[int]=0.5) -> Any:
"""simple docstring"""
if NLTK_VERSION >= version.Version('3.6.5'):
_UpperCAmelCase = [
meteor_score.single_meteor_score(
word_tokenize(A) , word_tokenize(A) , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
else:
_UpperCAmelCase = [
meteor_score.single_meteor_score(A , A , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
return {"meteor": np.mean(A)}
| 339 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Optional[int] , __a : Tuple , __a : Tuple=14 , __a : Dict=7 , __a : Any=True , __a : str=True , __a : Tuple=False , __a : Optional[int]=True , __a : Tuple=99 , __a : List[str]=32 , __a : List[str]=4 , __a : Dict=4 , __a : List[Any]=4 , __a : List[str]=37 , __a : Union[str, Any]="gelu" , __a : Dict=0.1 , __a : Tuple=0.1 , __a : int=5_12 , __a : Optional[Any]=0.02 , ):
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_input_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = rotary_dim
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = initializer_range
_a = None
_a = vocab_size - 1
_a = vocab_size - 1
_a = vocab_size - 1
def UpperCamelCase__ ( self : Optional[Any] ):
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_input_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=__a , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def UpperCamelCase__ ( self : Optional[int] ):
_a = self.prepare_config_and_inputs()
_a , _a , _a = config_and_inputs
_a = {"input_ids": input_ids, "attention_mask": attention_mask}
return config, inputs_dict
def UpperCamelCase__ ( self : Tuple , __a : List[str] , __a : str , __a : Any , __a : Any ):
_a = 20
_a = model_class_name(__a )
_a = model.init_cache(input_ids.shape[0] , __a )
_a = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype="i4" )
_a = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
_a = model(
input_ids[:, :-1] , attention_mask=__a , past_key_values=__a , position_ids=__a , )
_a = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="i4" )
_a = model(
input_ids[:, -1:] , attention_mask=__a , past_key_values=outputs_cache.past_key_values , position_ids=__a , )
_a = model(__a )
_a = 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 UpperCamelCase__ ( self : Dict , __a : List[Any] , __a : Optional[Any] , __a : Union[str, Any] , __a : Optional[Any] ):
_a = 20
_a = model_class_name(__a )
_a = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
_a = model.init_cache(input_ids.shape[0] , __a )
_a = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
_a = model(
input_ids[:, :-1] , attention_mask=__a , past_key_values=__a , position_ids=__a , )
_a = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="i4" )
_a = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=__a , position_ids=__a , )
_a = model(__a , attention_mask=__a )
_a = 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 __SCREAMING_SNAKE_CASE (lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__a =(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
__a =(FlaxGPTJForCausalLM,) if is_flax_available() else ()
def UpperCamelCase__ ( self : List[Any] ):
_a = FlaxGPTJModelTester(self )
def UpperCamelCase__ ( self : Optional[Any] ):
for model_class_name in self.all_model_classes:
_a , _a , _a = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(__a , __a , __a , __a )
def UpperCamelCase__ ( self : Union[str, Any] ):
for model_class_name in self.all_model_classes:
_a , _a , _a = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
__a , __a , __a , __a )
@tooslow
def UpperCamelCase__ ( self : str ):
_a = GPTaTokenizer.from_pretrained("gpt2" , pad_token="<|endoftext|>" , padding_side="left" )
_a = tokenizer(["Hello this is a long string", "Hey"] , return_tensors="np" , padding=__a , truncation=__a )
_a = FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B" )
_a = False
_a = model.config.eos_token_id
_a = jax.jit(model.generate )
_a = jit_generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , pad_token_id=tokenizer.pad_token_id ).sequences
_a = tokenizer.batch_decode(__a , skip_special_tokens=__a )
_a = [
"Hello this is a long string of text.\n\nI\'m trying to get the text of the",
"Hey, I\'m a little late to the party. I\'m going to",
]
self.assertListEqual(__a , __a )
@is_pt_flax_cross_test
def UpperCamelCase__ ( self : List[str] ):
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
_a = self._prepare_for_class(__a , __a )
_a = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
_a = model_class.__name__[4:] # Skip the "Flax" at the beginning
_a = getattr(__a , __a )
_a , _a = pt_inputs["input_ids"].shape
_a = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(__a ):
_a = 0
_a = 1
_a = 0
_a = 1
_a = pt_model_class(__a ).eval()
_a = model_class(__a , dtype=jnp.floataa )
_a = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , __a )
_a = fx_state
with torch.no_grad():
_a = pt_model(**__a ).to_tuple()
_a = fx_model(**__a ).to_tuple()
self.assertEqual(len(__a ) , len(__a ) , "Output lengths differ between Flax and PyTorch" )
for fx_output, pt_output in zip(__a , __a ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(__a )
_a = model_class.from_pretrained(__a , from_pt=__a )
_a = fx_model_loaded(**__a ).to_tuple()
self.assertEqual(
len(__a ) , len(__a ) , "Output lengths differ between Flax and PyTorch" )
for fx_output_loaded, pt_output in zip(__a , __a ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@is_pt_flax_cross_test
def UpperCamelCase__ ( self : Optional[int] ):
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
_a = self._prepare_for_class(__a , __a )
_a = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
_a = model_class.__name__[4:] # Skip the "Flax" at the beginning
_a = getattr(__a , __a )
_a = pt_model_class(__a ).eval()
_a = model_class(__a , dtype=jnp.floataa )
_a = load_flax_weights_in_pytorch_model(__a , fx_model.params )
_a , _a = pt_inputs["input_ids"].shape
_a = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(__a ):
_a = 0
_a = 1
_a = 0
_a = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
_a = pt_model(**__a ).to_tuple()
_a = fx_model(**__a ).to_tuple()
self.assertEqual(len(__a ) , len(__a ) , "Output lengths differ between Flax and PyTorch" )
for fx_output, pt_output in zip(__a , __a ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(__a )
_a = pt_model_class.from_pretrained(__a , from_flax=__a )
with torch.no_grad():
_a = pt_model_loaded(**__a ).to_tuple()
self.assertEqual(
len(__a ) , len(__a ) , "Output lengths differ between Flax and PyTorch" )
for fx_output, pt_output in zip(__a , __a ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@tooslow
def UpperCamelCase__ ( self : Dict ):
for model_class_name in self.all_model_classes:
_a = model_class_name.from_pretrained("EleutherAI/gpt-j-6B" )
_a = model(np.ones((1, 1) ) )
self.assertIsNotNone(__a )
| 63 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
UpperCAmelCase__ = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
"large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
_UpperCAmelCase = ['layers', 'blocks']
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase , _UpperCAmelCase )
UpperCAmelCase__ = {
"blocks": "layers",
"mlp.0": "fc1",
"mlp.2": "fc2",
"mlp_ln": "final_layer_norm",
".attn.query": ".self_attn.q_proj",
".attn.key": ".self_attn.k_proj",
".attn.value": ".self_attn.v_proj",
".attn_ln": ".self_attn_layer_norm",
".attn.out": ".self_attn.out_proj",
".cross_attn.query": ".encoder_attn.q_proj",
".cross_attn.key": ".encoder_attn.k_proj",
".cross_attn.value": ".encoder_attn.v_proj",
".cross_attn_ln": ".encoder_attn_layer_norm",
".cross_attn.out": ".encoder_attn.out_proj",
"decoder.ln.": "decoder.layer_norm.",
"encoder.ln.": "encoder.layer_norm.",
"token_embedding": "embed_tokens",
"encoder.positional_embedding": "encoder.embed_positions.weight",
"decoder.positional_embedding": "decoder.embed_positions.weight",
"ln_post": "layer_norm",
}
def A ( _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = list(s_dict.keys() )
for key in keys:
_UpperCAmelCase = key
for k, v in WHISPER_MAPPING.items():
if k in key:
_UpperCAmelCase = new_key.replace(_UpperCAmelCase , _UpperCAmelCase )
print(F"{key} -> {new_key}" )
_UpperCAmelCase = s_dict.pop(_UpperCAmelCase )
return s_dict
def A ( _UpperCAmelCase : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = emb.weight.shape
_UpperCAmelCase = nn.Linear(_UpperCAmelCase , _UpperCAmelCase , bias=_UpperCAmelCase )
_UpperCAmelCase = emb.weight.data
return lin_layer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> bytes:
'''simple docstring'''
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
_UpperCAmelCase = os.path.basename(_UpperCAmelCase )
_UpperCAmelCase = url.split('/' )[-2]
_UpperCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if os.path.exists(_UpperCAmelCase ) and not os.path.isfile(_UpperCAmelCase ):
raise RuntimeError(F"{download_target} exists and is not a regular file" )
if os.path.isfile(_UpperCAmelCase ):
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(F"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" )
with urllib.request.urlopen(_UpperCAmelCase ) as source, open(_UpperCAmelCase , 'wb' ) as output:
with tqdm(
total=int(source.info().get('Content-Length' ) ) , ncols=80 , unit='iB' , unit_scale=_UpperCAmelCase , unit_divisor=1_024 ) as loop:
while True:
_UpperCAmelCase = source.read(8_192 )
if not buffer:
break
output.write(_UpperCAmelCase )
loop.update(len(_UpperCAmelCase ) )
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() != expected_shaaaa:
raise RuntimeError(
'Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.' )
return model_bytes
def A ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
if ".pt" not in checkpoint_path:
_UpperCAmelCase = _download(_MODELS[checkpoint_path] )
else:
_UpperCAmelCase = torch.load(_UpperCAmelCase , map_location='cpu' )
_UpperCAmelCase = original_checkpoint['dims']
_UpperCAmelCase = original_checkpoint['model_state_dict']
_UpperCAmelCase = state_dict['decoder.token_embedding.weight']
remove_ignore_keys_(_UpperCAmelCase )
rename_keys(_UpperCAmelCase )
_UpperCAmelCase = True
_UpperCAmelCase = state_dict['decoder.layers.0.fc1.weight'].shape[0]
_UpperCAmelCase = WhisperConfig(
vocab_size=dimensions['n_vocab'] , encoder_ffn_dim=_UpperCAmelCase , decoder_ffn_dim=_UpperCAmelCase , num_mel_bins=dimensions['n_mels'] , d_model=dimensions['n_audio_state'] , max_target_positions=dimensions['n_text_ctx'] , encoder_layers=dimensions['n_audio_layer'] , encoder_attention_heads=dimensions['n_audio_head'] , decoder_layers=dimensions['n_text_layer'] , decoder_attention_heads=dimensions['n_text_state'] , max_source_positions=dimensions['n_audio_ctx'] , )
_UpperCAmelCase = WhisperForConditionalGeneration(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = model.model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0 and not set(_UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'
F" but all the following weights are missing {missing}" )
if tie_embeds:
_UpperCAmelCase = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
_UpperCAmelCase = proj_out_weights
model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# # Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCAmelCase__ = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 339 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def _lowerCAmelCase ( UpperCamelCase_ ):
return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items() )
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
__SCREAMING_SNAKE_CASE = key.replace("""heads.cmd.mim_head.cls.predictions""" , """mmm_image_head""" )
__SCREAMING_SNAKE_CASE = key.replace("""heads.cmd.mlm_head.cls.predictions""" , """mmm_text_head""" )
__SCREAMING_SNAKE_CASE = key.replace("""heads.cmd.itm_head.cls""" , """itm_head""" )
__SCREAMING_SNAKE_CASE = key.replace("""heads.cmd.itm_head.pooler""" , """itm_head.pooler""" )
__SCREAMING_SNAKE_CASE = key.replace("""heads.cmd.clip_head.logit_scale""" , """flava.logit_scale""" )
__SCREAMING_SNAKE_CASE = key.replace("""heads.fairseq_mlm.cls.predictions""" , """mlm_head""" )
__SCREAMING_SNAKE_CASE = key.replace("""heads.imagenet.mim_head.cls.predictions""" , """mim_head""" )
__SCREAMING_SNAKE_CASE = key.replace("""mm_text_projection""" , """flava.text_to_mm_projection""" )
__SCREAMING_SNAKE_CASE = key.replace("""mm_image_projection""" , """flava.image_to_mm_projection""" )
__SCREAMING_SNAKE_CASE = key.replace("""image_encoder.module""" , """flava.image_model""" )
__SCREAMING_SNAKE_CASE = key.replace("""text_encoder.module""" , """flava.text_model""" )
__SCREAMING_SNAKE_CASE = key.replace("""mm_encoder.module.encoder.cls_token""" , """flava.multimodal_model.cls_token""" )
__SCREAMING_SNAKE_CASE = key.replace("""mm_encoder.module""" , """flava.multimodal_model""" )
__SCREAMING_SNAKE_CASE = key.replace("""text_projection""" , """flava.text_projection""" )
__SCREAMING_SNAKE_CASE = key.replace("""image_projection""" , """flava.image_projection""" )
__SCREAMING_SNAKE_CASE = value.float()
for key, value in codebook_state_dict.items():
__SCREAMING_SNAKE_CASE = value
return upgrade
@torch.no_grad()
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=None ):
if config_path is not None:
__SCREAMING_SNAKE_CASE = FlavaConfig.from_pretrained(_UpperCAmelCase )
else:
__SCREAMING_SNAKE_CASE = FlavaConfig()
__SCREAMING_SNAKE_CASE = FlavaForPreTraining(_UpperCAmelCase ).eval()
__SCREAMING_SNAKE_CASE = convert_dalle_checkpoint(_UpperCAmelCase , _UpperCAmelCase , save_checkpoint=_UpperCAmelCase )
if os.path.exists(_UpperCAmelCase ):
__SCREAMING_SNAKE_CASE = torch.load(_UpperCAmelCase , map_location="""cpu""" )
else:
__SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(_UpperCAmelCase , map_location="""cpu""" )
__SCREAMING_SNAKE_CASE = upgrade_state_dict(_UpperCAmelCase , _UpperCAmelCase )
hf_model.load_state_dict(_UpperCAmelCase )
__SCREAMING_SNAKE_CASE = hf_model.state_dict()
__SCREAMING_SNAKE_CASE = count_parameters(_UpperCAmelCase )
__SCREAMING_SNAKE_CASE = count_parameters(_UpperCAmelCase ) + count_parameters(_UpperCAmelCase )
assert torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1e-3 )
hf_model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
__magic_name__ = 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 flava checkpoint")
parser.add_argument("--codebook_path", default=None, type=str, help="Path to flava codebook checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
__magic_name__ = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 100 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase__ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
UpperCamelCase = None
UpperCamelCase = None
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilder ):
UpperCamelCase = datasets.Audio()
UpperCamelCase = '''audio'''
UpperCamelCase = AudioFolderConfig
UpperCamelCase = 42 # definition at the bottom of the script
UpperCamelCase = AudioClassification(audio_column='''audio''' , label_column='''label''' )
UpperCAmelCase__ = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
UpperCAmelCase__ = AUDIO_EXTENSIONS
| 339 | 0 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __snake_case :
@staticmethod
def __a ( *_lowercase : Dict , **_lowercase : Any ):
"""simple docstring"""
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __snake_case ( unittest.TestCase ):
lowerCAmelCase_ = MODEL_FOR_OBJECT_DETECTION_MAPPING
def __a ( self : Tuple , _lowercase : Any , _lowercase : Dict , _lowercase : Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = ObjectDetectionPipeline(model=_lowercase , image_processor=_lowercase )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def __a ( self : Union[str, Any] , _lowercase : Optional[Any] , _lowercase : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png""" , threshold=0.0 )
self.assertGreater(len(_lowercase ) , 0 )
for detected_object in outputs:
self.assertEqual(
_lowercase , {
"""score""": ANY(_lowercase ),
"""label""": ANY(_lowercase ),
"""box""": {"""xmin""": ANY(_lowercase ), """ymin""": ANY(_lowercase ), """xmax""": ANY(_lowercase ), """ymax""": ANY(_lowercase )},
} , )
import datasets
SCREAMING_SNAKE_CASE__ = datasets.load_dataset("""hf-internal-testing/fixtures_image_utils""" , """image""" , split="""test""" )
SCREAMING_SNAKE_CASE__ = [
Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ),
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
# RGBA
dataset[0]["""file"""],
# LA
dataset[1]["""file"""],
# L
dataset[2]["""file"""],
]
SCREAMING_SNAKE_CASE__ = object_detector(_lowercase , threshold=0.0 )
self.assertEqual(len(_lowercase ) , len(_lowercase ) )
for outputs in batch_outputs:
self.assertGreater(len(_lowercase ) , 0 )
for detected_object in outputs:
self.assertEqual(
_lowercase , {
"""score""": ANY(_lowercase ),
"""label""": ANY(_lowercase ),
"""box""": {"""xmin""": ANY(_lowercase ), """ymin""": ANY(_lowercase ), """xmax""": ANY(_lowercase ), """ymax""": ANY(_lowercase )},
} , )
@require_tf
@unittest.skip("""Object detection not implemented in TF""" )
def __a ( self : Dict ):
"""simple docstring"""
pass
@require_torch
def __a ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = """hf-internal-testing/tiny-detr-mobilenetsv3"""
SCREAMING_SNAKE_CASE__ = AutoModelForObjectDetection.from_pretrained(_lowercase )
SCREAMING_SNAKE_CASE__ = AutoFeatureExtractor.from_pretrained(_lowercase )
SCREAMING_SNAKE_CASE__ = ObjectDetectionPipeline(model=_lowercase , feature_extractor=_lowercase )
SCREAMING_SNAKE_CASE__ = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" , threshold=0.0 )
self.assertEqual(
nested_simplify(_lowercase , decimals=4 ) , [
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
] , )
SCREAMING_SNAKE_CASE__ = object_detector(
[
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(_lowercase , decimals=4 ) , [
[
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
],
[
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
{"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
],
] , )
@require_torch
@slow
def __a ( self : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = """facebook/detr-resnet-50"""
SCREAMING_SNAKE_CASE__ = AutoModelForObjectDetection.from_pretrained(_lowercase )
SCREAMING_SNAKE_CASE__ = AutoFeatureExtractor.from_pretrained(_lowercase )
SCREAMING_SNAKE_CASE__ = ObjectDetectionPipeline(model=_lowercase , feature_extractor=_lowercase )
SCREAMING_SNAKE_CASE__ = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" )
self.assertEqual(
nested_simplify(_lowercase , decimals=4 ) , [
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
] , )
SCREAMING_SNAKE_CASE__ = object_detector(
[
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
] )
self.assertEqual(
nested_simplify(_lowercase , decimals=4 ) , [
[
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
],
[
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
],
] , )
@require_torch
@slow
def __a ( self : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = """facebook/detr-resnet-50"""
SCREAMING_SNAKE_CASE__ = pipeline("""object-detection""" , model=_lowercase )
SCREAMING_SNAKE_CASE__ = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" )
self.assertEqual(
nested_simplify(_lowercase , decimals=4 ) , [
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
] , )
SCREAMING_SNAKE_CASE__ = object_detector(
[
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
] )
self.assertEqual(
nested_simplify(_lowercase , decimals=4 ) , [
[
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
],
[
{"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
{"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
{"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
],
] , )
@require_torch
@slow
def __a ( self : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 0.99_85
SCREAMING_SNAKE_CASE__ = """facebook/detr-resnet-50"""
SCREAMING_SNAKE_CASE__ = pipeline("""object-detection""" , model=_lowercase )
SCREAMING_SNAKE_CASE__ = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" , threshold=_lowercase )
self.assertEqual(
nested_simplify(_lowercase , decimals=4 ) , [
{"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
{"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
] , )
@require_torch
@require_pytesseract
@slow
def __a ( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = """Narsil/layoutlmv3-finetuned-funsd"""
SCREAMING_SNAKE_CASE__ = 0.99_93
SCREAMING_SNAKE_CASE__ = pipeline("""object-detection""" , model=_lowercase , threshold=_lowercase )
SCREAMING_SNAKE_CASE__ = object_detector(
"""https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png""" )
self.assertEqual(
nested_simplify(_lowercase , decimals=4 ) , [
{"""score""": 0.99_93, """label""": """I-ANSWER""", """box""": {"""xmin""": 2_94, """ymin""": 2_54, """xmax""": 3_43, """ymax""": 2_64}},
{"""score""": 0.99_93, """label""": """I-ANSWER""", """box""": {"""xmin""": 2_94, """ymin""": 2_54, """xmax""": 3_43, """ymax""": 2_64}},
] , )
| 219 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
def __init__( self : int) -> str:
"""simple docstring"""
_UpperCAmelCase = []
def _lowerCamelCase ( self : Any , A : List[str]) -> int:
"""simple docstring"""
return self.node_position[vertex]
def _lowerCamelCase ( self : Optional[Any] , A : Optional[int] , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = pos
def _lowerCamelCase ( self : Tuple , A : Tuple , A : Dict , A : List[str] , A : Optional[Any]) -> Dict:
"""simple docstring"""
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_UpperCAmelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_UpperCAmelCase = 2 * start + 1
else:
_UpperCAmelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_UpperCAmelCase , _UpperCAmelCase = heap[smallest_child], positions[smallest_child]
_UpperCAmelCase , _UpperCAmelCase = (
heap[start],
positions[start],
)
_UpperCAmelCase , _UpperCAmelCase = temp, tempa
_UpperCAmelCase = self.get_position(positions[smallest_child])
self.set_position(
positions[smallest_child] , self.get_position(positions[start]))
self.set_position(positions[start] , A)
self.top_to_bottom(A , A , A , A)
def _lowerCamelCase ( self : Optional[int] , A : str , A : Optional[Any] , A : Optional[int] , A : str) -> Any:
"""simple docstring"""
_UpperCAmelCase = position[index]
while index != 0:
_UpperCAmelCase = int((index - 2) / 2) if index % 2 == 0 else int((index - 1) / 2)
if val < heap[parent]:
_UpperCAmelCase = heap[parent]
_UpperCAmelCase = position[parent]
self.set_position(position[parent] , A)
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , A)
break
_UpperCAmelCase = parent
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , 0)
def _lowerCamelCase ( self : Union[str, Any] , A : Optional[int] , A : Tuple) -> str:
"""simple docstring"""
_UpperCAmelCase = len(A) // 2 - 1
for i in range(A , -1 , -1):
self.top_to_bottom(A , A , len(A) , A)
def _lowerCamelCase ( self : Optional[int] , A : int , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = positions[0]
_UpperCAmelCase = sys.maxsize
self.top_to_bottom(A , 0 , len(A) , A)
return temp
def A ( _UpperCAmelCase : int ) -> Any:
'''simple docstring'''
_UpperCAmelCase = Heap()
_UpperCAmelCase = [0] * len(_UpperCAmelCase )
_UpperCAmelCase = [-1] * len(_UpperCAmelCase ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_UpperCAmelCase = [] # Heap of Distance of vertices from their neighboring vertex
_UpperCAmelCase = []
for vertex in range(len(_UpperCAmelCase ) ):
distance_tv.append(sys.maxsize )
positions.append(_UpperCAmelCase )
heap.node_position.append(_UpperCAmelCase )
_UpperCAmelCase = []
_UpperCAmelCase = 1
_UpperCAmelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_UpperCAmelCase = 0
_UpperCAmelCase = distance
heap.heapify(_UpperCAmelCase , _UpperCAmelCase )
for _ in range(1 , len(_UpperCAmelCase ) ):
_UpperCAmelCase = heap.delete_minimum(_UpperCAmelCase , _UpperCAmelCase )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_UpperCAmelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(_UpperCAmelCase )]
):
_UpperCAmelCase = distance
heap.bottom_to_top(
_UpperCAmelCase , heap.get_position(_UpperCAmelCase ) , _UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
UpperCAmelCase__ = int(input("Enter number of edges: ").strip())
UpperCAmelCase__ = defaultdict(list)
for _ in range(edges_number):
UpperCAmelCase__ = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 339 | 0 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
UpperCAmelCase_ : Optional[int] = 'Create a default config file for Accelerate with only a few flags set.'
def snake_case_ ( SCREAMING_SNAKE_CASE__="no" , SCREAMING_SNAKE_CASE__ = default_json_config_file , SCREAMING_SNAKE_CASE__ = False ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Optional[int] = Path(_UpperCAmelCase )
path.parent.mkdir(parents=_UpperCAmelCase , exist_ok=_UpperCAmelCase )
if path.exists():
print(
f"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.""" )
return False
_SCREAMING_SNAKE_CASE : Union[str, Any] = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
f"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}""" )
_SCREAMING_SNAKE_CASE : List[str] = {
"""compute_environment""": """LOCAL_MACHINE""",
"""mixed_precision""": mixed_precision,
}
if torch.cuda.is_available():
_SCREAMING_SNAKE_CASE : Optional[int] = torch.cuda.device_count()
_SCREAMING_SNAKE_CASE : Dict = num_gpus
_SCREAMING_SNAKE_CASE : Optional[int] = False
if num_gpus > 1:
_SCREAMING_SNAKE_CASE : Any = """MULTI_GPU"""
else:
_SCREAMING_SNAKE_CASE : int = """NO"""
elif is_xpu_available() and use_xpu:
_SCREAMING_SNAKE_CASE : Optional[int] = torch.xpu.device_count()
_SCREAMING_SNAKE_CASE : List[Any] = num_xpus
_SCREAMING_SNAKE_CASE : Optional[Any] = False
if num_xpus > 1:
_SCREAMING_SNAKE_CASE : int = """MULTI_XPU"""
else:
_SCREAMING_SNAKE_CASE : List[str] = """NO"""
elif is_npu_available():
_SCREAMING_SNAKE_CASE : Optional[int] = torch.npu.device_count()
_SCREAMING_SNAKE_CASE : List[str] = num_npus
_SCREAMING_SNAKE_CASE : Dict = False
if num_npus > 1:
_SCREAMING_SNAKE_CASE : List[Any] = """MULTI_NPU"""
else:
_SCREAMING_SNAKE_CASE : int = """NO"""
else:
_SCREAMING_SNAKE_CASE : List[str] = 0
_SCREAMING_SNAKE_CASE : List[str] = True
_SCREAMING_SNAKE_CASE : Tuple = 1
_SCREAMING_SNAKE_CASE : Optional[Any] = """NO"""
_SCREAMING_SNAKE_CASE : Dict = ClusterConfig(**_UpperCAmelCase )
config.to_json_file(_UpperCAmelCase )
return path
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : List[Any] = parser.add_parser("""default""" , parents=_UpperCAmelCase , help=_UpperCAmelCase , formatter_class=_UpperCAmelCase )
parser.add_argument(
"""--config_file""" , default=_UpperCAmelCase , help=(
"""The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
"""location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have """
"""such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed """
"""with \'huggingface\'."""
) , dest="""save_location""" , )
parser.add_argument(
"""--mixed_precision""" , choices=["""no""", """fp16""", """bf16"""] , type=_UpperCAmelCase , help="""Whether or not to use mixed precision training. """
"""Choose between FP16 and BF16 (bfloat16) training. """
"""BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.""" , default="""no""" , )
parser.set_defaults(func=_UpperCAmelCase )
return parser
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Union[str, Any] = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(f"""accelerate configuration saved at {config_file}""" )
| 200 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int]=5 ) -> List[Any]:
'''simple docstring'''
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('<mask>' ) == 1
_UpperCAmelCase = torch.tensor(tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) ).unsqueeze(0 ) # Batch size 1
_UpperCAmelCase = model(_UpperCAmelCase )[0] # The last hidden-state is the first element of the output tuple
_UpperCAmelCase = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
_UpperCAmelCase = logits[0, masked_index, :]
_UpperCAmelCase = logits.softmax(dim=0 )
_UpperCAmelCase , _UpperCAmelCase = prob.topk(k=_UpperCAmelCase , dim=0 )
_UpperCAmelCase = ' '.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(_UpperCAmelCase ) )] )
_UpperCAmelCase = tokenizer.mask_token
_UpperCAmelCase = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ' ) ):
_UpperCAmelCase = predicted_token_bpe.replace('\u2581' , ' ' )
if " {0}".format(_UpperCAmelCase ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(' {0}'.format(_UpperCAmelCase ) , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(_UpperCAmelCase , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
UpperCAmelCase__ = CamembertTokenizer.from_pretrained("camembert-base")
UpperCAmelCase__ = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
UpperCAmelCase__ = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 339 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
A_ :List[str] = logging.get_logger(__name__)
def A ( a_ ) -> List[str]:
__UpperCamelCase : str ='huggingface/label-files'
__UpperCamelCase : List[str] ='imagenet-1k-id2label.json'
__UpperCamelCase : Tuple =json.load(open(hf_hub_download(_UpperCAmelCase ,_UpperCAmelCase ,repo_type='dataset' ) ,'r' ) )
__UpperCamelCase : Union[str, Any] ={int(_UpperCAmelCase ): v for k, v in idalabel.items()}
__UpperCamelCase : Dict ={v: k for k, v in idalabel.items()}
__UpperCamelCase : Optional[int] ='std_conv' if 'bit' in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
__UpperCamelCase : Dict =BitConfig(
conv_layer=_UpperCAmelCase ,num_labels=1_000 ,idalabel=_UpperCAmelCase ,labelaid=_UpperCAmelCase ,)
return config
def A ( a_ ) -> str:
if "stem.conv" in name:
__UpperCamelCase : Optional[Any] =name.replace('stem.conv' ,'bit.embedder.convolution' )
if "blocks" in name:
__UpperCamelCase : Tuple =name.replace('blocks' ,'layers' )
if "head.fc" in name:
__UpperCamelCase : Optional[int] =name.replace('head.fc' ,'classifier.1' )
if name.startswith('norm' ):
__UpperCamelCase : int ='bit.' + name
if "bit" not in name and "classifier" not in name:
__UpperCamelCase : str ='bit.encoder.' + name
return name
def A ( ) -> int:
__UpperCamelCase : Any ='http://images.cocodataset.org/val2017/000000039769.jpg'
__UpperCamelCase : int =Image.open(requests.get(_UpperCAmelCase ,stream=_UpperCAmelCase ).raw )
return im
@torch.no_grad()
def A ( a_ ,a_ ,a_=False ) -> Tuple:
__UpperCamelCase : Any =get_config(_UpperCAmelCase )
# load original model from timm
__UpperCamelCase : Tuple =create_model(_UpperCAmelCase ,pretrained=_UpperCAmelCase )
timm_model.eval()
# load state_dict of original model
__UpperCamelCase : Optional[Any] =timm_model.state_dict()
for key in state_dict.copy().keys():
__UpperCamelCase : Optional[Any] =state_dict.pop(_UpperCAmelCase )
__UpperCamelCase : List[Any] =val.squeeze() if 'head' in key else val
# load HuggingFace model
__UpperCamelCase : Optional[int] =BitForImageClassification(_UpperCAmelCase )
model.eval()
model.load_state_dict(_UpperCAmelCase )
# create image processor
__UpperCamelCase : Any =create_transform(**resolve_data_config({} ,model=_UpperCAmelCase ) )
__UpperCamelCase : Tuple =transform.transforms
__UpperCamelCase : Any ={
'bilinear': PILImageResampling.BILINEAR,
'bicubic': PILImageResampling.BICUBIC,
'nearest': PILImageResampling.NEAREST,
}
__UpperCamelCase : int =BitImageProcessor(
do_resize=_UpperCAmelCase ,size={'shortest_edge': timm_transforms[0].size} ,resample=pillow_resamplings[timm_transforms[0].interpolation.value] ,do_center_crop=_UpperCAmelCase ,crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]} ,do_normalize=_UpperCAmelCase ,image_mean=timm_transforms[-1].mean.tolist() ,image_std=timm_transforms[-1].std.tolist() ,)
__UpperCamelCase : Tuple =prepare_img()
__UpperCamelCase : Any =transform(_UpperCAmelCase ).unsqueeze(0 )
__UpperCamelCase : List[Any] =processor(_UpperCAmelCase ,return_tensors='pt' ).pixel_values
# verify pixel values
assert torch.allclose(_UpperCAmelCase ,_UpperCAmelCase )
# verify logits
with torch.no_grad():
__UpperCamelCase : Tuple =model(_UpperCAmelCase )
__UpperCamelCase : Optional[Any] =outputs.logits
print('Logits:' ,logits[0, :3] )
print('Predicted class:' ,model.config.idalabel[logits.argmax(-1 ).item()] )
__UpperCamelCase : Tuple =timm_model(_UpperCAmelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_UpperCAmelCase ,outputs.logits ,atol=1e-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}' )
model.save_pretrained(_UpperCAmelCase )
processor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
print(F'Pushing model {model_name} and processor to the hub' )
model.push_to_hub(F'ybelkada/{model_name}' )
processor.push_to_hub(F'ybelkada/{model_name}' )
if __name__ == "__main__":
A_ :Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''resnetv2_50x1_bitm''',
type=str,
help='''Name of the BiT timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model to the hub.''',
)
A_ :int = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 71 |
import math
import unittest
def A ( _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Union[str, Any]:
"""simple docstring"""
self.assertTrue(is_prime(2))
self.assertTrue(is_prime(3))
self.assertTrue(is_prime(5))
self.assertTrue(is_prime(7))
self.assertTrue(is_prime(11))
self.assertTrue(is_prime(13))
self.assertTrue(is_prime(17))
self.assertTrue(is_prime(19))
self.assertTrue(is_prime(23))
self.assertTrue(is_prime(29))
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
with self.assertRaises(A):
is_prime(-19)
self.assertFalse(
is_prime(0) , 'Zero doesn\'t have any positive factors, primes must have exactly two.' , )
self.assertFalse(
is_prime(1) , 'One only has 1 positive factor, primes must have exactly two.' , )
self.assertFalse(is_prime(2 * 2))
self.assertFalse(is_prime(2 * 3))
self.assertFalse(is_prime(3 * 3))
self.assertFalse(is_prime(3 * 5))
self.assertFalse(is_prime(3 * 5 * 7))
if __name__ == "__main__":
unittest.main()
| 339 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class A__ ( A__ ):
def __init__( self : Dict ) -> Tuple:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =[]
def A ( self : str , _a : str , _a : Optional[int] , _a : List[Any] , **_a : str ) -> Optional[Any]:
'''simple docstring'''
self.events.append('on_init_end' )
def A ( self : str , _a : int , _a : Tuple , _a : List[str] , **_a : Union[str, Any] ) -> int:
'''simple docstring'''
self.events.append('on_train_begin' )
def A ( self : Dict , _a : Any , _a : Dict , _a : Optional[int] , **_a : str ) -> List[str]:
'''simple docstring'''
self.events.append('on_train_end' )
def A ( self : Union[str, Any] , _a : Union[str, Any] , _a : Optional[Any] , _a : Any , **_a : str ) -> Optional[int]:
'''simple docstring'''
self.events.append('on_epoch_begin' )
def A ( self : List[str] , _a : Optional[int] , _a : Optional[int] , _a : int , **_a : Optional[int] ) -> str:
'''simple docstring'''
self.events.append('on_epoch_end' )
def A ( self : Union[str, Any] , _a : Tuple , _a : Optional[int] , _a : Union[str, Any] , **_a : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
self.events.append('on_step_begin' )
def A ( self : Optional[int] , _a : Tuple , _a : List[str] , _a : int , **_a : str ) -> Dict:
'''simple docstring'''
self.events.append('on_step_end' )
def A ( self : Union[str, Any] , _a : Optional[Any] , _a : Tuple , _a : Optional[int] , **_a : Dict ) -> Tuple:
'''simple docstring'''
self.events.append('on_evaluate' )
def A ( self : Optional[int] , _a : List[str] , _a : str , _a : str , **_a : Optional[int] ) -> int:
'''simple docstring'''
self.events.append('on_predict' )
def A ( self : Optional[Any] , _a : List[str] , _a : List[str] , _a : Tuple , **_a : Any ) -> Union[str, Any]:
'''simple docstring'''
self.events.append('on_save' )
def A ( self : Any , _a : Union[str, Any] , _a : Any , _a : Dict , **_a : int ) -> Optional[int]:
'''simple docstring'''
self.events.append('on_log' )
def A ( self : Union[str, Any] , _a : Union[str, Any] , _a : List[Any] , _a : Dict , **_a : Optional[Any] ) -> List[Any]:
'''simple docstring'''
self.events.append('on_prediction_step' )
@require_torch
class A__ ( unittest.TestCase ):
def A ( self : Dict ) -> List[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =tempfile.mkdtemp()
def A ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
shutil.rmtree(self.output_dir )
def A ( self : Any , _a : int=0 , _a : Tuple=0 , _a : str=64 , _a : str=64 , _a : Optional[Any]=None , _a : Tuple=False , **_a : str ) -> Optional[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =RegressionDataset(length=_a )
_SCREAMING_SNAKE_CASE =RegressionDataset(length=_a )
_SCREAMING_SNAKE_CASE =RegressionModelConfig(a=_a , b=_a )
_SCREAMING_SNAKE_CASE =RegressionPreTrainedModel(_a )
_SCREAMING_SNAKE_CASE =TrainingArguments(self.output_dir , disable_tqdm=_a , report_to=[] , **_a )
return Trainer(
_a , _a , train_dataset=_a , eval_dataset=_a , callbacks=_a , )
def A ( self : List[Any] , _a : Union[str, Any] , _a : Dict ) -> Optional[int]:
'''simple docstring'''
self.assertEqual(len(_a ) , len(_a ) )
# Order doesn't matter
_SCREAMING_SNAKE_CASE =sorted(_a , key=lambda _a : cb.__name__ if isinstance(_a , _a ) else cb.__class__.__name__ )
_SCREAMING_SNAKE_CASE =sorted(_a , key=lambda _a : cb.__name__ if isinstance(_a , _a ) else cb.__class__.__name__ )
for cba, cba in zip(_a , _a ):
if isinstance(_a , _a ) and isinstance(_a , _a ):
self.assertEqual(_a , _a )
elif isinstance(_a , _a ) and not isinstance(_a , _a ):
self.assertEqual(_a , cba.__class__ )
elif not isinstance(_a , _a ) and isinstance(_a , _a ):
self.assertEqual(cba.__class__ , _a )
else:
self.assertEqual(_a , _a )
def A ( self : int , _a : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =['on_init_end', 'on_train_begin']
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =len(trainer.get_eval_dataloader() )
_SCREAMING_SNAKE_CASE =['on_prediction_step'] * len(trainer.get_eval_dataloader() ) + ['on_log', 'on_evaluate']
for _ in range(trainer.state.num_train_epochs ):
expected_events.append('on_epoch_begin' )
for _ in range(_a ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append('on_log' )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append('on_save' )
expected_events.append('on_epoch_end' )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def A ( self : Dict ) -> List[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.get_trainer()
_SCREAMING_SNAKE_CASE =DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
# Callbacks passed at init are added to the default callbacks
_SCREAMING_SNAKE_CASE =self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(_a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
_SCREAMING_SNAKE_CASE =self.get_trainer(disable_tqdm=_a )
_SCREAMING_SNAKE_CASE =DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
def A ( self : List[str] ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =DEFAULT_CALLBACKS.copy() + [ProgressCallback]
_SCREAMING_SNAKE_CASE =self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(_a )
expected_callbacks.remove(_a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
_SCREAMING_SNAKE_CASE =self.get_trainer()
_SCREAMING_SNAKE_CASE =trainer.pop_callback(_a )
self.assertEqual(cb.__class__ , _a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
trainer.add_callback(_a )
expected_callbacks.insert(0 , _a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
# We can also add, pop, or remove by instance
_SCREAMING_SNAKE_CASE =self.get_trainer()
_SCREAMING_SNAKE_CASE =trainer.callback_handler.callbacks[0]
trainer.remove_callback(_a )
expected_callbacks.remove(_a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
_SCREAMING_SNAKE_CASE =self.get_trainer()
_SCREAMING_SNAKE_CASE =trainer.callback_handler.callbacks[0]
_SCREAMING_SNAKE_CASE =trainer.pop_callback(_a )
self.assertEqual(_a , _a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
trainer.add_callback(_a )
expected_callbacks.insert(0 , _a )
self.check_callbacks_equality(trainer.callback_handler.callbacks , _a )
def A ( self : List[Any] ) -> Any:
'''simple docstring'''
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action='ignore' , category=_a )
_SCREAMING_SNAKE_CASE =self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
_SCREAMING_SNAKE_CASE =trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a , self.get_expected_events(_a ) )
# Independent log/save/eval
_SCREAMING_SNAKE_CASE =self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
_SCREAMING_SNAKE_CASE =trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a , self.get_expected_events(_a ) )
_SCREAMING_SNAKE_CASE =self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
_SCREAMING_SNAKE_CASE =trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a , self.get_expected_events(_a ) )
_SCREAMING_SNAKE_CASE =self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy='steps' )
trainer.train()
_SCREAMING_SNAKE_CASE =trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a , self.get_expected_events(_a ) )
_SCREAMING_SNAKE_CASE =self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy='epoch' )
trainer.train()
_SCREAMING_SNAKE_CASE =trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a , self.get_expected_events(_a ) )
# A bit of everything
_SCREAMING_SNAKE_CASE =self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=10 , eval_steps=5 , evaluation_strategy='steps' , )
trainer.train()
_SCREAMING_SNAKE_CASE =trainer.callback_handler.callbacks[-2].events
self.assertEqual(_a , self.get_expected_events(_a ) )
# warning should be emitted for duplicated callbacks
with patch('transformers.trainer_callback.logger.warning' ) as warn_mock:
_SCREAMING_SNAKE_CASE =self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(_a ) in warn_mock.call_args[0][0]
| 47 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase__ = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n"
UpperCAmelCase__ = "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"
UpperCAmelCase__ = "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n 'google_bleu': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.4\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : str) -> MetricInfo:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('string' , id='token') , id='sequence'),
'references': datasets.Sequence(
datasets.Sequence(datasets.Value('string' , id='token') , id='sequence') , id='references'),
}) , )
def _lowerCamelCase ( self : Union[str, Any] , A : List[List[List[str]]] , A : List[List[str]] , A : int = 1 , A : int = 4 , ) -> Dict[str, float]:
"""simple docstring"""
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=A , hypotheses=A , min_len=A , max_len=A)
}
| 339 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
return "\n".join(
f"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=1_0))
| 249 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
UpperCAmelCase__ = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
UpperCAmelCase__ = TaTokenizerFast
UpperCAmelCase__ = {"configuration_mt5": ["MT5Config", "MT5OnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"MT5EncoderModel",
"MT5ForConditionalGeneration",
"MT5ForQuestionAnswering",
"MT5Model",
"MT5PreTrainedModel",
"MT5Stack",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
UpperCAmelCase__ = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
module_spec=__spec__,
)
| 339 | 0 |
"""simple docstring"""
def lowercase (snake_case__ : Union[str, Any] , snake_case__ : List[str] , snake_case__ : Optional[int] , snake_case__ : Any , snake_case__ : List[Any] , snake_case__ : int ) -> str:
'''simple docstring'''
if index == r:
for j in range(_UpperCAmelCase ):
print(data[j] , end=""" """ )
print(""" """ )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
lowerCAmelCase = arr[i]
combination_util(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , index + 1 , _UpperCAmelCase , i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def lowercase (snake_case__ : List[str] , snake_case__ : Optional[int] , snake_case__ : List[Any] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , 0 , _UpperCAmelCase , 0 )
if __name__ == "__main__":
# Driver code to check the function above
a = [1_0, 2_0, 3_0, 4_0, 5_0]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 155 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class __lowerCAmelCase ( A ):
UpperCamelCase = '''open-llama'''
def __init__( self : str , A : List[Any]=10_00_00 , A : Tuple=40_96 , A : Tuple=1_10_08 , A : List[str]=32 , A : Tuple=32 , A : Optional[Any]="silu" , A : int=20_48 , A : Optional[Any]=0.0_2 , A : Dict=1E-6 , A : Optional[Any]=True , A : List[Any]=0 , A : Dict=1 , A : int=2 , A : Dict=False , A : Optional[int]=True , A : List[Any]=0.1 , A : str=0.1 , A : Dict=True , A : Optional[Any]=True , A : Dict=None , **A : Union[str, Any] , ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = vocab_size
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = hidden_size
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = initializer_range
_UpperCAmelCase = rms_norm_eps
_UpperCAmelCase = use_cache
_UpperCAmelCase = kwargs.pop(
'use_memorry_efficient_attention' , A)
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_dropout_prob
_UpperCAmelCase = use_stable_embedding
_UpperCAmelCase = shared_input_output_embedding
_UpperCAmelCase = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , tie_word_embeddings=A , **A , )
def _lowerCamelCase ( self : List[str]) -> Union[str, Any]:
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , A) or len(self.rope_scaling) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F"got {self.rope_scaling}")
_UpperCAmelCase = self.rope_scaling.get('type' , A)
_UpperCAmelCase = self.rope_scaling.get('factor' , A)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}")
if rope_scaling_factor is None or not isinstance(A , A) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
| 339 | 0 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
# TODO Update this
UpperCamelCase = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class snake_case_ ( __A ):
__A : Optional[Any] = "esm"
def __init__( self : Dict , lowercase_ : int=None , lowercase_ : Tuple=None , lowercase_ : str=None , lowercase_ : Dict=7_68 , lowercase_ : Optional[int]=12 , lowercase_ : Tuple=12 , lowercase_ : List[str]=30_72 , lowercase_ : int=0.1 , lowercase_ : Dict=0.1 , lowercase_ : Dict=10_26 , lowercase_ : Any=0.02 , lowercase_ : int=1E-12 , lowercase_ : Any="absolute" , lowercase_ : Any=True , lowercase_ : Any=None , lowercase_ : Optional[int]=False , lowercase_ : Optional[int]=False , lowercase_ : Tuple=None , lowercase_ : Union[str, Any]=None , **lowercase_ : Any , ) -> Dict:
super().__init__(pad_token_id=lowercase_ , mask_token_id=lowercase_ , **lowercase_ )
lowercase__ : Any = vocab_size
lowercase__ : str = hidden_size
lowercase__ : Tuple = num_hidden_layers
lowercase__ : str = num_attention_heads
lowercase__ : Optional[int] = intermediate_size
lowercase__ : Dict = hidden_dropout_prob
lowercase__ : str = attention_probs_dropout_prob
lowercase__ : List[Any] = max_position_embeddings
lowercase__ : Optional[Any] = initializer_range
lowercase__ : List[str] = layer_norm_eps
lowercase__ : Dict = position_embedding_type
lowercase__ : Any = use_cache
lowercase__ : Optional[Any] = emb_layer_norm_before
lowercase__ : Dict = token_dropout
lowercase__ : int = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("No esmfold_config supplied for folding model, using default values." )
lowercase__ : List[Any] = EsmFoldConfig()
elif isinstance(lowercase_ , lowercase_ ):
lowercase__ : str = EsmFoldConfig(**lowercase_ )
lowercase__ : str = esmfold_config
if vocab_list is None:
logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!" )
lowercase__ : Any = get_default_vocab_list()
else:
lowercase__ : List[str] = vocab_list
else:
lowercase__ : List[str] = None
lowercase__ : Union[str, Any] = None
if self.esmfold_config is not None and getattr(self.esmfold_config , "use_esm_attn_map" , lowercase_ ):
raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!" )
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]:
lowercase__ : int = super().to_dict()
if isinstance(self.esmfold_config , lowercase_ ):
lowercase__ : List[Any] = self.esmfold_config.to_dict()
return output
@dataclass
class snake_case_ :
__A : Tuple = None
__A : str = True
__A : Optional[int] = False
__A : str = False
__A : Dict = False
__A : Union[str, Any] = 0
__A : List[Any] = True
__A : List[Any] = False
__A : str = 128
__A : int = None
def __UpperCamelCase ( self : Optional[Any] ) -> List[str]:
if self.trunk is None:
lowercase__ : str = TrunkConfig()
elif isinstance(self.trunk , lowercase_ ):
lowercase__ : Optional[Any] = TrunkConfig(**self.trunk )
def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
lowercase__ : int = asdict(self )
lowercase__ : Any = self.trunk.to_dict()
return output
@dataclass
class snake_case_ :
__A : Union[str, Any] = 48
__A : Dict = 1024
__A : Dict = 128
__A : Dict = 32
__A : List[str] = 32
__A : List[Any] = 32
__A : Optional[int] = 0
__A : Optional[Any] = 0
__A : int = False
__A : str = 4
__A : Tuple = 128
__A : List[Any] = None
def __UpperCamelCase ( self : str ) -> List[str]:
if self.structure_module is None:
lowercase__ : Tuple = StructureModuleConfig()
elif isinstance(self.structure_module , lowercase_ ):
lowercase__ : List[str] = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
F''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
lowercase__ : int = self.sequence_state_dim // self.sequence_head_width
lowercase__ : str = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def __UpperCamelCase ( self : int ) -> int:
lowercase__ : Dict = asdict(self )
lowercase__ : Union[str, Any] = self.structure_module.to_dict()
return output
@dataclass
class snake_case_ :
__A : List[str] = 384
__A : str = 128
__A : Dict = 16
__A : Optional[Any] = 128
__A : Optional[Any] = 12
__A : int = 4
__A : Union[str, Any] = 8
__A : str = 0.1
__A : Dict = 8
__A : List[str] = 1
__A : Dict = 2
__A : Dict = 7
__A : Tuple = 10
__A : Optional[Any] = 1e-8
__A : List[str] = 1e5
def __UpperCamelCase ( self : Dict ) -> List[str]:
return asdict(self )
def lowercase_ ( ):
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 87 |
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = credit_card_number
_UpperCAmelCase = 0
_UpperCAmelCase = len(_UpperCAmelCase ) - 2
for i in range(_UpperCAmelCase , -1 , -2 ):
# double the value of every second digit
_UpperCAmelCase = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
_UpperCAmelCase = cc_number[:i] + str(_UpperCAmelCase ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(_UpperCAmelCase ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = F"{credit_card_number} is an invalid credit card number because"
if not credit_card_number.isdigit():
print(F"{error_message} it has nonnumerical characters." )
return False
if not 13 <= len(_UpperCAmelCase ) <= 16:
print(F"{error_message} of its length." )
return False
if not validate_initial_digits(_UpperCAmelCase ):
print(F"{error_message} of its first two digits." )
return False
if not luhn_validation(_UpperCAmelCase ):
print(F"{error_message} it fails the Luhn check." )
return False
print(F"{credit_card_number} is a valid credit card number." )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 339 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
)
from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST
class A :
'''simple docstring'''
def __init__(self , _UpperCAmelCase , _UpperCAmelCase=1_3 , _UpperCAmelCase=7 , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=9_9 , _UpperCAmelCase=3_2 , _UpperCAmelCase=5 , _UpperCAmelCase=4 , _UpperCAmelCase=3_7 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=1_2_8 , _UpperCAmelCase=3_2 , _UpperCAmelCase=1_6 , _UpperCAmelCase=2 , _UpperCAmelCase=0.02 , _UpperCAmelCase=3 , _UpperCAmelCase=4 , _UpperCAmelCase=None , ) -> str:
__UpperCamelCase : Union[str, Any] = parent
__UpperCamelCase : Optional[Any] = batch_size
__UpperCamelCase : List[str] = seq_length
__UpperCamelCase : int = is_training
__UpperCamelCase : Union[str, Any] = use_input_mask
__UpperCamelCase : Optional[int] = use_token_type_ids
__UpperCamelCase : Dict = use_labels
__UpperCamelCase : Tuple = vocab_size
__UpperCamelCase : Any = hidden_size
__UpperCamelCase : str = num_hidden_layers
__UpperCamelCase : List[str] = num_attention_heads
__UpperCamelCase : Any = intermediate_size
__UpperCamelCase : Any = hidden_act
__UpperCamelCase : Union[str, Any] = hidden_dropout_prob
__UpperCamelCase : Dict = attention_probs_dropout_prob
__UpperCamelCase : Optional[int] = max_position_embeddings
__UpperCamelCase : Union[str, Any] = type_vocab_size
__UpperCamelCase : Optional[Any] = type_sequence_label_size
__UpperCamelCase : str = initializer_range
__UpperCamelCase : Tuple = num_labels
__UpperCamelCase : Dict = num_choices
__UpperCamelCase : Any = scope
def a_ (self ) -> List[Any]:
__UpperCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase : Dict = None
if self.use_input_mask:
__UpperCamelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
__UpperCamelCase : List[Any] = None
if self.use_token_type_ids:
__UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCamelCase : Union[str, Any] = None
__UpperCamelCase : Tuple = None
__UpperCamelCase : str = None
if self.use_labels:
__UpperCamelCase : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.num_choices )
__UpperCamelCase : str = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ (self ) -> Tuple:
return NezhaConfig(
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=_UpperCAmelCase , initializer_range=self.initializer_range , )
def a_ (self ) -> List[Any]:
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) : Union[str, Any] = self.prepare_config_and_inputs()
__UpperCamelCase : List[Any] = True
__UpperCamelCase : Union[str, Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__UpperCamelCase : Dict = 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 , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> List[str]:
__UpperCamelCase : Union[str, Any] = NezhaModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__UpperCamelCase : str = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase )
__UpperCamelCase : Optional[int] = model(_UpperCAmelCase , token_type_ids=_UpperCAmelCase )
__UpperCamelCase : Optional[Any] = model(_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 , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ) -> Dict:
__UpperCamelCase : Optional[Any] = True
__UpperCamelCase : Dict = NezhaModel(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__UpperCamelCase : Tuple = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=_UpperCAmelCase , )
__UpperCamelCase : Optional[int] = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , )
__UpperCamelCase : str = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_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 , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Tuple:
__UpperCamelCase : Optional[Any] = NezhaForMaskedLM(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__UpperCamelCase : str = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Optional[Any]:
__UpperCamelCase : Union[str, Any] = NezhaForNextSentencePrediction(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__UpperCamelCase : List[Any] = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> List[str]:
__UpperCamelCase : str = NezhaForPreTraining(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__UpperCamelCase : List[str] = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase , next_sentence_label=_UpperCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Optional[int]:
__UpperCamelCase : int = NezhaForQuestionAnswering(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__UpperCamelCase : Dict = model(
_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 , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> int:
__UpperCamelCase : int = self.num_labels
__UpperCamelCase : Union[str, Any] = NezhaForSequenceClassification(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__UpperCamelCase : str = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Tuple:
__UpperCamelCase : int = self.num_labels
__UpperCamelCase : str = NezhaForTokenClassification(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__UpperCamelCase : Dict = model(_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 , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Any:
__UpperCamelCase : Tuple = self.num_choices
__UpperCamelCase : Tuple = NezhaForMultipleChoice(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__UpperCamelCase : List[Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__UpperCamelCase : Optional[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__UpperCamelCase : List[Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__UpperCamelCase : Any = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , labels=_UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ (self ) -> Dict:
__UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) : Optional[Any] = config_and_inputs
__UpperCamelCase : str = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class A ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
A = (
(
NezhaModel,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
)
if is_torch_available()
else ()
)
A = (
{
"feature-extraction": NezhaModel,
"fill-mask": NezhaForMaskedLM,
"question-answering": NezhaForQuestionAnswering,
"text-classification": NezhaForSequenceClassification,
"token-classification": NezhaForTokenClassification,
"zero-shot": NezhaForSequenceClassification,
}
if is_torch_available()
else {}
)
A = True
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=False ) -> List[str]:
__UpperCamelCase : List[str] = super()._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase , return_labels=_UpperCAmelCase )
if return_labels:
if model_class in get_values(_UpperCAmelCase ):
__UpperCamelCase : Optional[int] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=_UpperCAmelCase )
__UpperCamelCase : Any = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_UpperCAmelCase )
return inputs_dict
def a_ (self ) -> Any:
__UpperCamelCase : Dict = NezhaModelTester(self )
__UpperCamelCase : Optional[Any] = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=3_7 )
def a_ (self ) -> Dict:
self.config_tester.run_common_tests()
def a_ (self ) -> List[str]:
__UpperCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCAmelCase )
def a_ (self ) -> List[str]:
__UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*_UpperCAmelCase )
def a_ (self ) -> List[str]:
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) : List[str] = self.model_tester.prepare_config_and_inputs_for_decoder()
__UpperCamelCase : Tuple = None
self.model_tester.create_and_check_model_as_decoder(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
def a_ (self ) -> Dict:
__UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase )
def a_ (self ) -> Optional[Any]:
__UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase )
def a_ (self ) -> int:
__UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_next_sequence_prediction(*_UpperCAmelCase )
def a_ (self ) -> List[str]:
__UpperCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase )
def a_ (self ) -> List[Any]:
__UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase )
def a_ (self ) -> str:
__UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase )
def a_ (self ) -> Dict:
__UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase )
@slow
def a_ (self ) -> Any:
for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCamelCase : str = NezhaModel.from_pretrained(_UpperCAmelCase )
self.assertIsNotNone(_UpperCAmelCase )
@slow
@require_torch_gpu
def a_ (self ) -> str:
__UpperCamelCase , __UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# NezhaForMultipleChoice behaves incorrectly in JIT environments.
if model_class == NezhaForMultipleChoice:
return
__UpperCamelCase : Any = True
__UpperCamelCase : List[Any] = model_class(config=_UpperCAmelCase )
__UpperCamelCase : Optional[Any] = self._prepare_for_class(_UpperCAmelCase , _UpperCAmelCase )
__UpperCamelCase : Optional[int] = torch.jit.trace(
_UpperCAmelCase , (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_UpperCAmelCase , os.path.join(_UpperCAmelCase , "bert.pt" ) )
__UpperCamelCase : Union[str, Any] = torch.jit.load(os.path.join(_UpperCAmelCase , "bert.pt" ) , map_location=_UpperCAmelCase )
loaded(inputs_dict["input_ids"].to(_UpperCAmelCase ) , inputs_dict["attention_mask"].to(_UpperCAmelCase ) )
@require_torch
class A ( unittest.TestCase ):
'''simple docstring'''
@slow
def a_ (self ) -> Tuple:
__UpperCamelCase : Any = NezhaModel.from_pretrained("sijunhe/nezha-cn-base" )
__UpperCamelCase : List[str] = torch.tensor([[0, 1, 2, 3, 4, 5]] )
__UpperCamelCase : Union[str, Any] = torch.tensor([[0, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__UpperCamelCase : Any = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )[0]
__UpperCamelCase : Optional[int] = torch.Size((1, 6, 7_6_8) )
self.assertEqual(output.shape , _UpperCAmelCase )
__UpperCamelCase : Dict = torch.tensor([[[0.0_685, 0.2_441, 0.1_102], [0.0_600, 0.1_906, 0.1_349], [0.0_221, 0.0_819, 0.0_586]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _UpperCAmelCase , atol=1E-4 ) )
@slow
def a_ (self ) -> Tuple:
__UpperCamelCase : int = NezhaForMaskedLM.from_pretrained("sijunhe/nezha-cn-base" )
__UpperCamelCase : Union[str, Any] = torch.tensor([[0, 1, 2, 3, 4, 5]] )
__UpperCamelCase : Optional[Any] = torch.tensor([[1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__UpperCamelCase : List[Any] = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase )[0]
__UpperCamelCase : Optional[int] = torch.Size((1, 6, 2_1_1_2_8) )
self.assertEqual(output.shape , _UpperCAmelCase )
__UpperCamelCase : Optional[Any] = torch.tensor(
[[-2.7_939, -1.7_902, -2.2_189], [-2.8_585, -1.8_908, -2.3_723], [-2.6_499, -1.7_750, -2.2_558]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _UpperCAmelCase , atol=1E-4 ) )
| 298 |
from functools import reduce
UpperCAmelCase__ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def A ( _UpperCAmelCase : str = N ) -> int:
'''simple docstring'''
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda _UpperCAmelCase , _UpperCAmelCase : str(int(_UpperCAmelCase ) * int(_UpperCAmelCase ) ) , n[i : i + 13] ) )
for i in range(len(_UpperCAmelCase ) - 12 ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 0 |
def _lowerCamelCase( lowercase__ ) -> int:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
__lowercase= F'Input value of [number={number}] must be an integer'
raise TypeError(_UpperCAmelCase )
if number < 1:
__lowercase= F'Input value of [number={number}] must be > 0'
raise ValueError(_UpperCAmelCase )
__lowercase= 1
for i in range(1 , _UpperCAmelCase ):
current_number *= 4 * i - 2
current_number //= i + 1
return current_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 295 |
from __future__ import annotations
from collections.abc import Callable
UpperCAmelCase__ = list[list[float | int]]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : Matrix ) -> Matrix:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(size + 1 )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for row in range(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = matrix[row][col]
_UpperCAmelCase = vector[row][0]
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while row < size and col < size:
# pivoting
_UpperCAmelCase = max((abs(augmented[rowa][col] ), rowa) for rowa in range(_UpperCAmelCase , _UpperCAmelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
_UpperCAmelCase , _UpperCAmelCase = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , _UpperCAmelCase ):
_UpperCAmelCase = augmented[rowa][col] / augmented[row][col]
_UpperCAmelCase = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , _UpperCAmelCase ):
for row in range(_UpperCAmelCase ):
_UpperCAmelCase = augmented[row][col] / augmented[col][col]
for cola in range(_UpperCAmelCase , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(_UpperCAmelCase )
]
def A ( _UpperCAmelCase : list[int] ) -> Callable[[int], int]:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(_UpperCAmelCase )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = [[0] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for x_val, y_val in enumerate(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = (x_val + 1) ** (size - col - 1)
_UpperCAmelCase = y_val
_UpperCAmelCase = solve(_UpperCAmelCase , _UpperCAmelCase )
def interpolated_func(_UpperCAmelCase : int ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(_UpperCAmelCase ) )
return interpolated_func
def A ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def A ( _UpperCAmelCase : Callable[[int], int] = question_function , _UpperCAmelCase : int = 10 ) -> int:
'''simple docstring'''
_UpperCAmelCase = [func(_UpperCAmelCase ) for x_val in range(1 , order + 1 )]
_UpperCAmelCase = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
_UpperCAmelCase = 0
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for poly in polynomials:
_UpperCAmelCase = 1
while func(_UpperCAmelCase ) == poly(_UpperCAmelCase ):
x_val += 1
ret += poly(_UpperCAmelCase )
return ret
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 0 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : list[int] , lowercase : str ) -> list[int]:
_a = int(_UpperCAmelCase )
# Initialize Result
_a = []
# Traverse through all denomination
for denomination in reversed(_UpperCAmelCase ):
# Find denominations
while int(_UpperCAmelCase ) >= int(_UpperCAmelCase ):
total_value -= int(_UpperCAmelCase )
answer.append(_UpperCAmelCase ) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
lowerCAmelCase_ : Tuple = []
lowerCAmelCase_ : str = '0'
if (
input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
== "y"
):
lowerCAmelCase_ : Any = int(input('Enter the number of denominations you want to add: ').strip())
for i in range(0, n):
denominations.append(int(input(f"""Denomination {i}: """).strip()))
lowerCAmelCase_ : Tuple = input('Enter the change you want to make in Indian Currency: ').strip()
else:
# All denominations of Indian Currency if user does not enter
lowerCAmelCase_ : Union[str, Any] = [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
lowerCAmelCase_ : int = input('Enter the change you want to make: ').strip()
if int(value) == 0 or int(value) < 0:
print('The total value cannot be zero or negative.')
else:
print(f"""Following is minimal change for {value}: """)
lowerCAmelCase_ : Tuple = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=' ')
| 63 |
from __future__ import annotations
def A ( _UpperCAmelCase : list[int] ) -> bool:
'''simple docstring'''
return len(set(_UpperCAmelCase ) ) == len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 0 |
"""simple docstring"""
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
__magic_name__ = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
"large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}
def _lowerCAmelCase ( UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = ["""layers""", """blocks"""]
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase , _UpperCAmelCase )
__magic_name__ = {
"blocks": "layers",
"mlp.0": "fc1",
"mlp.2": "fc2",
"mlp_ln": "final_layer_norm",
".attn.query": ".self_attn.q_proj",
".attn.key": ".self_attn.k_proj",
".attn.value": ".self_attn.v_proj",
".attn_ln": ".self_attn_layer_norm",
".attn.out": ".self_attn.out_proj",
".cross_attn.query": ".encoder_attn.q_proj",
".cross_attn.key": ".encoder_attn.k_proj",
".cross_attn.value": ".encoder_attn.v_proj",
".cross_attn_ln": ".encoder_attn_layer_norm",
".cross_attn.out": ".encoder_attn.out_proj",
"decoder.ln.": "decoder.layer_norm.",
"encoder.ln.": "encoder.layer_norm.",
"token_embedding": "embed_tokens",
"encoder.positional_embedding": "encoder.embed_positions.weight",
"decoder.positional_embedding": "decoder.embed_positions.weight",
"ln_post": "layer_norm",
}
def _lowerCAmelCase ( UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = list(s_dict.keys() )
for key in keys:
__SCREAMING_SNAKE_CASE = key
for k, v in WHISPER_MAPPING.items():
if k in key:
__SCREAMING_SNAKE_CASE = new_key.replace(_UpperCAmelCase , _UpperCAmelCase )
print(f"{key} -> {new_key}" )
__SCREAMING_SNAKE_CASE = s_dict.pop(_UpperCAmelCase )
return s_dict
def _lowerCAmelCase ( UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = emb.weight.shape
__SCREAMING_SNAKE_CASE = nn.Linear(_UpperCAmelCase , _UpperCAmelCase , bias=_UpperCAmelCase )
__SCREAMING_SNAKE_CASE = emb.weight.data
return lin_layer
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
__SCREAMING_SNAKE_CASE = os.path.basename(_UpperCAmelCase )
__SCREAMING_SNAKE_CASE = url.split("""/""" )[-2]
__SCREAMING_SNAKE_CASE = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if os.path.exists(_UpperCAmelCase ) and not os.path.isfile(_UpperCAmelCase ):
raise RuntimeError(f"{download_target} exists and is not a regular file" )
if os.path.isfile(_UpperCAmelCase ):
__SCREAMING_SNAKE_CASE = open(_UpperCAmelCase , """rb""" ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" )
with urllib.request.urlopen(_UpperCAmelCase ) as source, open(_UpperCAmelCase , """wb""" ) as output:
with tqdm(
total=int(source.info().get("""Content-Length""" ) ) , ncols=80 , unit="""iB""" , unit_scale=_UpperCAmelCase , unit_divisor=1024 ) as loop:
while True:
__SCREAMING_SNAKE_CASE = source.read(8192 )
if not buffer:
break
output.write(_UpperCAmelCase )
loop.update(len(_UpperCAmelCase ) )
__SCREAMING_SNAKE_CASE = open(_UpperCAmelCase , """rb""" ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() != expected_shaaaa:
raise RuntimeError(
"""Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.""" )
return model_bytes
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
if ".pt" not in checkpoint_path:
__SCREAMING_SNAKE_CASE = _download(_MODELS[checkpoint_path] )
else:
__SCREAMING_SNAKE_CASE = torch.load(_UpperCAmelCase , map_location="""cpu""" )
__SCREAMING_SNAKE_CASE = original_checkpoint["""dims"""]
__SCREAMING_SNAKE_CASE = original_checkpoint["""model_state_dict"""]
__SCREAMING_SNAKE_CASE = state_dict["""decoder.token_embedding.weight"""]
remove_ignore_keys_(_UpperCAmelCase )
rename_keys(_UpperCAmelCase )
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = state_dict["""decoder.layers.0.fc1.weight"""].shape[0]
__SCREAMING_SNAKE_CASE = WhisperConfig(
vocab_size=dimensions["""n_vocab"""] , encoder_ffn_dim=_UpperCAmelCase , decoder_ffn_dim=_UpperCAmelCase , num_mel_bins=dimensions["""n_mels"""] , d_model=dimensions["""n_audio_state"""] , max_target_positions=dimensions["""n_text_ctx"""] , encoder_layers=dimensions["""n_audio_layer"""] , encoder_attention_heads=dimensions["""n_audio_head"""] , decoder_layers=dimensions["""n_text_layer"""] , decoder_attention_heads=dimensions["""n_text_state"""] , max_source_positions=dimensions["""n_audio_ctx"""] , )
__SCREAMING_SNAKE_CASE = WhisperForConditionalGeneration(_UpperCAmelCase )
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = model.model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0 and not set(_UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
"""Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,"""
f" but all the following weights are missing {missing}" )
if tie_embeds:
__SCREAMING_SNAKE_CASE = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
__SCREAMING_SNAKE_CASE = proj_out_weights
model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
# # Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
__magic_name__ = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 100 |
import os
UpperCAmelCase__ = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def A ( _UpperCAmelCase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(_UpperCAmelCase ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def A ( _UpperCAmelCase : int ) -> str:
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1_000
numerals += m_count * "M"
num %= 1_000
_UpperCAmelCase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
_UpperCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def A ( _UpperCAmelCase : str = "/p089_roman.txt" ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(_UpperCAmelCase ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(_UpperCAmelCase )
_UpperCAmelCase = generate_roman_numerals(_UpperCAmelCase )
savings += len(_UpperCAmelCase ) - len(_UpperCAmelCase )
return savings
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 0 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class __snake_case ( unittest.TestCase ):
@slow
def __a ( self : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small""" )
SCREAMING_SNAKE_CASE__ = AutoTokenizer.from_pretrained("""google/mt5-small""" )
SCREAMING_SNAKE_CASE__ = tokenizer("""Hello there""" , return_tensors="""np""" ).input_ids
SCREAMING_SNAKE_CASE__ = tokenizer("""Hi I am""" , return_tensors="""np""" ).input_ids
SCREAMING_SNAKE_CASE__ = shift_tokens_right(_lowercase , model.config.pad_token_id , model.config.decoder_start_token_id )
SCREAMING_SNAKE_CASE__ = model(_lowercase , decoder_input_ids=_lowercase ).logits
SCREAMING_SNAKE_CASE__ = optax.softmax_cross_entropy(_lowercase , onehot(_lowercase , logits.shape[-1] ) ).mean()
SCREAMING_SNAKE_CASE__ = -(labels.shape[-1] * loss.item())
SCREAMING_SNAKE_CASE__ = -84.91_27
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 219 |
import requests
from bsa import BeautifulSoup
def A ( _UpperCAmelCase : str , _UpperCAmelCase : dict ) -> str:
'''simple docstring'''
_UpperCAmelCase = BeautifulSoup(requests.get(_UpperCAmelCase , params=_UpperCAmelCase ).content , 'html.parser' )
_UpperCAmelCase = soup.find('div' , attrs={'class': 'gs_ri'} )
_UpperCAmelCase = div.find('div' , attrs={'class': 'gs_fl'} ).find_all('a' )
return anchors[2].get_text()
if __name__ == "__main__":
UpperCAmelCase__ = {
"title": (
"Precisely geometry controlled microsupercapacitors for ultrahigh areal "
"capacitance, volumetric capacitance, and energy density"
),
"journal": "Chem. Mater.",
"volume": 30,
"pages": "3979-3990",
"year": 2018,
"hl": "en",
}
print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
| 339 | 0 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Optional[Any] = BeautifulSoup(requests.get(_UpperCAmelCase , params=_UpperCAmelCase ).content , """html.parser""" )
_SCREAMING_SNAKE_CASE : Dict = soup.find("""div""" , attrs={"""class""": """gs_ri"""} )
_SCREAMING_SNAKE_CASE : Optional[Any] = div.find("""div""" , attrs={"""class""": """gs_fl"""} ).find_all("""a""" )
return anchors[2].get_text()
if __name__ == "__main__":
UpperCAmelCase_ : int = {
'title': (
'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
'capacitance, volumetric capacitance, and energy density'
),
'journal': 'Chem. Mater.',
'volume': 30,
'pages': '3979-3990',
'year': 2018,
'hl': 'en',
}
print(get_citation('https://scholar.google.com/scholar_lookup', params=params))
| 200 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self : Optional[Any] , A : Dict , A : Union[str, Any]=13 , A : Dict=7 , A : Dict=True , A : Tuple=True , A : Union[str, Any]=True , A : int=True , A : Optional[int]=99 , A : List[str]=32 , A : List[Any]=5 , A : int=4 , A : Any=37 , A : Optional[int]="gelu" , A : Optional[Any]=0.1 , A : Any=0.1 , A : Union[str, Any]=5_12 , A : int=16 , A : List[str]=2 , A : Union[str, Any]=0.0_2 , A : Union[str, Any]=4 , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_attention_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_choices
def _lowerCamelCase ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCAmelCase = None
if self.use_attention_mask:
_UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length])
_UpperCAmelCase = None
if self.use_token_type_ids:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
_UpperCAmelCase = RoFormerConfig(
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=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _lowerCamelCase ( self : List[Any]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class __lowerCAmelCase ( A , unittest.TestCase ):
UpperCamelCase = True
UpperCamelCase = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
_UpperCAmelCase = FlaxRoFormerModelTester(self)
@slow
def _lowerCamelCase ( self : List[Any]) -> Dict:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_UpperCAmelCase = model_class_name.from_pretrained('junnyu/roformer_chinese_small' , from_pt=A)
_UpperCAmelCase = model(np.ones((1, 1)))
self.assertIsNotNone(A)
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def _lowerCamelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base')
_UpperCAmelCase = jnp.array([[0, 1, 2, 3, 4, 5]])
_UpperCAmelCase = model(A)[0]
_UpperCAmelCase = 5_00_00
_UpperCAmelCase = (1, 6, vocab_size)
self.assertEqual(output.shape , A)
_UpperCAmelCase = jnp.array(
[[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]])
self.assertTrue(jnp.allclose(output[:, :3, :3] , A , atol=1E-4))
| 339 | 0 |
import unittest
import numpy as np
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
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class __A ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__=7 , lowerCamelCase__=3 , lowerCamelCase__=18 , lowerCamelCase__=30 , lowerCamelCase__=400 , lowerCamelCase__=True , lowerCamelCase__=None , lowerCamelCase__=True , lowerCamelCase__=None , lowerCamelCase__=True , lowerCamelCase__=[0.48_145_466, 0.4_578_275, 0.40_821_073] , lowerCamelCase__=[0.26_862_954, 0.26_130_258, 0.27_577_711] , lowerCamelCase__=True , ):
"""simple docstring"""
__UpperCamelCase : int =size if size is not None else {'height': 224, 'width': 224}
__UpperCamelCase : Optional[Any] =crop_size if crop_size is not None else {'height': 18, 'width': 18}
__UpperCamelCase : Optional[Any] =parent
__UpperCamelCase : List[str] =batch_size
__UpperCamelCase : Optional[Any] =num_channels
__UpperCamelCase : Any =image_size
__UpperCamelCase : Optional[Any] =min_resolution
__UpperCamelCase : Tuple =max_resolution
__UpperCamelCase : Tuple =do_resize
__UpperCamelCase : Optional[int] =size
__UpperCamelCase : Optional[int] =do_center_crop
__UpperCamelCase : Union[str, Any] =crop_size
__UpperCamelCase : Tuple =do_normalize
__UpperCamelCase : int =image_mean
__UpperCamelCase : Dict =image_std
__UpperCamelCase : List[str] =do_convert_rgb
def __lowercase ( self ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def __lowercase ( self , lowerCamelCase__=False , lowerCamelCase__=False , lowerCamelCase__=False ):
"""simple docstring"""
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
__UpperCamelCase : Union[str, Any] =[]
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
255 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta ) )
else:
__UpperCamelCase : Tuple =[]
for i in range(self.batch_size ):
__UpperCamelCase , __UpperCamelCase : Union[str, Any] =np.random.choice(np.arange(self.min_resolution , self.max_resolution ) , 2 )
image_inputs.append(np.random.randint(255 , size=(self.num_channels, width, height) , dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
__UpperCamelCase : Optional[int] =[Image.fromarray(np.moveaxis(lowerCamelCase__ , 0 , -1 ) ) for x in image_inputs]
if torchify:
__UpperCamelCase : Any =[torch.from_numpy(lowerCamelCase__ ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class __A ( a , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase__ : Any =ChineseCLIPImageProcessor if is_vision_available() else None
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Tuple =ChineseCLIPImageProcessingTester(self , do_center_crop=lowerCamelCase__ )
@property
def __lowercase ( self ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase__ , 'do_resize' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'size' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'do_center_crop' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'center_crop' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'do_normalize' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'image_mean' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'image_std' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'do_convert_rgb' ) )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] =self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 224, 'width': 224} )
self.assertEqual(image_processor.crop_size , {'height': 18, 'width': 18} )
__UpperCamelCase : Dict =self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'shortest_edge': 42} )
self.assertEqual(image_processor.crop_size , {'height': 84, 'width': 84} )
def __lowercase ( self ):
"""simple docstring"""
pass
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : int =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__UpperCamelCase : str =self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ , Image.Image )
# Test not batched input
__UpperCamelCase : Dict =image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
__UpperCamelCase : List[str] =image_processing(lowerCamelCase__ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Any =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__UpperCamelCase : Optional[Any] =self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__ , numpify=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ , np.ndarray )
# Test not batched input
__UpperCamelCase : List[str] =image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
__UpperCamelCase : Any =image_processing(lowerCamelCase__ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Tuple =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__UpperCamelCase : Union[str, Any] =self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__ , torchify=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ , torch.Tensor )
# Test not batched input
__UpperCamelCase : Any =image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
__UpperCamelCase : Any =image_processing(lowerCamelCase__ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
@require_torch
@require_vision
class __A ( a , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase__ : Any =ChineseCLIPImageProcessor if is_vision_available() else None
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : int =ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=lowerCamelCase__ )
__UpperCamelCase : int =3
@property
def __lowercase ( self ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[int] =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase__ , 'do_resize' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'size' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'do_center_crop' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'center_crop' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'do_normalize' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'image_mean' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'image_std' ) )
self.assertTrue(hasattr(lowerCamelCase__ , 'do_convert_rgb' ) )
def __lowercase ( self ):
"""simple docstring"""
pass
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Tuple =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__UpperCamelCase : Union[str, Any] =self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ , Image.Image )
# Test not batched input
__UpperCamelCase : List[str] =image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
__UpperCamelCase : List[Any] =image_processing(lowerCamelCase__ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
| 71 |
UpperCAmelCase__ = {}
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> 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 = (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 = _calculate(days - 1 , _UpperCAmelCase , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
_UpperCAmelCase = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
_UpperCAmelCase = _calculate(days - 1 , _UpperCAmelCase , 0 )
_UpperCAmelCase = state_late + state_absent + state_ontime
_UpperCAmelCase = prizestrings
return prizestrings
def A ( _UpperCAmelCase : int = 30 ) -> int:
'''simple docstring'''
return _calculate(_UpperCAmelCase , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 339 | 0 |
'''simple docstring'''
import itertools
import string
from collections.abc import Generator, Iterable
def _lowerCAmelCase ( _UpperCamelCase : Iterable[str] , _UpperCamelCase : int ) -> Generator[tuple[str, ...], None, None]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =iter(_UpperCAmelCase )
while True:
_SCREAMING_SNAKE_CASE =tuple(itertools.islice(_UpperCAmelCase , _UpperCAmelCase ) )
if not chunk:
return
yield chunk
def _lowerCAmelCase ( _UpperCamelCase : str ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =''.join([c.upper() for c in dirty if c in string.ascii_letters] )
_SCREAMING_SNAKE_CASE =''
if len(_UpperCAmelCase ) < 2:
return dirty
for i in range(len(_UpperCAmelCase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_UpperCAmelCase ) & 1:
clean += "X"
return clean
def _lowerCAmelCase ( _UpperCamelCase : str ) -> list[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE ='ABCDEFGHIKLMNOPQRSTUVWXYZ'
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
_SCREAMING_SNAKE_CASE =[]
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_UpperCAmelCase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_UpperCAmelCase )
return table
def _lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : str ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =generate_table(_UpperCAmelCase )
_SCREAMING_SNAKE_CASE =prepare_input(_UpperCAmelCase )
_SCREAMING_SNAKE_CASE =''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_UpperCAmelCase , 2 ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =divmod(table.index(_UpperCAmelCase ) , 5 )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =divmod(table.index(_UpperCAmelCase ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def _lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : str ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =generate_table(_UpperCAmelCase )
_SCREAMING_SNAKE_CASE =''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_UpperCAmelCase , 2 ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =divmod(table.index(_UpperCAmelCase ) , 5 )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =divmod(table.index(_UpperCAmelCase ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 47 |
import os
import sys
import unittest
UpperCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCAmelCase__ = os.path.join(git_repo_path, "src", "diffusers")
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = find_backend(' if not is_torch_available():')
self.assertEqual(A , 'torch')
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
_UpperCAmelCase = find_backend(' if not (is_torch_available() and is_transformers_available()):')
self.assertEqual(A , 'torch_and_transformers')
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
_UpperCAmelCase = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):')
self.assertEqual(A , 'torch_and_transformers_and_onnx')
def _lowerCamelCase ( self : int) -> Dict:
"""simple docstring"""
_UpperCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , A)
self.assertIn('torch_and_transformers' , A)
self.assertIn('flax_and_transformers' , A)
self.assertIn('torch_and_transformers_and_onnx' , A)
# Likewise, we can't assert on the exact content of a key
self.assertIn('UNet2DModel' , objects['torch'])
self.assertIn('FlaxUNet2DConditionModel' , objects['flax'])
self.assertIn('StableDiffusionPipeline' , objects['torch_and_transformers'])
self.assertIn('FlaxStableDiffusionPipeline' , objects['flax_and_transformers'])
self.assertIn('LMSDiscreteScheduler' , objects['torch_and_scipy'])
self.assertIn('OnnxStableDiffusionPipeline' , objects['torch_and_transformers_and_onnx'])
def _lowerCamelCase ( self : Union[str, Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = create_dummy_object('CONSTANT' , '\'torch\'')
self.assertEqual(A , '\nCONSTANT = None\n')
_UpperCAmelCase = create_dummy_object('function' , '\'torch\'')
self.assertEqual(
A , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n')
_UpperCAmelCase = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n'
_UpperCAmelCase = create_dummy_object('FakeClass' , '\'torch\'')
self.assertEqual(A , A)
def _lowerCamelCase ( self : Dict) -> int:
"""simple docstring"""
_UpperCAmelCase = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n'
_UpperCAmelCase = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']})
self.assertEqual(dummy_files['torch'] , A)
| 339 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase ):
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''') )
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : List[Any] = credit_card_number
__lowercase : List[str] = 0
__lowercase : str = len(_UpperCAmelCase ) - 2
for i in range(_UpperCAmelCase , -1 , -2 ):
# double the value of every second digit
__lowercase : Optional[int] = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
__lowercase : List[str] = cc_number[:i] + str(_UpperCAmelCase ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(_UpperCAmelCase ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : Dict = f"""{credit_card_number} is an invalid credit card number because"""
if not credit_card_number.isdigit():
print(f"""{error_message} it has nonnumerical characters.""" )
return False
if not 13 <= len(_UpperCAmelCase ) <= 16:
print(f"""{error_message} of its length.""" )
return False
if not validate_initial_digits(_UpperCAmelCase ):
print(f"""{error_message} of its first two digits.""" )
return False
if not luhn_validation(_UpperCAmelCase ):
print(f"""{error_message} it fails the Luhn check.""" )
return False
print(f"""{credit_card_number} is a valid credit card number.""" )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number('4111111111111111')
validate_credit_card_number('32323')
| 249 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
UpperCAmelCase__ = logging.getLogger(__name__)
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} , )
UpperCamelCase = field(
default=1_0_2_4 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
UpperCamelCase = field(
default=A , 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.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the training data.'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
UpperCamelCase = field(default=A , metadata={'''help''': '''A csv or a json file containing the test data.'''} )
def _lowerCamelCase ( self : str) -> List[Any]:
"""simple docstring"""
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.')
else:
_UpperCAmelCase = self.train_file.split('.')[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
_UpperCAmelCase = self.validation_file.split('.')[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default=A , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCamelCase = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
def A ( ) -> Optional[int]:
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCAmelCase = 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 , _UpperCAmelCase , _UpperCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_args_into_dataclasses()
# 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 )] , )
_UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(_UpperCAmelCase )
datasets.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
_UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase = 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 and training_args.resume_from_checkpoint is 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.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. 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 = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
_UpperCAmelCase = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
_UpperCAmelCase = data_args.train_file.split('.' )[-1]
_UpperCAmelCase = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
_UpperCAmelCase = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(F"load a local file for {key}: {data_files[key]}" )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
_UpperCAmelCase = load_dataset('csv' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
_UpperCAmelCase = load_dataset('json' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
_UpperCAmelCase = raw_datasets['train'].features['label'].names
_UpperCAmelCase = len(_UpperCAmelCase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
_UpperCAmelCase = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , 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 , add_prefix_space=_UpperCAmelCase , )
_UpperCAmelCase = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
_UpperCAmelCase = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
_UpperCAmelCase = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
_UpperCAmelCase = {'Refused': 0, 'Entailed': 1}
_UpperCAmelCase = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
F"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." )
_UpperCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(_UpperCAmelCase : Union[str, Any] ):
# Tokenize the texts
def _convert_table_text_to_pandas(_UpperCAmelCase : Dict ):
_UpperCAmelCase = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
_UpperCAmelCase = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
_UpperCAmelCase = examples['statement']
_UpperCAmelCase = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
_UpperCAmelCase = tokenizer(_UpperCAmelCase , _UpperCAmelCase , padding=_UpperCAmelCase , max_length=_UpperCAmelCase , truncation=_UpperCAmelCase )
_UpperCAmelCase = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
_UpperCAmelCase = raw_datasets.map(
_UpperCAmelCase , batched=_UpperCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
_UpperCAmelCase = raw_datasets['train']
if data_args.max_train_samples is not None:
_UpperCAmelCase = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
_UpperCAmelCase = raw_datasets['validation']
if data_args.max_eval_samples is not None:
_UpperCAmelCase = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
_UpperCAmelCase = raw_datasets['test']
if data_args.max_predict_samples is not None:
_UpperCAmelCase = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_UpperCAmelCase ) ) , 3 ):
logger.info(F"Sample {index} of the training set: {train_dataset[index]}." )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_UpperCAmelCase : EvalPrediction ):
_UpperCAmelCase = p.predictions[0] if isinstance(p.predictions , _UpperCAmelCase ) else p.predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
_UpperCAmelCase = default_data_collator
elif training_args.fpaa:
_UpperCAmelCase = DataCollatorWithPadding(_UpperCAmelCase , pad_to_multiple_of=8 )
else:
_UpperCAmelCase = None
# Initialize our Trainer
_UpperCAmelCase = Trainer(
model=_UpperCAmelCase , args=_UpperCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_UpperCAmelCase , tokenizer=_UpperCAmelCase , data_collator=_UpperCAmelCase , )
# Training
if training_args.do_train:
_UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase = last_checkpoint
_UpperCAmelCase = trainer.train(resume_from_checkpoint=_UpperCAmelCase )
_UpperCAmelCase = train_result.metrics
_UpperCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCAmelCase )
)
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _UpperCAmelCase )
trainer.save_metrics('train' , _UpperCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
_UpperCAmelCase = trainer.evaluate(eval_dataset=_UpperCAmelCase )
_UpperCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCAmelCase )
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.log_metrics('eval' , _UpperCAmelCase )
trainer.save_metrics('eval' , _UpperCAmelCase )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
_UpperCAmelCase = predict_dataset.remove_columns('label' )
_UpperCAmelCase = trainer.predict(_UpperCAmelCase , metric_key_prefix='predict' ).predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
_UpperCAmelCase = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(_UpperCAmelCase , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(_UpperCAmelCase ):
_UpperCAmelCase = label_list[item]
writer.write(F"{index}\t{item}\n" )
_UpperCAmelCase = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**_UpperCAmelCase )
else:
trainer.create_model_card(**_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 339 | 0 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase (snake_case__ : List[Any] , snake_case__ : Optional[int] , snake_case__ : Union[str, Any]=None ) -> Optional[Any]:
'''simple docstring'''
assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
lowerCAmelCase = nn.Parameter(_UpperCAmelCase )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
lowerCAmelCase = nn.Parameter(_UpperCAmelCase )
def lowercase (snake_case__ : Optional[Any] , snake_case__ : Dict , snake_case__ : str ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase = np.asarray(weights[0] )
lowerCAmelCase = np.asarray(weights[1] )
lowerCAmelCase = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(_UpperCAmelCase ).transpose(1 , 2 ).contiguous().view(-1 , _UpperCAmelCase ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(_UpperCAmelCase ).transpose(1 , 2 ).contiguous().view(-1 , _UpperCAmelCase ) , )
set_param(
torch_layer.output.dense , torch.tensor(_UpperCAmelCase ).view(-1 , _UpperCAmelCase ).contiguous().transpose(0 , 1 ) , )
def lowercase (snake_case__ : List[str] , snake_case__ : Union[str, Any] , snake_case__ : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase = np.asarray(weights[0] )
lowerCAmelCase = np.asarray(weights[1] )
lowerCAmelCase = np.asarray(weights[2] )
lowerCAmelCase = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(_UpperCAmelCase ).transpose(1 , 2 ).contiguous().view(-1 , _UpperCAmelCase ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(_UpperCAmelCase ).transpose(1 , 2 ).contiguous().view(-1 , _UpperCAmelCase ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(_UpperCAmelCase ).transpose(1 , 2 ).contiguous().view(-1 , _UpperCAmelCase ) , )
set_param(
torch_layer.output.dense , torch.tensor(_UpperCAmelCase ).view(-1 , _UpperCAmelCase ).contiguous().transpose(0 , 1 ) , )
def lowercase (snake_case__ : Dict , snake_case__ : List[Any] , snake_case__ : Dict ) -> Any:
'''simple docstring'''
lowerCAmelCase = weights[0][0][0]
lowerCAmelCase = np.asarray(layer_norm_a[0] )
lowerCAmelCase = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(_UpperCAmelCase ) , torch.tensor(_UpperCAmelCase ) , )
# lsh weights + output
lowerCAmelCase = weights[0][1]
if len(_UpperCAmelCase ) < 4:
set_layer_weights_in_torch_lsh(_UpperCAmelCase , torch_block.attention , _UpperCAmelCase )
else:
set_layer_weights_in_torch_local(_UpperCAmelCase , torch_block.attention , _UpperCAmelCase )
# intermediate weighs
lowerCAmelCase = weights[2][0][1][2]
# Chunked Feed Forward
if len(_UpperCAmelCase ) == 4:
lowerCAmelCase = intermediate_weights[2]
# layernorm 2
lowerCAmelCase = np.asarray(intermediate_weights[0][0] )
lowerCAmelCase = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(_UpperCAmelCase ) , torch.tensor(_UpperCAmelCase ) , )
# intermediate dense
lowerCAmelCase = np.asarray(intermediate_weights[1][0] )
lowerCAmelCase = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(_UpperCAmelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(_UpperCAmelCase ) , )
# intermediate out
lowerCAmelCase = np.asarray(intermediate_weights[4][0] )
lowerCAmelCase = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(_UpperCAmelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(_UpperCAmelCase ) , )
def lowercase (snake_case__ : Any , snake_case__ : Dict , snake_case__ : List[str] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase = torch_model.reformer
# word embeds
lowerCAmelCase = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(_UpperCAmelCase ) , )
if isinstance(weights[3] , _UpperCAmelCase ):
lowerCAmelCase = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
lowerCAmelCase = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), f'''{position_embeddings[emb_idx]} emb does not match'''
lowerCAmelCase = nn.Parameter(torch.tensor(_UpperCAmelCase ) )
lowerCAmelCase = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
_UpperCAmelCase ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
lowerCAmelCase = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# output layer norm
lowerCAmelCase = np.asarray(weights[7][0] )
lowerCAmelCase = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(_UpperCAmelCase ) , torch.tensor(_UpperCAmelCase ) , )
# output embeddings
lowerCAmelCase = np.asarray(weights[9][0] )
lowerCAmelCase = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(_UpperCAmelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(_UpperCAmelCase ) , )
def lowercase (snake_case__ : Union[str, Any] , snake_case__ : Dict , snake_case__ : Tuple ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase = ReformerConfig.from_json_file(_UpperCAmelCase )
print(f'''Building PyTorch model from configuration: {config}''' )
lowerCAmelCase = ReformerModelWithLMHead(_UpperCAmelCase )
with open(_UpperCAmelCase , """rb""" ) as f:
lowerCAmelCase = pickle.load(_UpperCAmelCase )["""weights"""]
set_model_weights_in_torch(_UpperCAmelCase , _UpperCAmelCase , config.hidden_size )
# Save pytorch-model
print(f'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , _UpperCAmelCase )
if __name__ == "__main__":
a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--trax_model_pkl_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained Reformer 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 = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 155 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def A ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict ) -> Any:
'''simple docstring'''
_UpperCAmelCase = multiprocessing.Manager()
_UpperCAmelCase = manager.list()
_UpperCAmelCase = multiprocessing.Process(target=_UpperCAmelCase , 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 ( _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
_UpperCAmelCase = shutil.rmtree
_UpperCAmelCase = os.rmdir
_UpperCAmelCase = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
_UpperCAmelCase = {}
with swallow_io():
with time_limit(_UpperCAmelCase ):
exec(_UpperCAmelCase , _UpperCAmelCase )
result.append('passed' )
except TimeoutException:
result.append('timed out' )
except BaseException as e:
result.append(F"failed: {e}" )
# Needed for cleaning up.
_UpperCAmelCase = rmtree
_UpperCAmelCase = rmdir
_UpperCAmelCase = chdir
@contextlib.contextmanager
def A ( _UpperCAmelCase : Union[str, Any] ) -> Any:
'''simple docstring'''
def signal_handler(_UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict ):
raise TimeoutException('Timed out!' )
signal.setitimer(signal.ITIMER_REAL , _UpperCAmelCase )
signal.signal(signal.SIGALRM , _UpperCAmelCase )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def A ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = WriteOnlyStringIO()
with contextlib.redirect_stdout(_UpperCAmelCase ):
with contextlib.redirect_stderr(_UpperCAmelCase ):
with redirect_stdin(_UpperCAmelCase ):
yield
@contextlib.contextmanager
def A ( ) -> Any:
'''simple docstring'''
with tempfile.TemporaryDirectory() as dirname:
with chdir(_UpperCAmelCase ):
yield dirname
class __lowerCAmelCase ( A ):
pass
class __lowerCAmelCase ( io.StringIO ):
def _lowerCamelCase ( self : Tuple , *A : str , **A : Any) -> Any:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : List[str] , *A : Optional[Any] , **A : Optional[Any]) -> Optional[int]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : str , *A : List[str] , **A : List[Any]) -> Union[str, Any]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : Union[str, Any] , *A : Optional[Any] , **A : List[str]) -> Optional[int]:
"""simple docstring"""
return False
class __lowerCAmelCase ( contextlib._RedirectStream ): # type: ignore
UpperCamelCase = '''stdin'''
@contextlib.contextmanager
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
if root == ".":
yield
return
_UpperCAmelCase = os.getcwd()
os.chdir(_UpperCAmelCase )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str]=None ) -> Any:
'''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 = None
_UpperCAmelCase = None
import os
_UpperCAmelCase = '1'
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import shutil
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import subprocess
_UpperCAmelCase = None # type: ignore
_UpperCAmelCase = None
import sys
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
| 339 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class snake_case_ ( unittest.TestCase ):
def __UpperCamelCase ( self : Optional[int] ) -> Tuple:
lowercase__ : Optional[Any] = inspect.getfile(accelerate.test_utils )
lowercase__ : Tuple = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_script.py"] )
lowercase__ : Any = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ["scripts", "test_distributed_data_loop.py"] )
lowercase__ : Any = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_ops.py"] )
@require_multi_gpu
def __UpperCamelCase ( self : Tuple ) -> List[str]:
print(F'''Found {torch.cuda.device_count()} devices.''' )
lowercase__ : str = ["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(lowercase_ , env=os.environ.copy() )
@require_multi_gpu
def __UpperCamelCase ( self : Union[str, Any] ) -> List[Any]:
print(F'''Found {torch.cuda.device_count()} devices.''' )
lowercase__ : str = ["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
print(F'''Command: {cmd}''' )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(lowercase_ , env=os.environ.copy() )
@require_multi_gpu
def __UpperCamelCase ( self : List[Any] ) -> Dict:
lowercase__ : Dict = ["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(lowercase_ , env=os.environ.copy() )
@require_multi_gpu
def __UpperCamelCase ( self : Any ) -> Tuple:
print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only''' )
lowercase__ : Any = ["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
with patch_environment(omp_num_threads=1 , cuda_visible_devices="0,1" ):
execute_subprocess_async(lowercase_ , env=os.environ.copy() )
if __name__ == "__main__":
UpperCamelCase = Accelerator()
UpperCamelCase = (accelerator.state.process_index + 2, 10)
UpperCamelCase = torch.randint(0, 10, shape).to(accelerator.device)
UpperCamelCase = ''''''
UpperCamelCase = accelerator.pad_across_processes(tensor)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
error_msg += "Padding was not done with the right value (0)."
UpperCamelCase = accelerator.pad_across_processes(tensor, pad_first=True)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
UpperCamelCase = accelerator.state.num_processes - accelerator.state.process_index - 1
if not torch.equal(tensora[index:], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[:index] == 0):
error_msg += "Padding was not done with the right value (0)."
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 87 |
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 ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any]=False ) -> str:
'''simple docstring'''
try:
_UpperCAmelCase = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_UpperCAmelCase = default
else:
# KEY is set, convert it to True or False.
try:
_UpperCAmelCase = strtobool(_UpperCAmelCase )
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
UpperCAmelCase__ = parse_flag_from_env("RUN_SLOW", default=False)
def A ( _UpperCAmelCase : List[str] ) -> List[str]:
'''simple docstring'''
return unittest.skip('Test was skipped' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> str:
'''simple docstring'''
return unittest.skipUnless(_run_slow_tests , 'test is slow' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> str:
'''simple docstring'''
return unittest.skipUnless(not torch.cuda.is_available() , 'test requires only a CPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.is_available() , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_xpu_available() , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> List[str]:
'''simple docstring'''
return unittest.skipUnless(is_mps_available() , 'test requires a `mps` backend support in `torch`' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , 'test requires the Hugging Face suite' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : str ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_bnb_available() , 'test requires the bitsandbytes library' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_tpu_available() , 'test requires TPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> str:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() == 1 , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> int:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() == 1 , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() > 1 , 'test requires multiple GPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> Any:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() > 1 , 'test requires multiple XPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_safetensors_available() , 'test requires safetensors' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(is_deepspeed_available() , 'test requires DeepSpeed' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_torch_version('>=' , '1.12.0' ) , 'test requires torch version >= 1.12.0' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any=None , _UpperCAmelCase : List[Any]=None ) -> Dict:
'''simple docstring'''
if test_case is None:
return partial(_UpperCAmelCase , version=_UpperCAmelCase )
return unittest.skipUnless(is_torch_version('>=' , _UpperCAmelCase ) , F"test requires torch version >= {version}" )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> int:
'''simple docstring'''
return unittest.skipUnless(is_tensorboard_available() , 'test requires Tensorboard' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return unittest.skipUnless(is_wandb_available() , 'test requires wandb' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_comet_ml_available() , 'test requires comet_ml' )(_UpperCAmelCase )
UpperCAmelCase__ = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def A ( _UpperCAmelCase : List[str] ) -> Any:
'''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' , )(_UpperCAmelCase )
class __lowerCAmelCase ( unittest.TestCase ):
UpperCamelCase = True
@classmethod
def _lowerCamelCase ( cls : List[Any]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = tempfile.mkdtemp()
@classmethod
def _lowerCamelCase ( cls : Union[str, Any]) -> str:
"""simple docstring"""
if os.path.exists(cls.tmpdir):
shutil.rmtree(cls.tmpdir)
def _lowerCamelCase ( self : List[str]) -> List[Any]:
"""simple docstring"""
if self.clear_on_setup:
for path in Path(self.tmpdir).glob('**/*'):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(A)
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Dict) -> Tuple:
"""simple docstring"""
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[int] , A : Union[mock.Mock, List[mock.Mock]]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = mocks if isinstance(A , (tuple, list)) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop)
def A ( _UpperCAmelCase : List[Any] ) -> int:
'''simple docstring'''
_UpperCAmelCase = AcceleratorState()
_UpperCAmelCase = tensor[None].clone().to(state.device )
_UpperCAmelCase = gather(_UpperCAmelCase ).cpu()
_UpperCAmelCase = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , _UpperCAmelCase ):
return False
return True
class __lowerCAmelCase :
def __init__( self : Optional[Any] , A : Union[str, Any] , A : Optional[int] , A : str) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = returncode
_UpperCAmelCase = stdout
_UpperCAmelCase = stderr
async def A ( _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
while True:
_UpperCAmelCase = await stream.readline()
if line:
callback(_UpperCAmelCase )
else:
break
async def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=None , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Union[str, Any]=False ) -> _RunOutput:
'''simple docstring'''
if echo:
print('\nRunning: ' , ' '.join(_UpperCAmelCase ) )
_UpperCAmelCase = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=_UpperCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_UpperCAmelCase , )
# 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 = []
_UpperCAmelCase = []
def tee(_UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : str="" ):
_UpperCAmelCase = line.decode('utf-8' ).rstrip()
sink.append(_UpperCAmelCase )
if not quiet:
print(_UpperCAmelCase , _UpperCAmelCase , file=_UpperCAmelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stdout , label='stdout:' ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stderr , label='stderr:' ) ) ),
] , timeout=_UpperCAmelCase , )
return _RunOutput(await p.wait() , _UpperCAmelCase , _UpperCAmelCase )
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Dict=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=180 , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : List[Any]=True ) -> _RunOutput:
'''simple docstring'''
_UpperCAmelCase = asyncio.get_event_loop()
_UpperCAmelCase = loop.run_until_complete(
_stream_subprocess(_UpperCAmelCase , env=_UpperCAmelCase , stdin=_UpperCAmelCase , timeout=_UpperCAmelCase , quiet=_UpperCAmelCase , echo=_UpperCAmelCase ) )
_UpperCAmelCase = ' '.join(_UpperCAmelCase )
if result.returncode > 0:
_UpperCAmelCase = '\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 ( A ):
pass
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : str=False ) -> Tuple:
'''simple docstring'''
try:
_UpperCAmelCase = subprocess.check_output(_UpperCAmelCase , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(_UpperCAmelCase , 'decode' ):
_UpperCAmelCase = output.decode('utf-8' )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F"Command `{' '.join(_UpperCAmelCase )}` failed with the following error:\n\n{e.output.decode()}" ) from e
| 339 | 0 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
A = CodeGenTokenizer
A = CodeGenTokenizerFast
A = True
A = {"add_prefix_space": True}
A = False
def a_ (self ) -> Union[str, Any]:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__UpperCamelCase : int = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
__UpperCamelCase : Optional[Any] = dict(zip(_UpperCAmelCase , range(len(_UpperCAmelCase ) ) ) )
__UpperCamelCase : int = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
__UpperCamelCase : List[Any] = {"unk_token": "<unk>"}
__UpperCamelCase : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
__UpperCamelCase : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(_UpperCAmelCase ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(_UpperCAmelCase ) )
def a_ (self , **_UpperCAmelCase ) -> Union[str, Any]:
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def a_ (self , **_UpperCAmelCase ) -> Any:
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def a_ (self , _UpperCAmelCase ) -> List[Any]:
__UpperCamelCase : Union[str, Any] = "lower newer"
__UpperCamelCase : Any = "lower newer"
return input_text, output_text
def a_ (self ) -> Tuple:
__UpperCamelCase : Union[str, Any] = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__UpperCamelCase : int = "lower newer"
__UpperCamelCase : Optional[Any] = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
__UpperCamelCase : Any = tokenizer.tokenize(_UpperCAmelCase , add_prefix_space=_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
__UpperCamelCase : Dict = tokens + [tokenizer.unk_token]
__UpperCamelCase : Union[str, Any] = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) , _UpperCAmelCase )
def a_ (self ) -> Dict:
if not self.test_rust_tokenizer:
return
__UpperCamelCase : Any = self.get_tokenizer()
__UpperCamelCase : Dict = self.get_rust_tokenizer(add_prefix_space=_UpperCAmelCase )
__UpperCamelCase : Any = "lower newer"
# Testing tokenization
__UpperCamelCase : List[str] = tokenizer.tokenize(_UpperCAmelCase , add_prefix_space=_UpperCAmelCase )
__UpperCamelCase : Optional[Any] = rust_tokenizer.tokenize(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
# Testing conversion to ids without special tokens
__UpperCamelCase : Optional[int] = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase )
__UpperCamelCase : Optional[Any] = rust_tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
# Testing conversion to ids with special tokens
__UpperCamelCase : str = self.get_rust_tokenizer(add_prefix_space=_UpperCAmelCase )
__UpperCamelCase : List[Any] = tokenizer.encode(_UpperCAmelCase , add_prefix_space=_UpperCAmelCase )
__UpperCamelCase : List[Any] = rust_tokenizer.encode(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
# Testing the unknown token
__UpperCamelCase : Dict = tokens + [rust_tokenizer.unk_token]
__UpperCamelCase : str = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) , _UpperCAmelCase )
def a_ (self , *_UpperCAmelCase , **_UpperCAmelCase ) -> Any:
pass
def a_ (self , _UpperCAmelCase=1_5 ) -> Any:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
__UpperCamelCase : Tuple = self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase , **_UpperCAmelCase )
# Simple input
__UpperCamelCase : Union[str, Any] = "This is a simple input"
__UpperCamelCase : Any = ["This is a simple input 1", "This is a simple input 2"]
__UpperCamelCase : List[Any] = ("This is a simple input", "This is a pair")
__UpperCamelCase : Optional[Any] = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(_UpperCAmelCase , tokenizer_r.encode , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="max_length" )
# Simple input
self.assertRaises(_UpperCAmelCase , tokenizer_r.encode_plus , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="max_length" )
# Simple input
self.assertRaises(
_UpperCAmelCase , tokenizer_r.batch_encode_plus , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="max_length" , )
# Pair input
self.assertRaises(_UpperCAmelCase , tokenizer_r.encode , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="max_length" )
# Pair input
self.assertRaises(_UpperCAmelCase , tokenizer_r.encode_plus , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="max_length" )
# Pair input
self.assertRaises(
_UpperCAmelCase , tokenizer_r.batch_encode_plus , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="max_length" , )
def a_ (self ) -> List[str]:
__UpperCamelCase : Optional[int] = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token="<pad>" )
# Simple input
__UpperCamelCase : Optional[int] = "This is a simple input"
__UpperCamelCase : List[Any] = ["This is a simple input looooooooong", "This is a simple input"]
__UpperCamelCase : Dict = ("This is a simple input", "This is a pair")
__UpperCamelCase : List[str] = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
__UpperCamelCase : Tuple = tokenizer.pad_token_id
__UpperCamelCase : Union[str, Any] = tokenizer(_UpperCAmelCase , padding="max_length" , max_length=3_0 , return_tensors="np" )
__UpperCamelCase : Union[str, Any] = tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase , truncate=_UpperCAmelCase , return_tensors="np" )
__UpperCamelCase : Optional[Any] = tokenizer(*_UpperCAmelCase , padding="max_length" , max_length=6_0 , return_tensors="np" )
__UpperCamelCase : Optional[Any] = tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase , truncate=_UpperCAmelCase , return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1] , 3_0 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1] , 3_3 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1] , 6_0 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1] , 5_2 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def a_ (self ) -> str:
__UpperCamelCase : Dict = "$$$"
__UpperCamelCase : Union[str, Any] = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=_UpperCAmelCase , add_bos_token=_UpperCAmelCase )
__UpperCamelCase : Optional[Any] = "This is a simple input"
__UpperCamelCase : Optional[int] = ["This is a simple input 1", "This is a simple input 2"]
__UpperCamelCase : Dict = tokenizer.bos_token_id
__UpperCamelCase : List[Any] = tokenizer(_UpperCAmelCase )
__UpperCamelCase : Dict = tokenizer(_UpperCAmelCase )
self.assertEqual(out_s.input_ids[0] , _UpperCAmelCase )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
__UpperCamelCase : Dict = tokenizer.decode(out_s.input_ids )
__UpperCamelCase : List[Any] = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , _UpperCAmelCase )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def a_ (self ) -> List[str]:
__UpperCamelCase : List[str] = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono" )
__UpperCamelCase : Dict = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
__UpperCamelCase : List[str] = "\nif len_a > len_b: result = a\nelse: result = b"
__UpperCamelCase : Union[str, Any] = tokenizer.encode(_UpperCAmelCase )
__UpperCamelCase : str = ["^#", re.escape("<|endoftext|>" ), "^\'\'\'", "^\"\"\"", "\n\n\n"]
__UpperCamelCase : int = tokenizer.decode(_UpperCAmelCase , truncate_before_pattern=_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
def a_ (self ) -> List[str]:
pass
| 298 |
from __future__ import annotations
UpperCAmelCase__ = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
UpperCAmelCase__ = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def A ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
'''simple docstring'''
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def A ( _UpperCAmelCase : Matrix ) -> Matrix | None:
'''simple docstring'''
if location := find_empty_location(_UpperCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
_UpperCAmelCase = 0
return None
def A ( _UpperCAmelCase : Matrix ) -> None:
'''simple docstring'''
for row in grid:
for cell in row:
print(_UpperCAmelCase , end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("\nExample grid:\n" + "=" * 20)
print_solution(example_grid)
print("\nExample grid solution:")
UpperCAmelCase__ = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("Cannot find a solution.")
| 339 | 0 |
from ...configuration_utils import PretrainedConfig
class A ( A_ ):
UpperCamelCase_ : List[Any] ='''bert-generation'''
def __init__(self , lowerCAmelCase=5_0_3_5_8 , lowerCAmelCase=1_0_2_4 , lowerCAmelCase=2_4 , lowerCAmelCase=1_6 , lowerCAmelCase=4_0_9_6 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=5_1_2 , lowerCAmelCase=0.02 , lowerCAmelCase=1E-12 , lowerCAmelCase=0 , lowerCAmelCase=2 , lowerCAmelCase=1 , lowerCAmelCase="absolute" , lowerCAmelCase=True , **lowerCAmelCase , ):
super().__init__(pad_token_id=lowerCAmelCase , bos_token_id=lowerCAmelCase , eos_token_id=lowerCAmelCase , **lowerCAmelCase )
__lowercase= vocab_size
__lowercase= hidden_size
__lowercase= num_hidden_layers
__lowercase= num_attention_heads
__lowercase= hidden_act
__lowercase= intermediate_size
__lowercase= hidden_dropout_prob
__lowercase= attention_probs_dropout_prob
__lowercase= max_position_embeddings
__lowercase= initializer_range
__lowercase= layer_norm_eps
__lowercase= position_embedding_type
__lowercase= use_cache
| 295 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
UpperCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
UpperCAmelCase__ = "\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n"
UpperCAmelCase__ = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"
UpperCAmelCase__ = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n 'meteor': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric('meteor')\n >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results[\"meteor\"], 4))\n 0.6944\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : List[Any]) -> List[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence'),
'references': datasets.Value('string' , id='sequence'),
}) , codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'] , reference_urls=[
'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
'https://en.wikipedia.org/wiki/METEOR',
] , )
def _lowerCamelCase ( self : Optional[Any] , A : List[str]) -> List[Any]:
"""simple docstring"""
import nltk
nltk.download('wordnet')
if NLTK_VERSION >= version.Version('3.6.5'):
nltk.download('punkt')
if NLTK_VERSION >= version.Version('3.6.6'):
nltk.download('omw-1.4')
def _lowerCamelCase ( self : Optional[Any] , A : Tuple , A : Optional[int] , A : List[Any]=0.9 , A : Optional[Any]=3 , A : Optional[int]=0.5) -> Any:
"""simple docstring"""
if NLTK_VERSION >= version.Version('3.6.5'):
_UpperCAmelCase = [
meteor_score.single_meteor_score(
word_tokenize(A) , word_tokenize(A) , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
else:
_UpperCAmelCase = [
meteor_score.single_meteor_score(A , A , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
return {"meteor": np.mean(A)}
| 339 | 0 |
'''simple docstring'''
from functools import lru_cache
def _lowerCamelCase ( lowercase : int ) -> set:
_a = 2
_a = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(_UpperCAmelCase )
if n > 1:
factors.add(_UpperCAmelCase )
return factors
@lru_cache
def _lowerCamelCase ( lowercase : int ) -> int:
return len(unique_prime_factors(_UpperCAmelCase ) )
def _lowerCamelCase ( lowercase : list ) -> bool:
return len(set(_UpperCAmelCase ) ) in (0, 1)
def _lowerCamelCase ( lowercase : int ) -> list:
_a = 2
while True:
# Increment each value of a generated range
_a = [base + i for i in range(_UpperCAmelCase )]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
_a = [upf_len(_UpperCAmelCase ) for x in group]
checker.append(_UpperCAmelCase )
# If all numbers in the list are equal, return the group variable.
if equality(_UpperCAmelCase ):
return group
# Increment our base variable by 1
base += 1
def _lowerCamelCase ( lowercase : int = 4 ) -> int:
_a = run(_UpperCAmelCase )
return results[0] if len(_UpperCAmelCase ) else None
if __name__ == "__main__":
print(solution())
| 63 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
UpperCAmelCase__ = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
"large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
_UpperCAmelCase = ['layers', 'blocks']
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase , _UpperCAmelCase )
UpperCAmelCase__ = {
"blocks": "layers",
"mlp.0": "fc1",
"mlp.2": "fc2",
"mlp_ln": "final_layer_norm",
".attn.query": ".self_attn.q_proj",
".attn.key": ".self_attn.k_proj",
".attn.value": ".self_attn.v_proj",
".attn_ln": ".self_attn_layer_norm",
".attn.out": ".self_attn.out_proj",
".cross_attn.query": ".encoder_attn.q_proj",
".cross_attn.key": ".encoder_attn.k_proj",
".cross_attn.value": ".encoder_attn.v_proj",
".cross_attn_ln": ".encoder_attn_layer_norm",
".cross_attn.out": ".encoder_attn.out_proj",
"decoder.ln.": "decoder.layer_norm.",
"encoder.ln.": "encoder.layer_norm.",
"token_embedding": "embed_tokens",
"encoder.positional_embedding": "encoder.embed_positions.weight",
"decoder.positional_embedding": "decoder.embed_positions.weight",
"ln_post": "layer_norm",
}
def A ( _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = list(s_dict.keys() )
for key in keys:
_UpperCAmelCase = key
for k, v in WHISPER_MAPPING.items():
if k in key:
_UpperCAmelCase = new_key.replace(_UpperCAmelCase , _UpperCAmelCase )
print(F"{key} -> {new_key}" )
_UpperCAmelCase = s_dict.pop(_UpperCAmelCase )
return s_dict
def A ( _UpperCAmelCase : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = emb.weight.shape
_UpperCAmelCase = nn.Linear(_UpperCAmelCase , _UpperCAmelCase , bias=_UpperCAmelCase )
_UpperCAmelCase = emb.weight.data
return lin_layer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> bytes:
'''simple docstring'''
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
_UpperCAmelCase = os.path.basename(_UpperCAmelCase )
_UpperCAmelCase = url.split('/' )[-2]
_UpperCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if os.path.exists(_UpperCAmelCase ) and not os.path.isfile(_UpperCAmelCase ):
raise RuntimeError(F"{download_target} exists and is not a regular file" )
if os.path.isfile(_UpperCAmelCase ):
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(F"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" )
with urllib.request.urlopen(_UpperCAmelCase ) as source, open(_UpperCAmelCase , 'wb' ) as output:
with tqdm(
total=int(source.info().get('Content-Length' ) ) , ncols=80 , unit='iB' , unit_scale=_UpperCAmelCase , unit_divisor=1_024 ) as loop:
while True:
_UpperCAmelCase = source.read(8_192 )
if not buffer:
break
output.write(_UpperCAmelCase )
loop.update(len(_UpperCAmelCase ) )
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() != expected_shaaaa:
raise RuntimeError(
'Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.' )
return model_bytes
def A ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
if ".pt" not in checkpoint_path:
_UpperCAmelCase = _download(_MODELS[checkpoint_path] )
else:
_UpperCAmelCase = torch.load(_UpperCAmelCase , map_location='cpu' )
_UpperCAmelCase = original_checkpoint['dims']
_UpperCAmelCase = original_checkpoint['model_state_dict']
_UpperCAmelCase = state_dict['decoder.token_embedding.weight']
remove_ignore_keys_(_UpperCAmelCase )
rename_keys(_UpperCAmelCase )
_UpperCAmelCase = True
_UpperCAmelCase = state_dict['decoder.layers.0.fc1.weight'].shape[0]
_UpperCAmelCase = WhisperConfig(
vocab_size=dimensions['n_vocab'] , encoder_ffn_dim=_UpperCAmelCase , decoder_ffn_dim=_UpperCAmelCase , num_mel_bins=dimensions['n_mels'] , d_model=dimensions['n_audio_state'] , max_target_positions=dimensions['n_text_ctx'] , encoder_layers=dimensions['n_audio_layer'] , encoder_attention_heads=dimensions['n_audio_head'] , decoder_layers=dimensions['n_text_layer'] , decoder_attention_heads=dimensions['n_text_state'] , max_source_positions=dimensions['n_audio_ctx'] , )
_UpperCAmelCase = WhisperForConditionalGeneration(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = model.model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0 and not set(_UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'
F" but all the following weights are missing {missing}" )
if tie_embeds:
_UpperCAmelCase = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
_UpperCAmelCase = proj_out_weights
model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# # Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCAmelCase__ = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 339 | 0 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
__magic_name__ = pytest.mark.integration
__magic_name__ = {"comet"}
__magic_name__ = importlib.util.find_spec("fairseq") is not None
__magic_name__ = {"code_eval"}
__magic_name__ = os.name == "nt"
__magic_name__ = {"bertscore", "frugalscore", "perplexity"}
__magic_name__ = importlib.util.find_spec("transformers") is not None
def _lowerCAmelCase ( UpperCamelCase_ ):
@wraps(_UpperCAmelCase )
def wrapper(self , UpperCamelCase_ ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest("""\"test requires Fairseq\"""" )
else:
test_case(self , _UpperCAmelCase )
return wrapper
def _lowerCAmelCase ( UpperCamelCase_ ):
@wraps(_UpperCAmelCase )
def wrapper(self , UpperCamelCase_ ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest("""\"test requires transformers\"""" )
else:
test_case(self , _UpperCAmelCase )
return wrapper
def _lowerCAmelCase ( UpperCamelCase_ ):
@wraps(_UpperCAmelCase )
def wrapper(self , UpperCamelCase_ ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest("""\"test not supported on Windows\"""" )
else:
test_case(self , _UpperCAmelCase )
return wrapper
def _lowerCAmelCase ( ):
__SCREAMING_SNAKE_CASE = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob("""./metrics/*/""" )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
__a , __a , __a )
@local
class SCREAMING_SNAKE_CASE_ ( parameterized.TestCase ):
"""simple docstring"""
__lowercase : Dict = {}
__lowercase : Optional[Any] = None
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""")
@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning""")
def snake_case_ ( self , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = """[...]"""
__SCREAMING_SNAKE_CASE = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("""metrics""" , lowerCAmelCase__)).module_path)
__SCREAMING_SNAKE_CASE = datasets.load.import_main_class(metric_module.__name__ , dataset=lowerCAmelCase__)
# check parameters
__SCREAMING_SNAKE_CASE = inspect.signature(metric._compute).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values())) # no **kwargs
# run doctest
with self.patch_intensive_calls(lowerCAmelCase__ , metric_module.__name__):
with self.use_local_metrics():
try:
__SCREAMING_SNAKE_CASE = doctest.testmod(lowerCAmelCase__ , verbose=lowerCAmelCase__ , raise_on_error=lowerCAmelCase__)
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0)
self.assertGreater(results.attempted , 1)
@slow
def snake_case_ ( self , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = """[...]"""
__SCREAMING_SNAKE_CASE = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("""metrics""" , lowerCAmelCase__)).module_path)
# run doctest
with self.use_local_metrics():
__SCREAMING_SNAKE_CASE = doctest.testmod(lowerCAmelCase__ , verbose=lowerCAmelCase__ , raise_on_error=lowerCAmelCase__)
self.assertEqual(results.failed , 0)
self.assertGreater(results.attempted , 1)
@contextmanager
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__):
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](lowerCAmelCase__):
yield
else:
yield
@contextmanager
def snake_case_ ( self):
def load_local_metric(lowerCAmelCase__ , *lowerCAmelCase__ , **lowerCAmelCase__):
return load_metric(os.path.join("""metrics""" , lowerCAmelCase__) , *lowerCAmelCase__ , **lowerCAmelCase__)
with patch("""datasets.load_metric""") as mock_load_metric:
__SCREAMING_SNAKE_CASE = load_local_metric
yield
@classmethod
def snake_case_ ( cls , lowerCAmelCase__):
def wrapper(lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = contextmanager(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher("""bleurt""" )
def _lowerCAmelCase ( UpperCamelCase_ ):
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string("""sv""" , """""" , """""" ) # handle pytest cli flags
class SCREAMING_SNAKE_CASE_ ( __a ):
"""simple docstring"""
def snake_case_ ( self , lowerCAmelCase__):
assert len(input_dict["""input_ids"""]) == 2
return np.array([1.03, 1.04])
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch("""bleurt.score._create_predictor""" ) as mock_create_predictor:
__SCREAMING_SNAKE_CASE = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher("""bertscore""" )
def _lowerCAmelCase ( UpperCamelCase_ ):
import torch
def bert_cos_score_idf(UpperCamelCase_ , UpperCamelCase_ , *UpperCamelCase_ , **UpperCamelCase_ ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(_UpperCAmelCase ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch("""bert_score.scorer.get_model""" ), patch(
"""bert_score.scorer.bert_cos_score_idf""" ) as mock_bert_cos_score_idf:
__SCREAMING_SNAKE_CASE = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher("""comet""" )
def _lowerCAmelCase ( UpperCamelCase_ ):
def load_from_checkpoint(UpperCamelCase_ ):
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def snake_case_ ( self , lowerCAmelCase__ , *lowerCAmelCase__ , **lowerCAmelCase__):
assert len(lowerCAmelCase__) == 2
__SCREAMING_SNAKE_CASE = [0.19, 0.92]
return scores, sum(lowerCAmelCase__) / len(lowerCAmelCase__)
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch("""comet.download_model""" ) as mock_download_model:
__SCREAMING_SNAKE_CASE = None
with patch("""comet.load_from_checkpoint""" ) as mock_load_from_checkpoint:
__SCREAMING_SNAKE_CASE = load_from_checkpoint
yield
def _lowerCAmelCase ( ):
__SCREAMING_SNAKE_CASE = load_metric(os.path.join("""metrics""" , """seqeval""" ) )
__SCREAMING_SNAKE_CASE = """ERROR"""
__SCREAMING_SNAKE_CASE = f"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"
with pytest.raises(_UpperCAmelCase , match=re.escape(_UpperCAmelCase ) ):
metric.compute(predictions=[] , references=[] , scheme=_UpperCAmelCase )
| 100 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase__ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
UpperCamelCase = None
UpperCamelCase = None
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilder ):
UpperCamelCase = datasets.Audio()
UpperCamelCase = '''audio'''
UpperCamelCase = AudioFolderConfig
UpperCamelCase = 42 # definition at the bottom of the script
UpperCamelCase = AudioClassification(audio_column='''audio''' , label_column='''label''' )
UpperCAmelCase__ = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
UpperCAmelCase__ = AUDIO_EXTENSIONS
| 339 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__lowerCamelCase : Optional[int] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Tuple = ['''MLukeTokenizer''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
__lowerCamelCase : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 219 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
def __init__( self : int) -> str:
"""simple docstring"""
_UpperCAmelCase = []
def _lowerCamelCase ( self : Any , A : List[str]) -> int:
"""simple docstring"""
return self.node_position[vertex]
def _lowerCamelCase ( self : Optional[Any] , A : Optional[int] , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = pos
def _lowerCamelCase ( self : Tuple , A : Tuple , A : Dict , A : List[str] , A : Optional[Any]) -> Dict:
"""simple docstring"""
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_UpperCAmelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_UpperCAmelCase = 2 * start + 1
else:
_UpperCAmelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_UpperCAmelCase , _UpperCAmelCase = heap[smallest_child], positions[smallest_child]
_UpperCAmelCase , _UpperCAmelCase = (
heap[start],
positions[start],
)
_UpperCAmelCase , _UpperCAmelCase = temp, tempa
_UpperCAmelCase = self.get_position(positions[smallest_child])
self.set_position(
positions[smallest_child] , self.get_position(positions[start]))
self.set_position(positions[start] , A)
self.top_to_bottom(A , A , A , A)
def _lowerCamelCase ( self : Optional[int] , A : str , A : Optional[Any] , A : Optional[int] , A : str) -> Any:
"""simple docstring"""
_UpperCAmelCase = position[index]
while index != 0:
_UpperCAmelCase = int((index - 2) / 2) if index % 2 == 0 else int((index - 1) / 2)
if val < heap[parent]:
_UpperCAmelCase = heap[parent]
_UpperCAmelCase = position[parent]
self.set_position(position[parent] , A)
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , A)
break
_UpperCAmelCase = parent
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , 0)
def _lowerCamelCase ( self : Union[str, Any] , A : Optional[int] , A : Tuple) -> str:
"""simple docstring"""
_UpperCAmelCase = len(A) // 2 - 1
for i in range(A , -1 , -1):
self.top_to_bottom(A , A , len(A) , A)
def _lowerCamelCase ( self : Optional[int] , A : int , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = positions[0]
_UpperCAmelCase = sys.maxsize
self.top_to_bottom(A , 0 , len(A) , A)
return temp
def A ( _UpperCAmelCase : int ) -> Any:
'''simple docstring'''
_UpperCAmelCase = Heap()
_UpperCAmelCase = [0] * len(_UpperCAmelCase )
_UpperCAmelCase = [-1] * len(_UpperCAmelCase ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_UpperCAmelCase = [] # Heap of Distance of vertices from their neighboring vertex
_UpperCAmelCase = []
for vertex in range(len(_UpperCAmelCase ) ):
distance_tv.append(sys.maxsize )
positions.append(_UpperCAmelCase )
heap.node_position.append(_UpperCAmelCase )
_UpperCAmelCase = []
_UpperCAmelCase = 1
_UpperCAmelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_UpperCAmelCase = 0
_UpperCAmelCase = distance
heap.heapify(_UpperCAmelCase , _UpperCAmelCase )
for _ in range(1 , len(_UpperCAmelCase ) ):
_UpperCAmelCase = heap.delete_minimum(_UpperCAmelCase , _UpperCAmelCase )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_UpperCAmelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(_UpperCAmelCase )]
):
_UpperCAmelCase = distance
heap.bottom_to_top(
_UpperCAmelCase , heap.get_position(_UpperCAmelCase ) , _UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
UpperCAmelCase__ = int(input("Enter number of edges: ").strip())
UpperCAmelCase__ = defaultdict(list)
for _ in range(edges_number):
UpperCAmelCase__ = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 339 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
UpperCAmelCase_ : Dict = list[list[float | int]]
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Tuple = len(_UpperCAmelCase )
_SCREAMING_SNAKE_CASE : Any = [[0 for _ in range(size + 1 )] for _ in range(_UpperCAmelCase )]
_SCREAMING_SNAKE_CASE : int = 42
_SCREAMING_SNAKE_CASE : List[str] = 42
_SCREAMING_SNAKE_CASE : Any = 42
_SCREAMING_SNAKE_CASE : Optional[int] = 42
_SCREAMING_SNAKE_CASE : Any = 42
_SCREAMING_SNAKE_CASE : Any = 42
for row in range(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_SCREAMING_SNAKE_CASE : Optional[Any] = matrix[row][col]
_SCREAMING_SNAKE_CASE : Tuple = vector[row][0]
_SCREAMING_SNAKE_CASE : Union[str, Any] = 0
_SCREAMING_SNAKE_CASE : Dict = 0
while row < size and col < size:
# pivoting
_SCREAMING_SNAKE_CASE : Optional[int] = max((abs(augmented[rowa][col] ), rowa) for rowa in range(_UpperCAmelCase , _UpperCAmelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Any = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , _UpperCAmelCase ):
_SCREAMING_SNAKE_CASE : Tuple = augmented[rowa][col] / augmented[row][col]
_SCREAMING_SNAKE_CASE : Any = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , _UpperCAmelCase ):
for row in range(_UpperCAmelCase ):
_SCREAMING_SNAKE_CASE : Optional[int] = augmented[row][col] / augmented[col][col]
for cola in range(_UpperCAmelCase , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(_UpperCAmelCase )
]
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : List[str] = len(_UpperCAmelCase )
_SCREAMING_SNAKE_CASE : List[Any] = [[0 for _ in range(_UpperCAmelCase )] for _ in range(_UpperCAmelCase )]
_SCREAMING_SNAKE_CASE : Tuple = [[0] for _ in range(_UpperCAmelCase )]
_SCREAMING_SNAKE_CASE : Optional[int] = 42
_SCREAMING_SNAKE_CASE : List[str] = 42
_SCREAMING_SNAKE_CASE : Optional[int] = 42
_SCREAMING_SNAKE_CASE : List[str] = 42
for x_val, y_val in enumerate(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = (x_val + 1) ** (size - col - 1)
_SCREAMING_SNAKE_CASE : Tuple = y_val
_SCREAMING_SNAKE_CASE : List[str] = solve(_UpperCAmelCase , _UpperCAmelCase )
def interpolated_func(SCREAMING_SNAKE_CASE__ ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(_UpperCAmelCase ) )
return interpolated_func
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def snake_case_ ( SCREAMING_SNAKE_CASE__ = question_function , SCREAMING_SNAKE_CASE__ = 10 ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Union[str, Any] = [func(_UpperCAmelCase ) for x_val in range(1 , order + 1 )]
_SCREAMING_SNAKE_CASE : Dict = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
_SCREAMING_SNAKE_CASE : int = 0
_SCREAMING_SNAKE_CASE : Optional[Any] = 42
_SCREAMING_SNAKE_CASE : int = 42
for poly in polynomials:
_SCREAMING_SNAKE_CASE : int = 1
while func(_UpperCAmelCase ) == poly(_UpperCAmelCase ):
x_val += 1
ret += poly(_UpperCAmelCase )
return ret
if __name__ == "__main__":
print(F"{solution() = }")
| 200 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int]=5 ) -> List[Any]:
'''simple docstring'''
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('<mask>' ) == 1
_UpperCAmelCase = torch.tensor(tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) ).unsqueeze(0 ) # Batch size 1
_UpperCAmelCase = model(_UpperCAmelCase )[0] # The last hidden-state is the first element of the output tuple
_UpperCAmelCase = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
_UpperCAmelCase = logits[0, masked_index, :]
_UpperCAmelCase = logits.softmax(dim=0 )
_UpperCAmelCase , _UpperCAmelCase = prob.topk(k=_UpperCAmelCase , dim=0 )
_UpperCAmelCase = ' '.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(_UpperCAmelCase ) )] )
_UpperCAmelCase = tokenizer.mask_token
_UpperCAmelCase = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ' ) ):
_UpperCAmelCase = predicted_token_bpe.replace('\u2581' , ' ' )
if " {0}".format(_UpperCAmelCase ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(' {0}'.format(_UpperCAmelCase ) , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(_UpperCAmelCase , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
UpperCAmelCase__ = CamembertTokenizer.from_pretrained("camembert-base")
UpperCAmelCase__ = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
UpperCAmelCase__ = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 339 | 0 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_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 __A ( a , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase__ : Tuple =RoCBertTokenizer
UpperCamelCase__ : List[str] =None
UpperCamelCase__ : Optional[int] =False
UpperCamelCase__ : Optional[int] =True
UpperCamelCase__ : Union[str, Any] =filter_non_english
def __lowercase ( self ):
"""simple docstring"""
super().setUp()
__UpperCamelCase : Optional[Any] =['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', '你', '好', '是', '谁', 'a', 'b', 'c', 'd']
__UpperCamelCase : List[Any] ={}
__UpperCamelCase : Any ={}
for i, value in enumerate(lowerCamelCase__ ):
__UpperCamelCase : List[Any] =i
__UpperCamelCase : Optional[int] =i
__UpperCamelCase : str =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__UpperCamelCase : Dict =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['word_shape_file'] )
__UpperCamelCase : List[Any] =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['word_pronunciation_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
with open(self.word_shape_file , 'w' , encoding='utf-8' ) as word_shape_writer:
json.dump(lowerCamelCase__ , lowerCamelCase__ , ensure_ascii=lowerCamelCase__ )
with open(self.word_pronunciation_file , 'w' , encoding='utf-8' ) as word_pronunciation_writer:
json.dump(lowerCamelCase__ , lowerCamelCase__ , ensure_ascii=lowerCamelCase__ )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Tuple =self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
__UpperCamelCase : Tuple =tokenizer.tokenize('你好[SEP]你是谁' )
self.assertListEqual(lowerCamelCase__ , ['你', '好', '[SEP]', '你', '是', '谁'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(lowerCamelCase__ ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(lowerCamelCase__ ) , [5, 6, 2, 5, 7, 8] )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] =RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Dict =RoCBertBasicTokenizer(do_lower_case=lowerCamelCase__ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] =RoCBertBasicTokenizer(do_lower_case=lowerCamelCase__ , strip_accents=lowerCamelCase__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Tuple =RoCBertBasicTokenizer(do_lower_case=lowerCamelCase__ , strip_accents=lowerCamelCase__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] =RoCBertBasicTokenizer(do_lower_case=lowerCamelCase__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[str] =RoCBertBasicTokenizer(do_lower_case=lowerCamelCase__ )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : str =RoCBertBasicTokenizer(do_lower_case=lowerCamelCase__ , strip_accents=lowerCamelCase__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] =RoCBertBasicTokenizer(do_lower_case=lowerCamelCase__ , strip_accents=lowerCamelCase__ )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] =RoCBertBasicTokenizer(do_lower_case=lowerCamelCase__ , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[str] =['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
__UpperCamelCase : Any ={}
for i, token in enumerate(lowerCamelCase__ ):
__UpperCamelCase : Any =i
__UpperCamelCase : Optional[int] =RoCBertWordpieceTokenizer(vocab=lowerCamelCase__ , 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 __lowercase ( self ):
"""simple docstring"""
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 __lowercase ( self ):
"""simple docstring"""
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 __lowercase ( self ):
"""simple docstring"""
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 __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Any =self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(lowerCamelCase__ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
if self.test_rust_tokenizer:
__UpperCamelCase : Optional[int] =self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(lowerCamelCase__ ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
def __lowercase ( self ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__UpperCamelCase : Union[str, Any] =self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
__UpperCamelCase : List[Any] =f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
__UpperCamelCase : str =tokenizer_r.encode_plus(
lowerCamelCase__ , return_attention_mask=lowerCamelCase__ , return_token_type_ids=lowerCamelCase__ , return_offsets_mapping=lowerCamelCase__ , add_special_tokens=lowerCamelCase__ , )
__UpperCamelCase : int =tokenizer_r.do_lower_case if hasattr(lowerCamelCase__ , 'do_lower_case' ) else False
__UpperCamelCase : List[Any] =(
[
((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 __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Dict =['的', '人', '有']
__UpperCamelCase : List[Any] =''.join(lowerCamelCase__ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__UpperCamelCase : Dict =True
__UpperCamelCase : List[str] =self.tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
__UpperCamelCase : List[Any] =self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
__UpperCamelCase : Optional[Any] =tokenizer_p.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
__UpperCamelCase : int =tokenizer_r.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
__UpperCamelCase : List[str] =tokenizer_r.convert_ids_to_tokens(lowerCamelCase__ )
__UpperCamelCase : List[str] =tokenizer_p.convert_ids_to_tokens(lowerCamelCase__ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
__UpperCamelCase : Optional[int] =False
__UpperCamelCase : List[str] =self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
__UpperCamelCase : Tuple =self.tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
__UpperCamelCase : List[str] =tokenizer_r.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
__UpperCamelCase : List[str] =tokenizer_p.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
__UpperCamelCase : Dict =tokenizer_r.convert_ids_to_tokens(lowerCamelCase__ )
__UpperCamelCase : int =tokenizer_p.convert_ids_to_tokens(lowerCamelCase__ )
# it is expected that only the first Chinese character is not preceded by "##".
__UpperCamelCase : str =[
f'##{token}' if idx != 0 else token for idx, token in enumerate(lowerCamelCase__ )
]
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@slow
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] =self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
__UpperCamelCase : Optional[int] =tokenizer.encode('你好' , add_special_tokens=lowerCamelCase__ )
__UpperCamelCase : Optional[int] =tokenizer.encode('你是谁' , add_special_tokens=lowerCamelCase__ )
__UpperCamelCase : Dict =tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ )
__UpperCamelCase : Tuple =tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ , lowerCamelCase__ )
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_a + [2]
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : List[str] =self.get_tokenizers(do_lower_case=lowerCamelCase__ )
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
__UpperCamelCase : Dict ='你好，你是谁'
__UpperCamelCase : str =tokenizer.tokenize(lowerCamelCase__ )
__UpperCamelCase : Optional[Any] =tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
__UpperCamelCase : Optional[Any] =tokenizer.convert_tokens_to_shape_ids(lowerCamelCase__ )
__UpperCamelCase : Dict =tokenizer.convert_tokens_to_pronunciation_ids(lowerCamelCase__ )
__UpperCamelCase : Optional[Any] =tokenizer.prepare_for_model(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
__UpperCamelCase : Any =tokenizer.encode_plus(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
self.assertEqual(lowerCamelCase__ , lowerCamelCase__ )
| 71 |
import math
import unittest
def A ( _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Union[str, Any]:
"""simple docstring"""
self.assertTrue(is_prime(2))
self.assertTrue(is_prime(3))
self.assertTrue(is_prime(5))
self.assertTrue(is_prime(7))
self.assertTrue(is_prime(11))
self.assertTrue(is_prime(13))
self.assertTrue(is_prime(17))
self.assertTrue(is_prime(19))
self.assertTrue(is_prime(23))
self.assertTrue(is_prime(29))
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
with self.assertRaises(A):
is_prime(-19)
self.assertFalse(
is_prime(0) , 'Zero doesn\'t have any positive factors, primes must have exactly two.' , )
self.assertFalse(
is_prime(1) , 'One only has 1 positive factor, primes must have exactly two.' , )
self.assertFalse(is_prime(2 * 2))
self.assertFalse(is_prime(2 * 3))
self.assertFalse(is_prime(3 * 3))
self.assertFalse(is_prime(3 * 5))
self.assertFalse(is_prime(3 * 5 * 7))
if __name__ == "__main__":
unittest.main()
| 339 | 0 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase : Tuple = {
"huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json",
}
class A__ ( A__ ):
A__ = 'autoformer'
A__ = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
'num_hidden_layers': 'encoder_layers',
}
def __init__( self : Any , _a : Optional[int] = None , _a : Optional[int] = None , _a : str = "student_t" , _a : str = "nll" , _a : int = 1 , _a : List[int] = [1, 2, 3, 4, 5, 6, 7] , _a : bool = True , _a : int = 0 , _a : int = 0 , _a : int = 0 , _a : int = 0 , _a : Optional[List[int]] = None , _a : Optional[List[int]] = None , _a : int = 64 , _a : int = 2 , _a : int = 2 , _a : int = 2 , _a : int = 2 , _a : int = 32 , _a : int = 32 , _a : str = "gelu" , _a : float = 0.1 , _a : float = 0.1 , _a : float = 0.1 , _a : float = 0.1 , _a : float = 0.1 , _a : int = 100 , _a : float = 0.02 , _a : bool = True , _a : List[str]=True , _a : int = 10 , _a : int = 25 , _a : int = 3 , **_a : Tuple , ) -> str:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =prediction_length
_SCREAMING_SNAKE_CASE =context_length if context_length is not None else prediction_length
_SCREAMING_SNAKE_CASE =distribution_output
_SCREAMING_SNAKE_CASE =loss
_SCREAMING_SNAKE_CASE =input_size
_SCREAMING_SNAKE_CASE =num_time_features
_SCREAMING_SNAKE_CASE =lags_sequence
_SCREAMING_SNAKE_CASE =scaling
_SCREAMING_SNAKE_CASE =num_dynamic_real_features
_SCREAMING_SNAKE_CASE =num_static_real_features
_SCREAMING_SNAKE_CASE =num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(_a ) != num_static_categorical_features:
raise ValueError(
'The cardinality should be a list of the same length as `num_static_categorical_features`' )
_SCREAMING_SNAKE_CASE =cardinality
else:
_SCREAMING_SNAKE_CASE =[0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(_a ) != num_static_categorical_features:
raise ValueError(
'The embedding dimension should be a list of the same length as `num_static_categorical_features`' )
_SCREAMING_SNAKE_CASE =embedding_dimension
else:
_SCREAMING_SNAKE_CASE =[min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
_SCREAMING_SNAKE_CASE =num_parallel_samples
# Transformer architecture configuration
_SCREAMING_SNAKE_CASE =input_size * len(self.lags_sequence ) + self._number_of_features
_SCREAMING_SNAKE_CASE =d_model
_SCREAMING_SNAKE_CASE =encoder_attention_heads
_SCREAMING_SNAKE_CASE =decoder_attention_heads
_SCREAMING_SNAKE_CASE =encoder_ffn_dim
_SCREAMING_SNAKE_CASE =decoder_ffn_dim
_SCREAMING_SNAKE_CASE =encoder_layers
_SCREAMING_SNAKE_CASE =decoder_layers
_SCREAMING_SNAKE_CASE =dropout
_SCREAMING_SNAKE_CASE =attention_dropout
_SCREAMING_SNAKE_CASE =activation_dropout
_SCREAMING_SNAKE_CASE =encoder_layerdrop
_SCREAMING_SNAKE_CASE =decoder_layerdrop
_SCREAMING_SNAKE_CASE =activation_function
_SCREAMING_SNAKE_CASE =init_std
_SCREAMING_SNAKE_CASE =use_cache
# Autoformer
_SCREAMING_SNAKE_CASE =label_length
_SCREAMING_SNAKE_CASE =moving_average
_SCREAMING_SNAKE_CASE =autocorrelation_factor
super().__init__(is_encoder_decoder=_a , **_a )
@property
def A ( self : List[Any] ) -> int:
'''simple docstring'''
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 47 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase__ = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n"
UpperCAmelCase__ = "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"
UpperCAmelCase__ = "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n 'google_bleu': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.4\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : str) -> MetricInfo:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('string' , id='token') , id='sequence'),
'references': datasets.Sequence(
datasets.Sequence(datasets.Value('string' , id='token') , id='sequence') , id='references'),
}) , )
def _lowerCamelCase ( self : Union[str, Any] , A : List[List[List[str]]] , A : List[List[str]] , A : int = 1 , A : int = 4 , ) -> Dict[str, float]:
"""simple docstring"""
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=A , hypotheses=A , min_len=A , max_len=A)
}
| 339 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase = 50 ):
__lowercase : Optional[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(F"{solution() = }")
| 249 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
UpperCAmelCase__ = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
UpperCAmelCase__ = TaTokenizerFast
UpperCAmelCase__ = {"configuration_mt5": ["MT5Config", "MT5OnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"MT5EncoderModel",
"MT5ForConditionalGeneration",
"MT5ForQuestionAnswering",
"MT5Model",
"MT5PreTrainedModel",
"MT5Stack",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
UpperCAmelCase__ = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
module_spec=__spec__,
)
| 339 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class SCREAMING_SNAKE_CASE__ ( _a , _a , unittest.TestCase ):
_a = IFPipeline
_a = TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
_a = TEXT_TO_IMAGE_BATCH_PARAMS
_a = PipelineTesterMixin.required_optional_params - {'latents'}
def __lowercase ( self : Optional[int] ):
return self._get_dummy_components()
def __lowercase ( self : Tuple , lowerCAmelCase : int , lowerCAmelCase : str=0 ):
if str(lowerCAmelCase ).startswith("""mps""" ):
lowerCAmelCase = torch.manual_seed(lowerCAmelCase )
else:
lowerCAmelCase = torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
lowerCAmelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def __lowercase ( self : Union[str, Any] ):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" )
def __lowercase ( self : List[str] ):
super().test_save_load_floataa(expected_max_diff=1e-1 )
def __lowercase ( self : List[Any] ):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def __lowercase ( self : Tuple ):
self._test_save_load_local()
def __lowercase ( self : List[str] ):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def __lowercase ( self : Optional[Any] ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowercase ( self : Optional[int] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self : int ):
lowerCAmelCase = IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0""" , variant="""fp16""" , torch_dtype=torch.floataa )
lowerCAmelCase = IFSuperResolutionPipeline.from_pretrained(
"""DeepFloyd/IF-II-L-v1.0""" , variant="""fp16""" , torch_dtype=torch.floataa , text_encoder=lowerCAmelCase , tokenizer=lowerCAmelCase )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to("""cuda""" )
lowerCAmelCase , lowerCAmelCase = pipe_a.encode_prompt("""anime turtle""" , device="""cuda""" )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
lowerCAmelCase = None
lowerCAmelCase = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
lowerCAmelCase = IFImgaImgPipeline(**pipe_a.components )
lowerCAmelCase = IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
lowerCAmelCase = IFInpaintingPipeline(**pipe_a.components )
lowerCAmelCase = IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
def __lowercase ( self : List[str] , lowerCAmelCase : Tuple , lowerCAmelCase : Any , lowerCAmelCase : List[str] , lowerCAmelCase : List[Any] ):
_start_torch_memory_measurement()
lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowerCAmelCase = pipe_a(
prompt_embeds=lowerCAmelCase , negative_prompt_embeds=lowerCAmelCase , num_inference_steps=2 , generator=lowerCAmelCase , output_type="""np""" , )
lowerCAmelCase = output.images[0]
assert image.shape == (64, 64, 3)
lowerCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy""" )
assert_mean_pixel_difference(lowerCAmelCase , lowerCAmelCase )
# pipeline 2
_start_torch_memory_measurement()
lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowerCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(lowerCAmelCase )
lowerCAmelCase = pipe_a(
prompt_embeds=lowerCAmelCase , negative_prompt_embeds=lowerCAmelCase , image=lowerCAmelCase , generator=lowerCAmelCase , num_inference_steps=2 , output_type="""np""" , )
lowerCAmelCase = output.images[0]
assert image.shape == (256, 256, 3)
lowerCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(lowerCAmelCase , lowerCAmelCase )
def __lowercase ( self : List[str] , lowerCAmelCase : List[Any] , lowerCAmelCase : int , lowerCAmelCase : Any , lowerCAmelCase : Tuple ):
_start_torch_memory_measurement()
lowerCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(lowerCAmelCase )
lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowerCAmelCase = pipe_a(
prompt_embeds=lowerCAmelCase , negative_prompt_embeds=lowerCAmelCase , image=lowerCAmelCase , num_inference_steps=2 , generator=lowerCAmelCase , output_type="""np""" , )
lowerCAmelCase = output.images[0]
assert image.shape == (64, 64, 3)
lowerCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy""" )
assert_mean_pixel_difference(lowerCAmelCase , lowerCAmelCase )
# pipeline 2
_start_torch_memory_measurement()
lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowerCAmelCase = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(lowerCAmelCase )
lowerCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(lowerCAmelCase )
lowerCAmelCase = pipe_a(
prompt_embeds=lowerCAmelCase , negative_prompt_embeds=lowerCAmelCase , image=lowerCAmelCase , original_image=lowerCAmelCase , generator=lowerCAmelCase , num_inference_steps=2 , output_type="""np""" , )
lowerCAmelCase = output.images[0]
assert image.shape == (256, 256, 3)
lowerCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(lowerCAmelCase , lowerCAmelCase )
def __lowercase ( self : Dict , lowerCAmelCase : Tuple , lowerCAmelCase : int , lowerCAmelCase : List[str] , lowerCAmelCase : Tuple ):
_start_torch_memory_measurement()
lowerCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(lowerCAmelCase )
lowerCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(lowerCAmelCase )
lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowerCAmelCase = pipe_a(
prompt_embeds=lowerCAmelCase , negative_prompt_embeds=lowerCAmelCase , image=lowerCAmelCase , mask_image=lowerCAmelCase , num_inference_steps=2 , generator=lowerCAmelCase , output_type="""np""" , )
lowerCAmelCase = output.images[0]
assert image.shape == (64, 64, 3)
lowerCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy""" )
assert_mean_pixel_difference(lowerCAmelCase , lowerCAmelCase )
# pipeline 2
_start_torch_memory_measurement()
lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowerCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(lowerCAmelCase )
lowerCAmelCase = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(lowerCAmelCase )
lowerCAmelCase = floats_tensor((1, 3, 256, 256) , rng=random.Random(1 ) ).to(lowerCAmelCase )
lowerCAmelCase = pipe_a(
prompt_embeds=lowerCAmelCase , negative_prompt_embeds=lowerCAmelCase , image=lowerCAmelCase , mask_image=lowerCAmelCase , original_image=lowerCAmelCase , generator=lowerCAmelCase , num_inference_steps=2 , output_type="""np""" , )
lowerCAmelCase = output.images[0]
assert image.shape == (256, 256, 3)
lowerCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(lowerCAmelCase , lowerCAmelCase )
def lowercase () -> List[str]:
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 155 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class __lowerCAmelCase ( A ):
UpperCamelCase = '''open-llama'''
def __init__( self : str , A : List[Any]=10_00_00 , A : Tuple=40_96 , A : Tuple=1_10_08 , A : List[str]=32 , A : Tuple=32 , A : Optional[Any]="silu" , A : int=20_48 , A : Optional[Any]=0.0_2 , A : Dict=1E-6 , A : Optional[Any]=True , A : List[Any]=0 , A : Dict=1 , A : int=2 , A : Dict=False , A : Optional[int]=True , A : List[Any]=0.1 , A : str=0.1 , A : Dict=True , A : Optional[Any]=True , A : Dict=None , **A : Union[str, Any] , ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = vocab_size
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = hidden_size
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = initializer_range
_UpperCAmelCase = rms_norm_eps
_UpperCAmelCase = use_cache
_UpperCAmelCase = kwargs.pop(
'use_memorry_efficient_attention' , A)
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_dropout_prob
_UpperCAmelCase = use_stable_embedding
_UpperCAmelCase = shared_input_output_embedding
_UpperCAmelCase = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , tie_word_embeddings=A , **A , )
def _lowerCamelCase ( self : List[str]) -> Union[str, Any]:
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , A) or len(self.rope_scaling) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F"got {self.rope_scaling}")
_UpperCAmelCase = self.rope_scaling.get('type' , A)
_UpperCAmelCase = self.rope_scaling.get('factor' , A)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}")
if rope_scaling_factor is None or not isinstance(A , A) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
| 339 | 0 |
def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int):
return abs(_UpperCAmelCase) if a == 0 else greatest_common_divisor(b % a , _UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int):
while y: # --> when y=0 then loop will terminate and return x as final GCD.
lowercase__ , lowercase__ : Any = y, x % y
return abs(_UpperCAmelCase)
def lowercase_ ( ):
try:
lowercase__ : str = input("Enter two integers separated by comma (,): ").split(",")
lowercase__ : int = int(nums[0])
lowercase__ : List[Any] = int(nums[1])
print(
f'''greatest_common_divisor({num_a}, {num_a}) = '''
f'''{greatest_common_divisor(_UpperCAmelCase , _UpperCAmelCase)}''')
print(f'''By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(_UpperCAmelCase , _UpperCAmelCase)}''')
except (IndexError, UnboundLocalError, ValueError):
print("Wrong input")
if __name__ == "__main__":
main()
| 87 |
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = credit_card_number
_UpperCAmelCase = 0
_UpperCAmelCase = len(_UpperCAmelCase ) - 2
for i in range(_UpperCAmelCase , -1 , -2 ):
# double the value of every second digit
_UpperCAmelCase = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
_UpperCAmelCase = cc_number[:i] + str(_UpperCAmelCase ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(_UpperCAmelCase ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = F"{credit_card_number} is an invalid credit card number because"
if not credit_card_number.isdigit():
print(F"{error_message} it has nonnumerical characters." )
return False
if not 13 <= len(_UpperCAmelCase ) <= 16:
print(F"{error_message} of its length." )
return False
if not validate_initial_digits(_UpperCAmelCase ):
print(F"{error_message} of its first two digits." )
return False
if not luhn_validation(_UpperCAmelCase ):
print(F"{error_message} it fails the Luhn check." )
return False
print(F"{credit_card_number} is a valid credit card number." )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 339 | 0 |
'''simple docstring'''
import cmath
import math
def __lowerCAmelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
__UpperCamelCase : Union[str, Any] = math.radians(_UpperCAmelCase )
__UpperCamelCase : int = math.radians(_UpperCAmelCase )
# Convert voltage and current to rectangular form
__UpperCamelCase : Tuple = cmath.rect(_UpperCAmelCase , _UpperCAmelCase )
__UpperCamelCase : Tuple = cmath.rect(_UpperCAmelCase , _UpperCAmelCase )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod()
| 298 |
from functools import reduce
UpperCAmelCase__ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def A ( _UpperCAmelCase : str = N ) -> int:
'''simple docstring'''
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda _UpperCAmelCase , _UpperCAmelCase : str(int(_UpperCAmelCase ) * int(_UpperCAmelCase ) ) , n[i : i + 13] ) )
for i in range(len(_UpperCAmelCase ) - 12 ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 0 |
from __future__ import annotations
from collections import Counter
from random import random
class A :
def __init__(self ):
__lowercase= {}
def _A (self , lowerCAmelCase ):
__lowercase= {}
def _A (self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
if nodea not in self.connections:
self.add_node(lowerCAmelCase )
if nodea not in self.connections:
self.add_node(lowerCAmelCase )
__lowercase= probability
def _A (self ):
return list(self.connections )
def _A (self , lowerCAmelCase ):
__lowercase= 0
__lowercase= random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def _lowerCamelCase( lowercase__ , lowercase__ , lowercase__ ) -> dict[str, int]:
'''simple docstring'''
__lowercase= MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
__lowercase= Counter(graph.get_nodes() )
__lowercase= start
for _ in range(_UpperCAmelCase ):
__lowercase= graph.transition(_UpperCAmelCase )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 295 |
from __future__ import annotations
from collections.abc import Callable
UpperCAmelCase__ = list[list[float | int]]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : Matrix ) -> Matrix:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(size + 1 )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for row in range(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = matrix[row][col]
_UpperCAmelCase = vector[row][0]
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while row < size and col < size:
# pivoting
_UpperCAmelCase = max((abs(augmented[rowa][col] ), rowa) for rowa in range(_UpperCAmelCase , _UpperCAmelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
_UpperCAmelCase , _UpperCAmelCase = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , _UpperCAmelCase ):
_UpperCAmelCase = augmented[rowa][col] / augmented[row][col]
_UpperCAmelCase = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , _UpperCAmelCase ):
for row in range(_UpperCAmelCase ):
_UpperCAmelCase = augmented[row][col] / augmented[col][col]
for cola in range(_UpperCAmelCase , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(_UpperCAmelCase )
]
def A ( _UpperCAmelCase : list[int] ) -> Callable[[int], int]:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(_UpperCAmelCase )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = [[0] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for x_val, y_val in enumerate(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = (x_val + 1) ** (size - col - 1)
_UpperCAmelCase = y_val
_UpperCAmelCase = solve(_UpperCAmelCase , _UpperCAmelCase )
def interpolated_func(_UpperCAmelCase : int ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(_UpperCAmelCase ) )
return interpolated_func
def A ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def A ( _UpperCAmelCase : Callable[[int], int] = question_function , _UpperCAmelCase : int = 10 ) -> int:
'''simple docstring'''
_UpperCAmelCase = [func(_UpperCAmelCase ) for x_val in range(1 , order + 1 )]
_UpperCAmelCase = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
_UpperCAmelCase = 0
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for poly in polynomials:
_UpperCAmelCase = 1
while func(_UpperCAmelCase ) == poly(_UpperCAmelCase ):
x_val += 1
ret += poly(_UpperCAmelCase )
return ret
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 0 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
lowerCAmelCase_ : Optional[int] = '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'
lowerCAmelCase_ : int = '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'
lowerCAmelCase_ : Union[str, Any] = '\nArgs:\n predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n Estimated target values.\n references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n Ground truth (correct) target values.\n sample_weight: array-like of shape (n_samples,), default=None\n Sample weights.\n multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n squared : bool, default=True\n If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n mse : mean squared error.\nExamples:\n\n >>> mse_metric = datasets.load_metric(\"mse\")\n >>> predictions = [2.5, 0.0, 2, 8]\n >>> references = [3, -0.5, 2, 7]\n >>> results = mse_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'mse\': 0.375}\n >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n >>> print(rmse_result)\n {\'mse\': 0.6123724356957945}\n\n If you\'re using multi-dimensional lists, then set the config as follows :\n\n >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n >>> references = [[0, 2], [-1, 2], [8, -5]]\n >>> results = mse_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'mse\': 0.7083333333333334}\n >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {\'mse\': array([0.41666667, 1. ])}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE (datasets.Metric ):
"""simple docstring"""
def UpperCamelCase__ ( self : Any ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
"https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
] , )
def UpperCamelCase__ ( self : List[Any] ):
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value("float" ) ),
"references": datasets.Sequence(datasets.Value("float" ) ),
}
else:
return {
"predictions": datasets.Value("float" ),
"references": datasets.Value("float" ),
}
def UpperCamelCase__ ( self : str , __a : Optional[int] , __a : List[str] , __a : str=None , __a : List[str]="uniform_average" , __a : Tuple=True ):
_a = mean_squared_error(
__a , __a , sample_weight=__a , multioutput=__a , squared=__a )
return {"mse": mse}
| 63 |
from __future__ import annotations
def A ( _UpperCAmelCase : list[int] ) -> bool:
'''simple docstring'''
return len(set(_UpperCAmelCase ) ) == len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 0 |
"""simple docstring"""
import unittest
import numpy as np
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 PIL import Image
from transformers import ViTImageProcessor
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=1_3 , lowerCAmelCase__=3 , lowerCAmelCase__=2_2_4 , lowerCAmelCase__=3_0 , lowerCAmelCase__=4_0_0 , lowerCAmelCase__=True , lowerCAmelCase__=None , lowerCAmelCase__=True , lowerCAmelCase__=[0.5, 0.5, 0.5] , lowerCAmelCase__=[0.5, 0.5, 0.5] , ):
__SCREAMING_SNAKE_CASE = size if size is not None else {"""height""": 1_8, """width""": 1_8}
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = min_resolution
__SCREAMING_SNAKE_CASE = max_resolution
__SCREAMING_SNAKE_CASE = do_resize
__SCREAMING_SNAKE_CASE = size
__SCREAMING_SNAKE_CASE = do_normalize
__SCREAMING_SNAKE_CASE = image_mean
__SCREAMING_SNAKE_CASE = image_std
def snake_case_ ( self):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_ ( __a , unittest.TestCase ):
"""simple docstring"""
__lowercase : Optional[Any] = ViTImageProcessor if is_vision_available() else None
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = EfficientFormerImageProcessorTester(self)
@property
def snake_case_ ( self):
return self.image_proc_tester.prepare_image_processor_dict()
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(lowerCAmelCase__ , """image_mean"""))
self.assertTrue(hasattr(lowerCAmelCase__ , """image_std"""))
self.assertTrue(hasattr(lowerCAmelCase__ , """do_normalize"""))
self.assertTrue(hasattr(lowerCAmelCase__ , """do_resize"""))
self.assertTrue(hasattr(lowerCAmelCase__ , """size"""))
def snake_case_ ( self):
pass
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
__SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_proc_tester , equal_resolution=lowerCAmelCase__)
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image)
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processor(image_inputs[0] , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
# Test batched
__SCREAMING_SNAKE_CASE = image_processor(lowerCAmelCase__ , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
__SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_proc_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__)
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray)
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processor(image_inputs[0] , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
# Test batched
__SCREAMING_SNAKE_CASE = image_processor(lowerCAmelCase__ , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_proc_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__)
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor)
# Test not batched input
__SCREAMING_SNAKE_CASE = image_processor(image_inputs[0] , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
# Test batched
__SCREAMING_SNAKE_CASE = image_processor(lowerCAmelCase__ , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["""height"""],
self.image_proc_tester.size["""width"""],
) , )
| 100 |
import os
UpperCAmelCase__ = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def A ( _UpperCAmelCase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(_UpperCAmelCase ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def A ( _UpperCAmelCase : int ) -> str:
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1_000
numerals += m_count * "M"
num %= 1_000
_UpperCAmelCase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
_UpperCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def A ( _UpperCAmelCase : str = "/p089_roman.txt" ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(_UpperCAmelCase ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(_UpperCAmelCase )
_UpperCAmelCase = generate_roman_numerals(_UpperCAmelCase )
savings += len(_UpperCAmelCase ) - len(_UpperCAmelCase )
return savings
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__lowerCamelCase : List[str] = ''''''
__lowerCamelCase : Any = ''''''
__lowerCamelCase : List[str] = ''''''
__lowerCamelCase : Dict = 1 # (0 is vertical, 1 is horizontal)
def __SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = get_dataset(_UpperCAmelCase , _UpperCAmelCase )
print("""Processing...""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = update_image_and_anno(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
for index, image in enumerate(_UpperCAmelCase ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
SCREAMING_SNAKE_CASE__ = random_chars(32 )
SCREAMING_SNAKE_CASE__ = paths[index].split(os.sep )[-1].rsplit(""".""" , 1 )[0]
SCREAMING_SNAKE_CASE__ = f"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
cva.imwrite(f"""/{file_root}.jpg""" , _UpperCAmelCase , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f"""Success {index+1}/{len(_UpperCAmelCase )} with {file_name}""" )
SCREAMING_SNAKE_CASE__ = []
for anno in new_annos[index]:
SCREAMING_SNAKE_CASE__ = f"""{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"""
annos_list.append(_UpperCAmelCase )
with open(f"""/{file_root}.txt""" , """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : str , __UpperCamelCase : str ) -> tuple[list, list]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = []
for label_file in glob.glob(os.path.join(_UpperCAmelCase , """*.txt""" ) ):
SCREAMING_SNAKE_CASE__ = label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
with open(_UpperCAmelCase ) as in_file:
SCREAMING_SNAKE_CASE__ = in_file.readlines()
SCREAMING_SNAKE_CASE__ = os.path.join(_UpperCAmelCase , f"""{label_name}.jpg""" )
SCREAMING_SNAKE_CASE__ = []
for obj_list in obj_lists:
SCREAMING_SNAKE_CASE__ = obj_list.rstrip("""\n""" ).split(""" """ )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(_UpperCAmelCase )
labels.append(_UpperCAmelCase )
return img_paths, labels
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : list , __UpperCamelCase : list , __UpperCamelCase : int = 1 ) -> tuple[list, list, list]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = []
for idx in range(len(_UpperCAmelCase ) ):
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = img_list[idx]
path_list.append(_UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = anno_list[idx]
SCREAMING_SNAKE_CASE__ = cva.imread(_UpperCAmelCase )
if flip_type == 1:
SCREAMING_SNAKE_CASE__ = cva.flip(_UpperCAmelCase , _UpperCAmelCase )
for bbox in img_annos:
SCREAMING_SNAKE_CASE__ = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
SCREAMING_SNAKE_CASE__ = cva.flip(_UpperCAmelCase , _UpperCAmelCase )
for bbox in img_annos:
SCREAMING_SNAKE_CASE__ = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(_UpperCAmelCase )
new_imgs_list.append(_UpperCAmelCase )
return new_imgs_list, new_annos_lists, path_list
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int = 32 ) -> str:
"""simple docstring"""
assert number_char > 1, "The number of character should greater than 1"
SCREAMING_SNAKE_CASE__ = ascii_lowercase + digits
return "".join(random.choice(_UpperCAmelCase ) for _ in range(_UpperCAmelCase ) )
if __name__ == "__main__":
main()
print('''DONE ✅''')
| 219 |
import requests
from bsa import BeautifulSoup
def A ( _UpperCAmelCase : str , _UpperCAmelCase : dict ) -> str:
'''simple docstring'''
_UpperCAmelCase = BeautifulSoup(requests.get(_UpperCAmelCase , params=_UpperCAmelCase ).content , 'html.parser' )
_UpperCAmelCase = soup.find('div' , attrs={'class': 'gs_ri'} )
_UpperCAmelCase = div.find('div' , attrs={'class': 'gs_fl'} ).find_all('a' )
return anchors[2].get_text()
if __name__ == "__main__":
UpperCAmelCase__ = {
"title": (
"Precisely geometry controlled microsupercapacitors for ultrahigh areal "
"capacitance, volumetric capacitance, and energy density"
),
"journal": "Chem. Mater.",
"volume": 30,
"pages": "3979-3990",
"year": 2018,
"hl": "en",
}
print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
| 339 | 0 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class lowercase__ ( _snake_case ):
'''simple docstring'''
def UpperCAmelCase_ ( self , __snake_case ):
with open(__snake_case , encoding="""utf-8""" ) as input_file:
_SCREAMING_SNAKE_CASE : Any = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" )
_SCREAMING_SNAKE_CASE : Union[str, Any] = input_file.read()
_SCREAMING_SNAKE_CASE : Optional[Any] = regexp.search(__snake_case )
return match
def UpperCAmelCase_ ( self , __snake_case ):
with open(__snake_case , encoding="""utf-8""" ) as input_file:
_SCREAMING_SNAKE_CASE : Tuple = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL )
_SCREAMING_SNAKE_CASE : List[str] = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
_SCREAMING_SNAKE_CASE : int = regexp.finditer(__snake_case )
_SCREAMING_SNAKE_CASE : str = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Dict = Path("""./datasets""" )
_SCREAMING_SNAKE_CASE : str = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(__snake_case ) ):
raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}""" )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : List[str] = Path("""./datasets""" )
_SCREAMING_SNAKE_CASE : Any = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_print_statements(str(__snake_case ) ):
raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead.""" )
| 200 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self : Optional[Any] , A : Dict , A : Union[str, Any]=13 , A : Dict=7 , A : Dict=True , A : Tuple=True , A : Union[str, Any]=True , A : int=True , A : Optional[int]=99 , A : List[str]=32 , A : List[Any]=5 , A : int=4 , A : Any=37 , A : Optional[int]="gelu" , A : Optional[Any]=0.1 , A : Any=0.1 , A : Union[str, Any]=5_12 , A : int=16 , A : List[str]=2 , A : Union[str, Any]=0.0_2 , A : Union[str, Any]=4 , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_attention_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_choices
def _lowerCamelCase ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCAmelCase = None
if self.use_attention_mask:
_UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length])
_UpperCAmelCase = None
if self.use_token_type_ids:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
_UpperCAmelCase = RoFormerConfig(
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=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _lowerCamelCase ( self : List[Any]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class __lowerCAmelCase ( A , unittest.TestCase ):
UpperCamelCase = True
UpperCamelCase = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
_UpperCAmelCase = FlaxRoFormerModelTester(self)
@slow
def _lowerCamelCase ( self : List[Any]) -> Dict:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_UpperCAmelCase = model_class_name.from_pretrained('junnyu/roformer_chinese_small' , from_pt=A)
_UpperCAmelCase = model(np.ones((1, 1)))
self.assertIsNotNone(A)
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def _lowerCamelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base')
_UpperCAmelCase = jnp.array([[0, 1, 2, 3, 4, 5]])
_UpperCAmelCase = model(A)[0]
_UpperCAmelCase = 5_00_00
_UpperCAmelCase = (1, 6, vocab_size)
self.assertEqual(output.shape , A)
_UpperCAmelCase = jnp.array(
[[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]])
self.assertTrue(jnp.allclose(output[:, :3, :3] , A , atol=1E-4))
| 339 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
A_ :Dict = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ :int = [
'''FALCON_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FalconForCausalLM''',
'''FalconModel''',
'''FalconPreTrainedModel''',
'''FalconForSequenceClassification''',
'''FalconForTokenClassification''',
'''FalconForQuestionAnswering''',
]
if TYPE_CHECKING:
from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_falcon import (
FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
FalconPreTrainedModel,
)
else:
import sys
A_ :Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 71 |
UpperCAmelCase__ = {}
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> 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 = (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 = _calculate(days - 1 , _UpperCAmelCase , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
_UpperCAmelCase = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
_UpperCAmelCase = _calculate(days - 1 , _UpperCAmelCase , 0 )
_UpperCAmelCase = state_late + state_absent + state_ontime
_UpperCAmelCase = prizestrings
return prizestrings
def A ( _UpperCAmelCase : int = 30 ) -> int:
'''simple docstring'''
return _calculate(_UpperCAmelCase , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 339 | 0 |
'''simple docstring'''
from math import loga
def _lowerCAmelCase ( _UpperCamelCase : int ) -> int:
"""simple docstring"""
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('Input value must be a \'int\' type' )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 47 |
import os
import sys
import unittest
UpperCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCAmelCase__ = os.path.join(git_repo_path, "src", "diffusers")
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = find_backend(' if not is_torch_available():')
self.assertEqual(A , 'torch')
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
_UpperCAmelCase = find_backend(' if not (is_torch_available() and is_transformers_available()):')
self.assertEqual(A , 'torch_and_transformers')
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
_UpperCAmelCase = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):')
self.assertEqual(A , 'torch_and_transformers_and_onnx')
def _lowerCamelCase ( self : int) -> Dict:
"""simple docstring"""
_UpperCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , A)
self.assertIn('torch_and_transformers' , A)
self.assertIn('flax_and_transformers' , A)
self.assertIn('torch_and_transformers_and_onnx' , A)
# Likewise, we can't assert on the exact content of a key
self.assertIn('UNet2DModel' , objects['torch'])
self.assertIn('FlaxUNet2DConditionModel' , objects['flax'])
self.assertIn('StableDiffusionPipeline' , objects['torch_and_transformers'])
self.assertIn('FlaxStableDiffusionPipeline' , objects['flax_and_transformers'])
self.assertIn('LMSDiscreteScheduler' , objects['torch_and_scipy'])
self.assertIn('OnnxStableDiffusionPipeline' , objects['torch_and_transformers_and_onnx'])
def _lowerCamelCase ( self : Union[str, Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = create_dummy_object('CONSTANT' , '\'torch\'')
self.assertEqual(A , '\nCONSTANT = None\n')
_UpperCAmelCase = create_dummy_object('function' , '\'torch\'')
self.assertEqual(
A , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n')
_UpperCAmelCase = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n'
_UpperCAmelCase = create_dummy_object('FakeClass' , '\'torch\'')
self.assertEqual(A , A)
def _lowerCamelCase ( self : Dict) -> int:
"""simple docstring"""
_UpperCAmelCase = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n'
_UpperCAmelCase = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']})
self.assertEqual(dummy_files['torch'] , A)
| 339 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
a_ = {
'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',
'adapter_layer': 'encoder.layers.*.adapter_layer',
'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',
'pooling_layer.linear': 'projector',
'pooling_layer.projection': 'classifier',
}
a_ = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
'projector',
'classifier',
]
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : List[Any] = {}
with open(_UpperCAmelCase , '''r''' ) as file:
for line_number, line in enumerate(_UpperCAmelCase ):
__lowercase : List[str] = line.strip()
if line:
__lowercase : Union[str, Any] = line.split()
__lowercase : Tuple = line_number
__lowercase : Tuple = words[0]
__lowercase : Any = value
return result
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
for attribute in key.split('''.''' ):
__lowercase : Optional[int] = getattr(_UpperCAmelCase , _UpperCAmelCase )
__lowercase : Dict = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(_UpperCAmelCase ):
__lowercase : Union[str, Any] = PARAM_MAPPING[full_name.split('''.''' )[-1]]
__lowercase : Dict = '''param'''
if weight_type is not None and weight_type != "param":
__lowercase : Union[str, Any] = getattr(_UpperCAmelCase , _UpperCAmelCase ).shape
elif weight_type is not None and weight_type == "param":
__lowercase : Tuple = hf_pointer
for attribute in hf_param_name.split('''.''' ):
__lowercase : Any = getattr(_UpperCAmelCase , _UpperCAmelCase )
__lowercase : List[Any] = shape_pointer.shape
# let's reduce dimension
__lowercase : Tuple = value[0]
else:
__lowercase : Tuple = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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":
__lowercase : Dict = value
elif weight_type == "weight_g":
__lowercase : Union[str, Any] = value
elif weight_type == "weight_v":
__lowercase : List[Any] = value
elif weight_type == "bias":
__lowercase : Union[str, Any] = value
elif weight_type == "param":
for attribute in hf_param_name.split('''.''' ):
__lowercase : Union[str, Any] = getattr(_UpperCAmelCase , _UpperCAmelCase )
__lowercase : List[str] = value
else:
__lowercase : List[Any] = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
__lowercase : Optional[Any] = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(_UpperCAmelCase ):
__lowercase : List[str] = PARAM_MAPPING[full_name.split('''.''' )[-1]]
__lowercase : Any = '''param'''
if weight_type is not None and weight_type != "param":
__lowercase : List[str] = '''.'''.join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
__lowercase : Optional[int] = '''.'''.join([key, hf_param_name] )
else:
__lowercase : Any = key
__lowercase : str = value if '''lm_head''' in full_key else value[0]
a_ = {
'W_a': 'linear_1.weight',
'W_b': 'linear_2.weight',
'b_a': 'linear_1.bias',
'b_b': 'linear_2.bias',
'ln_W': 'norm.weight',
'ln_b': 'norm.bias',
}
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None ):
__lowercase : List[Any] = False
for key, mapped_key in MAPPING.items():
__lowercase : Union[str, Any] = '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
__lowercase : List[Any] = True
if "*" in mapped_key:
__lowercase : int = name.split(_UpperCAmelCase )[0].split('''.''' )[-2]
__lowercase : Optional[Any] = mapped_key.replace('''*''' , _UpperCAmelCase )
if "weight_g" in name:
__lowercase : int = '''weight_g'''
elif "weight_v" in name:
__lowercase : Dict = '''weight_v'''
elif "bias" in name:
__lowercase : Any = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowercase : Optional[int] = '''weight'''
else:
__lowercase : List[Any] = None
if hf_dict is not None:
rename_dict(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
else:
set_recursively(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
return is_used
return is_used
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
__lowercase : List[str] = []
__lowercase : Tuple = fairseq_model.state_dict()
__lowercase : Tuple = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
__lowercase : List[str] = False
if "conv_layers" in name:
load_conv_layer(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , hf_model.config.feat_extract_norm == '''group''' , )
__lowercase : str = True
else:
__lowercase : Optional[int] = load_wavaveca_layer(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if not is_used:
unused_weights.append(_UpperCAmelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
__lowercase : List[str] = full_name.split('''conv_layers.''' )[-1]
__lowercase : Optional[Any] = name.split('''.''' )
__lowercase : Optional[Any] = int(items[0] )
__lowercase : Any = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
__lowercase : Optional[int] = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
__lowercase : List[Any] = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
__lowercase : Any = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
__lowercase : Optional[int] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_UpperCAmelCase )
@torch.no_grad()
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=True , __UpperCamelCase=False ):
if config_path is not None:
__lowercase : List[Any] = WavaVecaConfig.from_pretrained(_UpperCAmelCase )
else:
__lowercase : List[str] = WavaVecaConfig()
if is_seq_class:
__lowercase : Optional[int] = read_txt_into_dict(_UpperCAmelCase )
__lowercase : int = idalabel
__lowercase : Union[str, Any] = WavaVecaForSequenceClassification(_UpperCAmelCase )
__lowercase : Tuple = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=_UpperCAmelCase , return_attention_mask=_UpperCAmelCase , )
feature_extractor.save_pretrained(_UpperCAmelCase )
elif is_finetuned:
if dict_path:
__lowercase : Optional[Any] = Dictionary.load(_UpperCAmelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__lowercase : List[str] = target_dict.pad_index
__lowercase : Optional[int] = target_dict.bos_index
__lowercase : List[str] = target_dict.eos_index
__lowercase : List[str] = len(target_dict.symbols )
__lowercase : Tuple = os.path.join(_UpperCAmelCase , '''vocab.json''' )
if not os.path.isdir(_UpperCAmelCase ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_UpperCAmelCase ) )
return
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
__lowercase : Optional[Any] = target_dict.indices
# fairseq has the <pad> and <s> switched
__lowercase : List[Any] = 0
__lowercase : List[str] = 1
with open(_UpperCAmelCase , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
__lowercase : Any = WavaVecaCTCTokenizer(
_UpperCAmelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=_UpperCAmelCase , )
__lowercase : str = True if config.feat_extract_norm == '''layer''' else False
__lowercase : str = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=_UpperCAmelCase , return_attention_mask=_UpperCAmelCase , )
__lowercase : int = WavaVecaProcessor(feature_extractor=_UpperCAmelCase , tokenizer=_UpperCAmelCase )
processor.save_pretrained(_UpperCAmelCase )
__lowercase : Any = WavaVecaForCTC(_UpperCAmelCase )
else:
__lowercase : Optional[Any] = WavaVecaForPreTraining(_UpperCAmelCase )
if is_finetuned or is_seq_class:
__lowercase ,__lowercase ,__lowercase : Union[str, Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
__lowercase : int = argparse.Namespace(task='''audio_pretraining''' )
__lowercase : Dict = fairseq.tasks.setup_task(_UpperCAmelCase )
__lowercase ,__lowercase ,__lowercase : List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=_UpperCAmelCase )
__lowercase : Union[str, Any] = model[0].eval()
recursively_load_weights(_UpperCAmelCase , _UpperCAmelCase , not is_finetuned )
hf_wavavec.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
a_ = 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('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
parser.add_argument(
'--is_seq_class',
action='store_true',
help='Whether the model to convert is a fine-tuned sequence classification model or not',
)
a_ = parser.parse_args()
a_ = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 249 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
UpperCAmelCase__ = logging.getLogger(__name__)
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} , )
UpperCamelCase = field(
default=1_0_2_4 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
UpperCamelCase = field(
default=A , 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.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the training data.'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
UpperCamelCase = field(default=A , metadata={'''help''': '''A csv or a json file containing the test data.'''} )
def _lowerCamelCase ( self : str) -> List[Any]:
"""simple docstring"""
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.')
else:
_UpperCAmelCase = self.train_file.split('.')[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
_UpperCAmelCase = self.validation_file.split('.')[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default=A , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCamelCase = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
def A ( ) -> Optional[int]:
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCAmelCase = 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 , _UpperCAmelCase , _UpperCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_args_into_dataclasses()
# 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 )] , )
_UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(_UpperCAmelCase )
datasets.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
_UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase = 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 and training_args.resume_from_checkpoint is 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.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. 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 = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
_UpperCAmelCase = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
_UpperCAmelCase = data_args.train_file.split('.' )[-1]
_UpperCAmelCase = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
_UpperCAmelCase = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(F"load a local file for {key}: {data_files[key]}" )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
_UpperCAmelCase = load_dataset('csv' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
_UpperCAmelCase = load_dataset('json' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
_UpperCAmelCase = raw_datasets['train'].features['label'].names
_UpperCAmelCase = len(_UpperCAmelCase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
_UpperCAmelCase = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , 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 , add_prefix_space=_UpperCAmelCase , )
_UpperCAmelCase = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
_UpperCAmelCase = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
_UpperCAmelCase = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
_UpperCAmelCase = {'Refused': 0, 'Entailed': 1}
_UpperCAmelCase = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
F"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." )
_UpperCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(_UpperCAmelCase : Union[str, Any] ):
# Tokenize the texts
def _convert_table_text_to_pandas(_UpperCAmelCase : Dict ):
_UpperCAmelCase = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
_UpperCAmelCase = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
_UpperCAmelCase = examples['statement']
_UpperCAmelCase = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
_UpperCAmelCase = tokenizer(_UpperCAmelCase , _UpperCAmelCase , padding=_UpperCAmelCase , max_length=_UpperCAmelCase , truncation=_UpperCAmelCase )
_UpperCAmelCase = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
_UpperCAmelCase = raw_datasets.map(
_UpperCAmelCase , batched=_UpperCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
_UpperCAmelCase = raw_datasets['train']
if data_args.max_train_samples is not None:
_UpperCAmelCase = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
_UpperCAmelCase = raw_datasets['validation']
if data_args.max_eval_samples is not None:
_UpperCAmelCase = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
_UpperCAmelCase = raw_datasets['test']
if data_args.max_predict_samples is not None:
_UpperCAmelCase = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_UpperCAmelCase ) ) , 3 ):
logger.info(F"Sample {index} of the training set: {train_dataset[index]}." )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_UpperCAmelCase : EvalPrediction ):
_UpperCAmelCase = p.predictions[0] if isinstance(p.predictions , _UpperCAmelCase ) else p.predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
_UpperCAmelCase = default_data_collator
elif training_args.fpaa:
_UpperCAmelCase = DataCollatorWithPadding(_UpperCAmelCase , pad_to_multiple_of=8 )
else:
_UpperCAmelCase = None
# Initialize our Trainer
_UpperCAmelCase = Trainer(
model=_UpperCAmelCase , args=_UpperCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_UpperCAmelCase , tokenizer=_UpperCAmelCase , data_collator=_UpperCAmelCase , )
# Training
if training_args.do_train:
_UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase = last_checkpoint
_UpperCAmelCase = trainer.train(resume_from_checkpoint=_UpperCAmelCase )
_UpperCAmelCase = train_result.metrics
_UpperCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCAmelCase )
)
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _UpperCAmelCase )
trainer.save_metrics('train' , _UpperCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
_UpperCAmelCase = trainer.evaluate(eval_dataset=_UpperCAmelCase )
_UpperCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCAmelCase )
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.log_metrics('eval' , _UpperCAmelCase )
trainer.save_metrics('eval' , _UpperCAmelCase )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
_UpperCAmelCase = predict_dataset.remove_columns('label' )
_UpperCAmelCase = trainer.predict(_UpperCAmelCase , metric_key_prefix='predict' ).predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
_UpperCAmelCase = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(_UpperCAmelCase , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(_UpperCAmelCase ):
_UpperCAmelCase = label_list[item]
writer.write(F"{index}\t{item}\n" )
_UpperCAmelCase = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**_UpperCAmelCase )
else:
trainer.create_model_card(**_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 339 | 0 |
"""simple docstring"""
def lowercase () -> Dict:
'''simple docstring'''
lowerCAmelCase = 0
for i in range(1 , 1_001 ):
total += i**i
return str(_UpperCAmelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 155 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def A ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict ) -> Any:
'''simple docstring'''
_UpperCAmelCase = multiprocessing.Manager()
_UpperCAmelCase = manager.list()
_UpperCAmelCase = multiprocessing.Process(target=_UpperCAmelCase , 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 ( _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
_UpperCAmelCase = shutil.rmtree
_UpperCAmelCase = os.rmdir
_UpperCAmelCase = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
_UpperCAmelCase = {}
with swallow_io():
with time_limit(_UpperCAmelCase ):
exec(_UpperCAmelCase , _UpperCAmelCase )
result.append('passed' )
except TimeoutException:
result.append('timed out' )
except BaseException as e:
result.append(F"failed: {e}" )
# Needed for cleaning up.
_UpperCAmelCase = rmtree
_UpperCAmelCase = rmdir
_UpperCAmelCase = chdir
@contextlib.contextmanager
def A ( _UpperCAmelCase : Union[str, Any] ) -> Any:
'''simple docstring'''
def signal_handler(_UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict ):
raise TimeoutException('Timed out!' )
signal.setitimer(signal.ITIMER_REAL , _UpperCAmelCase )
signal.signal(signal.SIGALRM , _UpperCAmelCase )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def A ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = WriteOnlyStringIO()
with contextlib.redirect_stdout(_UpperCAmelCase ):
with contextlib.redirect_stderr(_UpperCAmelCase ):
with redirect_stdin(_UpperCAmelCase ):
yield
@contextlib.contextmanager
def A ( ) -> Any:
'''simple docstring'''
with tempfile.TemporaryDirectory() as dirname:
with chdir(_UpperCAmelCase ):
yield dirname
class __lowerCAmelCase ( A ):
pass
class __lowerCAmelCase ( io.StringIO ):
def _lowerCamelCase ( self : Tuple , *A : str , **A : Any) -> Any:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : List[str] , *A : Optional[Any] , **A : Optional[Any]) -> Optional[int]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : str , *A : List[str] , **A : List[Any]) -> Union[str, Any]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : Union[str, Any] , *A : Optional[Any] , **A : List[str]) -> Optional[int]:
"""simple docstring"""
return False
class __lowerCAmelCase ( contextlib._RedirectStream ): # type: ignore
UpperCamelCase = '''stdin'''
@contextlib.contextmanager
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
if root == ".":
yield
return
_UpperCAmelCase = os.getcwd()
os.chdir(_UpperCAmelCase )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str]=None ) -> Any:
'''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 = None
_UpperCAmelCase = None
import os
_UpperCAmelCase = '1'
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import shutil
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import subprocess
_UpperCAmelCase = None # type: ignore
_UpperCAmelCase = None
import sys
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
| 339 | 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 snake_case_ ( __A ,__A ,unittest.TestCase ):
__A : Union[str, Any] = StableDiffusionDiffEditPipeline
__A : Dict = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
__A : Optional[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
__A : Dict = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
__A : Any = frozenset([] )
def __UpperCamelCase ( self : List[str] ) -> Optional[Any]:
torch.manual_seed(0 )
lowercase__ : Union[str, Any] = 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=lowercase_ , )
lowercase__ : int = DDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule="scaled_linear" , clip_sample=lowercase_ , set_alpha_to_one=lowercase_ , )
lowercase__ : str = DDIMInverseScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule="scaled_linear" , clip_sample=lowercase_ , set_alpha_to_zero=lowercase_ , )
torch.manual_seed(0 )
lowercase__ : str = 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=1_28 , )
torch.manual_seed(0 )
lowercase__ : str = 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=10_00 , hidden_act="gelu" , projection_dim=5_12 , )
lowercase__ : str = CLIPTextModel(lowercase_ )
lowercase__ : List[Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowercase__ : List[str] = {
"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 __UpperCamelCase ( self : List[Any] , lowercase_ : List[Any] , lowercase_ : Tuple=0 ) -> str:
lowercase__ : Union[str, Any] = floats_tensor((1, 16, 16) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
lowercase__ : Optional[int] = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
if str(lowercase_ ).startswith("mps" ):
lowercase__ : List[Any] = torch.manual_seed(lowercase_ )
else:
lowercase__ : List[str] = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
lowercase__ : Optional[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 __UpperCamelCase ( self : Union[str, Any] , lowercase_ : Dict , lowercase_ : List[str]=0 ) -> str:
lowercase__ : Any = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
lowercase__ : Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ : List[str] = Image.fromarray(np.uinta(lowercase_ ) ).convert("RGB" )
if str(lowercase_ ).startswith("mps" ):
lowercase__ : Optional[Any] = torch.manual_seed(lowercase_ )
else:
lowercase__ : int = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
lowercase__ : Optional[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 __UpperCamelCase ( self : int , lowercase_ : Optional[int] , lowercase_ : str=0 ) -> int:
lowercase__ : List[Any] = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
lowercase__ : List[str] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ : List[Any] = Image.fromarray(np.uinta(lowercase_ ) ).convert("RGB" )
if str(lowercase_ ).startswith("mps" ):
lowercase__ : List[Any] = torch.manual_seed(lowercase_ )
else:
lowercase__ : List[Any] = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
lowercase__ : int = {
"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 __UpperCamelCase ( self : Optional[int] ) -> int:
if not hasattr(self.pipeline_class , "_optional_components" ):
return
lowercase__ : Tuple = self.get_dummy_components()
lowercase__ : Optional[int] = self.pipeline_class(**lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(lowercase_ , lowercase_ , lowercase_ )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
lowercase__ : List[str] = self.get_dummy_inputs(lowercase_ )
lowercase__ : Optional[int] = pipe(**lowercase_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowercase_ )
lowercase__ : Tuple = self.pipeline_class.from_pretrained(lowercase_ )
pipe_loaded.to(lowercase_ )
pipe_loaded.set_progress_bar_config(disable=lowercase_ )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(lowercase_ , lowercase_ ) is None , F'''`{optional_component}` did not stay set to None after loading.''' , )
lowercase__ : str = self.get_dummy_inputs(lowercase_ )
lowercase__ : Tuple = pipe_loaded(**lowercase_ )[0]
lowercase__ : List[Any] = np.abs(output - output_loaded ).max()
self.assertLess(lowercase_ , 1E-4 )
def __UpperCamelCase ( self : List[str] ) -> Optional[Any]:
lowercase__ : List[Any] = "cpu"
lowercase__ : Any = self.get_dummy_components()
lowercase__ : Union[str, Any] = self.pipeline_class(**lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase__ : List[Any] = self.get_dummy_mask_inputs(lowercase_ )
lowercase__ : int = pipe.generate_mask(**lowercase_ )
lowercase__ : int = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
lowercase__ : List[str] = np.array([0] * 9 )
lowercase__ : str = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase_ , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def __UpperCamelCase ( self : str ) -> str:
lowercase__ : Union[str, Any] = "cpu"
lowercase__ : Dict = self.get_dummy_components()
lowercase__ : List[Any] = self.pipeline_class(**lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase__ : List[str] = self.get_dummy_inversion_inputs(lowercase_ )
lowercase__ : Optional[int] = pipe.invert(**lowercase_ ).images
lowercase__ : List[Any] = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
lowercase__ : Union[str, Any] = np.array(
[0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99] , )
lowercase__ : Optional[int] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase_ , 1E-3 )
def __UpperCamelCase ( self : Optional[int] ) -> Union[str, Any]:
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def __UpperCamelCase ( self : Dict ) -> Dict:
lowercase__ : Tuple = "cpu"
lowercase__ : List[str] = self.get_dummy_components()
lowercase__ : Dict = {"beta_start": 0.0_00_85, "beta_end": 0.0_12, "beta_schedule": "scaled_linear"}
lowercase__ : Optional[int] = DPMSolverMultistepScheduler(**lowercase_ )
lowercase__ : Optional[int] = DPMSolverMultistepInverseScheduler(**lowercase_ )
lowercase__ : Tuple = self.pipeline_class(**lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase__ : Optional[Any] = self.get_dummy_inversion_inputs(lowercase_ )
lowercase__ : Tuple = pipe.invert(**lowercase_ ).images
lowercase__ : Union[str, Any] = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
lowercase__ : Tuple = np.array(
[0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99] , )
lowercase__ : List[str] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase_ , 1E-3 )
@require_torch_gpu
@slow
class snake_case_ ( unittest.TestCase ):
def __UpperCamelCase ( self : str ) -> Dict:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def __UpperCamelCase ( cls : int ) -> List[Any]:
lowercase__ : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png" )
lowercase__ : Any = raw_image.convert("RGB" ).resize((7_68, 7_68) )
lowercase__ : List[Any] = raw_image
def __UpperCamelCase ( self : Optional[int] ) -> List[str]:
lowercase__ : str = torch.manual_seed(0 )
lowercase__ : Optional[Any] = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=lowercase_ , torch_dtype=torch.floataa )
lowercase__ : Dict = DDIMScheduler.from_config(pipe.scheduler.config )
lowercase__ : List[Any] = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase__ : Optional[Any] = "a bowl of fruit"
lowercase__ : Tuple = "a bowl of pears"
lowercase__ : List[str] = pipe.generate_mask(
image=self.raw_image , source_prompt=lowercase_ , target_prompt=lowercase_ , generator=lowercase_ , )
lowercase__ : Optional[int] = pipe.invert(
prompt=lowercase_ , image=self.raw_image , inpaint_strength=0.7 , generator=lowercase_ ).latents
lowercase__ : Any = pipe(
prompt=lowercase_ , mask_image=lowercase_ , image_latents=lowercase_ , generator=lowercase_ , negative_prompt=lowercase_ , inpaint_strength=0.7 , output_type="numpy" , ).images[0]
lowercase__ : Dict = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png" ).resize((7_68, 7_68) ) )
/ 2_55
)
assert np.abs((expected_image - image).max() ) < 5E-1
def __UpperCamelCase ( self : Union[str, Any] ) -> Dict:
lowercase__ : Any = torch.manual_seed(0 )
lowercase__ : Optional[int] = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=lowercase_ , torch_dtype=torch.floataa )
lowercase__ : Optional[Any] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
lowercase__ : str = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=lowercase_ )
lowercase__ : Dict = "a bowl of fruit"
lowercase__ : Tuple = "a bowl of pears"
lowercase__ : str = pipe.generate_mask(
image=self.raw_image , source_prompt=lowercase_ , target_prompt=lowercase_ , generator=lowercase_ , )
lowercase__ : List[str] = pipe.invert(
prompt=lowercase_ , image=self.raw_image , inpaint_strength=0.7 , generator=lowercase_ , num_inference_steps=25 , ).latents
lowercase__ : Optional[Any] = pipe(
prompt=lowercase_ , mask_image=lowercase_ , image_latents=lowercase_ , generator=lowercase_ , negative_prompt=lowercase_ , inpaint_strength=0.7 , num_inference_steps=25 , output_type="numpy" , ).images[0]
lowercase__ : str = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png" ).resize((7_68, 7_68) ) )
/ 2_55
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 87 |
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 ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any]=False ) -> str:
'''simple docstring'''
try:
_UpperCAmelCase = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_UpperCAmelCase = default
else:
# KEY is set, convert it to True or False.
try:
_UpperCAmelCase = strtobool(_UpperCAmelCase )
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
UpperCAmelCase__ = parse_flag_from_env("RUN_SLOW", default=False)
def A ( _UpperCAmelCase : List[str] ) -> List[str]:
'''simple docstring'''
return unittest.skip('Test was skipped' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> str:
'''simple docstring'''
return unittest.skipUnless(_run_slow_tests , 'test is slow' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> str:
'''simple docstring'''
return unittest.skipUnless(not torch.cuda.is_available() , 'test requires only a CPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.is_available() , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_xpu_available() , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> List[str]:
'''simple docstring'''
return unittest.skipUnless(is_mps_available() , 'test requires a `mps` backend support in `torch`' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , 'test requires the Hugging Face suite' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : str ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_bnb_available() , 'test requires the bitsandbytes library' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_tpu_available() , 'test requires TPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> str:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() == 1 , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> int:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() == 1 , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() > 1 , 'test requires multiple GPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> Any:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() > 1 , 'test requires multiple XPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_safetensors_available() , 'test requires safetensors' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(is_deepspeed_available() , 'test requires DeepSpeed' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_torch_version('>=' , '1.12.0' ) , 'test requires torch version >= 1.12.0' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any=None , _UpperCAmelCase : List[Any]=None ) -> Dict:
'''simple docstring'''
if test_case is None:
return partial(_UpperCAmelCase , version=_UpperCAmelCase )
return unittest.skipUnless(is_torch_version('>=' , _UpperCAmelCase ) , F"test requires torch version >= {version}" )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> int:
'''simple docstring'''
return unittest.skipUnless(is_tensorboard_available() , 'test requires Tensorboard' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return unittest.skipUnless(is_wandb_available() , 'test requires wandb' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_comet_ml_available() , 'test requires comet_ml' )(_UpperCAmelCase )
UpperCAmelCase__ = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def A ( _UpperCAmelCase : List[str] ) -> Any:
'''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' , )(_UpperCAmelCase )
class __lowerCAmelCase ( unittest.TestCase ):
UpperCamelCase = True
@classmethod
def _lowerCamelCase ( cls : List[Any]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = tempfile.mkdtemp()
@classmethod
def _lowerCamelCase ( cls : Union[str, Any]) -> str:
"""simple docstring"""
if os.path.exists(cls.tmpdir):
shutil.rmtree(cls.tmpdir)
def _lowerCamelCase ( self : List[str]) -> List[Any]:
"""simple docstring"""
if self.clear_on_setup:
for path in Path(self.tmpdir).glob('**/*'):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(A)
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Dict) -> Tuple:
"""simple docstring"""
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[int] , A : Union[mock.Mock, List[mock.Mock]]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = mocks if isinstance(A , (tuple, list)) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop)
def A ( _UpperCAmelCase : List[Any] ) -> int:
'''simple docstring'''
_UpperCAmelCase = AcceleratorState()
_UpperCAmelCase = tensor[None].clone().to(state.device )
_UpperCAmelCase = gather(_UpperCAmelCase ).cpu()
_UpperCAmelCase = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , _UpperCAmelCase ):
return False
return True
class __lowerCAmelCase :
def __init__( self : Optional[Any] , A : Union[str, Any] , A : Optional[int] , A : str) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = returncode
_UpperCAmelCase = stdout
_UpperCAmelCase = stderr
async def A ( _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
while True:
_UpperCAmelCase = await stream.readline()
if line:
callback(_UpperCAmelCase )
else:
break
async def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=None , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Union[str, Any]=False ) -> _RunOutput:
'''simple docstring'''
if echo:
print('\nRunning: ' , ' '.join(_UpperCAmelCase ) )
_UpperCAmelCase = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=_UpperCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_UpperCAmelCase , )
# 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 = []
_UpperCAmelCase = []
def tee(_UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : str="" ):
_UpperCAmelCase = line.decode('utf-8' ).rstrip()
sink.append(_UpperCAmelCase )
if not quiet:
print(_UpperCAmelCase , _UpperCAmelCase , file=_UpperCAmelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stdout , label='stdout:' ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stderr , label='stderr:' ) ) ),
] , timeout=_UpperCAmelCase , )
return _RunOutput(await p.wait() , _UpperCAmelCase , _UpperCAmelCase )
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Dict=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=180 , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : List[Any]=True ) -> _RunOutput:
'''simple docstring'''
_UpperCAmelCase = asyncio.get_event_loop()
_UpperCAmelCase = loop.run_until_complete(
_stream_subprocess(_UpperCAmelCase , env=_UpperCAmelCase , stdin=_UpperCAmelCase , timeout=_UpperCAmelCase , quiet=_UpperCAmelCase , echo=_UpperCAmelCase ) )
_UpperCAmelCase = ' '.join(_UpperCAmelCase )
if result.returncode > 0:
_UpperCAmelCase = '\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 ( A ):
pass
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : str=False ) -> Tuple:
'''simple docstring'''
try:
_UpperCAmelCase = subprocess.check_output(_UpperCAmelCase , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(_UpperCAmelCase , 'decode' ):
_UpperCAmelCase = output.decode('utf-8' )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F"Command `{' '.join(_UpperCAmelCase )}` failed with the following error:\n\n{e.output.decode()}" ) from e
| 339 | 0 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __lowerCAmelCase ( snake_case__ ):
__UpperCamelCase : Optional[int] = filter(lambda snake_case__ : p.requires_grad , model.parameters() )
__UpperCamelCase : Optional[int] = sum([np.prod(p.size() ) for p in model_parameters] )
return params
_lowerCAmelCase = logging.getLogger(__name__)
def __lowerCAmelCase ( snake_case__ , snake_case__ ):
if metric == "rouge2":
__UpperCamelCase : Optional[Any] = "{val_avg_rouge2:.4f}-{step_count}"
elif metric == "bleu":
__UpperCamelCase : int = "{val_avg_bleu:.4f}-{step_count}"
elif metric == "em":
__UpperCamelCase : Optional[Any] = "{val_avg_em:.4f}-{step_count}"
elif metric == "loss":
__UpperCamelCase : List[Any] = "{val_avg_loss:.4f}-{step_count}"
else:
raise NotImplementedError(
F"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"
" function." )
__UpperCamelCase : int = ModelCheckpoint(
dirpath=_UpperCAmelCase , filename=_UpperCAmelCase , monitor=F"val_{metric}" , mode="max" , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def __lowerCAmelCase ( snake_case__ , snake_case__ ):
return EarlyStopping(
monitor=F"val_{metric}" , mode="min" if "loss" in metric else "max" , patience=_UpperCAmelCase , verbose=_UpperCAmelCase , )
class A ( pl.Callback ):
'''simple docstring'''
def a_ (self , _UpperCAmelCase , _UpperCAmelCase ) -> str:
__UpperCamelCase : Optional[int] = {f"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(_UpperCAmelCase )
@rank_zero_only
def a_ (self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=True ) -> None:
logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****" )
__UpperCamelCase : Optional[Any] = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} )
# Log results
__UpperCamelCase : Any = Path(pl_module.hparams.output_dir )
if type_path == "test":
__UpperCamelCase : Tuple = od / "test_results.txt"
__UpperCamelCase : int = od / "test_generations.txt"
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
__UpperCamelCase : Optional[int] = od / f"{type_path}_results/{trainer.global_step:05d}.txt"
__UpperCamelCase : Optional[Any] = od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
results_file.parent.mkdir(exist_ok=_UpperCAmelCase )
generations_file.parent.mkdir(exist_ok=_UpperCAmelCase )
with open(_UpperCAmelCase , "a+" ) as writer:
for key in sorted(_UpperCAmelCase ):
if key in ["log", "progress_bar", "preds"]:
continue
__UpperCamelCase : Optional[Any] = metrics[key]
if isinstance(_UpperCAmelCase , torch.Tensor ):
__UpperCamelCase : Optional[Any] = val.item()
__UpperCamelCase : List[Any] = f"{key}: {val:.6f}\n"
writer.write(_UpperCAmelCase )
if not save_generations:
return
if "preds" in metrics:
__UpperCamelCase : Optional[int] = "\n".join(metrics["preds"] )
generations_file.open("w+" ).write(_UpperCAmelCase )
@rank_zero_only
def a_ (self , _UpperCAmelCase , _UpperCAmelCase ) -> str:
try:
__UpperCamelCase : str = pl_module.model.model.num_parameters()
except AttributeError:
__UpperCamelCase : Any = pl_module.model.num_parameters()
__UpperCamelCase : List[Any] = count_trainable_parameters(_UpperCAmelCase )
# mp stands for million parameters
trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1E6, "grad_mp": n_trainable_pars / 1E6} )
@rank_zero_only
def a_ (self , _UpperCAmelCase , _UpperCAmelCase ) -> List[str]:
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(_UpperCAmelCase , _UpperCAmelCase , "test" )
@rank_zero_only
def a_ (self , _UpperCAmelCase , _UpperCAmelCase ) -> Dict:
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 298 |
from __future__ import annotations
UpperCAmelCase__ = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
UpperCAmelCase__ = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def A ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
'''simple docstring'''
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def A ( _UpperCAmelCase : Matrix ) -> Matrix | None:
'''simple docstring'''
if location := find_empty_location(_UpperCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
_UpperCAmelCase = 0
return None
def A ( _UpperCAmelCase : Matrix ) -> None:
'''simple docstring'''
for row in grid:
for cell in row:
print(_UpperCAmelCase , end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("\nExample grid:\n" + "=" * 20)
print_solution(example_grid)
print("\nExample grid solution:")
UpperCAmelCase__ = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("Cannot find a solution.")
| 339 | 0 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def _lowerCamelCase( lowercase__ ) -> List[Any]:
'''simple docstring'''
__lowercase= SwinaSRConfig()
if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
__lowercase= 4
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
__lowercase= 4
__lowercase= 4_8
__lowercase= 'pixelshuffle_aux'
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
__lowercase= [6, 6, 6, 6]
__lowercase= 6_0
__lowercase= [6, 6, 6, 6]
__lowercase= 'pixelshuffledirect'
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
__lowercase= 4
__lowercase= 'nearest+conv'
elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
__lowercase= 1
__lowercase= 1
__lowercase= 1_2_6
__lowercase= 7
__lowercase= 255.0
__lowercase= ''
return config
def _lowerCamelCase( lowercase__ , lowercase__ ) -> int:
'''simple docstring'''
if "patch_embed.proj" in name and "layers" not in name:
__lowercase= name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
__lowercase= name.replace('patch_embed.norm' , 'embeddings.patch_embeddings.layernorm' )
if "layers" in name:
__lowercase= name.replace('layers' , 'encoder.stages' )
if "residual_group.blocks" in name:
__lowercase= name.replace('residual_group.blocks' , 'layers' )
if "attn.proj" in name:
__lowercase= name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
__lowercase= name.replace('attn' , 'attention.self' )
if "norm1" in name:
__lowercase= name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
__lowercase= name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
__lowercase= name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
__lowercase= name.replace('mlp.fc2' , 'output.dense' )
if "q_bias" in name:
__lowercase= name.replace('q_bias' , 'query.bias' )
if "k_bias" in name:
__lowercase= name.replace('k_bias' , 'key.bias' )
if "v_bias" in name:
__lowercase= name.replace('v_bias' , 'value.bias' )
if "cpb_mlp" in name:
__lowercase= name.replace('cpb_mlp' , 'continuous_position_bias_mlp' )
if "patch_embed.proj" in name:
__lowercase= name.replace('patch_embed.proj' , 'patch_embed.projection' )
if name == "norm.weight":
__lowercase= 'layernorm.weight'
if name == "norm.bias":
__lowercase= 'layernorm.bias'
if "conv_first" in name:
__lowercase= name.replace('conv_first' , 'first_convolution' )
if (
"upsample" in name
or "conv_before_upsample" in name
or "conv_bicubic" in name
or "conv_up" in name
or "conv_hr" in name
or "conv_last" in name
or "aux" in name
):
# heads
if "conv_last" in name:
__lowercase= name.replace('conv_last' , 'final_convolution' )
if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
if "conv_before_upsample.0" in name:
__lowercase= name.replace('conv_before_upsample.0' , 'conv_before_upsample' )
if "upsample.0" in name:
__lowercase= name.replace('upsample.0' , 'upsample.convolution_0' )
if "upsample.2" in name:
__lowercase= name.replace('upsample.2' , 'upsample.convolution_1' )
__lowercase= 'upsample.' + name
elif config.upsampler == "pixelshuffledirect":
__lowercase= name.replace('upsample.0.weight' , 'upsample.conv.weight' )
__lowercase= name.replace('upsample.0.bias' , 'upsample.conv.bias' )
else:
pass
else:
__lowercase= 'swin2sr.' + name
return name
def _lowerCamelCase( lowercase__ , lowercase__ ) -> List[Any]:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
__lowercase= orig_state_dict.pop(_UpperCAmelCase )
if "qkv" in key:
__lowercase= key.split('.' )
__lowercase= int(key_split[1] )
__lowercase= int(key_split[4] )
__lowercase= config.embed_dim
if "weight" in key:
__lowercase= val[:dim, :]
__lowercase= val[dim : dim * 2, :]
__lowercase= val[-dim:, :]
else:
__lowercase= val[:dim]
__lowercase= val[dim : dim * 2]
__lowercase= val[-dim:]
pass
else:
__lowercase= val
return orig_state_dict
def _lowerCamelCase( lowercase__ , lowercase__ , lowercase__ ) -> str:
'''simple docstring'''
__lowercase= get_config(_UpperCAmelCase )
__lowercase= SwinaSRForImageSuperResolution(_UpperCAmelCase )
model.eval()
__lowercase= torch.hub.load_state_dict_from_url(_UpperCAmelCase , map_location='cpu' )
__lowercase= convert_state_dict(_UpperCAmelCase , _UpperCAmelCase )
__lowercase, __lowercase= model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0:
raise ValueError('Missing keys when converting: {}'.format(_UpperCAmelCase ) )
for key in unexpected_keys:
if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
raise ValueError(F'Unexpected key {key} in state_dict' )
# verify values
__lowercase= 'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
__lowercase= Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw ).convert('RGB' )
__lowercase= SwinaSRImageProcessor()
# pixel_values = processor(image, return_tensors="pt").pixel_values
__lowercase= 1_2_6 if 'Jpeg' in checkpoint_url else 2_5_6
__lowercase= Compose(
[
Resize((image_size, image_size) ),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] ),
] )
__lowercase= transforms(_UpperCAmelCase ).unsqueeze(0 )
if config.num_channels == 1:
__lowercase= pixel_values[:, 0, :, :].unsqueeze(1 )
__lowercase= model(_UpperCAmelCase )
# assert values
if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
__lowercase= torch.Size([1, 3, 5_1_2, 5_1_2] )
__lowercase= torch.tensor(
[[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]] )
elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
__lowercase= torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
__lowercase= torch.tensor(
[[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]] )
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
# TODO values didn't match exactly here
__lowercase= torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
__lowercase= torch.tensor(
[[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]] )
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
__lowercase= torch.Size([1, 3, 5_1_2, 5_1_2] )
__lowercase= torch.tensor(
[[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]] )
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
__lowercase= torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
__lowercase= torch.tensor(
[[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]] )
assert (
outputs.reconstruction.shape == expected_shape
), F'Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'
assert torch.allclose(outputs.reconstruction[0, 0, :3, :3] , _UpperCAmelCase , atol=1E-3 )
print('Looks ok!' )
__lowercase= {
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth': (
'swin2SR-classical-sr-x2-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth': (
'swin2SR-classical-sr-x4-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth': (
'swin2SR-compressed-sr-x4-48'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth': (
'swin2SR-lightweight-x2-64'
),
'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth': (
'swin2SR-realworld-sr-x4-64-bsrgan-psnr'
),
}
__lowercase= url_to_name[checkpoint_url]
if pytorch_dump_folder_path is not None:
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(_UpperCAmelCase )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
model.push_to_hub(F'caidas/{model_name}' )
processor.push_to_hub(F'caidas/{model_name}' )
if __name__ == "__main__":
lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
type=str,
help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the converted model to the hub.''')
lowerCAmelCase = parser.parse_args()
convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 295 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
UpperCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
UpperCAmelCase__ = "\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n"
UpperCAmelCase__ = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"
UpperCAmelCase__ = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n 'meteor': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric('meteor')\n >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results[\"meteor\"], 4))\n 0.6944\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : List[Any]) -> List[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence'),
'references': datasets.Value('string' , id='sequence'),
}) , codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'] , reference_urls=[
'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
'https://en.wikipedia.org/wiki/METEOR',
] , )
def _lowerCamelCase ( self : Optional[Any] , A : List[str]) -> List[Any]:
"""simple docstring"""
import nltk
nltk.download('wordnet')
if NLTK_VERSION >= version.Version('3.6.5'):
nltk.download('punkt')
if NLTK_VERSION >= version.Version('3.6.6'):
nltk.download('omw-1.4')
def _lowerCamelCase ( self : Optional[Any] , A : Tuple , A : Optional[int] , A : List[Any]=0.9 , A : Optional[Any]=3 , A : Optional[int]=0.5) -> Any:
"""simple docstring"""
if NLTK_VERSION >= version.Version('3.6.5'):
_UpperCAmelCase = [
meteor_score.single_meteor_score(
word_tokenize(A) , word_tokenize(A) , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
else:
_UpperCAmelCase = [
meteor_score.single_meteor_score(A , A , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
return {"meteor": np.mean(A)}
| 339 | 0 |
'''simple docstring'''
import argparse
import os
import re
lowerCAmelCase_ : Optional[int] = 'src/diffusers'
# Pattern that looks at the indentation in a line.
lowerCAmelCase_ : Any = re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCAmelCase_ : Tuple = re.compile(R'^\s*\"([^\"]+)\":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowerCAmelCase_ : List[str] = re.compile(R'^\s*_import_structure\[\"([^\"]+)\"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
lowerCAmelCase_ : Optional[Any] = re.compile(R'^\s*\"([^\"]+)\",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowerCAmelCase_ : List[str] = re.compile(R'\[([^\]]+)\]')
def _lowerCamelCase ( lowercase : Union[str, Any] ) -> Dict:
_a = _re_indent.search(_UpperCAmelCase )
return "" if search is None else search.groups()[0]
def _lowerCamelCase ( lowercase : Optional[int] , lowercase : str="" , lowercase : Dict=None , lowercase : Union[str, Any]=None ) -> str:
_a = 0
_a = code.split("\n" )
if start_prompt is not None:
while not lines[index].startswith(_UpperCAmelCase ):
index += 1
_a = ["\n".join(lines[:index] )]
else:
_a = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
_a = [lines[index]]
index += 1
while index < len(_UpperCAmelCase ) and (end_prompt is None or not lines[index].startswith(_UpperCAmelCase )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(_UpperCAmelCase ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + " " ):
current_block.append(lines[index] )
blocks.append("\n".join(_UpperCAmelCase ) )
if index < len(_UpperCAmelCase ) - 1:
_a = [lines[index + 1]]
index += 1
else:
_a = []
else:
blocks.append("\n".join(_UpperCAmelCase ) )
_a = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(_UpperCAmelCase ) > 0:
blocks.append("\n".join(_UpperCAmelCase ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(_UpperCAmelCase ):
blocks.append("\n".join(lines[index:] ) )
return blocks
def _lowerCamelCase ( lowercase : Any ) -> Tuple:
def _inner(lowercase : Optional[int] ):
return key(_UpperCAmelCase ).lower().replace("_" , "" )
return _inner
def _lowerCamelCase ( lowercase : str , lowercase : int=None ) -> Optional[Any]:
# If no key is provided, we use a noop.
def noop(lowercase : Optional[int] ):
return x
if key is None:
_a = noop
# Constants are all uppercase, they go first.
_a = [obj for obj in objects if key(_UpperCAmelCase ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
_a = [obj for obj in objects if key(_UpperCAmelCase )[0].isupper() and not key(_UpperCAmelCase ).isupper()]
# Functions begin with a lowercase, they go last.
_a = [obj for obj in objects if not key(_UpperCAmelCase )[0].isupper()]
_a = ignore_underscore(_UpperCAmelCase )
return sorted(_UpperCAmelCase , key=_UpperCAmelCase ) + sorted(_UpperCAmelCase , key=_UpperCAmelCase ) + sorted(_UpperCAmelCase , key=_UpperCAmelCase )
def _lowerCamelCase ( lowercase : List[str] ) -> Optional[Any]:
# This inner function sort imports between [ ].
def _replace(lowercase : Union[str, Any] ):
_a = match.groups()[0]
if "," not in imports:
return F'[{imports}]'
_a = [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:
_a = keys[:-1]
return "[" + ", ".join([F'\"{k}\"' for k in sort_objects(_UpperCAmelCase )] ) + "]"
_a = import_statement.split("\n" )
if len(_UpperCAmelCase ) > 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.
_a = 2 if lines[1].strip() == "[" else 1
_a = [(i, _re_strip_line.search(_UpperCAmelCase ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
_a = sort_objects(_UpperCAmelCase , key=lambda lowercase : x[1] )
_a = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(_UpperCAmelCase ) == 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:
_a = _re_bracket_content.sub(_replace , lines[1] )
else:
_a = [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:
_a = keys[:-1]
_a = get_indent(lines[1] ) + ", ".join([F'\"{k}\"' for k in sort_objects(_UpperCAmelCase )] )
return "\n".join(_UpperCAmelCase )
else:
# Finally we have to deal with imports fitting on one line
_a = _re_bracket_content.sub(_replace , _UpperCAmelCase )
return import_statement
def _lowerCamelCase ( lowercase : Optional[Any] , lowercase : Tuple=True ) -> Optional[int]:
with open(_UpperCAmelCase , "r" ) as f:
_a = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
_a = split_code_in_indented_blocks(
_UpperCAmelCase , 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(_UpperCAmelCase ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
_a = main_blocks[block_idx]
_a = block.split("\n" )
# Get to the start of the imports.
_a = 0
while line_idx < len(_UpperCAmelCase ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
_a = len(_UpperCAmelCase )
else:
line_idx += 1
if line_idx >= len(_UpperCAmelCase ):
continue
# Ignore beginning and last line: they don't contain anything.
_a = "\n".join(block_lines[line_idx:-1] )
_a = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
_a = split_code_in_indented_blocks(_UpperCAmelCase , indent_level=_UpperCAmelCase )
# We have two categories of import key: list or _import_structure[key].append/extend
_a = _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.
_a = [(pattern.search(_UpperCAmelCase ).groups()[0] if pattern.search(_UpperCAmelCase ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
_a = [(i, key) for i, key in enumerate(_UpperCAmelCase ) if key is not None]
_a = [x[0] for x in sorted(_UpperCAmelCase , key=lambda lowercase : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
_a = 0
_a = []
for i in range(len(_UpperCAmelCase ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
_a = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(_UpperCAmelCase )
count += 1
# And we put our main block back together with its first and last line.
_a = "\n".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(_UpperCAmelCase ):
if check_only:
return True
else:
print(F'Overwriting {file}.' )
with open(_UpperCAmelCase , "w" ) as f:
f.write("\n".join(_UpperCAmelCase ) )
def _lowerCamelCase ( lowercase : List[Any]=True ) -> Tuple:
_a = []
for root, _, files in os.walk(_UpperCAmelCase ):
if "__init__.py" in files:
_a = sort_imports(os.path.join(_UpperCAmelCase , "__init__.py" ) , check_only=_UpperCAmelCase )
if result:
_a = [os.path.join(_UpperCAmelCase , "__init__.py" )]
if len(_UpperCAmelCase ) > 0:
raise ValueError(F'Would overwrite {len(_UpperCAmelCase )} files, run `make style`.' )
if __name__ == "__main__":
lowerCAmelCase_ : Tuple = argparse.ArgumentParser()
parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
lowerCAmelCase_ : Any = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 63 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
UpperCAmelCase__ = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
"large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
_UpperCAmelCase = ['layers', 'blocks']
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase , _UpperCAmelCase )
UpperCAmelCase__ = {
"blocks": "layers",
"mlp.0": "fc1",
"mlp.2": "fc2",
"mlp_ln": "final_layer_norm",
".attn.query": ".self_attn.q_proj",
".attn.key": ".self_attn.k_proj",
".attn.value": ".self_attn.v_proj",
".attn_ln": ".self_attn_layer_norm",
".attn.out": ".self_attn.out_proj",
".cross_attn.query": ".encoder_attn.q_proj",
".cross_attn.key": ".encoder_attn.k_proj",
".cross_attn.value": ".encoder_attn.v_proj",
".cross_attn_ln": ".encoder_attn_layer_norm",
".cross_attn.out": ".encoder_attn.out_proj",
"decoder.ln.": "decoder.layer_norm.",
"encoder.ln.": "encoder.layer_norm.",
"token_embedding": "embed_tokens",
"encoder.positional_embedding": "encoder.embed_positions.weight",
"decoder.positional_embedding": "decoder.embed_positions.weight",
"ln_post": "layer_norm",
}
def A ( _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = list(s_dict.keys() )
for key in keys:
_UpperCAmelCase = key
for k, v in WHISPER_MAPPING.items():
if k in key:
_UpperCAmelCase = new_key.replace(_UpperCAmelCase , _UpperCAmelCase )
print(F"{key} -> {new_key}" )
_UpperCAmelCase = s_dict.pop(_UpperCAmelCase )
return s_dict
def A ( _UpperCAmelCase : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = emb.weight.shape
_UpperCAmelCase = nn.Linear(_UpperCAmelCase , _UpperCAmelCase , bias=_UpperCAmelCase )
_UpperCAmelCase = emb.weight.data
return lin_layer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> bytes:
'''simple docstring'''
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
_UpperCAmelCase = os.path.basename(_UpperCAmelCase )
_UpperCAmelCase = url.split('/' )[-2]
_UpperCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if os.path.exists(_UpperCAmelCase ) and not os.path.isfile(_UpperCAmelCase ):
raise RuntimeError(F"{download_target} exists and is not a regular file" )
if os.path.isfile(_UpperCAmelCase ):
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(F"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" )
with urllib.request.urlopen(_UpperCAmelCase ) as source, open(_UpperCAmelCase , 'wb' ) as output:
with tqdm(
total=int(source.info().get('Content-Length' ) ) , ncols=80 , unit='iB' , unit_scale=_UpperCAmelCase , unit_divisor=1_024 ) as loop:
while True:
_UpperCAmelCase = source.read(8_192 )
if not buffer:
break
output.write(_UpperCAmelCase )
loop.update(len(_UpperCAmelCase ) )
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() != expected_shaaaa:
raise RuntimeError(
'Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.' )
return model_bytes
def A ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
if ".pt" not in checkpoint_path:
_UpperCAmelCase = _download(_MODELS[checkpoint_path] )
else:
_UpperCAmelCase = torch.load(_UpperCAmelCase , map_location='cpu' )
_UpperCAmelCase = original_checkpoint['dims']
_UpperCAmelCase = original_checkpoint['model_state_dict']
_UpperCAmelCase = state_dict['decoder.token_embedding.weight']
remove_ignore_keys_(_UpperCAmelCase )
rename_keys(_UpperCAmelCase )
_UpperCAmelCase = True
_UpperCAmelCase = state_dict['decoder.layers.0.fc1.weight'].shape[0]
_UpperCAmelCase = WhisperConfig(
vocab_size=dimensions['n_vocab'] , encoder_ffn_dim=_UpperCAmelCase , decoder_ffn_dim=_UpperCAmelCase , num_mel_bins=dimensions['n_mels'] , d_model=dimensions['n_audio_state'] , max_target_positions=dimensions['n_text_ctx'] , encoder_layers=dimensions['n_audio_layer'] , encoder_attention_heads=dimensions['n_audio_head'] , decoder_layers=dimensions['n_text_layer'] , decoder_attention_heads=dimensions['n_text_state'] , max_source_positions=dimensions['n_audio_ctx'] , )
_UpperCAmelCase = WhisperForConditionalGeneration(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = model.model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0 and not set(_UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'
F" but all the following weights are missing {missing}" )
if tie_embeds:
_UpperCAmelCase = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
_UpperCAmelCase = proj_out_weights
model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# # Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCAmelCase__ = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 339 | 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, PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {"vocab_file": "sentencepiece.bpe.model"}
__magic_name__ = {
"vocab_file": {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
}
}
__magic_name__ = {
"camembert-base": 512,
}
__magic_name__ = "▁"
class SCREAMING_SNAKE_CASE_ ( __a ):
"""simple docstring"""
__lowercase : List[str] = VOCAB_FILES_NAMES
__lowercase : List[str] = PRETRAINED_VOCAB_FILES_MAP
__lowercase : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowercase : int = ['''input_ids''', '''attention_mask''']
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__="<s>" , lowerCAmelCase__="</s>" , lowerCAmelCase__="</s>" , lowerCAmelCase__="<s>" , lowerCAmelCase__="<unk>" , lowerCAmelCase__="<pad>" , lowerCAmelCase__="<mask>" , lowerCAmelCase__=["<s>NOTUSED", "</s>NOTUSED"] , lowerCAmelCase__ = None , **lowerCAmelCase__ , ):
__SCREAMING_SNAKE_CASE = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__ , lowerCAmelCase__) else mask_token
__SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , additional_special_tokens=lowerCAmelCase__ , sp_model_kwargs=self.sp_model_kwargs , **lowerCAmelCase__ , )
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(str(lowerCAmelCase__))
__SCREAMING_SNAKE_CASE = vocab_file
# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
# sentencepiece vocabulary (this is the case for <s> and </s>
__SCREAMING_SNAKE_CASE = {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
__SCREAMING_SNAKE_CASE = len(self.fairseq_tokens_to_ids)
__SCREAMING_SNAKE_CASE = len(self.sp_model) + len(self.fairseq_tokens_to_ids)
__SCREAMING_SNAKE_CASE = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__SCREAMING_SNAKE_CASE = [self.cls_token_id]
__SCREAMING_SNAKE_CASE = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = False):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase__ , token_ids_a=lowerCAmelCase__ , already_has_special_tokens=lowerCAmelCase__)
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase__)) + [1]
return [1] + ([0] * len(lowerCAmelCase__)) + [1, 1] + ([0] * len(lowerCAmelCase__)) + [1]
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None):
__SCREAMING_SNAKE_CASE = [self.sep_token_id]
__SCREAMING_SNAKE_CASE = [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]
@property
def snake_case_ ( self):
return len(self.fairseq_tokens_to_ids) + len(self.sp_model)
def snake_case_ ( self):
__SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(lowerCAmelCase__): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def snake_case_ ( self , lowerCAmelCase__):
return self.sp_model.encode(lowerCAmelCase__ , out_type=lowerCAmelCase__)
def snake_case_ ( self , lowerCAmelCase__):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
elif self.sp_model.PieceToId(lowerCAmelCase__) == 0:
# Convert sentence piece unk token to fairseq unk token index
return self.unk_token_id
return self.fairseq_offset + self.sp_model.PieceToId(lowerCAmelCase__)
def snake_case_ ( self , lowerCAmelCase__):
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 snake_case_ ( self , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = """"""
__SCREAMING_SNAKE_CASE = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowerCAmelCase__) + token
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = False
out_string += self.sp_model.decode(lowerCAmelCase__)
return out_string.strip()
def __getstate__( self):
__SCREAMING_SNAKE_CASE = self.__dict__.copy()
__SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs"""):
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None):
if not os.path.isdir(lowerCAmelCase__):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
__SCREAMING_SNAKE_CASE = os.path.join(
lowerCAmelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase__) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , lowerCAmelCase__)
elif not os.path.isfile(self.vocab_file):
with open(lowerCAmelCase__ , """wb""") as fi:
__SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase__)
return (out_vocab_file,)
| 100 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase__ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
UpperCamelCase = None
UpperCamelCase = None
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilder ):
UpperCamelCase = datasets.Audio()
UpperCamelCase = '''audio'''
UpperCamelCase = AudioFolderConfig
UpperCamelCase = 42 # definition at the bottom of the script
UpperCamelCase = AudioClassification(audio_column='''audio''' , label_column='''label''' )
UpperCAmelCase__ = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
UpperCAmelCase__ = AUDIO_EXTENSIONS
| 339 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class __snake_case ( unittest.TestCase ):
def __a ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = tempfile.mkdtemp()
# fmt: off
SCREAMING_SNAKE_CASE__ = ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
SCREAMING_SNAKE_CASE__ = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
SCREAMING_SNAKE_CASE__ = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
SCREAMING_SNAKE_CASE__ = {"""unk_token""": """<unk>"""}
SCREAMING_SNAKE_CASE__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
SCREAMING_SNAKE_CASE__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_lowercase ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(_lowercase ) )
SCREAMING_SNAKE_CASE__ = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
"""image_std""": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
}
SCREAMING_SNAKE_CASE__ = os.path.join(self.tmpdirname , _lowercase )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_lowercase , _lowercase )
def __a ( self : Union[str, Any] , **_lowercase : Optional[Any] ):
"""simple docstring"""
return CLIPTokenizer.from_pretrained(self.tmpdirname , **_lowercase )
def __a ( self : Optional[int] , **_lowercase : Tuple ):
"""simple docstring"""
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **_lowercase )
def __a ( self : Union[str, Any] , **_lowercase : Any ):
"""simple docstring"""
return CLIPImageProcessor.from_pretrained(self.tmpdirname , **_lowercase )
def __a ( self : Dict ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __a ( self : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE__ = [Image.fromarray(np.moveaxis(_lowercase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __a ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ = self.get_image_processor()
SCREAMING_SNAKE_CASE__ = CLIPProcessor(tokenizer=_lowercase , image_processor=_lowercase )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ = CLIPProcessor.from_pretrained(self.tmpdirname , use_fast=_lowercase )
SCREAMING_SNAKE_CASE__ = CLIPProcessor(tokenizer=_lowercase , image_processor=_lowercase )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ = CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _lowercase )
self.assertIsInstance(processor_fast.tokenizer , _lowercase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _lowercase )
self.assertIsInstance(processor_fast.image_processor , _lowercase )
def __a ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = CLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
SCREAMING_SNAKE_CASE__ = self.get_image_processor(do_normalize=_lowercase , padding_value=1.0 )
SCREAMING_SNAKE_CASE__ = CLIPProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_lowercase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _lowercase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowercase )
def __a ( self : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.get_image_processor()
SCREAMING_SNAKE_CASE__ = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ = CLIPProcessor(tokenizer=_lowercase , image_processor=_lowercase )
SCREAMING_SNAKE_CASE__ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ = image_processor(_lowercase , return_tensors="""np""" )
SCREAMING_SNAKE_CASE__ = processor(images=_lowercase , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __a ( self : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.get_image_processor()
SCREAMING_SNAKE_CASE__ = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ = CLIPProcessor(tokenizer=_lowercase , image_processor=_lowercase )
SCREAMING_SNAKE_CASE__ = """lower newer"""
SCREAMING_SNAKE_CASE__ = processor(text=_lowercase )
SCREAMING_SNAKE_CASE__ = tokenizer(_lowercase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __a ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.get_image_processor()
SCREAMING_SNAKE_CASE__ = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ = CLIPProcessor(tokenizer=_lowercase , image_processor=_lowercase )
SCREAMING_SNAKE_CASE__ = """lower newer"""
SCREAMING_SNAKE_CASE__ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ = processor(text=_lowercase , images=_lowercase )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_lowercase ):
processor()
def __a ( self : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.get_image_processor()
SCREAMING_SNAKE_CASE__ = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ = CLIPProcessor(tokenizer=_lowercase , image_processor=_lowercase )
SCREAMING_SNAKE_CASE__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE__ = processor.batch_decode(_lowercase )
SCREAMING_SNAKE_CASE__ = tokenizer.batch_decode(_lowercase )
self.assertListEqual(_lowercase , _lowercase )
def __a ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.get_image_processor()
SCREAMING_SNAKE_CASE__ = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ = CLIPProcessor(tokenizer=_lowercase , image_processor=_lowercase )
SCREAMING_SNAKE_CASE__ = """lower newer"""
SCREAMING_SNAKE_CASE__ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ = processor(text=_lowercase , images=_lowercase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 219 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
def __init__( self : int) -> str:
"""simple docstring"""
_UpperCAmelCase = []
def _lowerCamelCase ( self : Any , A : List[str]) -> int:
"""simple docstring"""
return self.node_position[vertex]
def _lowerCamelCase ( self : Optional[Any] , A : Optional[int] , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = pos
def _lowerCamelCase ( self : Tuple , A : Tuple , A : Dict , A : List[str] , A : Optional[Any]) -> Dict:
"""simple docstring"""
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_UpperCAmelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_UpperCAmelCase = 2 * start + 1
else:
_UpperCAmelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_UpperCAmelCase , _UpperCAmelCase = heap[smallest_child], positions[smallest_child]
_UpperCAmelCase , _UpperCAmelCase = (
heap[start],
positions[start],
)
_UpperCAmelCase , _UpperCAmelCase = temp, tempa
_UpperCAmelCase = self.get_position(positions[smallest_child])
self.set_position(
positions[smallest_child] , self.get_position(positions[start]))
self.set_position(positions[start] , A)
self.top_to_bottom(A , A , A , A)
def _lowerCamelCase ( self : Optional[int] , A : str , A : Optional[Any] , A : Optional[int] , A : str) -> Any:
"""simple docstring"""
_UpperCAmelCase = position[index]
while index != 0:
_UpperCAmelCase = int((index - 2) / 2) if index % 2 == 0 else int((index - 1) / 2)
if val < heap[parent]:
_UpperCAmelCase = heap[parent]
_UpperCAmelCase = position[parent]
self.set_position(position[parent] , A)
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , A)
break
_UpperCAmelCase = parent
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , 0)
def _lowerCamelCase ( self : Union[str, Any] , A : Optional[int] , A : Tuple) -> str:
"""simple docstring"""
_UpperCAmelCase = len(A) // 2 - 1
for i in range(A , -1 , -1):
self.top_to_bottom(A , A , len(A) , A)
def _lowerCamelCase ( self : Optional[int] , A : int , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = positions[0]
_UpperCAmelCase = sys.maxsize
self.top_to_bottom(A , 0 , len(A) , A)
return temp
def A ( _UpperCAmelCase : int ) -> Any:
'''simple docstring'''
_UpperCAmelCase = Heap()
_UpperCAmelCase = [0] * len(_UpperCAmelCase )
_UpperCAmelCase = [-1] * len(_UpperCAmelCase ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_UpperCAmelCase = [] # Heap of Distance of vertices from their neighboring vertex
_UpperCAmelCase = []
for vertex in range(len(_UpperCAmelCase ) ):
distance_tv.append(sys.maxsize )
positions.append(_UpperCAmelCase )
heap.node_position.append(_UpperCAmelCase )
_UpperCAmelCase = []
_UpperCAmelCase = 1
_UpperCAmelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_UpperCAmelCase = 0
_UpperCAmelCase = distance
heap.heapify(_UpperCAmelCase , _UpperCAmelCase )
for _ in range(1 , len(_UpperCAmelCase ) ):
_UpperCAmelCase = heap.delete_minimum(_UpperCAmelCase , _UpperCAmelCase )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_UpperCAmelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(_UpperCAmelCase )]
):
_UpperCAmelCase = distance
heap.bottom_to_top(
_UpperCAmelCase , heap.get_position(_UpperCAmelCase ) , _UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
UpperCAmelCase__ = int(input("Enter number of edges: ").strip())
UpperCAmelCase__ = defaultdict(list)
for _ in range(edges_number):
UpperCAmelCase__ = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 339 | 0 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Any = StableDiffusionPipeline.from_pretrained(_UpperCAmelCase , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
_SCREAMING_SNAKE_CASE : Optional[Any] = load_file(_UpperCAmelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
_SCREAMING_SNAKE_CASE : Union[str, Any] = key.split(""".""" )[0].split(LORA_PREFIX_TEXT_ENCODER + """_""" )[-1].split("""_""" )
_SCREAMING_SNAKE_CASE : List[Any] = pipeline.text_encoder
else:
_SCREAMING_SNAKE_CASE : Union[str, Any] = key.split(""".""" )[0].split(LORA_PREFIX_UNET + """_""" )[-1].split("""_""" )
_SCREAMING_SNAKE_CASE : Union[str, Any] = pipeline.unet
# find the target layer
_SCREAMING_SNAKE_CASE : List[str] = layer_infos.pop(0 )
while len(_UpperCAmelCase ) > -1:
try:
_SCREAMING_SNAKE_CASE : Any = curr_layer.__getattr__(_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0:
_SCREAMING_SNAKE_CASE : Union[str, Any] = layer_infos.pop(0 )
elif len(_UpperCAmelCase ) == 0:
break
except Exception:
if len(_UpperCAmelCase ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = layer_infos.pop(0 )
_SCREAMING_SNAKE_CASE : Optional[int] = []
if "lora_down" in key:
pair_keys.append(key.replace("""lora_down""" , """lora_up""" ) )
pair_keys.append(_UpperCAmelCase )
else:
pair_keys.append(_UpperCAmelCase )
pair_keys.append(key.replace("""lora_up""" , """lora_down""" ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
_SCREAMING_SNAKE_CASE : Dict = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
_SCREAMING_SNAKE_CASE : int = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(_UpperCAmelCase , _UpperCAmelCase ).unsqueeze(2 ).unsqueeze(3 )
else:
_SCREAMING_SNAKE_CASE : Any = state_dict[pair_keys[0]].to(torch.floataa )
_SCREAMING_SNAKE_CASE : Any = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(_UpperCAmelCase , _UpperCAmelCase )
# update visited list
for item in pair_keys:
visited.append(_UpperCAmelCase )
return pipeline
if __name__ == "__main__":
UpperCAmelCase_ : Any = argparse.ArgumentParser()
parser.add_argument(
'--base_model_path', default=None, type=str, required=True, help='Path to the base model in diffusers format.'
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--lora_prefix_unet', default='lora_unet', type=str, help='The prefix of UNet weight in safetensors'
)
parser.add_argument(
'--lora_prefix_text_encoder',
default='lora_te',
type=str,
help='The prefix of text encoder weight in safetensors',
)
parser.add_argument('--alpha', default=0.75, type=float, help='The merging ratio in W = W0 + alpha * deltaW')
parser.add_argument(
'--to_safetensors', action='store_true', help='Whether to store pipeline in safetensors format or not.'
)
parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
UpperCAmelCase_ : Optional[Any] = parser.parse_args()
UpperCAmelCase_ : Dict = args.base_model_path
UpperCAmelCase_ : str = args.checkpoint_path
UpperCAmelCase_ : Optional[Any] = args.dump_path
UpperCAmelCase_ : Optional[Any] = args.lora_prefix_unet
UpperCAmelCase_ : str = args.lora_prefix_text_encoder
UpperCAmelCase_ : Dict = args.alpha
UpperCAmelCase_ : Dict = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
UpperCAmelCase_ : str = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 200 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int]=5 ) -> List[Any]:
'''simple docstring'''
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('<mask>' ) == 1
_UpperCAmelCase = torch.tensor(tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) ).unsqueeze(0 ) # Batch size 1
_UpperCAmelCase = model(_UpperCAmelCase )[0] # The last hidden-state is the first element of the output tuple
_UpperCAmelCase = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
_UpperCAmelCase = logits[0, masked_index, :]
_UpperCAmelCase = logits.softmax(dim=0 )
_UpperCAmelCase , _UpperCAmelCase = prob.topk(k=_UpperCAmelCase , dim=0 )
_UpperCAmelCase = ' '.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(_UpperCAmelCase ) )] )
_UpperCAmelCase = tokenizer.mask_token
_UpperCAmelCase = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ' ) ):
_UpperCAmelCase = predicted_token_bpe.replace('\u2581' , ' ' )
if " {0}".format(_UpperCAmelCase ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(' {0}'.format(_UpperCAmelCase ) , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(_UpperCAmelCase , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
UpperCAmelCase__ = CamembertTokenizer.from_pretrained("camembert-base")
UpperCAmelCase__ = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
UpperCAmelCase__ = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 339 | 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_ :List[str] = '''\\n Text data.\n Second line of data.'''
A_ :Any = '''file'''
@pytest.fixture(scope='session' )
def A ( a_ ) -> Tuple:
__UpperCamelCase : Tuple =tmp_path_factory.mktemp('data' ) / (FILE_PATH + '.zstd')
__UpperCamelCase : int =bytes(_UpperCAmelCase ,'utf-8' )
with zstd.open(_UpperCAmelCase ,'wb' ) as f:
f.write(_UpperCAmelCase )
return path
@pytest.fixture
def A ( a_ ) -> Dict:
with open(os.path.join(tmpfs.local_root_dir ,_UpperCAmelCase ) ,'w' ) as f:
f.write(_UpperCAmelCase )
return FILE_PATH
@pytest.mark.parametrize('compression_format' ,['gzip', 'xz', 'zstd'] )
def A ( a_ ,a_ ,a_ ,a_ ,a_ ,a_ ) -> str:
__UpperCamelCase : Dict ={'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_path}
__UpperCamelCase : Optional[int] =input_paths[compression_format]
__UpperCamelCase : List[Any] =tmp_path / 'cache'
__UpperCamelCase : int =DownloadConfig(cache_dir=_UpperCAmelCase ,extract_compressed_file=_UpperCAmelCase )
__UpperCamelCase : str =cached_path(_UpperCAmelCase ,download_config=_UpperCAmelCase )
with open(_UpperCAmelCase ) as f:
__UpperCamelCase : Tuple =f.read()
with open(_UpperCAmelCase ) as f:
__UpperCamelCase : List[Any] =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 ( a_ ,a_ ,a_ ,a_ ,a_ ) -> Dict:
__UpperCamelCase : Tuple ='custom_cache'
__UpperCamelCase : List[Any] ='custom_extracted_dir'
__UpperCamelCase : Dict =tmp_path / 'custom_extracted_path'
if default_extracted:
__UpperCamelCase : Tuple =('downloads' if default_cache_dir else custom_cache_dir, 'extracted')
else:
monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR' ,_UpperCAmelCase )
monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH' ,str(_UpperCAmelCase ) )
__UpperCamelCase : Any =custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
__UpperCamelCase : Dict =xz_file
__UpperCamelCase : List[str] =(
DownloadConfig(extract_compressed_file=_UpperCAmelCase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir ,extract_compressed_file=_UpperCAmelCase )
)
__UpperCamelCase : int =cached_path(_UpperCAmelCase ,download_config=_UpperCAmelCase )
assert Path(_UpperCAmelCase ).parent.parts[-2:] == expected
def A ( a_ ) -> Optional[int]:
__UpperCamelCase : Dict =str(Path(_UpperCAmelCase ).resolve() )
assert cached_path(_UpperCAmelCase ) == text_file
# relative path
__UpperCamelCase : Dict =str(Path(_UpperCAmelCase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_UpperCAmelCase ) == text_file
def A ( a_ ) -> Optional[int]:
__UpperCamelCase : List[str] =str(tmp_path.resolve() / '__missing_file__.txt' )
with pytest.raises(_UpperCAmelCase ):
cached_path(_UpperCAmelCase )
# relative path
__UpperCamelCase : Any ='./__missing_file__.txt'
with pytest.raises(_UpperCAmelCase ):
cached_path(_UpperCAmelCase )
def A ( a_ ) -> List[Any]:
__UpperCamelCase : str =get_from_cache(F'tmp://{tmpfs_file}' )
with open(_UpperCAmelCase ) as f:
__UpperCamelCase : Optional[int] =f.read()
assert output_file_content == FILE_CONTENT
@patch('datasets.config.HF_DATASETS_OFFLINE' ,_UpperCAmelCase )
def A ( ) -> Dict:
with pytest.raises(_UpperCAmelCase ):
cached_path('https://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE' ,_UpperCAmelCase )
def A ( a_ ) -> Union[str, Any]:
__UpperCamelCase : Optional[int] =tmp_path_factory.mktemp('data' ) / 'file.html'
with pytest.raises(_UpperCAmelCase ):
http_get('https://huggingface.co' ,temp_file=_UpperCAmelCase )
with pytest.raises(_UpperCAmelCase ):
http_head('https://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE' ,_UpperCAmelCase )
def A ( a_ ) -> int:
__UpperCamelCase : str =tmp_path_factory.mktemp('data' ) / 'file.html'
with pytest.raises(_UpperCAmelCase ):
ftp_get('ftp://huggingface.co' ,temp_file=_UpperCAmelCase )
with pytest.raises(_UpperCAmelCase ):
ftp_head('ftp://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE' ,_UpperCAmelCase )
def A ( a_ ) -> int:
__UpperCamelCase : Dict =tmp_path_factory.mktemp('data' ) / 'file.html'
with pytest.raises(_UpperCAmelCase ):
fsspec_get('s3://huggingface.co' ,temp_file=_UpperCAmelCase )
with pytest.raises(_UpperCAmelCase ):
fsspec_head('s3://huggingface.co' )
| 71 |
import math
import unittest
def A ( _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Union[str, Any]:
"""simple docstring"""
self.assertTrue(is_prime(2))
self.assertTrue(is_prime(3))
self.assertTrue(is_prime(5))
self.assertTrue(is_prime(7))
self.assertTrue(is_prime(11))
self.assertTrue(is_prime(13))
self.assertTrue(is_prime(17))
self.assertTrue(is_prime(19))
self.assertTrue(is_prime(23))
self.assertTrue(is_prime(29))
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
with self.assertRaises(A):
is_prime(-19)
self.assertFalse(
is_prime(0) , 'Zero doesn\'t have any positive factors, primes must have exactly two.' , )
self.assertFalse(
is_prime(1) , 'One only has 1 positive factor, primes must have exactly two.' , )
self.assertFalse(is_prime(2 * 2))
self.assertFalse(is_prime(2 * 3))
self.assertFalse(is_prime(3 * 3))
self.assertFalse(is_prime(3 * 5))
self.assertFalse(is_prime(3 * 5 * 7))
if __name__ == "__main__":
unittest.main()
| 339 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : int ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_SCREAMING_SNAKE_CASE =1
_SCREAMING_SNAKE_CASE =1
while repunit:
_SCREAMING_SNAKE_CASE =(10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def _lowerCAmelCase ( _UpperCamelCase : int = 1_00_00_00 ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(_UpperCAmelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'''{solution() = }''')
| 47 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase__ = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n"
UpperCAmelCase__ = "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"
UpperCAmelCase__ = "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n 'google_bleu': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.4\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : str) -> MetricInfo:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('string' , id='token') , id='sequence'),
'references': datasets.Sequence(
datasets.Sequence(datasets.Value('string' , id='token') , id='sequence') , id='references'),
}) , )
def _lowerCamelCase ( self : Union[str, Any] , A : List[List[List[str]]] , A : List[List[str]] , A : int = 1 , A : int = 4 , ) -> Dict[str, float]:
"""simple docstring"""
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=A , hypotheses=A , min_len=A , max_len=A)
}
| 339 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'RWKV/rwkv-4-169m-pile': 'https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json',
'RWKV/rwkv-4-430m-pile': 'https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json',
'RWKV/rwkv-4-1b5-pile': 'https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json',
'RWKV/rwkv-4-3b-pile': 'https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json',
'RWKV/rwkv-4-7b-pile': 'https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json',
'RWKV/rwkv-4-14b-pile': 'https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json',
'RWKV/rwkv-raven-1b5': 'https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json',
'RWKV/rwkv-raven-3b': 'https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json',
'RWKV/rwkv-raven-7b': 'https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json',
'RWKV/rwkv-raven-14b': 'https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json',
}
class UpperCAmelCase_ ( snake_case ):
UpperCamelCase ="rwkv"
UpperCamelCase ={"max_position_embeddings": "context_length"}
def __init__( self , UpperCamelCase_=5_02_77 , UpperCamelCase_=10_24 , UpperCamelCase_=40_96 , UpperCamelCase_=32 , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_=1E-5 , UpperCamelCase_=0 , UpperCamelCase_=0 , UpperCamelCase_=6 , UpperCamelCase_=False , UpperCamelCase_=True , **UpperCamelCase_ , ) -> Optional[int]:
__lowercase : str = vocab_size
__lowercase : Union[str, Any] = context_length
__lowercase : List[str] = hidden_size
__lowercase : int = num_hidden_layers
__lowercase : Tuple = attention_hidden_size if attention_hidden_size is not None else hidden_size
__lowercase : List[Any] = intermediate_size if intermediate_size is not None else 4 * hidden_size
__lowercase : Dict = layer_norm_epsilon
__lowercase : int = rescale_every
__lowercase : str = use_cache
__lowercase : Tuple = bos_token_id
__lowercase : str = eos_token_id
super().__init__(
tie_word_embeddings=UpperCamelCase_ , bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , **UpperCamelCase_ )
| 249 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
UpperCAmelCase__ = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
UpperCAmelCase__ = TaTokenizerFast
UpperCAmelCase__ = {"configuration_mt5": ["MT5Config", "MT5OnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
"MT5EncoderModel",
"MT5ForConditionalGeneration",
"MT5ForQuestionAnswering",
"MT5Model",
"MT5PreTrainedModel",
"MT5Stack",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
UpperCAmelCase__ = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
module_spec=__spec__,
)
| 339 | 0 |
"""simple docstring"""
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
| 155 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class __lowerCAmelCase ( A ):
UpperCamelCase = '''open-llama'''
def __init__( self : str , A : List[Any]=10_00_00 , A : Tuple=40_96 , A : Tuple=1_10_08 , A : List[str]=32 , A : Tuple=32 , A : Optional[Any]="silu" , A : int=20_48 , A : Optional[Any]=0.0_2 , A : Dict=1E-6 , A : Optional[Any]=True , A : List[Any]=0 , A : Dict=1 , A : int=2 , A : Dict=False , A : Optional[int]=True , A : List[Any]=0.1 , A : str=0.1 , A : Dict=True , A : Optional[Any]=True , A : Dict=None , **A : Union[str, Any] , ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = vocab_size
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = hidden_size
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = initializer_range
_UpperCAmelCase = rms_norm_eps
_UpperCAmelCase = use_cache
_UpperCAmelCase = kwargs.pop(
'use_memorry_efficient_attention' , A)
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_dropout_prob
_UpperCAmelCase = use_stable_embedding
_UpperCAmelCase = shared_input_output_embedding
_UpperCAmelCase = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , tie_word_embeddings=A , **A , )
def _lowerCamelCase ( self : List[str]) -> Union[str, Any]:
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , A) or len(self.rope_scaling) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F"got {self.rope_scaling}")
_UpperCAmelCase = self.rope_scaling.get('type' , A)
_UpperCAmelCase = self.rope_scaling.get('factor' , A)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}")
if rope_scaling_factor is None or not isinstance(A , A) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
| 339 | 0 |
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 lowercase_ ( _lowerCamelCase : Tuple , _lowerCamelCase : Union[str, Any]=False):
try:
lowercase__ : Optional[int] = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
lowercase__ : List[str] = default
else:
# KEY is set, convert it to True or False.
try:
lowercase__ : Optional[Any] = strtobool(_UpperCAmelCase)
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
UpperCamelCase = parse_flag_from_env('''RUN_SLOW''', default=False)
def lowercase_ ( _lowerCamelCase : List[str]):
return unittest.skip("Test was skipped")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : Dict):
return unittest.skipUnless(_run_slow_tests , "test is slow")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : Any):
return unittest.skipUnless(not torch.cuda.is_available() , "test requires only a CPU")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : Dict):
return unittest.skipUnless(torch.cuda.is_available() , "test requires a GPU")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : Optional[Any]):
return unittest.skipUnless(is_xpu_available() , "test requires a XPU")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : Optional[int]):
return unittest.skipUnless(is_mps_available() , "test requires a `mps` backend support in `torch`")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : Union[str, Any]):
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , "test requires the Hugging Face suite")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : str):
return unittest.skipUnless(is_bnb_available() , "test requires the bitsandbytes library")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : Union[str, Any]):
return unittest.skipUnless(is_tpu_available() , "test requires TPU")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : Optional[Any]):
return unittest.skipUnless(torch.cuda.device_count() == 1 , "test requires a GPU")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : Tuple):
return unittest.skipUnless(torch.xpu.device_count() == 1 , "test requires a XPU")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : Any):
return unittest.skipUnless(torch.cuda.device_count() > 1 , "test requires multiple GPUs")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : Tuple):
return unittest.skipUnless(torch.xpu.device_count() > 1 , "test requires multiple XPUs")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : Any):
return unittest.skipUnless(is_safetensors_available() , "test requires safetensors")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : List[Any]):
return unittest.skipUnless(is_deepspeed_available() , "test requires DeepSpeed")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : Optional[int]):
return unittest.skipUnless(is_torch_version(">=" , "1.12.0") , "test requires torch version >= 1.12.0")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : Any=None , _lowerCamelCase : List[Any]=None):
if test_case is None:
return partial(_UpperCAmelCase , version=_UpperCAmelCase)
return unittest.skipUnless(is_torch_version(">=" , _UpperCAmelCase) , f'''test requires torch version >= {version}''')(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : List[str]):
return unittest.skipUnless(is_tensorboard_available() , "test requires Tensorboard")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : Union[str, Any]):
return unittest.skipUnless(is_wandb_available() , "test requires wandb")(_UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : List[str]):
return unittest.skipUnless(is_comet_ml_available() , "test requires comet_ml")(_UpperCAmelCase)
UpperCamelCase = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def lowercase_ ( _lowerCamelCase : List[str]):
return unittest.skipUnless(
_atleast_one_tracker_available , "test requires at least one tracker to be available and for `comet_ml` to not be installed" , )(_UpperCAmelCase)
class snake_case_ ( unittest.TestCase ):
__A : int = True
@classmethod
def __UpperCamelCase ( cls : List[Any] ) -> Tuple:
lowercase__ : str = tempfile.mkdtemp()
@classmethod
def __UpperCamelCase ( cls : Union[str, Any] ) -> str:
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def __UpperCamelCase ( self : List[str] ) -> List[Any]:
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob("**/*" ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(lowercase_ )
class snake_case_ ( unittest.TestCase ):
def __UpperCamelCase ( self : Dict ) -> Tuple:
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class snake_case_ ( unittest.TestCase ):
def __UpperCamelCase ( self : Optional[int] , lowercase_ : Union[mock.Mock, List[mock.Mock]] ) -> Tuple:
lowercase__ : Tuple = mocks if isinstance(lowercase_ , (tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def lowercase_ ( _lowerCamelCase : List[Any]):
lowercase__ : Any = AcceleratorState()
lowercase__ : Any = tensor[None].clone().to(state.device)
lowercase__ : int = gather(_UpperCAmelCase).cpu()
lowercase__ : Optional[Any] = tensor[0].cpu()
for i in range(tensors.shape[0]):
if not torch.equal(tensors[i] , _UpperCAmelCase):
return False
return True
class snake_case_ :
def __init__( self : Optional[Any] , lowercase_ : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : str ) -> Optional[int]:
lowercase__ : List[Any] = returncode
lowercase__ : List[str] = stdout
lowercase__ : int = stderr
async def lowercase_ ( _lowerCamelCase : str , _lowerCamelCase : Optional[int]):
while True:
lowercase__ : Tuple = await stream.readline()
if line:
callback(_UpperCAmelCase)
else:
break
async def lowercase_ ( _lowerCamelCase : Optional[int] , _lowerCamelCase : List[str]=None , _lowerCamelCase : str=None , _lowerCamelCase : str=None , _lowerCamelCase : Dict=False , _lowerCamelCase : Union[str, Any]=False):
if echo:
print("\nRunning: " , " ".join(_UpperCAmelCase))
lowercase__ : List[Any] = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=_UpperCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_UpperCAmelCase , )
# 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)
lowercase__ : Optional[Any] = []
lowercase__ : Optional[int] = []
def tee(_lowerCamelCase : Union[str, Any] , _lowerCamelCase : List[Any] , _lowerCamelCase : str , _lowerCamelCase : str=""):
lowercase__ : Tuple = line.decode("utf-8").rstrip()
sink.append(_UpperCAmelCase)
if not quiet:
print(_UpperCAmelCase , _UpperCAmelCase , file=_UpperCAmelCase)
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda _lowerCamelCase: tee(_UpperCAmelCase , _UpperCAmelCase , sys.stdout , label="stdout:"))),
asyncio.create_task(_read_stream(p.stderr , lambda _lowerCamelCase: tee(_UpperCAmelCase , _UpperCAmelCase , sys.stderr , label="stderr:"))),
] , timeout=_UpperCAmelCase , )
return _RunOutput(await p.wait() , _UpperCAmelCase , _UpperCAmelCase)
def lowercase_ ( _lowerCamelCase : str , _lowerCamelCase : Dict=None , _lowerCamelCase : str=None , _lowerCamelCase : str=180 , _lowerCamelCase : List[Any]=False , _lowerCamelCase : List[Any]=True):
lowercase__ : str = asyncio.get_event_loop()
lowercase__ : Optional[Any] = loop.run_until_complete(
_stream_subprocess(_UpperCAmelCase , env=_UpperCAmelCase , stdin=_UpperCAmelCase , timeout=_UpperCAmelCase , quiet=_UpperCAmelCase , echo=_UpperCAmelCase))
lowercase__ : Tuple = " ".join(_UpperCAmelCase)
if result.returncode > 0:
lowercase__ : List[str] = "\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 snake_case_ ( __A ):
pass
def lowercase_ ( _lowerCamelCase : List[str] , _lowerCamelCase : str=False):
try:
lowercase__ : Any = subprocess.check_output(_UpperCAmelCase , stderr=subprocess.STDOUT)
if return_stdout:
if hasattr(_UpperCAmelCase , "decode"):
lowercase__ : Union[str, Any] = output.decode("utf-8")
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
f'''Command `{" ".join(_UpperCAmelCase)}` failed with the following error:\n\n{e.output.decode()}''') from e
| 87 |
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = credit_card_number
_UpperCAmelCase = 0
_UpperCAmelCase = len(_UpperCAmelCase ) - 2
for i in range(_UpperCAmelCase , -1 , -2 ):
# double the value of every second digit
_UpperCAmelCase = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
_UpperCAmelCase = cc_number[:i] + str(_UpperCAmelCase ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(_UpperCAmelCase ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = F"{credit_card_number} is an invalid credit card number because"
if not credit_card_number.isdigit():
print(F"{error_message} it has nonnumerical characters." )
return False
if not 13 <= len(_UpperCAmelCase ) <= 16:
print(F"{error_message} of its length." )
return False
if not validate_initial_digits(_UpperCAmelCase ):
print(F"{error_message} of its first two digits." )
return False
if not luhn_validation(_UpperCAmelCase ):
print(F"{error_message} it fails the Luhn check." )
return False
print(F"{credit_card_number} is a valid credit card number." )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 339 | 0 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
_lowerCAmelCase = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
_lowerCAmelCase = {'''facebook/blenderbot_small-90M''': 512}
def __lowerCAmelCase ( snake_case__ ):
__UpperCamelCase : Union[str, Any] = set()
__UpperCamelCase : Optional[int] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__UpperCamelCase : Optional[Any] = char
__UpperCamelCase : List[str] = set(_UpperCAmelCase )
return pairs
class A ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
A = VOCAB_FILES_NAMES
A = PRETRAINED_VOCAB_FILES_MAP
A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A = ["input_ids", "attention_mask"]
def __init__(self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase="__start__" , _UpperCAmelCase="__end__" , _UpperCAmelCase="__unk__" , _UpperCAmelCase="__null__" , **_UpperCAmelCase , ) -> Optional[Any]:
super().__init__(unk_token=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , **_UpperCAmelCase )
with open(_UpperCAmelCase , encoding="utf-8" ) as vocab_handle:
__UpperCamelCase : Any = json.load(_UpperCAmelCase )
__UpperCamelCase : List[Any] = {v: k for k, v in self.encoder.items()}
with open(_UpperCAmelCase , encoding="utf-8" ) as merges_handle:
__UpperCamelCase : Any = merges_handle.read().split("\n" )[1:-1]
__UpperCamelCase : List[str] = [tuple(merge.split() ) for merge in merges]
__UpperCamelCase : List[Any] = dict(zip(_UpperCAmelCase , range(len(_UpperCAmelCase ) ) ) )
__UpperCamelCase : Tuple = {}
@property
def a_ (self ) -> int:
return len(self.encoder )
def a_ (self ) -> Dict:
return dict(self.encoder , **self.added_tokens_encoder )
def a_ (self , _UpperCAmelCase ) -> str:
if token in self.cache:
return self.cache[token]
__UpperCamelCase : str = re.sub("([.,!?()])" , R" \1" , _UpperCAmelCase )
__UpperCamelCase : Dict = re.sub("(\')" , R" \1 " , _UpperCAmelCase )
__UpperCamelCase : Any = re.sub(R"\s{2,}" , " " , _UpperCAmelCase )
if "\n" in token:
__UpperCamelCase : int = token.replace("\n" , " __newln__" )
__UpperCamelCase : Dict = token.split(" " )
__UpperCamelCase : Optional[Any] = []
for token in tokens:
if not len(_UpperCAmelCase ):
continue
__UpperCamelCase : List[str] = token.lower()
__UpperCamelCase : int = tuple(_UpperCAmelCase )
__UpperCamelCase : Union[str, Any] = tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
__UpperCamelCase : Tuple = get_pairs(_UpperCAmelCase )
if not pairs:
words.append(_UpperCAmelCase )
continue
while True:
__UpperCamelCase : List[Any] = min(_UpperCAmelCase , key=lambda _UpperCAmelCase : self.bpe_ranks.get(_UpperCAmelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
__UpperCamelCase , __UpperCamelCase : Any = bigram
__UpperCamelCase : str = []
__UpperCamelCase : Optional[int] = 0
while i < len(_UpperCAmelCase ):
try:
__UpperCamelCase : Any = word.index(_UpperCAmelCase , _UpperCAmelCase )
new_word.extend(word[i:j] )
__UpperCamelCase : Optional[Any] = j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(_UpperCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__UpperCamelCase : int = tuple(_UpperCAmelCase )
__UpperCamelCase : Optional[Any] = new_word
if len(_UpperCAmelCase ) == 1:
break
else:
__UpperCamelCase : str = get_pairs(_UpperCAmelCase )
__UpperCamelCase : Optional[int] = "@@ ".join(_UpperCAmelCase )
__UpperCamelCase : Union[str, Any] = word[:-4]
__UpperCamelCase : List[str] = word
words.append(_UpperCAmelCase )
return " ".join(_UpperCAmelCase )
def a_ (self , _UpperCAmelCase ) -> List[str]:
__UpperCamelCase : Dict = []
__UpperCamelCase : Optional[Any] = re.findall(R"\S+\n?" , _UpperCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(_UpperCAmelCase ).split(" " ) ) )
return split_tokens
def a_ (self , _UpperCAmelCase ) -> int:
__UpperCamelCase : Optional[int] = token.lower()
return self.encoder.get(_UpperCAmelCase , self.encoder.get(self.unk_token ) )
def a_ (self , _UpperCAmelCase ) -> str:
return self.decoder.get(_UpperCAmelCase , self.unk_token )
def a_ (self , _UpperCAmelCase ) -> str:
__UpperCamelCase : Optional[int] = " ".join(_UpperCAmelCase ).replace("@@ " , "" ).strip()
return out_string
def a_ (self , _UpperCAmelCase , _UpperCAmelCase = None ) -> Tuple[str]:
if not os.path.isdir(_UpperCAmelCase ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
__UpperCamelCase : List[Any] = os.path.join(
_UpperCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
__UpperCamelCase : int = os.path.join(
_UpperCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(_UpperCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_UpperCAmelCase , ensure_ascii=_UpperCAmelCase ) + "\n" )
__UpperCamelCase : Optional[int] = 0
with open(_UpperCAmelCase , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _UpperCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
" Please check that the tokenizer is not corrupted!" )
__UpperCamelCase : Optional[int] = token_index
writer.write(" ".join(_UpperCAmelCase ) + "\n" )
index += 1
return vocab_file, merge_file
| 298 |
from functools import reduce
UpperCAmelCase__ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def A ( _UpperCAmelCase : str = N ) -> int:
'''simple docstring'''
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda _UpperCAmelCase , _UpperCAmelCase : str(int(_UpperCAmelCase ) * int(_UpperCAmelCase ) ) , n[i : i + 13] ) )
for i in range(len(_UpperCAmelCase ) - 12 ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 0 |
from __future__ import annotations
lowerCAmelCase = 8.988E9 # units = N * m^s * C^-2
def _lowerCamelCase( lowercase__ , lowercase__ , lowercase__ , lowercase__ ) -> dict[str, float]:
'''simple docstring'''
__lowercase= abs(chargea * chargea )
if (force, chargea, chargea, distance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if distance < 0:
raise ValueError('Distance cannot be negative' )
if force == 0:
__lowercase= COULOMBS_CONSTANT * charge_product / (distance**2)
return {"force": force}
elif chargea == 0:
__lowercase= abs(_UpperCAmelCase ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge1": chargea}
elif chargea == 0:
__lowercase= abs(_UpperCAmelCase ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge2": chargea}
elif distance == 0:
__lowercase= (COULOMBS_CONSTANT * charge_product / abs(_UpperCAmelCase )) ** 0.5
return {"distance": distance}
raise ValueError('Exactly one argument must be 0' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 295 |
from __future__ import annotations
from collections.abc import Callable
UpperCAmelCase__ = list[list[float | int]]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : Matrix ) -> Matrix:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(size + 1 )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for row in range(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = matrix[row][col]
_UpperCAmelCase = vector[row][0]
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while row < size and col < size:
# pivoting
_UpperCAmelCase = max((abs(augmented[rowa][col] ), rowa) for rowa in range(_UpperCAmelCase , _UpperCAmelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
_UpperCAmelCase , _UpperCAmelCase = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , _UpperCAmelCase ):
_UpperCAmelCase = augmented[rowa][col] / augmented[row][col]
_UpperCAmelCase = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , _UpperCAmelCase ):
for row in range(_UpperCAmelCase ):
_UpperCAmelCase = augmented[row][col] / augmented[col][col]
for cola in range(_UpperCAmelCase , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(_UpperCAmelCase )
]
def A ( _UpperCAmelCase : list[int] ) -> Callable[[int], int]:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAmelCase )
_UpperCAmelCase = [[0 for _ in range(_UpperCAmelCase )] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = [[0] for _ in range(_UpperCAmelCase )]
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for x_val, y_val in enumerate(_UpperCAmelCase ):
for col in range(_UpperCAmelCase ):
_UpperCAmelCase = (x_val + 1) ** (size - col - 1)
_UpperCAmelCase = y_val
_UpperCAmelCase = solve(_UpperCAmelCase , _UpperCAmelCase )
def interpolated_func(_UpperCAmelCase : int ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(_UpperCAmelCase ) )
return interpolated_func
def A ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def A ( _UpperCAmelCase : Callable[[int], int] = question_function , _UpperCAmelCase : int = 10 ) -> int:
'''simple docstring'''
_UpperCAmelCase = [func(_UpperCAmelCase ) for x_val in range(1 , order + 1 )]
_UpperCAmelCase = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
_UpperCAmelCase = 0
_UpperCAmelCase = 42
_UpperCAmelCase = 42
for poly in polynomials:
_UpperCAmelCase = 1
while func(_UpperCAmelCase ) == poly(_UpperCAmelCase ):
x_val += 1
ret += poly(_UpperCAmelCase )
return ret
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 0 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def _lowerCamelCase ( lowercase : Any ) -> Tuple:
_a = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase , _UpperCAmelCase )
def _lowerCamelCase ( lowercase : Optional[int] ) -> str:
_a = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
_a = s_dict.pop(_UpperCAmelCase )
elif "subsample" in key:
_a = s_dict.pop(_UpperCAmelCase )
def _lowerCamelCase ( lowercase : List[Any] ) -> Union[str, Any]:
_a , _a = emb.weight.shape
_a = nn.Linear(_UpperCAmelCase , _UpperCAmelCase , bias=_UpperCAmelCase )
_a = emb.weight.data
return lin_layer
def _lowerCamelCase ( lowercase : Union[str, Any] , lowercase : List[Any] ) -> str:
_a = torch.load(_UpperCAmelCase , map_location="cpu" )
_a = mam_aaa["args"]
_a = mam_aaa["model"]
_a = state_dict["decoder.output_projection.weight"]
remove_ignore_keys_(_UpperCAmelCase )
rename_keys(_UpperCAmelCase )
_a = state_dict["decoder.embed_tokens.weight"].shape[0]
_a = args.share_decoder_input_output_embed
_a = [int(_UpperCAmelCase ) for i in args.conv_kernel_sizes.split("," )]
_a = SpeechaTextConfig(
vocab_size=_UpperCAmelCase , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="relu" , num_conv_layers=len(_UpperCAmelCase ) , conv_channels=args.conv_channels , conv_kernel_sizes=_UpperCAmelCase , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=_UpperCAmelCase , num_beams=5 , max_length=200 , use_cache=_UpperCAmelCase , decoder_start_token_id=2 , early_stopping=_UpperCAmelCase , )
_a = SpeechaTextForConditionalGeneration(_UpperCAmelCase )
_a , _a = model.model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0 and not set(_UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
"Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,"
F' but all the following weights are missing {missing}' )
if tie_embeds:
_a = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
_a = lm_head_weights
model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
lowerCAmelCase_ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
lowerCAmelCase_ : Tuple = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 63 |
from __future__ import annotations
def A ( _UpperCAmelCase : list[int] ) -> bool:
'''simple docstring'''
return len(set(_UpperCAmelCase ) ) == len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 0 |
"""simple docstring"""
from datetime import datetime as dt
import os
from github import Github
__magic_name__ = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def _lowerCAmelCase ( ):
__SCREAMING_SNAKE_CASE = Github(os.environ["""GITHUB_TOKEN"""] )
__SCREAMING_SNAKE_CASE = g.get_repo("""huggingface/transformers""" )
__SCREAMING_SNAKE_CASE = repo.get_issues(state="""open""" )
for issue in open_issues:
__SCREAMING_SNAKE_CASE = sorted([comment for comment in issue.get_comments()] , key=lambda UpperCamelCase_ : i.created_at , reverse=_UpperCAmelCase )
__SCREAMING_SNAKE_CASE = comments[0] if len(_UpperCAmelCase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state="""closed""" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
"""This issue has been automatically marked as stale because it has not had """
"""recent activity. If you think this still needs to be addressed """
"""please comment on this thread.\n\nPlease note that issues that do not follow the """
"""[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
"""are likely to be ignored.""" )
if __name__ == "__main__":
main()
| 100 |
import os
UpperCAmelCase__ = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def A ( _UpperCAmelCase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(_UpperCAmelCase ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def A ( _UpperCAmelCase : int ) -> str:
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1_000
numerals += m_count * "M"
num %= 1_000
_UpperCAmelCase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
_UpperCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def A ( _UpperCAmelCase : str = "/p089_roman.txt" ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(_UpperCAmelCase ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(_UpperCAmelCase )
_UpperCAmelCase = generate_roman_numerals(_UpperCAmelCase )
savings += len(_UpperCAmelCase ) - len(_UpperCAmelCase )
return savings
if __name__ == "__main__":
print(f"""{solution() = }""")
| 339 | 0 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lowerCamelCase : int = {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}
class __snake_case ( lowerCamelCase_ ):
lowerCAmelCase_ = "t5"
lowerCAmelCase_ = ["past_key_values"]
lowerCAmelCase_ = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__( self : Optional[Any] , _lowercase : Union[str, Any]=3_21_28 , _lowercase : Optional[Any]=5_12 , _lowercase : Optional[Any]=64 , _lowercase : Union[str, Any]=20_48 , _lowercase : Tuple=6 , _lowercase : Optional[int]=None , _lowercase : List[Any]=8 , _lowercase : Dict=32 , _lowercase : str=1_28 , _lowercase : Tuple=0.1 , _lowercase : List[str]=1E-6 , _lowercase : str=1.0 , _lowercase : Optional[Any]="relu" , _lowercase : Tuple=True , _lowercase : Optional[int]=True , _lowercase : Optional[Any]=0 , _lowercase : Optional[Any]=1 , **_lowercase : Dict , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = vocab_size
SCREAMING_SNAKE_CASE__ = d_model
SCREAMING_SNAKE_CASE__ = d_kv
SCREAMING_SNAKE_CASE__ = d_ff
SCREAMING_SNAKE_CASE__ = num_layers
SCREAMING_SNAKE_CASE__ = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
SCREAMING_SNAKE_CASE__ = num_heads
SCREAMING_SNAKE_CASE__ = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ = relative_attention_max_distance
SCREAMING_SNAKE_CASE__ = dropout_rate
SCREAMING_SNAKE_CASE__ = layer_norm_epsilon
SCREAMING_SNAKE_CASE__ = initializer_factor
SCREAMING_SNAKE_CASE__ = feed_forward_proj
SCREAMING_SNAKE_CASE__ = use_cache
SCREAMING_SNAKE_CASE__ = self.feed_forward_proj.split("""-""" )
SCREAMING_SNAKE_CASE__ = act_info[-1]
SCREAMING_SNAKE_CASE__ = act_info[0] == """gated"""
if len(_lowercase ) > 1 and act_info[0] != "gated" or len(_lowercase ) > 2:
raise ValueError(
f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""\'gated-gelu\' or \'relu\'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
SCREAMING_SNAKE_CASE__ = """gelu_new"""
super().__init__(
pad_token_id=_lowercase , eos_token_id=_lowercase , is_encoder_decoder=_lowercase , **_lowercase , )
class __snake_case ( lowerCamelCase_ ):
@property
def __a ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
SCREAMING_SNAKE_CASE__ = """past_encoder_sequence + sequence"""
SCREAMING_SNAKE_CASE__ = {0: """batch"""}
SCREAMING_SNAKE_CASE__ = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
SCREAMING_SNAKE_CASE__ = {0: """batch""", 1: """decoder_sequence"""}
SCREAMING_SNAKE_CASE__ = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_lowercase , direction="""inputs""" )
return common_inputs
@property
def __a ( self : Any ):
"""simple docstring"""
return 13
| 219 |
import requests
from bsa import BeautifulSoup
def A ( _UpperCAmelCase : str , _UpperCAmelCase : dict ) -> str:
'''simple docstring'''
_UpperCAmelCase = BeautifulSoup(requests.get(_UpperCAmelCase , params=_UpperCAmelCase ).content , 'html.parser' )
_UpperCAmelCase = soup.find('div' , attrs={'class': 'gs_ri'} )
_UpperCAmelCase = div.find('div' , attrs={'class': 'gs_fl'} ).find_all('a' )
return anchors[2].get_text()
if __name__ == "__main__":
UpperCAmelCase__ = {
"title": (
"Precisely geometry controlled microsupercapacitors for ultrahigh areal "
"capacitance, volumetric capacitance, and energy density"
),
"journal": "Chem. Mater.",
"volume": 30,
"pages": "3979-3990",
"year": 2018,
"hl": "en",
}
print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
| 339 | 0 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class lowercase__ ( _snake_case ):
'''simple docstring'''
A_ : List[Any] = """char"""
A_ : List[str] = """bpe"""
A_ : Tuple = """wp"""
UpperCAmelCase_ : Optional[int] = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class lowercase__ ( _snake_case ):
'''simple docstring'''
A_ : Tuple = ["""image_processor""", """char_tokenizer"""]
A_ : Optional[int] = """ViTImageProcessor"""
A_ : int = """MgpstrTokenizer"""
def __init__( self , __snake_case=None , __snake_case=None , **__snake_case ):
_SCREAMING_SNAKE_CASE : Tuple = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , __snake_case , )
_SCREAMING_SNAKE_CASE : Any = kwargs.pop("""feature_extractor""" )
_SCREAMING_SNAKE_CASE : int = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
_SCREAMING_SNAKE_CASE : int = tokenizer
_SCREAMING_SNAKE_CASE : Any = AutoTokenizer.from_pretrained("""gpt2""" )
_SCREAMING_SNAKE_CASE : Any = AutoTokenizer.from_pretrained("""bert-base-uncased""" )
super().__init__(__snake_case , __snake_case )
def __call__( self , __snake_case=None , __snake_case=None , __snake_case=None , **__snake_case ):
if images is None and text is None:
raise ValueError("""You need to specify either an `images` or `text` input to process.""" )
if images is not None:
_SCREAMING_SNAKE_CASE : Any = self.image_processor(__snake_case , return_tensors=__snake_case , **__snake_case )
if text is not None:
_SCREAMING_SNAKE_CASE : Dict = self.char_tokenizer(__snake_case , return_tensors=__snake_case , **__snake_case )
if text is None:
return inputs
elif images is None:
return encodings
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = encodings["""input_ids"""]
return inputs
def UpperCAmelCase_ ( self , __snake_case ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Any = sequences
_SCREAMING_SNAKE_CASE : List[str] = char_preds.size(0 )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[str] = self._decode_helper(__snake_case , """char""" )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Optional[Any] = self._decode_helper(__snake_case , """bpe""" )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : int = self._decode_helper(__snake_case , """wp""" )
_SCREAMING_SNAKE_CASE : Any = []
_SCREAMING_SNAKE_CASE : int = []
for i in range(__snake_case ):
_SCREAMING_SNAKE_CASE : int = [char_scores[i], bpe_scores[i], wp_scores[i]]
_SCREAMING_SNAKE_CASE : int = [char_strs[i], bpe_strs[i], wp_strs[i]]
_SCREAMING_SNAKE_CASE : List[Any] = scores.index(max(__snake_case ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
_SCREAMING_SNAKE_CASE : List[str] = {}
_SCREAMING_SNAKE_CASE : int = final_strs
_SCREAMING_SNAKE_CASE : Dict = final_scores
_SCREAMING_SNAKE_CASE : List[str] = char_strs
_SCREAMING_SNAKE_CASE : Optional[int] = bpe_strs
_SCREAMING_SNAKE_CASE : str = wp_strs
return out
def UpperCAmelCase_ ( self , __snake_case , __snake_case ):
if format == DecodeType.CHARACTER:
_SCREAMING_SNAKE_CASE : Any = self.char_decode
_SCREAMING_SNAKE_CASE : Union[str, Any] = 1
_SCREAMING_SNAKE_CASE : Union[str, Any] = """[s]"""
elif format == DecodeType.BPE:
_SCREAMING_SNAKE_CASE : List[Any] = self.bpe_decode
_SCREAMING_SNAKE_CASE : Any = 2
_SCREAMING_SNAKE_CASE : str = """#"""
elif format == DecodeType.WORDPIECE:
_SCREAMING_SNAKE_CASE : int = self.wp_decode
_SCREAMING_SNAKE_CASE : List[Any] = 102
_SCREAMING_SNAKE_CASE : Tuple = """[SEP]"""
else:
raise ValueError(f"""Format {format} is not supported.""" )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[Any] = [], []
_SCREAMING_SNAKE_CASE : Optional[int] = pred_logits.size(0 )
_SCREAMING_SNAKE_CASE : List[str] = pred_logits.size(1 )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Dict = pred_logits.topk(1 , dim=-1 , largest=__snake_case , sorted=__snake_case )
_SCREAMING_SNAKE_CASE : Dict = preds_index.view(-1 , __snake_case )[:, 1:]
_SCREAMING_SNAKE_CASE : Optional[Any] = decoder(__snake_case )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[str] = torch.nn.functional.softmax(__snake_case , dim=2 ).max(dim=2 )
_SCREAMING_SNAKE_CASE : Optional[Any] = preds_max_prob[:, 1:]
for index in range(__snake_case ):
_SCREAMING_SNAKE_CASE : Dict = preds_str[index].find(__snake_case )
_SCREAMING_SNAKE_CASE : int = preds_str[index][:pred_eos]
_SCREAMING_SNAKE_CASE : Optional[Any] = preds_index[index].cpu().tolist()
_SCREAMING_SNAKE_CASE : Union[str, Any] = pred_index.index(__snake_case ) if eos_token in pred_index else -1
_SCREAMING_SNAKE_CASE : Dict = preds_max_prob[index][: pred_eos_index + 1]
_SCREAMING_SNAKE_CASE : Optional[Any] = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(__snake_case )
conf_scores.append(__snake_case )
return dec_strs, conf_scores
def UpperCAmelCase_ ( self , __snake_case ):
_SCREAMING_SNAKE_CASE : Tuple = [seq.replace(""" """ , """""" ) for seq in self.char_tokenizer.batch_decode(__snake_case )]
return decode_strs
def UpperCAmelCase_ ( self , __snake_case ):
return self.bpe_tokenizer.batch_decode(__snake_case )
def UpperCAmelCase_ ( self , __snake_case ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = [seq.replace(""" """ , """""" ) for seq in self.wp_tokenizer.batch_decode(__snake_case )]
return decode_strs
| 200 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self : Optional[Any] , A : Dict , A : Union[str, Any]=13 , A : Dict=7 , A : Dict=True , A : Tuple=True , A : Union[str, Any]=True , A : int=True , A : Optional[int]=99 , A : List[str]=32 , A : List[Any]=5 , A : int=4 , A : Any=37 , A : Optional[int]="gelu" , A : Optional[Any]=0.1 , A : Any=0.1 , A : Union[str, Any]=5_12 , A : int=16 , A : List[str]=2 , A : Union[str, Any]=0.0_2 , A : Union[str, Any]=4 , ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_attention_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_choices
def _lowerCamelCase ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCAmelCase = None
if self.use_attention_mask:
_UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length])
_UpperCAmelCase = None
if self.use_token_type_ids:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
_UpperCAmelCase = RoFormerConfig(
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=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _lowerCamelCase ( self : List[Any]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class __lowerCAmelCase ( A , unittest.TestCase ):
UpperCamelCase = True
UpperCamelCase = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
_UpperCAmelCase = FlaxRoFormerModelTester(self)
@slow
def _lowerCamelCase ( self : List[Any]) -> Dict:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_UpperCAmelCase = model_class_name.from_pretrained('junnyu/roformer_chinese_small' , from_pt=A)
_UpperCAmelCase = model(np.ones((1, 1)))
self.assertIsNotNone(A)
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def _lowerCamelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base')
_UpperCAmelCase = jnp.array([[0, 1, 2, 3, 4, 5]])
_UpperCAmelCase = model(A)[0]
_UpperCAmelCase = 5_00_00
_UpperCAmelCase = (1, 6, vocab_size)
self.assertEqual(output.shape , A)
_UpperCAmelCase = jnp.array(
[[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]])
self.assertTrue(jnp.allclose(output[:, :3, :3] , A , atol=1E-4))
| 339 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class __A ( a , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase__ : Any =ShapEImgaImgPipeline
UpperCamelCase__ : str =["""image"""]
UpperCamelCase__ : Optional[Any] =["""image"""]
UpperCamelCase__ : List[str] =[
"""num_images_per_prompt""",
"""num_inference_steps""",
"""generator""",
"""latents""",
"""guidance_scale""",
"""frame_size""",
"""output_type""",
"""return_dict""",
]
UpperCamelCase__ : Any =False
@property
def __lowercase ( self ):
"""simple docstring"""
return 32
@property
def __lowercase ( self ):
"""simple docstring"""
return 32
@property
def __lowercase ( self ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def __lowercase ( self ):
"""simple docstring"""
return 8
@property
def __lowercase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
__UpperCamelCase : int =CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
__UpperCamelCase : str =CLIPVisionModel(lowerCamelCase__ )
return model
@property
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : int =CLIPImageProcessor(
crop_size=224 , do_center_crop=lowerCamelCase__ , do_normalize=lowerCamelCase__ , do_resize=lowerCamelCase__ , image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073] , image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711] , resample=3 , size=224 , )
return image_processor
@property
def __lowercase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
__UpperCamelCase : List[Any] ={
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'embedding_proj_norm_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
__UpperCamelCase : Any =PriorTransformer(**lowerCamelCase__ )
return model
@property
def __lowercase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
__UpperCamelCase : List[Any] ={
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
__UpperCamelCase : Dict =ShapERenderer(**lowerCamelCase__ )
return model
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[int] =self.dummy_prior
__UpperCamelCase : int =self.dummy_image_encoder
__UpperCamelCase : Union[str, Any] =self.dummy_image_processor
__UpperCamelCase : Optional[int] =self.dummy_renderer
__UpperCamelCase : str =HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1024 , prediction_type='sample' , use_karras_sigmas=lowerCamelCase__ , clip_sample=lowerCamelCase__ , clip_sample_range=1.0 , )
__UpperCamelCase : Optional[int] ={
'prior': prior,
'image_encoder': image_encoder,
'image_processor': image_processor,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__=0 ):
"""simple docstring"""
__UpperCamelCase : List[str] =floats_tensor((1, 3, 64, 64) , rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
if str(lowerCamelCase__ ).startswith('mps' ):
__UpperCamelCase : Optional[int] =torch.manual_seed(lowerCamelCase__ )
else:
__UpperCamelCase : int =torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
__UpperCamelCase : List[Any] ={
'image': input_image,
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Dict ='cpu'
__UpperCamelCase : Tuple =self.get_dummy_components()
__UpperCamelCase : List[str] =self.pipeline_class(**lowerCamelCase__ )
__UpperCamelCase : Optional[int] =pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__UpperCamelCase : List[Any] =pipe(**self.get_dummy_inputs(lowerCamelCase__ ) )
__UpperCamelCase : Optional[int] =output.images[0]
__UpperCamelCase : Dict =image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__UpperCamelCase : Union[str, Any] =np.array(
[
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def __lowercase ( self ):
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Tuple =torch_device == 'cpu'
__UpperCamelCase : Union[str, Any] =True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=lowerCamelCase__ , relax_max_difference=lowerCamelCase__ , )
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] =self.get_dummy_components()
__UpperCamelCase : List[Any] =self.pipeline_class(**lowerCamelCase__ )
__UpperCamelCase : int =pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__UpperCamelCase : List[Any] =1
__UpperCamelCase : Any =2
__UpperCamelCase : Optional[Any] =self.get_dummy_inputs(lowerCamelCase__ )
for key in inputs.keys():
if key in self.batch_params:
__UpperCamelCase : List[str] =batch_size * [inputs[key]]
__UpperCamelCase : Optional[Any] =pipe(**lowerCamelCase__ , num_images_per_prompt=lowerCamelCase__ )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class __A ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png' )
__UpperCamelCase : List[Any] =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_img2img_out.npy' )
__UpperCamelCase : List[str] =ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img' )
__UpperCamelCase : List[str] =pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__UpperCamelCase : List[str] =torch.Generator(device=lowerCamelCase__ ).manual_seed(0 )
__UpperCamelCase : Union[str, Any] =pipe(
lowerCamelCase__ , generator=lowerCamelCase__ , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(lowerCamelCase__ , lowerCamelCase__ )
| 71 |
UpperCAmelCase__ = {}
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> 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 = (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 = _calculate(days - 1 , _UpperCAmelCase , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
_UpperCAmelCase = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
_UpperCAmelCase = _calculate(days - 1 , _UpperCAmelCase , 0 )
_UpperCAmelCase = state_late + state_absent + state_ontime
_UpperCAmelCase = prizestrings
return prizestrings
def A ( _UpperCAmelCase : int = 30 ) -> int:
'''simple docstring'''
return _calculate(_UpperCAmelCase , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 339 | 0 |
'''simple docstring'''
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class A__ :
def __init__( self : Dict , _a : Optional[Any] , _a : List[str]=99 , _a : int=13 , _a : str=7 , _a : Optional[Any]=9 , _a : List[str]=True , _a : List[str]=True , _a : List[str]=False , _a : Tuple=32 , _a : Optional[int]=5 , _a : Any=4 , _a : Any=37 , _a : Tuple=8 , _a : Optional[int]=0.1 , _a : Union[str, Any]=0.0_02 , _a : Dict=1 , _a : int=0 , _a : Optional[int]=0 , _a : Any=None , _a : Tuple=None , ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =parent
_SCREAMING_SNAKE_CASE =batch_size
_SCREAMING_SNAKE_CASE =encoder_seq_length
_SCREAMING_SNAKE_CASE =decoder_seq_length
# For common tests
_SCREAMING_SNAKE_CASE =self.decoder_seq_length
_SCREAMING_SNAKE_CASE =is_training
_SCREAMING_SNAKE_CASE =use_attention_mask
_SCREAMING_SNAKE_CASE =use_labels
_SCREAMING_SNAKE_CASE =vocab_size
_SCREAMING_SNAKE_CASE =hidden_size
_SCREAMING_SNAKE_CASE =num_hidden_layers
_SCREAMING_SNAKE_CASE =num_attention_heads
_SCREAMING_SNAKE_CASE =d_ff
_SCREAMING_SNAKE_CASE =relative_attention_num_buckets
_SCREAMING_SNAKE_CASE =dropout_rate
_SCREAMING_SNAKE_CASE =initializer_factor
_SCREAMING_SNAKE_CASE =eos_token_id
_SCREAMING_SNAKE_CASE =pad_token_id
_SCREAMING_SNAKE_CASE =decoder_start_token_id
_SCREAMING_SNAKE_CASE =None
_SCREAMING_SNAKE_CASE =decoder_layers
def A ( self : int ) -> Optional[int]:
'''simple docstring'''
return TaConfig.from_pretrained('google/umt5-base' )
def A ( self : Tuple , _a : Optional[Any] , _a : Union[str, Any] , _a : int , _a : int=None , _a : int=None , _a : Any=None , _a : Any=None , _a : str=None , ) -> str:
'''simple docstring'''
if attention_mask is None:
_SCREAMING_SNAKE_CASE =input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
_SCREAMING_SNAKE_CASE =decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
_SCREAMING_SNAKE_CASE =torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_a )
if decoder_head_mask is None:
_SCREAMING_SNAKE_CASE =torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_a )
if cross_attn_head_mask is None:
_SCREAMING_SNAKE_CASE =torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=_a )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def A ( self : int ) -> Any:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
_SCREAMING_SNAKE_CASE =input_ids.clamp(self.pad_token_id + 1 )
_SCREAMING_SNAKE_CASE =decoder_input_ids.clamp(self.pad_token_id + 1 )
_SCREAMING_SNAKE_CASE =self.get_config()
_SCREAMING_SNAKE_CASE =config.num_attention_heads
_SCREAMING_SNAKE_CASE =self.prepare_inputs_dict(_a , _a , _a )
return config, input_dict
def A ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs()
return config, inputs_dict
def A ( self : Optional[int] ) -> int:
'''simple docstring'''
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def A ( self : Dict ) -> Any:
'''simple docstring'''
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def A ( self : str , _a : Dict , _a : str , _a : Dict , _a : int , _a : Any , _a : List[str] , ) -> Optional[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =UMTaModel(config=_a )
model.to(_a )
model.eval()
_SCREAMING_SNAKE_CASE =model(
input_ids=_a , decoder_input_ids=_a , attention_mask=_a , decoder_attention_mask=_a , )
_SCREAMING_SNAKE_CASE =model(input_ids=_a , decoder_input_ids=_a )
_SCREAMING_SNAKE_CASE =result.last_hidden_state
_SCREAMING_SNAKE_CASE =result.past_key_values
_SCREAMING_SNAKE_CASE =result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(_a ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def A ( self : Any , _a : Optional[Any] , _a : int , _a : Dict , _a : List[Any] , _a : Any , _a : Optional[Any] , ) -> Optional[int]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =UMTaModel(config=_a ).get_decoder().to(_a ).eval()
# first forward pass
_SCREAMING_SNAKE_CASE =model(_a , use_cache=_a )
_SCREAMING_SNAKE_CASE =model(_a )
_SCREAMING_SNAKE_CASE =model(_a , use_cache=_a )
self.parent.assertTrue(len(_a ) == len(_a ) )
self.parent.assertTrue(len(_a ) == len(_a ) + 1 )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
_SCREAMING_SNAKE_CASE =ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
_SCREAMING_SNAKE_CASE =torch.cat([input_ids, next_tokens] , dim=-1 )
_SCREAMING_SNAKE_CASE =model(_a )['last_hidden_state']
_SCREAMING_SNAKE_CASE =model(_a , past_key_values=_a )['last_hidden_state']
# select random slice
_SCREAMING_SNAKE_CASE =ids_tensor((1,) , output_from_past.shape[-1] ).item()
_SCREAMING_SNAKE_CASE =output_from_no_past[:, -1, random_slice_idx].detach()
_SCREAMING_SNAKE_CASE =output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_a , _a , atol=1e-3 ) )
def A ( self : str , _a : List[Any] , _a : List[Any] , ) -> str:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =UMTaModel(config=_a ).to(_a ).half().eval()
_SCREAMING_SNAKE_CASE =model(**_a )['last_hidden_state']
self.parent.assertFalse(torch.isnan(_a ).any().item() )
@require_torch
class A__ ( A__ , A__ , A__ , unittest.TestCase ):
A__ = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
A__ = (UMTaForConditionalGeneration,) if is_torch_available() else ()
A__ = (
{
'conversational': UMTaForConditionalGeneration,
'feature-extraction': UMTaModel,
'summarization': UMTaForConditionalGeneration,
'text2text-generation': UMTaForConditionalGeneration,
'translation': UMTaForConditionalGeneration,
'question-answering': UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
A__ = True
A__ = False
A__ = False
A__ = True
A__ = True
# The small UMT5 model needs higher percentages for CPU/MP tests
A__ = [0.8, 0.9]
def A ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =UMTaModelTester(self )
@unittest.skip('Test has a segmentation fault on torch 1.8.0' )
def A ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE =UMTaModel(config_and_inputs[0] ).to(_a )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
_a , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f"{tmpdirname}/t5_test.onnx" , export_params=_a , opset_version=9 , input_names=['input_ids', 'decoder_input_ids'] , )
@unittest.skipIf(torch_device == 'cpu' , 'Cant do half precision' )
def A ( self : Dict ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*_a )
def A ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =['encoder_attentions', 'decoder_attentions', 'cross_attentions']
_SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE =config_and_inputs[0]
_SCREAMING_SNAKE_CASE =UMTaForConditionalGeneration(_a ).eval()
model.to(_a )
_SCREAMING_SNAKE_CASE ={
'head_mask': torch.zeros(config.num_layers , config.num_heads , device=_a ),
'decoder_head_mask': torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
'cross_attn_head_mask': torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
}
for attn_name, (name, mask) in zip(_a , head_masking.items() ):
_SCREAMING_SNAKE_CASE ={name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
_SCREAMING_SNAKE_CASE =torch.ones(
config.num_decoder_layers , config.num_heads , device=_a )
_SCREAMING_SNAKE_CASE =model.generate(
config_and_inputs[1]['input_ids'] , num_beams=1 , max_length=3 , output_attentions=_a , return_dict_in_generate=_a , **_a , )
# We check the state of decoder_attentions and cross_attentions just from the last step
_SCREAMING_SNAKE_CASE =out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip('Does not work on the tiny model as we keep hitting edge cases.' )
def A ( self : List[Any] ) -> Tuple:
'''simple docstring'''
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class A__ ( unittest.TestCase ):
@slow
@unittest.skip(
'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged' )
def A ( self : Any ) -> Optional[int]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =UMTaForConditionalGeneration.from_pretrained('google/umt5-small' , return_dict=_a ).to(_a )
_SCREAMING_SNAKE_CASE =AutoTokenizer.from_pretrained('google/umt5-small' , use_fast=_a , legacy=_a )
_SCREAMING_SNAKE_CASE =[
'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
'No se como puedo <extra_id_0>.',
'This is the reason why we <extra_id_0> them.',
'The <extra_id_0> walks in <extra_id_1>, seats',
'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
]
_SCREAMING_SNAKE_CASE =tokenizer(_a , return_tensors='pt' , padding=_a ).input_ids
# fmt: off
_SCREAMING_SNAKE_CASE =torch.tensor(
[
[ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(_a , _a )
_SCREAMING_SNAKE_CASE =model.generate(input_ids.to(_a ) )
_SCREAMING_SNAKE_CASE =[
'<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
'<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
'<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
]
_SCREAMING_SNAKE_CASE =tokenizer.batch_decode(_a )
self.assertEqual(_a , _a )
| 47 |
import os
import sys
import unittest
UpperCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCAmelCase__ = os.path.join(git_repo_path, "src", "diffusers")
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = find_backend(' if not is_torch_available():')
self.assertEqual(A , 'torch')
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
_UpperCAmelCase = find_backend(' if not (is_torch_available() and is_transformers_available()):')
self.assertEqual(A , 'torch_and_transformers')
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
_UpperCAmelCase = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):')
self.assertEqual(A , 'torch_and_transformers_and_onnx')
def _lowerCamelCase ( self : int) -> Dict:
"""simple docstring"""
_UpperCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , A)
self.assertIn('torch_and_transformers' , A)
self.assertIn('flax_and_transformers' , A)
self.assertIn('torch_and_transformers_and_onnx' , A)
# Likewise, we can't assert on the exact content of a key
self.assertIn('UNet2DModel' , objects['torch'])
self.assertIn('FlaxUNet2DConditionModel' , objects['flax'])
self.assertIn('StableDiffusionPipeline' , objects['torch_and_transformers'])
self.assertIn('FlaxStableDiffusionPipeline' , objects['flax_and_transformers'])
self.assertIn('LMSDiscreteScheduler' , objects['torch_and_scipy'])
self.assertIn('OnnxStableDiffusionPipeline' , objects['torch_and_transformers_and_onnx'])
def _lowerCamelCase ( self : Union[str, Any]) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = create_dummy_object('CONSTANT' , '\'torch\'')
self.assertEqual(A , '\nCONSTANT = None\n')
_UpperCAmelCase = create_dummy_object('function' , '\'torch\'')
self.assertEqual(
A , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n')
_UpperCAmelCase = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n'
_UpperCAmelCase = create_dummy_object('FakeClass' , '\'torch\'')
self.assertEqual(A , A)
def _lowerCamelCase ( self : Dict) -> int:
"""simple docstring"""
_UpperCAmelCase = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = ["torch"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, ["torch"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, ["torch"])\n'
_UpperCAmelCase = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']})
self.assertEqual(dummy_files['torch'] , A)
| 339 | 0 |
"""simple docstring"""
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class UpperCAmelCase_ ( snake_case ):
UpperCamelCase ="data2vec-audio"
def __init__( self , UpperCamelCase_=32 , UpperCamelCase_=7_68 , UpperCamelCase_=12 , UpperCamelCase_=12 , UpperCamelCase_=30_72 , UpperCamelCase_="gelu" , UpperCamelCase_=0.1 , UpperCamelCase_=0.1 , UpperCamelCase_=0.1 , UpperCamelCase_=0.0 , UpperCamelCase_=0.1 , UpperCamelCase_=0.1 , UpperCamelCase_=0.0_2 , UpperCamelCase_=1E-5 , UpperCamelCase_="gelu" , UpperCamelCase_=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , UpperCamelCase_=(5, 2, 2, 2, 2, 2, 2) , UpperCamelCase_=(10, 3, 3, 3, 3, 2, 2) , UpperCamelCase_=False , UpperCamelCase_=16 , UpperCamelCase_=19 , UpperCamelCase_=5 , UpperCamelCase_=0.0_5 , UpperCamelCase_=10 , UpperCamelCase_=2 , UpperCamelCase_=0.0 , UpperCamelCase_=10 , UpperCamelCase_=0 , UpperCamelCase_="sum" , UpperCamelCase_=False , UpperCamelCase_=False , UpperCamelCase_=2_56 , UpperCamelCase_=(5_12, 5_12, 5_12, 5_12, 15_00) , UpperCamelCase_=(5, 3, 3, 1, 1) , UpperCamelCase_=(1, 2, 3, 1, 1) , UpperCamelCase_=5_12 , UpperCamelCase_=0 , UpperCamelCase_=1 , UpperCamelCase_=2 , UpperCamelCase_=False , UpperCamelCase_=3 , UpperCamelCase_=2 , UpperCamelCase_=3 , UpperCamelCase_=None , **UpperCamelCase_ , ) -> Tuple:
super().__init__(**UpperCamelCase_ , pad_token_id=UpperCamelCase_ , bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ )
__lowercase : Union[str, Any] = hidden_size
__lowercase : Optional[Any] = feat_extract_activation
__lowercase : Any = list(UpperCamelCase_ )
__lowercase : List[str] = list(UpperCamelCase_ )
__lowercase : Optional[int] = list(UpperCamelCase_ )
__lowercase : Dict = conv_bias
__lowercase : Optional[int] = num_conv_pos_embeddings
__lowercase : Any = num_conv_pos_embedding_groups
__lowercase : str = conv_pos_kernel_size
__lowercase : Dict = len(self.conv_dim )
__lowercase : str = num_hidden_layers
__lowercase : List[str] = intermediate_size
__lowercase : Tuple = hidden_act
__lowercase : str = num_attention_heads
__lowercase : Dict = hidden_dropout
__lowercase : Optional[Any] = attention_dropout
__lowercase : int = activation_dropout
__lowercase : List[str] = feat_proj_dropout
__lowercase : Union[str, Any] = final_dropout
__lowercase : List[str] = layerdrop
__lowercase : Tuple = layer_norm_eps
__lowercase : str = initializer_range
__lowercase : Optional[int] = vocab_size
__lowercase : Union[str, Any] = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
F""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowercase : int = mask_time_prob
__lowercase : int = mask_time_length
__lowercase : Tuple = mask_time_min_masks
__lowercase : List[Any] = mask_feature_prob
__lowercase : Union[str, Any] = mask_feature_length
__lowercase : List[str] = mask_feature_min_masks
# ctc loss
__lowercase : Optional[Any] = ctc_loss_reduction
__lowercase : Any = ctc_zero_infinity
# adapter
__lowercase : int = add_adapter
__lowercase : int = adapter_kernel_size
__lowercase : Union[str, Any] = adapter_stride
__lowercase : List[str] = num_adapter_layers
__lowercase : Optional[int] = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__lowercase : List[str] = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__lowercase : Union[str, Any] = list(UpperCamelCase_ )
__lowercase : int = list(UpperCamelCase_ )
__lowercase : str = list(UpperCamelCase_ )
__lowercase : Optional[int] = xvector_output_dim
@property
def _lowerCamelCase ( self ) -> List[Any]:
return math.prod(self.conv_stride )
| 249 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
UpperCAmelCase__ = logging.getLogger(__name__)
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
UpperCamelCase = field(
default='''tab_fact''' , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} , )
UpperCamelCase = field(
default=1_0_2_4 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
UpperCamelCase = field(
default=A , 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.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the training data.'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''A csv or a json file containing the validation data.'''} )
UpperCamelCase = field(default=A , metadata={'''help''': '''A csv or a json file containing the test data.'''} )
def _lowerCamelCase ( self : str) -> List[Any]:
"""simple docstring"""
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.')
else:
_UpperCAmelCase = self.train_file.split('.')[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
_UpperCAmelCase = self.validation_file.split('.')[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class __lowerCAmelCase :
UpperCamelCase = field(
default=A , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCamelCase = field(
default=A , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCamelCase = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCamelCase = field(
default=A , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
def A ( ) -> Optional[int]:
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_UpperCAmelCase = 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 , _UpperCAmelCase , _UpperCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_args_into_dataclasses()
# 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 )] , )
_UpperCAmelCase = training_args.get_process_log_level()
logger.setLevel(_UpperCAmelCase )
datasets.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.set_verbosity(_UpperCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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}" )
logger.info(F"Training/evaluation parameters {training_args}" )
# Detecting last checkpoint.
_UpperCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCAmelCase = 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 and training_args.resume_from_checkpoint is 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.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. 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 = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
_UpperCAmelCase = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
_UpperCAmelCase = data_args.train_file.split('.' )[-1]
_UpperCAmelCase = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
_UpperCAmelCase = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(F"load a local file for {key}: {data_files[key]}" )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
_UpperCAmelCase = load_dataset('csv' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
_UpperCAmelCase = load_dataset('json' , data_files=_UpperCAmelCase , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
_UpperCAmelCase = raw_datasets['train'].features['label'].names
_UpperCAmelCase = len(_UpperCAmelCase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
_UpperCAmelCase = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , 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 , add_prefix_space=_UpperCAmelCase , )
_UpperCAmelCase = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_UpperCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
_UpperCAmelCase = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
_UpperCAmelCase = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
_UpperCAmelCase = {'Refused': 0, 'Entailed': 1}
_UpperCAmelCase = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
F"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." )
_UpperCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(_UpperCAmelCase : Union[str, Any] ):
# Tokenize the texts
def _convert_table_text_to_pandas(_UpperCAmelCase : Dict ):
_UpperCAmelCase = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
_UpperCAmelCase = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
_UpperCAmelCase = examples['statement']
_UpperCAmelCase = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
_UpperCAmelCase = tokenizer(_UpperCAmelCase , _UpperCAmelCase , padding=_UpperCAmelCase , max_length=_UpperCAmelCase , truncation=_UpperCAmelCase )
_UpperCAmelCase = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
_UpperCAmelCase = raw_datasets.map(
_UpperCAmelCase , batched=_UpperCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
_UpperCAmelCase = raw_datasets['train']
if data_args.max_train_samples is not None:
_UpperCAmelCase = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
_UpperCAmelCase = raw_datasets['validation']
if data_args.max_eval_samples is not None:
_UpperCAmelCase = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
_UpperCAmelCase = raw_datasets['test']
if data_args.max_predict_samples is not None:
_UpperCAmelCase = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(_UpperCAmelCase ) ) , 3 ):
logger.info(F"Sample {index} of the training set: {train_dataset[index]}." )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_UpperCAmelCase : EvalPrediction ):
_UpperCAmelCase = p.predictions[0] if isinstance(p.predictions , _UpperCAmelCase ) else p.predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
_UpperCAmelCase = default_data_collator
elif training_args.fpaa:
_UpperCAmelCase = DataCollatorWithPadding(_UpperCAmelCase , pad_to_multiple_of=8 )
else:
_UpperCAmelCase = None
# Initialize our Trainer
_UpperCAmelCase = Trainer(
model=_UpperCAmelCase , args=_UpperCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_UpperCAmelCase , tokenizer=_UpperCAmelCase , data_collator=_UpperCAmelCase , )
# Training
if training_args.do_train:
_UpperCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_UpperCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCAmelCase = last_checkpoint
_UpperCAmelCase = trainer.train(resume_from_checkpoint=_UpperCAmelCase )
_UpperCAmelCase = train_result.metrics
_UpperCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCAmelCase )
)
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _UpperCAmelCase )
trainer.save_metrics('train' , _UpperCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
_UpperCAmelCase = trainer.evaluate(eval_dataset=_UpperCAmelCase )
_UpperCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCAmelCase )
_UpperCAmelCase = min(_UpperCAmelCase , len(_UpperCAmelCase ) )
trainer.log_metrics('eval' , _UpperCAmelCase )
trainer.save_metrics('eval' , _UpperCAmelCase )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
_UpperCAmelCase = predict_dataset.remove_columns('label' )
_UpperCAmelCase = trainer.predict(_UpperCAmelCase , metric_key_prefix='predict' ).predictions
_UpperCAmelCase = np.argmax(_UpperCAmelCase , axis=1 )
_UpperCAmelCase = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(_UpperCAmelCase , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(_UpperCAmelCase ):
_UpperCAmelCase = label_list[item]
writer.write(F"{index}\t{item}\n" )
_UpperCAmelCase = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**_UpperCAmelCase )
else:
trainer.create_model_card(**_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 339 | 0 |
"""simple docstring"""
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def lowercase (snake_case__ : Optional[Any] , snake_case__ : Union[str, Any] ) -> str:
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@require_sqlalchemy
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def lowercase (snake_case__ : Union[str, Any] , snake_case__ : Optional[Any] , snake_case__ : str , snake_case__ : Optional[int] ) -> Any:
'''simple docstring'''
lowerCAmelCase = tmp_path / """cache"""
lowerCAmelCase = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowerCAmelCase = SqlDatasetReader(
"""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=_UpperCAmelCase , keep_in_memory=_UpperCAmelCase ).read()
_check_sql_dataset(_UpperCAmelCase , _UpperCAmelCase )
@require_sqlalchemy
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def lowercase (snake_case__ : Tuple , snake_case__ : int , snake_case__ : Tuple , snake_case__ : Tuple ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase = tmp_path / """cache"""
lowerCAmelCase = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
lowerCAmelCase = features.copy() if features else default_expected_features
lowerCAmelCase = (
Features({feature: Value(_UpperCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
lowerCAmelCase = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , features=_UpperCAmelCase , cache_dir=_UpperCAmelCase ).read()
_check_sql_dataset(_UpperCAmelCase , _UpperCAmelCase )
def lowercase (snake_case__ : Any ) -> int:
'''simple docstring'''
with contextlib.closing(sqlitea.connect(_UpperCAmelCase ) ) as con:
lowerCAmelCase = con.cursor()
cur.execute("""SELECT * FROM dataset""" )
for row in cur:
yield row
@require_sqlalchemy
def lowercase (snake_case__ : Any , snake_case__ : Union[str, Any] , snake_case__ : Dict ) -> Dict:
'''simple docstring'''
lowerCAmelCase = tmp_path / """cache"""
lowerCAmelCase = os.path.join(_UpperCAmelCase , """tmp.sql""" )
lowerCAmelCase = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=_UpperCAmelCase ).read()
SqlDatasetWriter(_UpperCAmelCase , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=1 ).write()
lowerCAmelCase = iter_sql_file(_UpperCAmelCase )
lowerCAmelCase = iter_sql_file(_UpperCAmelCase )
for rowa, rowa in zip(_UpperCAmelCase , _UpperCAmelCase ):
assert rowa == rowa
@require_sqlalchemy
def lowercase (snake_case__ : Optional[int] , snake_case__ : List[str] , snake_case__ : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase = tmp_path / """cache"""
lowerCAmelCase = os.path.join(_UpperCAmelCase , """tmp.sql""" )
lowerCAmelCase = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=_UpperCAmelCase ).read()
SqlDatasetWriter(_UpperCAmelCase , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=2 ).write()
lowerCAmelCase = iter_sql_file(_UpperCAmelCase )
lowerCAmelCase = iter_sql_file(_UpperCAmelCase )
for rowa, rowa in zip(_UpperCAmelCase , _UpperCAmelCase ):
assert rowa == rowa
@require_sqlalchemy
def lowercase (snake_case__ : Union[str, Any] , snake_case__ : Optional[int] , snake_case__ : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase = tmp_path / """cache"""
lowerCAmelCase = os.path.join(_UpperCAmelCase , """tmp.sql""" )
lowerCAmelCase = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=_UpperCAmelCase ).read()
with pytest.raises(_UpperCAmelCase ):
SqlDatasetWriter(_UpperCAmelCase , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=0 ).write()
| 155 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def A ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : Dict ) -> Any:
'''simple docstring'''
_UpperCAmelCase = multiprocessing.Manager()
_UpperCAmelCase = manager.list()
_UpperCAmelCase = multiprocessing.Process(target=_UpperCAmelCase , 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 ( _UpperCAmelCase : str , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
with create_tempdir():
# These system calls are needed when cleaning up tempdir.
import os
import shutil
_UpperCAmelCase = shutil.rmtree
_UpperCAmelCase = os.rmdir
_UpperCAmelCase = os.chdir
# Disable functionalities that can make destructive changes to the test.
reliability_guard()
# Run program.
try:
_UpperCAmelCase = {}
with swallow_io():
with time_limit(_UpperCAmelCase ):
exec(_UpperCAmelCase , _UpperCAmelCase )
result.append('passed' )
except TimeoutException:
result.append('timed out' )
except BaseException as e:
result.append(F"failed: {e}" )
# Needed for cleaning up.
_UpperCAmelCase = rmtree
_UpperCAmelCase = rmdir
_UpperCAmelCase = chdir
@contextlib.contextmanager
def A ( _UpperCAmelCase : Union[str, Any] ) -> Any:
'''simple docstring'''
def signal_handler(_UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict ):
raise TimeoutException('Timed out!' )
signal.setitimer(signal.ITIMER_REAL , _UpperCAmelCase )
signal.signal(signal.SIGALRM , _UpperCAmelCase )
try:
yield
finally:
signal.setitimer(signal.ITIMER_REAL , 0 )
@contextlib.contextmanager
def A ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = WriteOnlyStringIO()
with contextlib.redirect_stdout(_UpperCAmelCase ):
with contextlib.redirect_stderr(_UpperCAmelCase ):
with redirect_stdin(_UpperCAmelCase ):
yield
@contextlib.contextmanager
def A ( ) -> Any:
'''simple docstring'''
with tempfile.TemporaryDirectory() as dirname:
with chdir(_UpperCAmelCase ):
yield dirname
class __lowerCAmelCase ( A ):
pass
class __lowerCAmelCase ( io.StringIO ):
def _lowerCamelCase ( self : Tuple , *A : str , **A : Any) -> Any:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : List[str] , *A : Optional[Any] , **A : Optional[Any]) -> Optional[int]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : str , *A : List[str] , **A : List[Any]) -> Union[str, Any]:
"""simple docstring"""
raise OSError
def _lowerCamelCase ( self : Union[str, Any] , *A : Optional[Any] , **A : List[str]) -> Optional[int]:
"""simple docstring"""
return False
class __lowerCAmelCase ( contextlib._RedirectStream ): # type: ignore
UpperCamelCase = '''stdin'''
@contextlib.contextmanager
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
if root == ".":
yield
return
_UpperCAmelCase = os.getcwd()
os.chdir(_UpperCAmelCase )
try:
yield
except BaseException as exc:
raise exc
finally:
os.chdir(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str]=None ) -> Any:
'''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 = None
_UpperCAmelCase = None
import os
_UpperCAmelCase = '1'
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import shutil
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
import subprocess
_UpperCAmelCase = None # type: ignore
_UpperCAmelCase = None
import sys
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
| 339 | 0 |
def lowercase_ ( _lowerCamelCase : int):
return str(_UpperCAmelCase) == str(_UpperCAmelCase)[::-1]
def lowercase_ ( _lowerCamelCase : int):
return int(_UpperCAmelCase) + int(str(_UpperCAmelCase)[::-1])
def lowercase_ ( _lowerCamelCase : int = 1_0000):
lowercase__ : Any = []
for num in range(1 , _UpperCAmelCase):
lowercase__ : str = 0
lowercase__ : Optional[int] = num
while iterations < 50:
lowercase__ : str = sum_reverse(_UpperCAmelCase)
iterations += 1
if is_palindrome(_UpperCAmelCase):
break
else:
lychrel_nums.append(_UpperCAmelCase)
return len(_UpperCAmelCase)
if __name__ == "__main__":
print(f"{solution() = }")
| 87 |
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 ( _UpperCAmelCase : Tuple , _UpperCAmelCase : Union[str, Any]=False ) -> str:
'''simple docstring'''
try:
_UpperCAmelCase = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_UpperCAmelCase = default
else:
# KEY is set, convert it to True or False.
try:
_UpperCAmelCase = strtobool(_UpperCAmelCase )
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
UpperCAmelCase__ = parse_flag_from_env("RUN_SLOW", default=False)
def A ( _UpperCAmelCase : List[str] ) -> List[str]:
'''simple docstring'''
return unittest.skip('Test was skipped' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> str:
'''simple docstring'''
return unittest.skipUnless(_run_slow_tests , 'test is slow' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> str:
'''simple docstring'''
return unittest.skipUnless(not torch.cuda.is_available() , 'test requires only a CPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Dict ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.is_available() , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_xpu_available() , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> List[str]:
'''simple docstring'''
return unittest.skipUnless(is_mps_available() , 'test requires a `mps` backend support in `torch`' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , 'test requires the Hugging Face suite' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : str ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_bnb_available() , 'test requires the bitsandbytes library' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
return unittest.skipUnless(is_tpu_available() , 'test requires TPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[Any] ) -> str:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() == 1 , 'test requires a GPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> int:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() == 1 , 'test requires a XPU' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(torch.cuda.device_count() > 1 , 'test requires multiple GPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Tuple ) -> Any:
'''simple docstring'''
return unittest.skipUnless(torch.xpu.device_count() > 1 , 'test requires multiple XPUs' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_safetensors_available() , 'test requires safetensors' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[Any] ) -> Dict:
'''simple docstring'''
return unittest.skipUnless(is_deepspeed_available() , 'test requires DeepSpeed' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
return unittest.skipUnless(is_torch_version('>=' , '1.12.0' ) , 'test requires torch version >= 1.12.0' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Any=None , _UpperCAmelCase : List[Any]=None ) -> Dict:
'''simple docstring'''
if test_case is None:
return partial(_UpperCAmelCase , version=_UpperCAmelCase )
return unittest.skipUnless(is_torch_version('>=' , _UpperCAmelCase ) , F"test requires torch version >= {version}" )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> int:
'''simple docstring'''
return unittest.skipUnless(is_tensorboard_available() , 'test requires Tensorboard' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
return unittest.skipUnless(is_wandb_available() , 'test requires wandb' )(_UpperCAmelCase )
def A ( _UpperCAmelCase : List[str] ) -> Optional[int]:
'''simple docstring'''
return unittest.skipUnless(is_comet_ml_available() , 'test requires comet_ml' )(_UpperCAmelCase )
UpperCAmelCase__ = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def A ( _UpperCAmelCase : List[str] ) -> Any:
'''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' , )(_UpperCAmelCase )
class __lowerCAmelCase ( unittest.TestCase ):
UpperCamelCase = True
@classmethod
def _lowerCamelCase ( cls : List[Any]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = tempfile.mkdtemp()
@classmethod
def _lowerCamelCase ( cls : Union[str, Any]) -> str:
"""simple docstring"""
if os.path.exists(cls.tmpdir):
shutil.rmtree(cls.tmpdir)
def _lowerCamelCase ( self : List[str]) -> List[Any]:
"""simple docstring"""
if self.clear_on_setup:
for path in Path(self.tmpdir).glob('**/*'):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(A)
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Dict) -> Tuple:
"""simple docstring"""
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[int] , A : Union[mock.Mock, List[mock.Mock]]) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = mocks if isinstance(A , (tuple, list)) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop)
def A ( _UpperCAmelCase : List[Any] ) -> int:
'''simple docstring'''
_UpperCAmelCase = AcceleratorState()
_UpperCAmelCase = tensor[None].clone().to(state.device )
_UpperCAmelCase = gather(_UpperCAmelCase ).cpu()
_UpperCAmelCase = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , _UpperCAmelCase ):
return False
return True
class __lowerCAmelCase :
def __init__( self : Optional[Any] , A : Union[str, Any] , A : Optional[int] , A : str) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = returncode
_UpperCAmelCase = stdout
_UpperCAmelCase = stderr
async def A ( _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
while True:
_UpperCAmelCase = await stream.readline()
if line:
callback(_UpperCAmelCase )
else:
break
async def A ( _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str]=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=None , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Union[str, Any]=False ) -> _RunOutput:
'''simple docstring'''
if echo:
print('\nRunning: ' , ' '.join(_UpperCAmelCase ) )
_UpperCAmelCase = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=_UpperCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_UpperCAmelCase , )
# 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 = []
_UpperCAmelCase = []
def tee(_UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : str , _UpperCAmelCase : str="" ):
_UpperCAmelCase = line.decode('utf-8' ).rstrip()
sink.append(_UpperCAmelCase )
if not quiet:
print(_UpperCAmelCase , _UpperCAmelCase , file=_UpperCAmelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stdout , label='stdout:' ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stderr , label='stderr:' ) ) ),
] , timeout=_UpperCAmelCase , )
return _RunOutput(await p.wait() , _UpperCAmelCase , _UpperCAmelCase )
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Dict=None , _UpperCAmelCase : str=None , _UpperCAmelCase : str=180 , _UpperCAmelCase : List[Any]=False , _UpperCAmelCase : List[Any]=True ) -> _RunOutput:
'''simple docstring'''
_UpperCAmelCase = asyncio.get_event_loop()
_UpperCAmelCase = loop.run_until_complete(
_stream_subprocess(_UpperCAmelCase , env=_UpperCAmelCase , stdin=_UpperCAmelCase , timeout=_UpperCAmelCase , quiet=_UpperCAmelCase , echo=_UpperCAmelCase ) )
_UpperCAmelCase = ' '.join(_UpperCAmelCase )
if result.returncode > 0:
_UpperCAmelCase = '\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 ( A ):
pass
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : str=False ) -> Tuple:
'''simple docstring'''
try:
_UpperCAmelCase = subprocess.check_output(_UpperCAmelCase , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(_UpperCAmelCase , 'decode' ):
_UpperCAmelCase = output.decode('utf-8' )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F"Command `{' '.join(_UpperCAmelCase )}` failed with the following error:\n\n{e.output.decode()}" ) from e
| 339 | 0 |
'''simple docstring'''
def __lowerCAmelCase ( snake_case__ ):
__UpperCamelCase : Any = set()
# edges = list of graph's edges
__UpperCamelCase : Any = get_edges(_UpperCAmelCase )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
__UpperCamelCase , __UpperCamelCase : int = edges.pop()
chosen_vertices.add(_UpperCAmelCase )
chosen_vertices.add(_UpperCAmelCase )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(_UpperCAmelCase )
return chosen_vertices
def __lowerCAmelCase ( snake_case__ ):
__UpperCamelCase : Tuple = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 298 |
from __future__ import annotations
UpperCAmelCase__ = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
UpperCAmelCase__ = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def A ( _UpperCAmelCase : Matrix ) -> tuple[int, int] | None:
'''simple docstring'''
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def A ( _UpperCAmelCase : Matrix ) -> Matrix | None:
'''simple docstring'''
if location := find_empty_location(_UpperCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase = digit
if sudoku(_UpperCAmelCase ) is not None:
return grid
_UpperCAmelCase = 0
return None
def A ( _UpperCAmelCase : Matrix ) -> None:
'''simple docstring'''
for row in grid:
for cell in row:
print(_UpperCAmelCase , end=' ' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("\nExample grid:\n" + "=" * 20)
print_solution(example_grid)
print("\nExample grid solution:")
UpperCAmelCase__ = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("Cannot find a solution.")
| 339 | 0 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCAmelCase = version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
from nltk import word_tokenize
lowerCAmelCase = '''\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n'''
lowerCAmelCase = '''\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'''
lowerCAmelCase = '''\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n \'meteor\': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric(\'meteor\')\n >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results[\"meteor\"], 4))\n 0.6944\n'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A ( datasets.Metric ):
def _A (self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'] , reference_urls=[
'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
'https://en.wikipedia.org/wiki/METEOR',
] , )
def _A (self , lowerCAmelCase ):
import nltk
nltk.download('wordnet' )
if NLTK_VERSION >= version.Version('3.6.5' ):
nltk.download('punkt' )
if NLTK_VERSION >= version.Version('3.6.6' ):
nltk.download('omw-1.4' )
def _A (self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=0.9 , lowerCAmelCase=3 , lowerCAmelCase=0.5 ):
if NLTK_VERSION >= version.Version('3.6.5' ):
__lowercase= [
meteor_score.single_meteor_score(
word_tokenize(lowerCAmelCase ) , word_tokenize(lowerCAmelCase ) , alpha=lowerCAmelCase , beta=lowerCAmelCase , gamma=lowerCAmelCase )
for ref, pred in zip(lowerCAmelCase , lowerCAmelCase )
]
else:
__lowercase= [
meteor_score.single_meteor_score(lowerCAmelCase , lowerCAmelCase , alpha=lowerCAmelCase , beta=lowerCAmelCase , gamma=lowerCAmelCase )
for ref, pred in zip(lowerCAmelCase , lowerCAmelCase )
]
return {"meteor": np.mean(lowerCAmelCase )}
| 295 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
UpperCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
UpperCAmelCase__ = "\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n"
UpperCAmelCase__ = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"
UpperCAmelCase__ = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n 'meteor': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric('meteor')\n >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results[\"meteor\"], 4))\n 0.6944\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _lowerCamelCase ( self : List[Any]) -> List[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence'),
'references': datasets.Value('string' , id='sequence'),
}) , codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'] , reference_urls=[
'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
'https://en.wikipedia.org/wiki/METEOR',
] , )
def _lowerCamelCase ( self : Optional[Any] , A : List[str]) -> List[Any]:
"""simple docstring"""
import nltk
nltk.download('wordnet')
if NLTK_VERSION >= version.Version('3.6.5'):
nltk.download('punkt')
if NLTK_VERSION >= version.Version('3.6.6'):
nltk.download('omw-1.4')
def _lowerCamelCase ( self : Optional[Any] , A : Tuple , A : Optional[int] , A : List[Any]=0.9 , A : Optional[Any]=3 , A : Optional[int]=0.5) -> Any:
"""simple docstring"""
if NLTK_VERSION >= version.Version('3.6.5'):
_UpperCAmelCase = [
meteor_score.single_meteor_score(
word_tokenize(A) , word_tokenize(A) , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
else:
_UpperCAmelCase = [
meteor_score.single_meteor_score(A , A , alpha=A , beta=A , gamma=A)
for ref, pred in zip(A , A)
]
return {"meteor": np.mean(A)}
| 339 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE (unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCamelCase__ ( self : int ):
_a = TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )
_a = tf.convert_to_tensor(
[[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
_a = model(__a )["last_hidden_state"]
_a = tf.TensorShape((1, 10, 7_68) )
self.assertEqual(output.shape , __a )
# compare the actual values for a slice.
_a = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 63 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
UpperCAmelCase__ = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
"large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
}
def A ( _UpperCAmelCase : Optional[int] ) -> str:
'''simple docstring'''
_UpperCAmelCase = ['layers', 'blocks']
for k in ignore_keys:
state_dict.pop(_UpperCAmelCase , _UpperCAmelCase )
UpperCAmelCase__ = {
"blocks": "layers",
"mlp.0": "fc1",
"mlp.2": "fc2",
"mlp_ln": "final_layer_norm",
".attn.query": ".self_attn.q_proj",
".attn.key": ".self_attn.k_proj",
".attn.value": ".self_attn.v_proj",
".attn_ln": ".self_attn_layer_norm",
".attn.out": ".self_attn.out_proj",
".cross_attn.query": ".encoder_attn.q_proj",
".cross_attn.key": ".encoder_attn.k_proj",
".cross_attn.value": ".encoder_attn.v_proj",
".cross_attn_ln": ".encoder_attn_layer_norm",
".cross_attn.out": ".encoder_attn.out_proj",
"decoder.ln.": "decoder.layer_norm.",
"encoder.ln.": "encoder.layer_norm.",
"token_embedding": "embed_tokens",
"encoder.positional_embedding": "encoder.embed_positions.weight",
"decoder.positional_embedding": "decoder.embed_positions.weight",
"ln_post": "layer_norm",
}
def A ( _UpperCAmelCase : Dict ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = list(s_dict.keys() )
for key in keys:
_UpperCAmelCase = key
for k, v in WHISPER_MAPPING.items():
if k in key:
_UpperCAmelCase = new_key.replace(_UpperCAmelCase , _UpperCAmelCase )
print(F"{key} -> {new_key}" )
_UpperCAmelCase = s_dict.pop(_UpperCAmelCase )
return s_dict
def A ( _UpperCAmelCase : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = emb.weight.shape
_UpperCAmelCase = nn.Linear(_UpperCAmelCase , _UpperCAmelCase , bias=_UpperCAmelCase )
_UpperCAmelCase = emb.weight.data
return lin_layer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> bytes:
'''simple docstring'''
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
_UpperCAmelCase = os.path.basename(_UpperCAmelCase )
_UpperCAmelCase = url.split('/' )[-2]
_UpperCAmelCase = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if os.path.exists(_UpperCAmelCase ) and not os.path.isfile(_UpperCAmelCase ):
raise RuntimeError(F"{download_target} exists and is not a regular file" )
if os.path.isfile(_UpperCAmelCase ):
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(F"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" )
with urllib.request.urlopen(_UpperCAmelCase ) as source, open(_UpperCAmelCase , 'wb' ) as output:
with tqdm(
total=int(source.info().get('Content-Length' ) ) , ncols=80 , unit='iB' , unit_scale=_UpperCAmelCase , unit_divisor=1_024 ) as loop:
while True:
_UpperCAmelCase = source.read(8_192 )
if not buffer:
break
output.write(_UpperCAmelCase )
loop.update(len(_UpperCAmelCase ) )
_UpperCAmelCase = open(_UpperCAmelCase , 'rb' ).read()
if hashlib.shaaaa(_UpperCAmelCase ).hexdigest() != expected_shaaaa:
raise RuntimeError(
'Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.' )
return model_bytes
def A ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any ) -> Optional[int]:
'''simple docstring'''
if ".pt" not in checkpoint_path:
_UpperCAmelCase = _download(_MODELS[checkpoint_path] )
else:
_UpperCAmelCase = torch.load(_UpperCAmelCase , map_location='cpu' )
_UpperCAmelCase = original_checkpoint['dims']
_UpperCAmelCase = original_checkpoint['model_state_dict']
_UpperCAmelCase = state_dict['decoder.token_embedding.weight']
remove_ignore_keys_(_UpperCAmelCase )
rename_keys(_UpperCAmelCase )
_UpperCAmelCase = True
_UpperCAmelCase = state_dict['decoder.layers.0.fc1.weight'].shape[0]
_UpperCAmelCase = WhisperConfig(
vocab_size=dimensions['n_vocab'] , encoder_ffn_dim=_UpperCAmelCase , decoder_ffn_dim=_UpperCAmelCase , num_mel_bins=dimensions['n_mels'] , d_model=dimensions['n_audio_state'] , max_target_positions=dimensions['n_text_ctx'] , encoder_layers=dimensions['n_audio_layer'] , encoder_attention_heads=dimensions['n_audio_head'] , decoder_layers=dimensions['n_text_layer'] , decoder_attention_heads=dimensions['n_text_state'] , max_source_positions=dimensions['n_audio_ctx'] , )
_UpperCAmelCase = WhisperForConditionalGeneration(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = model.model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
if len(_UpperCAmelCase ) > 0 and not set(_UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'
F" but all the following weights are missing {missing}" )
if tie_embeds:
_UpperCAmelCase = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
_UpperCAmelCase = proj_out_weights
model.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# # Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCAmelCase__ = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 339 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ = {
"configuration_mgp_str": ["MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP", "MgpstrConfig"],
"processing_mgp_str": ["MgpstrProcessor"],
"tokenization_mgp_str": ["MgpstrTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
"MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST",
"MgpstrModel",
"MgpstrPreTrainedModel",
"MgpstrForSceneTextRecognition",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 100 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase__ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
UpperCamelCase = None
UpperCamelCase = None
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilder ):
UpperCamelCase = datasets.Audio()
UpperCamelCase = '''audio'''
UpperCamelCase = AudioFolderConfig
UpperCamelCase = 42 # definition at the bottom of the script
UpperCamelCase = AudioClassification(audio_column='''audio''' , label_column='''label''' )
UpperCAmelCase__ = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
UpperCAmelCase__ = AUDIO_EXTENSIONS
| 339 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCamelCase : str = {
'''configuration_roberta_prelayernorm''': [
'''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''RobertaPreLayerNormConfig''',
'''RobertaPreLayerNormOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Union[str, Any] = [
'''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RobertaPreLayerNormForCausalLM''',
'''RobertaPreLayerNormForMaskedLM''',
'''RobertaPreLayerNormForMultipleChoice''',
'''RobertaPreLayerNormForQuestionAnswering''',
'''RobertaPreLayerNormForSequenceClassification''',
'''RobertaPreLayerNormForTokenClassification''',
'''RobertaPreLayerNormModel''',
'''RobertaPreLayerNormPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = [
'''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRobertaPreLayerNormForCausalLM''',
'''TFRobertaPreLayerNormForMaskedLM''',
'''TFRobertaPreLayerNormForMultipleChoice''',
'''TFRobertaPreLayerNormForQuestionAnswering''',
'''TFRobertaPreLayerNormForSequenceClassification''',
'''TFRobertaPreLayerNormForTokenClassification''',
'''TFRobertaPreLayerNormMainLayer''',
'''TFRobertaPreLayerNormModel''',
'''TFRobertaPreLayerNormPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Tuple = [
'''FlaxRobertaPreLayerNormForCausalLM''',
'''FlaxRobertaPreLayerNormForMaskedLM''',
'''FlaxRobertaPreLayerNormForMultipleChoice''',
'''FlaxRobertaPreLayerNormForQuestionAnswering''',
'''FlaxRobertaPreLayerNormForSequenceClassification''',
'''FlaxRobertaPreLayerNormForTokenClassification''',
'''FlaxRobertaPreLayerNormModel''',
'''FlaxRobertaPreLayerNormPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
__lowerCamelCase : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 219 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
def __init__( self : int) -> str:
"""simple docstring"""
_UpperCAmelCase = []
def _lowerCamelCase ( self : Any , A : List[str]) -> int:
"""simple docstring"""
return self.node_position[vertex]
def _lowerCamelCase ( self : Optional[Any] , A : Optional[int] , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = pos
def _lowerCamelCase ( self : Tuple , A : Tuple , A : Dict , A : List[str] , A : Optional[Any]) -> Dict:
"""simple docstring"""
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
_UpperCAmelCase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
_UpperCAmelCase = 2 * start + 1
else:
_UpperCAmelCase = 2 * start + 2
if heap[smallest_child] < heap[start]:
_UpperCAmelCase , _UpperCAmelCase = heap[smallest_child], positions[smallest_child]
_UpperCAmelCase , _UpperCAmelCase = (
heap[start],
positions[start],
)
_UpperCAmelCase , _UpperCAmelCase = temp, tempa
_UpperCAmelCase = self.get_position(positions[smallest_child])
self.set_position(
positions[smallest_child] , self.get_position(positions[start]))
self.set_position(positions[start] , A)
self.top_to_bottom(A , A , A , A)
def _lowerCamelCase ( self : Optional[int] , A : str , A : Optional[Any] , A : Optional[int] , A : str) -> Any:
"""simple docstring"""
_UpperCAmelCase = position[index]
while index != 0:
_UpperCAmelCase = int((index - 2) / 2) if index % 2 == 0 else int((index - 1) / 2)
if val < heap[parent]:
_UpperCAmelCase = heap[parent]
_UpperCAmelCase = position[parent]
self.set_position(position[parent] , A)
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , A)
break
_UpperCAmelCase = parent
else:
_UpperCAmelCase = val
_UpperCAmelCase = temp
self.set_position(A , 0)
def _lowerCamelCase ( self : Union[str, Any] , A : Optional[int] , A : Tuple) -> str:
"""simple docstring"""
_UpperCAmelCase = len(A) // 2 - 1
for i in range(A , -1 , -1):
self.top_to_bottom(A , A , len(A) , A)
def _lowerCamelCase ( self : Optional[int] , A : int , A : str) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = positions[0]
_UpperCAmelCase = sys.maxsize
self.top_to_bottom(A , 0 , len(A) , A)
return temp
def A ( _UpperCAmelCase : int ) -> Any:
'''simple docstring'''
_UpperCAmelCase = Heap()
_UpperCAmelCase = [0] * len(_UpperCAmelCase )
_UpperCAmelCase = [-1] * len(_UpperCAmelCase ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
_UpperCAmelCase = [] # Heap of Distance of vertices from their neighboring vertex
_UpperCAmelCase = []
for vertex in range(len(_UpperCAmelCase ) ):
distance_tv.append(sys.maxsize )
positions.append(_UpperCAmelCase )
heap.node_position.append(_UpperCAmelCase )
_UpperCAmelCase = []
_UpperCAmelCase = 1
_UpperCAmelCase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
_UpperCAmelCase = 0
_UpperCAmelCase = distance
heap.heapify(_UpperCAmelCase , _UpperCAmelCase )
for _ in range(1 , len(_UpperCAmelCase ) ):
_UpperCAmelCase = heap.delete_minimum(_UpperCAmelCase , _UpperCAmelCase )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
_UpperCAmelCase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(_UpperCAmelCase )]
):
_UpperCAmelCase = distance
heap.bottom_to_top(
_UpperCAmelCase , heap.get_position(_UpperCAmelCase ) , _UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
UpperCAmelCase__ = int(input("Enter number of edges: ").strip())
UpperCAmelCase__ = defaultdict(list)
for _ in range(edges_number):
UpperCAmelCase__ = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 339 | 0 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase_ : str = datasets.utils.logging.get_logger(__name__)
class lowercase__ ( folder_based_builder.FolderBasedBuilderConfig ):
'''simple docstring'''
A_ : Dict = None
A_ : Dict = None
class lowercase__ ( folder_based_builder.FolderBasedBuilder ):
'''simple docstring'''
A_ : Union[str, Any] = datasets.Audio()
A_ : Dict = """audio"""
A_ : Any = AudioFolderConfig
A_ : List[str] = 42 # definition at the bottom of the script
A_ : List[Any] = AudioClassification(audio_column="""audio""" , label_column="""label""" )
UpperCAmelCase_ : List[str] = [
'.aiff',
'.au',
'.avr',
'.caf',
'.flac',
'.htk',
'.svx',
'.mat4',
'.mat5',
'.mpc2k',
'.ogg',
'.paf',
'.pvf',
'.raw',
'.rf64',
'.sd2',
'.sds',
'.ircam',
'.voc',
'.w64',
'.wav',
'.nist',
'.wavex',
'.wve',
'.xi',
'.mp3',
'.opus',
]
UpperCAmelCase_ : int = AUDIO_EXTENSIONS
| 200 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int]=5 ) -> List[Any]:
'''simple docstring'''
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('<mask>' ) == 1
_UpperCAmelCase = torch.tensor(tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ) ).unsqueeze(0 ) # Batch size 1
_UpperCAmelCase = model(_UpperCAmelCase )[0] # The last hidden-state is the first element of the output tuple
_UpperCAmelCase = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
_UpperCAmelCase = logits[0, masked_index, :]
_UpperCAmelCase = logits.softmax(dim=0 )
_UpperCAmelCase , _UpperCAmelCase = prob.topk(k=_UpperCAmelCase , dim=0 )
_UpperCAmelCase = ' '.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(_UpperCAmelCase ) )] )
_UpperCAmelCase = tokenizer.mask_token
_UpperCAmelCase = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ' ) ):
_UpperCAmelCase = predicted_token_bpe.replace('\u2581' , ' ' )
if " {0}".format(_UpperCAmelCase ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(' {0}'.format(_UpperCAmelCase ) , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(_UpperCAmelCase , _UpperCAmelCase ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
UpperCAmelCase__ = CamembertTokenizer.from_pretrained("camembert-base")
UpperCAmelCase__ = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
UpperCAmelCase__ = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 339 | 0 |
import sys
from collections import defaultdict
class __A :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
__UpperCamelCase : Optional[int] =[]
def __lowercase ( self , lowerCamelCase__ ):
"""simple docstring"""
return self.node_position[vertex]
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : Any =pos
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
__UpperCamelCase : List[str] =2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
__UpperCamelCase : List[Any] =2 * start + 1
else:
__UpperCamelCase : Optional[int] =2 * start + 2
if heap[smallest_child] < heap[start]:
__UpperCamelCase , __UpperCamelCase : Dict =heap[smallest_child], positions[smallest_child]
__UpperCamelCase , __UpperCamelCase : int =(
heap[start],
positions[start],
)
__UpperCamelCase , __UpperCamelCase : List[Any] =temp, tempa
__UpperCamelCase : Optional[Any] =self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , lowerCamelCase__ )
self.top_to_bottom(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : Optional[int] =position[index]
while index != 0:
__UpperCamelCase : List[str] =int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
__UpperCamelCase : Optional[int] =heap[parent]
__UpperCamelCase : str =position[parent]
self.set_position(position[parent] , lowerCamelCase__ )
else:
__UpperCamelCase : Dict =val
__UpperCamelCase : Union[str, Any] =temp
self.set_position(lowerCamelCase__ , lowerCamelCase__ )
break
__UpperCamelCase : List[Any] =parent
else:
__UpperCamelCase : Any =val
__UpperCamelCase : str =temp
self.set_position(lowerCamelCase__ , 0 )
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : int =len(lowerCamelCase__ ) // 2 - 1
for i in range(lowerCamelCase__ , -1 , -1 ):
self.top_to_bottom(lowerCamelCase__ , lowerCamelCase__ , len(lowerCamelCase__ ) , lowerCamelCase__ )
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : Dict =positions[0]
__UpperCamelCase : List[Any] =sys.maxsize
self.top_to_bottom(lowerCamelCase__ , 0 , len(lowerCamelCase__ ) , lowerCamelCase__ )
return temp
def A ( a_ ) -> Any:
__UpperCamelCase : List[str] =Heap()
__UpperCamelCase : str =[0] * len(_UpperCAmelCase )
__UpperCamelCase : Optional[int] =[-1] * len(_UpperCAmelCase ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
__UpperCamelCase : List[Any] =[] # Heap of Distance of vertices from their neighboring vertex
__UpperCamelCase : int =[]
for vertex in range(len(_UpperCAmelCase ) ):
distance_tv.append(sys.maxsize )
positions.append(_UpperCAmelCase )
heap.node_position.append(_UpperCAmelCase )
__UpperCamelCase : str =[]
__UpperCamelCase : Union[str, Any] =1
__UpperCamelCase : Tuple =sys.maxsize
for neighbor, distance in adjacency_list[0]:
__UpperCamelCase : Union[str, Any] =0
__UpperCamelCase : str =distance
heap.heapify(_UpperCAmelCase ,_UpperCAmelCase )
for _ in range(1 ,len(_UpperCAmelCase ) ):
__UpperCamelCase : Any =heap.delete_minimum(_UpperCAmelCase ,_UpperCAmelCase )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
__UpperCamelCase : Optional[Any] =1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(_UpperCAmelCase )]
):
__UpperCamelCase : int =distance
heap.bottom_to_top(
_UpperCAmelCase ,heap.get_position(_UpperCAmelCase ) ,_UpperCAmelCase ,_UpperCAmelCase )
__UpperCamelCase : Union[str, Any] =vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
A_ :int = int(input('''Enter number of edges: ''').strip())
A_ :Tuple = defaultdict(list)
for _ in range(edges_number):
A_ :Tuple = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 71 |
import math
import unittest
def A ( _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( self : Tuple) -> Union[str, Any]:
"""simple docstring"""
self.assertTrue(is_prime(2))
self.assertTrue(is_prime(3))
self.assertTrue(is_prime(5))
self.assertTrue(is_prime(7))
self.assertTrue(is_prime(11))
self.assertTrue(is_prime(13))
self.assertTrue(is_prime(17))
self.assertTrue(is_prime(19))
self.assertTrue(is_prime(23))
self.assertTrue(is_prime(29))
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
with self.assertRaises(A):
is_prime(-19)
self.assertFalse(
is_prime(0) , 'Zero doesn\'t have any positive factors, primes must have exactly two.' , )
self.assertFalse(
is_prime(1) , 'One only has 1 positive factor, primes must have exactly two.' , )
self.assertFalse(is_prime(2 * 2))
self.assertFalse(is_prime(2 * 3))
self.assertFalse(is_prime(3 * 3))
self.assertFalse(is_prime(3 * 5))
self.assertFalse(is_prime(3 * 5 * 7))
if __name__ == "__main__":
unittest.main()
| 339 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase__ ( _UpperCAmelCase ):
a_ =["""image_processor""", """tokenizer"""]
a_ ="""CLIPImageProcessor"""
a_ =("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase )-> int:
'''simple docstring'''
lowerCAmelCase__ = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , __UpperCAmelCase , )
lowerCAmelCase__ = kwargs.pop("feature_extractor" )
lowerCAmelCase__ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(__UpperCAmelCase , __UpperCAmelCase )
def __call__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase )-> Optional[Any]:
'''simple docstring'''
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none." )
if text is not None:
lowerCAmelCase__ = self.tokenizer(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if images is not None:
lowerCAmelCase__ = self.image_processor(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if text is not None and images is not None:
lowerCAmelCase__ = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__UpperCAmelCase ) , tensor_type=__UpperCAmelCase )
def UpperCAmelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase )-> str:
'''simple docstring'''
return self.tokenizer.batch_decode(*__UpperCAmelCase , **__UpperCAmelCase )
def UpperCAmelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase )-> Optional[Any]:
'''simple docstring'''
return self.tokenizer.decode(*__UpperCAmelCase , **__UpperCAmelCase )
@property
def UpperCAmelCase ( self )-> str:
'''simple docstring'''
lowerCAmelCase__ = self.tokenizer.model_input_names
lowerCAmelCase__ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCAmelCase ( self )-> Dict:
'''simple docstring'''
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , __UpperCAmelCase , )
return self.image_processor_class
@property
def UpperCAmelCase ( self )-> Optional[Any]:
'''simple docstring'''
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , __UpperCAmelCase , )
return self.image_processor
| 340 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
a_ = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase )
class lowercase__ ( _UpperCAmelCase ):
def __init__( self , **__UpperCAmelCase )-> List[str]:
'''simple docstring'''
super().__init__(**__UpperCAmelCase )
requires_backends(self , "vision" )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self , __UpperCAmelCase , **__UpperCAmelCase )-> int:
'''simple docstring'''
return super().__call__(__UpperCAmelCase , **__UpperCAmelCase )
def UpperCAmelCase ( self , **__UpperCAmelCase )-> List[str]:
'''simple docstring'''
lowerCAmelCase__ = {}
if "candidate_labels" in kwargs:
lowerCAmelCase__ = kwargs["candidate_labels"]
if "hypothesis_template" in kwargs:
lowerCAmelCase__ = kwargs["hypothesis_template"]
return preprocess_params, {}, {}
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase="This is a photo of {}." )-> Optional[int]:
'''simple docstring'''
lowerCAmelCase__ = load_image(__UpperCAmelCase )
lowerCAmelCase__ = self.image_processor(images=[image] , return_tensors=self.framework )
lowerCAmelCase__ = candidate_labels
lowerCAmelCase__ = [hypothesis_template.format(__UpperCAmelCase ) for x in candidate_labels]
lowerCAmelCase__ = self.tokenizer(__UpperCAmelCase , return_tensors=self.framework , padding=__UpperCAmelCase )
lowerCAmelCase__ = [text_inputs]
return inputs
def UpperCAmelCase ( self , __UpperCAmelCase )-> int:
'''simple docstring'''
lowerCAmelCase__ = model_inputs.pop("candidate_labels" )
lowerCAmelCase__ = model_inputs.pop("text_inputs" )
if isinstance(text_inputs[0] , __UpperCAmelCase ):
lowerCAmelCase__ = text_inputs[0]
else:
# Batching case.
lowerCAmelCase__ = text_inputs[0][0]
lowerCAmelCase__ = self.model(**__UpperCAmelCase , **__UpperCAmelCase )
lowerCAmelCase__ = {
"candidate_labels": candidate_labels,
"logits": outputs.logits_per_image,
}
return model_outputs
def UpperCAmelCase ( self , __UpperCAmelCase )-> Tuple:
'''simple docstring'''
lowerCAmelCase__ = model_outputs.pop("candidate_labels" )
lowerCAmelCase__ = model_outputs["logits"][0]
if self.framework == "pt":
lowerCAmelCase__ = logits.softmax(dim=-1 ).squeeze(-1 )
lowerCAmelCase__ = probs.tolist()
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ = [scores]
elif self.framework == "tf":
lowerCAmelCase__ = stable_softmax(__UpperCAmelCase , axis=-1 )
lowerCAmelCase__ = probs.numpy().tolist()
else:
raise ValueError(F"Unsupported framework: {self.framework}" )
lowerCAmelCase__ = [
{"score": score, "label": candidate_label}
for score, candidate_label in sorted(zip(__UpperCAmelCase , __UpperCAmelCase ) , key=lambda __UpperCAmelCase : -x[0] )
]
return result
| 340 | 1 |
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