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'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
a_ = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class __SCREAMING_SNAKE_CASE ( snake_case__ ):
def __init__( self : List[str] , *__lowercase : Any , __lowercase : str=None , __lowercase : int=None , __lowercase : Tuple=None , **__lowercase : Tuple ) -> str:
super().__init__(*lowercase_ , **lowercase_ )
SCREAMING_SNAKE_CASE__ : List[str] =eval_examples
SCREAMING_SNAKE_CASE__ : str =post_process_function
SCREAMING_SNAKE_CASE__ : int =quant_trainer_args
SCREAMING_SNAKE_CASE__ : Optional[int] =1_28 # default number of calibration samples
def __magic_name__ ( self : Any , __lowercase : Union[str, Any]=None ) -> Tuple:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError('''Trainer: calibration requires an calib_dataset.''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] =calib_dataset if calib_dataset is not None else self.calib_dataset
SCREAMING_SNAKE_CASE__ : str =self._remove_unused_columns(lowercase_ , description='''Calibration''' )
return DataLoader(
lowercase_ , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=lowercase_ , )
def __magic_name__ ( self : List[Any] , __lowercase : Optional[int]=None ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Dict =self.train_dataset if calib_dataset is None else calib_dataset
SCREAMING_SNAKE_CASE__ : str =self.get_calib_dataloader(lowercase_ )
SCREAMING_SNAKE_CASE__ : Any =self.model
quant_trainer.configure_model(lowercase_ , self.quant_trainer_args , calib=lowercase_ )
model.eval()
quant_trainer.enable_calibration(lowercase_ )
logger.info('''***** Running calibration *****''' )
logger.info(F" Num examples = {self.calib_num}" )
logger.info(F" Batch size = {calib_dataloader.batch_size}" )
for step, inputs in enumerate(lowercase_ ):
# Prediction step
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.prediction_step(lowercase_ , lowercase_ , prediction_loss_only=lowercase_ )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(lowercase_ , self.quant_trainer_args )
SCREAMING_SNAKE_CASE__ : Optional[int] =model
def __magic_name__ ( self : Any , __lowercase : Dict=None , __lowercase : Optional[int]=None , __lowercase : Optional[Any]=None , __lowercase : Optional[int] = "eval" ) -> Dict:
SCREAMING_SNAKE_CASE__ : List[str] =self.eval_dataset if eval_dataset is None else eval_dataset
SCREAMING_SNAKE_CASE__ : Any =self.get_eval_dataloader(lowercase_ )
SCREAMING_SNAKE_CASE__ : int =self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
SCREAMING_SNAKE_CASE__ : List[str] =self.compute_metrics
SCREAMING_SNAKE_CASE__ : str =None
SCREAMING_SNAKE_CASE__ : Optional[int] =self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
SCREAMING_SNAKE_CASE__ : Any =eval_loop(
lowercase_ , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowercase_ , )
finally:
SCREAMING_SNAKE_CASE__ : Any =compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
SCREAMING_SNAKE_CASE__ : Tuple =self.post_process_function(lowercase_ , lowercase_ , output.predictions )
SCREAMING_SNAKE_CASE__ : Any =self.compute_metrics(lowercase_ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
SCREAMING_SNAKE_CASE__ : Dict =metrics.pop(lowercase_ )
self.log(lowercase_ )
else:
SCREAMING_SNAKE_CASE__ : List[Any] ={}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
SCREAMING_SNAKE_CASE__ : int =self.callback_handler.on_evaluate(self.args , self.state , self.control , lowercase_ )
return metrics
def __magic_name__ ( self : Optional[int] , __lowercase : int , __lowercase : Optional[int] , __lowercase : Tuple=None , __lowercase : Union[str, Any] = "test" ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Dict =self.get_test_dataloader(lowercase_ )
# Temporarily disable metric computation, we will do it in the loop here.
SCREAMING_SNAKE_CASE__ : Any =self.compute_metrics
SCREAMING_SNAKE_CASE__ : Optional[Any] =None
SCREAMING_SNAKE_CASE__ : Any =self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
SCREAMING_SNAKE_CASE__ : int =eval_loop(
lowercase_ , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowercase_ , )
finally:
SCREAMING_SNAKE_CASE__ : Dict =compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
SCREAMING_SNAKE_CASE__ : List[str] =self.post_process_function(lowercase_ , lowercase_ , output.predictions , '''predict''' )
SCREAMING_SNAKE_CASE__ : Any =self.compute_metrics(lowercase_ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
SCREAMING_SNAKE_CASE__ : int =metrics.pop(lowercase_ )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=lowercase_ )
def __magic_name__ ( self : Dict , __lowercase : Optional[int]="./" ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : int =self.eval_dataset
SCREAMING_SNAKE_CASE__ : Any =self.get_eval_dataloader(lowercase_ )
SCREAMING_SNAKE_CASE__ : int =next(iter(lowercase_ ) )
# saving device - to make it consistent
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
# convert to tuple
SCREAMING_SNAKE_CASE__ : str =tuple(v.to(lowercase_ ) for k, v in batch.items() )
logger.info('''Converting model to be onnx compatible''' )
from pytorch_quantization.nn import TensorQuantizer
SCREAMING_SNAKE_CASE__ : Union[str, Any] =True
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.model.to(lowercase_ )
model.eval()
model.float()
SCREAMING_SNAKE_CASE__ : Optional[Any] =model.module if hasattr(lowercase_ , '''module''' ) else model
quant_trainer.configure_model(lowercase_ , self.quant_trainer_args )
SCREAMING_SNAKE_CASE__ : List[Any] =os.path.join(lowercase_ , '''model.onnx''' )
logger.info(F"exporting model to {output_model_file}" )
SCREAMING_SNAKE_CASE__ : Dict ={0: "batch_size", 1: "seq_len"}
torch.onnx.export(
lowercase_ , lowercase_ , lowercase_ , export_params=lowercase_ , opset_version=13 , do_constant_folding=lowercase_ , input_names=['''input_ids''', '''attention_mask''', '''token_type_ids'''] , output_names=['''output_start_logits''', '''output_end_logits'''] , dynamic_axes={
'''input_ids''': axes,
'''attention_mask''': axes,
'''token_type_ids''': axes,
'''output_start_logits''': axes,
'''output_end_logits''': axes,
} , verbose=lowercase_ , )
logger.info('''onnx export finished''' )
| 704 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')
# TF training parameters
a_ = False
a_ = False
def _a( UpperCamelCase__ : Namespace ):
'''simple docstring'''
return TrainCommand(UpperCamelCase__ )
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
@staticmethod
def __magic_name__ ( __lowercase : ArgumentParser ) -> Any:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =parser.add_parser('''train''' , help='''CLI tool to train a model on a task.''' )
train_parser.add_argument(
'''--train_data''' , type=__lowercase , required=__lowercase , help='''path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.''' , )
train_parser.add_argument(
'''--column_label''' , type=__lowercase , default=0 , help='''Column of the dataset csv file with example labels.''' )
train_parser.add_argument(
'''--column_text''' , type=__lowercase , default=1 , help='''Column of the dataset csv file with example texts.''' )
train_parser.add_argument(
'''--column_id''' , type=__lowercase , default=2 , help='''Column of the dataset csv file with example ids.''' )
train_parser.add_argument(
'''--skip_first_row''' , action='''store_true''' , help='''Skip the first row of the csv file (headers).''' )
train_parser.add_argument('''--validation_data''' , type=__lowercase , default='''''' , help='''path to validation dataset.''' )
train_parser.add_argument(
'''--validation_split''' , type=__lowercase , default=0.1 , help='''if validation dataset is not provided, fraction of train dataset to use as validation dataset.''' , )
train_parser.add_argument('''--output''' , type=__lowercase , default='''./''' , help='''path to saved the trained model.''' )
train_parser.add_argument(
'''--task''' , type=__lowercase , default='''text_classification''' , help='''Task to train the model on.''' )
train_parser.add_argument(
'''--model''' , type=__lowercase , default='''bert-base-uncased''' , help='''Model\'s name or path to stored model.''' )
train_parser.add_argument('''--train_batch_size''' , type=__lowercase , default=32 , help='''Batch size for training.''' )
train_parser.add_argument('''--valid_batch_size''' , type=__lowercase , default=64 , help='''Batch size for validation.''' )
train_parser.add_argument('''--learning_rate''' , type=__lowercase , default=3e-5 , help='''Learning rate.''' )
train_parser.add_argument('''--adam_epsilon''' , type=__lowercase , default=1e-08 , help='''Epsilon for Adam optimizer.''' )
train_parser.set_defaults(func=__lowercase )
def __init__( self : Tuple , __lowercase : Namespace ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Tuple =logging.get_logger('''transformers-cli/training''' )
SCREAMING_SNAKE_CASE__ : int ='''tf''' if is_tf_available() else '''torch'''
os.makedirs(args.output , exist_ok=__lowercase )
SCREAMING_SNAKE_CASE__ : Any =args.output
SCREAMING_SNAKE_CASE__ : str =args.column_label
SCREAMING_SNAKE_CASE__ : List[Any] =args.column_text
SCREAMING_SNAKE_CASE__ : Tuple =args.column_id
self.logger.info(F"Loading {args.task} pipeline for {args.model}" )
if args.task == "text_classification":
SCREAMING_SNAKE_CASE__ : List[str] =TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(F"Loading dataset from {args.train_data}" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =None
if args.validation_data:
self.logger.info(F"Loading validation dataset from {args.validation_data}" )
SCREAMING_SNAKE_CASE__ : List[Any] =Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =args.validation_split
SCREAMING_SNAKE_CASE__ : List[Any] =args.train_batch_size
SCREAMING_SNAKE_CASE__ : Any =args.valid_batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =args.learning_rate
SCREAMING_SNAKE_CASE__ : int =args.adam_epsilon
def __magic_name__ ( self : Any ) -> str:
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def __magic_name__ ( self : Optional[int] ) -> Tuple:
raise NotImplementedError
def __magic_name__ ( self : Dict ) -> List[Any]:
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 665 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Dict ):
'''simple docstring'''
return number | (1 << position)
def _a( UpperCamelCase__ : List[str], UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
return number & ~(1 << position)
def _a( UpperCamelCase__ : int, UpperCamelCase__ : str ):
'''simple docstring'''
return number ^ (1 << position)
def _a( UpperCamelCase__ : Dict, UpperCamelCase__ : Dict ):
'''simple docstring'''
return ((number >> position) & 1) == 1
def _a( UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Tuple ):
'''simple docstring'''
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 705 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = KandinskyVaaImgaImgPipeline
snake_case_ = ["""image_embeds""", """negative_image_embeds""", """image"""]
snake_case_ = [
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
snake_case_ = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
snake_case_ = False
@property
def __magic_name__ ( self : List[str] ) -> Tuple:
return 32
@property
def __magic_name__ ( self : List[str] ) -> str:
return 32
@property
def __magic_name__ ( self : Any ) -> Optional[int]:
return self.time_input_dim
@property
def __magic_name__ ( self : List[Any] ) -> int:
return self.time_input_dim * 4
@property
def __magic_name__ ( self : Tuple ) -> Optional[int]:
return 1_00
@property
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] ={
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
SCREAMING_SNAKE_CASE__ : Optional[int] =UNetaDConditionModel(**__lowercase )
return model
@property
def __magic_name__ ( self : Dict ) -> Any:
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __magic_name__ ( self : Tuple ) -> Optional[Any]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] =VQModel(**self.dummy_movq_kwargs )
return model
def __magic_name__ ( self : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[str] =self.dummy_unet
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dummy_movq
SCREAMING_SNAKE_CASE__ : Optional[Any] ={
'''num_train_timesteps''': 10_00,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
SCREAMING_SNAKE_CASE__ : str =DDIMScheduler(**__lowercase )
SCREAMING_SNAKE_CASE__ : Any ={
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __magic_name__ ( self : str , __lowercase : Optional[Any] , __lowercase : Any=0 ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[int] =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__lowercase ) ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__lowercase )
# create init_image
SCREAMING_SNAKE_CASE__ : Optional[Any] =floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowercase ) ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE__ : Any =Image.fromarray(np.uinta(__lowercase ) ).convert('''RGB''' ).resize((2_56, 2_56) )
if str(__lowercase ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE__ : Dict =torch.manual_seed(__lowercase )
else:
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device=__lowercase ).manual_seed(__lowercase )
SCREAMING_SNAKE_CASE__ : str ={
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def __magic_name__ ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[Any] ='''cpu'''
SCREAMING_SNAKE_CASE__ : Tuple =self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Dict =self.pipeline_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =pipe(**self.get_dummy_inputs(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Tuple =output.images
SCREAMING_SNAKE_CASE__ : Union[str, Any] =pipe(
**self.get_dummy_inputs(__lowercase ) , return_dict=__lowercase , )[0]
SCREAMING_SNAKE_CASE__ : List[Any] =image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : List[str] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE__ : Tuple =np.array(
[0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : int ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : str =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
SCREAMING_SNAKE_CASE__ : List[Any] ='''A red cartoon frog, 4k'''
SCREAMING_SNAKE_CASE__ : Optional[int] =KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =KandinskyVaaImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : Dict =pipeline.to(__lowercase )
pipeline.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device='''cpu''' ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] =pipe_prior(
__lowercase , generator=__lowercase , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
image=__lowercase , image_embeds=__lowercase , negative_image_embeds=__lowercase , generator=__lowercase , num_inference_steps=1_00 , height=7_68 , width=7_68 , strength=0.2 , output_type='''np''' , )
SCREAMING_SNAKE_CASE__ : int =output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(__lowercase , __lowercase )
| 665 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'microsoft/beit-base-patch16-224-pt22k': (
'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class __SCREAMING_SNAKE_CASE ( __lowercase ):
snake_case_ = """beit"""
def __init__( self : str , __lowercase : Any=81_92 , __lowercase : List[Any]=7_68 , __lowercase : Optional[int]=12 , __lowercase : Optional[Any]=12 , __lowercase : Union[str, Any]=30_72 , __lowercase : Optional[int]="gelu" , __lowercase : Optional[int]=0.0 , __lowercase : Optional[Any]=0.0 , __lowercase : str=0.02 , __lowercase : List[Any]=1e-12 , __lowercase : List[Any]=2_24 , __lowercase : Optional[Any]=16 , __lowercase : Dict=3 , __lowercase : Tuple=False , __lowercase : Dict=False , __lowercase : Optional[int]=False , __lowercase : Optional[Any]=False , __lowercase : Any=0.1 , __lowercase : str=0.1 , __lowercase : str=True , __lowercase : Union[str, Any]=[3, 5, 7, 11] , __lowercase : Dict=[1, 2, 3, 6] , __lowercase : Union[str, Any]=True , __lowercase : int=0.4 , __lowercase : List[Any]=2_56 , __lowercase : List[Any]=1 , __lowercase : int=False , __lowercase : List[str]=2_55 , **__lowercase : List[Any] , ) -> Union[str, Any]:
super().__init__(**__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =hidden_size
SCREAMING_SNAKE_CASE__ : Dict =num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[Any] =num_attention_heads
SCREAMING_SNAKE_CASE__ : List[Any] =intermediate_size
SCREAMING_SNAKE_CASE__ : str =hidden_act
SCREAMING_SNAKE_CASE__ : List[str] =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Any =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Dict =initializer_range
SCREAMING_SNAKE_CASE__ : Dict =layer_norm_eps
SCREAMING_SNAKE_CASE__ : Tuple =image_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =patch_size
SCREAMING_SNAKE_CASE__ : List[str] =num_channels
SCREAMING_SNAKE_CASE__ : int =use_mask_token
SCREAMING_SNAKE_CASE__ : Optional[Any] =use_absolute_position_embeddings
SCREAMING_SNAKE_CASE__ : str =use_relative_position_bias
SCREAMING_SNAKE_CASE__ : Dict =use_shared_relative_position_bias
SCREAMING_SNAKE_CASE__ : Optional[Any] =layer_scale_init_value
SCREAMING_SNAKE_CASE__ : int =drop_path_rate
SCREAMING_SNAKE_CASE__ : Any =use_mean_pooling
# decode head attributes (semantic segmentation)
SCREAMING_SNAKE_CASE__ : Any =out_indices
SCREAMING_SNAKE_CASE__ : List[Any] =pool_scales
# auxiliary head attributes (semantic segmentation)
SCREAMING_SNAKE_CASE__ : List[Any] =use_auxiliary_head
SCREAMING_SNAKE_CASE__ : int =auxiliary_loss_weight
SCREAMING_SNAKE_CASE__ : Any =auxiliary_channels
SCREAMING_SNAKE_CASE__ : Tuple =auxiliary_num_convs
SCREAMING_SNAKE_CASE__ : Tuple =auxiliary_concat_input
SCREAMING_SNAKE_CASE__ : Any =semantic_loss_ignore_index
class __SCREAMING_SNAKE_CASE ( __lowercase ):
snake_case_ = version.parse("""1.11""" )
@property
def __magic_name__ ( self : List[Any] ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __magic_name__ ( self : Optional[int] ) -> float:
return 1e-4
| 706 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
a_ = TypeVar('T')
class __SCREAMING_SNAKE_CASE ( Generic[T] ):
snake_case_ = 42 # Cache store of keys
snake_case_ = 42 # References of the keys in cache
snake_case_ = 10 # Maximum capacity of cache
def __init__( self : Dict , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : Any =deque()
SCREAMING_SNAKE_CASE__ : str =set()
if not n:
SCREAMING_SNAKE_CASE__ : Optional[Any] =sys.maxsize
elif n < 0:
raise ValueError('''n should be an integer greater than 0.''' )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =n
def __magic_name__ ( self : List[str] , __lowercase : T ) -> None:
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
SCREAMING_SNAKE_CASE__ : int =self.dq_store.pop()
self.key_reference.remove(__lowercase )
else:
self.dq_store.remove(__lowercase )
self.dq_store.appendleft(__lowercase )
self.key_reference.add(__lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> None:
for k in self.dq_store:
print(__lowercase )
def __repr__( self : List[Any] ) -> str:
return F"LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}"
if __name__ == "__main__":
import doctest
doctest.testmod()
a_ = LRUCache(4)
lru_cache.refer('A')
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer('A')
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 665 | 0 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """"""
snake_case_ = """hf-legacy""" # "hf://"" is reserved for hffs
def __init__( self : Union[str, Any] , __lowercase : Optional[DatasetInfo] = None , __lowercase : Optional[str] = None , **__lowercase : Dict , ) -> List[Any]:
super().__init__(self , **__UpperCamelCase )
SCREAMING_SNAKE_CASE__ : List[Any] =repo_info
SCREAMING_SNAKE_CASE__ : Any =token
SCREAMING_SNAKE_CASE__ : int =None
def __magic_name__ ( self : Optional[int] ) -> Optional[int]:
if self.dir_cache is None:
SCREAMING_SNAKE_CASE__ : List[str] ={}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
SCREAMING_SNAKE_CASE__ : Union[str, Any] ={
'''name''': hf_file.rfilename,
'''size''': None,
'''type''': '''file''',
}
self.dir_cache.update(
{
str(__UpperCamelCase ): {'''name''': str(__UpperCamelCase ), '''size''': None, '''type''': '''directory'''}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def __magic_name__ ( self : Any , __lowercase : str , __lowercase : str = "rb" , **__lowercase : Dict , ) -> Any:
if not isinstance(self.repo_info , __UpperCamelCase ):
raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}" )
SCREAMING_SNAKE_CASE__ : Dict =hf_hub_url(self.repo_info.id , __UpperCamelCase , revision=self.repo_info.sha )
return fsspec.open(
__UpperCamelCase , mode=__UpperCamelCase , headers=get_authentication_headers_for_url(__UpperCamelCase , use_auth_token=self.token ) , client_kwargs={'''trust_env''': True} , ).open()
def __magic_name__ ( self : List[str] , __lowercase : List[str] , **__lowercase : Dict ) -> int:
self._get_dirs()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self._strip_protocol(__UpperCamelCase )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(__UpperCamelCase )
def __magic_name__ ( self : str , __lowercase : Optional[Any] , __lowercase : Union[str, Any]=False , **__lowercase : Any ) -> Any:
self._get_dirs()
SCREAMING_SNAKE_CASE__ : List[Any] =PurePosixPath(path.strip('''/''' ) )
SCREAMING_SNAKE_CASE__ : Any ={}
for p, f in self.dir_cache.items():
SCREAMING_SNAKE_CASE__ : Any =PurePosixPath(p.strip('''/''' ) )
SCREAMING_SNAKE_CASE__ : Any =p.parent
if root == path:
SCREAMING_SNAKE_CASE__ : List[Any] =f
SCREAMING_SNAKE_CASE__ : Dict =list(paths.values() )
if detail:
return out
else:
return sorted(f['''name'''] for f in out )
| 707 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a_ = list[list[float | int]]
def _a( UpperCamelCase__ : Matrix, UpperCamelCase__ : Matrix ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Matrix =[[0 for _ in range(size + 1 )] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : float
for row in range(UpperCamelCase__ ):
for col in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Tuple =matrix[row][col]
SCREAMING_SNAKE_CASE__ : Optional[int] =vector[row][0]
SCREAMING_SNAKE_CASE__ : Any =0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =0
while row < size and col < size:
# pivoting
SCREAMING_SNAKE_CASE__ : Any =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__ : Optional[Any] =augmented[pivot_row], augmented[row]
for rowa in range(row + 1, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] =augmented[rowa][col] / augmented[row][col]
SCREAMING_SNAKE_CASE__ : Tuple =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[Any] =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], 1_0 )] for row in range(UpperCamelCase__ )
]
def _a( UpperCamelCase__ : list[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Matrix =[[0 for _ in range(UpperCamelCase__ )] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Matrix =[[0] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Matrix
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
for x_val, y_val in enumerate(UpperCamelCase__ ):
for col in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[int] =(x_val + 1) ** (size - col - 1)
SCREAMING_SNAKE_CASE__ : Dict =y_val
SCREAMING_SNAKE_CASE__ : Optional[int] =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 ):
'''simple docstring'''
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**1_0
)
def _a( UpperCamelCase__ : Callable[[int], int] = question_function, UpperCamelCase__ : int = 1_0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : list[int] =[func(UpperCamelCase__ ) for x_val in range(1, order + 1 )]
SCREAMING_SNAKE_CASE__ : list[Callable[[int], int]] =[
interpolate(data_points[:max_coeff] ) for max_coeff in range(1, order + 1 )
]
SCREAMING_SNAKE_CASE__ : int =0
SCREAMING_SNAKE_CASE__ : Callable[[int], int]
SCREAMING_SNAKE_CASE__ : int
for poly in polynomials:
SCREAMING_SNAKE_CASE__ : Any =1
while func(UpperCamelCase__ ) == poly(UpperCamelCase__ ):
x_val += 1
ret += poly(UpperCamelCase__ )
return ret
if __name__ == "__main__":
print(F'''{solution() = }''')
| 665 | 0 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __SCREAMING_SNAKE_CASE ( A_ ):
'''simple docstring'''
def __init__( self : Tuple , __lowercase : List[Any] , __lowercase : List[str] = None , __lowercase : int = None , __lowercase : Tuple = None , __lowercase : Dict = False , __lowercase : Any = False , __lowercase : int = None , __lowercase : Optional[int] = None , **__lowercase : Union[str, Any] , ) -> int:
super().__init__(
__lowercase , split=__lowercase , features=__lowercase , cache_dir=__lowercase , keep_in_memory=__lowercase , streaming=__lowercase , num_proc=__lowercase , **__lowercase , )
SCREAMING_SNAKE_CASE__ : Any =field
SCREAMING_SNAKE_CASE__ : List[Any] =path_or_paths if isinstance(__lowercase , __lowercase ) else {self.split: path_or_paths}
SCREAMING_SNAKE_CASE__ : Optional[Any] =Json(
cache_dir=__lowercase , data_files=__lowercase , features=__lowercase , field=__lowercase , **__lowercase , )
def __magic_name__ ( self : Optional[int] ) -> Optional[int]:
# Build iterable dataset
if self.streaming:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
SCREAMING_SNAKE_CASE__ : Dict =None
SCREAMING_SNAKE_CASE__ : str =None
SCREAMING_SNAKE_CASE__ : int =None
SCREAMING_SNAKE_CASE__ : Optional[Any] =None
self.builder.download_and_prepare(
download_config=__lowercase , download_mode=__lowercase , verification_mode=__lowercase , base_path=__lowercase , num_proc=self.num_proc , )
SCREAMING_SNAKE_CASE__ : Any =self.builder.as_dataset(
split=self.split , verification_mode=__lowercase , in_memory=self.keep_in_memory )
return dataset
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Tuple , __lowercase : List[Any] , __lowercase : Tuple , __lowercase : Optional[int] = None , __lowercase : Optional[Any] = None , **__lowercase : Any , ) -> Dict:
if num_proc is not None and num_proc <= 0:
raise ValueError(F"num_proc {num_proc} must be an integer > 0." )
SCREAMING_SNAKE_CASE__ : Optional[Any] =dataset
SCREAMING_SNAKE_CASE__ : Union[str, Any] =path_or_buf
SCREAMING_SNAKE_CASE__ : Optional[Any] =batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
SCREAMING_SNAKE_CASE__ : List[Any] =num_proc
SCREAMING_SNAKE_CASE__ : List[str] ="""utf-8"""
SCREAMING_SNAKE_CASE__ : int =to_json_kwargs
def __magic_name__ ( self : int ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : List[Any] =self.to_json_kwargs.pop('''path_or_buf''' , __lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =self.to_json_kwargs.pop('''orient''' , '''records''' )
SCREAMING_SNAKE_CASE__ : List[str] =self.to_json_kwargs.pop('''lines''' , True if orient == '''records''' else False )
SCREAMING_SNAKE_CASE__ : str =self.to_json_kwargs.pop('''index''' , False if orient in ['''split''', '''table'''] else True )
SCREAMING_SNAKE_CASE__ : Tuple =self.to_json_kwargs.pop('''compression''' , __lowercase )
if compression not in [None, "infer", "gzip", "bz2", "xz"]:
raise NotImplementedError(F"`datasets` currently does not support {compression} compression" )
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with fsspec.open(self.path_or_buf , '''wb''' , compression=__lowercase ) as buffer:
SCREAMING_SNAKE_CASE__ : str =self._write(file_obj=__lowercase , orient=__lowercase , lines=__lowercase , index=__lowercase , **self.to_json_kwargs )
else:
if compression:
raise NotImplementedError(
F"The compression parameter is not supported when writing to a buffer, but compression={compression}"
''' was passed. Please provide a local path instead.''' )
SCREAMING_SNAKE_CASE__ : str =self._write(
file_obj=self.path_or_buf , orient=__lowercase , lines=__lowercase , index=__lowercase , **self.to_json_kwargs )
return written
def __magic_name__ ( self : int , __lowercase : Any ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : int =args
SCREAMING_SNAKE_CASE__ : Optional[Any] =query_table(
table=self.dataset.data , key=slice(__lowercase , offset + self.batch_size ) , indices=self.dataset._indices , )
SCREAMING_SNAKE_CASE__ : List[str] =batch.to_pandas().to_json(
path_or_buf=__lowercase , orient=__lowercase , lines=__lowercase , index=__lowercase , **__lowercase )
if not json_str.endswith('''\n''' ):
json_str += "\n"
return json_str.encode(self.encoding )
def __magic_name__ ( self : Optional[int] , __lowercase : Union[str, Any] , __lowercase : Optional[int] , __lowercase : List[Any] , __lowercase : Tuple , **__lowercase : Optional[int] , ) -> List[str]:
SCREAMING_SNAKE_CASE__ : List[str] =0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating json from Arrow format''' , ):
SCREAMING_SNAKE_CASE__ : Optional[int] =self._batch_json((offset, orient, lines, index, to_json_kwargs) )
written += file_obj.write(__lowercase )
else:
SCREAMING_SNAKE_CASE__ : int =len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for json_str in logging.tqdm(
pool.imap(
self._batch_json , [(offset, orient, lines, index, to_json_kwargs) for offset in range(0 , __lowercase , __lowercase )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating json from Arrow format''' , ):
written += file_obj.write(__lowercase )
return written
| 708 |
'''simple docstring'''
def _a( UpperCamelCase__ : Optional[int], UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =len(UpperCamelCase__ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
SCREAMING_SNAKE_CASE__ : Union[str, Any] =left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCamelCase__ ):
return None
SCREAMING_SNAKE_CASE__ : Optional[int] =sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =left
SCREAMING_SNAKE_CASE__ : Optional[Any] =point
elif point > right:
SCREAMING_SNAKE_CASE__ : Optional[int] =right
SCREAMING_SNAKE_CASE__ : Tuple =point
else:
if item < current_item:
SCREAMING_SNAKE_CASE__ : str =point - 1
else:
SCREAMING_SNAKE_CASE__ : Tuple =point + 1
return None
def _a( UpperCamelCase__ : List[str], UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
SCREAMING_SNAKE_CASE__ : Dict =left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCamelCase__ ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
elif point > right:
return interpolation_search_by_recursion(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, point - 1 )
else:
return interpolation_search_by_recursion(
UpperCamelCase__, UpperCamelCase__, point + 1, UpperCamelCase__ )
def _a( UpperCamelCase__ : Dict ):
'''simple docstring'''
if collection != sorted(UpperCamelCase__ ):
raise ValueError('''Collection must be ascending sorted''' )
return True
if __name__ == "__main__":
import sys
a_ = 0
if debug == 1:
a_ = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
try:
__assert_sorted(collection)
except ValueError:
sys.exit('Sequence must be ascending sorted to apply interpolation search')
a_ = 6_7
a_ = interpolation_search(collection, target)
if result is not None:
print(F'''{target} found at positions: {result}''')
else:
print('Not found')
| 665 | 0 |
'''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __SCREAMING_SNAKE_CASE :
snake_case_ = 42
snake_case_ = None
snake_case_ = None
def _a( UpperCamelCase__ : TreeNode | None ):
'''simple docstring'''
def is_valid_tree(UpperCamelCase__ : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(lowerCAmelCase__, lowerCAmelCase__ ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(lowerCAmelCase__ ):
raise ValueError(
'''Each node should be type of TreeNode and data should be float.''' )
def is_binary_search_tree_recursive_check(
UpperCamelCase__ : TreeNode | None, UpperCamelCase__ : float, UpperCamelCase__ : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left, lowerCAmelCase__, node.data )
and is_binary_search_tree_recursive_check(
node.right, node.data, lowerCAmelCase__ )
)
return is_binary_search_tree_recursive_check(lowerCAmelCase__, -float('''inf''' ), float('''inf''' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 709 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __SCREAMING_SNAKE_CASE :
@staticmethod
def __magic_name__ ( *__lowercase : int , **__lowercase : Optional[Any] ) -> Optional[Any]:
pass
@is_pipeline_test
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@require_torch
def __magic_name__ ( self : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , )
SCREAMING_SNAKE_CASE__ : List[str] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Any =image_classifier(__lowercase , candidate_labels=['''a''', '''b''', '''c'''] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(__lowercase ) , [
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
] , )
SCREAMING_SNAKE_CASE__ : int =image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
] , )
@require_tf
def __magic_name__ ( self : Union[str, Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , framework='''tf''' )
SCREAMING_SNAKE_CASE__ : Dict =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Tuple =image_classifier(__lowercase , candidate_labels=['''a''', '''b''', '''c'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}] , )
SCREAMING_SNAKE_CASE__ : List[Any] =image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
] , )
@slow
@require_torch
def __magic_name__ ( self : List[Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE__ : Any =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : List[str] =image_classifier(__lowercase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
@slow
@require_tf
def __magic_name__ ( self : Union[str, Any] ) -> int:
SCREAMING_SNAKE_CASE__ : str =pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , framework='''tf''' )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE__ : Optional[Any] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Any =image_classifier(__lowercase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
SCREAMING_SNAKE_CASE__ : List[Any] =image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
| 665 | 0 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _a( UpperCamelCase__ : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str =int(number**0.5 )
return number == sq * sq
def _a( UpperCamelCase__ : str, UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Optional[int], UpperCamelCase__ : List[Any], UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] =x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
SCREAMING_SNAKE_CASE__ : Optional[int] =x_den * y_den * z_den
SCREAMING_SNAKE_CASE__ : Union[str, Any] =gcd(UpperCamelCase__, UpperCamelCase__ )
top //= hcf
bottom //= hcf
return top, bottom
def _a( UpperCamelCase__ : Any = 3_5 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] =set()
SCREAMING_SNAKE_CASE__ : str =4_2
SCREAMING_SNAKE_CASE__ : Optional[Any] =Fraction(0 )
SCREAMING_SNAKE_CASE__ : Dict =4_2
for x_num in range(1, order + 1 ):
for x_den in range(x_num + 1, order + 1 ):
for y_num in range(1, order + 1 ):
for y_den in range(y_num + 1, order + 1 ):
# n=1
SCREAMING_SNAKE_CASE__ : int =x_num * y_den + x_den * y_num
SCREAMING_SNAKE_CASE__ : List[Any] =x_den * y_den
SCREAMING_SNAKE_CASE__ : Optional[int] =gcd(UpperCamelCase__, UpperCamelCase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE__ : Dict =add_three(
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
unique_s.add(UpperCamelCase__ )
# n=2
SCREAMING_SNAKE_CASE__ : Any =(
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
SCREAMING_SNAKE_CASE__ : List[Any] =x_den * x_den * y_den * y_den
if is_sq(UpperCamelCase__ ) and is_sq(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =int(sqrt(UpperCamelCase__ ) )
SCREAMING_SNAKE_CASE__ : Tuple =int(sqrt(UpperCamelCase__ ) )
SCREAMING_SNAKE_CASE__ : List[str] =gcd(UpperCamelCase__, UpperCamelCase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE__ : Optional[int] =add_three(
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
unique_s.add(UpperCamelCase__ )
# n=-1
SCREAMING_SNAKE_CASE__ : Optional[int] =x_num * y_num
SCREAMING_SNAKE_CASE__ : str =x_den * y_num + x_num * y_den
SCREAMING_SNAKE_CASE__ : Optional[Any] =gcd(UpperCamelCase__, UpperCamelCase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE__ : List[Any] =add_three(
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
unique_s.add(UpperCamelCase__ )
# n=2
SCREAMING_SNAKE_CASE__ : Tuple =x_num * x_num * y_num * y_num
SCREAMING_SNAKE_CASE__ : Optional[Any] =(
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(UpperCamelCase__ ) and is_sq(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : List[str] =int(sqrt(UpperCamelCase__ ) )
SCREAMING_SNAKE_CASE__ : Optional[int] =int(sqrt(UpperCamelCase__ ) )
SCREAMING_SNAKE_CASE__ : int =gcd(UpperCamelCase__, UpperCamelCase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE__ : List[str] =add_three(
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
unique_s.add(UpperCamelCase__ )
for num, den in unique_s:
total += Fraction(UpperCamelCase__, UpperCamelCase__ )
return total.denominator + total.numerator
if __name__ == "__main__":
print(F'''{solution() = }''')
| 710 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case_ = JukeboxTokenizer
snake_case_ = {
"""artist""": """Zac Brown Band""",
"""genres""": """Country""",
"""lyrics""": """I met a traveller from an antique land,
Who said \"Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
""",
}
@require_torch
def __magic_name__ ( self : Optional[int] ) -> str:
import torch
SCREAMING_SNAKE_CASE__ : List[str] =JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics''' )
SCREAMING_SNAKE_CASE__ : str =tokenizer(**self.metas )['''input_ids''']
# fmt: off
SCREAMING_SNAKE_CASE__ : str =[
torch.tensor([[
0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
76, 76]] ),
torch.tensor([[0, 0, 0, 10_69, 11]] ),
torch.tensor([[0, 0, 0, 10_69, 11]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
@require_torch
def __magic_name__ ( self : Any ) -> List[str]:
import torch
SCREAMING_SNAKE_CASE__ : int =JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics''' )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer(**self.metas )['''input_ids''']
# fmt: off
SCREAMING_SNAKE_CASE__ : Optional[int] =[
torch.tensor([[
0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
77, 77, 77, 77, 77, 77]] ),
torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
| 665 | 0 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
a_ = logging.get_logger(__name__)
@add_end_docstrings(lowercase_ )
class __SCREAMING_SNAKE_CASE ( lowercase_ ):
def __init__( self : int , **__lowercase : Any ) -> Dict:
super().__init__(**UpperCamelCase__ )
if self.framework != "pt":
raise ValueError(F"The {self.__class__} is only available in PyTorch." )
# No specific FOR_XXX available yet
def __call__( self : Optional[int] , __lowercase : Union[np.ndarray, bytes, str] , **__lowercase : Any ) -> Optional[Any]:
return super().__call__(UpperCamelCase__ , **UpperCamelCase__ )
def __magic_name__ ( self : int , **__lowercase : List[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Dict ={}
if "candidate_labels" in kwargs:
SCREAMING_SNAKE_CASE__ : List[Any] =kwargs['''candidate_labels''']
if "hypothesis_template" in kwargs:
SCREAMING_SNAKE_CASE__ : Tuple =kwargs['''hypothesis_template''']
return preprocess_params, {}, {}
def __magic_name__ ( self : Dict , __lowercase : Any , __lowercase : Dict=None , __lowercase : List[str]="This is a sound of {}." ) -> Optional[int]:
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
if audio.startswith('''http://''' ) or audio.startswith('''https://''' ):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
SCREAMING_SNAKE_CASE__ : List[Any] =requests.get(UpperCamelCase__ ).content
else:
with open(UpperCamelCase__ , '''rb''' ) as f:
SCREAMING_SNAKE_CASE__ : Tuple =f.read()
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Dict =ffmpeg_read(UpperCamelCase__ , self.feature_extractor.sampling_rate )
if not isinstance(UpperCamelCase__ , np.ndarray ):
raise ValueError('''We expect a numpy ndarray as input''' )
if len(audio.shape ) != 1:
raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline''' )
SCREAMING_SNAKE_CASE__ : List[str] =self.feature_extractor(
[audio] , sampling_rate=self.feature_extractor.sampling_rate , return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =candidate_labels
SCREAMING_SNAKE_CASE__ : Optional[int] =[hypothesis_template.format(UpperCamelCase__ ) for x in candidate_labels]
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.tokenizer(UpperCamelCase__ , return_tensors=self.framework , padding=UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : str =[text_inputs]
return inputs
def __magic_name__ ( self : int , __lowercase : Optional[int] ) -> str:
SCREAMING_SNAKE_CASE__ : int =model_inputs.pop('''candidate_labels''' )
SCREAMING_SNAKE_CASE__ : str =model_inputs.pop('''text_inputs''' )
if isinstance(text_inputs[0] , UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[int] =text_inputs[0]
else:
# Batching case.
SCREAMING_SNAKE_CASE__ : Dict =text_inputs[0][0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model(**UpperCamelCase__ , **UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : int ={
'''candidate_labels''': candidate_labels,
'''logits''': outputs.logits_per_audio,
}
return model_outputs
def __magic_name__ ( self : Dict , __lowercase : List[str] ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Optional[int] =model_outputs.pop('''candidate_labels''' )
SCREAMING_SNAKE_CASE__ : Any =model_outputs['''logits'''][0]
if self.framework == "pt":
SCREAMING_SNAKE_CASE__ : Union[str, Any] =logits.softmax(dim=0 )
SCREAMING_SNAKE_CASE__ : Dict =probs.tolist()
else:
raise ValueError('''`tf` framework not supported.''' )
SCREAMING_SNAKE_CASE__ : List[Any] =[
{'''score''': score, '''label''': candidate_label}
for score, candidate_label in sorted(zip(UpperCamelCase__ , UpperCamelCase__ ) , key=lambda __lowercase : -x[0] )
]
return result
| 711 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """gpt_neox"""
def __init__( self : List[Any] , __lowercase : Union[str, Any]=5_04_32 , __lowercase : int=61_44 , __lowercase : Tuple=44 , __lowercase : List[str]=64 , __lowercase : str=2_45_76 , __lowercase : Dict="gelu" , __lowercase : Tuple=0.25 , __lowercase : Tuple=1_00_00 , __lowercase : Tuple=0.0 , __lowercase : str=0.0 , __lowercase : List[Any]=0.1 , __lowercase : Dict=20_48 , __lowercase : Any=0.02 , __lowercase : Dict=1e-5 , __lowercase : List[Any]=True , __lowercase : str=0 , __lowercase : Optional[Any]=2 , __lowercase : Tuple=False , __lowercase : List[Any]=True , __lowercase : Optional[Any]=None , **__lowercase : Any , ) -> Dict:
super().__init__(bos_token_id=__lowercase , eos_token_id=__lowercase , **__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =max_position_embeddings
SCREAMING_SNAKE_CASE__ : Any =hidden_size
SCREAMING_SNAKE_CASE__ : str =num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any =num_attention_heads
SCREAMING_SNAKE_CASE__ : Union[str, Any] =intermediate_size
SCREAMING_SNAKE_CASE__ : Dict =hidden_act
SCREAMING_SNAKE_CASE__ : str =rotary_pct
SCREAMING_SNAKE_CASE__ : Optional[Any] =rotary_emb_base
SCREAMING_SNAKE_CASE__ : List[Any] =attention_dropout
SCREAMING_SNAKE_CASE__ : List[Any] =hidden_dropout
SCREAMING_SNAKE_CASE__ : str =classifier_dropout
SCREAMING_SNAKE_CASE__ : Any =initializer_range
SCREAMING_SNAKE_CASE__ : Dict =layer_norm_eps
SCREAMING_SNAKE_CASE__ : Any =use_cache
SCREAMING_SNAKE_CASE__ : Tuple =tie_word_embeddings
SCREAMING_SNAKE_CASE__ : Tuple =use_parallel_residual
SCREAMING_SNAKE_CASE__ : Union[str, Any] =rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )
def __magic_name__ ( self : Optional[Any] ) -> Optional[Any]:
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __lowercase ) 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}" )
SCREAMING_SNAKE_CASE__ : int =self.rope_scaling.get('''type''' , __lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =self.rope_scaling.get('''factor''' , __lowercase )
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(__lowercase , __lowercase ) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 665 | 0 |
'''simple docstring'''
import qiskit
def _a( UpperCamelCase__ : Union[str, Any] = 2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str =qubits
# Using Aer's simulator
SCREAMING_SNAKE_CASE__ : Optional[Any] =qiskit.Aer.get_backend('''aer_simulator''' )
# Creating a Quantum Circuit acting on the q register
SCREAMING_SNAKE_CASE__ : Tuple =qiskit.QuantumCircuit(__lowerCAmelCase, __lowerCAmelCase )
# Adding a H gate on qubit 0 (now q0 in superposition)
circuit.h(0 )
for i in range(1, __lowerCAmelCase ):
# Adding CX (CNOT) gate
circuit.cx(i - 1, __lowerCAmelCase )
# Mapping the quantum measurement to the classical bits
circuit.measure(list(range(__lowerCAmelCase ) ), list(range(__lowerCAmelCase ) ) )
# Now measuring any one qubit would affect other qubits to collapse
# their super position and have same state as the measured one.
# Executing the circuit on the simulator
SCREAMING_SNAKE_CASE__ : str =qiskit.execute(__lowerCAmelCase, __lowerCAmelCase, shots=1_0_0_0 )
return job.result().get_counts(__lowerCAmelCase )
if __name__ == "__main__":
print(F'''Total count for various states are: {quantum_entanglement(3)}''')
| 712 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
'tokenization_deberta': ['DebertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['DebertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'DebertaForMaskedLM',
'DebertaForQuestionAnswering',
'DebertaForSequenceClassification',
'DebertaForTokenClassification',
'DebertaModel',
'DebertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDebertaForMaskedLM',
'TFDebertaForQuestionAnswering',
'TFDebertaForSequenceClassification',
'TFDebertaForTokenClassification',
'TFDebertaModel',
'TFDebertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 665 | 0 |
'''simple docstring'''
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __SCREAMING_SNAKE_CASE ( _UpperCamelCase , unittest.TestCase ):
snake_case_ = FlaxAutoencoderKL
@property
def __magic_name__ ( self : Optional[Any] ) -> str:
SCREAMING_SNAKE_CASE__ : int =4
SCREAMING_SNAKE_CASE__ : Optional[Any] =3
SCREAMING_SNAKE_CASE__ : Optional[int] =(32, 32)
SCREAMING_SNAKE_CASE__ : Union[str, Any] =jax.random.PRNGKey(0 )
SCREAMING_SNAKE_CASE__ : Any =jax.random.uniform(_UpperCAmelCase , ((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def __magic_name__ ( self : Optional[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Dict ={
'''block_out_channels''': [32, 64],
'''in_channels''': 3,
'''out_channels''': 3,
'''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
'''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
'''latent_channels''': 4,
}
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.dummy_input
return init_dict, inputs_dict
| 713 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _a( UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : list[int], UpperCamelCase__ : int, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple =coefficient_matrix.shape
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple =constant_matrix.shape
if rowsa != colsa:
SCREAMING_SNAKE_CASE__ : Any =f"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
raise ValueError(UpperCamelCase__ )
if colsa != 1:
SCREAMING_SNAKE_CASE__ : str =f"Constant matrix must be nx1 but received {rowsa}x{colsa}"
raise ValueError(UpperCamelCase__ )
if rowsa != rowsa:
SCREAMING_SNAKE_CASE__ : str =(
'''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:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(
'''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''' )
SCREAMING_SNAKE_CASE__ : NDArray[floataa] =np.concatenate(
(coefficient_matrix, constant_matrix), axis=1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any =table.shape
strictly_diagonally_dominant(UpperCamelCase__ )
# Iterates the whole matrix for given number of times
for _ in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : List[str] =[]
for row in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =0
for col in range(UpperCamelCase__ ):
if col == row:
SCREAMING_SNAKE_CASE__ : int =table[row][col]
elif col == cols - 1:
SCREAMING_SNAKE_CASE__ : Any =table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
SCREAMING_SNAKE_CASE__ : int =(temp + val) / denom
new_val.append(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =new_val
return [float(UpperCamelCase__ ) for i in new_val]
def _a( UpperCamelCase__ : NDArray[floataa] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] =table.shape
SCREAMING_SNAKE_CASE__ : Any =True
for i in range(0, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : int =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()
| 665 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ = {'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
'ViTMAEForPreTraining',
'ViTMAELayer',
'ViTMAEModel',
'ViTMAEPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'TFViTMAEForPreTraining',
'TFViTMAEModel',
'TFViTMAEPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 714 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _a( UpperCamelCase__ : str, UpperCamelCase__ : Tuple, UpperCamelCase__ : Any, UpperCamelCase__ : List[str], UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
with open(UpperCamelCase__ ) as metadata_file:
SCREAMING_SNAKE_CASE__ : Optional[int] =json.load(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =LukeConfig(use_entity_aware_attention=UpperCamelCase__, **metadata['''model_config'''] )
# Load in the weights from the checkpoint_path
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch.load(UpperCamelCase__, map_location='''cpu''' )['''module''']
# Load the entity vocab file
SCREAMING_SNAKE_CASE__ : List[str] =load_original_entity_vocab(UpperCamelCase__ )
# add an entry for [MASK2]
SCREAMING_SNAKE_CASE__ : Optional[int] =max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
SCREAMING_SNAKE_CASE__ : Optional[int] =XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )
# Add special tokens to the token vocabulary for downstream tasks
SCREAMING_SNAKE_CASE__ : List[Any] =AddedToken('''<ent>''', lstrip=UpperCamelCase__, rstrip=UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Any =AddedToken('''<ent2>''', lstrip=UpperCamelCase__, rstrip=UpperCamelCase__ )
tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f"Saving tokenizer to {pytorch_dump_folder_path}" )
tokenizer.save_pretrained(UpperCamelCase__ )
with open(os.path.join(UpperCamelCase__, '''tokenizer_config.json''' ), '''r''' ) as f:
SCREAMING_SNAKE_CASE__ : Optional[Any] =json.load(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : int ='''MLukeTokenizer'''
with open(os.path.join(UpperCamelCase__, '''tokenizer_config.json''' ), '''w''' ) as f:
json.dump(UpperCamelCase__, UpperCamelCase__ )
with open(os.path.join(UpperCamelCase__, MLukeTokenizer.vocab_files_names['''entity_vocab_file'''] ), '''w''' ) as f:
json.dump(UpperCamelCase__, UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =MLukeTokenizer.from_pretrained(UpperCamelCase__ )
# Initialize the embeddings of the special tokens
SCREAMING_SNAKE_CASE__ : str =tokenizer.convert_tokens_to_ids(['''@'''] )[0]
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer.convert_tokens_to_ids(['''#'''] )[0]
SCREAMING_SNAKE_CASE__ : Dict =state_dict['''embeddings.word_embeddings.weight''']
SCREAMING_SNAKE_CASE__ : List[str] =word_emb[ent_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : Tuple =word_emb[enta_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : str =torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =state_dict[bias_name]
SCREAMING_SNAKE_CASE__ : List[Any] =decoder_bias[ent_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : str =decoder_bias[enta_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : List[str] =torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
SCREAMING_SNAKE_CASE__ : Tuple =f"encoder.layer.{layer_index}.attention.self."
SCREAMING_SNAKE_CASE__ : Optional[int] =state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE__ : Optional[int] =state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE__ : List[Any] =state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
SCREAMING_SNAKE_CASE__ : Any =state_dict['''entity_embeddings.entity_embeddings.weight''']
SCREAMING_SNAKE_CASE__ : Any =entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : Any =torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
SCREAMING_SNAKE_CASE__ : Optional[int] =state_dict['''entity_predictions.bias''']
SCREAMING_SNAKE_CASE__ : Tuple =entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : Any =torch.cat([entity_prediction_bias, entity_mask_bias] )
SCREAMING_SNAKE_CASE__ : int =LukeForMaskedLM(config=UpperCamelCase__ ).eval()
state_dict.pop('''entity_predictions.decoder.weight''' )
state_dict.pop('''lm_head.decoder.weight''' )
state_dict.pop('''lm_head.decoder.bias''' )
SCREAMING_SNAKE_CASE__ : Tuple =OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('''lm_head''' ) or key.startswith('''entity_predictions''' )):
SCREAMING_SNAKE_CASE__ : Optional[Any] =state_dict[key]
else:
SCREAMING_SNAKE_CASE__ : Any =state_dict[key]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] =model.load_state_dict(UpperCamelCase__, strict=UpperCamelCase__ )
if set(UpperCamelCase__ ) != {"luke.embeddings.position_ids"}:
raise ValueError(f"Unexpected unexpected_keys: {unexpected_keys}" )
if set(UpperCamelCase__ ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(f"Unexpected missing_keys: {missing_keys}" )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
SCREAMING_SNAKE_CASE__ : Any =MLukeTokenizer.from_pretrained(UpperCamelCase__, task='''entity_classification''' )
SCREAMING_SNAKE_CASE__ : str ='''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(0, 9)
SCREAMING_SNAKE_CASE__ : str =tokenizer(UpperCamelCase__, entity_spans=[span], return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : List[Any] =model(**UpperCamelCase__ )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
SCREAMING_SNAKE_CASE__ : str =torch.Size((1, 3_3, 7_6_8) )
SCREAMING_SNAKE_CASE__ : int =torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f"Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3], UpperCamelCase__, atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
SCREAMING_SNAKE_CASE__ : Any =torch.Size((1, 1, 7_6_8) )
SCREAMING_SNAKE_CASE__ : int =torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
f"Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"
f" {expected_shape}" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3], UpperCamelCase__, atol=1e-4 ):
raise ValueError
# Verify masked word/entity prediction
SCREAMING_SNAKE_CASE__ : str =MLukeTokenizer.from_pretrained(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] ='''Tokyo is the capital of <mask>.'''
SCREAMING_SNAKE_CASE__ : Dict =(2_4, 3_0)
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer(UpperCamelCase__, entity_spans=[span], return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : List[str] =model(**UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[Any] =encoding['''input_ids'''][0].tolist()
SCREAMING_SNAKE_CASE__ : Any =input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>''' ) )
SCREAMING_SNAKE_CASE__ : List[Any] =outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =outputs.entity_logits[0][0].argmax().item()
SCREAMING_SNAKE_CASE__ : Dict =[
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('''en:''' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('''Saving PyTorch model to {}'''.format(UpperCamelCase__ ) )
model.save_pretrained(UpperCamelCase__ )
def _a( UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] =['''[MASK]''', '''[PAD]''', '''[UNK]''']
SCREAMING_SNAKE_CASE__ : List[str] =[json.loads(UpperCamelCase__ ) for line in open(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Optional[int] ={}
for entry in data:
SCREAMING_SNAKE_CASE__ : Tuple =entry['''id''']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
SCREAMING_SNAKE_CASE__ : str =entity_id
break
SCREAMING_SNAKE_CASE__ : Union[str, Any] =f"{language}:{entity_name}"
SCREAMING_SNAKE_CASE__ : Union[str, Any] =entity_id
return new_mapping
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
parser.add_argument(
'--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
)
parser.add_argument(
'--entity_vocab_path',
default=None,
type=str,
help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
)
parser.add_argument(
'--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
)
a_ = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 665 | 0 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def _a( UpperCamelCase__ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict =[]
for line in lines:
SCREAMING_SNAKE_CASE__ : int =re.sub(R'''#.*''', '''''', UpperCAmelCase__ ) # remove comments
if line:
filtered_lines.append(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ : List[str] ='''\n'''.join(UpperCAmelCase__ )
# Make a hash from all this code
SCREAMING_SNAKE_CASE__ : str =full_str.encode('''utf-8''' )
return shaaaa(UpperCAmelCase__ ).hexdigest()
# get importable module names and hash for caching
a_ = {
'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
a_ = {
'.csv': ('csv', {}),
'.tsv': ('csv', {'sep': '\t'}),
'.json': ('json', {}),
'.jsonl': ('json', {}),
'.parquet': ('parquet', {}),
'.arrow': ('arrow', {}),
'.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
a_ = {'imagefolder', 'audiofolder'}
# Used to filter data files based on extensions given a module name
a_ = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')
| 715 |
'''simple docstring'''
def _a( UpperCamelCase__ : str, UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Tuple =[[False for _ in range(m + 1 )] for _ in range(n + 1 )]
SCREAMING_SNAKE_CASE__ : List[Any] =True
for i in range(UpperCamelCase__ ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
SCREAMING_SNAKE_CASE__ : Optional[int] =True
if a[i].islower():
SCREAMING_SNAKE_CASE__ : List[Any] =True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665 | 0 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class __SCREAMING_SNAKE_CASE :
def __init__( self : Tuple , __lowercase : int=None , __lowercase : List[Any]=None ) -> Any:
SCREAMING_SNAKE_CASE__ : Optional[Any] =list(poly_a or [0] )[:]
SCREAMING_SNAKE_CASE__ : Any =list(poly_b or [0] )[:]
# Remove leading zero coefficients
while self.polyA[-1] == 0:
self.polyA.pop()
SCREAMING_SNAKE_CASE__ : Any =len(self.polyA )
while self.polyB[-1] == 0:
self.polyB.pop()
SCREAMING_SNAKE_CASE__ : Optional[int] =len(self.polyB )
# Add 0 to make lengths equal a power of 2
SCREAMING_SNAKE_CASE__ : List[Any] =int(
2 ** np.ceil(np.loga(len(self.polyA ) + len(self.polyB ) - 1 ) ) )
while len(self.polyA ) < self.c_max_length:
self.polyA.append(0 )
while len(self.polyB ) < self.c_max_length:
self.polyB.append(0 )
# A complex root used for the fourier transform
SCREAMING_SNAKE_CASE__ : Dict =complex(mpmath.root(x=1 , n=self.c_max_length , k=1 ) )
# The product
SCREAMING_SNAKE_CASE__ : int =self.__multiply()
def __magic_name__ ( self : List[Any] , __lowercase : Optional[int] ) -> int:
SCREAMING_SNAKE_CASE__ : int =[[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
# Corner case
if len(__lowercase ) <= 1:
return dft[0]
#
SCREAMING_SNAKE_CASE__ : int =self.c_max_length // 2
while next_ncol > 0:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =[[] for i in range(__lowercase )]
SCREAMING_SNAKE_CASE__ : Tuple =self.root**next_ncol
# First half of next step
SCREAMING_SNAKE_CASE__ : int =1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(__lowercase ):
new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j] )
current_root *= root
# Second half of next step
SCREAMING_SNAKE_CASE__ : List[str] =1
for j in range(self.c_max_length // (next_ncol * 2) ):
for i in range(__lowercase ):
new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j] )
current_root *= root
# Update
SCREAMING_SNAKE_CASE__ : Dict =new_dft
SCREAMING_SNAKE_CASE__ : Union[str, Any] =next_ncol // 2
return dft[0]
def __magic_name__ ( self : Dict ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Any =self.__dft('''A''' )
SCREAMING_SNAKE_CASE__ : List[str] =self.__dft('''B''' )
SCREAMING_SNAKE_CASE__ : Any =[[dft_a[i] * dft_b[i] for i in range(self.c_max_length )]]
del dft_a
del dft_b
# Corner Case
if len(inverce_c[0] ) <= 1:
return inverce_c[0]
# Inverse DFT
SCREAMING_SNAKE_CASE__ : Dict =2
while next_ncol <= self.c_max_length:
SCREAMING_SNAKE_CASE__ : Tuple =[[] for i in range(__lowercase )]
SCREAMING_SNAKE_CASE__ : int =self.root ** (next_ncol // 2)
SCREAMING_SNAKE_CASE__ : Tuple =1
# First half of next step
for j in range(self.c_max_length // next_ncol ):
for i in range(next_ncol // 2 ):
# Even positions
new_inverse_c[i].append(
(
inverce_c[i][j]
+ inverce_c[i][j + self.c_max_length // next_ncol]
)
/ 2 )
# Odd positions
new_inverse_c[i + next_ncol // 2].append(
(
inverce_c[i][j]
- inverce_c[i][j + self.c_max_length // next_ncol]
)
/ (2 * current_root) )
current_root *= root
# Update
SCREAMING_SNAKE_CASE__ : List[str] =new_inverse_c
next_ncol *= 2
# Unpack
SCREAMING_SNAKE_CASE__ : str =[round(x[0].real , 8 ) + round(x[0].imag , 8 ) * 1J for x in inverce_c]
# Remove leading 0's
while inverce_c[-1] == 0:
inverce_c.pop()
return inverce_c
def __str__( self : Optional[int] ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[int] ='''A = ''' + ''' + '''.join(
F"{coef}*x^{i}" for coef, i in enumerate(self.polyA[: self.len_A] ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] ='''B = ''' + ''' + '''.join(
F"{coef}*x^{i}" for coef, i in enumerate(self.polyB[: self.len_B] ) )
SCREAMING_SNAKE_CASE__ : int ='''A*B = ''' + ''' + '''.join(
F"{coef}*x^{i}" for coef, i in enumerate(self.product ) )
return F"{a}\n{b}\n{c}"
# Unit tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 716 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , __lowercase : Optional[Any] , __lowercase : List[str]=13 , __lowercase : int=7 , __lowercase : Tuple=True , __lowercase : Optional[Any]=True , __lowercase : List[Any]=True , __lowercase : str=True , __lowercase : Tuple=99 , __lowercase : Union[str, Any]=64 , __lowercase : Tuple=32 , __lowercase : Optional[Any]=5 , __lowercase : Tuple=4 , __lowercase : Optional[int]=37 , __lowercase : int="gelu" , __lowercase : Union[str, Any]=0.1 , __lowercase : List[Any]=0.1 , __lowercase : List[Any]=5_12 , __lowercase : int=16 , __lowercase : Optional[int]=2 , __lowercase : Tuple=0.02 , __lowercase : List[str]=3 , __lowercase : List[str]=4 , __lowercase : List[str]=None , ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =parent
SCREAMING_SNAKE_CASE__ : Any =batch_size
SCREAMING_SNAKE_CASE__ : Optional[Any] =seq_length
SCREAMING_SNAKE_CASE__ : Dict =is_training
SCREAMING_SNAKE_CASE__ : List[Any] =use_input_mask
SCREAMING_SNAKE_CASE__ : Union[str, Any] =use_token_type_ids
SCREAMING_SNAKE_CASE__ : List[Any] =use_labels
SCREAMING_SNAKE_CASE__ : int =vocab_size
SCREAMING_SNAKE_CASE__ : str =hidden_size
SCREAMING_SNAKE_CASE__ : Any =embedding_size
SCREAMING_SNAKE_CASE__ : Tuple =num_hidden_layers
SCREAMING_SNAKE_CASE__ : str =num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple =intermediate_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =hidden_act
SCREAMING_SNAKE_CASE__ : Optional[int] =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : str =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Any =max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[Any] =type_vocab_size
SCREAMING_SNAKE_CASE__ : Any =type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Optional[Any] =initializer_range
SCREAMING_SNAKE_CASE__ : str =num_labels
SCREAMING_SNAKE_CASE__ : List[str] =num_choices
SCREAMING_SNAKE_CASE__ : List[str] =scope
def __magic_name__ ( self : str ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : int =None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : List[str] =random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : int =None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Any =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =None
SCREAMING_SNAKE_CASE__ : Optional[Any] =None
SCREAMING_SNAKE_CASE__ : Optional[int] =None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Tuple =ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : List[Any] =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : Any =ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __magic_name__ ( self : List[str] ) -> Any:
return MegatronBertConfig(
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 , embedding_size=self.embedding_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=__lowercase , initializer_range=self.initializer_range , )
def __magic_name__ ( self : Any , __lowercase : Tuple , __lowercase : Any , __lowercase : List[Any] , __lowercase : Optional[int] , __lowercase : List[str] , __lowercase : Dict , __lowercase : Union[str, Any] ) -> int:
SCREAMING_SNAKE_CASE__ : List[str] =MegatronBertModel(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(__lowercase , token_type_ids=__lowercase )
SCREAMING_SNAKE_CASE__ : str =model(__lowercase )
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 __magic_name__ ( self : Dict , __lowercase : Optional[int] , __lowercase : List[str] , __lowercase : str , __lowercase : Tuple , __lowercase : Any , __lowercase : Optional[int] , __lowercase : Any ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : str =MegatronBertForMaskedLM(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Tuple =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self : List[str] , __lowercase : Tuple , __lowercase : Tuple , __lowercase : Optional[Any] , __lowercase : Any , __lowercase : str , __lowercase : Any , __lowercase : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[Any] =MegatronBertForCausalLM(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self : Union[str, Any] , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : Union[str, Any] , __lowercase : Dict , __lowercase : Union[str, Any] , __lowercase : int , __lowercase : Any ) -> Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =MegatronBertForNextSentencePrediction(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] =model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def __magic_name__ ( self : List[str] , __lowercase : Optional[int] , __lowercase : str , __lowercase : Optional[int] , __lowercase : Optional[int] , __lowercase : Optional[Any] , __lowercase : str , __lowercase : Optional[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =MegatronBertForPreTraining(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] =model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , next_sentence_label=__lowercase , )
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 __magic_name__ ( self : Optional[int] , __lowercase : Tuple , __lowercase : Optional[int] , __lowercase : Union[str, Any] , __lowercase : int , __lowercase : Any , __lowercase : Any , __lowercase : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[Any] =MegatronBertForQuestionAnswering(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict =model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , start_positions=__lowercase , end_positions=__lowercase , )
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 __magic_name__ ( self : Optional[int] , __lowercase : Optional[int] , __lowercase : Optional[int] , __lowercase : List[Any] , __lowercase : List[Any] , __lowercase : Optional[int] , __lowercase : int , __lowercase : List[str] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.num_labels
SCREAMING_SNAKE_CASE__ : Dict =MegatronBertForSequenceClassification(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __magic_name__ ( self : Optional[int] , __lowercase : List[Any] , __lowercase : str , __lowercase : Optional[Any] , __lowercase : Any , __lowercase : Tuple , __lowercase : Optional[Any] , __lowercase : int ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Tuple =self.num_labels
SCREAMING_SNAKE_CASE__ : int =MegatronBertForTokenClassification(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __magic_name__ ( self : Dict , __lowercase : List[str] , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : Any , __lowercase : List[Any] , __lowercase : int , __lowercase : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : int =self.num_choices
SCREAMING_SNAKE_CASE__ : List[str] =MegatronBertForMultipleChoice(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Optional[int] =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Tuple =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __magic_name__ ( self : str ) -> Any:
SCREAMING_SNAKE_CASE__ : Tuple =self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : str =config_and_inputs
SCREAMING_SNAKE_CASE__ : Union[str, Any] ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
snake_case_ = (
{
"""feature-extraction""": MegatronBertModel,
"""fill-mask""": MegatronBertForMaskedLM,
"""question-answering""": MegatronBertForQuestionAnswering,
"""text-classification""": MegatronBertForSequenceClassification,
"""text-generation""": MegatronBertForCausalLM,
"""token-classification""": MegatronBertForTokenClassification,
"""zero-shot""": MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case_ = True
# test_resize_embeddings = False
snake_case_ = False
def __magic_name__ ( self : List[Any] , __lowercase : List[str] , __lowercase : Tuple , __lowercase : Tuple=False ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =super()._prepare_for_class(__lowercase , __lowercase , return_labels=__lowercase )
if return_labels:
if model_class in get_values(__lowercase ):
SCREAMING_SNAKE_CASE__ : List[str] =torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowercase )
return inputs_dict
def __magic_name__ ( self : Tuple ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =MegatronBertModelTester(self )
SCREAMING_SNAKE_CASE__ : Tuple =ConfigTester(self , config_class=__lowercase , hidden_size=37 )
def __magic_name__ ( self : str ) -> Dict:
self.config_tester.run_common_tests()
def __magic_name__ ( self : Tuple ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*__lowercase )
def __magic_name__ ( self : List[str] ) -> Any:
SCREAMING_SNAKE_CASE__ : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__lowercase )
def __magic_name__ ( self : Optional[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__lowercase )
def __magic_name__ ( self : Optional[int] ) -> Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__lowercase )
def __magic_name__ ( self : Dict ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*__lowercase )
def __magic_name__ ( self : int ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*__lowercase )
def __magic_name__ ( self : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*__lowercase )
def _a( UpperCamelCase__ : List[str] ):
'''simple docstring'''
return torch.tensor(
UpperCamelCase__, dtype=torch.long, device=UpperCamelCase__, )
a_ = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
@unittest.skip('''Model is not available.''' )
def __magic_name__ ( self : Any ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Any ='''nvidia/megatron-bert-uncased-345m'''
if "MYDIR" in os.environ:
SCREAMING_SNAKE_CASE__ : Optional[int] =os.path.join(os.environ['''MYDIR'''] , __lowercase )
SCREAMING_SNAKE_CASE__ : Any =MegatronBertModel.from_pretrained(__lowercase )
model.to(__lowercase )
model.half()
SCREAMING_SNAKE_CASE__ : Dict =_long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(__lowercase )[0]
SCREAMING_SNAKE_CASE__ : Dict =torch.Size((1, 9, 10_24) )
self.assertEqual(output.shape , __lowercase )
SCREAMING_SNAKE_CASE__ : str =[-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
for ii in range(3 ):
for jj in range(3 ):
SCREAMING_SNAKE_CASE__ : List[Any] =output[0, ii, jj]
SCREAMING_SNAKE_CASE__ : Tuple =expected[3 * ii + jj]
SCREAMING_SNAKE_CASE__ : List[str] ='''ii={} jj={} a={} b={}'''.format(__lowercase , __lowercase , __lowercase , __lowercase )
self.assertTrue(math.isclose(__lowercase , __lowercase , rel_tol=__lowercase , abs_tol=__lowercase ) , msg=__lowercase )
| 665 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a_ = {"""tokenization_bertweet""": ["""BertweetTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 717 |
'''simple docstring'''
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =inspect.getfile(accelerate.test_utils )
SCREAMING_SNAKE_CASE__ : Optional[Any] =os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_script.py'''] )
SCREAMING_SNAKE_CASE__ : Dict =os.path.sep.join(inspect.getfile(self.__class__ ).split(os.path.sep )[:-1] )
@require_tpu
def __magic_name__ ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Any =F"\n {self.test_dir}/xla_spawn.py\n --num_cores 8\n {self.test_file_path}\n ".split()
SCREAMING_SNAKE_CASE__ : List[str] =[sys.executable] + distributed_args
execute_subprocess_async(__lowercase , env=os.environ.copy() )
| 665 | 0 |
'''simple docstring'''
import argparse
from collections import defaultdict
def _a( UpperCamelCase__ : int, UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Tuple, UpperCamelCase__ : List[Any], UpperCamelCase__ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =f"{file}_{class_name}_{test_name}"
done_test[_id] += 1
with open(UpperCamelCase__, '''r''' ) as f:
SCREAMING_SNAKE_CASE__ : Optional[Any] =f.readlines()
SCREAMING_SNAKE_CASE__ : int =f"class {class_name}("
SCREAMING_SNAKE_CASE__ : Dict =f"{4 * ' '}def {test_name}("
SCREAMING_SNAKE_CASE__ : str =f"{8 * ' '}{correct_line.split()[0]}"
SCREAMING_SNAKE_CASE__ : str =f"{1_6 * ' '}{correct_line.split()[0]}"
SCREAMING_SNAKE_CASE__ : str =False
SCREAMING_SNAKE_CASE__ : Union[str, Any] =False
SCREAMING_SNAKE_CASE__ : Dict =False
SCREAMING_SNAKE_CASE__ : List[Any] =False
SCREAMING_SNAKE_CASE__ : int =0
SCREAMING_SNAKE_CASE__ : str =0
SCREAMING_SNAKE_CASE__ : List[str] =[]
for line in lines:
if line.startswith(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : List[str] =True
elif in_class and line.startswith(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : List[str] =True
elif in_class and in_func and (line.startswith(UpperCamelCase__ ) or line.startswith(UpperCamelCase__ )):
SCREAMING_SNAKE_CASE__ : List[str] =len(line.split(correct_line.split()[0] )[0] )
count += 1
if count == done_test[_id]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
SCREAMING_SNAKE_CASE__ : str =True
if in_class and in_func and in_line and insert_line:
new_lines.append(f"{spaces * ' '}{correct_line}" )
SCREAMING_SNAKE_CASE__ : str =False
else:
new_lines.append(UpperCamelCase__ )
with open(UpperCamelCase__, '''w''' ) as f:
for line in new_lines:
f.write(UpperCamelCase__ )
def _a( UpperCamelCase__ : List[Any], UpperCamelCase__ : List[Any]=None ):
'''simple docstring'''
if fail is not None:
with open(UpperCamelCase__, '''r''' ) as f:
SCREAMING_SNAKE_CASE__ : List[str] ={l.strip() for l in f.readlines()}
else:
SCREAMING_SNAKE_CASE__ : Tuple =None
with open(UpperCamelCase__, '''r''' ) as f:
SCREAMING_SNAKE_CASE__ : Optional[Any] =f.readlines()
SCREAMING_SNAKE_CASE__ : int =defaultdict(UpperCamelCase__ )
for line in correct_lines:
SCREAMING_SNAKE_CASE__ : Dict =line.split(''';''' )
if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
overwrite_file(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--correct_filename', help='filename of tests with expected result')
parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
a_ = parser.parse_args()
main(args.correct_filename, args.fail_filename)
| 718 |
'''simple docstring'''
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 __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = ShapEImgaImgPipeline
snake_case_ = ["""image"""]
snake_case_ = ["""image"""]
snake_case_ = [
"""num_images_per_prompt""",
"""num_inference_steps""",
"""generator""",
"""latents""",
"""guidance_scale""",
"""frame_size""",
"""output_type""",
"""return_dict""",
]
snake_case_ = False
@property
def __magic_name__ ( self : List[Any] ) -> List[Any]:
return 32
@property
def __magic_name__ ( self : List[str] ) -> Optional[int]:
return 32
@property
def __magic_name__ ( self : Optional[int] ) -> Optional[Any]:
return self.time_input_dim * 4
@property
def __magic_name__ ( self : Dict ) -> Union[str, Any]:
return 8
@property
def __magic_name__ ( self : Optional[int] ) -> Union[str, Any]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Dict =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 , )
SCREAMING_SNAKE_CASE__ : str =CLIPVisionModel(__lowercase )
return model
@property
def __magic_name__ ( self : Tuple ) -> List[str]:
SCREAMING_SNAKE_CASE__ : int =CLIPImageProcessor(
crop_size=2_24 , do_center_crop=__lowercase , do_normalize=__lowercase , do_resize=__lowercase , image_mean=[0.48145466, 0.4578275, 0.40821073] , image_std=[0.26862954, 0.26130258, 0.27577711] , resample=3 , size=2_24 , )
return image_processor
@property
def __magic_name__ ( self : List[str] ) -> Dict:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : str ={
'''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,
}
SCREAMING_SNAKE_CASE__ : str =PriorTransformer(**__lowercase )
return model
@property
def __magic_name__ ( self : Tuple ) -> List[str]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[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,
),
}
SCREAMING_SNAKE_CASE__ : str =ShapERenderer(**__lowercase )
return model
def __magic_name__ ( self : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : int =self.dummy_prior
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dummy_image_encoder
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dummy_image_processor
SCREAMING_SNAKE_CASE__ : Tuple =self.dummy_renderer
SCREAMING_SNAKE_CASE__ : int =HeunDiscreteScheduler(
beta_schedule='''exp''' , num_train_timesteps=10_24 , prediction_type='''sample''' , use_karras_sigmas=__lowercase , clip_sample=__lowercase , clip_sample_range=1.0 , )
SCREAMING_SNAKE_CASE__ : Any ={
'''prior''': prior,
'''image_encoder''': image_encoder,
'''image_processor''': image_processor,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def __magic_name__ ( self : Any , __lowercase : List[str] , __lowercase : Any=0 ) -> Any:
SCREAMING_SNAKE_CASE__ : int =floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowercase ) ).to(__lowercase )
if str(__lowercase ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE__ : List[str] =torch.manual_seed(__lowercase )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =torch.Generator(device=__lowercase ).manual_seed(__lowercase )
SCREAMING_SNAKE_CASE__ : Any ={
'''image''': input_image,
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 32,
'''output_type''': '''np''',
}
return inputs
def __magic_name__ ( self : List[str] ) -> str:
SCREAMING_SNAKE_CASE__ : int ='''cpu'''
SCREAMING_SNAKE_CASE__ : Any =self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : str =self.pipeline_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Any =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =pipe(**self.get_dummy_inputs(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Tuple =output.images[0]
SCREAMING_SNAKE_CASE__ : List[Any] =image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
SCREAMING_SNAKE_CASE__ : List[Any] =np.array(
[
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __magic_name__ ( self : List[Any] ) -> List[str]:
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __magic_name__ ( self : Optional[int] ) -> str:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch_device == '''cpu'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__lowercase , relax_max_difference=__lowercase , )
def __magic_name__ ( self : Dict ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Any =self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Dict =self.pipeline_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =1
SCREAMING_SNAKE_CASE__ : List[str] =2
SCREAMING_SNAKE_CASE__ : Dict =self.get_dummy_inputs(__lowercase )
for key in inputs.keys():
if key in self.batch_params:
SCREAMING_SNAKE_CASE__ : Tuple =batch_size * [inputs[key]]
SCREAMING_SNAKE_CASE__ : List[Any] =pipe(**__lowercase , num_images_per_prompt=__lowercase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : Optional[Any] ) -> int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : int ) -> Dict:
SCREAMING_SNAKE_CASE__ : List[str] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png''' )
SCREAMING_SNAKE_CASE__ : Dict =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_img2img_out.npy''' )
SCREAMING_SNAKE_CASE__ : List[Any] =ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img''' )
SCREAMING_SNAKE_CASE__ : Tuple =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device=__lowercase ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Tuple =pipe(
__lowercase , generator=__lowercase , 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(__lowercase , __lowercase )
| 665 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['BloomTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
'BloomForCausalLM',
'BloomModel',
'BloomPreTrainedModel',
'BloomForSequenceClassification',
'BloomForTokenClassification',
'BloomForQuestionAnswering',
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 719 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """gpt_bigcode"""
snake_case_ = ["""past_key_values"""]
snake_case_ = {
"""hidden_size""": """n_embd""",
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Any , __lowercase : Any=5_02_57 , __lowercase : int=10_24 , __lowercase : List[str]=7_68 , __lowercase : Optional[int]=12 , __lowercase : Dict=12 , __lowercase : List[str]=None , __lowercase : int="gelu_pytorch_tanh" , __lowercase : Union[str, Any]=0.1 , __lowercase : Optional[int]=0.1 , __lowercase : Optional[int]=0.1 , __lowercase : Optional[Any]=1e-5 , __lowercase : List[str]=0.02 , __lowercase : Tuple=True , __lowercase : Optional[Any]=True , __lowercase : Union[str, Any]=5_02_56 , __lowercase : List[Any]=5_02_56 , __lowercase : Union[str, Any]=True , __lowercase : List[str]=True , __lowercase : Dict=True , **__lowercase : List[Any] , ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =n_positions
SCREAMING_SNAKE_CASE__ : Dict =n_embd
SCREAMING_SNAKE_CASE__ : Dict =n_layer
SCREAMING_SNAKE_CASE__ : Union[str, Any] =n_head
SCREAMING_SNAKE_CASE__ : List[str] =n_inner
SCREAMING_SNAKE_CASE__ : List[str] =activation_function
SCREAMING_SNAKE_CASE__ : List[Any] =resid_pdrop
SCREAMING_SNAKE_CASE__ : List[Any] =embd_pdrop
SCREAMING_SNAKE_CASE__ : List[str] =attn_pdrop
SCREAMING_SNAKE_CASE__ : Dict =layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : List[str] =initializer_range
SCREAMING_SNAKE_CASE__ : List[Any] =scale_attn_weights
SCREAMING_SNAKE_CASE__ : Union[str, Any] =use_cache
SCREAMING_SNAKE_CASE__ : Dict =attention_softmax_in_fpaa
SCREAMING_SNAKE_CASE__ : int =scale_attention_softmax_in_fpaa
SCREAMING_SNAKE_CASE__ : Dict =multi_query
SCREAMING_SNAKE_CASE__ : Optional[Any] =bos_token_id
SCREAMING_SNAKE_CASE__ : Optional[Any] =eos_token_id
super().__init__(bos_token_id=__lowercase , eos_token_id=__lowercase , **__lowercase )
| 665 | 0 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def _a( UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple =np.max(UpperCamelCase__, axis=-1, keepdims=UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Any =np.exp(outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1, keepdims=UpperCamelCase__ )
class __SCREAMING_SNAKE_CASE ( lowercase_ ):
def __magic_name__ ( self : List[str] , **__lowercase : List[str] ) -> int:
SCREAMING_SNAKE_CASE__ : List[Any] ={}
if "second_text" in kwargs:
SCREAMING_SNAKE_CASE__ : List[str] =kwargs['''second_text''']
return preprocess_kwargs, {}, {}
def __magic_name__ ( self : List[Any] , __lowercase : List[str] , __lowercase : List[Any]=None ) -> Optional[Any]:
return self.tokenizer(__lowercase , text_pair=__lowercase , return_tensors=self.framework )
def __magic_name__ ( self : Union[str, Any] , __lowercase : int ) -> Dict:
return self.model(**__lowercase )
def __magic_name__ ( self : Any , __lowercase : Any ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[Any] =model_outputs.logits[0].numpy()
SCREAMING_SNAKE_CASE__ : List[Any] =softmax(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =np.argmax(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model.config.idalabel[best_class]
SCREAMING_SNAKE_CASE__ : str =probabilities[best_class].item()
SCREAMING_SNAKE_CASE__ : Optional[Any] =logits.tolist()
return {"label": label, "score": score, "logits": logits}
| 720 |
'''simple docstring'''
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =size
SCREAMING_SNAKE_CASE__ : List[Any] =[0] * size
SCREAMING_SNAKE_CASE__ : str =[0] * size
@staticmethod
def __magic_name__ ( __lowercase : int ) -> int:
return index | (index + 1)
@staticmethod
def __magic_name__ ( __lowercase : int ) -> int:
return (index & (index + 1)) - 1
def __magic_name__ ( self : Dict , __lowercase : int , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : List[str] =value
while index < self.size:
SCREAMING_SNAKE_CASE__ : Any =self.get_prev(__lowercase ) + 1
if current_left_border == index:
SCREAMING_SNAKE_CASE__ : List[str] =value
else:
SCREAMING_SNAKE_CASE__ : str =max(__lowercase , __lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.get_next(__lowercase )
def __magic_name__ ( self : Optional[int] , __lowercase : int , __lowercase : int ) -> int:
right -= 1 # Because of right is exclusive
SCREAMING_SNAKE_CASE__ : str =0
while left <= right:
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.get_prev(__lowercase )
if left <= current_left:
SCREAMING_SNAKE_CASE__ : List[Any] =max(__lowercase , self.tree[right] )
SCREAMING_SNAKE_CASE__ : Any =current_left
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =max(__lowercase , self.arr[right] )
right -= 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665 | 0 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def _a( UpperCamelCase__ : Tuple, UpperCamelCase__ : Any, UpperCamelCase__ : List[str]=1e-12 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =jnp.divide(emb_a.T, jnp.clip(jnp.linalg.norm(lowerCamelCase__, axis=1 ), a_min=lowerCamelCase__ ) ).T
SCREAMING_SNAKE_CASE__ : Dict =jnp.divide(emb_a.T, jnp.clip(jnp.linalg.norm(lowerCamelCase__, axis=1 ), a_min=lowerCamelCase__ ) ).T
return jnp.matmul(lowerCamelCase__, norm_emb_a.T )
class __SCREAMING_SNAKE_CASE ( nn.Module ):
snake_case_ = 42
snake_case_ = jnp.floataa
def __magic_name__ ( self : Any ) -> List[str]:
SCREAMING_SNAKE_CASE__ : List[Any] =FlaxCLIPVisionModule(self.config.vision_config )
SCREAMING_SNAKE_CASE__ : List[Any] =nn.Dense(self.config.projection_dim , use_bias=__lowercase , dtype=self.dtype )
SCREAMING_SNAKE_CASE__ : List[Any] =self.param('''concept_embeds''' , jax.nn.initializers.ones , (17, self.config.projection_dim) )
SCREAMING_SNAKE_CASE__ : Dict =self.param(
'''special_care_embeds''' , jax.nn.initializers.ones , (3, self.config.projection_dim) )
SCREAMING_SNAKE_CASE__ : List[Any] =self.param('''concept_embeds_weights''' , jax.nn.initializers.ones , (17,) )
SCREAMING_SNAKE_CASE__ : List[Any] =self.param('''special_care_embeds_weights''' , jax.nn.initializers.ones , (3,) )
def __call__( self : str , __lowercase : str ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Dict =self.vision_model(__lowercase )[1]
SCREAMING_SNAKE_CASE__ : Dict =self.visual_projection(__lowercase )
SCREAMING_SNAKE_CASE__ : int =jax_cosine_distance(__lowercase , self.special_care_embeds )
SCREAMING_SNAKE_CASE__ : List[Any] =jax_cosine_distance(__lowercase , self.concept_embeds )
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign image inputs
SCREAMING_SNAKE_CASE__ : Tuple =0.0
SCREAMING_SNAKE_CASE__ : int =special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
SCREAMING_SNAKE_CASE__ : List[str] =jnp.round(__lowercase , 3 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =jnp.any(special_scores > 0 , axis=1 , keepdims=__lowercase )
# Use a lower threshold if an image has any special care concept
SCREAMING_SNAKE_CASE__ : Optional[int] =is_special_care * 0.01
SCREAMING_SNAKE_CASE__ : Tuple =cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
SCREAMING_SNAKE_CASE__ : str =jnp.round(__lowercase , 3 )
SCREAMING_SNAKE_CASE__ : Dict =jnp.any(concept_scores > 0 , axis=1 )
return has_nsfw_concepts
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = CLIPConfig
snake_case_ = """clip_input"""
snake_case_ = FlaxStableDiffusionSafetyCheckerModule
def __init__( self : Optional[int] , __lowercase : CLIPConfig , __lowercase : Optional[Tuple] = None , __lowercase : int = 0 , __lowercase : jnp.dtype = jnp.floataa , __lowercase : bool = True , **__lowercase : List[str] , ) -> List[str]:
if input_shape is None:
SCREAMING_SNAKE_CASE__ : Optional[Any] =(1, 2_24, 2_24, 3)
SCREAMING_SNAKE_CASE__ : int =self.module_class(config=__lowercase , dtype=__lowercase , **__lowercase )
super().__init__(__lowercase , __lowercase , input_shape=__lowercase , seed=__lowercase , dtype=__lowercase , _do_init=_do_init )
def __magic_name__ ( self : List[Any] , __lowercase : jax.random.KeyArray , __lowercase : Tuple , __lowercase : FrozenDict = None ) -> FrozenDict:
SCREAMING_SNAKE_CASE__ : List[str] =jax.random.normal(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] =jax.random.split(__lowercase )
SCREAMING_SNAKE_CASE__ : int ={'''params''': params_rng, '''dropout''': dropout_rng}
SCREAMING_SNAKE_CASE__ : str =self.module.init(__lowercase , __lowercase )['''params''']
return random_params
def __call__( self : Optional[Any] , __lowercase : Union[str, Any] , __lowercase : dict = None , ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Any =jnp.transpose(__lowercase , (0, 2, 3, 1) )
return self.module.apply(
{'''params''': params or self.params} , jnp.array(__lowercase , dtype=jnp.floataa ) , rngs={} , )
| 721 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = ["""vqvae"""]
def __init__( self : int , __lowercase : AutoencoderKL , __lowercase : UNetaDConditionModel , __lowercase : Mel , __lowercase : Union[DDIMScheduler, DDPMScheduler] , ) -> int:
super().__init__()
self.register_modules(unet=__lowercase , scheduler=__lowercase , mel=__lowercase , vqvae=__lowercase )
def __magic_name__ ( self : List[str] ) -> int:
return 50 if isinstance(self.scheduler , __lowercase ) else 10_00
@torch.no_grad()
def __call__( self : Dict , __lowercase : int = 1 , __lowercase : str = None , __lowercase : np.ndarray = None , __lowercase : int = 0 , __lowercase : int = 0 , __lowercase : int = None , __lowercase : torch.Generator = None , __lowercase : float = 0 , __lowercase : float = 0 , __lowercase : torch.Generator = None , __lowercase : float = 0 , __lowercase : torch.Tensor = None , __lowercase : torch.Tensor = None , __lowercase : Dict=True , ) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =steps or self.get_default_steps()
self.scheduler.set_timesteps(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
SCREAMING_SNAKE_CASE__ : Optional[int] =randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=__lowercase , device=self.device , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =noise
SCREAMING_SNAKE_CASE__ : List[str] =None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Dict =self.mel.audio_slice_to_image(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =np.frombuffer(input_image.tobytes() , dtype='''uint8''' ).reshape(
(input_image.height, input_image.width) )
SCREAMING_SNAKE_CASE__ : int =(input_image / 2_55) * 2 - 1
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.vqvae.encode(torch.unsqueeze(__lowercase , 0 ) ).latent_dist.sample(
generator=__lowercase )[0]
SCREAMING_SNAKE_CASE__ : int =self.vqvae.config.scaling_factor * input_images
if start_step > 0:
SCREAMING_SNAKE_CASE__ : int =self.scheduler.add_noise(__lowercase , __lowercase , self.scheduler.timesteps[start_step - 1] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
SCREAMING_SNAKE_CASE__ : Optional[Any] =int(mask_start_secs * pixels_per_second )
SCREAMING_SNAKE_CASE__ : Tuple =int(mask_end_secs * pixels_per_second )
SCREAMING_SNAKE_CASE__ : int =self.scheduler.add_noise(__lowercase , __lowercase , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , __lowercase ):
SCREAMING_SNAKE_CASE__ : str =self.unet(__lowercase , __lowercase , __lowercase )['''sample''']
else:
SCREAMING_SNAKE_CASE__ : str =self.unet(__lowercase , __lowercase )['''sample''']
if isinstance(self.scheduler , __lowercase ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.scheduler.step(
model_output=__lowercase , timestep=__lowercase , sample=__lowercase , eta=__lowercase , generator=__lowercase , )['''prev_sample''']
else:
SCREAMING_SNAKE_CASE__ : Any =self.scheduler.step(
model_output=__lowercase , timestep=__lowercase , sample=__lowercase , generator=__lowercase , )['''prev_sample''']
if mask is not None:
if mask_start > 0:
SCREAMING_SNAKE_CASE__ : Optional[Any] =mask[:, step, :, :mask_start]
if mask_end > 0:
SCREAMING_SNAKE_CASE__ : List[str] =mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
SCREAMING_SNAKE_CASE__ : str =1 / self.vqvae.config.scaling_factor * images
SCREAMING_SNAKE_CASE__ : int =self.vqvae.decode(__lowercase )['''sample''']
SCREAMING_SNAKE_CASE__ : List[str] =(images / 2 + 0.5).clamp(0 , 1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] =images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
SCREAMING_SNAKE_CASE__ : Dict =(images * 2_55).round().astype('''uint8''' )
SCREAMING_SNAKE_CASE__ : Any =list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(__lowercase , mode='''RGB''' ).convert('''L''' ) for _ in images) )
SCREAMING_SNAKE_CASE__ : Optional[int] =[self.mel.image_to_audio(__lowercase ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(__lowercase )[:, np.newaxis, :] ) , **ImagePipelineOutput(__lowercase ) )
@torch.no_grad()
def __magic_name__ ( self : Optional[int] , __lowercase : List[Image.Image] , __lowercase : int = 50 ) -> np.ndarray:
assert isinstance(self.scheduler , __lowercase )
self.scheduler.set_timesteps(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =np.array(
[np.frombuffer(image.tobytes() , dtype='''uint8''' ).reshape((1, image.height, image.width) ) for image in images] )
SCREAMING_SNAKE_CASE__ : str =(sample / 2_55) * 2 - 1
SCREAMING_SNAKE_CASE__ : str =torch.Tensor(__lowercase ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
SCREAMING_SNAKE_CASE__ : Tuple =t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
SCREAMING_SNAKE_CASE__ : Any =self.scheduler.alphas_cumprod[t]
SCREAMING_SNAKE_CASE__ : int =(
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
SCREAMING_SNAKE_CASE__ : int =1 - alpha_prod_t
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.unet(__lowercase , __lowercase )['''sample''']
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(1 - alpha_prod_t_prev) ** 0.5 * model_output
SCREAMING_SNAKE_CASE__ : str =(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
SCREAMING_SNAKE_CASE__ : Any =sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def __magic_name__ ( __lowercase : torch.Tensor , __lowercase : torch.Tensor , __lowercase : float ) -> torch.Tensor:
SCREAMING_SNAKE_CASE__ : Optional[int] =acos(torch.dot(torch.flatten(__lowercase ) , torch.flatten(__lowercase ) ) / torch.norm(__lowercase ) / torch.norm(__lowercase ) )
return sin((1 - alpha) * theta ) * xa / sin(__lowercase ) + sin(alpha * theta ) * xa / sin(__lowercase )
| 665 | 0 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def _a( UpperCamelCase__ : Dict, UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple =old_name
if "patch_embed" in old_name:
SCREAMING_SNAKE_CASE__ : Dict =old_name.split('''.''' )
if layer == "0":
SCREAMING_SNAKE_CASE__ : Optional[int] =old_name.replace('''0''', '''convolution1''' )
elif layer == "1":
SCREAMING_SNAKE_CASE__ : Dict =old_name.replace('''1''', '''batchnorm_before''' )
elif layer == "3":
SCREAMING_SNAKE_CASE__ : Any =old_name.replace('''3''', '''convolution2''' )
else:
SCREAMING_SNAKE_CASE__ : List[Any] =old_name.replace('''4''', '''batchnorm_after''' )
if "network" in old_name and re.search(R'''\d\.\d''', UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] =R'''\b\d{2}\b'''
if bool(re.search(UpperCamelCase__, UpperCamelCase__ ) ):
SCREAMING_SNAKE_CASE__ : List[str] =re.search(R'''\d\.\d\d.''', UpperCamelCase__ ).group()
else:
SCREAMING_SNAKE_CASE__ : Any =re.search(R'''\d\.\d.''', UpperCamelCase__ ).group()
if int(match[0] ) < 6:
SCREAMING_SNAKE_CASE__ : Tuple =old_name.replace(UpperCamelCase__, '''''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =trimmed_name.replace('''network''', match[0] + '''.meta4D_layers.blocks.''' + match[2:-1] )
SCREAMING_SNAKE_CASE__ : int ='''intermediate_stages.''' + trimmed_name
else:
SCREAMING_SNAKE_CASE__ : Optional[int] =old_name.replace(UpperCamelCase__, '''''' )
if int(match[2] ) < num_meta4D_last_stage:
SCREAMING_SNAKE_CASE__ : Optional[int] =trimmed_name.replace('''network''', '''meta4D_layers.blocks.''' + match[2] )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =str(int(match[2] ) - num_meta4D_last_stage )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =trimmed_name.replace('''network''', '''meta3D_layers.blocks.''' + layer_index )
if "norm1" in old_name:
SCREAMING_SNAKE_CASE__ : List[Any] =trimmed_name.replace('''norm1''', '''layernorm1''' )
elif "norm2" in old_name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =trimmed_name.replace('''norm2''', '''layernorm2''' )
elif "fc1" in old_name:
SCREAMING_SNAKE_CASE__ : Optional[int] =trimmed_name.replace('''fc1''', '''linear_in''' )
elif "fc2" in old_name:
SCREAMING_SNAKE_CASE__ : List[Any] =trimmed_name.replace('''fc2''', '''linear_out''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] ='''last_stage.''' + trimmed_name
elif "network" in old_name and re.search(R'''.\d.''', UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Any =old_name.replace('''network''', '''intermediate_stages''' )
if "fc" in new_name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =new_name.replace('''fc''', '''convolution''' )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
SCREAMING_SNAKE_CASE__ : str =new_name.replace('''norm1''', '''batchnorm_before''' )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
SCREAMING_SNAKE_CASE__ : Optional[int] =new_name.replace('''norm2''', '''batchnorm_after''' )
if "proj" in new_name:
SCREAMING_SNAKE_CASE__ : List[Any] =new_name.replace('''proj''', '''projection''' )
if "dist_head" in new_name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =new_name.replace('''dist_head''', '''distillation_classifier''' )
elif "head" in new_name:
SCREAMING_SNAKE_CASE__ : List[Any] =new_name.replace('''head''', '''classifier''' )
elif "patch_embed" in new_name:
SCREAMING_SNAKE_CASE__ : Any ='''efficientformer.''' + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
SCREAMING_SNAKE_CASE__ : List[Any] =new_name.replace('''norm''', '''layernorm''' )
SCREAMING_SNAKE_CASE__ : List[str] ='''efficientformer.''' + new_name
else:
SCREAMING_SNAKE_CASE__ : Any ='''efficientformer.encoder.''' + new_name
return new_name
def _a( UpperCamelCase__ : List[Any], UpperCamelCase__ : Dict ):
'''simple docstring'''
for key in checkpoint.copy().keys():
SCREAMING_SNAKE_CASE__ : str =checkpoint.pop(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Any =val
return checkpoint
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
SCREAMING_SNAKE_CASE__ : Dict =Image.open(requests.get(UpperCamelCase__, stream=UpperCamelCase__ ).raw )
return image
def _a( UpperCamelCase__ : Path, UpperCamelCase__ : Path, UpperCamelCase__ : Path, UpperCamelCase__ : bool ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =torch.load(UpperCamelCase__, map_location='''cpu''' )['''model''']
SCREAMING_SNAKE_CASE__ : str =EfficientFormerConfig.from_json_file(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =EfficientFormerForImageClassificationWithTeacher(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Tuple ='''_'''.join(checkpoint_path.split('''/''' )[-1].split('''.''' )[0].split('''_''' )[:-1] )
SCREAMING_SNAKE_CASE__ : Optional[int] =config.depths[-1] - config.num_metaad_blocks + 1
SCREAMING_SNAKE_CASE__ : Optional[Any] =convert_torch_checkpoint(UpperCamelCase__, UpperCamelCase__ )
model.load_state_dict(UpperCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] ={
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
# prepare image
SCREAMING_SNAKE_CASE__ : List[Any] =prepare_img()
SCREAMING_SNAKE_CASE__ : Any =2_5_6
SCREAMING_SNAKE_CASE__ : str =2_2_4
SCREAMING_SNAKE_CASE__ : int =EfficientFormerImageProcessor(
size={'''shortest_edge''': image_size}, crop_size={'''height''': crop_size, '''width''': crop_size}, resample=pillow_resamplings['''bicubic'''], )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =processor(images=UpperCamelCase__, return_tensors='''pt''' ).pixel_values
# original processing pipeline
SCREAMING_SNAKE_CASE__ : Union[str, Any] =Compose(
[
Resize(UpperCamelCase__, interpolation=pillow_resamplings['''bicubic'''] ),
CenterCrop(UpperCamelCase__ ),
ToTensor(),
Normalize(UpperCamelCase__, UpperCamelCase__ ),
] )
SCREAMING_SNAKE_CASE__ : Dict =image_transforms(UpperCamelCase__ ).unsqueeze(0 )
assert torch.allclose(UpperCamelCase__, UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : str =model(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : str =outputs.logits
SCREAMING_SNAKE_CASE__ : Tuple =(1, 1_0_0_0)
if "l1" in model_name:
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.Tensor(
[-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8] )
assert torch.allclose(logits[0, :1_0], UpperCamelCase__, atol=1e-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
SCREAMING_SNAKE_CASE__ : Optional[Any] =torch.Tensor(
[-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7] )
assert torch.allclose(logits[0, :1_0], UpperCamelCase__, atol=1e-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
SCREAMING_SNAKE_CASE__ : int =torch.Tensor(
[-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8] )
assert logits.shape == expected_shape
else:
raise ValueError(
f"Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7" )
# Save Checkpoints
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
model.save_pretrained(UpperCamelCase__ )
print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}" )
processor.save_pretrained(UpperCamelCase__ )
print(f"Processor successfuly saved at {pytorch_dump_path}" )
if push_to_hub:
print('''Pushing model to the hub...''' )
model.push_to_hub(
repo_id=f"Bearnardd/{pytorch_dump_path}", commit_message='''Add model''', use_temp_dir=UpperCamelCase__, )
processor.push_to_hub(
repo_id=f"Bearnardd/{pytorch_dump_path}", commit_message='''Add image processor''', use_temp_dir=UpperCamelCase__, )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--pytorch_model_path',
default=None,
type=str,
required=True,
help='Path to EfficientFormer pytorch checkpoint.',
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The json file for EfficientFormer model config.',
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
parser.add_argument(
'--no-push_to_hub',
dest='push_to_hub',
action='store_false',
help='Do not push model and image processor to the hub',
)
parser.set_defaults(push_to_hub=True)
a_ = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
)
| 700 |
'''simple docstring'''
from math import isqrt
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =[True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2, UpperCamelCase__, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Any =False
return [i for i in range(2, UpperCamelCase__ ) if is_prime[i]]
def _a( UpperCamelCase__ : int = 1_0**8 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple =calculate_prime_numbers(max_number // 2 )
SCREAMING_SNAKE_CASE__ : int =0
SCREAMING_SNAKE_CASE__ : int =0
SCREAMING_SNAKE_CASE__ : Optional[int] =len(UpperCamelCase__ ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F'''{solution() = }''')
| 665 | 0 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class __SCREAMING_SNAKE_CASE ( nn.Module ):
snake_case_ = 42
snake_case_ = jnp.floataa
def __magic_name__ ( self : List[str] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : List[Any] =nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : Dict , __lowercase : Tuple ) -> Dict:
SCREAMING_SNAKE_CASE__ : List[Any] =hidden_states.shape
SCREAMING_SNAKE_CASE__ : Optional[Any] =jax.image.resize(
__lowercase , shape=(batch, height * 2, width * 2, channels) , method='''nearest''' , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.conv(__lowercase )
return hidden_states
class __SCREAMING_SNAKE_CASE ( nn.Module ):
snake_case_ = 42
snake_case_ = jnp.floataa
def __magic_name__ ( self : List[str] ) -> Any:
SCREAMING_SNAKE_CASE__ : Optional[Any] =nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : Optional[Any] , __lowercase : Optional[int] ) -> Optional[Any]:
# pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim
# hidden_states = jnp.pad(hidden_states, pad_width=pad)
SCREAMING_SNAKE_CASE__ : int =self.conv(__lowercase )
return hidden_states
class __SCREAMING_SNAKE_CASE ( nn.Module ):
snake_case_ = 42
snake_case_ = None
snake_case_ = 0.0
snake_case_ = None
snake_case_ = jnp.floataa
def __magic_name__ ( self : int ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.in_channels if self.out_channels is None else self.out_channels
SCREAMING_SNAKE_CASE__ : Optional[int] =nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
SCREAMING_SNAKE_CASE__ : str =nn.Conv(
__lowercase , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =nn.Dense(__lowercase , dtype=self.dtype )
SCREAMING_SNAKE_CASE__ : str =nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
SCREAMING_SNAKE_CASE__ : str =nn.Dropout(self.dropout_prob )
SCREAMING_SNAKE_CASE__ : Optional[int] =nn.Conv(
__lowercase , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
SCREAMING_SNAKE_CASE__ : str =self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
SCREAMING_SNAKE_CASE__ : Any =None
if use_nin_shortcut:
SCREAMING_SNAKE_CASE__ : List[Any] =nn.Conv(
__lowercase , kernel_size=(1, 1) , strides=(1, 1) , padding='''VALID''' , dtype=self.dtype , )
def __call__( self : int , __lowercase : List[Any] , __lowercase : Dict , __lowercase : List[Any]=True ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : List[str] =hidden_states
SCREAMING_SNAKE_CASE__ : List[str] =self.norma(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =nn.swish(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =self.conva(__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =self.time_emb_proj(nn.swish(__lowercase ) )
SCREAMING_SNAKE_CASE__ : int =jnp.expand_dims(jnp.expand_dims(__lowercase , 1 ) , 1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =hidden_states + temb
SCREAMING_SNAKE_CASE__ : Dict =self.norma(__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =nn.swish(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.dropout(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =self.conva(__lowercase )
if self.conv_shortcut is not None:
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.conv_shortcut(__lowercase )
return hidden_states + residual
| 701 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
a_ = get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = SpeechTaTokenizer
snake_case_ = False
snake_case_ = True
def __magic_name__ ( self : int ) -> Any:
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : Optional[Any] =SpeechTaTokenizer(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =AddedToken('''<mask>''' , lstrip=__lowercase , rstrip=__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =mask_token
tokenizer.add_special_tokens({'''mask_token''': mask_token} )
tokenizer.add_tokens(['''<ctc_blank>'''] )
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__ ( self : Dict , __lowercase : int ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Optional[Any] ='''this is a test'''
SCREAMING_SNAKE_CASE__ : int ='''this is a test'''
return input_text, output_text
def __magic_name__ ( self : List[Any] , __lowercase : int , __lowercase : Optional[Any]=False , __lowercase : Union[str, Any]=20 , __lowercase : Any=5 ) -> Any:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int =self.get_input_output_texts(__lowercase )
SCREAMING_SNAKE_CASE__ : str =tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
SCREAMING_SNAKE_CASE__ : str =tokenizer.decode(__lowercase , clean_up_tokenization_spaces=__lowercase )
return text, ids
def __magic_name__ ( self : Dict ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] ='''<pad>'''
SCREAMING_SNAKE_CASE__ : Optional[int] =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowercase ) , __lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowercase ) , __lowercase )
def __magic_name__ ( self : Tuple ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-4] , '''œ''' )
self.assertEqual(vocab_keys[-2] , '''<mask>''' )
self.assertEqual(vocab_keys[-1] , '''<ctc_blank>''' )
self.assertEqual(len(__lowercase ) , 81 )
def __magic_name__ ( self : Dict ) -> List[str]:
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def __magic_name__ ( self : Optional[Any] ) -> str:
SCREAMING_SNAKE_CASE__ : str =self.get_tokenizers(do_lower_case=__lowercase )
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
SCREAMING_SNAKE_CASE__ : Tuple =tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : Any =len(__lowercase )
self.assertNotEqual(__lowercase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
SCREAMING_SNAKE_CASE__ : int =['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.add_tokens(__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] =len(__lowercase )
self.assertNotEqual(__lowercase , 0 )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , len(__lowercase ) )
self.assertEqual(__lowercase , all_size + len(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Tuple =tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l''' , add_special_tokens=__lowercase )
self.assertGreaterEqual(len(__lowercase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
SCREAMING_SNAKE_CASE__ : str ={'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
SCREAMING_SNAKE_CASE__ : int =tokenizer.add_special_tokens(__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : int =len(__lowercase )
self.assertNotEqual(__lowercase , 0 )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , len(__lowercase ) )
self.assertEqual(__lowercase , all_size_a + len(__lowercase ) )
SCREAMING_SNAKE_CASE__ : List[Any] =tokenizer.encode(
'''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l''' , add_special_tokens=__lowercase )
self.assertGreaterEqual(len(__lowercase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def __magic_name__ ( self : Optional[Any] ) -> Any:
pass
def __magic_name__ ( self : List[str] ) -> List[Any]:
pass
def __magic_name__ ( self : Dict ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Dict =self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer.tokenize('''This is a test''' )
# fmt: off
self.assertListEqual(__lowercase , [SPIECE_UNDERLINE, '''T''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''a''', SPIECE_UNDERLINE, '''t''', '''e''', '''s''', '''t'''] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowercase ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__lowercase , [SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''92000''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =tokenizer.convert_tokens_to_ids(__lowercase )
# fmt: off
self.assertListEqual(__lowercase , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer.convert_ids_to_tokens(__lowercase )
self.assertListEqual(
__lowercase , [SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''<unk>''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''] )
@slow
def __magic_name__ ( self : List[str] ) -> List[str]:
# Use custom sequence because this tokenizer does not handle numbers.
SCREAMING_SNAKE_CASE__ : List[Any] =[
'''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
'''general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
'''Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained '''
'''models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
'''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
'''conditioning on both left and right context in all layers.''',
'''The quick brown fox jumps over the lazy dog.''',
]
# fmt: off
SCREAMING_SNAKE_CASE__ : str ={
'''input_ids''': [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
],
'''attention_mask''': [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__lowercase , model_name='''microsoft/speecht5_asr''' , revision='''c5ef64c71905caeccde0e4462ef3f9077224c524''' , sequences=__lowercase , )
| 665 | 0 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _a( UpperCamelCase__ : Dict ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple =[
'''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 _a( UpperCamelCase__ : int ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str =emb.weight.shape
SCREAMING_SNAKE_CASE__ : int =nn.Linear(UpperCamelCase__, UpperCamelCase__, bias=UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] =emb.weight.data
return lin_layer
def _a( UpperCamelCase__ : int ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =torch.load(UpperCamelCase__, map_location='''cpu''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] =mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
SCREAMING_SNAKE_CASE__ : int =mam_aaa['''model''']
remove_ignore_keys_(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[str] =state_dict['''encoder.embed_tokens.weight'''].shape[0]
SCREAMING_SNAKE_CASE__ : List[str] =MaMaaaConfig(
vocab_size=UpperCamelCase__, max_position_embeddings=1_0_2_4, 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, encoder_layerdrop=args.encoder_layerdrop, decoder_layerdrop=args.decoder_layerdrop, dropout=args.dropout, attention_dropout=args.attention_dropout, activation_dropout=args.activation_dropout, activation_function='''relu''', )
SCREAMING_SNAKE_CASE__ : Tuple =state_dict['''decoder.embed_tokens.weight''']
SCREAMING_SNAKE_CASE__ : str =MaMaaaForConditionalGeneration(UpperCamelCase__ )
model.model.load_state_dict(UpperCamelCase__, strict=UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Dict =make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
a_ = parser.parse_args()
a_ = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 702 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , __lowercase : Optional[int] , __lowercase : str=13 , __lowercase : int=10 , __lowercase : List[Any]=3 , __lowercase : List[str]=2 , __lowercase : int=2 , __lowercase : Dict=True , __lowercase : Optional[Any]=True , __lowercase : int=32 , __lowercase : List[Any]=5 , __lowercase : Union[str, Any]=4 , __lowercase : Any=37 , __lowercase : Optional[Any]="gelu" , __lowercase : Union[str, Any]=0.1 , __lowercase : Tuple=0.1 , __lowercase : Dict=10 , __lowercase : int=0.02 , __lowercase : str="divided_space_time" , __lowercase : Union[str, Any]=None , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : int =parent
SCREAMING_SNAKE_CASE__ : List[str] =batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =image_size
SCREAMING_SNAKE_CASE__ : List[Any] =num_channels
SCREAMING_SNAKE_CASE__ : int =patch_size
SCREAMING_SNAKE_CASE__ : Tuple =num_frames
SCREAMING_SNAKE_CASE__ : List[Any] =is_training
SCREAMING_SNAKE_CASE__ : List[str] =use_labels
SCREAMING_SNAKE_CASE__ : Optional[Any] =hidden_size
SCREAMING_SNAKE_CASE__ : str =num_hidden_layers
SCREAMING_SNAKE_CASE__ : int =num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple =intermediate_size
SCREAMING_SNAKE_CASE__ : List[str] =hidden_act
SCREAMING_SNAKE_CASE__ : Dict =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[Any] =attention_type
SCREAMING_SNAKE_CASE__ : Union[str, Any] =initializer_range
SCREAMING_SNAKE_CASE__ : Any =scope
SCREAMING_SNAKE_CASE__ : int =num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
SCREAMING_SNAKE_CASE__ : List[str] =(image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE__ : str =(num_frames) * self.num_patches_per_frame + 1
def __magic_name__ ( self : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : List[Any] =None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Any =ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE__ : int =self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : int ) -> Any:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , 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 , initializer_range=self.initializer_range , attention_type=self.attention_type , )
SCREAMING_SNAKE_CASE__ : List[Any] =self.num_labels
return config
def __magic_name__ ( self : int , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : List[Any] ) -> int:
SCREAMING_SNAKE_CASE__ : Tuple =TimesformerModel(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] =model(__lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : List[str] , __lowercase : str , __lowercase : Tuple , __lowercase : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : str =TimesformerForVideoClassification(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(__lowercase )
# verify the logits shape
SCREAMING_SNAKE_CASE__ : Tuple =torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , __lowercase )
def __magic_name__ ( self : Any ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : int =self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str =config_and_inputs
SCREAMING_SNAKE_CASE__ : Any ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
snake_case_ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
snake_case_ = (
{"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def __magic_name__ ( self : str ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Dict =TimesformerModelTester(self )
SCREAMING_SNAKE_CASE__ : List[Any] =ConfigTester(
self , config_class=__lowercase , has_text_modality=__lowercase , hidden_size=37 )
def __magic_name__ ( self : str , __lowercase : Dict , __lowercase : str , __lowercase : Optional[int]=False ) -> int:
SCREAMING_SNAKE_CASE__ : str =copy.deepcopy(__lowercase )
if return_labels:
if model_class in get_values(__lowercase ):
SCREAMING_SNAKE_CASE__ : Any =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowercase )
return inputs_dict
def __magic_name__ ( self : List[Any] ) -> Any:
self.config_tester.run_common_tests()
@unittest.skip(reason='''TimeSformer does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ) -> Optional[int]:
pass
def __magic_name__ ( self : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : str =model_class(__lowercase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE__ : Any =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__lowercase , nn.Linear ) )
def __magic_name__ ( self : Any ) -> Any:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Optional[Any] =model_class(__lowercase )
SCREAMING_SNAKE_CASE__ : str =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE__ : List[str] =[*signature.parameters.keys()]
SCREAMING_SNAKE_CASE__ : Dict =['''pixel_values''']
self.assertListEqual(arg_names[:1] , __lowercase )
def __magic_name__ ( self : int ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase )
def __magic_name__ ( self : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*__lowercase )
@slow
def __magic_name__ ( self : Optional[int] ) -> Optional[Any]:
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =TimesformerModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> List[str]:
if not self.has_attentions:
pass
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[Any] =True
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.seq_length
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.num_frames
SCREAMING_SNAKE_CASE__ : Optional[Any] =True
SCREAMING_SNAKE_CASE__ : str =False
SCREAMING_SNAKE_CASE__ : Tuple =True
SCREAMING_SNAKE_CASE__ : Dict =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(**self._prepare_for_class(__lowercase , __lowercase ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =outputs.attentions
self.assertEqual(len(__lowercase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
SCREAMING_SNAKE_CASE__ : List[Any] =True
SCREAMING_SNAKE_CASE__ : List[str] =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Any =model(**self._prepare_for_class(__lowercase , __lowercase ) )
SCREAMING_SNAKE_CASE__ : List[Any] =outputs.attentions
self.assertEqual(len(__lowercase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
SCREAMING_SNAKE_CASE__ : Optional[int] =len(__lowercase )
# Check attention is always last and order is fine
SCREAMING_SNAKE_CASE__ : Optional[int] =True
SCREAMING_SNAKE_CASE__ : Union[str, Any] =True
SCREAMING_SNAKE_CASE__ : Optional[Any] =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(**self._prepare_for_class(__lowercase , __lowercase ) )
self.assertEqual(out_len + 1 , len(__lowercase ) )
SCREAMING_SNAKE_CASE__ : List[str] =outputs.attentions
self.assertEqual(len(__lowercase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def __magic_name__ ( self : Tuple ) -> List[Any]:
def check_hidden_states_output(__lowercase : Tuple , __lowercase : Dict , __lowercase : Optional[int] ):
SCREAMING_SNAKE_CASE__ : List[Any] =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : int =model(**self._prepare_for_class(__lowercase , __lowercase ) )
SCREAMING_SNAKE_CASE__ : List[Any] =outputs.hidden_states
SCREAMING_SNAKE_CASE__ : Tuple =self.model_tester.num_hidden_layers + 1
self.assertEqual(len(__lowercase ) , __lowercase )
SCREAMING_SNAKE_CASE__ : int =self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Tuple =True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE__ : List[str] =True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''', filename='''eating_spaghetti.npy''', repo_type='''dataset''' )
SCREAMING_SNAKE_CASE__ : Any =np.load(UpperCamelCase__ )
return list(UpperCamelCase__ )
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : Any ) -> List[str]:
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def __magic_name__ ( self : Any ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : int =TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400''' ).to(
__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =self.default_image_processor
SCREAMING_SNAKE_CASE__ : Tuple =prepare_video()
SCREAMING_SNAKE_CASE__ : Any =image_processor(video[:8] , return_tensors='''pt''' ).to(__lowercase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Optional[int] =model(**__lowercase )
# verify the logits
SCREAMING_SNAKE_CASE__ : List[str] =torch.Size((1, 4_00) )
self.assertEqual(outputs.logits.shape , __lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =torch.tensor([-0.3016, -0.7713, -0.4205] ).to(__lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowercase , atol=1e-4 ) )
| 665 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : str, UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Tuple =[[False for _ in range(m + 1 )] for _ in range(n + 1 )]
SCREAMING_SNAKE_CASE__ : List[Any] =True
for i in range(UpperCamelCase__ ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
SCREAMING_SNAKE_CASE__ : Optional[int] =True
if a[i].islower():
SCREAMING_SNAKE_CASE__ : List[Any] =True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 703 |
'''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_bert import BertTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
a_ = {
'vocab_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
),
'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
),
'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
),
'bert-base-multilingual-cased': (
'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
),
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-cased': (
'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
),
},
}
a_ = {
'bert-base-uncased': 5_1_2,
'bert-large-uncased': 5_1_2,
'bert-base-cased': 5_1_2,
'bert-large-cased': 5_1_2,
'bert-base-multilingual-uncased': 5_1_2,
'bert-base-multilingual-cased': 5_1_2,
'bert-base-chinese': 5_1_2,
'bert-base-german-cased': 5_1_2,
'bert-large-uncased-whole-word-masking': 5_1_2,
'bert-large-cased-whole-word-masking': 5_1_2,
'bert-large-uncased-whole-word-masking-finetuned-squad': 5_1_2,
'bert-large-cased-whole-word-masking-finetuned-squad': 5_1_2,
'bert-base-cased-finetuned-mrpc': 5_1_2,
'bert-base-german-dbmdz-cased': 5_1_2,
'bert-base-german-dbmdz-uncased': 5_1_2,
'TurkuNLP/bert-base-finnish-cased-v1': 5_1_2,
'TurkuNLP/bert-base-finnish-uncased-v1': 5_1_2,
'wietsedv/bert-base-dutch-cased': 5_1_2,
}
a_ = {
'bert-base-uncased': {'do_lower_case': True},
'bert-large-uncased': {'do_lower_case': True},
'bert-base-cased': {'do_lower_case': False},
'bert-large-cased': {'do_lower_case': False},
'bert-base-multilingual-uncased': {'do_lower_case': True},
'bert-base-multilingual-cased': {'do_lower_case': False},
'bert-base-chinese': {'do_lower_case': False},
'bert-base-german-cased': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
'bert-large-cased-whole-word-masking': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
'bert-base-german-dbmdz-cased': {'do_lower_case': False},
'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_INIT_CONFIGURATION
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = BertTokenizer
def __init__( self : int , __lowercase : Union[str, Any]=None , __lowercase : Tuple=None , __lowercase : str=True , __lowercase : Optional[Any]="[UNK]" , __lowercase : Tuple="[SEP]" , __lowercase : Any="[PAD]" , __lowercase : List[Any]="[CLS]" , __lowercase : Union[str, Any]="[MASK]" , __lowercase : Tuple=True , __lowercase : str=None , **__lowercase : Any , ) -> Optional[Any]:
super().__init__(
__lowercase , tokenizer_file=__lowercase , do_lower_case=__lowercase , unk_token=__lowercase , sep_token=__lowercase , pad_token=__lowercase , cls_token=__lowercase , mask_token=__lowercase , tokenize_chinese_chars=__lowercase , strip_accents=__lowercase , **__lowercase , )
SCREAMING_SNAKE_CASE__ : str =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __lowercase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __lowercase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __lowercase ) != tokenize_chinese_chars
):
SCREAMING_SNAKE_CASE__ : int =getattr(__lowercase , normalizer_state.pop('''type''' ) )
SCREAMING_SNAKE_CASE__ : Any =do_lower_case
SCREAMING_SNAKE_CASE__ : Any =strip_accents
SCREAMING_SNAKE_CASE__ : Dict =tokenize_chinese_chars
SCREAMING_SNAKE_CASE__ : Union[str, Any] =normalizer_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =do_lower_case
def __magic_name__ ( self : int , __lowercase : Optional[Any] , __lowercase : Union[str, Any]=None ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] =[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 __magic_name__ ( self : Union[str, Any] , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ) -> List[int]:
SCREAMING_SNAKE_CASE__ : List[str] =[self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] =[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 __magic_name__ ( self : List[Any] , __lowercase : str , __lowercase : Optional[str] = None ) -> Tuple[str]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self._tokenizer.model.save(__lowercase , name=__lowercase )
return tuple(__lowercase )
| 665 | 0 |
'''simple docstring'''
import cva
import numpy as np
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , __lowercase : float , __lowercase : int ) -> Any:
if k in (0.04, 0.06):
SCREAMING_SNAKE_CASE__ : Dict =k
SCREAMING_SNAKE_CASE__ : List[str] =window_size
else:
raise ValueError('''invalid k value''' )
def __str__( self : Tuple ) -> str:
return str(self.k )
def __magic_name__ ( self : List[str] , __lowercase : str ) -> tuple[cva.Mat, list[list[int]]]:
SCREAMING_SNAKE_CASE__ : str =cva.imread(__lowercase , 0 )
SCREAMING_SNAKE_CASE__ : Dict =img.shape
SCREAMING_SNAKE_CASE__ : list[list[int]] =[]
SCREAMING_SNAKE_CASE__ : int =img.copy()
SCREAMING_SNAKE_CASE__ : Optional[int] =cva.cvtColor(__lowercase , cva.COLOR_GRAY2RGB )
SCREAMING_SNAKE_CASE__ : List[str] =np.gradient(__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =dx**2
SCREAMING_SNAKE_CASE__ : List[str] =dy**2
SCREAMING_SNAKE_CASE__ : Optional[Any] =dx * dy
SCREAMING_SNAKE_CASE__ : Optional[int] =0.04
SCREAMING_SNAKE_CASE__ : Tuple =self.window_size // 2
for y in range(__lowercase , h - offset ):
for x in range(__lowercase , w - offset ):
SCREAMING_SNAKE_CASE__ : Dict =ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
SCREAMING_SNAKE_CASE__ : Dict =iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
SCREAMING_SNAKE_CASE__ : Optional[Any] =(wxx * wyy) - (wxy**2)
SCREAMING_SNAKE_CASE__ : Optional[Any] =wxx + wyy
SCREAMING_SNAKE_CASE__ : Optional[int] =det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) , 0 )
color_img.itemset((y, x, 1) , 0 )
color_img.itemset((y, x, 2) , 2_55 )
return color_img, corner_list
if __name__ == "__main__":
a_ = HarrisCorner(0.04, 3)
a_ , a_ = edge_detect.detect('path_to_image')
cva.imwrite('detect.png', color_img)
| 704 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')
# TF training parameters
a_ = False
a_ = False
def _a( UpperCamelCase__ : Namespace ):
'''simple docstring'''
return TrainCommand(UpperCamelCase__ )
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
@staticmethod
def __magic_name__ ( __lowercase : ArgumentParser ) -> Any:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =parser.add_parser('''train''' , help='''CLI tool to train a model on a task.''' )
train_parser.add_argument(
'''--train_data''' , type=__lowercase , required=__lowercase , help='''path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.''' , )
train_parser.add_argument(
'''--column_label''' , type=__lowercase , default=0 , help='''Column of the dataset csv file with example labels.''' )
train_parser.add_argument(
'''--column_text''' , type=__lowercase , default=1 , help='''Column of the dataset csv file with example texts.''' )
train_parser.add_argument(
'''--column_id''' , type=__lowercase , default=2 , help='''Column of the dataset csv file with example ids.''' )
train_parser.add_argument(
'''--skip_first_row''' , action='''store_true''' , help='''Skip the first row of the csv file (headers).''' )
train_parser.add_argument('''--validation_data''' , type=__lowercase , default='''''' , help='''path to validation dataset.''' )
train_parser.add_argument(
'''--validation_split''' , type=__lowercase , default=0.1 , help='''if validation dataset is not provided, fraction of train dataset to use as validation dataset.''' , )
train_parser.add_argument('''--output''' , type=__lowercase , default='''./''' , help='''path to saved the trained model.''' )
train_parser.add_argument(
'''--task''' , type=__lowercase , default='''text_classification''' , help='''Task to train the model on.''' )
train_parser.add_argument(
'''--model''' , type=__lowercase , default='''bert-base-uncased''' , help='''Model\'s name or path to stored model.''' )
train_parser.add_argument('''--train_batch_size''' , type=__lowercase , default=32 , help='''Batch size for training.''' )
train_parser.add_argument('''--valid_batch_size''' , type=__lowercase , default=64 , help='''Batch size for validation.''' )
train_parser.add_argument('''--learning_rate''' , type=__lowercase , default=3e-5 , help='''Learning rate.''' )
train_parser.add_argument('''--adam_epsilon''' , type=__lowercase , default=1e-08 , help='''Epsilon for Adam optimizer.''' )
train_parser.set_defaults(func=__lowercase )
def __init__( self : Tuple , __lowercase : Namespace ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Tuple =logging.get_logger('''transformers-cli/training''' )
SCREAMING_SNAKE_CASE__ : int ='''tf''' if is_tf_available() else '''torch'''
os.makedirs(args.output , exist_ok=__lowercase )
SCREAMING_SNAKE_CASE__ : Any =args.output
SCREAMING_SNAKE_CASE__ : str =args.column_label
SCREAMING_SNAKE_CASE__ : List[Any] =args.column_text
SCREAMING_SNAKE_CASE__ : Tuple =args.column_id
self.logger.info(F"Loading {args.task} pipeline for {args.model}" )
if args.task == "text_classification":
SCREAMING_SNAKE_CASE__ : List[str] =TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(F"Loading dataset from {args.train_data}" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =None
if args.validation_data:
self.logger.info(F"Loading validation dataset from {args.validation_data}" )
SCREAMING_SNAKE_CASE__ : List[Any] =Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =args.validation_split
SCREAMING_SNAKE_CASE__ : List[Any] =args.train_batch_size
SCREAMING_SNAKE_CASE__ : Any =args.valid_batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =args.learning_rate
SCREAMING_SNAKE_CASE__ : int =args.adam_epsilon
def __magic_name__ ( self : Any ) -> str:
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def __magic_name__ ( self : Optional[int] ) -> Tuple:
raise NotImplementedError
def __magic_name__ ( self : Dict ) -> List[Any]:
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 665 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def _a( UpperCamelCase__ : Any, UpperCamelCase__ : Tuple=1_0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =[]
for _ in range(UpperCamelCase__ ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def _a( UpperCamelCase__ : Optional[int], UpperCamelCase__ : Union[str, Any]=1_0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str =[]
for step in range(UpperCamelCase__ ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE__ : str =os.path.join(UpperCamelCase__, '''schedule.bin''' )
torch.save(scheduler.state_dict(), UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Dict =torch.load(UpperCamelCase__ )
scheduler.load_state_dict(UpperCamelCase__ )
return lrs
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : Optional[int] , __lowercase : Union[str, Any] , __lowercase : Optional[int] , __lowercase : List[str] ) -> int:
self.assertEqual(len(__lowercase ) , len(__lowercase ) )
for a, b in zip(__lowercase , __lowercase ):
self.assertAlmostEqual(__lowercase , __lowercase , delta=__lowercase )
def __magic_name__ ( self : Optional[int] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =torch.tensor([0.1, -0.2, -0.1] , requires_grad=__lowercase )
SCREAMING_SNAKE_CASE__ : Any =torch.tensor([0.4, 0.2, -0.5] )
SCREAMING_SNAKE_CASE__ : Dict =nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
SCREAMING_SNAKE_CASE__ : Optional[int] =AdamW(params=[w] , lr=2e-1 , weight_decay=0.0 )
for _ in range(1_00 ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] =criterion(__lowercase , __lowercase )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1e-2 )
def __magic_name__ ( self : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[str] =torch.tensor([0.1, -0.2, -0.1] , requires_grad=__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch.tensor([0.4, 0.2, -0.5] )
SCREAMING_SNAKE_CASE__ : List[str] =nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
SCREAMING_SNAKE_CASE__ : Dict =Adafactor(
params=[w] , lr=1e-2 , eps=(1e-30, 1e-3) , clip_threshold=1.0 , decay_rate=-0.8 , betaa=__lowercase , weight_decay=0.0 , relative_step=__lowercase , scale_parameter=__lowercase , warmup_init=__lowercase , )
for _ in range(10_00 ):
SCREAMING_SNAKE_CASE__ : List[Any] =criterion(__lowercase , __lowercase )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1e-2 )
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case_ = nn.Linear(50 , 50 ) if is_torch_available() else None
snake_case_ = AdamW(m.parameters() , lr=10.0 ) if is_torch_available() else None
snake_case_ = 10
def __magic_name__ ( self : List[Any] , __lowercase : Optional[Any] , __lowercase : List[Any] , __lowercase : List[Any] , __lowercase : Optional[int]=None ) -> Union[str, Any]:
self.assertEqual(len(__lowercase ) , len(__lowercase ) )
for a, b in zip(__lowercase , __lowercase ):
self.assertAlmostEqual(__lowercase , __lowercase , delta=__lowercase , msg=__lowercase )
def __magic_name__ ( self : List[Any] ) -> str:
SCREAMING_SNAKE_CASE__ : int ={'''num_warmup_steps''': 2, '''num_training_steps''': 10}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
SCREAMING_SNAKE_CASE__ : Any ={
get_constant_schedule: ({}, [10.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{'''num_warmup_steps''': 4},
[0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, '''num_cycles''': 2},
[0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, '''power''': 2.0, '''lr_end''': 1e-7},
[0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
),
get_inverse_sqrt_schedule: (
{'''num_warmup_steps''': 2},
[0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
),
}
for scheduler_func, data in scheds.items():
SCREAMING_SNAKE_CASE__ : Optional[int] =data
SCREAMING_SNAKE_CASE__ : List[Any] =scheduler_func(self.optimizer , **__lowercase )
self.assertEqual(len([scheduler.get_lr()[0]] ) , 1 )
SCREAMING_SNAKE_CASE__ : List[str] =unwrap_schedule(__lowercase , self.num_steps )
self.assertListAlmostEqual(
__lowercase , __lowercase , tol=1e-2 , msg=F"failed for {scheduler_func} in normal scheduler" , )
SCREAMING_SNAKE_CASE__ : str =scheduler_func(self.optimizer , **__lowercase )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(__lowercase ) # wrap to test picklability of the schedule
SCREAMING_SNAKE_CASE__ : Union[str, Any] =unwrap_and_save_reload_schedule(__lowercase , self.num_steps )
self.assertListEqual(__lowercase , __lowercase , msg=F"failed for {scheduler_func} in save and reload" )
class __SCREAMING_SNAKE_CASE :
def __init__( self : str , __lowercase : Any ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : str =fn
def __call__( self : Union[str, Any] , *__lowercase : Any , **__lowercase : Optional[Any] ) -> int:
return self.fn(*__lowercase , **__lowercase )
@classmethod
def __magic_name__ ( self : Tuple , __lowercase : Dict ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =list(map(self , scheduler.lr_lambdas ) )
| 705 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = KandinskyVaaImgaImgPipeline
snake_case_ = ["""image_embeds""", """negative_image_embeds""", """image"""]
snake_case_ = [
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
snake_case_ = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
snake_case_ = False
@property
def __magic_name__ ( self : List[str] ) -> Tuple:
return 32
@property
def __magic_name__ ( self : List[str] ) -> str:
return 32
@property
def __magic_name__ ( self : Any ) -> Optional[int]:
return self.time_input_dim
@property
def __magic_name__ ( self : List[Any] ) -> int:
return self.time_input_dim * 4
@property
def __magic_name__ ( self : Tuple ) -> Optional[int]:
return 1_00
@property
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] ={
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
SCREAMING_SNAKE_CASE__ : Optional[int] =UNetaDConditionModel(**__lowercase )
return model
@property
def __magic_name__ ( self : Dict ) -> Any:
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __magic_name__ ( self : Tuple ) -> Optional[Any]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] =VQModel(**self.dummy_movq_kwargs )
return model
def __magic_name__ ( self : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[str] =self.dummy_unet
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dummy_movq
SCREAMING_SNAKE_CASE__ : Optional[Any] ={
'''num_train_timesteps''': 10_00,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
SCREAMING_SNAKE_CASE__ : str =DDIMScheduler(**__lowercase )
SCREAMING_SNAKE_CASE__ : Any ={
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __magic_name__ ( self : str , __lowercase : Optional[Any] , __lowercase : Any=0 ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[int] =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__lowercase ) ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__lowercase )
# create init_image
SCREAMING_SNAKE_CASE__ : Optional[Any] =floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowercase ) ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE__ : Any =Image.fromarray(np.uinta(__lowercase ) ).convert('''RGB''' ).resize((2_56, 2_56) )
if str(__lowercase ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE__ : Dict =torch.manual_seed(__lowercase )
else:
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device=__lowercase ).manual_seed(__lowercase )
SCREAMING_SNAKE_CASE__ : str ={
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def __magic_name__ ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[Any] ='''cpu'''
SCREAMING_SNAKE_CASE__ : Tuple =self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Dict =self.pipeline_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =pipe(**self.get_dummy_inputs(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Tuple =output.images
SCREAMING_SNAKE_CASE__ : Union[str, Any] =pipe(
**self.get_dummy_inputs(__lowercase ) , return_dict=__lowercase , )[0]
SCREAMING_SNAKE_CASE__ : List[Any] =image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : List[str] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE__ : Tuple =np.array(
[0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : int ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : str =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
SCREAMING_SNAKE_CASE__ : List[Any] ='''A red cartoon frog, 4k'''
SCREAMING_SNAKE_CASE__ : Optional[int] =KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =KandinskyVaaImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : Dict =pipeline.to(__lowercase )
pipeline.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device='''cpu''' ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] =pipe_prior(
__lowercase , generator=__lowercase , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
image=__lowercase , image_embeds=__lowercase , negative_image_embeds=__lowercase , generator=__lowercase , num_inference_steps=1_00 , height=7_68 , width=7_68 , strength=0.2 , output_type='''np''' , )
SCREAMING_SNAKE_CASE__ : int =output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(__lowercase , __lowercase )
| 665 | 0 |
'''simple docstring'''
from __future__ import annotations
a_ = 1.6021E-19 # units = C
def _a( UpperCamelCase__ : float, UpperCamelCase__ : float, UpperCamelCase__ : float, ):
'''simple docstring'''
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif conductivity < 0:
raise ValueError('''Conductivity cannot be negative''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative''' )
elif mobility < 0:
raise ValueError('''mobility cannot be negative''' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 706 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
a_ = TypeVar('T')
class __SCREAMING_SNAKE_CASE ( Generic[T] ):
snake_case_ = 42 # Cache store of keys
snake_case_ = 42 # References of the keys in cache
snake_case_ = 10 # Maximum capacity of cache
def __init__( self : Dict , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : Any =deque()
SCREAMING_SNAKE_CASE__ : str =set()
if not n:
SCREAMING_SNAKE_CASE__ : Optional[Any] =sys.maxsize
elif n < 0:
raise ValueError('''n should be an integer greater than 0.''' )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =n
def __magic_name__ ( self : List[str] , __lowercase : T ) -> None:
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
SCREAMING_SNAKE_CASE__ : int =self.dq_store.pop()
self.key_reference.remove(__lowercase )
else:
self.dq_store.remove(__lowercase )
self.dq_store.appendleft(__lowercase )
self.key_reference.add(__lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> None:
for k in self.dq_store:
print(__lowercase )
def __repr__( self : List[Any] ) -> str:
return F"LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}"
if __name__ == "__main__":
import doctest
doctest.testmod()
a_ = LRUCache(4)
lru_cache.refer('A')
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer('A')
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 665 | 0 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
a_ = True
except (ImportError, ModuleNotFoundError):
a_ = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
re.sub('''<n>''', '''''', UpperCamelCase__ ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(UpperCamelCase__ ) )
| 707 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a_ = list[list[float | int]]
def _a( UpperCamelCase__ : Matrix, UpperCamelCase__ : Matrix ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Matrix =[[0 for _ in range(size + 1 )] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : float
for row in range(UpperCamelCase__ ):
for col in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Tuple =matrix[row][col]
SCREAMING_SNAKE_CASE__ : Optional[int] =vector[row][0]
SCREAMING_SNAKE_CASE__ : Any =0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =0
while row < size and col < size:
# pivoting
SCREAMING_SNAKE_CASE__ : Any =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__ : Optional[Any] =augmented[pivot_row], augmented[row]
for rowa in range(row + 1, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] =augmented[rowa][col] / augmented[row][col]
SCREAMING_SNAKE_CASE__ : Tuple =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[Any] =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], 1_0 )] for row in range(UpperCamelCase__ )
]
def _a( UpperCamelCase__ : list[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Matrix =[[0 for _ in range(UpperCamelCase__ )] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Matrix =[[0] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Matrix
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
for x_val, y_val in enumerate(UpperCamelCase__ ):
for col in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[int] =(x_val + 1) ** (size - col - 1)
SCREAMING_SNAKE_CASE__ : Dict =y_val
SCREAMING_SNAKE_CASE__ : Optional[int] =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 ):
'''simple docstring'''
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**1_0
)
def _a( UpperCamelCase__ : Callable[[int], int] = question_function, UpperCamelCase__ : int = 1_0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : list[int] =[func(UpperCamelCase__ ) for x_val in range(1, order + 1 )]
SCREAMING_SNAKE_CASE__ : list[Callable[[int], int]] =[
interpolate(data_points[:max_coeff] ) for max_coeff in range(1, order + 1 )
]
SCREAMING_SNAKE_CASE__ : int =0
SCREAMING_SNAKE_CASE__ : Callable[[int], int]
SCREAMING_SNAKE_CASE__ : int
for poly in polynomials:
SCREAMING_SNAKE_CASE__ : Any =1
while func(UpperCamelCase__ ) == poly(UpperCamelCase__ ):
x_val += 1
ret += poly(UpperCamelCase__ )
return ret
if __name__ == "__main__":
print(F'''{solution() = }''')
| 665 | 0 |
'''simple docstring'''
from collections import defaultdict
def _a( UpperCamelCase__ : str, UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =first_str.lower().strip()
SCREAMING_SNAKE_CASE__ : Optional[Any] =second_str.lower().strip()
# Remove whitespace
SCREAMING_SNAKE_CASE__ : Tuple =first_str.replace(''' ''', '''''' )
SCREAMING_SNAKE_CASE__ : List[str] =second_str.replace(''' ''', '''''' )
# Strings of different lengths are not anagrams
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
return False
# Default values for count should be 0
SCREAMING_SNAKE_CASE__ : defaultdict[str, int] =defaultdict(UpperCamelCase__ )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(UpperCamelCase__ ) ):
count[first_str[i]] += 1
count[second_str[i]] -= 1
return all(_count == 0 for _count in count.values() )
if __name__ == "__main__":
from doctest import testmod
testmod()
a_ = input('Enter the first string ').strip()
a_ = input('Enter the second string ').strip()
a_ = check_anagrams(input_a, input_b)
print(F'''{input_a} and {input_b} are {'' if status else 'not '}anagrams.''')
| 708 |
'''simple docstring'''
def _a( UpperCamelCase__ : Optional[int], UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =len(UpperCamelCase__ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
SCREAMING_SNAKE_CASE__ : Union[str, Any] =left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCamelCase__ ):
return None
SCREAMING_SNAKE_CASE__ : Optional[int] =sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =left
SCREAMING_SNAKE_CASE__ : Optional[Any] =point
elif point > right:
SCREAMING_SNAKE_CASE__ : Optional[int] =right
SCREAMING_SNAKE_CASE__ : Tuple =point
else:
if item < current_item:
SCREAMING_SNAKE_CASE__ : str =point - 1
else:
SCREAMING_SNAKE_CASE__ : Tuple =point + 1
return None
def _a( UpperCamelCase__ : List[str], UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
SCREAMING_SNAKE_CASE__ : Dict =left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCamelCase__ ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
elif point > right:
return interpolation_search_by_recursion(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, point - 1 )
else:
return interpolation_search_by_recursion(
UpperCamelCase__, UpperCamelCase__, point + 1, UpperCamelCase__ )
def _a( UpperCamelCase__ : Dict ):
'''simple docstring'''
if collection != sorted(UpperCamelCase__ ):
raise ValueError('''Collection must be ascending sorted''' )
return True
if __name__ == "__main__":
import sys
a_ = 0
if debug == 1:
a_ = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
try:
__assert_sorted(collection)
except ValueError:
sys.exit('Sequence must be ascending sorted to apply interpolation search')
a_ = 6_7
a_ = interpolation_search(collection, target)
if result is not None:
print(F'''{target} found at positions: {result}''')
else:
print('Not found')
| 665 | 0 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
a_ = [
# tf -> hf
('/', '.'),
('layer_', 'layers.'),
('kernel', 'weight'),
('beta', 'bias'),
('gamma', 'weight'),
('pegasus', 'model'),
]
a_ = [
('.output.dense', '.fc2'),
('intermediate.LayerNorm', 'final_layer_norm'),
('intermediate.dense', 'fc1'),
]
a_ = (
INIT_COMMON
+ [
('attention.self.LayerNorm', 'self_attn_layer_norm'),
('attention.output.dense', 'self_attn.out_proj'),
('attention.self', 'self_attn'),
('attention.encdec.LayerNorm', 'encoder_attn_layer_norm'),
('attention.encdec_output.dense', 'encoder_attn.out_proj'),
('attention.encdec', 'encoder_attn'),
('key', 'k_proj'),
('value', 'v_proj'),
('query', 'q_proj'),
('decoder.LayerNorm', 'decoder.layernorm_embedding'),
]
+ END_COMMON
)
a_ = (
INIT_COMMON
+ [
('embeddings.word_embeddings', 'shared.weight'),
('embeddings.position_embeddings', 'embed_positions.weight'),
('attention.self.LayerNorm', 'self_attn_layer_norm'),
('attention.output.dense', 'self_attn.output'),
('attention.self', 'self_attn.self'),
('encoder.LayerNorm', 'encoder.layernorm_embedding'),
]
+ END_COMMON
)
a_ = [
'encdec/key/bias',
'encdec/query/bias',
'encdec/value/bias',
'self/key/bias',
'self/query/bias',
'self/value/bias',
'encdec_output/dense/bias',
'attention/output/dense/bias',
]
def _a( UpperCamelCase__ : Tuple, UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
for tf_name, hf_name in patterns:
SCREAMING_SNAKE_CASE__ : str =k.replace(UpperCamelCase__, UpperCamelCase__ )
return k
def _a( UpperCamelCase__ : dict, UpperCamelCase__ : dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =BigBirdPegasusConfig(**UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : int =BigBirdPegasusForConditionalGeneration(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Tuple =torch_model.state_dict()
SCREAMING_SNAKE_CASE__ : Dict ={}
# separating decoder weights
SCREAMING_SNAKE_CASE__ : int ={k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder''' )}
SCREAMING_SNAKE_CASE__ : List[Any] ={k: tf_weights[k] for k in tf_weights if not k.startswith('''pegasus/decoder''' )}
for k, v in tqdm(decoder_weights.items(), '''tf -> hf conversion''' ):
SCREAMING_SNAKE_CASE__ : Any =[k.endswith(UpperCamelCase__ ) for ending in KEYS_TO_IGNORE]
if any(UpperCamelCase__ ):
continue
SCREAMING_SNAKE_CASE__ : Optional[int] =DECODER_PATTERNS
SCREAMING_SNAKE_CASE__ : Union[str, Any] =rename_state_dict_key(UpperCamelCase__, UpperCamelCase__ )
if new_k not in state_dict:
raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] =v.T
SCREAMING_SNAKE_CASE__ : int =torch.from_numpy(UpperCamelCase__ )
assert v.shape == state_dict[new_k].shape, f"{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"
for k, v in tqdm(remaining_weights.items(), '''tf -> hf conversion''' ):
SCREAMING_SNAKE_CASE__ : Dict =[k.endswith(UpperCamelCase__ ) for ending in KEYS_TO_IGNORE]
if any(UpperCamelCase__ ):
continue
SCREAMING_SNAKE_CASE__ : List[Any] =REMAINING_PATTERNS
SCREAMING_SNAKE_CASE__ : Any =rename_state_dict_key(UpperCamelCase__, UpperCamelCase__ )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})" )
if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value'''] ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =v.T
SCREAMING_SNAKE_CASE__ : List[str] =torch.from_numpy(UpperCamelCase__ )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, f"{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"
SCREAMING_SNAKE_CASE__ : Dict =mapping['''model.embed_positions.weight''']
SCREAMING_SNAKE_CASE__ : Optional[int] =mapping.pop('''model.embed_positions.weight''' )
SCREAMING_SNAKE_CASE__ : int =torch_model.load_state_dict(UpperCamelCase__, strict=UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : str =[
k
for k in missing
if k
not in [
'''final_logits_bias''',
'''model.encoder.embed_tokens.weight''',
'''model.decoder.embed_tokens.weight''',
'''lm_head.weight''',
]
]
assert unexpected_missing == [], f"no matches found for the following torch keys {unexpected_missing}"
assert extra == [], f"no matches found for the following tf keys {extra}"
return torch_model
def _a( UpperCamelCase__ : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =tf.train.list_variables(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Dict ={}
SCREAMING_SNAKE_CASE__ : int =['''global_step''']
for name, shape in tqdm(UpperCamelCase__, desc='''converting tf checkpoint to dict''' ):
SCREAMING_SNAKE_CASE__ : List[str] =any(pat in name for pat in ignore_name )
if skip_key:
continue
SCREAMING_SNAKE_CASE__ : Any =tf.train.load_variable(UpperCamelCase__, UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Dict =array
return tf_weights
def _a( UpperCamelCase__ : str, UpperCamelCase__ : str, UpperCamelCase__ : dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] =get_tf_weights_as_numpy(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[str] =convert_bigbird_pegasus(UpperCamelCase__, UpperCamelCase__ )
torch_model.save_pretrained(UpperCamelCase__ )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument('--tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
parser.add_argument('--save_dir', default=None, type=str, help='Path to the output PyTorch model.')
a_ = parser.parse_args()
a_ = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 709 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __SCREAMING_SNAKE_CASE :
@staticmethod
def __magic_name__ ( *__lowercase : int , **__lowercase : Optional[Any] ) -> Optional[Any]:
pass
@is_pipeline_test
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@require_torch
def __magic_name__ ( self : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , )
SCREAMING_SNAKE_CASE__ : List[str] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Any =image_classifier(__lowercase , candidate_labels=['''a''', '''b''', '''c'''] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(__lowercase ) , [
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
] , )
SCREAMING_SNAKE_CASE__ : int =image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
] , )
@require_tf
def __magic_name__ ( self : Union[str, Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , framework='''tf''' )
SCREAMING_SNAKE_CASE__ : Dict =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Tuple =image_classifier(__lowercase , candidate_labels=['''a''', '''b''', '''c'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}] , )
SCREAMING_SNAKE_CASE__ : List[Any] =image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
] , )
@slow
@require_torch
def __magic_name__ ( self : List[Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE__ : Any =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : List[str] =image_classifier(__lowercase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
@slow
@require_tf
def __magic_name__ ( self : Union[str, Any] ) -> int:
SCREAMING_SNAKE_CASE__ : str =pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , framework='''tf''' )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE__ : Optional[Any] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Any =image_classifier(__lowercase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
SCREAMING_SNAKE_CASE__ : List[Any] =image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
| 665 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
a_ = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['DPTFeatureExtractor']
a_ = ['DPTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DPTForDepthEstimation',
'DPTForSemanticSegmentation',
'DPTModel',
'DPTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 710 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case_ = JukeboxTokenizer
snake_case_ = {
"""artist""": """Zac Brown Band""",
"""genres""": """Country""",
"""lyrics""": """I met a traveller from an antique land,
Who said \"Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
""",
}
@require_torch
def __magic_name__ ( self : Optional[int] ) -> str:
import torch
SCREAMING_SNAKE_CASE__ : List[str] =JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics''' )
SCREAMING_SNAKE_CASE__ : str =tokenizer(**self.metas )['''input_ids''']
# fmt: off
SCREAMING_SNAKE_CASE__ : str =[
torch.tensor([[
0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
76, 76]] ),
torch.tensor([[0, 0, 0, 10_69, 11]] ),
torch.tensor([[0, 0, 0, 10_69, 11]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
@require_torch
def __magic_name__ ( self : Any ) -> List[str]:
import torch
SCREAMING_SNAKE_CASE__ : int =JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics''' )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer(**self.metas )['''input_ids''']
# fmt: off
SCREAMING_SNAKE_CASE__ : Optional[int] =[
torch.tensor([[
0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
77, 77, 77, 77, 77, 77]] ),
torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
| 665 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
def _a( UpperCamelCase__ : Any, UpperCamelCase__ : Dict=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str =[]
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias") )
# projection layer + position embeddings
rename_keys.extend(
[
('''cls_token''', '''vit.embeddings.cls_token'''),
('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
('''pos_embed''', '''vit.embeddings.position_embeddings'''),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
SCREAMING_SNAKE_CASE__ : Tuple =[(pair[0], pair[1][4:]) if pair[1].startswith('''vit''' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('''norm.weight''', '''vit.layernorm.weight'''),
('''norm.bias''', '''vit.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def _a( UpperCamelCase__ : List[str], UpperCamelCase__ : List[Any], UpperCamelCase__ : Optional[int]=False ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
SCREAMING_SNAKE_CASE__ : List[Any] =''''''
else:
SCREAMING_SNAKE_CASE__ : Optional[int] ='''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE__ : List[Any] =state_dict.pop(f"blocks.{i}.attn.qkv.weight" )
SCREAMING_SNAKE_CASE__ : Optional[int] =state_dict.pop(f"blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE__ : int =in_proj_weight[
: config.hidden_size, :
]
SCREAMING_SNAKE_CASE__ : str =in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE__ : str =in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE__ : str =in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE__ : List[str] =in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE__ : Dict =in_proj_bias[-config.hidden_size :]
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(UpperCamelCase__, UpperCamelCase__ )
def _a( UpperCamelCase__ : Optional[Any], UpperCamelCase__ : str, UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =dct.pop(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] =val
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
SCREAMING_SNAKE_CASE__ : List[Any] =Image.open(requests.get(UpperCamelCase__, stream=UpperCamelCase__ ).raw )
return im
@torch.no_grad()
def _a( UpperCamelCase__ : int, UpperCamelCase__ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] =ViTConfig()
SCREAMING_SNAKE_CASE__ : int =False
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
if vit_name[-5:] == "in21k":
SCREAMING_SNAKE_CASE__ : Tuple =True
SCREAMING_SNAKE_CASE__ : Union[str, Any] =int(vit_name[-1_2:-1_0] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =int(vit_name[-9:-6] )
else:
SCREAMING_SNAKE_CASE__ : Any =1_0_0_0
SCREAMING_SNAKE_CASE__ : List[Any] ='''huggingface/label-files'''
SCREAMING_SNAKE_CASE__ : int ='''imagenet-1k-id2label.json'''
SCREAMING_SNAKE_CASE__ : List[Any] =json.load(open(hf_hub_download(UpperCamelCase__, UpperCamelCase__, repo_type='''dataset''' ), '''r''' ) )
SCREAMING_SNAKE_CASE__ : List[str] ={int(UpperCamelCase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : int =idalabel
SCREAMING_SNAKE_CASE__ : str ={v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : List[Any] =int(vit_name[-6:-4] )
SCREAMING_SNAKE_CASE__ : int =int(vit_name[-3:] )
# size of the architecture
if "deit" in vit_name:
if vit_name[9:].startswith('''tiny''' ):
SCREAMING_SNAKE_CASE__ : Optional[int] =1_9_2
SCREAMING_SNAKE_CASE__ : Dict =7_6_8
SCREAMING_SNAKE_CASE__ : Optional[int] =1_2
SCREAMING_SNAKE_CASE__ : Tuple =3
elif vit_name[9:].startswith('''small''' ):
SCREAMING_SNAKE_CASE__ : Any =3_8_4
SCREAMING_SNAKE_CASE__ : Any =1_5_3_6
SCREAMING_SNAKE_CASE__ : Any =1_2
SCREAMING_SNAKE_CASE__ : Union[str, Any] =6
else:
pass
else:
if vit_name[4:].startswith('''small''' ):
SCREAMING_SNAKE_CASE__ : str =7_6_8
SCREAMING_SNAKE_CASE__ : Optional[int] =2_3_0_4
SCREAMING_SNAKE_CASE__ : List[str] =8
SCREAMING_SNAKE_CASE__ : List[Any] =8
elif vit_name[4:].startswith('''base''' ):
pass
elif vit_name[4:].startswith('''large''' ):
SCREAMING_SNAKE_CASE__ : str =1_0_2_4
SCREAMING_SNAKE_CASE__ : Optional[Any] =4_0_9_6
SCREAMING_SNAKE_CASE__ : Optional[Any] =2_4
SCREAMING_SNAKE_CASE__ : Dict =1_6
elif vit_name[4:].startswith('''huge''' ):
SCREAMING_SNAKE_CASE__ : List[Any] =1_2_8_0
SCREAMING_SNAKE_CASE__ : int =5_1_2_0
SCREAMING_SNAKE_CASE__ : Tuple =3_2
SCREAMING_SNAKE_CASE__ : int =1_6
# load original model from timm
SCREAMING_SNAKE_CASE__ : str =timm.create_model(UpperCamelCase__, pretrained=UpperCamelCase__ )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
SCREAMING_SNAKE_CASE__ : Optional[int] =timm_model.state_dict()
if base_model:
remove_classification_head_(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[str] =create_rename_keys(UpperCamelCase__, UpperCamelCase__ )
for src, dest in rename_keys:
rename_key(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
read_in_q_k_v(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
# load HuggingFace model
if vit_name[-5:] == "in21k":
SCREAMING_SNAKE_CASE__ : List[str] =ViTModel(UpperCamelCase__ ).eval()
else:
SCREAMING_SNAKE_CASE__ : Dict =ViTForImageClassification(UpperCamelCase__ ).eval()
model.load_state_dict(UpperCamelCase__ )
# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
if "deit" in vit_name:
SCREAMING_SNAKE_CASE__ : Optional[Any] =DeiTImageProcessor(size=config.image_size )
else:
SCREAMING_SNAKE_CASE__ : Any =ViTImageProcessor(size=config.image_size )
SCREAMING_SNAKE_CASE__ : Optional[Any] =image_processor(images=prepare_img(), return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : Dict =encoding['''pixel_values''']
SCREAMING_SNAKE_CASE__ : Any =model(UpperCamelCase__ )
if base_model:
SCREAMING_SNAKE_CASE__ : str =timm_model.forward_features(UpperCamelCase__ )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(UpperCamelCase__, outputs.pooler_output, atol=1e-3 )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =timm_model(UpperCamelCase__ )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(UpperCamelCase__, outputs.logits, atol=1e-3 )
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
print(f"Saving model {vit_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(UpperCamelCase__ )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(UpperCamelCase__ )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--vit_name',
default='vit_base_patch16_224',
type=str,
help='Name of the ViT 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.'
)
a_ = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
| 711 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """gpt_neox"""
def __init__( self : List[Any] , __lowercase : Union[str, Any]=5_04_32 , __lowercase : int=61_44 , __lowercase : Tuple=44 , __lowercase : List[str]=64 , __lowercase : str=2_45_76 , __lowercase : Dict="gelu" , __lowercase : Tuple=0.25 , __lowercase : Tuple=1_00_00 , __lowercase : Tuple=0.0 , __lowercase : str=0.0 , __lowercase : List[Any]=0.1 , __lowercase : Dict=20_48 , __lowercase : Any=0.02 , __lowercase : Dict=1e-5 , __lowercase : List[Any]=True , __lowercase : str=0 , __lowercase : Optional[Any]=2 , __lowercase : Tuple=False , __lowercase : List[Any]=True , __lowercase : Optional[Any]=None , **__lowercase : Any , ) -> Dict:
super().__init__(bos_token_id=__lowercase , eos_token_id=__lowercase , **__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =max_position_embeddings
SCREAMING_SNAKE_CASE__ : Any =hidden_size
SCREAMING_SNAKE_CASE__ : str =num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any =num_attention_heads
SCREAMING_SNAKE_CASE__ : Union[str, Any] =intermediate_size
SCREAMING_SNAKE_CASE__ : Dict =hidden_act
SCREAMING_SNAKE_CASE__ : str =rotary_pct
SCREAMING_SNAKE_CASE__ : Optional[Any] =rotary_emb_base
SCREAMING_SNAKE_CASE__ : List[Any] =attention_dropout
SCREAMING_SNAKE_CASE__ : List[Any] =hidden_dropout
SCREAMING_SNAKE_CASE__ : str =classifier_dropout
SCREAMING_SNAKE_CASE__ : Any =initializer_range
SCREAMING_SNAKE_CASE__ : Dict =layer_norm_eps
SCREAMING_SNAKE_CASE__ : Any =use_cache
SCREAMING_SNAKE_CASE__ : Tuple =tie_word_embeddings
SCREAMING_SNAKE_CASE__ : Tuple =use_parallel_residual
SCREAMING_SNAKE_CASE__ : Union[str, Any] =rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )
def __magic_name__ ( self : Optional[Any] ) -> Optional[Any]:
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __lowercase ) 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}" )
SCREAMING_SNAKE_CASE__ : int =self.rope_scaling.get('''type''' , __lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =self.rope_scaling.get('''factor''' , __lowercase )
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(__lowercase , __lowercase ) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 665 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
from .camera import create_pan_cameras
from .pipeline_shap_e import ShapEPipeline
from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
from .renderer import (
BoundingBoxVolume,
ImportanceRaySampler,
MLPNeRFModelOutput,
MLPNeRSTFModel,
ShapEParamsProjModel,
ShapERenderer,
StratifiedRaySampler,
VoidNeRFModel,
)
| 712 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
'tokenization_deberta': ['DebertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['DebertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'DebertaForMaskedLM',
'DebertaForQuestionAnswering',
'DebertaForSequenceClassification',
'DebertaForTokenClassification',
'DebertaModel',
'DebertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDebertaForMaskedLM',
'TFDebertaForQuestionAnswering',
'TFDebertaForSequenceClassification',
'TFDebertaForTokenClassification',
'TFDebertaModel',
'TFDebertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 665 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : list, UpperCamelCase__ : int, UpperCamelCase__ : int = 0, UpperCamelCase__ : int = 0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =right or len(UpperCamelCase__ ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(UpperCamelCase__, UpperCamelCase__, left + 1, right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 713 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _a( UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : list[int], UpperCamelCase__ : int, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple =coefficient_matrix.shape
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple =constant_matrix.shape
if rowsa != colsa:
SCREAMING_SNAKE_CASE__ : Any =f"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
raise ValueError(UpperCamelCase__ )
if colsa != 1:
SCREAMING_SNAKE_CASE__ : str =f"Constant matrix must be nx1 but received {rowsa}x{colsa}"
raise ValueError(UpperCamelCase__ )
if rowsa != rowsa:
SCREAMING_SNAKE_CASE__ : str =(
'''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:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(
'''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''' )
SCREAMING_SNAKE_CASE__ : NDArray[floataa] =np.concatenate(
(coefficient_matrix, constant_matrix), axis=1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any =table.shape
strictly_diagonally_dominant(UpperCamelCase__ )
# Iterates the whole matrix for given number of times
for _ in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : List[str] =[]
for row in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =0
for col in range(UpperCamelCase__ ):
if col == row:
SCREAMING_SNAKE_CASE__ : int =table[row][col]
elif col == cols - 1:
SCREAMING_SNAKE_CASE__ : Any =table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
SCREAMING_SNAKE_CASE__ : int =(temp + val) / denom
new_val.append(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =new_val
return [float(UpperCamelCase__ ) for i in new_val]
def _a( UpperCamelCase__ : NDArray[floataa] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] =table.shape
SCREAMING_SNAKE_CASE__ : Any =True
for i in range(0, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : int =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()
| 665 | 0 |
'''simple docstring'''
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def _a( UpperCamelCase__ : ndarray ):
'''simple docstring'''
return np.dot(UpperCamelCase__, UpperCamelCase__ )
class __SCREAMING_SNAKE_CASE :
def __init__( self : Dict , *,
__lowercase : float = np.inf , __lowercase : str = "linear" , __lowercase : float = 0.0 , ) -> None:
SCREAMING_SNAKE_CASE__ : List[str] =regularization
SCREAMING_SNAKE_CASE__ : Optional[Any] =gamma
if kernel == "linear":
SCREAMING_SNAKE_CASE__ : List[Any] =self.__linear
elif kernel == "rbf":
if self.gamma == 0:
raise ValueError('''rbf kernel requires gamma''' )
if not isinstance(self.gamma , (float, int) ):
raise ValueError('''gamma must be float or int''' )
if not self.gamma > 0:
raise ValueError('''gamma must be > 0''' )
SCREAMING_SNAKE_CASE__ : List[str] =self.__rbf
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
else:
SCREAMING_SNAKE_CASE__ : List[str] =F"Unknown kernel: {kernel}"
raise ValueError(__lowercase )
def __magic_name__ ( self : List[Any] , __lowercase : ndarray , __lowercase : ndarray ) -> float:
return np.dot(__lowercase , __lowercase )
def __magic_name__ ( self : Tuple , __lowercase : ndarray , __lowercase : ndarray ) -> float:
return np.exp(-(self.gamma * norm_squared(vectora - vectora )) )
def __magic_name__ ( self : Optional[int] , __lowercase : list[ndarray] , __lowercase : ndarray ) -> None:
SCREAMING_SNAKE_CASE__ : Tuple =observations
SCREAMING_SNAKE_CASE__ : Union[str, Any] =classes
# using Wolfe's Dual to calculate w.
# Primal problem: minimize 1/2*norm_squared(w)
# constraint: yn(w . xn + b) >= 1
#
# With l a vector
# Dual problem: maximize sum_n(ln) -
# 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
# constraint: self.C >= ln >= 0
# and sum_n(ln*yn) = 0
# Then we get w using w = sum_n(ln*yn*xn)
# At the end we can get b ~= mean(yn - w . xn)
#
# Since we use kernels, we only need l_star to calculate b
# and to classify observations
(SCREAMING_SNAKE_CASE__ ) : Union[str, Any] =np.shape(__lowercase )
def to_minimize(__lowercase : ndarray ) -> float:
SCREAMING_SNAKE_CASE__ : Optional[Any] =0
(SCREAMING_SNAKE_CASE__ ) : str =np.shape(__lowercase )
for i in range(__lowercase ):
for j in range(__lowercase ):
s += (
candidate[i]
* candidate[j]
* classes[i]
* classes[j]
* self.kernel(observations[i] , observations[j] )
)
return 1 / 2 * s - sum(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =LinearConstraint(__lowercase , 0 , 0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =Bounds(0 , self.regularization )
SCREAMING_SNAKE_CASE__ : Dict =minimize(
__lowercase , np.ones(__lowercase ) , bounds=__lowercase , constraints=[ly_contraint] ).x
SCREAMING_SNAKE_CASE__ : List[Any] =l_star
# calculating mean offset of separation plane to points
SCREAMING_SNAKE_CASE__ : Optional[int] =0
for i in range(__lowercase ):
for j in range(__lowercase ):
s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
observations[i] , observations[j] )
SCREAMING_SNAKE_CASE__ : List[Any] =s / n
def __magic_name__ ( self : Union[str, Any] , __lowercase : ndarray ) -> int:
SCREAMING_SNAKE_CASE__ : Any =sum(
self.optimum[n]
* self.classes[n]
* self.kernel(self.observations[n] , __lowercase )
for n in range(len(self.classes ) ) )
return 1 if s + self.offset >= 0 else -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 714 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _a( UpperCamelCase__ : str, UpperCamelCase__ : Tuple, UpperCamelCase__ : Any, UpperCamelCase__ : List[str], UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
with open(UpperCamelCase__ ) as metadata_file:
SCREAMING_SNAKE_CASE__ : Optional[int] =json.load(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =LukeConfig(use_entity_aware_attention=UpperCamelCase__, **metadata['''model_config'''] )
# Load in the weights from the checkpoint_path
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch.load(UpperCamelCase__, map_location='''cpu''' )['''module''']
# Load the entity vocab file
SCREAMING_SNAKE_CASE__ : List[str] =load_original_entity_vocab(UpperCamelCase__ )
# add an entry for [MASK2]
SCREAMING_SNAKE_CASE__ : Optional[int] =max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
SCREAMING_SNAKE_CASE__ : Optional[int] =XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )
# Add special tokens to the token vocabulary for downstream tasks
SCREAMING_SNAKE_CASE__ : List[Any] =AddedToken('''<ent>''', lstrip=UpperCamelCase__, rstrip=UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Any =AddedToken('''<ent2>''', lstrip=UpperCamelCase__, rstrip=UpperCamelCase__ )
tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f"Saving tokenizer to {pytorch_dump_folder_path}" )
tokenizer.save_pretrained(UpperCamelCase__ )
with open(os.path.join(UpperCamelCase__, '''tokenizer_config.json''' ), '''r''' ) as f:
SCREAMING_SNAKE_CASE__ : Optional[Any] =json.load(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : int ='''MLukeTokenizer'''
with open(os.path.join(UpperCamelCase__, '''tokenizer_config.json''' ), '''w''' ) as f:
json.dump(UpperCamelCase__, UpperCamelCase__ )
with open(os.path.join(UpperCamelCase__, MLukeTokenizer.vocab_files_names['''entity_vocab_file'''] ), '''w''' ) as f:
json.dump(UpperCamelCase__, UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =MLukeTokenizer.from_pretrained(UpperCamelCase__ )
# Initialize the embeddings of the special tokens
SCREAMING_SNAKE_CASE__ : str =tokenizer.convert_tokens_to_ids(['''@'''] )[0]
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer.convert_tokens_to_ids(['''#'''] )[0]
SCREAMING_SNAKE_CASE__ : Dict =state_dict['''embeddings.word_embeddings.weight''']
SCREAMING_SNAKE_CASE__ : List[str] =word_emb[ent_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : Tuple =word_emb[enta_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : str =torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =state_dict[bias_name]
SCREAMING_SNAKE_CASE__ : List[Any] =decoder_bias[ent_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : str =decoder_bias[enta_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : List[str] =torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
SCREAMING_SNAKE_CASE__ : Tuple =f"encoder.layer.{layer_index}.attention.self."
SCREAMING_SNAKE_CASE__ : Optional[int] =state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE__ : Optional[int] =state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE__ : List[Any] =state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
SCREAMING_SNAKE_CASE__ : Any =state_dict['''entity_embeddings.entity_embeddings.weight''']
SCREAMING_SNAKE_CASE__ : Any =entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : Any =torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
SCREAMING_SNAKE_CASE__ : Optional[int] =state_dict['''entity_predictions.bias''']
SCREAMING_SNAKE_CASE__ : Tuple =entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : Any =torch.cat([entity_prediction_bias, entity_mask_bias] )
SCREAMING_SNAKE_CASE__ : int =LukeForMaskedLM(config=UpperCamelCase__ ).eval()
state_dict.pop('''entity_predictions.decoder.weight''' )
state_dict.pop('''lm_head.decoder.weight''' )
state_dict.pop('''lm_head.decoder.bias''' )
SCREAMING_SNAKE_CASE__ : Tuple =OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('''lm_head''' ) or key.startswith('''entity_predictions''' )):
SCREAMING_SNAKE_CASE__ : Optional[Any] =state_dict[key]
else:
SCREAMING_SNAKE_CASE__ : Any =state_dict[key]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] =model.load_state_dict(UpperCamelCase__, strict=UpperCamelCase__ )
if set(UpperCamelCase__ ) != {"luke.embeddings.position_ids"}:
raise ValueError(f"Unexpected unexpected_keys: {unexpected_keys}" )
if set(UpperCamelCase__ ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(f"Unexpected missing_keys: {missing_keys}" )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
SCREAMING_SNAKE_CASE__ : Any =MLukeTokenizer.from_pretrained(UpperCamelCase__, task='''entity_classification''' )
SCREAMING_SNAKE_CASE__ : str ='''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(0, 9)
SCREAMING_SNAKE_CASE__ : str =tokenizer(UpperCamelCase__, entity_spans=[span], return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : List[Any] =model(**UpperCamelCase__ )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
SCREAMING_SNAKE_CASE__ : str =torch.Size((1, 3_3, 7_6_8) )
SCREAMING_SNAKE_CASE__ : int =torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f"Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3], UpperCamelCase__, atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
SCREAMING_SNAKE_CASE__ : Any =torch.Size((1, 1, 7_6_8) )
SCREAMING_SNAKE_CASE__ : int =torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
f"Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"
f" {expected_shape}" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3], UpperCamelCase__, atol=1e-4 ):
raise ValueError
# Verify masked word/entity prediction
SCREAMING_SNAKE_CASE__ : str =MLukeTokenizer.from_pretrained(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] ='''Tokyo is the capital of <mask>.'''
SCREAMING_SNAKE_CASE__ : Dict =(2_4, 3_0)
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer(UpperCamelCase__, entity_spans=[span], return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : List[str] =model(**UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[Any] =encoding['''input_ids'''][0].tolist()
SCREAMING_SNAKE_CASE__ : Any =input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>''' ) )
SCREAMING_SNAKE_CASE__ : List[Any] =outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =outputs.entity_logits[0][0].argmax().item()
SCREAMING_SNAKE_CASE__ : Dict =[
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('''en:''' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('''Saving PyTorch model to {}'''.format(UpperCamelCase__ ) )
model.save_pretrained(UpperCamelCase__ )
def _a( UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] =['''[MASK]''', '''[PAD]''', '''[UNK]''']
SCREAMING_SNAKE_CASE__ : List[str] =[json.loads(UpperCamelCase__ ) for line in open(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Optional[int] ={}
for entry in data:
SCREAMING_SNAKE_CASE__ : Tuple =entry['''id''']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
SCREAMING_SNAKE_CASE__ : str =entity_id
break
SCREAMING_SNAKE_CASE__ : Union[str, Any] =f"{language}:{entity_name}"
SCREAMING_SNAKE_CASE__ : Union[str, Any] =entity_id
return new_mapping
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
parser.add_argument(
'--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
)
parser.add_argument(
'--entity_vocab_path',
default=None,
type=str,
help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
)
parser.add_argument(
'--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
)
a_ = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 665 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
a_ = {
'config': [
'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
'OnnxConfig',
'OnnxConfigWithPast',
'OnnxSeq2SeqConfigWithPast',
'PatchingSpec',
],
'convert': ['export', 'validate_model_outputs'],
'features': ['FeaturesManager'],
'utils': ['ParameterFormat', 'compute_serialized_parameters_size'],
}
if TYPE_CHECKING:
from .config import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
OnnxSeqaSeqConfigWithPast,
PatchingSpec,
)
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import ParameterFormat, compute_serialized_parameters_size
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 715 |
'''simple docstring'''
def _a( UpperCamelCase__ : str, UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Tuple =[[False for _ in range(m + 1 )] for _ in range(n + 1 )]
SCREAMING_SNAKE_CASE__ : List[Any] =True
for i in range(UpperCamelCase__ ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
SCREAMING_SNAKE_CASE__ : Optional[int] =True
if a[i].islower():
SCREAMING_SNAKE_CASE__ : List[Any] =True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _a( UpperCamelCase__ : List[str], UpperCamelCase__ : str, UpperCamelCase__ : str, UpperCamelCase__ : Path, UpperCamelCase__ : str = None, UpperCamelCase__ : str = None, UpperCamelCase__ : str = None, ):
'''simple docstring'''
if config_name_or_path is None:
SCREAMING_SNAKE_CASE__ : List[Any] ='''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''
if generator_tokenizer_name_or_path is None:
SCREAMING_SNAKE_CASE__ : int =generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
SCREAMING_SNAKE_CASE__ : List[str] =question_encoder_name_or_path
SCREAMING_SNAKE_CASE__ : int =RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration
# Save model.
SCREAMING_SNAKE_CASE__ : Dict =RagConfig.from_pretrained(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[str] =AutoConfig.from_pretrained(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[str] =AutoConfig.from_pretrained(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] =gen_config
SCREAMING_SNAKE_CASE__ : int =question_encoder_config
SCREAMING_SNAKE_CASE__ : Optional[Any] =model_class.from_pretrained_question_encoder_generator(
UpperCamelCase__, UpperCamelCase__, config=UpperCamelCase__ )
rag_model.save_pretrained(UpperCamelCase__ )
# Sanity check.
model_class.from_pretrained(UpperCamelCase__ )
# Save tokenizers.
SCREAMING_SNAKE_CASE__ : List[str] =AutoTokenizer.from_pretrained(UpperCamelCase__ )
gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/''' )
SCREAMING_SNAKE_CASE__ : int =AutoTokenizer.from_pretrained(UpperCamelCase__ )
question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/''' )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
'--model_type',
choices=['rag_sequence', 'rag_token'],
required=True,
type=str,
help='RAG model type: rag_sequence, rag_token',
)
parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
parser.add_argument(
'--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
)
parser.add_argument(
'--generator_tokenizer_name_or_path',
type=str,
help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
)
parser.add_argument(
'--question_encoder_tokenizer_name_or_path',
type=str,
help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
)
parser.add_argument(
'--config_name_or_path',
type=str,
help=(
'Identifier of the model config to use, if not provided, resolves to a base config for a given'
' ``model_type``'
),
)
a_ = parser.parse_args()
a_ = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 716 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , __lowercase : Optional[Any] , __lowercase : List[str]=13 , __lowercase : int=7 , __lowercase : Tuple=True , __lowercase : Optional[Any]=True , __lowercase : List[Any]=True , __lowercase : str=True , __lowercase : Tuple=99 , __lowercase : Union[str, Any]=64 , __lowercase : Tuple=32 , __lowercase : Optional[Any]=5 , __lowercase : Tuple=4 , __lowercase : Optional[int]=37 , __lowercase : int="gelu" , __lowercase : Union[str, Any]=0.1 , __lowercase : List[Any]=0.1 , __lowercase : List[Any]=5_12 , __lowercase : int=16 , __lowercase : Optional[int]=2 , __lowercase : Tuple=0.02 , __lowercase : List[str]=3 , __lowercase : List[str]=4 , __lowercase : List[str]=None , ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =parent
SCREAMING_SNAKE_CASE__ : Any =batch_size
SCREAMING_SNAKE_CASE__ : Optional[Any] =seq_length
SCREAMING_SNAKE_CASE__ : Dict =is_training
SCREAMING_SNAKE_CASE__ : List[Any] =use_input_mask
SCREAMING_SNAKE_CASE__ : Union[str, Any] =use_token_type_ids
SCREAMING_SNAKE_CASE__ : List[Any] =use_labels
SCREAMING_SNAKE_CASE__ : int =vocab_size
SCREAMING_SNAKE_CASE__ : str =hidden_size
SCREAMING_SNAKE_CASE__ : Any =embedding_size
SCREAMING_SNAKE_CASE__ : Tuple =num_hidden_layers
SCREAMING_SNAKE_CASE__ : str =num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple =intermediate_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =hidden_act
SCREAMING_SNAKE_CASE__ : Optional[int] =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : str =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Any =max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[Any] =type_vocab_size
SCREAMING_SNAKE_CASE__ : Any =type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Optional[Any] =initializer_range
SCREAMING_SNAKE_CASE__ : str =num_labels
SCREAMING_SNAKE_CASE__ : List[str] =num_choices
SCREAMING_SNAKE_CASE__ : List[str] =scope
def __magic_name__ ( self : str ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : int =None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : List[str] =random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : int =None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Any =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =None
SCREAMING_SNAKE_CASE__ : Optional[Any] =None
SCREAMING_SNAKE_CASE__ : Optional[int] =None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Tuple =ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : List[Any] =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : Any =ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __magic_name__ ( self : List[str] ) -> Any:
return MegatronBertConfig(
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 , embedding_size=self.embedding_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=__lowercase , initializer_range=self.initializer_range , )
def __magic_name__ ( self : Any , __lowercase : Tuple , __lowercase : Any , __lowercase : List[Any] , __lowercase : Optional[int] , __lowercase : List[str] , __lowercase : Dict , __lowercase : Union[str, Any] ) -> int:
SCREAMING_SNAKE_CASE__ : List[str] =MegatronBertModel(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(__lowercase , token_type_ids=__lowercase )
SCREAMING_SNAKE_CASE__ : str =model(__lowercase )
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 __magic_name__ ( self : Dict , __lowercase : Optional[int] , __lowercase : List[str] , __lowercase : str , __lowercase : Tuple , __lowercase : Any , __lowercase : Optional[int] , __lowercase : Any ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : str =MegatronBertForMaskedLM(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Tuple =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self : List[str] , __lowercase : Tuple , __lowercase : Tuple , __lowercase : Optional[Any] , __lowercase : Any , __lowercase : str , __lowercase : Any , __lowercase : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[Any] =MegatronBertForCausalLM(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self : Union[str, Any] , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : Union[str, Any] , __lowercase : Dict , __lowercase : Union[str, Any] , __lowercase : int , __lowercase : Any ) -> Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =MegatronBertForNextSentencePrediction(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] =model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def __magic_name__ ( self : List[str] , __lowercase : Optional[int] , __lowercase : str , __lowercase : Optional[int] , __lowercase : Optional[int] , __lowercase : Optional[Any] , __lowercase : str , __lowercase : Optional[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =MegatronBertForPreTraining(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] =model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , next_sentence_label=__lowercase , )
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 __magic_name__ ( self : Optional[int] , __lowercase : Tuple , __lowercase : Optional[int] , __lowercase : Union[str, Any] , __lowercase : int , __lowercase : Any , __lowercase : Any , __lowercase : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[Any] =MegatronBertForQuestionAnswering(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict =model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , start_positions=__lowercase , end_positions=__lowercase , )
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 __magic_name__ ( self : Optional[int] , __lowercase : Optional[int] , __lowercase : Optional[int] , __lowercase : List[Any] , __lowercase : List[Any] , __lowercase : Optional[int] , __lowercase : int , __lowercase : List[str] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.num_labels
SCREAMING_SNAKE_CASE__ : Dict =MegatronBertForSequenceClassification(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __magic_name__ ( self : Optional[int] , __lowercase : List[Any] , __lowercase : str , __lowercase : Optional[Any] , __lowercase : Any , __lowercase : Tuple , __lowercase : Optional[Any] , __lowercase : int ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Tuple =self.num_labels
SCREAMING_SNAKE_CASE__ : int =MegatronBertForTokenClassification(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __magic_name__ ( self : Dict , __lowercase : List[str] , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : Any , __lowercase : List[Any] , __lowercase : int , __lowercase : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : int =self.num_choices
SCREAMING_SNAKE_CASE__ : List[str] =MegatronBertForMultipleChoice(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Optional[int] =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Tuple =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __magic_name__ ( self : str ) -> Any:
SCREAMING_SNAKE_CASE__ : Tuple =self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : str =config_and_inputs
SCREAMING_SNAKE_CASE__ : Union[str, Any] ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
snake_case_ = (
{
"""feature-extraction""": MegatronBertModel,
"""fill-mask""": MegatronBertForMaskedLM,
"""question-answering""": MegatronBertForQuestionAnswering,
"""text-classification""": MegatronBertForSequenceClassification,
"""text-generation""": MegatronBertForCausalLM,
"""token-classification""": MegatronBertForTokenClassification,
"""zero-shot""": MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case_ = True
# test_resize_embeddings = False
snake_case_ = False
def __magic_name__ ( self : List[Any] , __lowercase : List[str] , __lowercase : Tuple , __lowercase : Tuple=False ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =super()._prepare_for_class(__lowercase , __lowercase , return_labels=__lowercase )
if return_labels:
if model_class in get_values(__lowercase ):
SCREAMING_SNAKE_CASE__ : List[str] =torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowercase )
return inputs_dict
def __magic_name__ ( self : Tuple ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =MegatronBertModelTester(self )
SCREAMING_SNAKE_CASE__ : Tuple =ConfigTester(self , config_class=__lowercase , hidden_size=37 )
def __magic_name__ ( self : str ) -> Dict:
self.config_tester.run_common_tests()
def __magic_name__ ( self : Tuple ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*__lowercase )
def __magic_name__ ( self : List[str] ) -> Any:
SCREAMING_SNAKE_CASE__ : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__lowercase )
def __magic_name__ ( self : Optional[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__lowercase )
def __magic_name__ ( self : Optional[int] ) -> Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__lowercase )
def __magic_name__ ( self : Dict ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*__lowercase )
def __magic_name__ ( self : int ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*__lowercase )
def __magic_name__ ( self : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*__lowercase )
def _a( UpperCamelCase__ : List[str] ):
'''simple docstring'''
return torch.tensor(
UpperCamelCase__, dtype=torch.long, device=UpperCamelCase__, )
a_ = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
@unittest.skip('''Model is not available.''' )
def __magic_name__ ( self : Any ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Any ='''nvidia/megatron-bert-uncased-345m'''
if "MYDIR" in os.environ:
SCREAMING_SNAKE_CASE__ : Optional[int] =os.path.join(os.environ['''MYDIR'''] , __lowercase )
SCREAMING_SNAKE_CASE__ : Any =MegatronBertModel.from_pretrained(__lowercase )
model.to(__lowercase )
model.half()
SCREAMING_SNAKE_CASE__ : Dict =_long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(__lowercase )[0]
SCREAMING_SNAKE_CASE__ : Dict =torch.Size((1, 9, 10_24) )
self.assertEqual(output.shape , __lowercase )
SCREAMING_SNAKE_CASE__ : str =[-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
for ii in range(3 ):
for jj in range(3 ):
SCREAMING_SNAKE_CASE__ : List[Any] =output[0, ii, jj]
SCREAMING_SNAKE_CASE__ : Tuple =expected[3 * ii + jj]
SCREAMING_SNAKE_CASE__ : List[str] ='''ii={} jj={} a={} b={}'''.format(__lowercase , __lowercase , __lowercase , __lowercase )
self.assertTrue(math.isclose(__lowercase , __lowercase , rel_tol=__lowercase , abs_tol=__lowercase ) , msg=__lowercase )
| 665 | 0 |
'''simple docstring'''
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 _a( UpperCamelCase__ : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] =SwinaSRConfig()
if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Optional[int] =4
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : int =4
SCREAMING_SNAKE_CASE__ : Any =4_8
SCREAMING_SNAKE_CASE__ : List[Any] ='''pixelshuffle_aux'''
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Optional[int] =[6, 6, 6, 6]
SCREAMING_SNAKE_CASE__ : Tuple =6_0
SCREAMING_SNAKE_CASE__ : List[Any] =[6, 6, 6, 6]
SCREAMING_SNAKE_CASE__ : Tuple ='''pixelshuffledirect'''
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Optional[Any] =4
SCREAMING_SNAKE_CASE__ : Any ='''nearest+conv'''
elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =1
SCREAMING_SNAKE_CASE__ : int =1
SCREAMING_SNAKE_CASE__ : Any =1_2_6
SCREAMING_SNAKE_CASE__ : Dict =7
SCREAMING_SNAKE_CASE__ : Optional[Any] =2_5_5.0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =''''''
return config
def _a( UpperCamelCase__ : Tuple, UpperCamelCase__ : Dict ):
'''simple docstring'''
if "patch_embed.proj" in name and "layers" not in name:
SCREAMING_SNAKE_CASE__ : str =name.replace('''patch_embed.proj''', '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
SCREAMING_SNAKE_CASE__ : List[Any] =name.replace('''patch_embed.norm''', '''embeddings.patch_embeddings.layernorm''' )
if "layers" in name:
SCREAMING_SNAKE_CASE__ : List[str] =name.replace('''layers''', '''encoder.stages''' )
if "residual_group.blocks" in name:
SCREAMING_SNAKE_CASE__ : List[Any] =name.replace('''residual_group.blocks''', '''layers''' )
if "attn.proj" in name:
SCREAMING_SNAKE_CASE__ : List[Any] =name.replace('''attn.proj''', '''attention.output.dense''' )
if "attn" in name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =name.replace('''attn''', '''attention.self''' )
if "norm1" in name:
SCREAMING_SNAKE_CASE__ : Any =name.replace('''norm1''', '''layernorm_before''' )
if "norm2" in name:
SCREAMING_SNAKE_CASE__ : str =name.replace('''norm2''', '''layernorm_after''' )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE__ : Optional[Any] =name.replace('''mlp.fc1''', '''intermediate.dense''' )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE__ : Optional[int] =name.replace('''mlp.fc2''', '''output.dense''' )
if "q_bias" in name:
SCREAMING_SNAKE_CASE__ : List[Any] =name.replace('''q_bias''', '''query.bias''' )
if "k_bias" in name:
SCREAMING_SNAKE_CASE__ : List[str] =name.replace('''k_bias''', '''key.bias''' )
if "v_bias" in name:
SCREAMING_SNAKE_CASE__ : List[str] =name.replace('''v_bias''', '''value.bias''' )
if "cpb_mlp" in name:
SCREAMING_SNAKE_CASE__ : int =name.replace('''cpb_mlp''', '''continuous_position_bias_mlp''' )
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE__ : Tuple =name.replace('''patch_embed.proj''', '''patch_embed.projection''' )
if name == "norm.weight":
SCREAMING_SNAKE_CASE__ : Optional[Any] ='''layernorm.weight'''
if name == "norm.bias":
SCREAMING_SNAKE_CASE__ : Optional[int] ='''layernorm.bias'''
if "conv_first" in name:
SCREAMING_SNAKE_CASE__ : str =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:
SCREAMING_SNAKE_CASE__ : Any =name.replace('''conv_last''', '''final_convolution''' )
if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
if "conv_before_upsample.0" in name:
SCREAMING_SNAKE_CASE__ : str =name.replace('''conv_before_upsample.0''', '''conv_before_upsample''' )
if "upsample.0" in name:
SCREAMING_SNAKE_CASE__ : str =name.replace('''upsample.0''', '''upsample.convolution_0''' )
if "upsample.2" in name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =name.replace('''upsample.2''', '''upsample.convolution_1''' )
SCREAMING_SNAKE_CASE__ : Tuple ='''upsample.''' + name
elif config.upsampler == "pixelshuffledirect":
SCREAMING_SNAKE_CASE__ : int =name.replace('''upsample.0.weight''', '''upsample.conv.weight''' )
SCREAMING_SNAKE_CASE__ : str =name.replace('''upsample.0.bias''', '''upsample.conv.bias''' )
else:
pass
else:
SCREAMING_SNAKE_CASE__ : int ='''swin2sr.''' + name
return name
def _a( UpperCamelCase__ : List[str], UpperCamelCase__ : Tuple ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE__ : List[Any] =orig_state_dict.pop(UpperCamelCase__ )
if "qkv" in key:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =key.split('''.''' )
SCREAMING_SNAKE_CASE__ : List[Any] =int(key_split[1] )
SCREAMING_SNAKE_CASE__ : Dict =int(key_split[4] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =config.embed_dim
if "weight" in key:
SCREAMING_SNAKE_CASE__ : List[Any] =val[:dim, :]
SCREAMING_SNAKE_CASE__ : Union[str, Any] =val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE__ : Tuple =val[-dim:, :]
else:
SCREAMING_SNAKE_CASE__ : str =val[:dim]
SCREAMING_SNAKE_CASE__ : List[Any] =val[dim : dim * 2]
SCREAMING_SNAKE_CASE__ : Dict =val[-dim:]
pass
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =val
return orig_state_dict
def _a( UpperCamelCase__ : str, UpperCamelCase__ : Optional[int], UpperCamelCase__ : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =get_config(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Dict =SwinaSRForImageSuperResolution(UpperCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] =torch.hub.load_state_dict_from_url(UpperCamelCase__, map_location='''cpu''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] =convert_state_dict(UpperCamelCase__, UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[Any] =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
SCREAMING_SNAKE_CASE__ : Any ='''https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =Image.open(requests.get(UpperCamelCase__, stream=UpperCamelCase__ ).raw ).convert('''RGB''' )
SCREAMING_SNAKE_CASE__ : List[Any] =SwinaSRImageProcessor()
# pixel_values = processor(image, return_tensors="pt").pixel_values
SCREAMING_SNAKE_CASE__ : List[Any] =1_2_6 if '''Jpeg''' in checkpoint_url else 2_5_6
SCREAMING_SNAKE_CASE__ : str =Compose(
[
Resize((image_size, image_size) ),
ToTensor(),
Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6], std=[0.2_2_9, 0.2_2_4, 0.2_2_5] ),
] )
SCREAMING_SNAKE_CASE__ : int =transforms(UpperCamelCase__ ).unsqueeze(0 )
if config.num_channels == 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] =pixel_values[:, 0, :, :].unsqueeze(1 )
SCREAMING_SNAKE_CASE__ : List[str] =model(UpperCamelCase__ )
# assert values
if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : int =torch.Size([1, 3, 5_1_2, 5_1_2] )
SCREAMING_SNAKE_CASE__ : Tuple =torch.tensor(
[[-0.7_0_8_7, -0.7_1_3_8, -0.6_7_2_1], [-0.8_3_4_0, -0.8_0_9_5, -0.7_2_9_8], [-0.9_1_4_9, -0.8_4_1_4, -0.7_9_4_0]] )
elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Any =torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
SCREAMING_SNAKE_CASE__ : Dict =torch.tensor(
[[-0.7_7_7_5, -0.8_1_0_5, -0.8_9_3_3], [-0.7_7_6_4, -0.8_3_5_6, -0.9_2_2_5], [-0.7_9_7_6, -0.8_6_8_6, -0.9_5_7_9]] )
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
# TODO values didn't match exactly here
SCREAMING_SNAKE_CASE__ : Tuple =torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.tensor(
[[-0.8_0_3_5, -0.7_5_0_4, -0.7_4_9_1], [-0.8_5_3_8, -0.8_1_2_4, -0.7_7_8_2], [-0.8_8_0_4, -0.8_6_5_1, -0.8_4_9_3]] )
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : List[Any] =torch.Size([1, 3, 5_1_2, 5_1_2] )
SCREAMING_SNAKE_CASE__ : Tuple =torch.tensor(
[[-0.7_6_6_9, -0.8_6_6_2, -0.8_7_6_7], [-0.8_8_1_0, -0.9_9_6_2, -0.9_8_2_0], [-0.9_3_4_0, -1.0_3_2_2, -1.1_1_4_9]] )
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.Size([1, 3, 1_0_2_4, 1_0_2_4] )
SCREAMING_SNAKE_CASE__ : Optional[Any] =torch.tensor(
[[-0.5_2_3_8, -0.5_5_5_7, -0.6_3_2_1], [-0.6_0_1_6, -0.5_9_0_3, -0.6_3_9_1], [-0.6_2_4_4, -0.6_3_3_4, -0.6_8_8_9]] )
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!''' )
SCREAMING_SNAKE_CASE__ : List[str] ={
'''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'''
),
}
SCREAMING_SNAKE_CASE__ : int =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__":
a_ = 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.')
a_ = parser.parse_args()
convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 717 |
'''simple docstring'''
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =inspect.getfile(accelerate.test_utils )
SCREAMING_SNAKE_CASE__ : Optional[Any] =os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_script.py'''] )
SCREAMING_SNAKE_CASE__ : Dict =os.path.sep.join(inspect.getfile(self.__class__ ).split(os.path.sep )[:-1] )
@require_tpu
def __magic_name__ ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Any =F"\n {self.test_dir}/xla_spawn.py\n --num_cores 8\n {self.test_file_path}\n ".split()
SCREAMING_SNAKE_CASE__ : List[str] =[sys.executable] + distributed_args
execute_subprocess_async(__lowercase , env=os.environ.copy() )
| 665 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a_ = list[list[float | int]]
def _a( UpperCamelCase__ : Matrix, UpperCamelCase__ : Matrix ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Matrix =[[0 for _ in range(size + 1 )] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : float
for row in range(UpperCamelCase__ ):
for col in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Tuple =matrix[row][col]
SCREAMING_SNAKE_CASE__ : Optional[int] =vector[row][0]
SCREAMING_SNAKE_CASE__ : Any =0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =0
while row < size and col < size:
# pivoting
SCREAMING_SNAKE_CASE__ : Any =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__ : Optional[Any] =augmented[pivot_row], augmented[row]
for rowa in range(row + 1, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] =augmented[rowa][col] / augmented[row][col]
SCREAMING_SNAKE_CASE__ : Tuple =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[Any] =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], 1_0 )] for row in range(UpperCamelCase__ )
]
def _a( UpperCamelCase__ : list[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Matrix =[[0 for _ in range(UpperCamelCase__ )] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Matrix =[[0] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Matrix
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
for x_val, y_val in enumerate(UpperCamelCase__ ):
for col in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[int] =(x_val + 1) ** (size - col - 1)
SCREAMING_SNAKE_CASE__ : Dict =y_val
SCREAMING_SNAKE_CASE__ : Optional[int] =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 ):
'''simple docstring'''
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**1_0
)
def _a( UpperCamelCase__ : Callable[[int], int] = question_function, UpperCamelCase__ : int = 1_0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : list[int] =[func(UpperCamelCase__ ) for x_val in range(1, order + 1 )]
SCREAMING_SNAKE_CASE__ : list[Callable[[int], int]] =[
interpolate(data_points[:max_coeff] ) for max_coeff in range(1, order + 1 )
]
SCREAMING_SNAKE_CASE__ : int =0
SCREAMING_SNAKE_CASE__ : Callable[[int], int]
SCREAMING_SNAKE_CASE__ : int
for poly in polynomials:
SCREAMING_SNAKE_CASE__ : Any =1
while func(UpperCamelCase__ ) == poly(UpperCamelCase__ ):
x_val += 1
ret += poly(UpperCamelCase__ )
return ret
if __name__ == "__main__":
print(F'''{solution() = }''')
| 718 |
'''simple docstring'''
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 __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = ShapEImgaImgPipeline
snake_case_ = ["""image"""]
snake_case_ = ["""image"""]
snake_case_ = [
"""num_images_per_prompt""",
"""num_inference_steps""",
"""generator""",
"""latents""",
"""guidance_scale""",
"""frame_size""",
"""output_type""",
"""return_dict""",
]
snake_case_ = False
@property
def __magic_name__ ( self : List[Any] ) -> List[Any]:
return 32
@property
def __magic_name__ ( self : List[str] ) -> Optional[int]:
return 32
@property
def __magic_name__ ( self : Optional[int] ) -> Optional[Any]:
return self.time_input_dim * 4
@property
def __magic_name__ ( self : Dict ) -> Union[str, Any]:
return 8
@property
def __magic_name__ ( self : Optional[int] ) -> Union[str, Any]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Dict =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 , )
SCREAMING_SNAKE_CASE__ : str =CLIPVisionModel(__lowercase )
return model
@property
def __magic_name__ ( self : Tuple ) -> List[str]:
SCREAMING_SNAKE_CASE__ : int =CLIPImageProcessor(
crop_size=2_24 , do_center_crop=__lowercase , do_normalize=__lowercase , do_resize=__lowercase , image_mean=[0.48145466, 0.4578275, 0.40821073] , image_std=[0.26862954, 0.26130258, 0.27577711] , resample=3 , size=2_24 , )
return image_processor
@property
def __magic_name__ ( self : List[str] ) -> Dict:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : str ={
'''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,
}
SCREAMING_SNAKE_CASE__ : str =PriorTransformer(**__lowercase )
return model
@property
def __magic_name__ ( self : Tuple ) -> List[str]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[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,
),
}
SCREAMING_SNAKE_CASE__ : str =ShapERenderer(**__lowercase )
return model
def __magic_name__ ( self : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : int =self.dummy_prior
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dummy_image_encoder
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dummy_image_processor
SCREAMING_SNAKE_CASE__ : Tuple =self.dummy_renderer
SCREAMING_SNAKE_CASE__ : int =HeunDiscreteScheduler(
beta_schedule='''exp''' , num_train_timesteps=10_24 , prediction_type='''sample''' , use_karras_sigmas=__lowercase , clip_sample=__lowercase , clip_sample_range=1.0 , )
SCREAMING_SNAKE_CASE__ : Any ={
'''prior''': prior,
'''image_encoder''': image_encoder,
'''image_processor''': image_processor,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def __magic_name__ ( self : Any , __lowercase : List[str] , __lowercase : Any=0 ) -> Any:
SCREAMING_SNAKE_CASE__ : int =floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowercase ) ).to(__lowercase )
if str(__lowercase ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE__ : List[str] =torch.manual_seed(__lowercase )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =torch.Generator(device=__lowercase ).manual_seed(__lowercase )
SCREAMING_SNAKE_CASE__ : Any ={
'''image''': input_image,
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 32,
'''output_type''': '''np''',
}
return inputs
def __magic_name__ ( self : List[str] ) -> str:
SCREAMING_SNAKE_CASE__ : int ='''cpu'''
SCREAMING_SNAKE_CASE__ : Any =self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : str =self.pipeline_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Any =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =pipe(**self.get_dummy_inputs(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Tuple =output.images[0]
SCREAMING_SNAKE_CASE__ : List[Any] =image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
SCREAMING_SNAKE_CASE__ : List[Any] =np.array(
[
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __magic_name__ ( self : List[Any] ) -> List[str]:
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __magic_name__ ( self : Optional[int] ) -> str:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch_device == '''cpu'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__lowercase , relax_max_difference=__lowercase , )
def __magic_name__ ( self : Dict ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Any =self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Dict =self.pipeline_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =1
SCREAMING_SNAKE_CASE__ : List[str] =2
SCREAMING_SNAKE_CASE__ : Dict =self.get_dummy_inputs(__lowercase )
for key in inputs.keys():
if key in self.batch_params:
SCREAMING_SNAKE_CASE__ : Tuple =batch_size * [inputs[key]]
SCREAMING_SNAKE_CASE__ : List[Any] =pipe(**__lowercase , num_images_per_prompt=__lowercase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : Optional[Any] ) -> int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : int ) -> Dict:
SCREAMING_SNAKE_CASE__ : List[str] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png''' )
SCREAMING_SNAKE_CASE__ : Dict =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_img2img_out.npy''' )
SCREAMING_SNAKE_CASE__ : List[Any] =ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img''' )
SCREAMING_SNAKE_CASE__ : Tuple =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device=__lowercase ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Tuple =pipe(
__lowercase , generator=__lowercase , 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(__lowercase , __lowercase )
| 665 | 0 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 719 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """gpt_bigcode"""
snake_case_ = ["""past_key_values"""]
snake_case_ = {
"""hidden_size""": """n_embd""",
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Any , __lowercase : Any=5_02_57 , __lowercase : int=10_24 , __lowercase : List[str]=7_68 , __lowercase : Optional[int]=12 , __lowercase : Dict=12 , __lowercase : List[str]=None , __lowercase : int="gelu_pytorch_tanh" , __lowercase : Union[str, Any]=0.1 , __lowercase : Optional[int]=0.1 , __lowercase : Optional[int]=0.1 , __lowercase : Optional[Any]=1e-5 , __lowercase : List[str]=0.02 , __lowercase : Tuple=True , __lowercase : Optional[Any]=True , __lowercase : Union[str, Any]=5_02_56 , __lowercase : List[Any]=5_02_56 , __lowercase : Union[str, Any]=True , __lowercase : List[str]=True , __lowercase : Dict=True , **__lowercase : List[Any] , ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =n_positions
SCREAMING_SNAKE_CASE__ : Dict =n_embd
SCREAMING_SNAKE_CASE__ : Dict =n_layer
SCREAMING_SNAKE_CASE__ : Union[str, Any] =n_head
SCREAMING_SNAKE_CASE__ : List[str] =n_inner
SCREAMING_SNAKE_CASE__ : List[str] =activation_function
SCREAMING_SNAKE_CASE__ : List[Any] =resid_pdrop
SCREAMING_SNAKE_CASE__ : List[Any] =embd_pdrop
SCREAMING_SNAKE_CASE__ : List[str] =attn_pdrop
SCREAMING_SNAKE_CASE__ : Dict =layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : List[str] =initializer_range
SCREAMING_SNAKE_CASE__ : List[Any] =scale_attn_weights
SCREAMING_SNAKE_CASE__ : Union[str, Any] =use_cache
SCREAMING_SNAKE_CASE__ : Dict =attention_softmax_in_fpaa
SCREAMING_SNAKE_CASE__ : int =scale_attention_softmax_in_fpaa
SCREAMING_SNAKE_CASE__ : Dict =multi_query
SCREAMING_SNAKE_CASE__ : Optional[Any] =bos_token_id
SCREAMING_SNAKE_CASE__ : Optional[Any] =eos_token_id
super().__init__(bos_token_id=__lowercase , eos_token_id=__lowercase , **__lowercase )
| 665 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : int, UpperCamelCase__ : int ):
'''simple docstring'''
while b:
SCREAMING_SNAKE_CASE__ : Dict =b, a % b
return a
def _a( UpperCamelCase__ : int, UpperCamelCase__ : int ):
'''simple docstring'''
return a if b == 0 else euclidean_gcd_recursive(UpperCamelCase__, a % b )
def _a( ):
'''simple docstring'''
print(f"euclidean_gcd(3, 5) = {euclidean_gcd(3, 5 )}" )
print(f"euclidean_gcd(5, 3) = {euclidean_gcd(5, 3 )}" )
print(f"euclidean_gcd(1, 3) = {euclidean_gcd(1, 3 )}" )
print(f"euclidean_gcd(3, 6) = {euclidean_gcd(3, 6 )}" )
print(f"euclidean_gcd(6, 3) = {euclidean_gcd(6, 3 )}" )
print(f"euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3, 5 )}" )
print(f"euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5, 3 )}" )
print(f"euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1, 3 )}" )
print(f"euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3, 6 )}" )
print(f"euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6, 3 )}" )
if __name__ == "__main__":
main()
| 720 |
'''simple docstring'''
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =size
SCREAMING_SNAKE_CASE__ : List[Any] =[0] * size
SCREAMING_SNAKE_CASE__ : str =[0] * size
@staticmethod
def __magic_name__ ( __lowercase : int ) -> int:
return index | (index + 1)
@staticmethod
def __magic_name__ ( __lowercase : int ) -> int:
return (index & (index + 1)) - 1
def __magic_name__ ( self : Dict , __lowercase : int , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : List[str] =value
while index < self.size:
SCREAMING_SNAKE_CASE__ : Any =self.get_prev(__lowercase ) + 1
if current_left_border == index:
SCREAMING_SNAKE_CASE__ : List[str] =value
else:
SCREAMING_SNAKE_CASE__ : str =max(__lowercase , __lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.get_next(__lowercase )
def __magic_name__ ( self : Optional[int] , __lowercase : int , __lowercase : int ) -> int:
right -= 1 # Because of right is exclusive
SCREAMING_SNAKE_CASE__ : str =0
while left <= right:
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.get_prev(__lowercase )
if left <= current_left:
SCREAMING_SNAKE_CASE__ : List[Any] =max(__lowercase , self.tree[right] )
SCREAMING_SNAKE_CASE__ : Any =current_left
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =max(__lowercase , self.arr[right] )
right -= 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __SCREAMING_SNAKE_CASE :
snake_case_ = 42
snake_case_ = 42
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , __lowercase : int ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : list[list[Edge]] =[[] for _ in range(__lowercase )]
SCREAMING_SNAKE_CASE__ : Dict =size
def __getitem__( self : Dict , __lowercase : int ) -> Iterator[Edge]:
return iter(self._graph[vertex] )
@property
def __magic_name__ ( self : List[str] ) -> str:
return self._size
def __magic_name__ ( self : Tuple , __lowercase : int , __lowercase : int , __lowercase : int ) -> List[Any]:
if weight not in (0, 1):
raise ValueError('''Edge weight must be either 0 or 1.''' )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError('''Vertex indexes must be in [0; size).''' )
self._graph[from_vertex].append(Edge(__lowercase , __lowercase ) )
def __magic_name__ ( self : str , __lowercase : int , __lowercase : int ) -> int | None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =deque([start_vertex] )
SCREAMING_SNAKE_CASE__ : list[int | None] =[None] * self.size
SCREAMING_SNAKE_CASE__ : Dict =0
while queue:
SCREAMING_SNAKE_CASE__ : List[str] =queue.popleft()
SCREAMING_SNAKE_CASE__ : Optional[Any] =distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
SCREAMING_SNAKE_CASE__ : str =current_distance + edge.weight
SCREAMING_SNAKE_CASE__ : Optional[int] =distances[edge.destination_vertex]
if (
isinstance(__lowercase , __lowercase )
and new_distance >= dest_vertex_distance
):
continue
SCREAMING_SNAKE_CASE__ : List[Any] =new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError('''No path from start_vertex to finish_vertex.''' )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 721 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = ["""vqvae"""]
def __init__( self : int , __lowercase : AutoencoderKL , __lowercase : UNetaDConditionModel , __lowercase : Mel , __lowercase : Union[DDIMScheduler, DDPMScheduler] , ) -> int:
super().__init__()
self.register_modules(unet=__lowercase , scheduler=__lowercase , mel=__lowercase , vqvae=__lowercase )
def __magic_name__ ( self : List[str] ) -> int:
return 50 if isinstance(self.scheduler , __lowercase ) else 10_00
@torch.no_grad()
def __call__( self : Dict , __lowercase : int = 1 , __lowercase : str = None , __lowercase : np.ndarray = None , __lowercase : int = 0 , __lowercase : int = 0 , __lowercase : int = None , __lowercase : torch.Generator = None , __lowercase : float = 0 , __lowercase : float = 0 , __lowercase : torch.Generator = None , __lowercase : float = 0 , __lowercase : torch.Tensor = None , __lowercase : torch.Tensor = None , __lowercase : Dict=True , ) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =steps or self.get_default_steps()
self.scheduler.set_timesteps(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
SCREAMING_SNAKE_CASE__ : Optional[int] =randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=__lowercase , device=self.device , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =noise
SCREAMING_SNAKE_CASE__ : List[str] =None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Dict =self.mel.audio_slice_to_image(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =np.frombuffer(input_image.tobytes() , dtype='''uint8''' ).reshape(
(input_image.height, input_image.width) )
SCREAMING_SNAKE_CASE__ : int =(input_image / 2_55) * 2 - 1
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.vqvae.encode(torch.unsqueeze(__lowercase , 0 ) ).latent_dist.sample(
generator=__lowercase )[0]
SCREAMING_SNAKE_CASE__ : int =self.vqvae.config.scaling_factor * input_images
if start_step > 0:
SCREAMING_SNAKE_CASE__ : int =self.scheduler.add_noise(__lowercase , __lowercase , self.scheduler.timesteps[start_step - 1] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
SCREAMING_SNAKE_CASE__ : Optional[Any] =int(mask_start_secs * pixels_per_second )
SCREAMING_SNAKE_CASE__ : Tuple =int(mask_end_secs * pixels_per_second )
SCREAMING_SNAKE_CASE__ : int =self.scheduler.add_noise(__lowercase , __lowercase , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , __lowercase ):
SCREAMING_SNAKE_CASE__ : str =self.unet(__lowercase , __lowercase , __lowercase )['''sample''']
else:
SCREAMING_SNAKE_CASE__ : str =self.unet(__lowercase , __lowercase )['''sample''']
if isinstance(self.scheduler , __lowercase ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.scheduler.step(
model_output=__lowercase , timestep=__lowercase , sample=__lowercase , eta=__lowercase , generator=__lowercase , )['''prev_sample''']
else:
SCREAMING_SNAKE_CASE__ : Any =self.scheduler.step(
model_output=__lowercase , timestep=__lowercase , sample=__lowercase , generator=__lowercase , )['''prev_sample''']
if mask is not None:
if mask_start > 0:
SCREAMING_SNAKE_CASE__ : Optional[Any] =mask[:, step, :, :mask_start]
if mask_end > 0:
SCREAMING_SNAKE_CASE__ : List[str] =mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
SCREAMING_SNAKE_CASE__ : str =1 / self.vqvae.config.scaling_factor * images
SCREAMING_SNAKE_CASE__ : int =self.vqvae.decode(__lowercase )['''sample''']
SCREAMING_SNAKE_CASE__ : List[str] =(images / 2 + 0.5).clamp(0 , 1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] =images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
SCREAMING_SNAKE_CASE__ : Dict =(images * 2_55).round().astype('''uint8''' )
SCREAMING_SNAKE_CASE__ : Any =list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(__lowercase , mode='''RGB''' ).convert('''L''' ) for _ in images) )
SCREAMING_SNAKE_CASE__ : Optional[int] =[self.mel.image_to_audio(__lowercase ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(__lowercase )[:, np.newaxis, :] ) , **ImagePipelineOutput(__lowercase ) )
@torch.no_grad()
def __magic_name__ ( self : Optional[int] , __lowercase : List[Image.Image] , __lowercase : int = 50 ) -> np.ndarray:
assert isinstance(self.scheduler , __lowercase )
self.scheduler.set_timesteps(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =np.array(
[np.frombuffer(image.tobytes() , dtype='''uint8''' ).reshape((1, image.height, image.width) ) for image in images] )
SCREAMING_SNAKE_CASE__ : str =(sample / 2_55) * 2 - 1
SCREAMING_SNAKE_CASE__ : str =torch.Tensor(__lowercase ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
SCREAMING_SNAKE_CASE__ : Tuple =t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
SCREAMING_SNAKE_CASE__ : Any =self.scheduler.alphas_cumprod[t]
SCREAMING_SNAKE_CASE__ : int =(
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
SCREAMING_SNAKE_CASE__ : int =1 - alpha_prod_t
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.unet(__lowercase , __lowercase )['''sample''']
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(1 - alpha_prod_t_prev) ** 0.5 * model_output
SCREAMING_SNAKE_CASE__ : str =(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
SCREAMING_SNAKE_CASE__ : Any =sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def __magic_name__ ( __lowercase : torch.Tensor , __lowercase : torch.Tensor , __lowercase : float ) -> torch.Tensor:
SCREAMING_SNAKE_CASE__ : Optional[int] =acos(torch.dot(torch.flatten(__lowercase ) , torch.flatten(__lowercase ) ) / torch.norm(__lowercase ) / torch.norm(__lowercase ) )
return sin((1 - alpha) * theta ) * xa / sin(__lowercase ) + sin(alpha * theta ) * xa / sin(__lowercase )
| 665 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , __lowercase : Optional[int] , __lowercase : str=13 , __lowercase : int=10 , __lowercase : List[Any]=3 , __lowercase : List[str]=2 , __lowercase : int=2 , __lowercase : Dict=True , __lowercase : Optional[Any]=True , __lowercase : int=32 , __lowercase : List[Any]=5 , __lowercase : Union[str, Any]=4 , __lowercase : Any=37 , __lowercase : Optional[Any]="gelu" , __lowercase : Union[str, Any]=0.1 , __lowercase : Tuple=0.1 , __lowercase : Dict=10 , __lowercase : int=0.02 , __lowercase : str="divided_space_time" , __lowercase : Union[str, Any]=None , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : int =parent
SCREAMING_SNAKE_CASE__ : List[str] =batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =image_size
SCREAMING_SNAKE_CASE__ : List[Any] =num_channels
SCREAMING_SNAKE_CASE__ : int =patch_size
SCREAMING_SNAKE_CASE__ : Tuple =num_frames
SCREAMING_SNAKE_CASE__ : List[Any] =is_training
SCREAMING_SNAKE_CASE__ : List[str] =use_labels
SCREAMING_SNAKE_CASE__ : Optional[Any] =hidden_size
SCREAMING_SNAKE_CASE__ : str =num_hidden_layers
SCREAMING_SNAKE_CASE__ : int =num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple =intermediate_size
SCREAMING_SNAKE_CASE__ : List[str] =hidden_act
SCREAMING_SNAKE_CASE__ : Dict =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[Any] =attention_type
SCREAMING_SNAKE_CASE__ : Union[str, Any] =initializer_range
SCREAMING_SNAKE_CASE__ : Any =scope
SCREAMING_SNAKE_CASE__ : int =num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
SCREAMING_SNAKE_CASE__ : List[str] =(image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE__ : str =(num_frames) * self.num_patches_per_frame + 1
def __magic_name__ ( self : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : List[Any] =None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Any =ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE__ : int =self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : int ) -> Any:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , 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 , initializer_range=self.initializer_range , attention_type=self.attention_type , )
SCREAMING_SNAKE_CASE__ : List[Any] =self.num_labels
return config
def __magic_name__ ( self : int , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : List[Any] ) -> int:
SCREAMING_SNAKE_CASE__ : Tuple =TimesformerModel(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] =model(__lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : List[str] , __lowercase : str , __lowercase : Tuple , __lowercase : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : str =TimesformerForVideoClassification(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(__lowercase )
# verify the logits shape
SCREAMING_SNAKE_CASE__ : Tuple =torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , __lowercase )
def __magic_name__ ( self : Any ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : int =self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : str =config_and_inputs
SCREAMING_SNAKE_CASE__ : Any ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
snake_case_ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
snake_case_ = (
{"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def __magic_name__ ( self : str ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Dict =TimesformerModelTester(self )
SCREAMING_SNAKE_CASE__ : List[Any] =ConfigTester(
self , config_class=__lowercase , has_text_modality=__lowercase , hidden_size=37 )
def __magic_name__ ( self : str , __lowercase : Dict , __lowercase : str , __lowercase : Optional[int]=False ) -> int:
SCREAMING_SNAKE_CASE__ : str =copy.deepcopy(__lowercase )
if return_labels:
if model_class in get_values(__lowercase ):
SCREAMING_SNAKE_CASE__ : Any =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowercase )
return inputs_dict
def __magic_name__ ( self : List[Any] ) -> Any:
self.config_tester.run_common_tests()
@unittest.skip(reason='''TimeSformer does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ) -> Optional[int]:
pass
def __magic_name__ ( self : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : str =model_class(__lowercase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE__ : Any =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__lowercase , nn.Linear ) )
def __magic_name__ ( self : Any ) -> Any:
SCREAMING_SNAKE_CASE__ : str =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Optional[Any] =model_class(__lowercase )
SCREAMING_SNAKE_CASE__ : str =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE__ : List[str] =[*signature.parameters.keys()]
SCREAMING_SNAKE_CASE__ : Dict =['''pixel_values''']
self.assertListEqual(arg_names[:1] , __lowercase )
def __magic_name__ ( self : int ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase )
def __magic_name__ ( self : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*__lowercase )
@slow
def __magic_name__ ( self : Optional[int] ) -> Optional[Any]:
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =TimesformerModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> List[str]:
if not self.has_attentions:
pass
else:
SCREAMING_SNAKE_CASE__ : Any =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[Any] =True
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.seq_length
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.num_frames
SCREAMING_SNAKE_CASE__ : Optional[Any] =True
SCREAMING_SNAKE_CASE__ : str =False
SCREAMING_SNAKE_CASE__ : Tuple =True
SCREAMING_SNAKE_CASE__ : Dict =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(**self._prepare_for_class(__lowercase , __lowercase ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =outputs.attentions
self.assertEqual(len(__lowercase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
SCREAMING_SNAKE_CASE__ : List[Any] =True
SCREAMING_SNAKE_CASE__ : List[str] =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Any =model(**self._prepare_for_class(__lowercase , __lowercase ) )
SCREAMING_SNAKE_CASE__ : List[Any] =outputs.attentions
self.assertEqual(len(__lowercase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
SCREAMING_SNAKE_CASE__ : Optional[int] =len(__lowercase )
# Check attention is always last and order is fine
SCREAMING_SNAKE_CASE__ : Optional[int] =True
SCREAMING_SNAKE_CASE__ : Union[str, Any] =True
SCREAMING_SNAKE_CASE__ : Optional[Any] =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(**self._prepare_for_class(__lowercase , __lowercase ) )
self.assertEqual(out_len + 1 , len(__lowercase ) )
SCREAMING_SNAKE_CASE__ : List[str] =outputs.attentions
self.assertEqual(len(__lowercase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def __magic_name__ ( self : Tuple ) -> List[Any]:
def check_hidden_states_output(__lowercase : Tuple , __lowercase : Dict , __lowercase : Optional[int] ):
SCREAMING_SNAKE_CASE__ : List[Any] =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : int =model(**self._prepare_for_class(__lowercase , __lowercase ) )
SCREAMING_SNAKE_CASE__ : List[Any] =outputs.hidden_states
SCREAMING_SNAKE_CASE__ : Tuple =self.model_tester.num_hidden_layers + 1
self.assertEqual(len(__lowercase ) , __lowercase )
SCREAMING_SNAKE_CASE__ : int =self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
SCREAMING_SNAKE_CASE__ : int =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Tuple =True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE__ : List[str] =True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''', filename='''eating_spaghetti.npy''', repo_type='''dataset''' )
SCREAMING_SNAKE_CASE__ : Any =np.load(UpperCamelCase__ )
return list(UpperCamelCase__ )
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : Any ) -> List[str]:
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def __magic_name__ ( self : Any ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : int =TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400''' ).to(
__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =self.default_image_processor
SCREAMING_SNAKE_CASE__ : Tuple =prepare_video()
SCREAMING_SNAKE_CASE__ : Any =image_processor(video[:8] , return_tensors='''pt''' ).to(__lowercase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Optional[int] =model(**__lowercase )
# verify the logits
SCREAMING_SNAKE_CASE__ : List[str] =torch.Size((1, 4_00) )
self.assertEqual(outputs.logits.shape , __lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =torch.tensor([-0.3016, -0.7713, -0.4205] ).to(__lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowercase , atol=1e-4 ) )
| 700 |
'''simple docstring'''
from math import isqrt
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =[True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2, UpperCamelCase__, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Any =False
return [i for i in range(2, UpperCamelCase__ ) if is_prime[i]]
def _a( UpperCamelCase__ : int = 1_0**8 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple =calculate_prime_numbers(max_number // 2 )
SCREAMING_SNAKE_CASE__ : int =0
SCREAMING_SNAKE_CASE__ : int =0
SCREAMING_SNAKE_CASE__ : Optional[int] =len(UpperCamelCase__ ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F'''{solution() = }''')
| 665 | 0 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
a_ = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Arora\n and Steven Basart\n and Eric Tang\n and Dawn Song\n and Jacob Steinhardt},\n journal={arXiv preprint arXiv:2103.03874},\n year={2021}\n}\n'
a_ = '\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'
a_ = R'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n predictions: list of predictions to score. Each prediction\n is a string that contains natural language and LaTex.\n references: list of reference for each prediction. Each\n reference is a string that contains natural language\n and LaTex.\nReturns:\n accuracy: accuracy after canonicalizing inputs\n (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n >>> metric = datasets.load_metric("competition_math")\n >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n >>> print(results)\n {\'accuracy\': 1.0}\n'
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE ( datasets.Metric ):
def __magic_name__ ( self : Optional[int] ) -> List[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' ),
'''references''': datasets.Value('''string''' ),
} ) , homepage='''https://github.com/hendrycks/math''' , codebase_urls=['''https://github.com/hendrycks/math'''] , )
def __magic_name__ ( self : int , __lowercase : Union[str, Any] , __lowercase : Union[str, Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : int =0.0
for i, j in zip(__lowercase , __lowercase ):
n_correct += 1.0 if math_equivalence.is_equiv(__lowercase , __lowercase ) else 0.0
SCREAMING_SNAKE_CASE__ : str =n_correct / len(__lowercase )
return {
"accuracy": accuracy,
}
| 701 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
a_ = get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = SpeechTaTokenizer
snake_case_ = False
snake_case_ = True
def __magic_name__ ( self : int ) -> Any:
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : Optional[Any] =SpeechTaTokenizer(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =AddedToken('''<mask>''' , lstrip=__lowercase , rstrip=__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =mask_token
tokenizer.add_special_tokens({'''mask_token''': mask_token} )
tokenizer.add_tokens(['''<ctc_blank>'''] )
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__ ( self : Dict , __lowercase : int ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Optional[Any] ='''this is a test'''
SCREAMING_SNAKE_CASE__ : int ='''this is a test'''
return input_text, output_text
def __magic_name__ ( self : List[Any] , __lowercase : int , __lowercase : Optional[Any]=False , __lowercase : Union[str, Any]=20 , __lowercase : Any=5 ) -> Any:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int =self.get_input_output_texts(__lowercase )
SCREAMING_SNAKE_CASE__ : str =tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
SCREAMING_SNAKE_CASE__ : str =tokenizer.decode(__lowercase , clean_up_tokenization_spaces=__lowercase )
return text, ids
def __magic_name__ ( self : Dict ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] ='''<pad>'''
SCREAMING_SNAKE_CASE__ : Optional[int] =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowercase ) , __lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowercase ) , __lowercase )
def __magic_name__ ( self : Tuple ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-4] , '''œ''' )
self.assertEqual(vocab_keys[-2] , '''<mask>''' )
self.assertEqual(vocab_keys[-1] , '''<ctc_blank>''' )
self.assertEqual(len(__lowercase ) , 81 )
def __magic_name__ ( self : Dict ) -> List[str]:
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def __magic_name__ ( self : Optional[Any] ) -> str:
SCREAMING_SNAKE_CASE__ : str =self.get_tokenizers(do_lower_case=__lowercase )
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
SCREAMING_SNAKE_CASE__ : Tuple =tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : Any =len(__lowercase )
self.assertNotEqual(__lowercase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
SCREAMING_SNAKE_CASE__ : int =['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.add_tokens(__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] =len(__lowercase )
self.assertNotEqual(__lowercase , 0 )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , len(__lowercase ) )
self.assertEqual(__lowercase , all_size + len(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Tuple =tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l''' , add_special_tokens=__lowercase )
self.assertGreaterEqual(len(__lowercase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
SCREAMING_SNAKE_CASE__ : str ={'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
SCREAMING_SNAKE_CASE__ : int =tokenizer.add_special_tokens(__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : int =len(__lowercase )
self.assertNotEqual(__lowercase , 0 )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , len(__lowercase ) )
self.assertEqual(__lowercase , all_size_a + len(__lowercase ) )
SCREAMING_SNAKE_CASE__ : List[Any] =tokenizer.encode(
'''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l''' , add_special_tokens=__lowercase )
self.assertGreaterEqual(len(__lowercase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def __magic_name__ ( self : Optional[Any] ) -> Any:
pass
def __magic_name__ ( self : List[str] ) -> List[Any]:
pass
def __magic_name__ ( self : Dict ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Dict =self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer.tokenize('''This is a test''' )
# fmt: off
self.assertListEqual(__lowercase , [SPIECE_UNDERLINE, '''T''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''a''', SPIECE_UNDERLINE, '''t''', '''e''', '''s''', '''t'''] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowercase ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__lowercase , [SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''92000''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =tokenizer.convert_tokens_to_ids(__lowercase )
# fmt: off
self.assertListEqual(__lowercase , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer.convert_ids_to_tokens(__lowercase )
self.assertListEqual(
__lowercase , [SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''<unk>''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''] )
@slow
def __magic_name__ ( self : List[str] ) -> List[str]:
# Use custom sequence because this tokenizer does not handle numbers.
SCREAMING_SNAKE_CASE__ : List[Any] =[
'''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
'''general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
'''Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained '''
'''models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
'''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
'''conditioning on both left and right context in all layers.''',
'''The quick brown fox jumps over the lazy dog.''',
]
# fmt: off
SCREAMING_SNAKE_CASE__ : str ={
'''input_ids''': [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
],
'''attention_mask''': [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__lowercase , model_name='''microsoft/speecht5_asr''' , revision='''c5ef64c71905caeccde0e4462ef3f9077224c524''' , sequences=__lowercase , )
| 665 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """wavlm"""
def __init__( self : List[Any] , __lowercase : Any=32 , __lowercase : List[str]=7_68 , __lowercase : Any=12 , __lowercase : Any=12 , __lowercase : Dict=30_72 , __lowercase : Union[str, Any]="gelu" , __lowercase : List[str]=0.1 , __lowercase : List[str]=0.1 , __lowercase : Dict=0.1 , __lowercase : Union[str, Any]=0.0 , __lowercase : str=0.1 , __lowercase : List[Any]=0.1 , __lowercase : Dict=0.02 , __lowercase : Any=1e-5 , __lowercase : Union[str, Any]="group" , __lowercase : List[str]="gelu" , __lowercase : Optional[Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __lowercase : Union[str, Any]=(5, 2, 2, 2, 2, 2, 2) , __lowercase : Dict=(10, 3, 3, 3, 3, 2, 2) , __lowercase : str=False , __lowercase : Optional[int]=1_28 , __lowercase : Dict=16 , __lowercase : int=3_20 , __lowercase : Optional[Any]=8_00 , __lowercase : Optional[int]=False , __lowercase : int=True , __lowercase : str=0.05 , __lowercase : Any=10 , __lowercase : List[Any]=2 , __lowercase : Optional[Any]=0.0 , __lowercase : Any=10 , __lowercase : Dict=3_20 , __lowercase : Optional[Any]=2 , __lowercase : int=0.1 , __lowercase : Union[str, Any]=1_00 , __lowercase : Optional[Any]=2_56 , __lowercase : Dict=2_56 , __lowercase : Dict=0.1 , __lowercase : Any="mean" , __lowercase : List[Any]=False , __lowercase : List[Any]=False , __lowercase : Any=2_56 , __lowercase : List[Any]=(5_12, 5_12, 5_12, 5_12, 15_00) , __lowercase : Optional[int]=(5, 3, 3, 1, 1) , __lowercase : Optional[int]=(1, 2, 3, 1, 1) , __lowercase : str=5_12 , __lowercase : Any=80 , __lowercase : List[Any]=0 , __lowercase : Optional[Any]=1 , __lowercase : int=2 , __lowercase : Tuple=False , __lowercase : int=3 , __lowercase : Tuple=2 , __lowercase : List[Any]=3 , __lowercase : Tuple=None , **__lowercase : Any , ) -> Any:
super().__init__(**__lowercase , pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase )
SCREAMING_SNAKE_CASE__ : int =hidden_size
SCREAMING_SNAKE_CASE__ : str =feat_extract_norm
SCREAMING_SNAKE_CASE__ : str =feat_extract_activation
SCREAMING_SNAKE_CASE__ : int =list(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =list(__lowercase )
SCREAMING_SNAKE_CASE__ : int =list(__lowercase )
SCREAMING_SNAKE_CASE__ : int =conv_bias
SCREAMING_SNAKE_CASE__ : Tuple =num_buckets
SCREAMING_SNAKE_CASE__ : List[str] =max_bucket_distance
SCREAMING_SNAKE_CASE__ : Union[str, Any] =num_conv_pos_embeddings
SCREAMING_SNAKE_CASE__ : Union[str, Any] =num_conv_pos_embedding_groups
SCREAMING_SNAKE_CASE__ : Optional[int] =len(self.conv_dim )
SCREAMING_SNAKE_CASE__ : Optional[int] =num_hidden_layers
SCREAMING_SNAKE_CASE__ : Dict =intermediate_size
SCREAMING_SNAKE_CASE__ : int =hidden_act
SCREAMING_SNAKE_CASE__ : List[Any] =num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple =hidden_dropout
SCREAMING_SNAKE_CASE__ : Dict =attention_dropout
SCREAMING_SNAKE_CASE__ : List[str] =activation_dropout
SCREAMING_SNAKE_CASE__ : List[Any] =feat_proj_dropout
SCREAMING_SNAKE_CASE__ : str =final_dropout
SCREAMING_SNAKE_CASE__ : List[Any] =layerdrop
SCREAMING_SNAKE_CASE__ : List[str] =layer_norm_eps
SCREAMING_SNAKE_CASE__ : int =initializer_range
SCREAMING_SNAKE_CASE__ : List[str] =num_ctc_classes
SCREAMING_SNAKE_CASE__ : Tuple =vocab_size
SCREAMING_SNAKE_CASE__ : Optional[Any] =do_stable_layer_norm
SCREAMING_SNAKE_CASE__ : str =use_weighted_layer_sum
SCREAMING_SNAKE_CASE__ : Tuple =classifier_proj_size
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
SCREAMING_SNAKE_CASE__ : int =apply_spec_augment
SCREAMING_SNAKE_CASE__ : Tuple =mask_time_prob
SCREAMING_SNAKE_CASE__ : Any =mask_time_length
SCREAMING_SNAKE_CASE__ : Dict =mask_time_min_masks
SCREAMING_SNAKE_CASE__ : Optional[Any] =mask_feature_prob
SCREAMING_SNAKE_CASE__ : List[Any] =mask_feature_length
# parameters for pretraining with codevector quantized representations
SCREAMING_SNAKE_CASE__ : int =num_codevectors_per_group
SCREAMING_SNAKE_CASE__ : List[Any] =num_codevector_groups
SCREAMING_SNAKE_CASE__ : Union[str, Any] =contrastive_logits_temperature
SCREAMING_SNAKE_CASE__ : Optional[Any] =num_negatives
SCREAMING_SNAKE_CASE__ : List[Any] =codevector_dim
SCREAMING_SNAKE_CASE__ : Optional[Any] =proj_codevector_dim
SCREAMING_SNAKE_CASE__ : int =diversity_loss_weight
# ctc loss
SCREAMING_SNAKE_CASE__ : List[Any] =ctc_loss_reduction
SCREAMING_SNAKE_CASE__ : Any =ctc_zero_infinity
# adapter
SCREAMING_SNAKE_CASE__ : Optional[int] =add_adapter
SCREAMING_SNAKE_CASE__ : List[str] =adapter_kernel_size
SCREAMING_SNAKE_CASE__ : int =adapter_stride
SCREAMING_SNAKE_CASE__ : Union[str, Any] =num_adapter_layers
SCREAMING_SNAKE_CASE__ : Optional[int] =output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE__ : Any =classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE__ : Dict =list(__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =list(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =list(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =xvector_output_dim
@property
def __magic_name__ ( self : Tuple ) -> Optional[Any]:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 702 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , __lowercase : Optional[int] , __lowercase : str=13 , __lowercase : int=10 , __lowercase : List[Any]=3 , __lowercase : List[str]=2 , __lowercase : int=2 , __lowercase : Dict=True , __lowercase : Optional[Any]=True , __lowercase : int=32 , __lowercase : List[Any]=5 , __lowercase : Union[str, Any]=4 , __lowercase : Any=37 , __lowercase : Optional[Any]="gelu" , __lowercase : Union[str, Any]=0.1 , __lowercase : Tuple=0.1 , __lowercase : Dict=10 , __lowercase : int=0.02 , __lowercase : str="divided_space_time" , __lowercase : Union[str, Any]=None , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : int =parent
SCREAMING_SNAKE_CASE__ : List[str] =batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =image_size
SCREAMING_SNAKE_CASE__ : List[Any] =num_channels
SCREAMING_SNAKE_CASE__ : int =patch_size
SCREAMING_SNAKE_CASE__ : Tuple =num_frames
SCREAMING_SNAKE_CASE__ : List[Any] =is_training
SCREAMING_SNAKE_CASE__ : List[str] =use_labels
SCREAMING_SNAKE_CASE__ : Optional[Any] =hidden_size
SCREAMING_SNAKE_CASE__ : str =num_hidden_layers
SCREAMING_SNAKE_CASE__ : int =num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple =intermediate_size
SCREAMING_SNAKE_CASE__ : List[str] =hidden_act
SCREAMING_SNAKE_CASE__ : Dict =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[Any] =attention_type
SCREAMING_SNAKE_CASE__ : Union[str, Any] =initializer_range
SCREAMING_SNAKE_CASE__ : Any =scope
SCREAMING_SNAKE_CASE__ : int =num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
SCREAMING_SNAKE_CASE__ : List[str] =(image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE__ : str =(num_frames) * self.num_patches_per_frame + 1
def __magic_name__ ( self : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : List[Any] =None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Any =ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE__ : int =self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : int ) -> Any:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , 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 , initializer_range=self.initializer_range , attention_type=self.attention_type , )
SCREAMING_SNAKE_CASE__ : List[Any] =self.num_labels
return config
def __magic_name__ ( self : int , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : List[Any] ) -> int:
SCREAMING_SNAKE_CASE__ : Tuple =TimesformerModel(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] =model(__lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : List[str] , __lowercase : str , __lowercase : Tuple , __lowercase : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : str =TimesformerForVideoClassification(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(__lowercase )
# verify the logits shape
SCREAMING_SNAKE_CASE__ : Tuple =torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , __lowercase )
def __magic_name__ ( self : Any ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : int =self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str =config_and_inputs
SCREAMING_SNAKE_CASE__ : Any ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
snake_case_ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
snake_case_ = (
{"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def __magic_name__ ( self : str ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Dict =TimesformerModelTester(self )
SCREAMING_SNAKE_CASE__ : List[Any] =ConfigTester(
self , config_class=__lowercase , has_text_modality=__lowercase , hidden_size=37 )
def __magic_name__ ( self : str , __lowercase : Dict , __lowercase : str , __lowercase : Optional[int]=False ) -> int:
SCREAMING_SNAKE_CASE__ : str =copy.deepcopy(__lowercase )
if return_labels:
if model_class in get_values(__lowercase ):
SCREAMING_SNAKE_CASE__ : Any =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowercase )
return inputs_dict
def __magic_name__ ( self : List[Any] ) -> Any:
self.config_tester.run_common_tests()
@unittest.skip(reason='''TimeSformer does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ) -> Optional[int]:
pass
def __magic_name__ ( self : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : str =model_class(__lowercase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE__ : Any =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__lowercase , nn.Linear ) )
def __magic_name__ ( self : Any ) -> Any:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Optional[Any] =model_class(__lowercase )
SCREAMING_SNAKE_CASE__ : str =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE__ : List[str] =[*signature.parameters.keys()]
SCREAMING_SNAKE_CASE__ : Dict =['''pixel_values''']
self.assertListEqual(arg_names[:1] , __lowercase )
def __magic_name__ ( self : int ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase )
def __magic_name__ ( self : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*__lowercase )
@slow
def __magic_name__ ( self : Optional[int] ) -> Optional[Any]:
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =TimesformerModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> List[str]:
if not self.has_attentions:
pass
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[Any] =True
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.seq_length
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.num_frames
SCREAMING_SNAKE_CASE__ : Optional[Any] =True
SCREAMING_SNAKE_CASE__ : str =False
SCREAMING_SNAKE_CASE__ : Tuple =True
SCREAMING_SNAKE_CASE__ : Dict =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(**self._prepare_for_class(__lowercase , __lowercase ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =outputs.attentions
self.assertEqual(len(__lowercase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
SCREAMING_SNAKE_CASE__ : List[Any] =True
SCREAMING_SNAKE_CASE__ : List[str] =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Any =model(**self._prepare_for_class(__lowercase , __lowercase ) )
SCREAMING_SNAKE_CASE__ : List[Any] =outputs.attentions
self.assertEqual(len(__lowercase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
SCREAMING_SNAKE_CASE__ : Optional[int] =len(__lowercase )
# Check attention is always last and order is fine
SCREAMING_SNAKE_CASE__ : Optional[int] =True
SCREAMING_SNAKE_CASE__ : Union[str, Any] =True
SCREAMING_SNAKE_CASE__ : Optional[Any] =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(**self._prepare_for_class(__lowercase , __lowercase ) )
self.assertEqual(out_len + 1 , len(__lowercase ) )
SCREAMING_SNAKE_CASE__ : List[str] =outputs.attentions
self.assertEqual(len(__lowercase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def __magic_name__ ( self : Tuple ) -> List[Any]:
def check_hidden_states_output(__lowercase : Tuple , __lowercase : Dict , __lowercase : Optional[int] ):
SCREAMING_SNAKE_CASE__ : List[Any] =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : int =model(**self._prepare_for_class(__lowercase , __lowercase ) )
SCREAMING_SNAKE_CASE__ : List[Any] =outputs.hidden_states
SCREAMING_SNAKE_CASE__ : Tuple =self.model_tester.num_hidden_layers + 1
self.assertEqual(len(__lowercase ) , __lowercase )
SCREAMING_SNAKE_CASE__ : int =self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Tuple =True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE__ : List[str] =True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''', filename='''eating_spaghetti.npy''', repo_type='''dataset''' )
SCREAMING_SNAKE_CASE__ : Any =np.load(UpperCamelCase__ )
return list(UpperCamelCase__ )
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : Any ) -> List[str]:
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def __magic_name__ ( self : Any ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : int =TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400''' ).to(
__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =self.default_image_processor
SCREAMING_SNAKE_CASE__ : Tuple =prepare_video()
SCREAMING_SNAKE_CASE__ : Any =image_processor(video[:8] , return_tensors='''pt''' ).to(__lowercase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Optional[int] =model(**__lowercase )
# verify the logits
SCREAMING_SNAKE_CASE__ : List[str] =torch.Size((1, 4_00) )
self.assertEqual(outputs.logits.shape , __lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =torch.tensor([-0.3016, -0.7713, -0.4205] ).to(__lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowercase , atol=1e-4 ) )
| 665 | 0 |
'''simple docstring'''
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_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
a_ = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase )
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : Any , *__lowercase : List[Any] , **__lowercase : List[Any] ) -> Union[str, Any]:
super().__init__(*__lowercase , **__lowercase )
requires_backends(self , '''vision''' )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING )
def __magic_name__ ( self : Union[str, Any] , __lowercase : str=None , __lowercase : str=None , __lowercase : Any=None ) -> Any:
SCREAMING_SNAKE_CASE__ : List[Any] ={}
SCREAMING_SNAKE_CASE__ : Tuple ={}
if prompt is not None:
SCREAMING_SNAKE_CASE__ : Optional[int] =prompt
if generate_kwargs is not None:
SCREAMING_SNAKE_CASE__ : int =generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
SCREAMING_SNAKE_CASE__ : Optional[int] ={}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
'''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
''' please use only one''' )
SCREAMING_SNAKE_CASE__ : List[str] =max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self : Any , __lowercase : Union[str, List[str], "Image.Image", List["Image.Image"]] , **__lowercase : Optional[int] ) -> Tuple:
return super().__call__(__lowercase , **__lowercase )
def __magic_name__ ( self : int , __lowercase : str , __lowercase : str=None ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : str =load_image(__lowercase )
if prompt is not None:
if not isinstance(__lowercase , __lowercase ):
raise ValueError(
F"Received an invalid text input, got - {type(__lowercase )} - but expected a single string. "
'''Note also that one single text can be provided for conditional image to text generation.''' )
SCREAMING_SNAKE_CASE__ : Tuple =self.model.config.model_type
if model_type == "git":
SCREAMING_SNAKE_CASE__ : Any =self.image_processor(images=__lowercase , return_tensors=self.framework )
SCREAMING_SNAKE_CASE__ : List[str] =self.tokenizer(text=__lowercase , add_special_tokens=__lowercase ).input_ids
SCREAMING_SNAKE_CASE__ : Optional[int] =[self.tokenizer.cls_token_id] + input_ids
SCREAMING_SNAKE_CASE__ : str =torch.tensor(__lowercase ).unsqueeze(0 )
model_inputs.update({'''input_ids''': input_ids} )
elif model_type == "pix2struct":
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.image_processor(images=__lowercase , header_text=__lowercase , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
SCREAMING_SNAKE_CASE__ : List[str] =self.image_processor(images=__lowercase , return_tensors=self.framework )
SCREAMING_SNAKE_CASE__ : Dict =self.tokenizer(__lowercase , return_tensors=self.framework )
model_inputs.update(__lowercase )
else:
raise ValueError(F"Model type {model_type} does not support conditional text generation" )
else:
SCREAMING_SNAKE_CASE__ : List[Any] =self.image_processor(images=__lowercase , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
SCREAMING_SNAKE_CASE__ : List[Any] =None
return model_inputs
def __magic_name__ ( self : int , __lowercase : Any , __lowercase : Optional[int]=None ) -> Optional[int]:
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs['''input_ids'''] , __lowercase )
and all(x is None for x in model_inputs['''input_ids'''] )
):
SCREAMING_SNAKE_CASE__ : List[Any] =None
if generate_kwargs is None:
SCREAMING_SNAKE_CASE__ : Any ={}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
SCREAMING_SNAKE_CASE__ : int =model_inputs.pop(self.model.main_input_name )
SCREAMING_SNAKE_CASE__ : int =self.model.generate(__lowercase , **__lowercase , **__lowercase )
return model_outputs
def __magic_name__ ( self : int , __lowercase : Dict ) -> str:
SCREAMING_SNAKE_CASE__ : Any =[]
for output_ids in model_outputs:
SCREAMING_SNAKE_CASE__ : Optional[Any] ={
'''generated_text''': self.tokenizer.decode(
__lowercase , skip_special_tokens=__lowercase , )
}
records.append(__lowercase )
return records
| 703 |
'''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_bert import BertTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
a_ = {
'vocab_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
),
'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
),
'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
),
'bert-base-multilingual-cased': (
'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
),
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-cased': (
'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
),
},
}
a_ = {
'bert-base-uncased': 5_1_2,
'bert-large-uncased': 5_1_2,
'bert-base-cased': 5_1_2,
'bert-large-cased': 5_1_2,
'bert-base-multilingual-uncased': 5_1_2,
'bert-base-multilingual-cased': 5_1_2,
'bert-base-chinese': 5_1_2,
'bert-base-german-cased': 5_1_2,
'bert-large-uncased-whole-word-masking': 5_1_2,
'bert-large-cased-whole-word-masking': 5_1_2,
'bert-large-uncased-whole-word-masking-finetuned-squad': 5_1_2,
'bert-large-cased-whole-word-masking-finetuned-squad': 5_1_2,
'bert-base-cased-finetuned-mrpc': 5_1_2,
'bert-base-german-dbmdz-cased': 5_1_2,
'bert-base-german-dbmdz-uncased': 5_1_2,
'TurkuNLP/bert-base-finnish-cased-v1': 5_1_2,
'TurkuNLP/bert-base-finnish-uncased-v1': 5_1_2,
'wietsedv/bert-base-dutch-cased': 5_1_2,
}
a_ = {
'bert-base-uncased': {'do_lower_case': True},
'bert-large-uncased': {'do_lower_case': True},
'bert-base-cased': {'do_lower_case': False},
'bert-large-cased': {'do_lower_case': False},
'bert-base-multilingual-uncased': {'do_lower_case': True},
'bert-base-multilingual-cased': {'do_lower_case': False},
'bert-base-chinese': {'do_lower_case': False},
'bert-base-german-cased': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
'bert-large-cased-whole-word-masking': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
'bert-base-german-dbmdz-cased': {'do_lower_case': False},
'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_INIT_CONFIGURATION
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = BertTokenizer
def __init__( self : int , __lowercase : Union[str, Any]=None , __lowercase : Tuple=None , __lowercase : str=True , __lowercase : Optional[Any]="[UNK]" , __lowercase : Tuple="[SEP]" , __lowercase : Any="[PAD]" , __lowercase : List[Any]="[CLS]" , __lowercase : Union[str, Any]="[MASK]" , __lowercase : Tuple=True , __lowercase : str=None , **__lowercase : Any , ) -> Optional[Any]:
super().__init__(
__lowercase , tokenizer_file=__lowercase , do_lower_case=__lowercase , unk_token=__lowercase , sep_token=__lowercase , pad_token=__lowercase , cls_token=__lowercase , mask_token=__lowercase , tokenize_chinese_chars=__lowercase , strip_accents=__lowercase , **__lowercase , )
SCREAMING_SNAKE_CASE__ : str =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __lowercase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __lowercase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __lowercase ) != tokenize_chinese_chars
):
SCREAMING_SNAKE_CASE__ : int =getattr(__lowercase , normalizer_state.pop('''type''' ) )
SCREAMING_SNAKE_CASE__ : Any =do_lower_case
SCREAMING_SNAKE_CASE__ : Any =strip_accents
SCREAMING_SNAKE_CASE__ : Dict =tokenize_chinese_chars
SCREAMING_SNAKE_CASE__ : Union[str, Any] =normalizer_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =do_lower_case
def __magic_name__ ( self : int , __lowercase : Optional[Any] , __lowercase : Union[str, Any]=None ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] =[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 __magic_name__ ( self : Union[str, Any] , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ) -> List[int]:
SCREAMING_SNAKE_CASE__ : List[str] =[self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] =[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 __magic_name__ ( self : List[Any] , __lowercase : str , __lowercase : Optional[str] = None ) -> Tuple[str]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self._tokenizer.model.save(__lowercase , name=__lowercase )
return tuple(__lowercase )
| 665 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : int = 1_0_0_0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] =2**power
SCREAMING_SNAKE_CASE__ : str =str(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] =list(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Any =0
for i in list_num:
sum_of_num += int(UpperCamelCase__ )
return sum_of_num
if __name__ == "__main__":
a_ = int(input('Enter the power of 2: ').strip())
print('2 ^ ', power, ' = ', 2**power)
a_ = solution(power)
print('Sum of the digits is: ', result)
| 704 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')
# TF training parameters
a_ = False
a_ = False
def _a( UpperCamelCase__ : Namespace ):
'''simple docstring'''
return TrainCommand(UpperCamelCase__ )
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
@staticmethod
def __magic_name__ ( __lowercase : ArgumentParser ) -> Any:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =parser.add_parser('''train''' , help='''CLI tool to train a model on a task.''' )
train_parser.add_argument(
'''--train_data''' , type=__lowercase , required=__lowercase , help='''path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.''' , )
train_parser.add_argument(
'''--column_label''' , type=__lowercase , default=0 , help='''Column of the dataset csv file with example labels.''' )
train_parser.add_argument(
'''--column_text''' , type=__lowercase , default=1 , help='''Column of the dataset csv file with example texts.''' )
train_parser.add_argument(
'''--column_id''' , type=__lowercase , default=2 , help='''Column of the dataset csv file with example ids.''' )
train_parser.add_argument(
'''--skip_first_row''' , action='''store_true''' , help='''Skip the first row of the csv file (headers).''' )
train_parser.add_argument('''--validation_data''' , type=__lowercase , default='''''' , help='''path to validation dataset.''' )
train_parser.add_argument(
'''--validation_split''' , type=__lowercase , default=0.1 , help='''if validation dataset is not provided, fraction of train dataset to use as validation dataset.''' , )
train_parser.add_argument('''--output''' , type=__lowercase , default='''./''' , help='''path to saved the trained model.''' )
train_parser.add_argument(
'''--task''' , type=__lowercase , default='''text_classification''' , help='''Task to train the model on.''' )
train_parser.add_argument(
'''--model''' , type=__lowercase , default='''bert-base-uncased''' , help='''Model\'s name or path to stored model.''' )
train_parser.add_argument('''--train_batch_size''' , type=__lowercase , default=32 , help='''Batch size for training.''' )
train_parser.add_argument('''--valid_batch_size''' , type=__lowercase , default=64 , help='''Batch size for validation.''' )
train_parser.add_argument('''--learning_rate''' , type=__lowercase , default=3e-5 , help='''Learning rate.''' )
train_parser.add_argument('''--adam_epsilon''' , type=__lowercase , default=1e-08 , help='''Epsilon for Adam optimizer.''' )
train_parser.set_defaults(func=__lowercase )
def __init__( self : Tuple , __lowercase : Namespace ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Tuple =logging.get_logger('''transformers-cli/training''' )
SCREAMING_SNAKE_CASE__ : int ='''tf''' if is_tf_available() else '''torch'''
os.makedirs(args.output , exist_ok=__lowercase )
SCREAMING_SNAKE_CASE__ : Any =args.output
SCREAMING_SNAKE_CASE__ : str =args.column_label
SCREAMING_SNAKE_CASE__ : List[Any] =args.column_text
SCREAMING_SNAKE_CASE__ : Tuple =args.column_id
self.logger.info(F"Loading {args.task} pipeline for {args.model}" )
if args.task == "text_classification":
SCREAMING_SNAKE_CASE__ : List[str] =TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(F"Loading dataset from {args.train_data}" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =None
if args.validation_data:
self.logger.info(F"Loading validation dataset from {args.validation_data}" )
SCREAMING_SNAKE_CASE__ : List[Any] =Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =args.validation_split
SCREAMING_SNAKE_CASE__ : List[Any] =args.train_batch_size
SCREAMING_SNAKE_CASE__ : Any =args.valid_batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =args.learning_rate
SCREAMING_SNAKE_CASE__ : int =args.adam_epsilon
def __magic_name__ ( self : Any ) -> str:
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def __magic_name__ ( self : Optional[int] ) -> Tuple:
raise NotImplementedError
def __magic_name__ ( self : Dict ) -> List[Any]:
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 665 | 0 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
lowerCAmelCase_ = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'
lowerCAmelCase_ = '\nArgs:\n predictions (`List[ndarray]`):\n List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n references (`List[ndarray]`):\n List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n num_labels (`int`):\n Number of classes (categories).\n ignore_index (`int`):\n Index that will be ignored during evaluation.\n nan_to_num (`int`, *optional*):\n If specified, NaN values will be replaced by the number defined by the user.\n label_map (`dict`, *optional*):\n If specified, dictionary mapping old label indices to new label indices.\n reduce_labels (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n `Dict[str, float | ndarray]` comprising various elements:\n - *mean_iou* (`float`):\n Mean Intersection-over-Union (IoU averaged over all categories).\n - *mean_accuracy* (`float`):\n Mean accuracy (averaged over all categories).\n - *overall_accuracy* (`float`):\n Overall accuracy on all images.\n - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n Per category accuracy.\n - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n Per category IoU.\n\nExamples:\n\n >>> import numpy as np\n\n >>> mean_iou = datasets.load_metric("mean_iou")\n\n >>> # suppose one has 3 different segmentation maps predicted\n >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n >>> predicted = [predicted_1, predicted_2, predicted_3]\n >>> ground_truth = [actual_1, actual_2, actual_3]\n\n >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), \'per_category_accuracy\': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}\n'
lowerCAmelCase_ = '\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'
def _a( UpperCamelCase__ : Tuple, UpperCamelCase__ : int, UpperCamelCase__ : Optional[int], UpperCamelCase__ : bool, UpperCamelCase__ : Optional[Dict[int, int]] = None, UpperCamelCase__ : bool = False, ):
'''simple docstring'''
if label_map is not None:
for old_id, new_id in label_map.items():
SCREAMING_SNAKE_CASE__ : Optional[int] =new_id
# turn into Numpy arrays
SCREAMING_SNAKE_CASE__ : int =np.array(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : int =np.array(UpperCamelCase__ )
if reduce_labels:
SCREAMING_SNAKE_CASE__ : str =2_5_5
SCREAMING_SNAKE_CASE__ : str =label - 1
SCREAMING_SNAKE_CASE__ : Tuple =2_5_5
SCREAMING_SNAKE_CASE__ : Union[str, Any] =label != ignore_index
SCREAMING_SNAKE_CASE__ : str =np.not_equal(UpperCamelCase__, UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Any =pred_label[mask]
SCREAMING_SNAKE_CASE__ : Tuple =np.array(UpperCamelCase__ )[mask]
SCREAMING_SNAKE_CASE__ : List[Any] =pred_label[pred_label == label]
SCREAMING_SNAKE_CASE__ : List[Any] =np.histogram(UpperCamelCase__, bins=UpperCamelCase__, range=(0, num_labels - 1) )[0]
SCREAMING_SNAKE_CASE__ : Optional[int] =np.histogram(UpperCamelCase__, bins=UpperCamelCase__, range=(0, num_labels - 1) )[0]
SCREAMING_SNAKE_CASE__ : List[str] =np.histogram(UpperCamelCase__, bins=UpperCamelCase__, range=(0, num_labels - 1) )[0]
SCREAMING_SNAKE_CASE__ : str =area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def _a( UpperCamelCase__ : List[Any], UpperCamelCase__ : Any, UpperCamelCase__ : int, UpperCamelCase__ : bool, UpperCamelCase__ : Optional[Dict[int, int]] = None, UpperCamelCase__ : bool = False, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =np.zeros((num_labels,), dtype=np.floataa )
SCREAMING_SNAKE_CASE__ : List[str] =np.zeros((num_labels,), dtype=np.floataa )
SCREAMING_SNAKE_CASE__ : Dict =np.zeros((num_labels,), dtype=np.floataa )
SCREAMING_SNAKE_CASE__ : List[str] =np.zeros((num_labels,), dtype=np.floataa )
for result, gt_seg_map in zip(UpperCamelCase__, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =intersect_and_union(
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def _a( UpperCamelCase__ : Optional[int], UpperCamelCase__ : str, UpperCamelCase__ : List[Any], UpperCamelCase__ : bool, UpperCamelCase__ : Optional[int] = None, UpperCamelCase__ : Optional[Dict[int, int]] = None, UpperCamelCase__ : bool = False, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] =total_intersect_and_union(
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
# compute metrics
SCREAMING_SNAKE_CASE__ : Tuple ={}
SCREAMING_SNAKE_CASE__ : List[Any] =total_area_intersect.sum() / total_area_label.sum()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =total_area_intersect / total_area_union
SCREAMING_SNAKE_CASE__ : int =total_area_intersect / total_area_label
SCREAMING_SNAKE_CASE__ : Tuple =np.nanmean(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[Any] =np.nanmean(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : int =all_acc
SCREAMING_SNAKE_CASE__ : int =iou
SCREAMING_SNAKE_CASE__ : Optional[Any] =acc
if nan_to_num is not None:
SCREAMING_SNAKE_CASE__ : Tuple ={metric: np.nan_to_num(UpperCamelCase__, nan=UpperCamelCase__ ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE ( datasets.Metric ):
def __magic_name__ ( self : Dict ) -> Any:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
'''predictions''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16''' ) ) ),
'''references''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16''' ) ) ),
} ) , reference_urls=[
'''https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'''
] , )
def __magic_name__ ( self : int , __lowercase : List[str] , __lowercase : str , __lowercase : int , __lowercase : bool , __lowercase : Optional[int] = None , __lowercase : Optional[Dict[int, int]] = None , __lowercase : bool = False , ) -> int:
SCREAMING_SNAKE_CASE__ : Dict =mean_iou(
results=__lowercase , gt_seg_maps=__lowercase , num_labels=__lowercase , ignore_index=__lowercase , nan_to_num=__lowercase , label_map=__lowercase , reduce_labels=__lowercase , )
return iou_result
| 705 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = KandinskyVaaImgaImgPipeline
snake_case_ = ["""image_embeds""", """negative_image_embeds""", """image"""]
snake_case_ = [
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
snake_case_ = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
snake_case_ = False
@property
def __magic_name__ ( self : List[str] ) -> Tuple:
return 32
@property
def __magic_name__ ( self : List[str] ) -> str:
return 32
@property
def __magic_name__ ( self : Any ) -> Optional[int]:
return self.time_input_dim
@property
def __magic_name__ ( self : List[Any] ) -> int:
return self.time_input_dim * 4
@property
def __magic_name__ ( self : Tuple ) -> Optional[int]:
return 1_00
@property
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] ={
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
SCREAMING_SNAKE_CASE__ : Optional[int] =UNetaDConditionModel(**__lowercase )
return model
@property
def __magic_name__ ( self : Dict ) -> Any:
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __magic_name__ ( self : Tuple ) -> Optional[Any]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] =VQModel(**self.dummy_movq_kwargs )
return model
def __magic_name__ ( self : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[str] =self.dummy_unet
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dummy_movq
SCREAMING_SNAKE_CASE__ : Optional[Any] ={
'''num_train_timesteps''': 10_00,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
SCREAMING_SNAKE_CASE__ : str =DDIMScheduler(**__lowercase )
SCREAMING_SNAKE_CASE__ : Any ={
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __magic_name__ ( self : str , __lowercase : Optional[Any] , __lowercase : Any=0 ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[int] =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__lowercase ) ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__lowercase )
# create init_image
SCREAMING_SNAKE_CASE__ : Optional[Any] =floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowercase ) ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE__ : Any =Image.fromarray(np.uinta(__lowercase ) ).convert('''RGB''' ).resize((2_56, 2_56) )
if str(__lowercase ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE__ : Dict =torch.manual_seed(__lowercase )
else:
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device=__lowercase ).manual_seed(__lowercase )
SCREAMING_SNAKE_CASE__ : str ={
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def __magic_name__ ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[Any] ='''cpu'''
SCREAMING_SNAKE_CASE__ : Tuple =self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Dict =self.pipeline_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =pipe(**self.get_dummy_inputs(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Tuple =output.images
SCREAMING_SNAKE_CASE__ : Union[str, Any] =pipe(
**self.get_dummy_inputs(__lowercase ) , return_dict=__lowercase , )[0]
SCREAMING_SNAKE_CASE__ : List[Any] =image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : List[str] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE__ : Tuple =np.array(
[0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : int ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : str =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
SCREAMING_SNAKE_CASE__ : List[Any] ='''A red cartoon frog, 4k'''
SCREAMING_SNAKE_CASE__ : Optional[int] =KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =KandinskyVaaImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : Dict =pipeline.to(__lowercase )
pipeline.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device='''cpu''' ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] =pipe_prior(
__lowercase , generator=__lowercase , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
image=__lowercase , image_embeds=__lowercase , negative_image_embeds=__lowercase , generator=__lowercase , num_inference_steps=1_00 , height=7_68 , width=7_68 , strength=0.2 , output_type='''np''' , )
SCREAMING_SNAKE_CASE__ : int =output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(__lowercase , __lowercase )
| 665 | 0 |
'''simple docstring'''
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if is_timm_available():
import timm
if is_torch_available():
from torch import Tensor
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , lowerCamelCase ):
snake_case_ = """pixel_values"""
snake_case_ = False
snake_case_ = TimmBackboneConfig
def __init__( self : Union[str, Any] , __lowercase : Optional[Any] , **__lowercase : Optional[int] ) -> Any:
requires_backends(self , '''timm''' )
super().__init__(__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =config
if config.backbone is None:
raise ValueError('''backbone is not set in the config. Please set it to a timm model name.''' )
if config.backbone not in timm.list_models():
raise ValueError(F"backbone {config.backbone} is not supported by timm." )
if hasattr(__lowercase , '''out_features''' ) and config.out_features is not None:
raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.''' )
SCREAMING_SNAKE_CASE__ : Any =getattr(__lowercase , '''use_pretrained_backbone''' , __lowercase )
if pretrained is None:
raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.''' )
# We just take the final layer by default. This matches the default for the transformers models.
SCREAMING_SNAKE_CASE__ : Optional[int] =config.out_indices if getattr(__lowercase , '''out_indices''' , __lowercase ) is not None else (-1,)
SCREAMING_SNAKE_CASE__ : str =timm.create_model(
config.backbone , pretrained=__lowercase , features_only=config.features_only , in_chans=config.num_channels , out_indices=__lowercase , **__lowercase , )
# These are used to control the output of the model when called. If output_hidden_states is True, then
# return_layers is modified to include all layers.
SCREAMING_SNAKE_CASE__ : List[str] =self._backbone.return_layers
SCREAMING_SNAKE_CASE__ : List[Any] ={layer['''module''']: str(__lowercase ) for i, layer in enumerate(self._backbone.feature_info.info )}
super()._init_backbone(__lowercase )
@classmethod
def __magic_name__ ( cls : str , __lowercase : Optional[Any] , *__lowercase : str , **__lowercase : Optional[Any] ) -> Dict:
requires_backends(cls , ['''vision''', '''timm'''] )
from ...models.timm_backbone import TimmBackboneConfig
SCREAMING_SNAKE_CASE__ : Optional[int] =kwargs.pop('''config''' , TimmBackboneConfig() )
SCREAMING_SNAKE_CASE__ : str =kwargs.pop('''use_timm_backbone''' , __lowercase )
if not use_timm:
raise ValueError('''use_timm_backbone must be True for timm backbones''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] =kwargs.pop('''num_channels''' , config.num_channels )
SCREAMING_SNAKE_CASE__ : Tuple =kwargs.pop('''features_only''' , config.features_only )
SCREAMING_SNAKE_CASE__ : str =kwargs.pop('''use_pretrained_backbone''' , config.use_pretrained_backbone )
SCREAMING_SNAKE_CASE__ : Any =kwargs.pop('''out_indices''' , config.out_indices )
SCREAMING_SNAKE_CASE__ : List[Any] =TimmBackboneConfig(
backbone=__lowercase , num_channels=__lowercase , features_only=__lowercase , use_pretrained_backbone=__lowercase , out_indices=__lowercase , )
return super()._from_config(__lowercase , **__lowercase )
def __magic_name__ ( self : Optional[int] , __lowercase : str ) -> str:
pass
def __magic_name__ ( self : Tuple , __lowercase : Tuple , __lowercase : int=None , __lowercase : Optional[int]=None , __lowercase : List[str]=None , **__lowercase : Dict ) -> Union[BackboneOutput, Tuple[Tensor, ...]]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =return_dict if return_dict is not None else self.config.use_return_dict
SCREAMING_SNAKE_CASE__ : Optional[Any] =(
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
SCREAMING_SNAKE_CASE__ : List[Any] =output_attentions if output_attentions is not None else self.config.output_attentions
if output_attentions:
raise ValueError('''Cannot output attentions for timm backbones at the moment''' )
if output_hidden_states:
# We modify the return layers to include all the stages of the backbone
SCREAMING_SNAKE_CASE__ : Optional[int] =self._all_layers
SCREAMING_SNAKE_CASE__ : str =self._backbone(__lowercase , **__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =self._return_layers
SCREAMING_SNAKE_CASE__ : Optional[Any] =tuple(hidden_states[i] for i in self.out_indices )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self._backbone(__lowercase , **__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =None
SCREAMING_SNAKE_CASE__ : Tuple =tuple(__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =tuple(__lowercase ) if hidden_states is not None else None
if not return_dict:
SCREAMING_SNAKE_CASE__ : Dict =(feature_maps,)
if output_hidden_states:
SCREAMING_SNAKE_CASE__ : Dict =output + (hidden_states,)
return output
return BackboneOutput(feature_maps=__lowercase , hidden_states=__lowercase , attentions=__lowercase )
| 706 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
a_ = TypeVar('T')
class __SCREAMING_SNAKE_CASE ( Generic[T] ):
snake_case_ = 42 # Cache store of keys
snake_case_ = 42 # References of the keys in cache
snake_case_ = 10 # Maximum capacity of cache
def __init__( self : Dict , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : Any =deque()
SCREAMING_SNAKE_CASE__ : str =set()
if not n:
SCREAMING_SNAKE_CASE__ : Optional[Any] =sys.maxsize
elif n < 0:
raise ValueError('''n should be an integer greater than 0.''' )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =n
def __magic_name__ ( self : List[str] , __lowercase : T ) -> None:
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
SCREAMING_SNAKE_CASE__ : int =self.dq_store.pop()
self.key_reference.remove(__lowercase )
else:
self.dq_store.remove(__lowercase )
self.dq_store.appendleft(__lowercase )
self.key_reference.add(__lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> None:
for k in self.dq_store:
print(__lowercase )
def __repr__( self : List[Any] ) -> str:
return F"LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}"
if __name__ == "__main__":
import doctest
doctest.testmod()
a_ = LRUCache(4)
lru_cache.refer('A')
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer('A')
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 665 | 0 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
a_ = TypeVar('T')
class __SCREAMING_SNAKE_CASE ( Generic[T] ):
snake_case_ = 42 # Cache store of keys
snake_case_ = 42 # References of the keys in cache
snake_case_ = 10 # Maximum capacity of cache
def __init__( self : Dict , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : Any =deque()
SCREAMING_SNAKE_CASE__ : str =set()
if not n:
SCREAMING_SNAKE_CASE__ : Optional[Any] =sys.maxsize
elif n < 0:
raise ValueError('''n should be an integer greater than 0.''' )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =n
def __magic_name__ ( self : List[str] , __lowercase : T ) -> None:
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
SCREAMING_SNAKE_CASE__ : int =self.dq_store.pop()
self.key_reference.remove(__lowercase )
else:
self.dq_store.remove(__lowercase )
self.dq_store.appendleft(__lowercase )
self.key_reference.add(__lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> None:
for k in self.dq_store:
print(__lowercase )
def __repr__( self : List[Any] ) -> str:
return F"LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}"
if __name__ == "__main__":
import doctest
doctest.testmod()
a_ = LRUCache(4)
lru_cache.refer('A')
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer('A')
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 707 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a_ = list[list[float | int]]
def _a( UpperCamelCase__ : Matrix, UpperCamelCase__ : Matrix ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Matrix =[[0 for _ in range(size + 1 )] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : float
for row in range(UpperCamelCase__ ):
for col in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Tuple =matrix[row][col]
SCREAMING_SNAKE_CASE__ : Optional[int] =vector[row][0]
SCREAMING_SNAKE_CASE__ : Any =0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =0
while row < size and col < size:
# pivoting
SCREAMING_SNAKE_CASE__ : Any =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__ : Optional[Any] =augmented[pivot_row], augmented[row]
for rowa in range(row + 1, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] =augmented[rowa][col] / augmented[row][col]
SCREAMING_SNAKE_CASE__ : Tuple =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[Any] =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], 1_0 )] for row in range(UpperCamelCase__ )
]
def _a( UpperCamelCase__ : list[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Matrix =[[0 for _ in range(UpperCamelCase__ )] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Matrix =[[0] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Matrix
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
for x_val, y_val in enumerate(UpperCamelCase__ ):
for col in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[int] =(x_val + 1) ** (size - col - 1)
SCREAMING_SNAKE_CASE__ : Dict =y_val
SCREAMING_SNAKE_CASE__ : Optional[int] =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 ):
'''simple docstring'''
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**1_0
)
def _a( UpperCamelCase__ : Callable[[int], int] = question_function, UpperCamelCase__ : int = 1_0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : list[int] =[func(UpperCamelCase__ ) for x_val in range(1, order + 1 )]
SCREAMING_SNAKE_CASE__ : list[Callable[[int], int]] =[
interpolate(data_points[:max_coeff] ) for max_coeff in range(1, order + 1 )
]
SCREAMING_SNAKE_CASE__ : int =0
SCREAMING_SNAKE_CASE__ : Callable[[int], int]
SCREAMING_SNAKE_CASE__ : int
for poly in polynomials:
SCREAMING_SNAKE_CASE__ : Any =1
while func(UpperCamelCase__ ) == poly(UpperCamelCase__ ):
x_val += 1
ret += poly(UpperCamelCase__ )
return ret
if __name__ == "__main__":
print(F'''{solution() = }''')
| 665 | 0 |
'''simple docstring'''
from math import ceil, sqrt
def _a( UpperCamelCase__ : int = 1_0_0_0_0_0_0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =0
for outer_width in range(3, (limit // 4) + 2 ):
if outer_width**2 > limit:
SCREAMING_SNAKE_CASE__ : List[str] =max(ceil(sqrt(outer_width**2 - limit ) ), 1 )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(F'''{solution() = }''')
| 708 |
'''simple docstring'''
def _a( UpperCamelCase__ : Optional[int], UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =len(UpperCamelCase__ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
SCREAMING_SNAKE_CASE__ : Union[str, Any] =left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCamelCase__ ):
return None
SCREAMING_SNAKE_CASE__ : Optional[int] =sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =left
SCREAMING_SNAKE_CASE__ : Optional[Any] =point
elif point > right:
SCREAMING_SNAKE_CASE__ : Optional[int] =right
SCREAMING_SNAKE_CASE__ : Tuple =point
else:
if item < current_item:
SCREAMING_SNAKE_CASE__ : str =point - 1
else:
SCREAMING_SNAKE_CASE__ : Tuple =point + 1
return None
def _a( UpperCamelCase__ : List[str], UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
SCREAMING_SNAKE_CASE__ : Dict =left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCamelCase__ ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
elif point > right:
return interpolation_search_by_recursion(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, point - 1 )
else:
return interpolation_search_by_recursion(
UpperCamelCase__, UpperCamelCase__, point + 1, UpperCamelCase__ )
def _a( UpperCamelCase__ : Dict ):
'''simple docstring'''
if collection != sorted(UpperCamelCase__ ):
raise ValueError('''Collection must be ascending sorted''' )
return True
if __name__ == "__main__":
import sys
a_ = 0
if debug == 1:
a_ = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
try:
__assert_sorted(collection)
except ValueError:
sys.exit('Sequence must be ascending sorted to apply interpolation search')
a_ = 6_7
a_ = interpolation_search(collection, target)
if result is not None:
print(F'''{target} found at positions: {result}''')
else:
print('Not found')
| 665 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
a_ = TypeVar('T')
class __SCREAMING_SNAKE_CASE ( Generic[T] ):
def __init__( self : str , __lowercase : list[T] , __lowercase : Callable[[T, T], T] ) -> None:
SCREAMING_SNAKE_CASE__ : Any | T =None
SCREAMING_SNAKE_CASE__ : int =len(__lowercase )
SCREAMING_SNAKE_CASE__ : list[T] =[any_type for _ in range(self.N )] + arr
SCREAMING_SNAKE_CASE__ : List[Any] =fnc
self.build()
def __magic_name__ ( self : List[Any] ) -> None:
for p in range(self.N - 1 , 0 , -1 ):
SCREAMING_SNAKE_CASE__ : Any =self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def __magic_name__ ( self : Dict , __lowercase : int , __lowercase : T ) -> None:
p += self.N
SCREAMING_SNAKE_CASE__ : Dict =v
while p > 1:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =p // 2
SCREAMING_SNAKE_CASE__ : int =self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def __magic_name__ ( self : Optional[int] , __lowercase : int , __lowercase : int ) -> T | None: # noqa: E741
SCREAMING_SNAKE_CASE__ : Tuple =l + self.N, r + self.N
SCREAMING_SNAKE_CASE__ : T | None =None
while l <= r:
if l % 2 == 1:
SCREAMING_SNAKE_CASE__ : Tuple =self.st[l] if res is None else self.fn(__lowercase , self.st[l] )
if r % 2 == 0:
SCREAMING_SNAKE_CASE__ : List[Any] =self.st[r] if res is None else self.fn(__lowercase , self.st[r] )
SCREAMING_SNAKE_CASE__ : str =(l + 1) // 2, (r - 1) // 2
return res
if __name__ == "__main__":
from functools import reduce
a_ = [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]
a_ = {
0: 7,
1: 2,
2: 6,
3: -1_4,
4: 5,
5: 4,
6: 7,
7: -1_0,
8: 9,
9: 1_0,
1_0: 1_2,
1_1: 1,
}
a_ = SegmentTree(test_array, min)
a_ = SegmentTree(test_array, max)
a_ = SegmentTree(test_array, lambda a, b: a + b)
def _a( ):
'''simple docstring'''
for i in range(len(UpperCamelCase__ ) ):
for j in range(UpperCamelCase__, len(UpperCamelCase__ ) ):
SCREAMING_SNAKE_CASE__ : Optional[int] =reduce(UpperCamelCase__, test_array[i : j + 1] )
SCREAMING_SNAKE_CASE__ : Optional[Any] =reduce(UpperCamelCase__, test_array[i : j + 1] )
SCREAMING_SNAKE_CASE__ : Any =reduce(lambda UpperCamelCase__, UpperCamelCase__ : a + b, test_array[i : j + 1] )
assert min_range == min_segment_tree.query(UpperCamelCase__, UpperCamelCase__ )
assert max_range == max_segment_tree.query(UpperCamelCase__, UpperCamelCase__ )
assert sum_range == sum_segment_tree.query(UpperCamelCase__, UpperCamelCase__ )
test_all_segments()
for index, value in test_updates.items():
a_ = value
min_segment_tree.update(index, value)
max_segment_tree.update(index, value)
sum_segment_tree.update(index, value)
test_all_segments()
| 709 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __SCREAMING_SNAKE_CASE :
@staticmethod
def __magic_name__ ( *__lowercase : int , **__lowercase : Optional[Any] ) -> Optional[Any]:
pass
@is_pipeline_test
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@require_torch
def __magic_name__ ( self : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , )
SCREAMING_SNAKE_CASE__ : List[str] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Any =image_classifier(__lowercase , candidate_labels=['''a''', '''b''', '''c'''] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(__lowercase ) , [
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
] , )
SCREAMING_SNAKE_CASE__ : int =image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
] , )
@require_tf
def __magic_name__ ( self : Union[str, Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , framework='''tf''' )
SCREAMING_SNAKE_CASE__ : Dict =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Tuple =image_classifier(__lowercase , candidate_labels=['''a''', '''b''', '''c'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}] , )
SCREAMING_SNAKE_CASE__ : List[Any] =image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
] , )
@slow
@require_torch
def __magic_name__ ( self : List[Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE__ : Any =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : List[str] =image_classifier(__lowercase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
@slow
@require_tf
def __magic_name__ ( self : Union[str, Any] ) -> int:
SCREAMING_SNAKE_CASE__ : str =pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , framework='''tf''' )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE__ : Optional[Any] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Any =image_classifier(__lowercase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
SCREAMING_SNAKE_CASE__ : List[Any] =image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
| 665 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a_ = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST',
'LongT5EncoderModel',
'LongT5ForConditionalGeneration',
'LongT5Model',
'LongT5PreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'FlaxLongT5ForConditionalGeneration',
'FlaxLongT5Model',
'FlaxLongT5PreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longta import (
LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
LongTaEncoderModel,
LongTaForConditionalGeneration,
LongTaModel,
LongTaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_longta import (
FlaxLongTaForConditionalGeneration,
FlaxLongTaModel,
FlaxLongTaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 710 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case_ = JukeboxTokenizer
snake_case_ = {
"""artist""": """Zac Brown Band""",
"""genres""": """Country""",
"""lyrics""": """I met a traveller from an antique land,
Who said \"Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
""",
}
@require_torch
def __magic_name__ ( self : Optional[int] ) -> str:
import torch
SCREAMING_SNAKE_CASE__ : List[str] =JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics''' )
SCREAMING_SNAKE_CASE__ : str =tokenizer(**self.metas )['''input_ids''']
# fmt: off
SCREAMING_SNAKE_CASE__ : str =[
torch.tensor([[
0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
76, 76]] ),
torch.tensor([[0, 0, 0, 10_69, 11]] ),
torch.tensor([[0, 0, 0, 10_69, 11]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
@require_torch
def __magic_name__ ( self : Any ) -> List[str]:
import torch
SCREAMING_SNAKE_CASE__ : int =JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics''' )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer(**self.metas )['''input_ids''']
# fmt: off
SCREAMING_SNAKE_CASE__ : Optional[int] =[
torch.tensor([[
0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
77, 77, 77, 77, 77, 77]] ),
torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
| 665 | 0 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Generic, TypeVar
a_ = TypeVar('_T')
class __SCREAMING_SNAKE_CASE ( Generic[_T] ):
def __init__( self : List[Any] , __lowercase : Iterable[_T] | None = None ) -> None:
SCREAMING_SNAKE_CASE__ : list[_T] =list(iterable or [] )
SCREAMING_SNAKE_CASE__ : list[_T] =[]
def __len__( self : List[str] ) -> int:
return len(self._stacka ) + len(self._stacka )
def __repr__( self : List[str] ) -> str:
return F"Queue({tuple(self._stacka[::-1] + self._stacka )})"
def __magic_name__ ( self : List[Any] , __lowercase : _T ) -> None:
self._stacka.append(__lowercase )
def __magic_name__ ( self : Optional[Any] ) -> _T:
SCREAMING_SNAKE_CASE__ : List[str] =self._stacka.pop
SCREAMING_SNAKE_CASE__ : List[Any] =self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError('''Queue is empty''' )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 711 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """gpt_neox"""
def __init__( self : List[Any] , __lowercase : Union[str, Any]=5_04_32 , __lowercase : int=61_44 , __lowercase : Tuple=44 , __lowercase : List[str]=64 , __lowercase : str=2_45_76 , __lowercase : Dict="gelu" , __lowercase : Tuple=0.25 , __lowercase : Tuple=1_00_00 , __lowercase : Tuple=0.0 , __lowercase : str=0.0 , __lowercase : List[Any]=0.1 , __lowercase : Dict=20_48 , __lowercase : Any=0.02 , __lowercase : Dict=1e-5 , __lowercase : List[Any]=True , __lowercase : str=0 , __lowercase : Optional[Any]=2 , __lowercase : Tuple=False , __lowercase : List[Any]=True , __lowercase : Optional[Any]=None , **__lowercase : Any , ) -> Dict:
super().__init__(bos_token_id=__lowercase , eos_token_id=__lowercase , **__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =max_position_embeddings
SCREAMING_SNAKE_CASE__ : Any =hidden_size
SCREAMING_SNAKE_CASE__ : str =num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any =num_attention_heads
SCREAMING_SNAKE_CASE__ : Union[str, Any] =intermediate_size
SCREAMING_SNAKE_CASE__ : Dict =hidden_act
SCREAMING_SNAKE_CASE__ : str =rotary_pct
SCREAMING_SNAKE_CASE__ : Optional[Any] =rotary_emb_base
SCREAMING_SNAKE_CASE__ : List[Any] =attention_dropout
SCREAMING_SNAKE_CASE__ : List[Any] =hidden_dropout
SCREAMING_SNAKE_CASE__ : str =classifier_dropout
SCREAMING_SNAKE_CASE__ : Any =initializer_range
SCREAMING_SNAKE_CASE__ : Dict =layer_norm_eps
SCREAMING_SNAKE_CASE__ : Any =use_cache
SCREAMING_SNAKE_CASE__ : Tuple =tie_word_embeddings
SCREAMING_SNAKE_CASE__ : Tuple =use_parallel_residual
SCREAMING_SNAKE_CASE__ : Union[str, Any] =rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )
def __magic_name__ ( self : Optional[Any] ) -> Optional[Any]:
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __lowercase ) 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}" )
SCREAMING_SNAKE_CASE__ : int =self.rope_scaling.get('''type''' , __lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =self.rope_scaling.get('''factor''' , __lowercase )
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(__lowercase , __lowercase ) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 665 | 0 |
'''simple docstring'''
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[Any] , __lowercase : int , __lowercase : List[Any]=1_00 , __lowercase : Optional[int]=13 , __lowercase : Union[str, Any]=30 , __lowercase : List[Any]=2 , __lowercase : Any=3 , __lowercase : List[Any]=True , __lowercase : Any=True , __lowercase : Optional[Any]=32 , __lowercase : Tuple=4 , __lowercase : str=4 , __lowercase : Optional[Any]=37 , __lowercase : Union[str, Any]="gelu" , __lowercase : List[str]=0.1 , __lowercase : Union[str, Any]=0.1 , __lowercase : Any=10 , __lowercase : Optional[Any]=0.02 , __lowercase : Union[str, Any]=3 , __lowercase : Union[str, Any]=None , __lowercase : Tuple=[0, 1, 2, 3] , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Dict =parent
SCREAMING_SNAKE_CASE__ : Dict =1_00
SCREAMING_SNAKE_CASE__ : Dict =batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =image_size
SCREAMING_SNAKE_CASE__ : str =patch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =num_channels
SCREAMING_SNAKE_CASE__ : List[Any] =is_training
SCREAMING_SNAKE_CASE__ : Any =use_labels
SCREAMING_SNAKE_CASE__ : Optional[Any] =hidden_size
SCREAMING_SNAKE_CASE__ : Optional[int] =num_hidden_layers
SCREAMING_SNAKE_CASE__ : int =num_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] =intermediate_size
SCREAMING_SNAKE_CASE__ : List[str] =hidden_act
SCREAMING_SNAKE_CASE__ : str =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Any =type_sequence_label_size
SCREAMING_SNAKE_CASE__ : List[Any] =initializer_range
SCREAMING_SNAKE_CASE__ : Optional[int] =scope
SCREAMING_SNAKE_CASE__ : Optional[int] =out_indices
SCREAMING_SNAKE_CASE__ : Any =num_labels
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
SCREAMING_SNAKE_CASE__ : Tuple =(image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE__ : Dict =num_patches + 1
def __magic_name__ ( self : Tuple ) -> List[str]:
SCREAMING_SNAKE_CASE__ : int =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : Dict =None
SCREAMING_SNAKE_CASE__ : List[Any] =None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Optional[int] =ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.get_config()
return config, pixel_values, labels, pixel_labels
def __magic_name__ ( self : Any ) -> int:
return BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=__lowercase , initializer_range=self.initializer_range , out_indices=self.out_indices , )
def __magic_name__ ( self : List[Any] , __lowercase : Tuple , __lowercase : List[Any] , __lowercase : Optional[Any] , __lowercase : Any ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[Any] =BeitModel(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] =model(__lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Any , __lowercase : List[str] , __lowercase : str , __lowercase : List[str] , __lowercase : str ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : str =BeitForMaskedImageModeling(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] =model(__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def __magic_name__ ( self : Tuple , __lowercase : Optional[int] , __lowercase : Any , __lowercase : Any , __lowercase : Tuple ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Tuple =self.type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Optional[Any] =BeitForImageClassification(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] =model(__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE__ : Tuple =1
SCREAMING_SNAKE_CASE__ : Tuple =BeitForImageClassification(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : Optional[int] =model(__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __magic_name__ ( self : List[str] , __lowercase : Optional[Any] , __lowercase : Optional[int] , __lowercase : List[str] , __lowercase : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[str] =self.num_labels
SCREAMING_SNAKE_CASE__ : str =BeitForSemanticSegmentation(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] =model(__lowercase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
SCREAMING_SNAKE_CASE__ : Optional[int] =model(__lowercase , labels=__lowercase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def __magic_name__ ( self : int ) -> Dict:
SCREAMING_SNAKE_CASE__ : str =self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Tuple =config_and_inputs
SCREAMING_SNAKE_CASE__ : List[Any] ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
snake_case_ = (
{
"""feature-extraction""": BeitModel,
"""image-classification""": BeitForImageClassification,
"""image-segmentation""": BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
def __magic_name__ ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =BeitModelTester(self )
SCREAMING_SNAKE_CASE__ : str =ConfigTester(self , config_class=__lowercase , has_text_modality=__lowercase , hidden_size=37 )
def __magic_name__ ( self : int ) -> int:
self.config_tester.run_common_tests()
@unittest.skip(reason='''BEiT does not use inputs_embeds''' )
def __magic_name__ ( self : Optional[Any] ) -> Optional[Any]:
pass
@require_torch_multi_gpu
@unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def __magic_name__ ( self : Tuple ) -> Any:
pass
def __magic_name__ ( self : Optional[int] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model_class(__lowercase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE__ : Dict =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__lowercase , nn.Linear ) )
def __magic_name__ ( self : Dict ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Optional[Any] =model_class(__lowercase )
SCREAMING_SNAKE_CASE__ : str =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE__ : List[str] =[*signature.parameters.keys()]
SCREAMING_SNAKE_CASE__ : Optional[Any] =['''pixel_values''']
self.assertListEqual(arg_names[:1] , __lowercase )
def __magic_name__ ( self : str ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase )
def __magic_name__ ( self : Dict ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__lowercase )
def __magic_name__ ( self : Optional[Any] ) -> str:
SCREAMING_SNAKE_CASE__ : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowercase )
def __magic_name__ ( self : Optional[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> List[Any]:
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE__ : int =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[Any] =True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(__lowercase ), BeitForMaskedImageModeling]:
continue
SCREAMING_SNAKE_CASE__ : Tuple =model_class(__lowercase )
model.to(__lowercase )
model.train()
SCREAMING_SNAKE_CASE__ : Optional[int] =self._prepare_for_class(__lowercase , __lowercase , return_labels=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =model(**__lowercase ).loss
loss.backward()
def __magic_name__ ( self : Optional[int] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
SCREAMING_SNAKE_CASE__ : int =False
SCREAMING_SNAKE_CASE__ : str =True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(__lowercase ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
SCREAMING_SNAKE_CASE__ : Optional[int] =model_class(__lowercase )
model.gradient_checkpointing_enable()
model.to(__lowercase )
model.train()
SCREAMING_SNAKE_CASE__ : str =self._prepare_for_class(__lowercase , __lowercase , return_labels=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =model(**__lowercase ).loss
loss.backward()
def __magic_name__ ( self : Union[str, Any] ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[str] =_config_zero_init(__lowercase )
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : str =model_class(config=__lowercase )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F"Parameter {name} of model {model_class} seems not properly initialized" , )
@slow
def __magic_name__ ( self : Dict ) -> str:
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =BeitModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : List[Any] ) -> Dict:
return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224''' ) if is_vision_available() else None
@slow
def __magic_name__ ( self : Any ) -> int:
SCREAMING_SNAKE_CASE__ : Any =BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k''' ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =self.default_image_processor
SCREAMING_SNAKE_CASE__ : List[str] =prepare_img()
SCREAMING_SNAKE_CASE__ : Any =image_processor(images=__lowercase , return_tensors='''pt''' ).pixel_values.to(__lowercase )
# prepare bool_masked_pos
SCREAMING_SNAKE_CASE__ : Optional[Any] =torch.ones((1, 1_96) , dtype=torch.bool ).to(__lowercase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[Any] =model(pixel_values=__lowercase , bool_masked_pos=__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE__ : str =torch.Size((1, 1_96, 81_92) )
self.assertEqual(logits.shape , __lowercase )
SCREAMING_SNAKE_CASE__ : str =torch.tensor(
[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] ).to(__lowercase )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] , __lowercase , atol=1e-2 ) )
@slow
def __magic_name__ ( self : List[Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ : List[str] =BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224''' ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : int =self.default_image_processor
SCREAMING_SNAKE_CASE__ : int =prepare_img()
SCREAMING_SNAKE_CASE__ : int =image_processor(images=__lowercase , return_tensors='''pt''' ).to(__lowercase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Any =model(**__lowercase )
SCREAMING_SNAKE_CASE__ : str =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE__ : Tuple =torch.Size((1, 10_00) )
self.assertEqual(logits.shape , __lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =torch.tensor([-1.2385, -1.0987, -1.0108] ).to(__lowercase )
self.assertTrue(torch.allclose(logits[0, :3] , __lowercase , atol=1e-4 ) )
SCREAMING_SNAKE_CASE__ : Any =2_81
self.assertEqual(logits.argmax(-1 ).item() , __lowercase )
@slow
def __magic_name__ ( self : Optional[int] ) -> str:
SCREAMING_SNAKE_CASE__ : Dict =BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k''' ).to(
__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =self.default_image_processor
SCREAMING_SNAKE_CASE__ : int =prepare_img()
SCREAMING_SNAKE_CASE__ : Optional[Any] =image_processor(images=__lowercase , return_tensors='''pt''' ).to(__lowercase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(**__lowercase )
SCREAMING_SNAKE_CASE__ : int =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE__ : int =torch.Size((1, 2_18_41) )
self.assertEqual(logits.shape , __lowercase )
SCREAMING_SNAKE_CASE__ : Any =torch.tensor([1.6881, -0.2787, 0.5901] ).to(__lowercase )
self.assertTrue(torch.allclose(logits[0, :3] , __lowercase , atol=1e-4 ) )
SCREAMING_SNAKE_CASE__ : List[str] =23_96
self.assertEqual(logits.argmax(-1 ).item() , __lowercase )
@slow
def __magic_name__ ( self : Optional[int] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : int =BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model.to(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =BeitImageProcessor(do_resize=__lowercase , size=6_40 , do_center_crop=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
SCREAMING_SNAKE_CASE__ : int =Image.open(ds[0]['''file'''] )
SCREAMING_SNAKE_CASE__ : Any =image_processor(images=__lowercase , return_tensors='''pt''' ).to(__lowercase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Any =model(**__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE__ : Tuple =torch.Size((1, 1_50, 1_60, 1_60) )
self.assertEqual(logits.shape , __lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =version.parse(PIL.__version__ ) < version.parse('''9.0.0''' )
if is_pillow_less_than_a:
SCREAMING_SNAKE_CASE__ : int =torch.tensor(
[
[[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
[[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
[[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
] , device=__lowercase , )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.tensor(
[
[[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
[[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
[[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
] , device=__lowercase , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __lowercase , atol=1e-4 ) )
@slow
def __magic_name__ ( self : Dict ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : int =BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640''' )
SCREAMING_SNAKE_CASE__ : Optional[int] =model.to(__lowercase )
SCREAMING_SNAKE_CASE__ : str =BeitImageProcessor(do_resize=__lowercase , size=6_40 , do_center_crop=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
SCREAMING_SNAKE_CASE__ : List[Any] =Image.open(ds[0]['''file'''] )
SCREAMING_SNAKE_CASE__ : Optional[int] =image_processor(images=__lowercase , return_tensors='''pt''' ).to(__lowercase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =outputs.logits.detach().cpu()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =image_processor.post_process_semantic_segmentation(outputs=__lowercase , target_sizes=[(5_00, 3_00)] )
SCREAMING_SNAKE_CASE__ : Any =torch.Size((5_00, 3_00) )
self.assertEqual(segmentation[0].shape , __lowercase )
SCREAMING_SNAKE_CASE__ : Any =image_processor.post_process_semantic_segmentation(outputs=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =torch.Size((1_60, 1_60) )
self.assertEqual(segmentation[0].shape , __lowercase )
| 712 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
'tokenization_deberta': ['DebertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['DebertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'DebertaForMaskedLM',
'DebertaForQuestionAnswering',
'DebertaForSequenceClassification',
'DebertaForTokenClassification',
'DebertaModel',
'DebertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDebertaForMaskedLM',
'TFDebertaForQuestionAnswering',
'TFDebertaForSequenceClassification',
'TFDebertaForTokenClassification',
'TFDebertaModel',
'TFDebertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 665 | 0 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
a_ = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class __SCREAMING_SNAKE_CASE ( datasets.BuilderConfig ):
snake_case_ = None
def _a( UpperCamelCase__ : "pyspark.sql.DataFrame", UpperCamelCase__ : List[int], ):
'''simple docstring'''
import pyspark
def generate_fn():
SCREAMING_SNAKE_CASE__ : str =df.select('''*''', pyspark.sql.functions.spark_partition_id().alias('''part_id''' ) )
for partition_id in partition_order:
SCREAMING_SNAKE_CASE__ : List[str] =df_with_partition_id.select('''*''' ).where(f"part_id = {partition_id}" ).drop('''part_id''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] =partition_df.collect()
SCREAMING_SNAKE_CASE__ : Any =0
for row in rows:
yield f"{partition_id}_{row_id}", row.asDict()
row_id += 1
return generate_fn
class __SCREAMING_SNAKE_CASE ( _BaseExamplesIterable ):
def __init__( self : Optional[Any] , __lowercase : "pyspark.sql.DataFrame" , __lowercase : Optional[int]=None , ) -> Dict:
SCREAMING_SNAKE_CASE__ : str =df
SCREAMING_SNAKE_CASE__ : List[Any] =partition_order or range(self.df.rdd.getNumPartitions() )
SCREAMING_SNAKE_CASE__ : List[str] =_generate_iterable_examples(self.df , self.partition_order )
def __iter__( self : Optional[int] ) -> Tuple:
yield from self.generate_examples_fn()
def __magic_name__ ( self : str , __lowercase : np.random.Generator ) -> "SparkExamplesIterable":
SCREAMING_SNAKE_CASE__ : str =list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(__lowercase )
return SparkExamplesIterable(self.df , partition_order=__lowercase )
def __magic_name__ ( self : str , __lowercase : int , __lowercase : int ) -> "SparkExamplesIterable":
SCREAMING_SNAKE_CASE__ : List[str] =self.split_shard_indices_by_worker(__lowercase , __lowercase )
return SparkExamplesIterable(self.df , partition_order=__lowercase )
@property
def __magic_name__ ( self : int ) -> int:
return len(self.partition_order )
class __SCREAMING_SNAKE_CASE ( datasets.DatasetBuilder ):
snake_case_ = SparkConfig
def __init__( self : Tuple , __lowercase : "pyspark.sql.DataFrame" , __lowercase : str = None , __lowercase : str = None , **__lowercase : List[str] , ) -> Dict:
import pyspark
SCREAMING_SNAKE_CASE__ : Union[str, Any] =pyspark.sql.SparkSession.builder.getOrCreate()
SCREAMING_SNAKE_CASE__ : List[Any] =df
SCREAMING_SNAKE_CASE__ : Dict =working_dir
super().__init__(
cache_dir=__lowercase , config_name=str(self.df.semanticHash() ) , **__lowercase , )
def __magic_name__ ( self : Tuple ) -> List[Any]:
# Returns the path of the created file.
def create_cache_and_write_probe(__lowercase : Any ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir , exist_ok=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =os.path.join(self._cache_dir , '''fs_test''' + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(__lowercase , '''a''' )
return [probe_file]
if self._spark.conf.get('''spark.master''' , '''''' ).startswith('''local''' ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
SCREAMING_SNAKE_CASE__ : List[str] =(
self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(__lowercase ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
'''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir''' )
def __magic_name__ ( self : Optional[Any] ) -> Dict:
return datasets.DatasetInfo(features=self.config.features )
def __magic_name__ ( self : str , __lowercase : datasets.download.download_manager.DownloadManager ) -> List[str]:
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def __magic_name__ ( self : Any , __lowercase : Any ) -> Tuple:
import pyspark
def get_arrow_batch_size(__lowercase : str ):
for batch in it:
yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]} )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.df.count()
SCREAMING_SNAKE_CASE__ : Optional[Any] =df_num_rows if df_num_rows <= 1_00 else 1_00
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
SCREAMING_SNAKE_CASE__ : List[Any] =(
self.df.limit(__lowercase )
.repartition(1 )
.mapInArrow(__lowercase , '''batch_bytes: long''' )
.agg(pyspark.sql.functions.sum('''batch_bytes''' ).alias('''sample_bytes''' ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
SCREAMING_SNAKE_CASE__ : Optional[int] =approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
SCREAMING_SNAKE_CASE__ : int =min(__lowercase , int(approx_total_size / max_shard_size ) )
SCREAMING_SNAKE_CASE__ : Tuple =self.df.repartition(__lowercase )
def __magic_name__ ( self : Dict , __lowercase : str , __lowercase : str , __lowercase : int , ) -> Iterable[Tuple[int, bool, Union[int, tuple]]]:
import pyspark
SCREAMING_SNAKE_CASE__ : str =ParquetWriter if file_format == '''parquet''' else ArrowWriter
SCREAMING_SNAKE_CASE__ : Tuple =os.path.join(self._working_dir , os.path.basename(__lowercase ) ) if self._working_dir else fpath
SCREAMING_SNAKE_CASE__ : Optional[int] =file_format == '''parquet'''
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
SCREAMING_SNAKE_CASE__ : Any =self.config.features
SCREAMING_SNAKE_CASE__ : Any =self._writer_batch_size
SCREAMING_SNAKE_CASE__ : Tuple =self._fs.storage_options
def write_arrow(__lowercase : Optional[Any] ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
SCREAMING_SNAKE_CASE__ : List[Any] =pyspark.TaskContext().taskAttemptId()
SCREAMING_SNAKE_CASE__ : int =next(__lowercase , __lowercase )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =0
SCREAMING_SNAKE_CASE__ : Optional[int] =writer_class(
features=__lowercase , path=working_fpath.replace('''SSSSS''' , F"{shard_id:05d}" ).replace('''TTTTT''' , F"{task_id:05d}" ) , writer_batch_size=__lowercase , storage_options=__lowercase , embed_local_files=__lowercase , )
SCREAMING_SNAKE_CASE__ : List[Any] =pa.Table.from_batches([first_batch] )
writer.write_table(__lowercase )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
SCREAMING_SNAKE_CASE__ : int =writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
shard_id += 1
SCREAMING_SNAKE_CASE__ : List[Any] =writer_class(
features=writer._features , path=working_fpath.replace('''SSSSS''' , F"{shard_id:05d}" ).replace('''TTTTT''' , F"{task_id:05d}" ) , writer_batch_size=__lowercase , storage_options=__lowercase , embed_local_files=__lowercase , )
SCREAMING_SNAKE_CASE__ : str =pa.Table.from_batches([batch] )
writer.write_table(__lowercase )
if writer._num_bytes > 0:
SCREAMING_SNAKE_CASE__ : str =writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] , names=['''task_id''', '''num_examples''', '''num_bytes'''] , )
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(__lowercase ) ):
SCREAMING_SNAKE_CASE__ : str =os.path.join(os.path.dirname(__lowercase ) , os.path.basename(__lowercase ) )
shutil.move(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(
self.df.mapInArrow(__lowercase , '''task_id: long, num_examples: long, num_bytes: long''' )
.groupBy('''task_id''' )
.agg(
pyspark.sql.functions.sum('''num_examples''' ).alias('''total_num_examples''' ) , pyspark.sql.functions.sum('''num_bytes''' ).alias('''total_num_bytes''' ) , pyspark.sql.functions.count('''num_bytes''' ).alias('''num_shards''' ) , pyspark.sql.functions.collect_list('''num_examples''' ).alias('''shard_lengths''' ) , )
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def __magic_name__ ( self : Union[str, Any] , __lowercase : "datasets.SplitGenerator" , __lowercase : str = "arrow" , __lowercase : Optional[Union[str, int]] = None , __lowercase : Optional[int] = None , **__lowercase : str , ) -> Any:
self._validate_cache_dir()
SCREAMING_SNAKE_CASE__ : Any =convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =not is_remote_filesystem(self._fs )
SCREAMING_SNAKE_CASE__ : Optional[int] =os.path.join if is_local else posixpath.join
SCREAMING_SNAKE_CASE__ : Dict ='''-TTTTT-SSSSS-of-NNNNN'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =F"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
SCREAMING_SNAKE_CASE__ : Optional[Any] =path_join(self._output_dir , __lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =0
SCREAMING_SNAKE_CASE__ : Any =0
SCREAMING_SNAKE_CASE__ : List[Any] =0
SCREAMING_SNAKE_CASE__ : Dict =[]
SCREAMING_SNAKE_CASE__ : Tuple =[]
for task_id, content in self._prepare_split_single(__lowercase , __lowercase , __lowercase ):
(
SCREAMING_SNAKE_CASE__
) : Any =content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =total_num_examples
SCREAMING_SNAKE_CASE__ : List[Any] =total_num_bytes
# should rename everything at the end
logger.debug(F"Renaming {total_shards} shards." )
if total_shards > 1:
SCREAMING_SNAKE_CASE__ : Dict =all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
SCREAMING_SNAKE_CASE__ : str =self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
__lowercase : int , __lowercase : int , __lowercase : int , ):
rename(
__lowercase , fpath.replace('''SSSSS''' , F"{shard_id:05d}" ).replace('''TTTTT''' , F"{task_id:05d}" ) , fpath.replace('''TTTTT-SSSSS''' , F"{global_shard_id:05d}" ).replace('''NNNNN''' , F"{total_shards:05d}" ) , )
SCREAMING_SNAKE_CASE__ : Dict =[]
SCREAMING_SNAKE_CASE__ : Dict =0
for i in range(len(__lowercase ) ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =task_id_and_num_shards[i]
for shard_id in range(__lowercase ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(__lowercase , len(__lowercase ) ).map(lambda __lowercase : _rename_shard(*__lowercase ) ).collect()
else:
# don't use any pattern
SCREAMING_SNAKE_CASE__ : Optional[Any] =0
SCREAMING_SNAKE_CASE__ : Optional[Any] =task_id_and_num_shards[0][0]
self._rename(
fpath.replace('''SSSSS''' , F"{shard_id:05d}" ).replace('''TTTTT''' , F"{task_id:05d}" ) , fpath.replace(__lowercase , '''''' ) , )
def __magic_name__ ( self : Union[str, Any] , __lowercase : "datasets.SplitGenerator" , ) -> SparkExamplesIterable:
return SparkExamplesIterable(self.df )
| 713 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _a( UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : list[int], UpperCamelCase__ : int, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple =coefficient_matrix.shape
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple =constant_matrix.shape
if rowsa != colsa:
SCREAMING_SNAKE_CASE__ : Any =f"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
raise ValueError(UpperCamelCase__ )
if colsa != 1:
SCREAMING_SNAKE_CASE__ : str =f"Constant matrix must be nx1 but received {rowsa}x{colsa}"
raise ValueError(UpperCamelCase__ )
if rowsa != rowsa:
SCREAMING_SNAKE_CASE__ : str =(
'''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:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(
'''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''' )
SCREAMING_SNAKE_CASE__ : NDArray[floataa] =np.concatenate(
(coefficient_matrix, constant_matrix), axis=1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any =table.shape
strictly_diagonally_dominant(UpperCamelCase__ )
# Iterates the whole matrix for given number of times
for _ in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : List[str] =[]
for row in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =0
for col in range(UpperCamelCase__ ):
if col == row:
SCREAMING_SNAKE_CASE__ : int =table[row][col]
elif col == cols - 1:
SCREAMING_SNAKE_CASE__ : Any =table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
SCREAMING_SNAKE_CASE__ : int =(temp + val) / denom
new_val.append(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =new_val
return [float(UpperCamelCase__ ) for i in new_val]
def _a( UpperCamelCase__ : NDArray[floataa] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] =table.shape
SCREAMING_SNAKE_CASE__ : Any =True
for i in range(0, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : int =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()
| 665 | 0 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
a_ = [
'cross_validation.py',
'gradient_accumulation.py',
'local_sgd.py',
'multi_process_metrics.py',
'memory.py',
'automatic_gradient_accumulation.py',
'fsdp_with_peak_mem_tracking.py',
'deepspeed_with_config_support.py',
'megatron_lm_gpt_pretraining.py',
]
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : Dict , __lowercase : str , __lowercase : bool , __lowercase : str = None , __lowercase : list = None ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =None
SCREAMING_SNAKE_CASE__ : int =os.path.abspath(os.path.join('''examples''' , '''by_feature''' ) )
SCREAMING_SNAKE_CASE__ : Any =os.path.abspath('''examples''' )
for item in os.listdir(__lowercase ):
if item not in EXCLUDE_EXAMPLES:
SCREAMING_SNAKE_CASE__ : Any =os.path.join(__lowercase , __lowercase )
if os.path.isfile(__lowercase ) and ".py" in item_path:
with self.subTest(
tested_script=__lowercase , feature_script=__lowercase , tested_section='''main()''' if parser_only else '''training_function()''' , ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =compare_against_test(
os.path.join(__lowercase , __lowercase ) , __lowercase , __lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] ='''\n'''.join(__lowercase )
if special_strings is not None:
for string in special_strings:
SCREAMING_SNAKE_CASE__ : List[str] =diff.replace(__lowercase , '''''' )
self.assertEqual(__lowercase , '''''' )
def __magic_name__ ( self : int ) -> int:
self.one_complete_example('''complete_nlp_example.py''' , __lowercase )
self.one_complete_example('''complete_nlp_example.py''' , __lowercase )
def __magic_name__ ( self : Dict ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =os.path.abspath(os.path.join('''examples''' , '''cv_example.py''' ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] =[
''' ''' * 16 + '''{\n\n''',
''' ''' * 20 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 20 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 20 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 20 + '''"epoch": epoch,\n\n''',
''' ''' * 16 + '''},\n\n''',
''' ''' * 16 + '''step=epoch,\n''',
''' ''' * 12,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example('''complete_cv_example.py''' , __lowercase , __lowercase , __lowercase )
self.one_complete_example('''complete_cv_example.py''' , __lowercase , __lowercase , __lowercase )
@mock.patch.dict(os.environ , {"""TESTING_MOCKED_DATALOADERS""": """1"""} )
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = False
@classmethod
def __magic_name__ ( cls : Dict ) -> int:
super().setUpClass()
SCREAMING_SNAKE_CASE__ : List[Any] =tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : Tuple =os.path.join(cls._tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
SCREAMING_SNAKE_CASE__ : Optional[int] =['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def __magic_name__ ( cls : Union[str, Any] ) -> Union[str, Any]:
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def __magic_name__ ( self : Dict ) -> Dict:
SCREAMING_SNAKE_CASE__ : Dict =F"\n examples/by_feature/checkpointing.py\n --checkpointing_steps epoch\n --output_dir {self.tmpdir}\n ".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0''' ) ) )
def __magic_name__ ( self : List[Any] ) -> int:
SCREAMING_SNAKE_CASE__ : List[str] =F"\n examples/by_feature/checkpointing.py\n --checkpointing_steps 1\n --output_dir {self.tmpdir}\n ".split()
SCREAMING_SNAKE_CASE__ : List[Any] =run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2''' ) ) )
def __magic_name__ ( self : List[Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =F"\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}\n ".split()
SCREAMING_SNAKE_CASE__ : str =run_command(self._launch_args + testargs , return_stdout=__lowercase )
self.assertNotIn('''epoch 0:''' , __lowercase )
self.assertIn('''epoch 1:''' , __lowercase )
def __magic_name__ ( self : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : str =F"\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}\n ".split()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =run_command(self._launch_args + testargs , return_stdout=__lowercase )
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =1
if num_processes > 1:
self.assertNotIn('''epoch 0:''' , __lowercase )
self.assertIn('''epoch 1:''' , __lowercase )
else:
self.assertIn('''epoch 0:''' , __lowercase )
self.assertIn('''epoch 1:''' , __lowercase )
@slow
def __magic_name__ ( self : Optional[int] ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[Any] ='''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''} ):
SCREAMING_SNAKE_CASE__ : List[Any] =run_command(self._launch_args + testargs , return_stdout=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =re.findall('''({.+})''' , __lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =[r for r in results if '''accuracy''' in r][-1]
SCREAMING_SNAKE_CASE__ : Tuple =ast.literal_eval(__lowercase )
self.assertGreaterEqual(results['''accuracy'''] , 0.75 )
def __magic_name__ ( self : Dict ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : List[str] =['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def __magic_name__ ( self : Tuple ) -> str:
with tempfile.TemporaryDirectory() as tmpdir:
SCREAMING_SNAKE_CASE__ : Optional[Any] =F"\n examples/by_feature/tracking.py\n --with_tracking\n --project_dir {tmpdir}\n ".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(__lowercase , '''tracking''' ) ) )
def __magic_name__ ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Dict =['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def __magic_name__ ( self : Union[str, Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ : str =['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 714 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _a( UpperCamelCase__ : str, UpperCamelCase__ : Tuple, UpperCamelCase__ : Any, UpperCamelCase__ : List[str], UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
with open(UpperCamelCase__ ) as metadata_file:
SCREAMING_SNAKE_CASE__ : Optional[int] =json.load(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =LukeConfig(use_entity_aware_attention=UpperCamelCase__, **metadata['''model_config'''] )
# Load in the weights from the checkpoint_path
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch.load(UpperCamelCase__, map_location='''cpu''' )['''module''']
# Load the entity vocab file
SCREAMING_SNAKE_CASE__ : List[str] =load_original_entity_vocab(UpperCamelCase__ )
# add an entry for [MASK2]
SCREAMING_SNAKE_CASE__ : Optional[int] =max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
SCREAMING_SNAKE_CASE__ : Optional[int] =XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )
# Add special tokens to the token vocabulary for downstream tasks
SCREAMING_SNAKE_CASE__ : List[Any] =AddedToken('''<ent>''', lstrip=UpperCamelCase__, rstrip=UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Any =AddedToken('''<ent2>''', lstrip=UpperCamelCase__, rstrip=UpperCamelCase__ )
tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f"Saving tokenizer to {pytorch_dump_folder_path}" )
tokenizer.save_pretrained(UpperCamelCase__ )
with open(os.path.join(UpperCamelCase__, '''tokenizer_config.json''' ), '''r''' ) as f:
SCREAMING_SNAKE_CASE__ : Optional[Any] =json.load(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : int ='''MLukeTokenizer'''
with open(os.path.join(UpperCamelCase__, '''tokenizer_config.json''' ), '''w''' ) as f:
json.dump(UpperCamelCase__, UpperCamelCase__ )
with open(os.path.join(UpperCamelCase__, MLukeTokenizer.vocab_files_names['''entity_vocab_file'''] ), '''w''' ) as f:
json.dump(UpperCamelCase__, UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =MLukeTokenizer.from_pretrained(UpperCamelCase__ )
# Initialize the embeddings of the special tokens
SCREAMING_SNAKE_CASE__ : str =tokenizer.convert_tokens_to_ids(['''@'''] )[0]
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer.convert_tokens_to_ids(['''#'''] )[0]
SCREAMING_SNAKE_CASE__ : Dict =state_dict['''embeddings.word_embeddings.weight''']
SCREAMING_SNAKE_CASE__ : List[str] =word_emb[ent_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : Tuple =word_emb[enta_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : str =torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =state_dict[bias_name]
SCREAMING_SNAKE_CASE__ : List[Any] =decoder_bias[ent_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : str =decoder_bias[enta_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : List[str] =torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
SCREAMING_SNAKE_CASE__ : Tuple =f"encoder.layer.{layer_index}.attention.self."
SCREAMING_SNAKE_CASE__ : Optional[int] =state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE__ : Optional[int] =state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE__ : List[Any] =state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
SCREAMING_SNAKE_CASE__ : Any =state_dict['''entity_embeddings.entity_embeddings.weight''']
SCREAMING_SNAKE_CASE__ : Any =entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : Any =torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
SCREAMING_SNAKE_CASE__ : Optional[int] =state_dict['''entity_predictions.bias''']
SCREAMING_SNAKE_CASE__ : Tuple =entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : Any =torch.cat([entity_prediction_bias, entity_mask_bias] )
SCREAMING_SNAKE_CASE__ : int =LukeForMaskedLM(config=UpperCamelCase__ ).eval()
state_dict.pop('''entity_predictions.decoder.weight''' )
state_dict.pop('''lm_head.decoder.weight''' )
state_dict.pop('''lm_head.decoder.bias''' )
SCREAMING_SNAKE_CASE__ : Tuple =OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('''lm_head''' ) or key.startswith('''entity_predictions''' )):
SCREAMING_SNAKE_CASE__ : Optional[Any] =state_dict[key]
else:
SCREAMING_SNAKE_CASE__ : Any =state_dict[key]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] =model.load_state_dict(UpperCamelCase__, strict=UpperCamelCase__ )
if set(UpperCamelCase__ ) != {"luke.embeddings.position_ids"}:
raise ValueError(f"Unexpected unexpected_keys: {unexpected_keys}" )
if set(UpperCamelCase__ ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(f"Unexpected missing_keys: {missing_keys}" )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
SCREAMING_SNAKE_CASE__ : Any =MLukeTokenizer.from_pretrained(UpperCamelCase__, task='''entity_classification''' )
SCREAMING_SNAKE_CASE__ : str ='''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(0, 9)
SCREAMING_SNAKE_CASE__ : str =tokenizer(UpperCamelCase__, entity_spans=[span], return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : List[Any] =model(**UpperCamelCase__ )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
SCREAMING_SNAKE_CASE__ : str =torch.Size((1, 3_3, 7_6_8) )
SCREAMING_SNAKE_CASE__ : int =torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f"Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3], UpperCamelCase__, atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
SCREAMING_SNAKE_CASE__ : Any =torch.Size((1, 1, 7_6_8) )
SCREAMING_SNAKE_CASE__ : int =torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
f"Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"
f" {expected_shape}" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3], UpperCamelCase__, atol=1e-4 ):
raise ValueError
# Verify masked word/entity prediction
SCREAMING_SNAKE_CASE__ : str =MLukeTokenizer.from_pretrained(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] ='''Tokyo is the capital of <mask>.'''
SCREAMING_SNAKE_CASE__ : Dict =(2_4, 3_0)
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer(UpperCamelCase__, entity_spans=[span], return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : List[str] =model(**UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[Any] =encoding['''input_ids'''][0].tolist()
SCREAMING_SNAKE_CASE__ : Any =input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>''' ) )
SCREAMING_SNAKE_CASE__ : List[Any] =outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =outputs.entity_logits[0][0].argmax().item()
SCREAMING_SNAKE_CASE__ : Dict =[
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('''en:''' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('''Saving PyTorch model to {}'''.format(UpperCamelCase__ ) )
model.save_pretrained(UpperCamelCase__ )
def _a( UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] =['''[MASK]''', '''[PAD]''', '''[UNK]''']
SCREAMING_SNAKE_CASE__ : List[str] =[json.loads(UpperCamelCase__ ) for line in open(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Optional[int] ={}
for entry in data:
SCREAMING_SNAKE_CASE__ : Tuple =entry['''id''']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
SCREAMING_SNAKE_CASE__ : str =entity_id
break
SCREAMING_SNAKE_CASE__ : Union[str, Any] =f"{language}:{entity_name}"
SCREAMING_SNAKE_CASE__ : Union[str, Any] =entity_id
return new_mapping
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
parser.add_argument(
'--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
)
parser.add_argument(
'--entity_vocab_path',
default=None,
type=str,
help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
)
parser.add_argument(
'--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
)
a_ = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 665 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """biogpt"""
def __init__( self : Optional[int] , __lowercase : Any=4_23_84 , __lowercase : Optional[int]=10_24 , __lowercase : Tuple=24 , __lowercase : Any=16 , __lowercase : List[str]=40_96 , __lowercase : Optional[int]="gelu" , __lowercase : Optional[int]=0.1 , __lowercase : Any=0.1 , __lowercase : Dict=10_24 , __lowercase : Union[str, Any]=0.02 , __lowercase : List[str]=1e-12 , __lowercase : Tuple=True , __lowercase : List[str]=True , __lowercase : str=0.0 , __lowercase : Optional[Any]=0.0 , __lowercase : Optional[int]=1 , __lowercase : int=0 , __lowercase : Any=2 , **__lowercase : str , ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Any =vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[Any] =hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] =num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any =num_attention_heads
SCREAMING_SNAKE_CASE__ : int =intermediate_size
SCREAMING_SNAKE_CASE__ : List[str] =hidden_act
SCREAMING_SNAKE_CASE__ : List[str] =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Tuple =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] =initializer_range
SCREAMING_SNAKE_CASE__ : str =layer_norm_eps
SCREAMING_SNAKE_CASE__ : Dict =scale_embedding
SCREAMING_SNAKE_CASE__ : Dict =use_cache
SCREAMING_SNAKE_CASE__ : str =layerdrop
SCREAMING_SNAKE_CASE__ : Any =activation_dropout
super().__init__(pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase , **__lowercase )
| 715 |
'''simple docstring'''
def _a( UpperCamelCase__ : str, UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Tuple =[[False for _ in range(m + 1 )] for _ in range(n + 1 )]
SCREAMING_SNAKE_CASE__ : List[Any] =True
for i in range(UpperCamelCase__ ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
SCREAMING_SNAKE_CASE__ : Optional[int] =True
if a[i].islower():
SCREAMING_SNAKE_CASE__ : List[Any] =True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
a_ = (3, 9, -1_1, 0, 7, 5, 1, -1)
a_ = (4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class __SCREAMING_SNAKE_CASE :
snake_case_ = 42
snake_case_ = 42
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , __lowercase : Iterable[int] ) -> None:
SCREAMING_SNAKE_CASE__ : Node | None =None
for i in sorted(__lowercase , reverse=__lowercase ):
SCREAMING_SNAKE_CASE__ : Any =Node(__lowercase , self.head )
def __iter__( self : Any ) -> Iterator[int]:
SCREAMING_SNAKE_CASE__ : List[Any] =self.head
while node:
yield node.data
SCREAMING_SNAKE_CASE__ : Any =node.next_node
def __len__( self : List[Any] ) -> int:
return sum(1 for _ in self )
def __str__( self : List[Any] ) -> str:
return " -> ".join([str(__lowercase ) for node in self] )
def _a( UpperCamelCase__ : SortedLinkedList, UpperCamelCase__ : SortedLinkedList ):
'''simple docstring'''
return SortedLinkedList(list(UpperCamelCase__ ) + list(UpperCamelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
a_ = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 716 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , __lowercase : Optional[Any] , __lowercase : List[str]=13 , __lowercase : int=7 , __lowercase : Tuple=True , __lowercase : Optional[Any]=True , __lowercase : List[Any]=True , __lowercase : str=True , __lowercase : Tuple=99 , __lowercase : Union[str, Any]=64 , __lowercase : Tuple=32 , __lowercase : Optional[Any]=5 , __lowercase : Tuple=4 , __lowercase : Optional[int]=37 , __lowercase : int="gelu" , __lowercase : Union[str, Any]=0.1 , __lowercase : List[Any]=0.1 , __lowercase : List[Any]=5_12 , __lowercase : int=16 , __lowercase : Optional[int]=2 , __lowercase : Tuple=0.02 , __lowercase : List[str]=3 , __lowercase : List[str]=4 , __lowercase : List[str]=None , ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =parent
SCREAMING_SNAKE_CASE__ : Any =batch_size
SCREAMING_SNAKE_CASE__ : Optional[Any] =seq_length
SCREAMING_SNAKE_CASE__ : Dict =is_training
SCREAMING_SNAKE_CASE__ : List[Any] =use_input_mask
SCREAMING_SNAKE_CASE__ : Union[str, Any] =use_token_type_ids
SCREAMING_SNAKE_CASE__ : List[Any] =use_labels
SCREAMING_SNAKE_CASE__ : int =vocab_size
SCREAMING_SNAKE_CASE__ : str =hidden_size
SCREAMING_SNAKE_CASE__ : Any =embedding_size
SCREAMING_SNAKE_CASE__ : Tuple =num_hidden_layers
SCREAMING_SNAKE_CASE__ : str =num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple =intermediate_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =hidden_act
SCREAMING_SNAKE_CASE__ : Optional[int] =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : str =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Any =max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[Any] =type_vocab_size
SCREAMING_SNAKE_CASE__ : Any =type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Optional[Any] =initializer_range
SCREAMING_SNAKE_CASE__ : str =num_labels
SCREAMING_SNAKE_CASE__ : List[str] =num_choices
SCREAMING_SNAKE_CASE__ : List[str] =scope
def __magic_name__ ( self : str ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : int =None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : List[str] =random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : int =None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Any =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =None
SCREAMING_SNAKE_CASE__ : Optional[Any] =None
SCREAMING_SNAKE_CASE__ : Optional[int] =None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Tuple =ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : List[Any] =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : Any =ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __magic_name__ ( self : List[str] ) -> Any:
return MegatronBertConfig(
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 , embedding_size=self.embedding_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=__lowercase , initializer_range=self.initializer_range , )
def __magic_name__ ( self : Any , __lowercase : Tuple , __lowercase : Any , __lowercase : List[Any] , __lowercase : Optional[int] , __lowercase : List[str] , __lowercase : Dict , __lowercase : Union[str, Any] ) -> int:
SCREAMING_SNAKE_CASE__ : List[str] =MegatronBertModel(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(__lowercase , token_type_ids=__lowercase )
SCREAMING_SNAKE_CASE__ : str =model(__lowercase )
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 __magic_name__ ( self : Dict , __lowercase : Optional[int] , __lowercase : List[str] , __lowercase : str , __lowercase : Tuple , __lowercase : Any , __lowercase : Optional[int] , __lowercase : Any ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : str =MegatronBertForMaskedLM(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Tuple =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self : List[str] , __lowercase : Tuple , __lowercase : Tuple , __lowercase : Optional[Any] , __lowercase : Any , __lowercase : str , __lowercase : Any , __lowercase : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[Any] =MegatronBertForCausalLM(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self : Union[str, Any] , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : Union[str, Any] , __lowercase : Dict , __lowercase : Union[str, Any] , __lowercase : int , __lowercase : Any ) -> Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =MegatronBertForNextSentencePrediction(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] =model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def __magic_name__ ( self : List[str] , __lowercase : Optional[int] , __lowercase : str , __lowercase : Optional[int] , __lowercase : Optional[int] , __lowercase : Optional[Any] , __lowercase : str , __lowercase : Optional[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =MegatronBertForPreTraining(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] =model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , next_sentence_label=__lowercase , )
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 __magic_name__ ( self : Optional[int] , __lowercase : Tuple , __lowercase : Optional[int] , __lowercase : Union[str, Any] , __lowercase : int , __lowercase : Any , __lowercase : Any , __lowercase : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[Any] =MegatronBertForQuestionAnswering(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict =model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , start_positions=__lowercase , end_positions=__lowercase , )
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 __magic_name__ ( self : Optional[int] , __lowercase : Optional[int] , __lowercase : Optional[int] , __lowercase : List[Any] , __lowercase : List[Any] , __lowercase : Optional[int] , __lowercase : int , __lowercase : List[str] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.num_labels
SCREAMING_SNAKE_CASE__ : Dict =MegatronBertForSequenceClassification(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __magic_name__ ( self : Optional[int] , __lowercase : List[Any] , __lowercase : str , __lowercase : Optional[Any] , __lowercase : Any , __lowercase : Tuple , __lowercase : Optional[Any] , __lowercase : int ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Tuple =self.num_labels
SCREAMING_SNAKE_CASE__ : int =MegatronBertForTokenClassification(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __magic_name__ ( self : Dict , __lowercase : List[str] , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : Any , __lowercase : List[Any] , __lowercase : int , __lowercase : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : int =self.num_choices
SCREAMING_SNAKE_CASE__ : List[str] =MegatronBertForMultipleChoice(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Optional[int] =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Tuple =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __magic_name__ ( self : str ) -> Any:
SCREAMING_SNAKE_CASE__ : Tuple =self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : str =config_and_inputs
SCREAMING_SNAKE_CASE__ : Union[str, Any] ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
snake_case_ = (
{
"""feature-extraction""": MegatronBertModel,
"""fill-mask""": MegatronBertForMaskedLM,
"""question-answering""": MegatronBertForQuestionAnswering,
"""text-classification""": MegatronBertForSequenceClassification,
"""text-generation""": MegatronBertForCausalLM,
"""token-classification""": MegatronBertForTokenClassification,
"""zero-shot""": MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case_ = True
# test_resize_embeddings = False
snake_case_ = False
def __magic_name__ ( self : List[Any] , __lowercase : List[str] , __lowercase : Tuple , __lowercase : Tuple=False ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =super()._prepare_for_class(__lowercase , __lowercase , return_labels=__lowercase )
if return_labels:
if model_class in get_values(__lowercase ):
SCREAMING_SNAKE_CASE__ : List[str] =torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowercase )
return inputs_dict
def __magic_name__ ( self : Tuple ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =MegatronBertModelTester(self )
SCREAMING_SNAKE_CASE__ : Tuple =ConfigTester(self , config_class=__lowercase , hidden_size=37 )
def __magic_name__ ( self : str ) -> Dict:
self.config_tester.run_common_tests()
def __magic_name__ ( self : Tuple ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*__lowercase )
def __magic_name__ ( self : List[str] ) -> Any:
SCREAMING_SNAKE_CASE__ : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__lowercase )
def __magic_name__ ( self : Optional[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__lowercase )
def __magic_name__ ( self : Optional[int] ) -> Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__lowercase )
def __magic_name__ ( self : Dict ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*__lowercase )
def __magic_name__ ( self : int ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*__lowercase )
def __magic_name__ ( self : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*__lowercase )
def _a( UpperCamelCase__ : List[str] ):
'''simple docstring'''
return torch.tensor(
UpperCamelCase__, dtype=torch.long, device=UpperCamelCase__, )
a_ = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
@unittest.skip('''Model is not available.''' )
def __magic_name__ ( self : Any ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Any ='''nvidia/megatron-bert-uncased-345m'''
if "MYDIR" in os.environ:
SCREAMING_SNAKE_CASE__ : Optional[int] =os.path.join(os.environ['''MYDIR'''] , __lowercase )
SCREAMING_SNAKE_CASE__ : Any =MegatronBertModel.from_pretrained(__lowercase )
model.to(__lowercase )
model.half()
SCREAMING_SNAKE_CASE__ : Dict =_long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(__lowercase )[0]
SCREAMING_SNAKE_CASE__ : Dict =torch.Size((1, 9, 10_24) )
self.assertEqual(output.shape , __lowercase )
SCREAMING_SNAKE_CASE__ : str =[-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
for ii in range(3 ):
for jj in range(3 ):
SCREAMING_SNAKE_CASE__ : List[Any] =output[0, ii, jj]
SCREAMING_SNAKE_CASE__ : Tuple =expected[3 * ii + jj]
SCREAMING_SNAKE_CASE__ : List[str] ='''ii={} jj={} a={} b={}'''.format(__lowercase , __lowercase , __lowercase , __lowercase )
self.assertTrue(math.isclose(__lowercase , __lowercase , rel_tol=__lowercase , abs_tol=__lowercase ) , msg=__lowercase )
| 665 | 0 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def _a( UpperCamelCase__ : int, UpperCamelCase__ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any =int(UpperCamelCase__ )
assert noofclusters < len(UpperCamelCase__ )
# Find out the dimensionality
SCREAMING_SNAKE_CASE__ : List[str] =len(vectors[0] )
# Will help select random centroids from among the available vectors
SCREAMING_SNAKE_CASE__ : Optional[Any] =list(range(len(UpperCamelCase__ ) ) )
shuffle(UpperCamelCase__ )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
SCREAMING_SNAKE_CASE__ : Any =tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
SCREAMING_SNAKE_CASE__ : int =tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
SCREAMING_SNAKE_CASE__ : Dict =[
tf.Variable(vectors[vector_indices[i]] ) for i in range(UpperCamelCase__ )
]
##These nodes will assign the centroid Variables the appropriate
##values
SCREAMING_SNAKE_CASE__ : str =tf.placeholder('''float64''', [dim] )
SCREAMING_SNAKE_CASE__ : Any =[]
for centroid in centroids:
cent_assigns.append(tf.assign(UpperCamelCase__, UpperCamelCase__ ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
SCREAMING_SNAKE_CASE__ : Tuple =[tf.Variable(0 ) for i in range(len(UpperCamelCase__ ) )]
##These nodes will assign an assignment Variable the appropriate
##value
SCREAMING_SNAKE_CASE__ : List[str] =tf.placeholder('''int32''' )
SCREAMING_SNAKE_CASE__ : List[str] =[]
for assignment in assignments:
cluster_assigns.append(tf.assign(UpperCamelCase__, UpperCamelCase__ ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
SCREAMING_SNAKE_CASE__ : str =tf.placeholder('''float''', [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
SCREAMING_SNAKE_CASE__ : List[str] =tf.reduce_mean(UpperCamelCase__, 0 )
##Node for computing Euclidean distances
# Placeholders for input
SCREAMING_SNAKE_CASE__ : Any =tf.placeholder('''float''', [dim] )
SCREAMING_SNAKE_CASE__ : List[str] =tf.placeholder('''float''', [dim] )
SCREAMING_SNAKE_CASE__ : Any =tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(UpperCamelCase__, UpperCamelCase__ ), 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
SCREAMING_SNAKE_CASE__ : Optional[Any] =tf.placeholder('''float''', [noofclusters] )
SCREAMING_SNAKE_CASE__ : str =tf.argmin(UpperCamelCase__, 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
SCREAMING_SNAKE_CASE__ : List[str] =tf.initialize_all_variables()
# Initialize all variables
sess.run(UpperCamelCase__ )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
SCREAMING_SNAKE_CASE__ : Dict =1_0_0
for _ in range(UpperCamelCase__ ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(UpperCamelCase__ ) ):
SCREAMING_SNAKE_CASE__ : Tuple =vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
SCREAMING_SNAKE_CASE__ : Union[str, Any] =[
sess.run(UpperCamelCase__, feed_dict={va: vect, va: sess.run(UpperCamelCase__ )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
SCREAMING_SNAKE_CASE__ : int =sess.run(
UpperCamelCase__, feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n], feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(UpperCamelCase__ ):
# Collect all the vectors assigned to this cluster
SCREAMING_SNAKE_CASE__ : List[Any] =[
vectors[i]
for i in range(len(UpperCamelCase__ ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
SCREAMING_SNAKE_CASE__ : int =sess.run(
UpperCamelCase__, feed_dict={mean_input: array(UpperCamelCase__ )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n], feed_dict={centroid_value: new_location} )
# Return centroids and assignments
SCREAMING_SNAKE_CASE__ : Tuple =sess.run(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[Any] =sess.run(UpperCamelCase__ )
return centroids, assignments
| 717 |
'''simple docstring'''
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =inspect.getfile(accelerate.test_utils )
SCREAMING_SNAKE_CASE__ : Optional[Any] =os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_script.py'''] )
SCREAMING_SNAKE_CASE__ : Dict =os.path.sep.join(inspect.getfile(self.__class__ ).split(os.path.sep )[:-1] )
@require_tpu
def __magic_name__ ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Any =F"\n {self.test_dir}/xla_spawn.py\n --num_cores 8\n {self.test_file_path}\n ".split()
SCREAMING_SNAKE_CASE__ : List[str] =[sys.executable] + distributed_args
execute_subprocess_async(__lowercase , env=os.environ.copy() )
| 665 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTextConfig',
'AltCLIPVisionConfig',
],
'processing_altclip': ['AltCLIPProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'AltCLIPPreTrainedModel',
'AltCLIPModel',
'AltCLIPTextModel',
'AltCLIPVisionModel',
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 718 |
'''simple docstring'''
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 __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = ShapEImgaImgPipeline
snake_case_ = ["""image"""]
snake_case_ = ["""image"""]
snake_case_ = [
"""num_images_per_prompt""",
"""num_inference_steps""",
"""generator""",
"""latents""",
"""guidance_scale""",
"""frame_size""",
"""output_type""",
"""return_dict""",
]
snake_case_ = False
@property
def __magic_name__ ( self : List[Any] ) -> List[Any]:
return 32
@property
def __magic_name__ ( self : List[str] ) -> Optional[int]:
return 32
@property
def __magic_name__ ( self : Optional[int] ) -> Optional[Any]:
return self.time_input_dim * 4
@property
def __magic_name__ ( self : Dict ) -> Union[str, Any]:
return 8
@property
def __magic_name__ ( self : Optional[int] ) -> Union[str, Any]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Dict =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 , )
SCREAMING_SNAKE_CASE__ : str =CLIPVisionModel(__lowercase )
return model
@property
def __magic_name__ ( self : Tuple ) -> List[str]:
SCREAMING_SNAKE_CASE__ : int =CLIPImageProcessor(
crop_size=2_24 , do_center_crop=__lowercase , do_normalize=__lowercase , do_resize=__lowercase , image_mean=[0.48145466, 0.4578275, 0.40821073] , image_std=[0.26862954, 0.26130258, 0.27577711] , resample=3 , size=2_24 , )
return image_processor
@property
def __magic_name__ ( self : List[str] ) -> Dict:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : str ={
'''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,
}
SCREAMING_SNAKE_CASE__ : str =PriorTransformer(**__lowercase )
return model
@property
def __magic_name__ ( self : Tuple ) -> List[str]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[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,
),
}
SCREAMING_SNAKE_CASE__ : str =ShapERenderer(**__lowercase )
return model
def __magic_name__ ( self : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : int =self.dummy_prior
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dummy_image_encoder
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dummy_image_processor
SCREAMING_SNAKE_CASE__ : Tuple =self.dummy_renderer
SCREAMING_SNAKE_CASE__ : int =HeunDiscreteScheduler(
beta_schedule='''exp''' , num_train_timesteps=10_24 , prediction_type='''sample''' , use_karras_sigmas=__lowercase , clip_sample=__lowercase , clip_sample_range=1.0 , )
SCREAMING_SNAKE_CASE__ : Any ={
'''prior''': prior,
'''image_encoder''': image_encoder,
'''image_processor''': image_processor,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def __magic_name__ ( self : Any , __lowercase : List[str] , __lowercase : Any=0 ) -> Any:
SCREAMING_SNAKE_CASE__ : int =floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowercase ) ).to(__lowercase )
if str(__lowercase ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE__ : List[str] =torch.manual_seed(__lowercase )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =torch.Generator(device=__lowercase ).manual_seed(__lowercase )
SCREAMING_SNAKE_CASE__ : Any ={
'''image''': input_image,
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 32,
'''output_type''': '''np''',
}
return inputs
def __magic_name__ ( self : List[str] ) -> str:
SCREAMING_SNAKE_CASE__ : int ='''cpu'''
SCREAMING_SNAKE_CASE__ : Any =self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : str =self.pipeline_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Any =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =pipe(**self.get_dummy_inputs(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Tuple =output.images[0]
SCREAMING_SNAKE_CASE__ : List[Any] =image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
SCREAMING_SNAKE_CASE__ : List[Any] =np.array(
[
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __magic_name__ ( self : List[Any] ) -> List[str]:
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __magic_name__ ( self : Optional[int] ) -> str:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch_device == '''cpu'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__lowercase , relax_max_difference=__lowercase , )
def __magic_name__ ( self : Dict ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Any =self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Dict =self.pipeline_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =1
SCREAMING_SNAKE_CASE__ : List[str] =2
SCREAMING_SNAKE_CASE__ : Dict =self.get_dummy_inputs(__lowercase )
for key in inputs.keys():
if key in self.batch_params:
SCREAMING_SNAKE_CASE__ : Tuple =batch_size * [inputs[key]]
SCREAMING_SNAKE_CASE__ : List[Any] =pipe(**__lowercase , num_images_per_prompt=__lowercase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : Optional[Any] ) -> int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : int ) -> Dict:
SCREAMING_SNAKE_CASE__ : List[str] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png''' )
SCREAMING_SNAKE_CASE__ : Dict =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_img2img_out.npy''' )
SCREAMING_SNAKE_CASE__ : List[Any] =ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img''' )
SCREAMING_SNAKE_CASE__ : Tuple =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device=__lowercase ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Tuple =pipe(
__lowercase , generator=__lowercase , 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(__lowercase , __lowercase )
| 665 | 0 |
'''simple docstring'''
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
'''compression_format, is_archive''', [
('''7z''', True),
('''bz2''', False),
('''gzip''', False),
('''lz4''', False),
('''tar''', True),
('''xz''', False),
('''zip''', True),
('''zstd''', False),
], )
def _a( UpperCamelCase__ : int, UpperCamelCase__ : int, UpperCamelCase__ : Dict, UpperCamelCase__ : Dict, UpperCamelCase__ : List[Any], UpperCamelCase__ : Dict, UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Optional[int], UpperCamelCase__ : Dict, UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Dict, UpperCamelCase__ : List[str], ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any ={
'''7z''': (seven_zip_file, SevenZipExtractor),
'''bz2''': (bza_file, BzipaExtractor),
'''gzip''': (gz_file, GzipExtractor),
'''lz4''': (lza_file, LzaExtractor),
'''tar''': (tar_file, TarExtractor),
'''xz''': (xz_file, XzExtractor),
'''zip''': (zip_file, ZipExtractor),
'''zstd''': (zstd_file, ZstdExtractor),
}
SCREAMING_SNAKE_CASE__ : List[Any] =input_paths_and_base_extractors[compression_format]
if input_path is None:
SCREAMING_SNAKE_CASE__ : Dict =f"for '{compression_format}' compression_format, "
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(UpperCamelCase__ )
assert base_extractor.is_extractable(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[str] =tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
base_extractor.extract(UpperCamelCase__, UpperCamelCase__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
SCREAMING_SNAKE_CASE__ : List[Any] =file_path.read_text(encoding='''utf-8''' )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =output_path.read_text(encoding='''utf-8''' )
SCREAMING_SNAKE_CASE__ : Dict =text_file.read_text(encoding='''utf-8''' )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
'''compression_format, is_archive''', [
('''7z''', True),
('''bz2''', False),
('''gzip''', False),
('''lz4''', False),
('''tar''', True),
('''xz''', False),
('''zip''', True),
('''zstd''', False),
], )
def _a( UpperCamelCase__ : Dict, UpperCamelCase__ : List[str], UpperCamelCase__ : Optional[int], UpperCamelCase__ : int, UpperCamelCase__ : Dict, UpperCamelCase__ : Optional[Any], UpperCamelCase__ : List[str], UpperCamelCase__ : List[str], UpperCamelCase__ : Optional[int], UpperCamelCase__ : Optional[int], UpperCamelCase__ : Optional[Any], UpperCamelCase__ : List[str], ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any ={
'''7z''': seven_zip_file,
'''bz2''': bza_file,
'''gzip''': gz_file,
'''lz4''': lza_file,
'''tar''': tar_file,
'''xz''': xz_file,
'''zip''': zip_file,
'''zstd''': zstd_file,
}
SCREAMING_SNAKE_CASE__ : Optional[int] =input_paths[compression_format]
if input_path is None:
SCREAMING_SNAKE_CASE__ : str =f"for '{compression_format}' compression_format, "
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Dict =Extractor.infer_extractor_format(UpperCamelCase__ )
assert extractor_format is not None
SCREAMING_SNAKE_CASE__ : Tuple =tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
Extractor.extract(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
SCREAMING_SNAKE_CASE__ : str =file_path.read_text(encoding='''utf-8''' )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] =output_path.read_text(encoding='''utf-8''' )
SCREAMING_SNAKE_CASE__ : int =text_file.read_text(encoding='''utf-8''' )
assert extracted_file_content == expected_file_content
@pytest.fixture
def _a( UpperCamelCase__ : Tuple, UpperCamelCase__ : Any ):
'''simple docstring'''
import tarfile
SCREAMING_SNAKE_CASE__ : Any =tmp_path / '''data_dot_dot'''
directory.mkdir()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =directory / '''tar_file_with_dot_dot.tar'''
with tarfile.TarFile(UpperCamelCase__, '''w''' ) as f:
f.add(UpperCamelCase__, arcname=os.path.join('''..''', text_file.name ) )
return path
@pytest.fixture
def _a( UpperCamelCase__ : List[str] ):
'''simple docstring'''
import tarfile
SCREAMING_SNAKE_CASE__ : Optional[int] =tmp_path / '''data_sym_link'''
directory.mkdir()
SCREAMING_SNAKE_CASE__ : List[Any] =directory / '''tar_file_with_sym_link.tar'''
os.symlink('''..''', directory / '''subdir''', target_is_directory=UpperCamelCase__ )
with tarfile.TarFile(UpperCamelCase__, '''w''' ) as f:
f.add(str(directory / '''subdir''' ), arcname='''subdir''' ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
'''insecure_tar_file, error_log''', [('''tar_file_with_dot_dot''', '''illegal path'''), ('''tar_file_with_sym_link''', '''Symlink''')], )
def _a( UpperCamelCase__ : str, UpperCamelCase__ : int, UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Tuple, UpperCamelCase__ : Any, UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] ={
'''tar_file_with_dot_dot''': tar_file_with_dot_dot,
'''tar_file_with_sym_link''': tar_file_with_sym_link,
}
SCREAMING_SNAKE_CASE__ : List[str] =insecure_tar_files[insecure_tar_file]
SCREAMING_SNAKE_CASE__ : Tuple =tmp_path / '''extracted'''
TarExtractor.extract(UpperCamelCase__, UpperCamelCase__ )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def _a( UpperCamelCase__ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict =tmpdir / '''not_a_zip_file'''
# From: https://github.com/python/cpython/pull/5053
SCREAMING_SNAKE_CASE__ : Dict =(
B'''\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'''
B'''\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'''
B'''DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'''
B'''\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'''
)
with not_a_zip_file.open('''wb''' ) as f:
f.write(UpperCamelCase__ )
assert zipfile.is_zipfile(str(UpperCamelCase__ ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(UpperCamelCase__ ) # but we're right
| 719 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """gpt_bigcode"""
snake_case_ = ["""past_key_values"""]
snake_case_ = {
"""hidden_size""": """n_embd""",
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Any , __lowercase : Any=5_02_57 , __lowercase : int=10_24 , __lowercase : List[str]=7_68 , __lowercase : Optional[int]=12 , __lowercase : Dict=12 , __lowercase : List[str]=None , __lowercase : int="gelu_pytorch_tanh" , __lowercase : Union[str, Any]=0.1 , __lowercase : Optional[int]=0.1 , __lowercase : Optional[int]=0.1 , __lowercase : Optional[Any]=1e-5 , __lowercase : List[str]=0.02 , __lowercase : Tuple=True , __lowercase : Optional[Any]=True , __lowercase : Union[str, Any]=5_02_56 , __lowercase : List[Any]=5_02_56 , __lowercase : Union[str, Any]=True , __lowercase : List[str]=True , __lowercase : Dict=True , **__lowercase : List[Any] , ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =n_positions
SCREAMING_SNAKE_CASE__ : Dict =n_embd
SCREAMING_SNAKE_CASE__ : Dict =n_layer
SCREAMING_SNAKE_CASE__ : Union[str, Any] =n_head
SCREAMING_SNAKE_CASE__ : List[str] =n_inner
SCREAMING_SNAKE_CASE__ : List[str] =activation_function
SCREAMING_SNAKE_CASE__ : List[Any] =resid_pdrop
SCREAMING_SNAKE_CASE__ : List[Any] =embd_pdrop
SCREAMING_SNAKE_CASE__ : List[str] =attn_pdrop
SCREAMING_SNAKE_CASE__ : Dict =layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : List[str] =initializer_range
SCREAMING_SNAKE_CASE__ : List[Any] =scale_attn_weights
SCREAMING_SNAKE_CASE__ : Union[str, Any] =use_cache
SCREAMING_SNAKE_CASE__ : Dict =attention_softmax_in_fpaa
SCREAMING_SNAKE_CASE__ : int =scale_attention_softmax_in_fpaa
SCREAMING_SNAKE_CASE__ : Dict =multi_query
SCREAMING_SNAKE_CASE__ : Optional[Any] =bos_token_id
SCREAMING_SNAKE_CASE__ : Optional[Any] =eos_token_id
super().__init__(bos_token_id=__lowercase , eos_token_id=__lowercase , **__lowercase )
| 665 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : str , *__lowercase : int , **__lowercase : List[Any] ) -> None:
warnings.warn(
'''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use GLPNImageProcessor instead.''' , __lowercase , )
super().__init__(*__lowercase , **__lowercase )
| 720 |
'''simple docstring'''
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =size
SCREAMING_SNAKE_CASE__ : List[Any] =[0] * size
SCREAMING_SNAKE_CASE__ : str =[0] * size
@staticmethod
def __magic_name__ ( __lowercase : int ) -> int:
return index | (index + 1)
@staticmethod
def __magic_name__ ( __lowercase : int ) -> int:
return (index & (index + 1)) - 1
def __magic_name__ ( self : Dict , __lowercase : int , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : List[str] =value
while index < self.size:
SCREAMING_SNAKE_CASE__ : Any =self.get_prev(__lowercase ) + 1
if current_left_border == index:
SCREAMING_SNAKE_CASE__ : List[str] =value
else:
SCREAMING_SNAKE_CASE__ : str =max(__lowercase , __lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.get_next(__lowercase )
def __magic_name__ ( self : Optional[int] , __lowercase : int , __lowercase : int ) -> int:
right -= 1 # Because of right is exclusive
SCREAMING_SNAKE_CASE__ : str =0
while left <= right:
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.get_prev(__lowercase )
if left <= current_left:
SCREAMING_SNAKE_CASE__ : List[Any] =max(__lowercase , self.tree[right] )
SCREAMING_SNAKE_CASE__ : Any =current_left
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =max(__lowercase , self.arr[right] )
right -= 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['ReformerTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['ReformerTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'ReformerAttention',
'ReformerForMaskedLM',
'ReformerForQuestionAnswering',
'ReformerForSequenceClassification',
'ReformerLayer',
'ReformerModel',
'ReformerModelWithLMHead',
'ReformerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer import ReformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer_fast import ReformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_reformer import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerAttention,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerLayer,
ReformerModel,
ReformerModelWithLMHead,
ReformerPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 721 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = ["""vqvae"""]
def __init__( self : int , __lowercase : AutoencoderKL , __lowercase : UNetaDConditionModel , __lowercase : Mel , __lowercase : Union[DDIMScheduler, DDPMScheduler] , ) -> int:
super().__init__()
self.register_modules(unet=__lowercase , scheduler=__lowercase , mel=__lowercase , vqvae=__lowercase )
def __magic_name__ ( self : List[str] ) -> int:
return 50 if isinstance(self.scheduler , __lowercase ) else 10_00
@torch.no_grad()
def __call__( self : Dict , __lowercase : int = 1 , __lowercase : str = None , __lowercase : np.ndarray = None , __lowercase : int = 0 , __lowercase : int = 0 , __lowercase : int = None , __lowercase : torch.Generator = None , __lowercase : float = 0 , __lowercase : float = 0 , __lowercase : torch.Generator = None , __lowercase : float = 0 , __lowercase : torch.Tensor = None , __lowercase : torch.Tensor = None , __lowercase : Dict=True , ) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =steps or self.get_default_steps()
self.scheduler.set_timesteps(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
SCREAMING_SNAKE_CASE__ : Optional[int] =randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=__lowercase , device=self.device , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =noise
SCREAMING_SNAKE_CASE__ : List[str] =None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Dict =self.mel.audio_slice_to_image(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =np.frombuffer(input_image.tobytes() , dtype='''uint8''' ).reshape(
(input_image.height, input_image.width) )
SCREAMING_SNAKE_CASE__ : int =(input_image / 2_55) * 2 - 1
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.vqvae.encode(torch.unsqueeze(__lowercase , 0 ) ).latent_dist.sample(
generator=__lowercase )[0]
SCREAMING_SNAKE_CASE__ : int =self.vqvae.config.scaling_factor * input_images
if start_step > 0:
SCREAMING_SNAKE_CASE__ : int =self.scheduler.add_noise(__lowercase , __lowercase , self.scheduler.timesteps[start_step - 1] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
SCREAMING_SNAKE_CASE__ : Optional[Any] =int(mask_start_secs * pixels_per_second )
SCREAMING_SNAKE_CASE__ : Tuple =int(mask_end_secs * pixels_per_second )
SCREAMING_SNAKE_CASE__ : int =self.scheduler.add_noise(__lowercase , __lowercase , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , __lowercase ):
SCREAMING_SNAKE_CASE__ : str =self.unet(__lowercase , __lowercase , __lowercase )['''sample''']
else:
SCREAMING_SNAKE_CASE__ : str =self.unet(__lowercase , __lowercase )['''sample''']
if isinstance(self.scheduler , __lowercase ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.scheduler.step(
model_output=__lowercase , timestep=__lowercase , sample=__lowercase , eta=__lowercase , generator=__lowercase , )['''prev_sample''']
else:
SCREAMING_SNAKE_CASE__ : Any =self.scheduler.step(
model_output=__lowercase , timestep=__lowercase , sample=__lowercase , generator=__lowercase , )['''prev_sample''']
if mask is not None:
if mask_start > 0:
SCREAMING_SNAKE_CASE__ : Optional[Any] =mask[:, step, :, :mask_start]
if mask_end > 0:
SCREAMING_SNAKE_CASE__ : List[str] =mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
SCREAMING_SNAKE_CASE__ : str =1 / self.vqvae.config.scaling_factor * images
SCREAMING_SNAKE_CASE__ : int =self.vqvae.decode(__lowercase )['''sample''']
SCREAMING_SNAKE_CASE__ : List[str] =(images / 2 + 0.5).clamp(0 , 1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] =images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
SCREAMING_SNAKE_CASE__ : Dict =(images * 2_55).round().astype('''uint8''' )
SCREAMING_SNAKE_CASE__ : Any =list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(__lowercase , mode='''RGB''' ).convert('''L''' ) for _ in images) )
SCREAMING_SNAKE_CASE__ : Optional[int] =[self.mel.image_to_audio(__lowercase ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(__lowercase )[:, np.newaxis, :] ) , **ImagePipelineOutput(__lowercase ) )
@torch.no_grad()
def __magic_name__ ( self : Optional[int] , __lowercase : List[Image.Image] , __lowercase : int = 50 ) -> np.ndarray:
assert isinstance(self.scheduler , __lowercase )
self.scheduler.set_timesteps(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =np.array(
[np.frombuffer(image.tobytes() , dtype='''uint8''' ).reshape((1, image.height, image.width) ) for image in images] )
SCREAMING_SNAKE_CASE__ : str =(sample / 2_55) * 2 - 1
SCREAMING_SNAKE_CASE__ : str =torch.Tensor(__lowercase ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
SCREAMING_SNAKE_CASE__ : Tuple =t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
SCREAMING_SNAKE_CASE__ : Any =self.scheduler.alphas_cumprod[t]
SCREAMING_SNAKE_CASE__ : int =(
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
SCREAMING_SNAKE_CASE__ : int =1 - alpha_prod_t
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.unet(__lowercase , __lowercase )['''sample''']
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(1 - alpha_prod_t_prev) ** 0.5 * model_output
SCREAMING_SNAKE_CASE__ : str =(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
SCREAMING_SNAKE_CASE__ : Any =sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def __magic_name__ ( __lowercase : torch.Tensor , __lowercase : torch.Tensor , __lowercase : float ) -> torch.Tensor:
SCREAMING_SNAKE_CASE__ : Optional[int] =acos(torch.dot(torch.flatten(__lowercase ) , torch.flatten(__lowercase ) ) / torch.norm(__lowercase ) / torch.norm(__lowercase ) )
return sin((1 - alpha) * theta ) * xa / sin(__lowercase ) + sin(alpha * theta ) * xa / sin(__lowercase )
| 665 | 0 |
import functools
def _a( UpperCamelCase__ : list[int], UpperCamelCase__ : list[int] ):
'''simple docstring'''
if not isinstance(UpperCamelCase__, UpperCamelCase__ ) or not all(isinstance(UpperCamelCase__, UpperCamelCase__ ) for day in days ):
raise ValueError('''The parameter days should be a list of integers''' )
if len(UpperCamelCase__ ) != 3 or not all(isinstance(UpperCamelCase__, UpperCamelCase__ ) for cost in costs ):
raise ValueError('''The parameter costs should be a list of three integers''' )
if len(UpperCamelCase__ ) == 0:
return 0
if min(UpperCamelCase__ ) <= 0:
raise ValueError('''All days elements should be greater than 0''' )
if max(UpperCamelCase__ ) >= 3_6_6:
raise ValueError('''All days elements should be less than 366''' )
SCREAMING_SNAKE_CASE__ : Dict =set(UpperCamelCase__ )
@functools.cache
def dynamic_programming(UpperCamelCase__ : int ) -> int:
if index > 3_6_5:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ), costs[1] + dynamic_programming(index + 7 ), costs[2] + dynamic_programming(index + 3_0 ), )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 700 |
'''simple docstring'''
from math import isqrt
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =[True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2, UpperCamelCase__, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Any =False
return [i for i in range(2, UpperCamelCase__ ) if is_prime[i]]
def _a( UpperCamelCase__ : int = 1_0**8 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple =calculate_prime_numbers(max_number // 2 )
SCREAMING_SNAKE_CASE__ : int =0
SCREAMING_SNAKE_CASE__ : int =0
SCREAMING_SNAKE_CASE__ : Optional[int] =len(UpperCamelCase__ ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F'''{solution() = }''')
| 665 | 0 |
'''simple docstring'''
from math import sqrt
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
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(sqrt(UpperCamelCase__ ) + 1 ), 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _a( UpperCamelCase__ : int = 1_0_0_0_1 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any =0
SCREAMING_SNAKE_CASE__ : Dict =1
while count != nth and number < 3:
number += 1
if is_prime(UpperCamelCase__ ):
count += 1
while count != nth:
number += 2
if is_prime(UpperCamelCase__ ):
count += 1
return number
if __name__ == "__main__":
print(F'''{solution() = }''')
| 701 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
a_ = get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = SpeechTaTokenizer
snake_case_ = False
snake_case_ = True
def __magic_name__ ( self : int ) -> Any:
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : Optional[Any] =SpeechTaTokenizer(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =AddedToken('''<mask>''' , lstrip=__lowercase , rstrip=__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =mask_token
tokenizer.add_special_tokens({'''mask_token''': mask_token} )
tokenizer.add_tokens(['''<ctc_blank>'''] )
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__ ( self : Dict , __lowercase : int ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Optional[Any] ='''this is a test'''
SCREAMING_SNAKE_CASE__ : int ='''this is a test'''
return input_text, output_text
def __magic_name__ ( self : List[Any] , __lowercase : int , __lowercase : Optional[Any]=False , __lowercase : Union[str, Any]=20 , __lowercase : Any=5 ) -> Any:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int =self.get_input_output_texts(__lowercase )
SCREAMING_SNAKE_CASE__ : str =tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
SCREAMING_SNAKE_CASE__ : str =tokenizer.decode(__lowercase , clean_up_tokenization_spaces=__lowercase )
return text, ids
def __magic_name__ ( self : Dict ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] ='''<pad>'''
SCREAMING_SNAKE_CASE__ : Optional[int] =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowercase ) , __lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowercase ) , __lowercase )
def __magic_name__ ( self : Tuple ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-4] , '''œ''' )
self.assertEqual(vocab_keys[-2] , '''<mask>''' )
self.assertEqual(vocab_keys[-1] , '''<ctc_blank>''' )
self.assertEqual(len(__lowercase ) , 81 )
def __magic_name__ ( self : Dict ) -> List[str]:
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def __magic_name__ ( self : Optional[Any] ) -> str:
SCREAMING_SNAKE_CASE__ : str =self.get_tokenizers(do_lower_case=__lowercase )
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
SCREAMING_SNAKE_CASE__ : Tuple =tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : Any =len(__lowercase )
self.assertNotEqual(__lowercase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
SCREAMING_SNAKE_CASE__ : int =['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.add_tokens(__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] =len(__lowercase )
self.assertNotEqual(__lowercase , 0 )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , len(__lowercase ) )
self.assertEqual(__lowercase , all_size + len(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Tuple =tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l''' , add_special_tokens=__lowercase )
self.assertGreaterEqual(len(__lowercase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
SCREAMING_SNAKE_CASE__ : str ={'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
SCREAMING_SNAKE_CASE__ : int =tokenizer.add_special_tokens(__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : int =len(__lowercase )
self.assertNotEqual(__lowercase , 0 )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , len(__lowercase ) )
self.assertEqual(__lowercase , all_size_a + len(__lowercase ) )
SCREAMING_SNAKE_CASE__ : List[Any] =tokenizer.encode(
'''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l''' , add_special_tokens=__lowercase )
self.assertGreaterEqual(len(__lowercase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def __magic_name__ ( self : Optional[Any] ) -> Any:
pass
def __magic_name__ ( self : List[str] ) -> List[Any]:
pass
def __magic_name__ ( self : Dict ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Dict =self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer.tokenize('''This is a test''' )
# fmt: off
self.assertListEqual(__lowercase , [SPIECE_UNDERLINE, '''T''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''a''', SPIECE_UNDERLINE, '''t''', '''e''', '''s''', '''t'''] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowercase ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__lowercase , [SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''92000''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =tokenizer.convert_tokens_to_ids(__lowercase )
# fmt: off
self.assertListEqual(__lowercase , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer.convert_ids_to_tokens(__lowercase )
self.assertListEqual(
__lowercase , [SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''<unk>''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''] )
@slow
def __magic_name__ ( self : List[str] ) -> List[str]:
# Use custom sequence because this tokenizer does not handle numbers.
SCREAMING_SNAKE_CASE__ : List[Any] =[
'''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
'''general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
'''Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained '''
'''models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
'''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
'''conditioning on both left and right context in all layers.''',
'''The quick brown fox jumps over the lazy dog.''',
]
# fmt: off
SCREAMING_SNAKE_CASE__ : str ={
'''input_ids''': [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
],
'''attention_mask''': [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__lowercase , model_name='''microsoft/speecht5_asr''' , revision='''c5ef64c71905caeccde0e4462ef3f9077224c524''' , sequences=__lowercase , )
| 665 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}
a_ = {
'vocab_file': {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
'allenai/longformer-large-4096': (
'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
),
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
),
},
'merges_file': {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
'allenai/longformer-large-4096': (
'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
),
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
),
},
}
a_ = {
'allenai/longformer-base-4096': 4_0_9_6,
'allenai/longformer-large-4096': 4_0_9_6,
'allenai/longformer-large-4096-finetuned-triviaqa': 4_0_9_6,
'allenai/longformer-base-4096-extra.pos.embd.only': 4_0_9_6,
'allenai/longformer-large-4096-extra.pos.embd.only': 4_0_9_6,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _a( ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str =(
list(range(ord('''!''' ), ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ), ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ), ord('''ÿ''' ) + 1 ) )
)
SCREAMING_SNAKE_CASE__ : List[str] =bs[:]
SCREAMING_SNAKE_CASE__ : Any =0
for b in range(2**8 ):
if b not in bs:
bs.append(UpperCamelCase__ )
cs.append(2**8 + n )
n += 1
SCREAMING_SNAKE_CASE__ : int =[chr(UpperCamelCase__ ) for n in cs]
return dict(zip(UpperCamelCase__, UpperCamelCase__ ) )
def _a( UpperCamelCase__ : Optional[Any] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any =set()
SCREAMING_SNAKE_CASE__ : Optional[Any] =word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
SCREAMING_SNAKE_CASE__ : int =char
return pairs
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ["""input_ids""", """attention_mask"""]
def __init__( self : Optional[Any] , __lowercase : Union[str, Any] , __lowercase : List[Any] , __lowercase : List[Any]="replace" , __lowercase : Tuple="<s>" , __lowercase : List[Any]="</s>" , __lowercase : Any="</s>" , __lowercase : Dict="<s>" , __lowercase : Dict="<unk>" , __lowercase : Optional[Any]="<pad>" , __lowercase : Optional[int]="<mask>" , __lowercase : Union[str, Any]=False , **__lowercase : str , ) -> Any:
SCREAMING_SNAKE_CASE__ : Optional[Any] =AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else bos_token
SCREAMING_SNAKE_CASE__ : List[Any] =AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else eos_token
SCREAMING_SNAKE_CASE__ : int =AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else sep_token
SCREAMING_SNAKE_CASE__ : Union[str, Any] =AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else cls_token
SCREAMING_SNAKE_CASE__ : Optional[Any] =AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else unk_token
SCREAMING_SNAKE_CASE__ : List[Any] =AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE__ : Dict =AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else mask_token
super().__init__(
errors=__lowercase , bos_token=__lowercase , eos_token=__lowercase , unk_token=__lowercase , sep_token=__lowercase , cls_token=__lowercase , pad_token=__lowercase , mask_token=__lowercase , add_prefix_space=__lowercase , **__lowercase , )
with open(__lowercase , encoding='''utf-8''' ) as vocab_handle:
SCREAMING_SNAKE_CASE__ : str =json.load(__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] ={v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE__ : Dict =errors # how to handle errors in decoding
SCREAMING_SNAKE_CASE__ : Optional[int] =bytes_to_unicode()
SCREAMING_SNAKE_CASE__ : List[str] ={v: k for k, v in self.byte_encoder.items()}
with open(__lowercase , encoding='''utf-8''' ) as merges_handle:
SCREAMING_SNAKE_CASE__ : int =merges_handle.read().split('''\n''' )[1:-1]
SCREAMING_SNAKE_CASE__ : Optional[int] =[tuple(merge.split() ) for merge in bpe_merges]
SCREAMING_SNAKE_CASE__ : Union[str, Any] =dict(zip(__lowercase , range(len(__lowercase ) ) ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] ={}
SCREAMING_SNAKE_CASE__ : Optional[Any] =add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
SCREAMING_SNAKE_CASE__ : Optional[Any] =re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
def __magic_name__ ( self : Optional[Any] ) -> List[Any]:
return len(self.encoder )
def __magic_name__ ( self : Optional[int] ) -> Dict:
return dict(self.encoder , **self.added_tokens_encoder )
def __magic_name__ ( self : Optional[int] , __lowercase : str ) -> Tuple:
if token in self.cache:
return self.cache[token]
SCREAMING_SNAKE_CASE__ : Any =tuple(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =get_pairs(__lowercase )
if not pairs:
return token
while True:
SCREAMING_SNAKE_CASE__ : Any =min(__lowercase , key=lambda __lowercase : self.bpe_ranks.get(__lowercase , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
SCREAMING_SNAKE_CASE__ : Optional[Any] =bigram
SCREAMING_SNAKE_CASE__ : List[str] =[]
SCREAMING_SNAKE_CASE__ : List[Any] =0
while i < len(__lowercase ):
try:
SCREAMING_SNAKE_CASE__ : Any =word.index(__lowercase , __lowercase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
SCREAMING_SNAKE_CASE__ : Optional[int] =j
if word[i] == first and i < len(__lowercase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
SCREAMING_SNAKE_CASE__ : Tuple =tuple(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =new_word
if len(__lowercase ) == 1:
break
else:
SCREAMING_SNAKE_CASE__ : Optional[int] =get_pairs(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =''' '''.join(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =word
return word
def __magic_name__ ( self : Dict , __lowercase : List[Any] ) -> Any:
SCREAMING_SNAKE_CASE__ : Any =[]
for token in re.findall(self.pat , __lowercase ):
SCREAMING_SNAKE_CASE__ : List[str] =''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowercase ).split(''' ''' ) )
return bpe_tokens
def __magic_name__ ( self : Union[str, Any] , __lowercase : Optional[int] ) -> Optional[int]:
return self.encoder.get(__lowercase , self.encoder.get(self.unk_token ) )
def __magic_name__ ( self : Tuple , __lowercase : Dict ) -> Any:
return self.decoder.get(__lowercase )
def __magic_name__ ( self : Any , __lowercase : str ) -> Any:
SCREAMING_SNAKE_CASE__ : List[Any] =''''''.join(__lowercase )
SCREAMING_SNAKE_CASE__ : int =bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def __magic_name__ ( self : Optional[int] , __lowercase : str , __lowercase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__lowercase ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
SCREAMING_SNAKE_CASE__ : Dict =os.path.join(
__lowercase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
SCREAMING_SNAKE_CASE__ : Dict =os.path.join(
__lowercase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(__lowercase , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowercase , ensure_ascii=__lowercase ) + '''\n''' )
SCREAMING_SNAKE_CASE__ : Optional[int] =0
with open(__lowercase , '''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 __lowercase : 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!''' )
SCREAMING_SNAKE_CASE__ : Dict =token_index
writer.write(''' '''.join(__lowercase ) + '''\n''' )
index += 1
return vocab_file, merge_file
def __magic_name__ ( self : Any , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : str =[self.cls_token_id]
SCREAMING_SNAKE_CASE__ : List[str] =[self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __magic_name__ ( self : List[str] , __lowercase : List[int] , __lowercase : Optional[List[int]] = None , __lowercase : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowercase , token_ids_a=__lowercase , already_has_special_tokens=__lowercase )
if token_ids_a is None:
return [1] + ([0] * len(__lowercase )) + [1]
return [1] + ([0] * len(__lowercase )) + [1, 1] + ([0] * len(__lowercase )) + [1]
def __magic_name__ ( self : Union[str, Any] , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ) -> List[int]:
SCREAMING_SNAKE_CASE__ : Tuple =[self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __magic_name__ ( self : Tuple , __lowercase : Union[str, Any] , __lowercase : Optional[int]=False , **__lowercase : List[str] ) -> Any:
SCREAMING_SNAKE_CASE__ : Optional[int] =kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__lowercase ) > 0 and not text[0].isspace()):
SCREAMING_SNAKE_CASE__ : int =''' ''' + text
return (text, kwargs)
| 702 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , __lowercase : Optional[int] , __lowercase : str=13 , __lowercase : int=10 , __lowercase : List[Any]=3 , __lowercase : List[str]=2 , __lowercase : int=2 , __lowercase : Dict=True , __lowercase : Optional[Any]=True , __lowercase : int=32 , __lowercase : List[Any]=5 , __lowercase : Union[str, Any]=4 , __lowercase : Any=37 , __lowercase : Optional[Any]="gelu" , __lowercase : Union[str, Any]=0.1 , __lowercase : Tuple=0.1 , __lowercase : Dict=10 , __lowercase : int=0.02 , __lowercase : str="divided_space_time" , __lowercase : Union[str, Any]=None , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : int =parent
SCREAMING_SNAKE_CASE__ : List[str] =batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =image_size
SCREAMING_SNAKE_CASE__ : List[Any] =num_channels
SCREAMING_SNAKE_CASE__ : int =patch_size
SCREAMING_SNAKE_CASE__ : Tuple =num_frames
SCREAMING_SNAKE_CASE__ : List[Any] =is_training
SCREAMING_SNAKE_CASE__ : List[str] =use_labels
SCREAMING_SNAKE_CASE__ : Optional[Any] =hidden_size
SCREAMING_SNAKE_CASE__ : str =num_hidden_layers
SCREAMING_SNAKE_CASE__ : int =num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple =intermediate_size
SCREAMING_SNAKE_CASE__ : List[str] =hidden_act
SCREAMING_SNAKE_CASE__ : Dict =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[Any] =attention_type
SCREAMING_SNAKE_CASE__ : Union[str, Any] =initializer_range
SCREAMING_SNAKE_CASE__ : Any =scope
SCREAMING_SNAKE_CASE__ : int =num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
SCREAMING_SNAKE_CASE__ : List[str] =(image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE__ : str =(num_frames) * self.num_patches_per_frame + 1
def __magic_name__ ( self : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : List[Any] =None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Any =ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE__ : int =self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : int ) -> Any:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , 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 , initializer_range=self.initializer_range , attention_type=self.attention_type , )
SCREAMING_SNAKE_CASE__ : List[Any] =self.num_labels
return config
def __magic_name__ ( self : int , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : List[Any] ) -> int:
SCREAMING_SNAKE_CASE__ : Tuple =TimesformerModel(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] =model(__lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : List[str] , __lowercase : str , __lowercase : Tuple , __lowercase : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : str =TimesformerForVideoClassification(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(__lowercase )
# verify the logits shape
SCREAMING_SNAKE_CASE__ : Tuple =torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , __lowercase )
def __magic_name__ ( self : Any ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : int =self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str =config_and_inputs
SCREAMING_SNAKE_CASE__ : Any ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
snake_case_ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
snake_case_ = (
{"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def __magic_name__ ( self : str ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Dict =TimesformerModelTester(self )
SCREAMING_SNAKE_CASE__ : List[Any] =ConfigTester(
self , config_class=__lowercase , has_text_modality=__lowercase , hidden_size=37 )
def __magic_name__ ( self : str , __lowercase : Dict , __lowercase : str , __lowercase : Optional[int]=False ) -> int:
SCREAMING_SNAKE_CASE__ : str =copy.deepcopy(__lowercase )
if return_labels:
if model_class in get_values(__lowercase ):
SCREAMING_SNAKE_CASE__ : Any =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowercase )
return inputs_dict
def __magic_name__ ( self : List[Any] ) -> Any:
self.config_tester.run_common_tests()
@unittest.skip(reason='''TimeSformer does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ) -> Optional[int]:
pass
def __magic_name__ ( self : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : str =model_class(__lowercase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE__ : Any =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__lowercase , nn.Linear ) )
def __magic_name__ ( self : Any ) -> Any:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Optional[Any] =model_class(__lowercase )
SCREAMING_SNAKE_CASE__ : str =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE__ : List[str] =[*signature.parameters.keys()]
SCREAMING_SNAKE_CASE__ : Dict =['''pixel_values''']
self.assertListEqual(arg_names[:1] , __lowercase )
def __magic_name__ ( self : int ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase )
def __magic_name__ ( self : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*__lowercase )
@slow
def __magic_name__ ( self : Optional[int] ) -> Optional[Any]:
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =TimesformerModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> List[str]:
if not self.has_attentions:
pass
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[Any] =True
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.seq_length
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.num_frames
SCREAMING_SNAKE_CASE__ : Optional[Any] =True
SCREAMING_SNAKE_CASE__ : str =False
SCREAMING_SNAKE_CASE__ : Tuple =True
SCREAMING_SNAKE_CASE__ : Dict =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(**self._prepare_for_class(__lowercase , __lowercase ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =outputs.attentions
self.assertEqual(len(__lowercase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
SCREAMING_SNAKE_CASE__ : List[Any] =True
SCREAMING_SNAKE_CASE__ : List[str] =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Any =model(**self._prepare_for_class(__lowercase , __lowercase ) )
SCREAMING_SNAKE_CASE__ : List[Any] =outputs.attentions
self.assertEqual(len(__lowercase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
SCREAMING_SNAKE_CASE__ : Optional[int] =len(__lowercase )
# Check attention is always last and order is fine
SCREAMING_SNAKE_CASE__ : Optional[int] =True
SCREAMING_SNAKE_CASE__ : Union[str, Any] =True
SCREAMING_SNAKE_CASE__ : Optional[Any] =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(**self._prepare_for_class(__lowercase , __lowercase ) )
self.assertEqual(out_len + 1 , len(__lowercase ) )
SCREAMING_SNAKE_CASE__ : List[str] =outputs.attentions
self.assertEqual(len(__lowercase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def __magic_name__ ( self : Tuple ) -> List[Any]:
def check_hidden_states_output(__lowercase : Tuple , __lowercase : Dict , __lowercase : Optional[int] ):
SCREAMING_SNAKE_CASE__ : List[Any] =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : int =model(**self._prepare_for_class(__lowercase , __lowercase ) )
SCREAMING_SNAKE_CASE__ : List[Any] =outputs.hidden_states
SCREAMING_SNAKE_CASE__ : Tuple =self.model_tester.num_hidden_layers + 1
self.assertEqual(len(__lowercase ) , __lowercase )
SCREAMING_SNAKE_CASE__ : int =self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Tuple =True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE__ : List[str] =True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''', filename='''eating_spaghetti.npy''', repo_type='''dataset''' )
SCREAMING_SNAKE_CASE__ : Any =np.load(UpperCamelCase__ )
return list(UpperCamelCase__ )
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : Any ) -> List[str]:
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def __magic_name__ ( self : Any ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : int =TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400''' ).to(
__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =self.default_image_processor
SCREAMING_SNAKE_CASE__ : Tuple =prepare_video()
SCREAMING_SNAKE_CASE__ : Any =image_processor(video[:8] , return_tensors='''pt''' ).to(__lowercase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Optional[int] =model(**__lowercase )
# verify the logits
SCREAMING_SNAKE_CASE__ : List[str] =torch.Size((1, 4_00) )
self.assertEqual(outputs.logits.shape , __lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =torch.tensor([-0.3016, -0.7713, -0.4205] ).to(__lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowercase , atol=1e-4 ) )
| 665 | 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 MobileViTImageProcessor
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __init__( self : Any , __lowercase : Dict , __lowercase : Optional[int]=7 , __lowercase : Dict=3 , __lowercase : Optional[Any]=18 , __lowercase : int=30 , __lowercase : Dict=4_00 , __lowercase : Any=True , __lowercase : Optional[Any]=None , __lowercase : Optional[int]=True , __lowercase : Optional[int]=None , __lowercase : str=True , ) -> str:
SCREAMING_SNAKE_CASE__ : List[Any] =size if size is not None else {'''shortest_edge''': 20}
SCREAMING_SNAKE_CASE__ : Optional[Any] =crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
SCREAMING_SNAKE_CASE__ : List[Any] =parent
SCREAMING_SNAKE_CASE__ : int =batch_size
SCREAMING_SNAKE_CASE__ : Any =num_channels
SCREAMING_SNAKE_CASE__ : Union[str, Any] =image_size
SCREAMING_SNAKE_CASE__ : List[str] =min_resolution
SCREAMING_SNAKE_CASE__ : Union[str, Any] =max_resolution
SCREAMING_SNAKE_CASE__ : Tuple =do_resize
SCREAMING_SNAKE_CASE__ : str =size
SCREAMING_SNAKE_CASE__ : int =do_center_crop
SCREAMING_SNAKE_CASE__ : str =crop_size
SCREAMING_SNAKE_CASE__ : Optional[int] =do_flip_channel_order
def __magic_name__ ( self : str ) -> int:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = MobileViTImageProcessor if is_vision_available() else None
def __magic_name__ ( self : Union[str, Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : List[Any] =MobileViTImageProcessingTester(self )
@property
def __magic_name__ ( self : Union[str, Any] ) -> int:
return self.image_processor_tester.prepare_image_processor_dict()
def __magic_name__ ( self : List[Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[Any] =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowercase , '''do_resize''' ) )
self.assertTrue(hasattr(__lowercase , '''size''' ) )
self.assertTrue(hasattr(__lowercase , '''do_center_crop''' ) )
self.assertTrue(hasattr(__lowercase , '''center_crop''' ) )
self.assertTrue(hasattr(__lowercase , '''do_flip_channel_order''' ) )
def __magic_name__ ( self : Tuple ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : int =self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
SCREAMING_SNAKE_CASE__ : List[Any] =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 __magic_name__ ( self : List[Any] ) -> Union[str, Any]:
pass
def __magic_name__ ( self : Any ) -> Optional[Any]:
# Initialize image_processing
SCREAMING_SNAKE_CASE__ : Any =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : List[str] =prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : 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
SCREAMING_SNAKE_CASE__ : Optional[int] =image_processing(__lowercase , 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 __magic_name__ ( self : Dict ) -> List[Any]:
# Initialize image_processing
SCREAMING_SNAKE_CASE__ : Dict =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__ : Optional[Any] =prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , numpify=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Optional[int] =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
SCREAMING_SNAKE_CASE__ : Optional[int] =image_processing(__lowercase , 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 __magic_name__ ( self : str ) -> Dict:
# Initialize image_processing
SCREAMING_SNAKE_CASE__ : Optional[int] =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__ : str =prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , torchify=__lowercase )
for image in image_inputs:
self.assertIsInstance(__lowercase , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE__ : 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
SCREAMING_SNAKE_CASE__ : Any =image_processing(__lowercase , 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'''],
) , )
| 703 |
'''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_bert import BertTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
a_ = {
'vocab_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
),
'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
),
'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
),
'bert-base-multilingual-cased': (
'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
),
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-cased': (
'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
),
},
}
a_ = {
'bert-base-uncased': 5_1_2,
'bert-large-uncased': 5_1_2,
'bert-base-cased': 5_1_2,
'bert-large-cased': 5_1_2,
'bert-base-multilingual-uncased': 5_1_2,
'bert-base-multilingual-cased': 5_1_2,
'bert-base-chinese': 5_1_2,
'bert-base-german-cased': 5_1_2,
'bert-large-uncased-whole-word-masking': 5_1_2,
'bert-large-cased-whole-word-masking': 5_1_2,
'bert-large-uncased-whole-word-masking-finetuned-squad': 5_1_2,
'bert-large-cased-whole-word-masking-finetuned-squad': 5_1_2,
'bert-base-cased-finetuned-mrpc': 5_1_2,
'bert-base-german-dbmdz-cased': 5_1_2,
'bert-base-german-dbmdz-uncased': 5_1_2,
'TurkuNLP/bert-base-finnish-cased-v1': 5_1_2,
'TurkuNLP/bert-base-finnish-uncased-v1': 5_1_2,
'wietsedv/bert-base-dutch-cased': 5_1_2,
}
a_ = {
'bert-base-uncased': {'do_lower_case': True},
'bert-large-uncased': {'do_lower_case': True},
'bert-base-cased': {'do_lower_case': False},
'bert-large-cased': {'do_lower_case': False},
'bert-base-multilingual-uncased': {'do_lower_case': True},
'bert-base-multilingual-cased': {'do_lower_case': False},
'bert-base-chinese': {'do_lower_case': False},
'bert-base-german-cased': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
'bert-large-cased-whole-word-masking': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
'bert-base-german-dbmdz-cased': {'do_lower_case': False},
'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_INIT_CONFIGURATION
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = BertTokenizer
def __init__( self : int , __lowercase : Union[str, Any]=None , __lowercase : Tuple=None , __lowercase : str=True , __lowercase : Optional[Any]="[UNK]" , __lowercase : Tuple="[SEP]" , __lowercase : Any="[PAD]" , __lowercase : List[Any]="[CLS]" , __lowercase : Union[str, Any]="[MASK]" , __lowercase : Tuple=True , __lowercase : str=None , **__lowercase : Any , ) -> Optional[Any]:
super().__init__(
__lowercase , tokenizer_file=__lowercase , do_lower_case=__lowercase , unk_token=__lowercase , sep_token=__lowercase , pad_token=__lowercase , cls_token=__lowercase , mask_token=__lowercase , tokenize_chinese_chars=__lowercase , strip_accents=__lowercase , **__lowercase , )
SCREAMING_SNAKE_CASE__ : str =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __lowercase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __lowercase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __lowercase ) != tokenize_chinese_chars
):
SCREAMING_SNAKE_CASE__ : int =getattr(__lowercase , normalizer_state.pop('''type''' ) )
SCREAMING_SNAKE_CASE__ : Any =do_lower_case
SCREAMING_SNAKE_CASE__ : Any =strip_accents
SCREAMING_SNAKE_CASE__ : Dict =tokenize_chinese_chars
SCREAMING_SNAKE_CASE__ : Union[str, Any] =normalizer_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =do_lower_case
def __magic_name__ ( self : int , __lowercase : Optional[Any] , __lowercase : Union[str, Any]=None ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] =[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 __magic_name__ ( self : Union[str, Any] , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ) -> List[int]:
SCREAMING_SNAKE_CASE__ : List[str] =[self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] =[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 __magic_name__ ( self : List[Any] , __lowercase : str , __lowercase : Optional[str] = None ) -> Tuple[str]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self._tokenizer.model.save(__lowercase , name=__lowercase )
return tuple(__lowercase )
| 665 | 0 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , __lowercase : List[str] , __lowercase : Optional[Any]=13 , __lowercase : List[str]=7 , __lowercase : Dict=True , __lowercase : str=True , __lowercase : List[str]=False , __lowercase : Union[str, Any]=True , __lowercase : List[Any]=99 , __lowercase : Optional[int]=32 , __lowercase : Any=5 , __lowercase : List[Any]=4 , __lowercase : Optional[Any]=37 , __lowercase : Optional[Any]="gelu" , __lowercase : str=0.1 , __lowercase : List[str]=0.1 , __lowercase : Tuple=5_12 , __lowercase : Optional[Any]=16 , __lowercase : List[Any]=2 , __lowercase : List[Any]=0.02 , __lowercase : List[Any]=3 , __lowercase : Tuple=4 , __lowercase : List[Any]=None , ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] =parent
SCREAMING_SNAKE_CASE__ : Any =batch_size
SCREAMING_SNAKE_CASE__ : Dict =seq_length
SCREAMING_SNAKE_CASE__ : Union[str, Any] =is_training
SCREAMING_SNAKE_CASE__ : Dict =use_input_mask
SCREAMING_SNAKE_CASE__ : Tuple =use_token_type_ids
SCREAMING_SNAKE_CASE__ : Optional[int] =use_labels
SCREAMING_SNAKE_CASE__ : Optional[Any] =vocab_size
SCREAMING_SNAKE_CASE__ : Any =hidden_size
SCREAMING_SNAKE_CASE__ : Any =num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any =num_attention_heads
SCREAMING_SNAKE_CASE__ : List[Any] =intermediate_size
SCREAMING_SNAKE_CASE__ : List[str] =hidden_act
SCREAMING_SNAKE_CASE__ : List[Any] =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : List[Any] =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[Any] =max_position_embeddings
SCREAMING_SNAKE_CASE__ : Union[str, Any] =type_vocab_size
SCREAMING_SNAKE_CASE__ : Tuple =type_sequence_label_size
SCREAMING_SNAKE_CASE__ : str =initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] =num_labels
SCREAMING_SNAKE_CASE__ : int =num_choices
SCREAMING_SNAKE_CASE__ : List[str] =scope
def __magic_name__ ( self : Union[str, Any] ) -> List[str]:
SCREAMING_SNAKE_CASE__ : str =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : int =None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : Tuple =random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Optional[int] =None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : int =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : int =None
SCREAMING_SNAKE_CASE__ : Optional[Any] =None
SCREAMING_SNAKE_CASE__ : Union[str, Any] =None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : str =ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Tuple =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : List[str] =ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : Optional[int] =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __magic_name__ ( self : Tuple ) -> Tuple:
return LlamaConfig(
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=__lowercase , initializer_range=self.initializer_range , )
def __magic_name__ ( self : Union[str, Any] , __lowercase : List[str] , __lowercase : str , __lowercase : int , __lowercase : Optional[Any] , __lowercase : Tuple , __lowercase : List[str] , __lowercase : int ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[Any] =LlamaModel(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(__lowercase , attention_mask=__lowercase )
SCREAMING_SNAKE_CASE__ : int =model(__lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : Tuple , __lowercase : Tuple , __lowercase : List[str] , __lowercase : List[str] , __lowercase : Optional[int] , __lowercase : Optional[Any] , __lowercase : Any , __lowercase : int , __lowercase : Union[str, Any] , __lowercase : Union[str, Any] , ) -> Dict:
SCREAMING_SNAKE_CASE__ : int =True
SCREAMING_SNAKE_CASE__ : int =LlamaModel(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict =model(
__lowercase , attention_mask=__lowercase , encoder_hidden_states=__lowercase , encoder_attention_mask=__lowercase , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(
__lowercase , attention_mask=__lowercase , encoder_hidden_states=__lowercase , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(__lowercase , attention_mask=__lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : List[Any] , __lowercase : List[Any] , __lowercase : List[str] , __lowercase : Dict , __lowercase : Any , __lowercase : str , __lowercase : Dict , __lowercase : Union[str, Any] , __lowercase : int , __lowercase : List[str] , ) -> int:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =LlamaForCausalLM(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Tuple =model(__lowercase , attention_mask=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self : Tuple , __lowercase : List[Any] , __lowercase : Optional[Any] , __lowercase : Tuple , __lowercase : List[Any] , __lowercase : List[str] , __lowercase : Dict , __lowercase : Any , __lowercase : int , __lowercase : Optional[Any] , ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Tuple =True
SCREAMING_SNAKE_CASE__ : Optional[int] =True
SCREAMING_SNAKE_CASE__ : Any =LlamaForCausalLM(config=__lowercase )
model.to(__lowercase )
model.eval()
# first forward pass
SCREAMING_SNAKE_CASE__ : Any =model(
__lowercase , attention_mask=__lowercase , encoder_hidden_states=__lowercase , encoder_attention_mask=__lowercase , use_cache=__lowercase , )
SCREAMING_SNAKE_CASE__ : Optional[int] =outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : Optional[Any] =ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : Tuple =torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : int =torch.cat([input_mask, next_mask] , dim=-1 )
SCREAMING_SNAKE_CASE__ : List[str] =model(
__lowercase , attention_mask=__lowercase , encoder_hidden_states=__lowercase , encoder_attention_mask=__lowercase , output_hidden_states=__lowercase , )['''hidden_states'''][0]
SCREAMING_SNAKE_CASE__ : Any =model(
__lowercase , attention_mask=__lowercase , encoder_hidden_states=__lowercase , encoder_attention_mask=__lowercase , past_key_values=__lowercase , output_hidden_states=__lowercase , )['''hidden_states'''][0]
# select random slice
SCREAMING_SNAKE_CASE__ : str =ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : str =output_from_no_past[:, -3:, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : Tuple =output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__lowercase , __lowercase , atol=1e-3 ) )
def __magic_name__ ( self : Optional[Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Any =self.prepare_config_and_inputs()
(
SCREAMING_SNAKE_CASE__
) : Union[str, Any] =config_and_inputs
SCREAMING_SNAKE_CASE__ : Tuple ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , lowerCamelCase , lowerCamelCase , unittest.TestCase ):
snake_case_ = (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
snake_case_ = (LlamaForCausalLM,) if is_torch_available() else ()
snake_case_ = (
{
"""feature-extraction""": LlamaModel,
"""text-classification""": LlamaForSequenceClassification,
"""text-generation""": LlamaForCausalLM,
"""zero-shot""": LlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
def __magic_name__ ( self : Optional[int] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : str =LlamaModelTester(self )
SCREAMING_SNAKE_CASE__ : Tuple =ConfigTester(self , config_class=__lowercase , hidden_size=37 )
def __magic_name__ ( self : Dict ) -> str:
self.config_tester.run_common_tests()
def __magic_name__ ( self : List[str] ) -> List[str]:
SCREAMING_SNAKE_CASE__ : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase )
def __magic_name__ ( self : Optional[Any] ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
SCREAMING_SNAKE_CASE__ : str =type
self.model_tester.create_and_check_model(*__lowercase )
def __magic_name__ ( self : Optional[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[Any] =3
SCREAMING_SNAKE_CASE__ : Optional[Any] =input_dict['''input_ids''']
SCREAMING_SNAKE_CASE__ : Any =input_ids.ne(1 ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Any =LlamaForSequenceClassification(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : str =model(__lowercase , attention_mask=__lowercase , labels=__lowercase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __magic_name__ ( self : Union[str, Any] ) -> Any:
SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : Dict =3
SCREAMING_SNAKE_CASE__ : List[str] ='''single_label_classification'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =input_dict['''input_ids''']
SCREAMING_SNAKE_CASE__ : Any =input_ids.ne(1 ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Any =LlamaForSequenceClassification(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict =model(__lowercase , attention_mask=__lowercase , labels=__lowercase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def __magic_name__ ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[Any] =3
SCREAMING_SNAKE_CASE__ : Tuple ='''multi_label_classification'''
SCREAMING_SNAKE_CASE__ : Dict =input_dict['''input_ids''']
SCREAMING_SNAKE_CASE__ : str =input_ids.ne(1 ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
SCREAMING_SNAKE_CASE__ : List[str] =LlamaForSequenceClassification(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] =model(__lowercase , attention_mask=__lowercase , labels=__lowercase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('''LLaMA buffers include complex numbers, which breaks this test''' )
def __magic_name__ ( self : Any ) -> str:
pass
@parameterized.expand([('''linear''',), ('''dynamic''',)] )
def __magic_name__ ( self : Tuple , __lowercase : Optional[int] ) -> Any:
SCREAMING_SNAKE_CASE__ : int =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : int =ids_tensor([1, 10] , config.vocab_size )
SCREAMING_SNAKE_CASE__ : List[str] =ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
SCREAMING_SNAKE_CASE__ : Tuple =LlamaModel(__lowercase )
original_model.to(__lowercase )
original_model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] =original_model(__lowercase ).last_hidden_state
SCREAMING_SNAKE_CASE__ : Any =original_model(__lowercase ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
SCREAMING_SNAKE_CASE__ : Optional[Any] ={'''type''': scaling_type, '''factor''': 10.0}
SCREAMING_SNAKE_CASE__ : Union[str, Any] =LlamaModel(__lowercase )
scaled_model.to(__lowercase )
scaled_model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =scaled_model(__lowercase ).last_hidden_state
SCREAMING_SNAKE_CASE__ : Optional[Any] =scaled_model(__lowercase ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(__lowercase , __lowercase , atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(__lowercase , __lowercase , atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(__lowercase , __lowercase , atol=1e-5 ) )
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!''' )
@slow
def __magic_name__ ( self : List[Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ : str =[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
SCREAMING_SNAKE_CASE__ : Optional[Any] =LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf''' , device_map='''auto''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(torch.tensor([input_ids] ) )
# Expected mean on dim = -1
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]] )
torch.testing.assert_close(out.mean(-1 ) , __lowercase , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
SCREAMING_SNAKE_CASE__ : List[Any] =torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , __lowercase , atol=1e-5 , rtol=1e-5 )
@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!''' )
@slow
def __magic_name__ ( self : Optional[Any] ) -> List[str]:
SCREAMING_SNAKE_CASE__ : str =[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
SCREAMING_SNAKE_CASE__ : List[Any] =LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf''' , device_map='''auto''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(torch.tensor(__lowercase ) )
# Expected mean on dim = -1
SCREAMING_SNAKE_CASE__ : int =torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]] )
torch.testing.assert_close(out.mean(-1 ) , __lowercase , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
SCREAMING_SNAKE_CASE__ : Tuple =torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , __lowercase , atol=1e-5 , rtol=1e-5 )
@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!''' )
@slow
def __magic_name__ ( self : str ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : List[Any] =[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
SCREAMING_SNAKE_CASE__ : Optional[Any] =LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf''' , device_map='''auto''' )
SCREAMING_SNAKE_CASE__ : str =model(torch.tensor(__lowercase ) )
# Expected mean on dim = -1
SCREAMING_SNAKE_CASE__ : List[Any] =torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]] )
torch.testing.assert_close(out.mean(-1 ) , __lowercase , atol=1e-2 , rtol=1e-2 )
# slicing logits[0, 0, 0:30]
# fmt: off
SCREAMING_SNAKE_CASE__ : str =torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513] )
# fmt: on
torch.testing.assert_close(out.mean(-1 ) , __lowercase , atol=1e-2 , rtol=1e-2 )
@unittest.skip(
'''Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test''' )
@slow
def __magic_name__ ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[Any] =[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
SCREAMING_SNAKE_CASE__ : List[Any] =LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf''' , device_map='''auto''' )
SCREAMING_SNAKE_CASE__ : int =model(torch.tensor(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] =torch.tensor(
[[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]] , dtype=torch.floataa )
torch.testing.assert_close(out.mean(-1 ) , __lowercase , atol=1e-2 , rtol=1e-2 )
# fmt: off
SCREAMING_SNAKE_CASE__ : Dict =torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312] )
# fmt: on
torch.testing.assert_close(out[0, 0, :30] , __lowercase , atol=1e-5 , rtol=1e-5 )
@unittest.skip('''Model is curently gated''' )
@slow
def __magic_name__ ( self : List[Any] ) -> Any:
SCREAMING_SNAKE_CASE__ : Optional[int] ='''Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'''
SCREAMING_SNAKE_CASE__ : Dict ='''Simply put, the theory of relativity states that '''
SCREAMING_SNAKE_CASE__ : Optional[int] =LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf''' )
SCREAMING_SNAKE_CASE__ : Any =tokenizer.encode(__lowercase , return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : Dict =LlamaForCausalLM.from_pretrained(
'''meta-llama/Llama-2-13b-chat-hf''' , device_map='''sequential''' , use_safetensors=__lowercase )
# greedy generation outputs
SCREAMING_SNAKE_CASE__ : int =model.generate(__lowercase , max_new_tokens=64 , top_p=__lowercase , temperature=1 , do_sample=__lowercase )
SCREAMING_SNAKE_CASE__ : int =tokenizer.decode(generated_ids[0] , skip_special_tokens=__lowercase )
self.assertEqual(__lowercase , __lowercase )
| 704 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')
# TF training parameters
a_ = False
a_ = False
def _a( UpperCamelCase__ : Namespace ):
'''simple docstring'''
return TrainCommand(UpperCamelCase__ )
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
@staticmethod
def __magic_name__ ( __lowercase : ArgumentParser ) -> Any:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =parser.add_parser('''train''' , help='''CLI tool to train a model on a task.''' )
train_parser.add_argument(
'''--train_data''' , type=__lowercase , required=__lowercase , help='''path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.''' , )
train_parser.add_argument(
'''--column_label''' , type=__lowercase , default=0 , help='''Column of the dataset csv file with example labels.''' )
train_parser.add_argument(
'''--column_text''' , type=__lowercase , default=1 , help='''Column of the dataset csv file with example texts.''' )
train_parser.add_argument(
'''--column_id''' , type=__lowercase , default=2 , help='''Column of the dataset csv file with example ids.''' )
train_parser.add_argument(
'''--skip_first_row''' , action='''store_true''' , help='''Skip the first row of the csv file (headers).''' )
train_parser.add_argument('''--validation_data''' , type=__lowercase , default='''''' , help='''path to validation dataset.''' )
train_parser.add_argument(
'''--validation_split''' , type=__lowercase , default=0.1 , help='''if validation dataset is not provided, fraction of train dataset to use as validation dataset.''' , )
train_parser.add_argument('''--output''' , type=__lowercase , default='''./''' , help='''path to saved the trained model.''' )
train_parser.add_argument(
'''--task''' , type=__lowercase , default='''text_classification''' , help='''Task to train the model on.''' )
train_parser.add_argument(
'''--model''' , type=__lowercase , default='''bert-base-uncased''' , help='''Model\'s name or path to stored model.''' )
train_parser.add_argument('''--train_batch_size''' , type=__lowercase , default=32 , help='''Batch size for training.''' )
train_parser.add_argument('''--valid_batch_size''' , type=__lowercase , default=64 , help='''Batch size for validation.''' )
train_parser.add_argument('''--learning_rate''' , type=__lowercase , default=3e-5 , help='''Learning rate.''' )
train_parser.add_argument('''--adam_epsilon''' , type=__lowercase , default=1e-08 , help='''Epsilon for Adam optimizer.''' )
train_parser.set_defaults(func=__lowercase )
def __init__( self : Tuple , __lowercase : Namespace ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Tuple =logging.get_logger('''transformers-cli/training''' )
SCREAMING_SNAKE_CASE__ : int ='''tf''' if is_tf_available() else '''torch'''
os.makedirs(args.output , exist_ok=__lowercase )
SCREAMING_SNAKE_CASE__ : Any =args.output
SCREAMING_SNAKE_CASE__ : str =args.column_label
SCREAMING_SNAKE_CASE__ : List[Any] =args.column_text
SCREAMING_SNAKE_CASE__ : Tuple =args.column_id
self.logger.info(F"Loading {args.task} pipeline for {args.model}" )
if args.task == "text_classification":
SCREAMING_SNAKE_CASE__ : List[str] =TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(F"Loading dataset from {args.train_data}" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =None
if args.validation_data:
self.logger.info(F"Loading validation dataset from {args.validation_data}" )
SCREAMING_SNAKE_CASE__ : List[Any] =Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =args.validation_split
SCREAMING_SNAKE_CASE__ : List[Any] =args.train_batch_size
SCREAMING_SNAKE_CASE__ : Any =args.valid_batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =args.learning_rate
SCREAMING_SNAKE_CASE__ : int =args.adam_epsilon
def __magic_name__ ( self : Any ) -> str:
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def __magic_name__ ( self : Optional[int] ) -> Tuple:
raise NotImplementedError
def __magic_name__ ( self : Dict ) -> List[Any]:
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 665 | 0 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[Any] =0
def __magic_name__ ( self : str ) -> Dict:
SCREAMING_SNAKE_CASE__ : List[str] =AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(__lowercase , __lowercase )
def __magic_name__ ( self : Optional[Any] ) -> List[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE__ : List[Any] =Path(__lowercase ) / '''preprocessor_config.json'''
SCREAMING_SNAKE_CASE__ : int =Path(__lowercase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__lowercase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__lowercase , '''w''' ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] =AutoImageProcessor.from_pretrained(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
def __magic_name__ ( self : Any ) -> List[Any]:
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =Path(__lowercase ) / '''preprocessor_config.json'''
SCREAMING_SNAKE_CASE__ : List[str] =Path(__lowercase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(__lowercase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__lowercase , '''w''' ) )
SCREAMING_SNAKE_CASE__ : List[Any] =AutoImageProcessor.from_pretrained(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
def __magic_name__ ( self : Optional[Any] ) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE__ : Any =CLIPConfig()
# Create a dummy config file with image_proceesor_type
SCREAMING_SNAKE_CASE__ : Optional[int] =Path(__lowercase ) / '''preprocessor_config.json'''
SCREAMING_SNAKE_CASE__ : str =Path(__lowercase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__lowercase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__lowercase , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
SCREAMING_SNAKE_CASE__ : str =AutoImageProcessor.from_pretrained(__lowercase ).to_dict()
config_dict.pop('''image_processor_type''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] =CLIPImageProcessor(**__lowercase )
# save in new folder
model_config.save_pretrained(__lowercase )
config.save_pretrained(__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =AutoImageProcessor.from_pretrained(__lowercase )
# make sure private variable is not incorrectly saved
SCREAMING_SNAKE_CASE__ : Any =json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(__lowercase , __lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> Union[str, Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =Path(__lowercase ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(__lowercase , '''w''' ) , )
SCREAMING_SNAKE_CASE__ : Dict =AutoImageProcessor.from_pretrained(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
def __magic_name__ ( self : Optional[int] ) -> List[Any]:
with self.assertRaisesRegex(
__lowercase , '''clip-base is not a local folder and is not a valid model identifier''' ):
SCREAMING_SNAKE_CASE__ : Any =AutoImageProcessor.from_pretrained('''clip-base''' )
def __magic_name__ ( self : Optional[int] ) -> List[Any]:
with self.assertRaisesRegex(
__lowercase , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
SCREAMING_SNAKE_CASE__ : List[Any] =AutoImageProcessor.from_pretrained(__lowercase , revision='''aaaaaa''' )
def __magic_name__ ( self : Tuple ) -> Optional[int]:
with self.assertRaisesRegex(
__lowercase , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
SCREAMING_SNAKE_CASE__ : List[str] =AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def __magic_name__ ( self : Dict ) -> str:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(__lowercase ):
SCREAMING_SNAKE_CASE__ : Dict =AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(__lowercase ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__lowercase )
SCREAMING_SNAKE_CASE__ : int =AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__lowercase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =AutoImageProcessor.from_pretrained(__lowercase , trust_remote_code=__lowercase )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def __magic_name__ ( self : int ) -> Any:
try:
AutoConfig.register('''custom''' , __lowercase )
AutoImageProcessor.register(__lowercase , __lowercase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__lowercase ):
AutoImageProcessor.register(__lowercase , __lowercase )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE__ : Optional[int] =Path(__lowercase ) / '''preprocessor_config.json'''
SCREAMING_SNAKE_CASE__ : Dict =Path(__lowercase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(__lowercase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(__lowercase , '''w''' ) )
SCREAMING_SNAKE_CASE__ : List[Any] =CustomImageProcessor.from_pretrained(__lowercase )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =AutoImageProcessor.from_pretrained(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def __magic_name__ ( self : str ) -> str:
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = True
try:
AutoConfig.register('''custom''' , __lowercase )
AutoImageProcessor.register(__lowercase , __lowercase )
# If remote code is not set, the default is to use local
SCREAMING_SNAKE_CASE__ : Any =AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
SCREAMING_SNAKE_CASE__ : Tuple =AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__lowercase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
SCREAMING_SNAKE_CASE__ : str =AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=__lowercase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(__lowercase , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 705 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = KandinskyVaaImgaImgPipeline
snake_case_ = ["""image_embeds""", """negative_image_embeds""", """image"""]
snake_case_ = [
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
snake_case_ = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
snake_case_ = False
@property
def __magic_name__ ( self : List[str] ) -> Tuple:
return 32
@property
def __magic_name__ ( self : List[str] ) -> str:
return 32
@property
def __magic_name__ ( self : Any ) -> Optional[int]:
return self.time_input_dim
@property
def __magic_name__ ( self : List[Any] ) -> int:
return self.time_input_dim * 4
@property
def __magic_name__ ( self : Tuple ) -> Optional[int]:
return 1_00
@property
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] ={
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
SCREAMING_SNAKE_CASE__ : Optional[int] =UNetaDConditionModel(**__lowercase )
return model
@property
def __magic_name__ ( self : Dict ) -> Any:
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __magic_name__ ( self : Tuple ) -> Optional[Any]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] =VQModel(**self.dummy_movq_kwargs )
return model
def __magic_name__ ( self : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[str] =self.dummy_unet
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dummy_movq
SCREAMING_SNAKE_CASE__ : Optional[Any] ={
'''num_train_timesteps''': 10_00,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
SCREAMING_SNAKE_CASE__ : str =DDIMScheduler(**__lowercase )
SCREAMING_SNAKE_CASE__ : Any ={
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __magic_name__ ( self : str , __lowercase : Optional[Any] , __lowercase : Any=0 ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[int] =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__lowercase ) ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__lowercase )
# create init_image
SCREAMING_SNAKE_CASE__ : Optional[Any] =floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowercase ) ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE__ : Any =Image.fromarray(np.uinta(__lowercase ) ).convert('''RGB''' ).resize((2_56, 2_56) )
if str(__lowercase ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE__ : Dict =torch.manual_seed(__lowercase )
else:
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device=__lowercase ).manual_seed(__lowercase )
SCREAMING_SNAKE_CASE__ : str ={
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def __magic_name__ ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[Any] ='''cpu'''
SCREAMING_SNAKE_CASE__ : Tuple =self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Dict =self.pipeline_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =pipe(**self.get_dummy_inputs(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Tuple =output.images
SCREAMING_SNAKE_CASE__ : Union[str, Any] =pipe(
**self.get_dummy_inputs(__lowercase ) , return_dict=__lowercase , )[0]
SCREAMING_SNAKE_CASE__ : List[Any] =image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : List[str] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE__ : Tuple =np.array(
[0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : int ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : str =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
SCREAMING_SNAKE_CASE__ : List[Any] ='''A red cartoon frog, 4k'''
SCREAMING_SNAKE_CASE__ : Optional[int] =KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =KandinskyVaaImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : Dict =pipeline.to(__lowercase )
pipeline.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device='''cpu''' ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] =pipe_prior(
__lowercase , generator=__lowercase , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
image=__lowercase , image_embeds=__lowercase , negative_image_embeds=__lowercase , generator=__lowercase , num_inference_steps=1_00 , height=7_68 , width=7_68 , strength=0.2 , output_type='''np''' , )
SCREAMING_SNAKE_CASE__ : int =output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(__lowercase , __lowercase )
| 665 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = 42
@flax_register_to_config
class __SCREAMING_SNAKE_CASE ( nn.Module , lowerCamelCase , lowerCamelCase ):
snake_case_ = 32
snake_case_ = 4
snake_case_ = 4
snake_case_ = (
"""CrossAttnDownBlock2D""",
"""CrossAttnDownBlock2D""",
"""CrossAttnDownBlock2D""",
"""DownBlock2D""",
)
snake_case_ = ("""UpBlock2D""", """CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""")
snake_case_ = False
snake_case_ = (320, 640, 1280, 1280)
snake_case_ = 2
snake_case_ = 8
snake_case_ = None
snake_case_ = 1280
snake_case_ = 0.0
snake_case_ = False
snake_case_ = jnp.floataa
snake_case_ = True
snake_case_ = 0
snake_case_ = False
def __magic_name__ ( self : List[Any] , __lowercase : jax.random.KeyArray ) -> FrozenDict:
# init input tensors
SCREAMING_SNAKE_CASE__ : int =(1, self.in_channels, self.sample_size, self.sample_size)
SCREAMING_SNAKE_CASE__ : List[Any] =jnp.zeros(__lowercase , dtype=jnp.floataa )
SCREAMING_SNAKE_CASE__ : int =jnp.ones((1,) , dtype=jnp.intaa )
SCREAMING_SNAKE_CASE__ : List[Any] =jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
SCREAMING_SNAKE_CASE__ : List[str] =jax.random.split(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] ={'''params''': params_rng, '''dropout''': dropout_rng}
return self.init(__lowercase , __lowercase , __lowercase , __lowercase )["params"]
def __magic_name__ ( self : List[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : int =self.block_out_channels
SCREAMING_SNAKE_CASE__ : Optional[Any] =block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
'''At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.''' )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.num_attention_heads or self.attention_head_dim
# input
SCREAMING_SNAKE_CASE__ : Tuple =nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
SCREAMING_SNAKE_CASE__ : Optional[int] =FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
SCREAMING_SNAKE_CASE__ : Optional[int] =FlaxTimestepEmbedding(__lowercase , dtype=self.dtype )
SCREAMING_SNAKE_CASE__ : Optional[int] =self.only_cross_attention
if isinstance(__lowercase , __lowercase ):
SCREAMING_SNAKE_CASE__ : List[Any] =(only_cross_attention,) * len(self.down_block_types )
if isinstance(__lowercase , __lowercase ):
SCREAMING_SNAKE_CASE__ : Any =(num_attention_heads,) * len(self.down_block_types )
# down
SCREAMING_SNAKE_CASE__ : Union[str, Any] =[]
SCREAMING_SNAKE_CASE__ : Optional[Any] =block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
SCREAMING_SNAKE_CASE__ : List[str] =output_channel
SCREAMING_SNAKE_CASE__ : Tuple =block_out_channels[i]
SCREAMING_SNAKE_CASE__ : Optional[int] =i == len(__lowercase ) - 1
if down_block_type == "CrossAttnDownBlock2D":
SCREAMING_SNAKE_CASE__ : Tuple =FlaxCrossAttnDownBlockaD(
in_channels=__lowercase , out_channels=__lowercase , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
SCREAMING_SNAKE_CASE__ : str =FlaxDownBlockaD(
in_channels=__lowercase , out_channels=__lowercase , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(__lowercase )
SCREAMING_SNAKE_CASE__ : int =down_blocks
# mid
SCREAMING_SNAKE_CASE__ : List[Any] =FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
# up
SCREAMING_SNAKE_CASE__ : int =[]
SCREAMING_SNAKE_CASE__ : Any =list(reversed(__lowercase ) )
SCREAMING_SNAKE_CASE__ : List[Any] =list(reversed(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Dict =list(reversed(__lowercase ) )
SCREAMING_SNAKE_CASE__ : List[str] =reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
SCREAMING_SNAKE_CASE__ : Optional[int] =output_channel
SCREAMING_SNAKE_CASE__ : List[Any] =reversed_block_out_channels[i]
SCREAMING_SNAKE_CASE__ : List[Any] =reversed_block_out_channels[min(i + 1 , len(__lowercase ) - 1 )]
SCREAMING_SNAKE_CASE__ : Optional[int] =i == len(__lowercase ) - 1
if up_block_type == "CrossAttnUpBlock2D":
SCREAMING_SNAKE_CASE__ : List[Any] =FlaxCrossAttnUpBlockaD(
in_channels=__lowercase , out_channels=__lowercase , prev_output_channel=__lowercase , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
SCREAMING_SNAKE_CASE__ : List[Any] =FlaxUpBlockaD(
in_channels=__lowercase , out_channels=__lowercase , prev_output_channel=__lowercase , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =output_channel
SCREAMING_SNAKE_CASE__ : Optional[Any] =up_blocks
# out
SCREAMING_SNAKE_CASE__ : Optional[int] =nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
SCREAMING_SNAKE_CASE__ : str =nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : Union[str, Any] , __lowercase : Optional[Any] , __lowercase : List[str] , __lowercase : str , __lowercase : Union[str, Any]=None , __lowercase : List[str]=None , __lowercase : bool = True , __lowercase : bool = False , ) -> Union[FlaxUNetaDConditionOutput, Tuple]:
# 1. time
if not isinstance(__lowercase , jnp.ndarray ):
SCREAMING_SNAKE_CASE__ : List[Any] =jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(__lowercase , jnp.ndarray ) and len(timesteps.shape ) == 0:
SCREAMING_SNAKE_CASE__ : Optional[Any] =timesteps.astype(dtype=jnp.floataa )
SCREAMING_SNAKE_CASE__ : List[Any] =jnp.expand_dims(__lowercase , 0 )
SCREAMING_SNAKE_CASE__ : Optional[int] =self.time_proj(__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =self.time_embedding(__lowercase )
# 2. pre-process
SCREAMING_SNAKE_CASE__ : str =jnp.transpose(__lowercase , (0, 2, 3, 1) )
SCREAMING_SNAKE_CASE__ : Tuple =self.conv_in(__lowercase )
# 3. down
SCREAMING_SNAKE_CASE__ : Tuple =(sample,)
for down_block in self.down_blocks:
if isinstance(__lowercase , __lowercase ):
SCREAMING_SNAKE_CASE__ : str =down_block(__lowercase , __lowercase , __lowercase , deterministic=not train )
else:
SCREAMING_SNAKE_CASE__ : Any =down_block(__lowercase , __lowercase , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
SCREAMING_SNAKE_CASE__ : int =()
for down_block_res_sample, down_block_additional_residual in zip(
__lowercase , __lowercase ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
SCREAMING_SNAKE_CASE__ : Dict =new_down_block_res_samples
# 4. mid
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.mid_block(__lowercase , __lowercase , __lowercase , deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
SCREAMING_SNAKE_CASE__ : List[str] =down_block_res_samples[-(self.layers_per_block + 1) :]
SCREAMING_SNAKE_CASE__ : Optional[Any] =down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(__lowercase , __lowercase ):
SCREAMING_SNAKE_CASE__ : Optional[int] =up_block(
__lowercase , temb=__lowercase , encoder_hidden_states=__lowercase , res_hidden_states_tuple=__lowercase , deterministic=not train , )
else:
SCREAMING_SNAKE_CASE__ : int =up_block(__lowercase , temb=__lowercase , res_hidden_states_tuple=__lowercase , deterministic=not train )
# 6. post-process
SCREAMING_SNAKE_CASE__ : List[Any] =self.conv_norm_out(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =nn.silu(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.conv_out(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =jnp.transpose(__lowercase , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=__lowercase )
| 706 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
a_ = TypeVar('T')
class __SCREAMING_SNAKE_CASE ( Generic[T] ):
snake_case_ = 42 # Cache store of keys
snake_case_ = 42 # References of the keys in cache
snake_case_ = 10 # Maximum capacity of cache
def __init__( self : Dict , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : Any =deque()
SCREAMING_SNAKE_CASE__ : str =set()
if not n:
SCREAMING_SNAKE_CASE__ : Optional[Any] =sys.maxsize
elif n < 0:
raise ValueError('''n should be an integer greater than 0.''' )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =n
def __magic_name__ ( self : List[str] , __lowercase : T ) -> None:
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
SCREAMING_SNAKE_CASE__ : int =self.dq_store.pop()
self.key_reference.remove(__lowercase )
else:
self.dq_store.remove(__lowercase )
self.dq_store.appendleft(__lowercase )
self.key_reference.add(__lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> None:
for k in self.dq_store:
print(__lowercase )
def __repr__( self : List[Any] ) -> str:
return F"LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}"
if __name__ == "__main__":
import doctest
doctest.testmod()
a_ = LRUCache(4)
lru_cache.refer('A')
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer('A')
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 665 | 0 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _a( UpperCamelCase__ : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =int(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[str] =t // 3_6_0_0, (t // 6_0) % 6_0, t % 6_0
return f"{h}:{m:02d}:{s:02d}" if h != 0 else f"{m:02d}:{s:02d}"
def _a( UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Dict, UpperCamelCase__ : int, UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Dict=3_0_0 ):
'''simple docstring'''
return f"\n <div>\n {prefix}\n <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>\n {label}\n </div>\n "
def _a( UpperCamelCase__ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] ='''<table border="1" class="dataframe">\n'''
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += f" <th>{i}</th>\n"
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
SCREAMING_SNAKE_CASE__ : List[Any] =f"{elt:.6f}" if isinstance(UpperCamelCase__, UpperCamelCase__ ) else str(UpperCamelCase__ )
html_code += f" <td>{elt}</td>\n"
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class __SCREAMING_SNAKE_CASE :
snake_case_ = 5
snake_case_ = 0.2
def __init__( self : str , __lowercase : int , __lowercase : Optional[str] = None , __lowercase : bool = True , __lowercase : Optional["NotebookTrainingTracker"] = None , __lowercase : int = 3_00 , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : List[Any] =total
SCREAMING_SNAKE_CASE__ : List[str] ='''''' if prefix is None else prefix
SCREAMING_SNAKE_CASE__ : Tuple =leave
SCREAMING_SNAKE_CASE__ : Optional[Any] =parent
SCREAMING_SNAKE_CASE__ : List[Any] =width
SCREAMING_SNAKE_CASE__ : List[Any] =None
SCREAMING_SNAKE_CASE__ : List[str] =None
SCREAMING_SNAKE_CASE__ : str =None
def __magic_name__ ( self : int , __lowercase : int , __lowercase : bool = False , __lowercase : str = None ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Tuple =value
if comment is not None:
SCREAMING_SNAKE_CASE__ : Tuple =comment
if self.last_value is None:
SCREAMING_SNAKE_CASE__ : str =time.time()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =value
SCREAMING_SNAKE_CASE__ : Optional[Any] =None
SCREAMING_SNAKE_CASE__ : Any =self.warmup
SCREAMING_SNAKE_CASE__ : Any =1
self.update_bar(__lowercase )
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for , self.total ):
if self.first_calls > 0:
self.first_calls -= 1
SCREAMING_SNAKE_CASE__ : Optional[int] =time.time()
SCREAMING_SNAKE_CASE__ : Any =current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
SCREAMING_SNAKE_CASE__ : Any =self.elapsed_time / (value - self.start_value)
else:
SCREAMING_SNAKE_CASE__ : Dict =None
if value >= self.total:
SCREAMING_SNAKE_CASE__ : int =self.total
SCREAMING_SNAKE_CASE__ : Optional[int] =None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
SCREAMING_SNAKE_CASE__ : List[Any] =self.average_time_per_item * (self.total - value)
self.update_bar(__lowercase )
SCREAMING_SNAKE_CASE__ : int =value
SCREAMING_SNAKE_CASE__ : Optional[int] =current_time
if self.average_time_per_item is None:
SCREAMING_SNAKE_CASE__ : List[str] =1
else:
SCREAMING_SNAKE_CASE__ : int =max(int(self.update_every / self.average_time_per_item ) , 1 )
def __magic_name__ ( self : str , __lowercase : List[str] , __lowercase : Union[str, Any]=None ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Tuple =''' ''' * (len(str(self.total ) ) - len(str(__lowercase ) )) + str(__lowercase )
if self.elapsed_time is None:
SCREAMING_SNAKE_CASE__ : str =F"[{spaced_value}/{self.total} : < :"
elif self.predicted_remaining is None:
SCREAMING_SNAKE_CASE__ : Any =F"[{spaced_value}/{self.total} {format_time(self.elapsed_time )}"
else:
SCREAMING_SNAKE_CASE__ : List[str] =(
F"[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <"
F" {format_time(self.predicted_remaining )}"
)
self.label += F", {1/self.average_time_per_item:.2f} it/s"
self.label += "]" if self.comment is None or len(self.comment ) == 0 else F", {self.comment}]"
self.display()
def __magic_name__ ( self : Dict ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : int =html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
SCREAMING_SNAKE_CASE__ : Optional[int] =disp.display(disp.HTML(self.html_code ) , display_id=__lowercase )
else:
self.output.update(disp.HTML(self.html_code ) )
def __magic_name__ ( self : Dict ) -> str:
if self.parent is None and self.output is not None:
self.output.update(disp.HTML('''''' ) )
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : Union[str, Any] , __lowercase : Dict , __lowercase : Union[str, Any]=None ) -> Optional[int]:
super().__init__(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =None if column_names is None else [column_names]
SCREAMING_SNAKE_CASE__ : Dict =None
def __magic_name__ ( self : Optional[int] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : str =html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table )
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
SCREAMING_SNAKE_CASE__ : Optional[Any] =disp.display(disp.HTML(self.html_code ) , display_id=__lowercase )
else:
self.output.update(disp.HTML(self.html_code ) )
def __magic_name__ ( self : int , __lowercase : str ) -> List[str]:
if self.inner_table is None:
SCREAMING_SNAKE_CASE__ : List[str] =[list(values.keys() ), list(values.values() )]
else:
SCREAMING_SNAKE_CASE__ : Dict =self.inner_table[0]
if len(self.inner_table ) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =columns
self.inner_table.append([values[c] for c in columns] )
def __magic_name__ ( self : Optional[Any] , __lowercase : str , __lowercase : Dict=None , __lowercase : List[Any]=3_00 ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : int =NotebookProgressBar(__lowercase , prefix=__lowercase , parent=self , width=__lowercase )
return self.child_bar
def __magic_name__ ( self : Union[str, Any] ) -> int:
SCREAMING_SNAKE_CASE__ : Any =None
self.display()
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : Tuple ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : int =None
SCREAMING_SNAKE_CASE__ : Optional[int] =None
SCREAMING_SNAKE_CASE__ : Any =False
def __magic_name__ ( self : Dict , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : List[Any] , **__lowercase : Any ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] ='''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
SCREAMING_SNAKE_CASE__ : Any =0
SCREAMING_SNAKE_CASE__ : List[Any] =0
SCREAMING_SNAKE_CASE__ : Tuple =[self.first_column] + ['''Training Loss''']
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append('''Validation Loss''' )
SCREAMING_SNAKE_CASE__ : Dict =NotebookTrainingTracker(state.max_steps , __lowercase )
def __magic_name__ ( self : Optional[int] , __lowercase : List[str] , __lowercase : str , __lowercase : int , **__lowercase : Optional[int] ) -> str:
SCREAMING_SNAKE_CASE__ : Dict =int(state.epoch ) if int(state.epoch ) == state.epoch else F"{state.epoch:.2f}"
self.training_tracker.update(
state.global_step + 1 , comment=F"Epoch {epoch}/{state.num_train_epochs}" , force_update=self._force_next_update , )
SCREAMING_SNAKE_CASE__ : List[str] =False
def __magic_name__ ( self : Union[str, Any] , __lowercase : Dict , __lowercase : int , __lowercase : Optional[int] , __lowercase : str=None , **__lowercase : int ) -> List[str]:
if not has_length(__lowercase ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
SCREAMING_SNAKE_CASE__ : List[str] =self.training_tracker.add_child(len(__lowercase ) )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =NotebookProgressBar(len(__lowercase ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def __magic_name__ ( self : Dict , __lowercase : List[Any] , __lowercase : Any , __lowercase : Dict , **__lowercase : Dict ) -> Optional[int]:
if self.prediction_bar is not None:
self.prediction_bar.close()
SCREAMING_SNAKE_CASE__ : int =None
def __magic_name__ ( self : Optional[Any] , __lowercase : Optional[Any] , __lowercase : Dict , __lowercase : List[str] , __lowercase : Tuple=None , **__lowercase : List[str] ) -> int:
# Only for when there is no evaluation
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
SCREAMING_SNAKE_CASE__ : List[Any] ={'''Training Loss''': logs['''loss''']}
# First column is necessarily Step sine we're not in epoch eval strategy
SCREAMING_SNAKE_CASE__ : List[Any] =state.global_step
self.training_tracker.write_line(__lowercase )
def __magic_name__ ( self : Optional[Any] , __lowercase : List[str] , __lowercase : List[str] , __lowercase : str , __lowercase : List[str]=None , **__lowercase : List[str] ) -> Tuple:
if self.training_tracker is not None:
SCREAMING_SNAKE_CASE__ : Tuple ={'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
for log in reversed(state.log_history ):
if "loss" in log:
SCREAMING_SNAKE_CASE__ : Optional[Any] =log['''loss''']
break
if self.first_column == "Epoch":
SCREAMING_SNAKE_CASE__ : Optional[Any] =int(state.epoch )
else:
SCREAMING_SNAKE_CASE__ : int =state.global_step
SCREAMING_SNAKE_CASE__ : Tuple ='''eval'''
for k in metrics:
if k.endswith('''_loss''' ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =re.sub(r'''\_loss$''' , '''''' , __lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =metrics.pop('''total_flos''' , __lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =metrics.pop('''epoch''' , __lowercase )
SCREAMING_SNAKE_CASE__ : int =metrics.pop(F"{metric_key_prefix}_runtime" , __lowercase )
SCREAMING_SNAKE_CASE__ : Any =metrics.pop(F"{metric_key_prefix}_samples_per_second" , __lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =metrics.pop(F"{metric_key_prefix}_steps_per_second" , __lowercase )
SCREAMING_SNAKE_CASE__ : int =metrics.pop(F"{metric_key_prefix}_jit_compilation_time" , __lowercase )
for k, v in metrics.items():
if k == F"{metric_key_prefix}_loss":
SCREAMING_SNAKE_CASE__ : int =v
else:
SCREAMING_SNAKE_CASE__ : Tuple =k.split('''_''' )
SCREAMING_SNAKE_CASE__ : Dict =''' '''.join([part.capitalize() for part in splits[1:]] )
SCREAMING_SNAKE_CASE__ : str =v
self.training_tracker.write_line(__lowercase )
self.training_tracker.remove_child()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =None
# Evaluation takes a long time so we should force the next update.
SCREAMING_SNAKE_CASE__ : Optional[Any] =True
def __magic_name__ ( self : Union[str, Any] , __lowercase : Union[str, Any] , __lowercase : Tuple , __lowercase : int , **__lowercase : Optional[int] ) -> Tuple:
self.training_tracker.update(
state.global_step , comment=F"Epoch {int(state.epoch )}/{state.num_train_epochs}" , force_update=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =None
| 707 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a_ = list[list[float | int]]
def _a( UpperCamelCase__ : Matrix, UpperCamelCase__ : Matrix ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Matrix =[[0 for _ in range(size + 1 )] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : float
for row in range(UpperCamelCase__ ):
for col in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Tuple =matrix[row][col]
SCREAMING_SNAKE_CASE__ : Optional[int] =vector[row][0]
SCREAMING_SNAKE_CASE__ : Any =0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =0
while row < size and col < size:
# pivoting
SCREAMING_SNAKE_CASE__ : Any =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__ : Optional[Any] =augmented[pivot_row], augmented[row]
for rowa in range(row + 1, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] =augmented[rowa][col] / augmented[row][col]
SCREAMING_SNAKE_CASE__ : Tuple =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[Any] =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], 1_0 )] for row in range(UpperCamelCase__ )
]
def _a( UpperCamelCase__ : list[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Matrix =[[0 for _ in range(UpperCamelCase__ )] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Matrix =[[0] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Matrix
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
for x_val, y_val in enumerate(UpperCamelCase__ ):
for col in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[int] =(x_val + 1) ** (size - col - 1)
SCREAMING_SNAKE_CASE__ : Dict =y_val
SCREAMING_SNAKE_CASE__ : Optional[int] =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 ):
'''simple docstring'''
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**1_0
)
def _a( UpperCamelCase__ : Callable[[int], int] = question_function, UpperCamelCase__ : int = 1_0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : list[int] =[func(UpperCamelCase__ ) for x_val in range(1, order + 1 )]
SCREAMING_SNAKE_CASE__ : list[Callable[[int], int]] =[
interpolate(data_points[:max_coeff] ) for max_coeff in range(1, order + 1 )
]
SCREAMING_SNAKE_CASE__ : int =0
SCREAMING_SNAKE_CASE__ : Callable[[int], int]
SCREAMING_SNAKE_CASE__ : int
for poly in polynomials:
SCREAMING_SNAKE_CASE__ : Any =1
while func(UpperCamelCase__ ) == poly(UpperCamelCase__ ):
x_val += 1
ret += poly(UpperCamelCase__ )
return ret
if __name__ == "__main__":
print(F'''{solution() = }''')
| 665 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : int = 1_0**1_2 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] =1
SCREAMING_SNAKE_CASE__ : str =0
SCREAMING_SNAKE_CASE__ : int =1
SCREAMING_SNAKE_CASE__ : Tuple =1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev_numerator
prev_denominator += 2 * denominator
denominator += 2 * prev_denominator
return (denominator + 1) // 2
if __name__ == "__main__":
print(F'''{solution() = }''')
| 708 |
'''simple docstring'''
def _a( UpperCamelCase__ : Optional[int], UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =len(UpperCamelCase__ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
SCREAMING_SNAKE_CASE__ : Union[str, Any] =left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCamelCase__ ):
return None
SCREAMING_SNAKE_CASE__ : Optional[int] =sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =left
SCREAMING_SNAKE_CASE__ : Optional[Any] =point
elif point > right:
SCREAMING_SNAKE_CASE__ : Optional[int] =right
SCREAMING_SNAKE_CASE__ : Tuple =point
else:
if item < current_item:
SCREAMING_SNAKE_CASE__ : str =point - 1
else:
SCREAMING_SNAKE_CASE__ : Tuple =point + 1
return None
def _a( UpperCamelCase__ : List[str], UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
SCREAMING_SNAKE_CASE__ : Dict =left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCamelCase__ ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
elif point > right:
return interpolation_search_by_recursion(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, point - 1 )
else:
return interpolation_search_by_recursion(
UpperCamelCase__, UpperCamelCase__, point + 1, UpperCamelCase__ )
def _a( UpperCamelCase__ : Dict ):
'''simple docstring'''
if collection != sorted(UpperCamelCase__ ):
raise ValueError('''Collection must be ascending sorted''' )
return True
if __name__ == "__main__":
import sys
a_ = 0
if debug == 1:
a_ = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
try:
__assert_sorted(collection)
except ValueError:
sys.exit('Sequence must be ascending sorted to apply interpolation search')
a_ = 6_7
a_ = interpolation_search(collection, target)
if result is not None:
print(F'''{target} found at positions: {result}''')
else:
print('Not found')
| 665 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
a_ = logging.get_logger(__name__)
def _a( UpperCamelCase__ : List[Any] ):
'''simple docstring'''
if isinstance(UpperCamelCase__, (list, tuple) ) and isinstance(videos[0], (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(UpperCamelCase__, (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(UpperCamelCase__ ):
return [[videos]]
raise ValueError(f"Could not make batched video from {videos}" )
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = ["""pixel_values"""]
def __init__( self : int , __lowercase : bool = True , __lowercase : Dict[str, int] = None , __lowercase : PILImageResampling = PILImageResampling.BILINEAR , __lowercase : bool = True , __lowercase : Dict[str, int] = None , __lowercase : bool = True , __lowercase : Union[int, float] = 1 / 2_55 , __lowercase : bool = True , __lowercase : bool = True , __lowercase : Optional[Union[float, List[float]]] = None , __lowercase : Optional[Union[float, List[float]]] = None , **__lowercase : Optional[int] , ) -> None:
super().__init__(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =size if size is not None else {'''shortest_edge''': 2_56}
SCREAMING_SNAKE_CASE__ : str =get_size_dict(__lowercase , default_to_square=__lowercase )
SCREAMING_SNAKE_CASE__ : str =crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
SCREAMING_SNAKE_CASE__ : str =get_size_dict(__lowercase , param_name='''crop_size''' )
SCREAMING_SNAKE_CASE__ : int =do_resize
SCREAMING_SNAKE_CASE__ : Tuple =size
SCREAMING_SNAKE_CASE__ : int =do_center_crop
SCREAMING_SNAKE_CASE__ : Dict =crop_size
SCREAMING_SNAKE_CASE__ : Tuple =resample
SCREAMING_SNAKE_CASE__ : List[Any] =do_rescale
SCREAMING_SNAKE_CASE__ : Optional[Any] =rescale_factor
SCREAMING_SNAKE_CASE__ : Optional[Any] =offset
SCREAMING_SNAKE_CASE__ : Tuple =do_normalize
SCREAMING_SNAKE_CASE__ : List[str] =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
SCREAMING_SNAKE_CASE__ : List[Any] =image_std if image_std is not None else IMAGENET_STANDARD_STD
def __magic_name__ ( self : Optional[Any] , __lowercase : np.ndarray , __lowercase : Dict[str, int] , __lowercase : PILImageResampling = PILImageResampling.BILINEAR , __lowercase : Optional[Union[str, ChannelDimension]] = None , **__lowercase : str , ) -> np.ndarray:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =get_size_dict(__lowercase , default_to_square=__lowercase )
if "shortest_edge" in size:
SCREAMING_SNAKE_CASE__ : Optional[int] =get_resize_output_image_size(__lowercase , size['''shortest_edge'''] , default_to_square=__lowercase )
elif "height" in size and "width" in size:
SCREAMING_SNAKE_CASE__ : Any =(size['''height'''], size['''width'''])
else:
raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
return resize(__lowercase , size=__lowercase , resample=__lowercase , data_format=__lowercase , **__lowercase )
def __magic_name__ ( self : Optional[Any] , __lowercase : np.ndarray , __lowercase : Dict[str, int] , __lowercase : Optional[Union[str, ChannelDimension]] = None , **__lowercase : List[Any] , ) -> np.ndarray:
SCREAMING_SNAKE_CASE__ : Any =get_size_dict(__lowercase )
if "height" not in size or "width" not in size:
raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}" )
return center_crop(__lowercase , size=(size['''height'''], size['''width''']) , data_format=__lowercase , **__lowercase )
def __magic_name__ ( self : Tuple , __lowercase : np.ndarray , __lowercase : Union[int, float] , __lowercase : bool = True , __lowercase : Optional[Union[str, ChannelDimension]] = None , **__lowercase : Optional[int] , ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : str =image.astype(np.floataa )
if offset:
SCREAMING_SNAKE_CASE__ : int =image - (scale / 2)
return rescale(__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase )
def __magic_name__ ( self : Optional[Any] , __lowercase : np.ndarray , __lowercase : Union[float, List[float]] , __lowercase : Union[float, List[float]] , __lowercase : Optional[Union[str, ChannelDimension]] = None , **__lowercase : Any , ) -> np.ndarray:
return normalize(__lowercase , mean=__lowercase , std=__lowercase , data_format=__lowercase , **__lowercase )
def __magic_name__ ( self : Dict , __lowercase : ImageInput , __lowercase : bool = None , __lowercase : Dict[str, int] = None , __lowercase : PILImageResampling = None , __lowercase : bool = None , __lowercase : Dict[str, int] = None , __lowercase : bool = None , __lowercase : float = None , __lowercase : bool = None , __lowercase : bool = None , __lowercase : Optional[Union[float, List[float]]] = None , __lowercase : Optional[Union[float, List[float]]] = None , __lowercase : Optional[ChannelDimension] = ChannelDimension.FIRST , ) -> np.ndarray:
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
if offset and not do_rescale:
raise ValueError('''For offset, do_rescale must also be set to True.''' )
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE__ : List[str] =to_numpy_array(__lowercase )
if do_resize:
SCREAMING_SNAKE_CASE__ : str =self.resize(image=__lowercase , size=__lowercase , resample=__lowercase )
if do_center_crop:
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.center_crop(__lowercase , size=__lowercase )
if do_rescale:
SCREAMING_SNAKE_CASE__ : List[str] =self.rescale(image=__lowercase , scale=__lowercase , offset=__lowercase )
if do_normalize:
SCREAMING_SNAKE_CASE__ : int =self.normalize(image=__lowercase , mean=__lowercase , std=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =to_channel_dimension_format(__lowercase , __lowercase )
return image
def __magic_name__ ( self : Any , __lowercase : ImageInput , __lowercase : bool = None , __lowercase : Dict[str, int] = None , __lowercase : PILImageResampling = None , __lowercase : bool = None , __lowercase : Dict[str, int] = None , __lowercase : bool = None , __lowercase : float = None , __lowercase : bool = None , __lowercase : bool = None , __lowercase : Optional[Union[float, List[float]]] = None , __lowercase : Optional[Union[float, List[float]]] = None , __lowercase : Optional[Union[str, TensorType]] = None , __lowercase : ChannelDimension = ChannelDimension.FIRST , **__lowercase : Dict , ) -> PIL.Image.Image:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE__ : List[str] =resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE__ : str =do_center_crop if do_center_crop is not None else self.do_center_crop
SCREAMING_SNAKE_CASE__ : Dict =do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE__ : Optional[Any] =rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE__ : Any =offset if offset is not None else self.offset
SCREAMING_SNAKE_CASE__ : List[Any] =do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE__ : Union[str, Any] =image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE__ : Optional[Any] =image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE__ : Optional[int] =size if size is not None else self.size
SCREAMING_SNAKE_CASE__ : str =get_size_dict(__lowercase , default_to_square=__lowercase )
SCREAMING_SNAKE_CASE__ : str =crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE__ : Optional[int] =get_size_dict(__lowercase , param_name='''crop_size''' )
if not valid_images(__lowercase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] =make_batched(__lowercase )
SCREAMING_SNAKE_CASE__ : int =[
[
self._preprocess_image(
image=__lowercase , do_resize=__lowercase , size=__lowercase , resample=__lowercase , do_center_crop=__lowercase , crop_size=__lowercase , do_rescale=__lowercase , rescale_factor=__lowercase , offset=__lowercase , do_normalize=__lowercase , image_mean=__lowercase , image_std=__lowercase , data_format=__lowercase , )
for img in video
]
for video in videos
]
SCREAMING_SNAKE_CASE__ : Tuple ={'''pixel_values''': videos}
return BatchFeature(data=__lowercase , tensor_type=__lowercase )
| 709 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __SCREAMING_SNAKE_CASE :
@staticmethod
def __magic_name__ ( *__lowercase : int , **__lowercase : Optional[Any] ) -> Optional[Any]:
pass
@is_pipeline_test
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@require_torch
def __magic_name__ ( self : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , )
SCREAMING_SNAKE_CASE__ : List[str] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Any =image_classifier(__lowercase , candidate_labels=['''a''', '''b''', '''c'''] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(__lowercase ) , [
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
] , )
SCREAMING_SNAKE_CASE__ : int =image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
] , )
@require_tf
def __magic_name__ ( self : Union[str, Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , framework='''tf''' )
SCREAMING_SNAKE_CASE__ : Dict =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Tuple =image_classifier(__lowercase , candidate_labels=['''a''', '''b''', '''c'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}] , )
SCREAMING_SNAKE_CASE__ : List[Any] =image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
] , )
@slow
@require_torch
def __magic_name__ ( self : List[Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE__ : Any =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : List[str] =image_classifier(__lowercase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
@slow
@require_tf
def __magic_name__ ( self : Union[str, Any] ) -> int:
SCREAMING_SNAKE_CASE__ : str =pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , framework='''tf''' )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE__ : Optional[Any] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Any =image_classifier(__lowercase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
SCREAMING_SNAKE_CASE__ : List[Any] =image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
| 665 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = KandinskyVaaImgaImgPipeline
snake_case_ = ["""image_embeds""", """negative_image_embeds""", """image"""]
snake_case_ = [
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
snake_case_ = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
snake_case_ = False
@property
def __magic_name__ ( self : List[str] ) -> Tuple:
return 32
@property
def __magic_name__ ( self : List[str] ) -> str:
return 32
@property
def __magic_name__ ( self : Any ) -> Optional[int]:
return self.time_input_dim
@property
def __magic_name__ ( self : List[Any] ) -> int:
return self.time_input_dim * 4
@property
def __magic_name__ ( self : Tuple ) -> Optional[int]:
return 1_00
@property
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] ={
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
SCREAMING_SNAKE_CASE__ : Optional[int] =UNetaDConditionModel(**__lowercase )
return model
@property
def __magic_name__ ( self : Dict ) -> Any:
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __magic_name__ ( self : Tuple ) -> Optional[Any]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] =VQModel(**self.dummy_movq_kwargs )
return model
def __magic_name__ ( self : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[str] =self.dummy_unet
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dummy_movq
SCREAMING_SNAKE_CASE__ : Optional[Any] ={
'''num_train_timesteps''': 10_00,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
SCREAMING_SNAKE_CASE__ : str =DDIMScheduler(**__lowercase )
SCREAMING_SNAKE_CASE__ : Any ={
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __magic_name__ ( self : str , __lowercase : Optional[Any] , __lowercase : Any=0 ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[int] =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__lowercase ) ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__lowercase )
# create init_image
SCREAMING_SNAKE_CASE__ : Optional[Any] =floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowercase ) ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE__ : Any =Image.fromarray(np.uinta(__lowercase ) ).convert('''RGB''' ).resize((2_56, 2_56) )
if str(__lowercase ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE__ : Dict =torch.manual_seed(__lowercase )
else:
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device=__lowercase ).manual_seed(__lowercase )
SCREAMING_SNAKE_CASE__ : str ={
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def __magic_name__ ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[Any] ='''cpu'''
SCREAMING_SNAKE_CASE__ : Tuple =self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Dict =self.pipeline_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =pipe(**self.get_dummy_inputs(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Tuple =output.images
SCREAMING_SNAKE_CASE__ : Union[str, Any] =pipe(
**self.get_dummy_inputs(__lowercase ) , return_dict=__lowercase , )[0]
SCREAMING_SNAKE_CASE__ : List[Any] =image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : List[str] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE__ : Tuple =np.array(
[0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : int ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : str =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
SCREAMING_SNAKE_CASE__ : List[Any] ='''A red cartoon frog, 4k'''
SCREAMING_SNAKE_CASE__ : Optional[int] =KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =KandinskyVaaImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : Dict =pipeline.to(__lowercase )
pipeline.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device='''cpu''' ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : List[str] =pipe_prior(
__lowercase , generator=__lowercase , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
image=__lowercase , image_embeds=__lowercase , negative_image_embeds=__lowercase , generator=__lowercase , num_inference_steps=1_00 , height=7_68 , width=7_68 , strength=0.2 , output_type='''np''' , )
SCREAMING_SNAKE_CASE__ : int =output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(__lowercase , __lowercase )
| 710 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case_ = JukeboxTokenizer
snake_case_ = {
"""artist""": """Zac Brown Band""",
"""genres""": """Country""",
"""lyrics""": """I met a traveller from an antique land,
Who said \"Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
""",
}
@require_torch
def __magic_name__ ( self : Optional[int] ) -> str:
import torch
SCREAMING_SNAKE_CASE__ : List[str] =JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics''' )
SCREAMING_SNAKE_CASE__ : str =tokenizer(**self.metas )['''input_ids''']
# fmt: off
SCREAMING_SNAKE_CASE__ : str =[
torch.tensor([[
0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
76, 76]] ),
torch.tensor([[0, 0, 0, 10_69, 11]] ),
torch.tensor([[0, 0, 0, 10_69, 11]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
@require_torch
def __magic_name__ ( self : Any ) -> List[str]:
import torch
SCREAMING_SNAKE_CASE__ : int =JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics''' )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer(**self.metas )['''input_ids''']
# fmt: off
SCREAMING_SNAKE_CASE__ : Optional[int] =[
torch.tensor([[
0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
77, 77, 77, 77, 77, 77]] ),
torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
| 665 | 0 |
'''simple docstring'''
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def _a( UpperCamelCase__ : List[str] ):
'''simple docstring'''
if isinstance(UpperCamelCase__, collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class __SCREAMING_SNAKE_CASE :
def __magic_name__ ( self : Tuple , __lowercase : Tuple , __lowercase : Tuple ) -> Optional[Any]:
pass
def __magic_name__ ( self : List[Any] ) -> Any:
pass
def __magic_name__ ( self : Optional[Any] ) -> Union[str, Any]:
pass
def __magic_name__ ( self : int , __lowercase : List[str] , __lowercase : Optional[int] , __lowercase : Any , __lowercase : Dict , __lowercase : Optional[int]=None , **__lowercase : List[str] ) -> int:
SCREAMING_SNAKE_CASE__ : Tuple =VisionTextDualEncoderConfig.from_vision_text_configs(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =TFVisionTextDualEncoderModel(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =model(input_ids=__lowercase , pixel_values=__lowercase , attention_mask=__lowercase )
self.assertEqual(output['''text_embeds'''].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output['''image_embeds'''].shape , (pixel_values.shape[0], config.projection_dim) )
def __magic_name__ ( self : int , __lowercase : str , __lowercase : int , __lowercase : int , __lowercase : int , __lowercase : str=None , **__lowercase : Optional[int] ) -> Dict:
SCREAMING_SNAKE_CASE__ : Tuple =self.get_vision_text_model(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =TFVisionTextDualEncoderModel(vision_model=__lowercase , text_model=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =model(input_ids=__lowercase , pixel_values=__lowercase , attention_mask=__lowercase )
self.assertEqual(output['''text_embeds'''].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output['''image_embeds'''].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __magic_name__ ( self : List[Any] , __lowercase : int , __lowercase : Any , __lowercase : int , __lowercase : Tuple , __lowercase : Union[str, Any]=None , **__lowercase : Optional[Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : int =self.get_vision_text_model(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : List[str] ={'''vision_model''': vision_model, '''text_model''': text_model}
SCREAMING_SNAKE_CASE__ : Optional[Any] =TFVisionTextDualEncoderModel.from_vision_text_pretrained(**__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =model(input_ids=__lowercase , pixel_values=__lowercase , attention_mask=__lowercase )
self.assertEqual(output['''text_embeds'''].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output['''image_embeds'''].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __magic_name__ ( self : str , __lowercase : Optional[int] , __lowercase : Union[str, Any] , __lowercase : Any , __lowercase : Any , __lowercase : str=None , **__lowercase : str ) -> str:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.get_vision_text_model(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Dict =TFVisionTextDualEncoderModel(vision_model=__lowercase , text_model=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(input_ids=__lowercase , pixel_values=__lowercase , attention_mask=__lowercase )
SCREAMING_SNAKE_CASE__ : Any =output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__lowercase )
SCREAMING_SNAKE_CASE__ : str =TFVisionTextDualEncoderModel.from_pretrained(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =model(input_ids=__lowercase , pixel_values=__lowercase , attention_mask=__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =after_output[0].numpy()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__lowercase , 1e-5 )
def __magic_name__ ( self : List[Any] , __lowercase : Optional[Any] , __lowercase : Optional[Any] , __lowercase : List[Any] , __lowercase : List[Any] , __lowercase : List[Any]=None , **__lowercase : Tuple ) -> int:
SCREAMING_SNAKE_CASE__ : List[Any] =self.get_vision_text_model(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =TFVisionTextDualEncoderModel(vision_model=__lowercase , text_model=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =model(
input_ids=__lowercase , pixel_values=__lowercase , attention_mask=__lowercase , output_attentions=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =output.vision_model_output.attentions
self.assertEqual(len(__lowercase ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
SCREAMING_SNAKE_CASE__ : int =to_atuple(vision_model.config.image_size )
SCREAMING_SNAKE_CASE__ : Dict =to_atuple(vision_model.config.patch_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
SCREAMING_SNAKE_CASE__ : Union[str, Any] =num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
SCREAMING_SNAKE_CASE__ : Optional[Any] =output.text_model_output.attentions
self.assertEqual(len(__lowercase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __magic_name__ ( self : int , __lowercase : np.ndarray , __lowercase : np.ndarray , __lowercase : float ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =np.abs((a - b) ).max()
self.assertLessEqual(__lowercase , __lowercase , F"Difference between torch and flax is {diff} (>= {tol})." )
def __magic_name__ ( self : str ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Tuple =self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**__lowercase )
def __magic_name__ ( self : Any ) -> str:
SCREAMING_SNAKE_CASE__ : Tuple =self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**__lowercase )
def __magic_name__ ( self : List[str] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**__lowercase )
def __magic_name__ ( self : List[str] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : List[str] =self.prepare_config_and_inputs()
self.check_save_load(**__lowercase )
def __magic_name__ ( self : int ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**__lowercase )
@slow
def __magic_name__ ( self : Optional[int] ) -> Any:
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.get_pretrained_model_and_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] =model_a(**__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(__lowercase )
SCREAMING_SNAKE_CASE__ : str =TFVisionTextDualEncoderModel.from_pretrained(__lowercase )
SCREAMING_SNAKE_CASE__ : int =model_a(**__lowercase )
SCREAMING_SNAKE_CASE__ : str =after_outputs[0].numpy()
SCREAMING_SNAKE_CASE__ : Optional[int] =np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__lowercase , 1e-5 )
@require_tf
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
def __magic_name__ ( self : Tuple ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Tuple =TFVisionTextDualEncoderModel.from_vision_text_pretrained(
'''hf-internal-testing/tiny-random-vit''' , '''hf-internal-testing/tiny-random-bert''' )
SCREAMING_SNAKE_CASE__ : List[str] =13
SCREAMING_SNAKE_CASE__ : Optional[Any] =floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
SCREAMING_SNAKE_CASE__ : List[Any] =random_attention_mask([batch_size, 4] )
SCREAMING_SNAKE_CASE__ : Dict ={'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def __magic_name__ ( self : Dict , __lowercase : str , __lowercase : List[str] ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] =TFViTModel(__lowercase , name='''vision_model''' )
SCREAMING_SNAKE_CASE__ : Dict =TFBertModel(__lowercase , name='''text_model''' )
return vision_model, text_model
def __magic_name__ ( self : Tuple ) -> Dict:
SCREAMING_SNAKE_CASE__ : int =TFViTModelTester(self )
SCREAMING_SNAKE_CASE__ : Any =TFBertModelTester(self )
SCREAMING_SNAKE_CASE__ : Optional[int] =vit_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Any =bert_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] =vision_config_and_inputs
(
SCREAMING_SNAKE_CASE__
) : Tuple =text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
def __magic_name__ ( self : List[str] ) -> int:
# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
# just reinitialize it.
SCREAMING_SNAKE_CASE__ : List[str] =TFVisionTextDualEncoderModel.from_vision_text_pretrained(
'''Rocketknight1/tiny-random-deit-tf''' , '''hf-internal-testing/tiny-random-roberta''' )
SCREAMING_SNAKE_CASE__ : Any =13
SCREAMING_SNAKE_CASE__ : Tuple =floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
SCREAMING_SNAKE_CASE__ : List[Any] =ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[Any] =random_attention_mask([batch_size, 4] )
SCREAMING_SNAKE_CASE__ : Optional[int] ={'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def __magic_name__ ( self : List[str] , __lowercase : Optional[int] , __lowercase : Optional[int] , __lowercase : Tuple , __lowercase : Optional[Any] , __lowercase : str=None , **__lowercase : Tuple ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.get_vision_text_model(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =TFVisionTextDualEncoderModel(vision_model=__lowercase , text_model=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =model(
input_ids=__lowercase , pixel_values=__lowercase , attention_mask=__lowercase , output_attentions=__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =output.vision_model_output.attentions
self.assertEqual(len(__lowercase ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
SCREAMING_SNAKE_CASE__ : Union[str, Any] =to_atuple(vision_model.config.image_size )
SCREAMING_SNAKE_CASE__ : int =to_atuple(vision_model.config.patch_size )
SCREAMING_SNAKE_CASE__ : str =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
SCREAMING_SNAKE_CASE__ : Tuple =num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
SCREAMING_SNAKE_CASE__ : Any =output.text_model_output.attentions
self.assertEqual(len(__lowercase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __magic_name__ ( self : str , __lowercase : List[str] , __lowercase : List[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Dict =TFDeiTModel(__lowercase , name='''vision_model''' )
SCREAMING_SNAKE_CASE__ : List[Any] =TFRobertaModel(__lowercase , name='''text_model''' )
return vision_model, text_model
def __magic_name__ ( self : Dict ) -> Any:
SCREAMING_SNAKE_CASE__ : Optional[Any] =TFDeiTModelTester(self )
SCREAMING_SNAKE_CASE__ : str =TFRobertaModelTester(self )
SCREAMING_SNAKE_CASE__ : str =vit_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Any =bert_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : int =vision_config_and_inputs
(
SCREAMING_SNAKE_CASE__
) : Dict =text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
def __magic_name__ ( self : str ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =TFVisionTextDualEncoderModel.from_vision_text_pretrained(
'''Rocketknight1/tiny-random-clip-tf''' , '''hf-internal-testing/tiny-random-bert''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =13
SCREAMING_SNAKE_CASE__ : Any =floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
SCREAMING_SNAKE_CASE__ : Dict =ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
SCREAMING_SNAKE_CASE__ : Dict =random_attention_mask([batch_size, 4] )
SCREAMING_SNAKE_CASE__ : Optional[Any] ={'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}
return model, inputs
def __magic_name__ ( self : int , __lowercase : Optional[int] , __lowercase : str ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =TFCLIPVisionModel(__lowercase , name='''vision_model''' )
SCREAMING_SNAKE_CASE__ : Any =TFBertModel(__lowercase , name='''text_model''' )
return vision_model, text_model
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =TFCLIPVisionModelTester(self )
SCREAMING_SNAKE_CASE__ : Optional[Any] =TFBertModelTester(self )
SCREAMING_SNAKE_CASE__ : List[str] =clip_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : List[str] =bert_model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Dict =vision_config_and_inputs
(
SCREAMING_SNAKE_CASE__
) : Any =text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def __magic_name__ ( self : Any ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[Any] =TFVisionTextDualEncoderModel.from_pretrained(
'''clip-italian/clip-italian''' , logit_scale_init_value=1.0 , from_pt=__lowercase )
SCREAMING_SNAKE_CASE__ : str =VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian''' )
SCREAMING_SNAKE_CASE__ : Dict =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] =processor(
text=['''una foto di un gatto''', '''una foto di un cane'''] , images=__lowercase , padding=__lowercase , return_tensors='''np''' )
SCREAMING_SNAKE_CASE__ : Dict =model(**__lowercase )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =np.array([[1.2284727, 0.3104122]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , __lowercase , atol=1e-3 ) )
| 711 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """gpt_neox"""
def __init__( self : List[Any] , __lowercase : Union[str, Any]=5_04_32 , __lowercase : int=61_44 , __lowercase : Tuple=44 , __lowercase : List[str]=64 , __lowercase : str=2_45_76 , __lowercase : Dict="gelu" , __lowercase : Tuple=0.25 , __lowercase : Tuple=1_00_00 , __lowercase : Tuple=0.0 , __lowercase : str=0.0 , __lowercase : List[Any]=0.1 , __lowercase : Dict=20_48 , __lowercase : Any=0.02 , __lowercase : Dict=1e-5 , __lowercase : List[Any]=True , __lowercase : str=0 , __lowercase : Optional[Any]=2 , __lowercase : Tuple=False , __lowercase : List[Any]=True , __lowercase : Optional[Any]=None , **__lowercase : Any , ) -> Dict:
super().__init__(bos_token_id=__lowercase , eos_token_id=__lowercase , **__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =max_position_embeddings
SCREAMING_SNAKE_CASE__ : Any =hidden_size
SCREAMING_SNAKE_CASE__ : str =num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any =num_attention_heads
SCREAMING_SNAKE_CASE__ : Union[str, Any] =intermediate_size
SCREAMING_SNAKE_CASE__ : Dict =hidden_act
SCREAMING_SNAKE_CASE__ : str =rotary_pct
SCREAMING_SNAKE_CASE__ : Optional[Any] =rotary_emb_base
SCREAMING_SNAKE_CASE__ : List[Any] =attention_dropout
SCREAMING_SNAKE_CASE__ : List[Any] =hidden_dropout
SCREAMING_SNAKE_CASE__ : str =classifier_dropout
SCREAMING_SNAKE_CASE__ : Any =initializer_range
SCREAMING_SNAKE_CASE__ : Dict =layer_norm_eps
SCREAMING_SNAKE_CASE__ : Any =use_cache
SCREAMING_SNAKE_CASE__ : Tuple =tie_word_embeddings
SCREAMING_SNAKE_CASE__ : Tuple =use_parallel_residual
SCREAMING_SNAKE_CASE__ : Union[str, Any] =rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )
def __magic_name__ ( self : Optional[Any] ) -> Optional[Any]:
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __lowercase ) 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}" )
SCREAMING_SNAKE_CASE__ : int =self.rope_scaling.get('''type''' , __lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =self.rope_scaling.get('''factor''' , __lowercase )
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(__lowercase , __lowercase ) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 665 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
a_ = logging.get_logger(__name__)
# TODO: upload to AWS
a_ = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'
),
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """retribert"""
def __init__( self : Any , __lowercase : Dict=3_05_22 , __lowercase : Tuple=7_68 , __lowercase : Any=8 , __lowercase : Dict=12 , __lowercase : Any=30_72 , __lowercase : Optional[int]="gelu" , __lowercase : List[Any]=0.1 , __lowercase : Tuple=0.1 , __lowercase : Optional[Any]=5_12 , __lowercase : List[Any]=2 , __lowercase : Any=0.02 , __lowercase : str=1e-12 , __lowercase : List[str]=True , __lowercase : Optional[Any]=1_28 , __lowercase : Optional[int]=0 , **__lowercase : Tuple , ) -> Optional[Any]:
super().__init__(pad_token_id=__lowercase , **__lowercase )
SCREAMING_SNAKE_CASE__ : str =vocab_size
SCREAMING_SNAKE_CASE__ : Dict =hidden_size
SCREAMING_SNAKE_CASE__ : int =num_hidden_layers
SCREAMING_SNAKE_CASE__ : List[Any] =num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[Any] =hidden_act
SCREAMING_SNAKE_CASE__ : Union[str, Any] =intermediate_size
SCREAMING_SNAKE_CASE__ : List[str] =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[int] =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[str] =max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[int] =type_vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =initializer_range
SCREAMING_SNAKE_CASE__ : Dict =layer_norm_eps
SCREAMING_SNAKE_CASE__ : int =share_encoders
SCREAMING_SNAKE_CASE__ : Union[str, Any] =projection_dim
| 712 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
'tokenization_deberta': ['DebertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['DebertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'DebertaForMaskedLM',
'DebertaForQuestionAnswering',
'DebertaForSequenceClassification',
'DebertaForTokenClassification',
'DebertaModel',
'DebertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDebertaForMaskedLM',
'TFDebertaForQuestionAnswering',
'TFDebertaForSequenceClassification',
'TFDebertaForTokenClassification',
'TFDebertaModel',
'TFDebertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 665 | 0 |
'''simple docstring'''
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
a_ = 0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
a_ = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
class __SCREAMING_SNAKE_CASE :
def __init__( self : Tuple ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =WATERMARK_BITS
SCREAMING_SNAKE_CASE__ : Tuple =WatermarkEncoder()
self.encoder.set_watermark('''bits''' , self.watermark )
def __magic_name__ ( self : Tuple , __lowercase : torch.FloatTensor ) -> int:
# can't encode images that are smaller than 256
if images.shape[-1] < 2_56:
return images
SCREAMING_SNAKE_CASE__ : str =(2_55 * (images / 2 + 0.5)).cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =[self.encoder.encode(__lowercase , '''dwtDct''' ) for image in images]
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.from_numpy(np.array(__lowercase ) ).permute(0 , 3 , 1 , 2 )
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.clamp(2 * (images / 2_55 - 0.5) , min=-1.0 , max=1.0 )
return images
| 713 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _a( UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : list[int], UpperCamelCase__ : int, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple =coefficient_matrix.shape
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple =constant_matrix.shape
if rowsa != colsa:
SCREAMING_SNAKE_CASE__ : Any =f"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
raise ValueError(UpperCamelCase__ )
if colsa != 1:
SCREAMING_SNAKE_CASE__ : str =f"Constant matrix must be nx1 but received {rowsa}x{colsa}"
raise ValueError(UpperCamelCase__ )
if rowsa != rowsa:
SCREAMING_SNAKE_CASE__ : str =(
'''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:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(
'''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''' )
SCREAMING_SNAKE_CASE__ : NDArray[floataa] =np.concatenate(
(coefficient_matrix, constant_matrix), axis=1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any =table.shape
strictly_diagonally_dominant(UpperCamelCase__ )
# Iterates the whole matrix for given number of times
for _ in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : List[str] =[]
for row in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =0
for col in range(UpperCamelCase__ ):
if col == row:
SCREAMING_SNAKE_CASE__ : int =table[row][col]
elif col == cols - 1:
SCREAMING_SNAKE_CASE__ : Any =table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
SCREAMING_SNAKE_CASE__ : int =(temp + val) / denom
new_val.append(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =new_val
return [float(UpperCamelCase__ ) for i in new_val]
def _a( UpperCamelCase__ : NDArray[floataa] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] =table.shape
SCREAMING_SNAKE_CASE__ : Any =True
for i in range(0, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : int =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()
| 665 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , __lowercase : Union[str, Any] , __lowercase : Optional[int]=12 , __lowercase : Any=7 , __lowercase : int=True , __lowercase : Optional[int]=True , __lowercase : Optional[Any]=True , __lowercase : Union[str, Any]=99 , __lowercase : int=32 , __lowercase : Dict=32 , __lowercase : int=2 , __lowercase : List[Any]=4 , __lowercase : Any=37 , __lowercase : Union[str, Any]=0.1 , __lowercase : str=0.1 , __lowercase : Dict=5_12 , __lowercase : Optional[Any]=0.02 , __lowercase : str=0 , __lowercase : Optional[int]=None , ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : str =parent
SCREAMING_SNAKE_CASE__ : Any =batch_size
SCREAMING_SNAKE_CASE__ : str =seq_length
SCREAMING_SNAKE_CASE__ : str =is_training
SCREAMING_SNAKE_CASE__ : int =use_input_mask
SCREAMING_SNAKE_CASE__ : Tuple =use_labels
SCREAMING_SNAKE_CASE__ : Optional[Any] =vocab_size
SCREAMING_SNAKE_CASE__ : Optional[Any] =hidden_size
SCREAMING_SNAKE_CASE__ : Optional[int] =projection_dim
SCREAMING_SNAKE_CASE__ : Optional[int] =num_hidden_layers
SCREAMING_SNAKE_CASE__ : List[str] =num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[int] =intermediate_size
SCREAMING_SNAKE_CASE__ : Optional[Any] =dropout
SCREAMING_SNAKE_CASE__ : List[str] =attention_dropout
SCREAMING_SNAKE_CASE__ : Dict =max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[Any] =initializer_range
SCREAMING_SNAKE_CASE__ : Dict =scope
SCREAMING_SNAKE_CASE__ : List[str] =bos_token_id
def __magic_name__ ( self : int ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : str =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : str =None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : str =random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
SCREAMING_SNAKE_CASE__ : List[Any] =input_mask.numpy()
SCREAMING_SNAKE_CASE__ : List[Any] =input_mask.shape
SCREAMING_SNAKE_CASE__ : List[Any] =np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(__lowercase ):
SCREAMING_SNAKE_CASE__ : Any =1
SCREAMING_SNAKE_CASE__ : Union[str, Any] =0
SCREAMING_SNAKE_CASE__ : str =self.get_config()
return config, input_ids, tf.convert_to_tensor(__lowercase )
def __magic_name__ ( self : List[str] ) -> int:
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def __magic_name__ ( self : int , __lowercase : Any , __lowercase : Dict , __lowercase : List[Any] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =TFBlipTextModel(config=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =model(__lowercase , attention_mask=__lowercase , training=__lowercase )
SCREAMING_SNAKE_CASE__ : str =model(__lowercase , training=__lowercase )
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 __magic_name__ ( self : Any ) -> str:
SCREAMING_SNAKE_CASE__ : List[Any] =self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Optional[int] =config_and_inputs
SCREAMING_SNAKE_CASE__ : Union[str, Any] ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = (TFBlipTextModel,) if is_tf_available() else ()
snake_case_ = False
snake_case_ = False
snake_case_ = False
def __magic_name__ ( self : Tuple ) -> Any:
SCREAMING_SNAKE_CASE__ : Optional[int] =BlipTextModelTester(self )
SCREAMING_SNAKE_CASE__ : Optional[Any] =ConfigTester(self , config_class=__lowercase , hidden_size=37 )
def __magic_name__ ( self : List[str] ) -> Union[str, Any]:
self.config_tester.run_common_tests()
def __magic_name__ ( self : Optional[Any] ) -> int:
SCREAMING_SNAKE_CASE__ : Tuple =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase )
def __magic_name__ ( self : Optional[Any] ) -> Dict:
pass
def __magic_name__ ( self : Tuple ) -> List[str]:
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''' )
def __magic_name__ ( self : Any ) -> Optional[int]:
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def __magic_name__ ( self : List[str] ) -> List[Any]:
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def __magic_name__ ( self : Union[str, Any] ) -> int:
pass
@slow
def __magic_name__ ( self : str ) -> str:
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =TFBlipTextModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
def __magic_name__ ( self : int , __lowercase : List[Any]=True ) -> Any:
super().test_pt_tf_model_equivalence(allow_missing_keys=__lowercase )
| 714 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _a( UpperCamelCase__ : str, UpperCamelCase__ : Tuple, UpperCamelCase__ : Any, UpperCamelCase__ : List[str], UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
with open(UpperCamelCase__ ) as metadata_file:
SCREAMING_SNAKE_CASE__ : Optional[int] =json.load(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =LukeConfig(use_entity_aware_attention=UpperCamelCase__, **metadata['''model_config'''] )
# Load in the weights from the checkpoint_path
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch.load(UpperCamelCase__, map_location='''cpu''' )['''module''']
# Load the entity vocab file
SCREAMING_SNAKE_CASE__ : List[str] =load_original_entity_vocab(UpperCamelCase__ )
# add an entry for [MASK2]
SCREAMING_SNAKE_CASE__ : Optional[int] =max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
SCREAMING_SNAKE_CASE__ : Optional[int] =XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )
# Add special tokens to the token vocabulary for downstream tasks
SCREAMING_SNAKE_CASE__ : List[Any] =AddedToken('''<ent>''', lstrip=UpperCamelCase__, rstrip=UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Any =AddedToken('''<ent2>''', lstrip=UpperCamelCase__, rstrip=UpperCamelCase__ )
tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f"Saving tokenizer to {pytorch_dump_folder_path}" )
tokenizer.save_pretrained(UpperCamelCase__ )
with open(os.path.join(UpperCamelCase__, '''tokenizer_config.json''' ), '''r''' ) as f:
SCREAMING_SNAKE_CASE__ : Optional[Any] =json.load(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : int ='''MLukeTokenizer'''
with open(os.path.join(UpperCamelCase__, '''tokenizer_config.json''' ), '''w''' ) as f:
json.dump(UpperCamelCase__, UpperCamelCase__ )
with open(os.path.join(UpperCamelCase__, MLukeTokenizer.vocab_files_names['''entity_vocab_file'''] ), '''w''' ) as f:
json.dump(UpperCamelCase__, UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =MLukeTokenizer.from_pretrained(UpperCamelCase__ )
# Initialize the embeddings of the special tokens
SCREAMING_SNAKE_CASE__ : str =tokenizer.convert_tokens_to_ids(['''@'''] )[0]
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer.convert_tokens_to_ids(['''#'''] )[0]
SCREAMING_SNAKE_CASE__ : Dict =state_dict['''embeddings.word_embeddings.weight''']
SCREAMING_SNAKE_CASE__ : List[str] =word_emb[ent_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : Tuple =word_emb[enta_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : str =torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =state_dict[bias_name]
SCREAMING_SNAKE_CASE__ : List[Any] =decoder_bias[ent_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : str =decoder_bias[enta_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : List[str] =torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
SCREAMING_SNAKE_CASE__ : Tuple =f"encoder.layer.{layer_index}.attention.self."
SCREAMING_SNAKE_CASE__ : Optional[int] =state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE__ : Optional[int] =state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE__ : List[Any] =state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
SCREAMING_SNAKE_CASE__ : Any =state_dict['''entity_embeddings.entity_embeddings.weight''']
SCREAMING_SNAKE_CASE__ : Any =entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : Any =torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
SCREAMING_SNAKE_CASE__ : Optional[int] =state_dict['''entity_predictions.bias''']
SCREAMING_SNAKE_CASE__ : Tuple =entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : Any =torch.cat([entity_prediction_bias, entity_mask_bias] )
SCREAMING_SNAKE_CASE__ : int =LukeForMaskedLM(config=UpperCamelCase__ ).eval()
state_dict.pop('''entity_predictions.decoder.weight''' )
state_dict.pop('''lm_head.decoder.weight''' )
state_dict.pop('''lm_head.decoder.bias''' )
SCREAMING_SNAKE_CASE__ : Tuple =OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('''lm_head''' ) or key.startswith('''entity_predictions''' )):
SCREAMING_SNAKE_CASE__ : Optional[Any] =state_dict[key]
else:
SCREAMING_SNAKE_CASE__ : Any =state_dict[key]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] =model.load_state_dict(UpperCamelCase__, strict=UpperCamelCase__ )
if set(UpperCamelCase__ ) != {"luke.embeddings.position_ids"}:
raise ValueError(f"Unexpected unexpected_keys: {unexpected_keys}" )
if set(UpperCamelCase__ ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(f"Unexpected missing_keys: {missing_keys}" )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
SCREAMING_SNAKE_CASE__ : Any =MLukeTokenizer.from_pretrained(UpperCamelCase__, task='''entity_classification''' )
SCREAMING_SNAKE_CASE__ : str ='''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(0, 9)
SCREAMING_SNAKE_CASE__ : str =tokenizer(UpperCamelCase__, entity_spans=[span], return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : List[Any] =model(**UpperCamelCase__ )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
SCREAMING_SNAKE_CASE__ : str =torch.Size((1, 3_3, 7_6_8) )
SCREAMING_SNAKE_CASE__ : int =torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f"Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3], UpperCamelCase__, atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
SCREAMING_SNAKE_CASE__ : Any =torch.Size((1, 1, 7_6_8) )
SCREAMING_SNAKE_CASE__ : int =torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
f"Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"
f" {expected_shape}" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3], UpperCamelCase__, atol=1e-4 ):
raise ValueError
# Verify masked word/entity prediction
SCREAMING_SNAKE_CASE__ : str =MLukeTokenizer.from_pretrained(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] ='''Tokyo is the capital of <mask>.'''
SCREAMING_SNAKE_CASE__ : Dict =(2_4, 3_0)
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer(UpperCamelCase__, entity_spans=[span], return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : List[str] =model(**UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[Any] =encoding['''input_ids'''][0].tolist()
SCREAMING_SNAKE_CASE__ : Any =input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>''' ) )
SCREAMING_SNAKE_CASE__ : List[Any] =outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =outputs.entity_logits[0][0].argmax().item()
SCREAMING_SNAKE_CASE__ : Dict =[
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('''en:''' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('''Saving PyTorch model to {}'''.format(UpperCamelCase__ ) )
model.save_pretrained(UpperCamelCase__ )
def _a( UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] =['''[MASK]''', '''[PAD]''', '''[UNK]''']
SCREAMING_SNAKE_CASE__ : List[str] =[json.loads(UpperCamelCase__ ) for line in open(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Optional[int] ={}
for entry in data:
SCREAMING_SNAKE_CASE__ : Tuple =entry['''id''']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
SCREAMING_SNAKE_CASE__ : str =entity_id
break
SCREAMING_SNAKE_CASE__ : Union[str, Any] =f"{language}:{entity_name}"
SCREAMING_SNAKE_CASE__ : Union[str, Any] =entity_id
return new_mapping
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
parser.add_argument(
'--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
)
parser.add_argument(
'--entity_vocab_path',
default=None,
type=str,
help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
)
parser.add_argument(
'--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
)
a_ = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 665 | 0 |
'''simple docstring'''
import os
import re
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
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'spiece.model'}
a_ = {
'vocab_file': {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
'google/bigbird-roberta-large': (
'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
),
'google/bigbird-base-trivia-itc': (
'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
),
}
}
a_ = {
'google/bigbird-roberta-base': 4_0_9_6,
'google/bigbird-roberta-large': 4_0_9_6,
'google/bigbird-base-trivia-itc': 4_0_9_6,
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ["""input_ids""", """attention_mask"""]
snake_case_ = []
def __init__( self : Any , __lowercase : Union[str, Any] , __lowercase : Tuple="<unk>" , __lowercase : Optional[Any]="<s>" , __lowercase : List[str]="</s>" , __lowercase : Dict="<pad>" , __lowercase : Dict="[SEP]" , __lowercase : Union[str, Any]="[MASK]" , __lowercase : Dict="[CLS]" , __lowercase : Optional[Dict[str, Any]] = None , **__lowercase : str , ) -> None:
SCREAMING_SNAKE_CASE__ : Optional[Any] =AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else bos_token
SCREAMING_SNAKE_CASE__ : List[str] =AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else eos_token
SCREAMING_SNAKE_CASE__ : Optional[Any] =AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else unk_token
SCREAMING_SNAKE_CASE__ : Optional[Any] =AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else pad_token
SCREAMING_SNAKE_CASE__ : Union[str, Any] =AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else cls_token
SCREAMING_SNAKE_CASE__ : Optional[Any] =AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE__ : str =AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else mask_token
SCREAMING_SNAKE_CASE__ : List[str] ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__lowercase , eos_token=__lowercase , unk_token=__lowercase , pad_token=__lowercase , sep_token=__lowercase , mask_token=__lowercase , cls_token=__lowercase , sp_model_kwargs=self.sp_model_kwargs , **__lowercase , )
SCREAMING_SNAKE_CASE__ : Dict =vocab_file
SCREAMING_SNAKE_CASE__ : Tuple =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__lowercase )
@property
def __magic_name__ ( self : Union[str, Any] ) -> List[str]:
return self.sp_model.get_piece_size()
def __magic_name__ ( self : Dict ) -> Dict:
SCREAMING_SNAKE_CASE__ : Tuple ={self.convert_ids_to_tokens(__lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Tuple =self.__dict__.copy()
SCREAMING_SNAKE_CASE__ : Dict =None
return state
def __setstate__( self : str , __lowercase : Any ) -> Any:
SCREAMING_SNAKE_CASE__ : Tuple =d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
SCREAMING_SNAKE_CASE__ : Optional[int] ={}
SCREAMING_SNAKE_CASE__ : List[Any] =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __magic_name__ ( self : Optional[int] , __lowercase : str ) -> List[str]:
return self.sp_model.encode(__lowercase , out_type=__lowercase )
def __magic_name__ ( self : int , __lowercase : Any ) -> List[str]:
return self.sp_model.piece_to_id(__lowercase )
def __magic_name__ ( self : Dict , __lowercase : int ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.sp_model.IdToPiece(__lowercase )
return token
def __magic_name__ ( self : List[str] , __lowercase : Optional[int] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Tuple =[]
SCREAMING_SNAKE_CASE__ : Union[str, Any] =''''''
SCREAMING_SNAKE_CASE__ : Optional[int] =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(__lowercase ) + token
SCREAMING_SNAKE_CASE__ : Optional[Any] =True
SCREAMING_SNAKE_CASE__ : Tuple =[]
else:
current_sub_tokens.append(__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =False
out_string += self.sp_model.decode(__lowercase )
return out_string.strip()
def __magic_name__ ( self : List[str] , __lowercase : List[int] , __lowercase : bool = False , __lowercase : bool = None , __lowercase : bool = True , **__lowercase : Tuple , ) -> str:
SCREAMING_SNAKE_CASE__ : Dict =kwargs.pop('''use_source_tokenizer''' , __lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.convert_ids_to_tokens(__lowercase , skip_special_tokens=__lowercase )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
SCREAMING_SNAKE_CASE__ : List[str] =[]
SCREAMING_SNAKE_CASE__ : List[Any] =[]
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(__lowercase ) )
SCREAMING_SNAKE_CASE__ : str =[]
sub_texts.append(__lowercase )
else:
current_sub_text.append(__lowercase )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(__lowercase ) )
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
SCREAMING_SNAKE_CASE__ : str =re.sub(r''' (\[(MASK|SEP)\])''' , r'''\1''' , ''' '''.join(__lowercase ) )
else:
SCREAMING_SNAKE_CASE__ : List[str] =''''''.join(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =(
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
SCREAMING_SNAKE_CASE__ : int =self.clean_up_tokenization(__lowercase )
return clean_text
else:
return text
def __magic_name__ ( self : List[str] , __lowercase : str , __lowercase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__lowercase ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
SCREAMING_SNAKE_CASE__ : Optional[Any] =os.path.join(
__lowercase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowercase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __lowercase )
elif not os.path.isfile(self.vocab_file ):
with open(__lowercase , '''wb''' ) as fi:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.sp_model.serialized_model_proto()
fi.write(__lowercase )
return (out_vocab_file,)
def __magic_name__ ( self : Union[str, Any] , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Union[str, Any] =[self.cls_token_id]
SCREAMING_SNAKE_CASE__ : int =[self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def __magic_name__ ( self : Any , __lowercase : List[int] , __lowercase : Optional[List[int]] = None , __lowercase : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowercase , token_ids_a=__lowercase , already_has_special_tokens=__lowercase )
if token_ids_a is None:
return [1] + ([0] * len(__lowercase )) + [1]
return [1] + ([0] * len(__lowercase )) + [1] + ([0] * len(__lowercase )) + [1]
def __magic_name__ ( self : Optional[int] , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ) -> List[int]:
SCREAMING_SNAKE_CASE__ : str =[self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] =[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]
| 715 |
'''simple docstring'''
def _a( UpperCamelCase__ : str, UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Tuple =[[False for _ in range(m + 1 )] for _ in range(n + 1 )]
SCREAMING_SNAKE_CASE__ : List[Any] =True
for i in range(UpperCamelCase__ ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
SCREAMING_SNAKE_CASE__ : Optional[int] =True
if a[i].islower():
SCREAMING_SNAKE_CASE__ : List[Any] =True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665 | 0 |
'''simple docstring'''
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _a( UpperCamelCase__ : str, UpperCamelCase__ : List[str], UpperCamelCase__ : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any =AlbertConfig.from_json_file(UpperCamelCase__ )
print(f"Building PyTorch model from configuration: {config}" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =AlbertForPreTraining(UpperCamelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_albert(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
# 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(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--albert_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained ALBERT model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
a_ = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 716 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , __lowercase : Optional[Any] , __lowercase : List[str]=13 , __lowercase : int=7 , __lowercase : Tuple=True , __lowercase : Optional[Any]=True , __lowercase : List[Any]=True , __lowercase : str=True , __lowercase : Tuple=99 , __lowercase : Union[str, Any]=64 , __lowercase : Tuple=32 , __lowercase : Optional[Any]=5 , __lowercase : Tuple=4 , __lowercase : Optional[int]=37 , __lowercase : int="gelu" , __lowercase : Union[str, Any]=0.1 , __lowercase : List[Any]=0.1 , __lowercase : List[Any]=5_12 , __lowercase : int=16 , __lowercase : Optional[int]=2 , __lowercase : Tuple=0.02 , __lowercase : List[str]=3 , __lowercase : List[str]=4 , __lowercase : List[str]=None , ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =parent
SCREAMING_SNAKE_CASE__ : Any =batch_size
SCREAMING_SNAKE_CASE__ : Optional[Any] =seq_length
SCREAMING_SNAKE_CASE__ : Dict =is_training
SCREAMING_SNAKE_CASE__ : List[Any] =use_input_mask
SCREAMING_SNAKE_CASE__ : Union[str, Any] =use_token_type_ids
SCREAMING_SNAKE_CASE__ : List[Any] =use_labels
SCREAMING_SNAKE_CASE__ : int =vocab_size
SCREAMING_SNAKE_CASE__ : str =hidden_size
SCREAMING_SNAKE_CASE__ : Any =embedding_size
SCREAMING_SNAKE_CASE__ : Tuple =num_hidden_layers
SCREAMING_SNAKE_CASE__ : str =num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple =intermediate_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =hidden_act
SCREAMING_SNAKE_CASE__ : Optional[int] =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : str =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Any =max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[Any] =type_vocab_size
SCREAMING_SNAKE_CASE__ : Any =type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Optional[Any] =initializer_range
SCREAMING_SNAKE_CASE__ : str =num_labels
SCREAMING_SNAKE_CASE__ : List[str] =num_choices
SCREAMING_SNAKE_CASE__ : List[str] =scope
def __magic_name__ ( self : str ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : int =None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : List[str] =random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : int =None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Any =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =None
SCREAMING_SNAKE_CASE__ : Optional[Any] =None
SCREAMING_SNAKE_CASE__ : Optional[int] =None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Tuple =ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : List[Any] =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ : Any =ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __magic_name__ ( self : List[str] ) -> Any:
return MegatronBertConfig(
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 , embedding_size=self.embedding_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=__lowercase , initializer_range=self.initializer_range , )
def __magic_name__ ( self : Any , __lowercase : Tuple , __lowercase : Any , __lowercase : List[Any] , __lowercase : Optional[int] , __lowercase : List[str] , __lowercase : Dict , __lowercase : Union[str, Any] ) -> int:
SCREAMING_SNAKE_CASE__ : List[str] =MegatronBertModel(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(__lowercase , token_type_ids=__lowercase )
SCREAMING_SNAKE_CASE__ : str =model(__lowercase )
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 __magic_name__ ( self : Dict , __lowercase : Optional[int] , __lowercase : List[str] , __lowercase : str , __lowercase : Tuple , __lowercase : Any , __lowercase : Optional[int] , __lowercase : Any ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : str =MegatronBertForMaskedLM(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Tuple =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self : List[str] , __lowercase : Tuple , __lowercase : Tuple , __lowercase : Optional[Any] , __lowercase : Any , __lowercase : str , __lowercase : Any , __lowercase : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[Any] =MegatronBertForCausalLM(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __magic_name__ ( self : Union[str, Any] , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : Union[str, Any] , __lowercase : Dict , __lowercase : Union[str, Any] , __lowercase : int , __lowercase : Any ) -> Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =MegatronBertForNextSentencePrediction(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] =model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def __magic_name__ ( self : List[str] , __lowercase : Optional[int] , __lowercase : str , __lowercase : Optional[int] , __lowercase : Optional[int] , __lowercase : Optional[Any] , __lowercase : str , __lowercase : Optional[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =MegatronBertForPreTraining(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] =model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , next_sentence_label=__lowercase , )
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 __magic_name__ ( self : Optional[int] , __lowercase : Tuple , __lowercase : Optional[int] , __lowercase : Union[str, Any] , __lowercase : int , __lowercase : Any , __lowercase : Any , __lowercase : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[Any] =MegatronBertForQuestionAnswering(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict =model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , start_positions=__lowercase , end_positions=__lowercase , )
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 __magic_name__ ( self : Optional[int] , __lowercase : Optional[int] , __lowercase : Optional[int] , __lowercase : List[Any] , __lowercase : List[Any] , __lowercase : Optional[int] , __lowercase : int , __lowercase : List[str] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.num_labels
SCREAMING_SNAKE_CASE__ : Dict =MegatronBertForSequenceClassification(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __magic_name__ ( self : Optional[int] , __lowercase : List[Any] , __lowercase : str , __lowercase : Optional[Any] , __lowercase : Any , __lowercase : Tuple , __lowercase : Optional[Any] , __lowercase : int ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Tuple =self.num_labels
SCREAMING_SNAKE_CASE__ : int =MegatronBertForTokenClassification(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] =model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __magic_name__ ( self : Dict , __lowercase : List[str] , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : Any , __lowercase : List[Any] , __lowercase : int , __lowercase : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : int =self.num_choices
SCREAMING_SNAKE_CASE__ : List[str] =MegatronBertForMultipleChoice(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[Any] =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Optional[int] =token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Tuple =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(
__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , labels=__lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __magic_name__ ( self : str ) -> Any:
SCREAMING_SNAKE_CASE__ : Tuple =self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) : str =config_and_inputs
SCREAMING_SNAKE_CASE__ : Union[str, Any] ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
snake_case_ = (
{
"""feature-extraction""": MegatronBertModel,
"""fill-mask""": MegatronBertForMaskedLM,
"""question-answering""": MegatronBertForQuestionAnswering,
"""text-classification""": MegatronBertForSequenceClassification,
"""text-generation""": MegatronBertForCausalLM,
"""token-classification""": MegatronBertForTokenClassification,
"""zero-shot""": MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case_ = True
# test_resize_embeddings = False
snake_case_ = False
def __magic_name__ ( self : List[Any] , __lowercase : List[str] , __lowercase : Tuple , __lowercase : Tuple=False ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =super()._prepare_for_class(__lowercase , __lowercase , return_labels=__lowercase )
if return_labels:
if model_class in get_values(__lowercase ):
SCREAMING_SNAKE_CASE__ : List[str] =torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowercase )
return inputs_dict
def __magic_name__ ( self : Tuple ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =MegatronBertModelTester(self )
SCREAMING_SNAKE_CASE__ : Tuple =ConfigTester(self , config_class=__lowercase , hidden_size=37 )
def __magic_name__ ( self : str ) -> Dict:
self.config_tester.run_common_tests()
def __magic_name__ ( self : Tuple ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*__lowercase )
def __magic_name__ ( self : List[str] ) -> Any:
SCREAMING_SNAKE_CASE__ : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__lowercase )
def __magic_name__ ( self : Optional[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__lowercase )
def __magic_name__ ( self : Optional[int] ) -> Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__lowercase )
def __magic_name__ ( self : Dict ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*__lowercase )
def __magic_name__ ( self : int ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*__lowercase )
def __magic_name__ ( self : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*__lowercase )
def _a( UpperCamelCase__ : List[str] ):
'''simple docstring'''
return torch.tensor(
UpperCamelCase__, dtype=torch.long, device=UpperCamelCase__, )
a_ = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
@unittest.skip('''Model is not available.''' )
def __magic_name__ ( self : Any ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Any ='''nvidia/megatron-bert-uncased-345m'''
if "MYDIR" in os.environ:
SCREAMING_SNAKE_CASE__ : Optional[int] =os.path.join(os.environ['''MYDIR'''] , __lowercase )
SCREAMING_SNAKE_CASE__ : Any =MegatronBertModel.from_pretrained(__lowercase )
model.to(__lowercase )
model.half()
SCREAMING_SNAKE_CASE__ : Dict =_long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(__lowercase )[0]
SCREAMING_SNAKE_CASE__ : Dict =torch.Size((1, 9, 10_24) )
self.assertEqual(output.shape , __lowercase )
SCREAMING_SNAKE_CASE__ : str =[-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
for ii in range(3 ):
for jj in range(3 ):
SCREAMING_SNAKE_CASE__ : List[Any] =output[0, ii, jj]
SCREAMING_SNAKE_CASE__ : Tuple =expected[3 * ii + jj]
SCREAMING_SNAKE_CASE__ : List[str] ='''ii={} jj={} a={} b={}'''.format(__lowercase , __lowercase , __lowercase , __lowercase )
self.assertTrue(math.isclose(__lowercase , __lowercase , rel_tol=__lowercase , abs_tol=__lowercase ) , msg=__lowercase )
| 665 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] =0
SCREAMING_SNAKE_CASE__ : str =len(UpperCamelCase__ )
for i in range(n - 1 ):
for j in range(i + 1, UpperCamelCase__ ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
if len(UpperCamelCase__ ) <= 1:
return arr, 0
SCREAMING_SNAKE_CASE__ : int =len(UpperCamelCase__ ) // 2
SCREAMING_SNAKE_CASE__ : List[Any] =arr[0:mid]
SCREAMING_SNAKE_CASE__ : List[str] =arr[mid:]
SCREAMING_SNAKE_CASE__ : Dict =count_inversions_recursive(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[Any] =count_inversions_recursive(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : str =_count_cross_inversions(UpperCamelCase__, UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Any =inversion_p + inversions_q + cross_inversions
return c, num_inversions
def _a( UpperCamelCase__ : Any, UpperCamelCase__ : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =[]
SCREAMING_SNAKE_CASE__ : Optional[Any] =0
while i < len(UpperCamelCase__ ) and j < len(UpperCamelCase__ ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(UpperCamelCase__ ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(UpperCamelCase__ ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =[1_0, 2, 1, 5, 5, 2, 1_1]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
SCREAMING_SNAKE_CASE__ : Dict =count_inversions_bf(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Tuple =count_inversions_recursive(UpperCamelCase__ )
assert num_inversions_bf == num_inversions_recursive == 8
print('''number of inversions = ''', UpperCamelCase__ )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
SCREAMING_SNAKE_CASE__ : Optional[Any] =count_inversions_bf(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =count_inversions_recursive(UpperCamelCase__ )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''', UpperCamelCase__ )
# an empty list should also have zero inversions
SCREAMING_SNAKE_CASE__ : Dict =[]
SCREAMING_SNAKE_CASE__ : List[str] =count_inversions_bf(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] =count_inversions_recursive(UpperCamelCase__ )
assert num_inversions_bf == num_inversions_recursive == 0
print('''number of inversions = ''', UpperCamelCase__ )
if __name__ == "__main__":
main()
| 717 |
'''simple docstring'''
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =inspect.getfile(accelerate.test_utils )
SCREAMING_SNAKE_CASE__ : Optional[Any] =os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_script.py'''] )
SCREAMING_SNAKE_CASE__ : Dict =os.path.sep.join(inspect.getfile(self.__class__ ).split(os.path.sep )[:-1] )
@require_tpu
def __magic_name__ ( self : int ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Any =F"\n {self.test_dir}/xla_spawn.py\n --num_cores 8\n {self.test_file_path}\n ".split()
SCREAMING_SNAKE_CASE__ : List[str] =[sys.executable] + distributed_args
execute_subprocess_async(__lowercase , env=os.environ.copy() )
| 665 | 0 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
a_ = logging.getLogger(__name__)
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : Union[str, Any] , __lowercase : Dict=-1 ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict =label_idx
def __magic_name__ ( self : List[str] , __lowercase : Optional[int] , __lowercase : Union[Split, str] ) -> List[InputExample]:
'''simple docstring'''
if isinstance(__lowercase , __lowercase ):
SCREAMING_SNAKE_CASE__ : Dict =mode.value
SCREAMING_SNAKE_CASE__ : List[Any] =os.path.join(__lowercase , F"{mode}.txt" )
SCREAMING_SNAKE_CASE__ : List[Any] =1
SCREAMING_SNAKE_CASE__ : Optional[int] =[]
with open(__lowercase , encoding='''utf-8''' ) as f:
SCREAMING_SNAKE_CASE__ : Tuple =[]
SCREAMING_SNAKE_CASE__ : int =[]
for line in f:
if line.startswith('''-DOCSTART-''' ) or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=__lowercase , labels=__lowercase ) )
guid_index += 1
SCREAMING_SNAKE_CASE__ : str =[]
SCREAMING_SNAKE_CASE__ : List[Any] =[]
else:
SCREAMING_SNAKE_CASE__ : Tuple =line.split(''' ''' )
words.append(splits[0] )
if len(__lowercase ) > 1:
labels.append(splits[self.label_idx].replace('''\n''' , '''''' ) )
else:
# Examples could have no label for mode = "test"
labels.append('''O''' )
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=__lowercase , labels=__lowercase ) )
return examples
def __magic_name__ ( self : Dict , __lowercase : TextIO , __lowercase : TextIO , __lowercase : List ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple =0
for line in test_input_reader:
if line.startswith('''-DOCSTART-''' ) or line == "" or line == "\n":
writer.write(__lowercase )
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
SCREAMING_SNAKE_CASE__ : List[Any] =line.split()[0] + ''' ''' + preds_list[example_id].pop(0 ) + '''\n'''
writer.write(__lowercase )
else:
logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.''' , line.split()[0] )
def __magic_name__ ( self : Tuple , __lowercase : str ) -> List[str]:
'''simple docstring'''
if path:
with open(__lowercase , '''r''' ) as f:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =f.read().splitlines()
if "O" not in labels:
SCREAMING_SNAKE_CASE__ : Dict =['''O'''] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : int ) -> Dict:
'''simple docstring'''
super().__init__(label_idx=-2 )
def __magic_name__ ( self : Optional[int] , __lowercase : str ) -> List[str]:
'''simple docstring'''
if path:
with open(__lowercase , '''r''' ) as f:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =f.read().splitlines()
if "O" not in labels:
SCREAMING_SNAKE_CASE__ : Dict =['''O'''] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __magic_name__ ( self : Optional[Any] , __lowercase : Optional[int] , __lowercase : Union[Split, str] ) -> List[InputExample]:
'''simple docstring'''
if isinstance(__lowercase , __lowercase ):
SCREAMING_SNAKE_CASE__ : str =mode.value
SCREAMING_SNAKE_CASE__ : Optional[Any] =os.path.join(__lowercase , F"{mode}.txt" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =1
SCREAMING_SNAKE_CASE__ : List[Any] =[]
with open(__lowercase , encoding='''utf-8''' ) as f:
for sentence in parse_incr(__lowercase ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =[]
SCREAMING_SNAKE_CASE__ : int =[]
for token in sentence:
words.append(token['''form'''] )
labels.append(token['''upos'''] )
assert len(__lowercase ) == len(__lowercase )
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=__lowercase , labels=__lowercase ) )
guid_index += 1
return examples
def __magic_name__ ( self : Tuple , __lowercase : TextIO , __lowercase : TextIO , __lowercase : List ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] =0
for sentence in parse_incr(__lowercase ):
SCREAMING_SNAKE_CASE__ : Optional[int] =preds_list[example_id]
SCREAMING_SNAKE_CASE__ : int =''''''
for token in sentence:
out += F"{token['form']} ({token['upos']}|{s_p.pop(0 )}) "
out += "\n"
writer.write(__lowercase )
example_id += 1
def __magic_name__ ( self : int , __lowercase : str ) -> List[str]:
'''simple docstring'''
if path:
with open(__lowercase , '''r''' ) as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
| 718 |
'''simple docstring'''
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 __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = ShapEImgaImgPipeline
snake_case_ = ["""image"""]
snake_case_ = ["""image"""]
snake_case_ = [
"""num_images_per_prompt""",
"""num_inference_steps""",
"""generator""",
"""latents""",
"""guidance_scale""",
"""frame_size""",
"""output_type""",
"""return_dict""",
]
snake_case_ = False
@property
def __magic_name__ ( self : List[Any] ) -> List[Any]:
return 32
@property
def __magic_name__ ( self : List[str] ) -> Optional[int]:
return 32
@property
def __magic_name__ ( self : Optional[int] ) -> Optional[Any]:
return self.time_input_dim * 4
@property
def __magic_name__ ( self : Dict ) -> Union[str, Any]:
return 8
@property
def __magic_name__ ( self : Optional[int] ) -> Union[str, Any]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Dict =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 , )
SCREAMING_SNAKE_CASE__ : str =CLIPVisionModel(__lowercase )
return model
@property
def __magic_name__ ( self : Tuple ) -> List[str]:
SCREAMING_SNAKE_CASE__ : int =CLIPImageProcessor(
crop_size=2_24 , do_center_crop=__lowercase , do_normalize=__lowercase , do_resize=__lowercase , image_mean=[0.48145466, 0.4578275, 0.40821073] , image_std=[0.26862954, 0.26130258, 0.27577711] , resample=3 , size=2_24 , )
return image_processor
@property
def __magic_name__ ( self : List[str] ) -> Dict:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : str ={
'''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,
}
SCREAMING_SNAKE_CASE__ : str =PriorTransformer(**__lowercase )
return model
@property
def __magic_name__ ( self : Tuple ) -> List[str]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[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,
),
}
SCREAMING_SNAKE_CASE__ : str =ShapERenderer(**__lowercase )
return model
def __magic_name__ ( self : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : int =self.dummy_prior
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dummy_image_encoder
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dummy_image_processor
SCREAMING_SNAKE_CASE__ : Tuple =self.dummy_renderer
SCREAMING_SNAKE_CASE__ : int =HeunDiscreteScheduler(
beta_schedule='''exp''' , num_train_timesteps=10_24 , prediction_type='''sample''' , use_karras_sigmas=__lowercase , clip_sample=__lowercase , clip_sample_range=1.0 , )
SCREAMING_SNAKE_CASE__ : Any ={
'''prior''': prior,
'''image_encoder''': image_encoder,
'''image_processor''': image_processor,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def __magic_name__ ( self : Any , __lowercase : List[str] , __lowercase : Any=0 ) -> Any:
SCREAMING_SNAKE_CASE__ : int =floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowercase ) ).to(__lowercase )
if str(__lowercase ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE__ : List[str] =torch.manual_seed(__lowercase )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =torch.Generator(device=__lowercase ).manual_seed(__lowercase )
SCREAMING_SNAKE_CASE__ : Any ={
'''image''': input_image,
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 32,
'''output_type''': '''np''',
}
return inputs
def __magic_name__ ( self : List[str] ) -> str:
SCREAMING_SNAKE_CASE__ : int ='''cpu'''
SCREAMING_SNAKE_CASE__ : Any =self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : str =self.pipeline_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Any =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =pipe(**self.get_dummy_inputs(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Tuple =output.images[0]
SCREAMING_SNAKE_CASE__ : List[Any] =image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
SCREAMING_SNAKE_CASE__ : List[Any] =np.array(
[
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __magic_name__ ( self : List[Any] ) -> List[str]:
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __magic_name__ ( self : Optional[int] ) -> str:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch_device == '''cpu'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__lowercase , relax_max_difference=__lowercase , )
def __magic_name__ ( self : Dict ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Any =self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Dict =self.pipeline_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =1
SCREAMING_SNAKE_CASE__ : List[str] =2
SCREAMING_SNAKE_CASE__ : Dict =self.get_dummy_inputs(__lowercase )
for key in inputs.keys():
if key in self.batch_params:
SCREAMING_SNAKE_CASE__ : Tuple =batch_size * [inputs[key]]
SCREAMING_SNAKE_CASE__ : List[Any] =pipe(**__lowercase , num_images_per_prompt=__lowercase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : Optional[Any] ) -> int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : int ) -> Dict:
SCREAMING_SNAKE_CASE__ : List[str] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png''' )
SCREAMING_SNAKE_CASE__ : Dict =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_img2img_out.npy''' )
SCREAMING_SNAKE_CASE__ : List[Any] =ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img''' )
SCREAMING_SNAKE_CASE__ : Tuple =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device=__lowercase ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Tuple =pipe(
__lowercase , generator=__lowercase , 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(__lowercase , __lowercase )
| 665 | 0 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from transformers.utils import is_sentencepiece_available
if is_sentencepiece_available():
from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
if is_sentencepiece_available():
a_ = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
a_ = 1_2_8_0_2_2
a_ = 1_2_8_0_2_8
@require_sentencepiece
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = MaMaaaTokenizer
snake_case_ = False
snake_case_ = False
snake_case_ = True
def __magic_name__ ( self : Dict ) -> Union[str, Any]:
super().setUp()
SCREAMING_SNAKE_CASE__ : Dict =['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
SCREAMING_SNAKE_CASE__ : Any =dict(zip(__lowercase , range(len(__lowercase ) ) ) )
SCREAMING_SNAKE_CASE__ : int =Path(self.tmpdirname )
save_json(__lowercase , save_dir / VOCAB_FILES_NAMES['''vocab_file'''] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(__lowercase , save_dir / VOCAB_FILES_NAMES['''spm_file'''] )
SCREAMING_SNAKE_CASE__ : List[str] =MaMaaaTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__ ( self : Any , **__lowercase : Dict ) -> int:
return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **__lowercase )
def __magic_name__ ( self : Tuple , __lowercase : Any ) -> Any:
return (
"This is a test",
"This is a test",
)
def __magic_name__ ( self : Optional[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Tuple ='''</s>'''
SCREAMING_SNAKE_CASE__ : Any =0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowercase ) , __lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowercase ) , __lowercase )
def __magic_name__ ( self : List[Any] ) -> str:
SCREAMING_SNAKE_CASE__ : Dict =self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[Any] =list(tokenizer.get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''</s>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''<s>''' )
self.assertEqual(len(__lowercase ) , tokenizer.vocab_size + len(tokenizer.get_added_vocab() ) )
@unittest.skip('''Skip this test while all models are still to be uploaded.''' )
def __magic_name__ ( self : List[Any] ) -> Union[str, Any]:
pass
def __magic_name__ ( self : Tuple ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : List[str] =self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Tuple =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__lowercase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowercase ) , [2, 3, 4, 5, 6] , )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6] )
self.assertListEqual(__lowercase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =tokenizer.convert_tokens_to_string(__lowercase )
self.assertEqual(__lowercase , '''This is a test''' )
@slow
def __magic_name__ ( self : str ) -> List[Any]:
# fmt: off
SCREAMING_SNAKE_CASE__ : str ={'''input_ids''': [[12_80_22, 11_01_08, 3_97, 11, 3_82_72, 22_47, 12_48_11, 2_85, 1_81_05, 15_86, 2_07, 7, 3_95_34, 44_28, 3_97, 10_19, 1_81_05, 15_86, 2_07, 7, 4_13_37, 1_67_86, 2_41, 7, 2_02_14, 17, 12_56_90, 1_03_98, 7, 4_43_78, 5_80_69, 6_83_42, 77_98, 73_43, 11, 2_99, 3_33_10, 4, 1_58, 3_73_50, 9_40_77, 45_69, 2_99, 3_33_10, 90, 4, 5_28_40, 2_90, 4, 3_12_70, 1_12, 2_99, 6_82, 4, 5_28_40, 3_99_53, 1_40_79, 1_93, 5_25_19, 9_08_94, 1_78_94, 12_06_97, 11, 4_04_45, 5_51, 17, 10_19, 5_25_19, 9_08_94, 1_77_56, 9_63, 11, 4_04_45, 4_80, 17, 97_92, 11_20, 51_73, 13_93, 62_40, 1_67_86, 2_41, 12_09_96, 28, 12_45, 13_93, 11_82_40, 1_11_23, 10_19, 9_36_12, 26_91, 1_06_18, 9_80_58, 12_04_09, 19_28, 2_79, 4, 4_06_83, 3_67, 1_78, 2_07, 10_19, 1_03, 10_31_21, 5_06, 6_52_96, 5, 2], [12_80_22, 2_12_17, 3_67, 1_17, 12_54_50, 1_28, 7_19, 7, 73_08, 40, 9_36_12, 1_26_69, 11_16, 1_67_04, 71, 1_77_85, 36_99, 1_55_92, 35, 1_44, 95_84, 2_41, 1_19_43, 7_13, 9_50, 7_99, 22_47, 8_84_27, 1_50, 1_49, 11_88_13, 12_07_06, 10_19, 10_69_06, 8_15_18, 28, 12_24, 2_27_99, 3_97, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [12_80_22, 16_58, 12_33_11, 51_55, 55_78, 47_22, 2_79, 1_49_47, 23_66, 11_20, 11_97, 14, 13_48, 92_32, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__lowercase , model_name='''facebook/m2m100_418M''' , revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e''' , )
@require_torch
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case_ = """facebook/m2m100_418M"""
snake_case_ = [
"""In my opinion, there are two levels of response from the French government.""",
"""NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
]
snake_case_ = [
"""Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
"""L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
]
# fmt: off
snake_case_ = [EN_CODE, 593, 1949, 11_5781, 4, 7_1586, 4234, 6_0633, 12_6233, 432, 12_3808, 1_5592, 1197, 11_7132, 12_0618, 5, 2]
@classmethod
def __magic_name__ ( cls : int ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : MaMaaaTokenizer =MaMaaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en''' , tgt_lang='''fr''' )
SCREAMING_SNAKE_CASE__ : Tuple =1
return cls
def __magic_name__ ( self : Tuple ) -> Tuple:
self.assertEqual(self.tokenizer.get_lang_id('''ar''' ) , 12_80_06 )
self.assertEqual(self.tokenizer.get_lang_id('''en''' ) , 12_80_22 )
self.assertEqual(self.tokenizer.get_lang_id('''ro''' ) , 12_80_76 )
self.assertEqual(self.tokenizer.get_lang_id('''mr''' ) , 12_80_63 )
def __magic_name__ ( self : List[Any] ) -> int:
SCREAMING_SNAKE_CASE__ : List[Any] =self.tokenizer.get_vocab()
self.assertEqual(len(__lowercase ) , self.tokenizer.vocab_size )
self.assertEqual(vocab['''<unk>'''] , 3 )
self.assertIn(self.tokenizer.get_lang_token('''en''' ) , __lowercase )
def __magic_name__ ( self : List[Any] ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] ='''en'''
SCREAMING_SNAKE_CASE__ : List[str] =self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __lowercase )
def __magic_name__ ( self : str ) -> Dict:
self.assertIn(__lowercase , self.tokenizer.all_special_ids )
# fmt: off
SCREAMING_SNAKE_CASE__ : Tuple =[FR_CODE, 53_64, 82, 86_42, 4, 2_94, 47, 8, 1_40_28, 1_36, 32_86, 97_06, 6, 9_07_97, 6, 14_40_12, 1_62, 8_81_28, 3_00_61, 5, 2]
# fmt: on
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.tokenizer.decode(__lowercase , skip_special_tokens=__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__lowercase )
self.assertEqual(__lowercase , __lowercase )
self.assertNotIn(self.tokenizer.eos_token , __lowercase )
def __magic_name__ ( self : Optional[int] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[str] =tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : List[Any] =self.tokenizer.lang_token_to_id
self.tokenizer.save_pretrained(__lowercase )
SCREAMING_SNAKE_CASE__ : int =MaMaaaTokenizer.from_pretrained(__lowercase )
self.assertDictEqual(new_tok.lang_token_to_id , __lowercase )
@require_torch
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[str] ='''en'''
SCREAMING_SNAKE_CASE__ : Tuple ='''fr'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.tokenizer(self.src_text , text_target=self.tgt_text , padding=__lowercase , return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : Optional[int] =shift_tokens_right(
batch['''labels'''] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id )
for k in batch:
SCREAMING_SNAKE_CASE__ : Dict =batch[k].tolist()
# batch = {k: v.tolist() for k,v in batch.items()}
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
# batch.decoder_inputs_ids[0][0] ==
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == FR_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
@require_torch
def __magic_name__ ( self : int ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Tuple ='''mr'''
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''mr''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
SCREAMING_SNAKE_CASE__ : int ='''zh'''
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''zh''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
@require_torch
def __magic_name__ ( self : str ) -> List[str]:
SCREAMING_SNAKE_CASE__ : List[str] ='''mr'''
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''mr''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
SCREAMING_SNAKE_CASE__ : Dict ='''zh'''
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('''zh''' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
@require_torch
def __magic_name__ ( self : Dict ) -> Tuple:
SCREAMING_SNAKE_CASE__ : int =self.tokenizer._build_translation_inputs('''A test''' , return_tensors='''pt''' , src_lang='''en''' , tgt_lang='''ar''' )
self.assertEqual(
nested_simplify(__lowercase ) , {
# en_XX, A, test, EOS
'''input_ids''': [[12_80_22, 58, 41_83, 2]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 12_80_06,
} , )
| 719 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """gpt_bigcode"""
snake_case_ = ["""past_key_values"""]
snake_case_ = {
"""hidden_size""": """n_embd""",
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : Any , __lowercase : Any=5_02_57 , __lowercase : int=10_24 , __lowercase : List[str]=7_68 , __lowercase : Optional[int]=12 , __lowercase : Dict=12 , __lowercase : List[str]=None , __lowercase : int="gelu_pytorch_tanh" , __lowercase : Union[str, Any]=0.1 , __lowercase : Optional[int]=0.1 , __lowercase : Optional[int]=0.1 , __lowercase : Optional[Any]=1e-5 , __lowercase : List[str]=0.02 , __lowercase : Tuple=True , __lowercase : Optional[Any]=True , __lowercase : Union[str, Any]=5_02_56 , __lowercase : List[Any]=5_02_56 , __lowercase : Union[str, Any]=True , __lowercase : List[str]=True , __lowercase : Dict=True , **__lowercase : List[Any] , ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =n_positions
SCREAMING_SNAKE_CASE__ : Dict =n_embd
SCREAMING_SNAKE_CASE__ : Dict =n_layer
SCREAMING_SNAKE_CASE__ : Union[str, Any] =n_head
SCREAMING_SNAKE_CASE__ : List[str] =n_inner
SCREAMING_SNAKE_CASE__ : List[str] =activation_function
SCREAMING_SNAKE_CASE__ : List[Any] =resid_pdrop
SCREAMING_SNAKE_CASE__ : List[Any] =embd_pdrop
SCREAMING_SNAKE_CASE__ : List[str] =attn_pdrop
SCREAMING_SNAKE_CASE__ : Dict =layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : List[str] =initializer_range
SCREAMING_SNAKE_CASE__ : List[Any] =scale_attn_weights
SCREAMING_SNAKE_CASE__ : Union[str, Any] =use_cache
SCREAMING_SNAKE_CASE__ : Dict =attention_softmax_in_fpaa
SCREAMING_SNAKE_CASE__ : int =scale_attention_softmax_in_fpaa
SCREAMING_SNAKE_CASE__ : Dict =multi_query
SCREAMING_SNAKE_CASE__ : Optional[Any] =bos_token_id
SCREAMING_SNAKE_CASE__ : Optional[Any] =eos_token_id
super().__init__(bos_token_id=__lowercase , eos_token_id=__lowercase , **__lowercase )
| 665 | 0 |
'''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
a_ = 2_9_9_7_9_2_4_5_8
# Symbols
a_ , a_ , a_ , a_ = symbols('ct x y z')
def _a( UpperCamelCase__ : float ):
'''simple docstring'''
if velocity > c:
raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!''' )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError('''Speed must be greater than or equal to 1!''' )
return velocity / c
def _a( UpperCamelCase__ : float ):
'''simple docstring'''
return 1 / sqrt(1 - beta(UpperCamelCase__ ) ** 2 )
def _a( UpperCamelCase__ : float ):
'''simple docstring'''
return np.array(
[
[gamma(UpperCamelCase__ ), -gamma(UpperCamelCase__ ) * beta(UpperCamelCase__ ), 0, 0],
[-gamma(UpperCamelCase__ ) * beta(UpperCamelCase__ ), gamma(UpperCamelCase__ ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def _a( UpperCamelCase__ : float, UpperCamelCase__ : np.ndarray | None = None ):
'''simple docstring'''
if event is None:
SCREAMING_SNAKE_CASE__ : Optional[Any] =np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(UpperCamelCase__ ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
a_ = transform(2_9_9_7_9_2_4_5)
print('Example of four vector: ')
print(F'''ct\' = {four_vector[0]}''')
print(F'''x\' = {four_vector[1]}''')
print(F'''y\' = {four_vector[2]}''')
print(F'''z\' = {four_vector[3]}''')
# Substitute symbols with numerical values
a_ = {ct: c, x: 1, y: 1, z: 1}
a_ = [four_vector[i].subs(sub_dict) for i in range(4)]
print(F'''\n{numerical_vector}''')
| 720 |
'''simple docstring'''
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =size
SCREAMING_SNAKE_CASE__ : List[Any] =[0] * size
SCREAMING_SNAKE_CASE__ : str =[0] * size
@staticmethod
def __magic_name__ ( __lowercase : int ) -> int:
return index | (index + 1)
@staticmethod
def __magic_name__ ( __lowercase : int ) -> int:
return (index & (index + 1)) - 1
def __magic_name__ ( self : Dict , __lowercase : int , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : List[str] =value
while index < self.size:
SCREAMING_SNAKE_CASE__ : Any =self.get_prev(__lowercase ) + 1
if current_left_border == index:
SCREAMING_SNAKE_CASE__ : List[str] =value
else:
SCREAMING_SNAKE_CASE__ : str =max(__lowercase , __lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.get_next(__lowercase )
def __magic_name__ ( self : Optional[int] , __lowercase : int , __lowercase : int ) -> int:
right -= 1 # Because of right is exclusive
SCREAMING_SNAKE_CASE__ : str =0
while left <= right:
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.get_prev(__lowercase )
if left <= current_left:
SCREAMING_SNAKE_CASE__ : List[Any] =max(__lowercase , self.tree[right] )
SCREAMING_SNAKE_CASE__ : Any =current_left
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =max(__lowercase , self.arr[right] )
right -= 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
a_ = logging.get_logger(__name__)
a_ = {
'openai/whisper-base': 'https://huggingface.co/openai/whisper-base/resolve/main/config.json',
}
# fmt: off
a_ = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
a_ = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """whisper"""
snake_case_ = ["""past_key_values"""]
snake_case_ = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : Any , __lowercase : Any=5_18_65 , __lowercase : Optional[Any]=80 , __lowercase : Union[str, Any]=6 , __lowercase : Optional[int]=4 , __lowercase : Optional[int]=6 , __lowercase : int=4 , __lowercase : Dict=15_36 , __lowercase : Any=15_36 , __lowercase : Dict=0.0 , __lowercase : Dict=0.0 , __lowercase : Optional[int]=5_02_57 , __lowercase : Optional[int]=True , __lowercase : List[str]=True , __lowercase : Optional[int]="gelu" , __lowercase : Dict=2_56 , __lowercase : Union[str, Any]=0.0 , __lowercase : Optional[int]=0.0 , __lowercase : Optional[Any]=0.0 , __lowercase : Any=0.02 , __lowercase : Optional[Any]=False , __lowercase : int=15_00 , __lowercase : Any=4_48 , __lowercase : int=5_02_56 , __lowercase : Dict=5_02_56 , __lowercase : Dict=5_02_56 , __lowercase : List[Any]=None , __lowercase : Tuple=[2_20, 5_02_56] , __lowercase : Dict=False , __lowercase : Dict=2_56 , __lowercase : List[Any]=False , __lowercase : Union[str, Any]=0.05 , __lowercase : List[Any]=10 , __lowercase : List[str]=2 , __lowercase : Dict=0.0 , __lowercase : int=10 , __lowercase : Any=0 , __lowercase : int=7 , **__lowercase : int , ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : List[Any] =vocab_size
SCREAMING_SNAKE_CASE__ : int =num_mel_bins
SCREAMING_SNAKE_CASE__ : Optional[int] =d_model
SCREAMING_SNAKE_CASE__ : Optional[int] =encoder_layers
SCREAMING_SNAKE_CASE__ : Optional[Any] =encoder_attention_heads
SCREAMING_SNAKE_CASE__ : str =decoder_layers
SCREAMING_SNAKE_CASE__ : Optional[Any] =decoder_attention_heads
SCREAMING_SNAKE_CASE__ : Union[str, Any] =decoder_ffn_dim
SCREAMING_SNAKE_CASE__ : Optional[int] =encoder_ffn_dim
SCREAMING_SNAKE_CASE__ : Union[str, Any] =dropout
SCREAMING_SNAKE_CASE__ : Optional[int] =attention_dropout
SCREAMING_SNAKE_CASE__ : str =activation_dropout
SCREAMING_SNAKE_CASE__ : str =activation_function
SCREAMING_SNAKE_CASE__ : Tuple =init_std
SCREAMING_SNAKE_CASE__ : List[str] =encoder_layerdrop
SCREAMING_SNAKE_CASE__ : int =decoder_layerdrop
SCREAMING_SNAKE_CASE__ : List[Any] =use_cache
SCREAMING_SNAKE_CASE__ : Union[str, Any] =encoder_layers
SCREAMING_SNAKE_CASE__ : List[str] =scale_embedding # scale factor will be sqrt(d_model) if True
SCREAMING_SNAKE_CASE__ : Any =max_source_positions
SCREAMING_SNAKE_CASE__ : List[str] =max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE__ : List[Any] =classifier_proj_size
SCREAMING_SNAKE_CASE__ : List[str] =use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
SCREAMING_SNAKE_CASE__ : List[str] =apply_spec_augment
SCREAMING_SNAKE_CASE__ : str =mask_time_prob
SCREAMING_SNAKE_CASE__ : List[str] =mask_time_length
SCREAMING_SNAKE_CASE__ : int =mask_time_min_masks
SCREAMING_SNAKE_CASE__ : Any =mask_feature_prob
SCREAMING_SNAKE_CASE__ : List[str] =mask_feature_length
SCREAMING_SNAKE_CASE__ : int =mask_feature_min_masks
SCREAMING_SNAKE_CASE__ : Optional[int] =median_filter_width
super().__init__(
pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase , is_encoder_decoder=__lowercase , decoder_start_token_id=__lowercase , suppress_tokens=__lowercase , begin_suppress_tokens=__lowercase , **__lowercase , )
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
@property
def __magic_name__ ( self : Dict ) -> Mapping[str, Mapping[int, str]]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =OrderedDict(
[
('''input_features''', {0: '''batch''', 1: '''feature_size''', 2: '''encoder_sequence'''}),
] )
if self.use_past:
SCREAMING_SNAKE_CASE__ : List[str] ={0: '''batch'''}
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] ={0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(__lowercase , direction='''inputs''' )
return common_inputs
def __magic_name__ ( self : int , __lowercase : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , __lowercase : int = -1 , __lowercase : int = -1 , __lowercase : bool = False , __lowercase : Optional["TensorType"] = None , __lowercase : int = 2_20_50 , __lowercase : float = 5.0 , __lowercase : int = 2_20 , ) -> Mapping[str, Any]:
SCREAMING_SNAKE_CASE__ : List[Any] =OrderedDict()
SCREAMING_SNAKE_CASE__ : Optional[int] =OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=__lowercase , framework=__lowercase , sampling_rate=__lowercase , time_duration=__lowercase , frequency=__lowercase , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =encoder_inputs['''input_features'''].shape[2]
SCREAMING_SNAKE_CASE__ : List[Any] =encoder_sequence_length // 2 if self.use_past else seq_length
SCREAMING_SNAKE_CASE__ : Any =super().generate_dummy_inputs(
preprocessor.tokenizer , __lowercase , __lowercase , __lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =encoder_inputs.pop('''input_features''' )
SCREAMING_SNAKE_CASE__ : List[str] =decoder_inputs.pop('''decoder_input_ids''' )
if "past_key_values" in decoder_inputs:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =decoder_inputs.pop('''past_key_values''' )
return dummy_inputs
@property
def __magic_name__ ( self : Dict ) -> float:
return 1e-3
| 721 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = ["""vqvae"""]
def __init__( self : int , __lowercase : AutoencoderKL , __lowercase : UNetaDConditionModel , __lowercase : Mel , __lowercase : Union[DDIMScheduler, DDPMScheduler] , ) -> int:
super().__init__()
self.register_modules(unet=__lowercase , scheduler=__lowercase , mel=__lowercase , vqvae=__lowercase )
def __magic_name__ ( self : List[str] ) -> int:
return 50 if isinstance(self.scheduler , __lowercase ) else 10_00
@torch.no_grad()
def __call__( self : Dict , __lowercase : int = 1 , __lowercase : str = None , __lowercase : np.ndarray = None , __lowercase : int = 0 , __lowercase : int = 0 , __lowercase : int = None , __lowercase : torch.Generator = None , __lowercase : float = 0 , __lowercase : float = 0 , __lowercase : torch.Generator = None , __lowercase : float = 0 , __lowercase : torch.Tensor = None , __lowercase : torch.Tensor = None , __lowercase : Dict=True , ) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =steps or self.get_default_steps()
self.scheduler.set_timesteps(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
SCREAMING_SNAKE_CASE__ : Optional[int] =randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=__lowercase , device=self.device , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =noise
SCREAMING_SNAKE_CASE__ : List[str] =None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Dict =self.mel.audio_slice_to_image(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =np.frombuffer(input_image.tobytes() , dtype='''uint8''' ).reshape(
(input_image.height, input_image.width) )
SCREAMING_SNAKE_CASE__ : int =(input_image / 2_55) * 2 - 1
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.vqvae.encode(torch.unsqueeze(__lowercase , 0 ) ).latent_dist.sample(
generator=__lowercase )[0]
SCREAMING_SNAKE_CASE__ : int =self.vqvae.config.scaling_factor * input_images
if start_step > 0:
SCREAMING_SNAKE_CASE__ : int =self.scheduler.add_noise(__lowercase , __lowercase , self.scheduler.timesteps[start_step - 1] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
SCREAMING_SNAKE_CASE__ : Optional[Any] =int(mask_start_secs * pixels_per_second )
SCREAMING_SNAKE_CASE__ : Tuple =int(mask_end_secs * pixels_per_second )
SCREAMING_SNAKE_CASE__ : int =self.scheduler.add_noise(__lowercase , __lowercase , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , __lowercase ):
SCREAMING_SNAKE_CASE__ : str =self.unet(__lowercase , __lowercase , __lowercase )['''sample''']
else:
SCREAMING_SNAKE_CASE__ : str =self.unet(__lowercase , __lowercase )['''sample''']
if isinstance(self.scheduler , __lowercase ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.scheduler.step(
model_output=__lowercase , timestep=__lowercase , sample=__lowercase , eta=__lowercase , generator=__lowercase , )['''prev_sample''']
else:
SCREAMING_SNAKE_CASE__ : Any =self.scheduler.step(
model_output=__lowercase , timestep=__lowercase , sample=__lowercase , generator=__lowercase , )['''prev_sample''']
if mask is not None:
if mask_start > 0:
SCREAMING_SNAKE_CASE__ : Optional[Any] =mask[:, step, :, :mask_start]
if mask_end > 0:
SCREAMING_SNAKE_CASE__ : List[str] =mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
SCREAMING_SNAKE_CASE__ : str =1 / self.vqvae.config.scaling_factor * images
SCREAMING_SNAKE_CASE__ : int =self.vqvae.decode(__lowercase )['''sample''']
SCREAMING_SNAKE_CASE__ : List[str] =(images / 2 + 0.5).clamp(0 , 1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] =images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
SCREAMING_SNAKE_CASE__ : Dict =(images * 2_55).round().astype('''uint8''' )
SCREAMING_SNAKE_CASE__ : Any =list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(__lowercase , mode='''RGB''' ).convert('''L''' ) for _ in images) )
SCREAMING_SNAKE_CASE__ : Optional[int] =[self.mel.image_to_audio(__lowercase ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(__lowercase )[:, np.newaxis, :] ) , **ImagePipelineOutput(__lowercase ) )
@torch.no_grad()
def __magic_name__ ( self : Optional[int] , __lowercase : List[Image.Image] , __lowercase : int = 50 ) -> np.ndarray:
assert isinstance(self.scheduler , __lowercase )
self.scheduler.set_timesteps(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =np.array(
[np.frombuffer(image.tobytes() , dtype='''uint8''' ).reshape((1, image.height, image.width) ) for image in images] )
SCREAMING_SNAKE_CASE__ : str =(sample / 2_55) * 2 - 1
SCREAMING_SNAKE_CASE__ : str =torch.Tensor(__lowercase ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
SCREAMING_SNAKE_CASE__ : Tuple =t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
SCREAMING_SNAKE_CASE__ : Any =self.scheduler.alphas_cumprod[t]
SCREAMING_SNAKE_CASE__ : int =(
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
SCREAMING_SNAKE_CASE__ : int =1 - alpha_prod_t
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.unet(__lowercase , __lowercase )['''sample''']
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(1 - alpha_prod_t_prev) ** 0.5 * model_output
SCREAMING_SNAKE_CASE__ : str =(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
SCREAMING_SNAKE_CASE__ : Any =sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def __magic_name__ ( __lowercase : torch.Tensor , __lowercase : torch.Tensor , __lowercase : float ) -> torch.Tensor:
SCREAMING_SNAKE_CASE__ : Optional[int] =acos(torch.dot(torch.flatten(__lowercase ) , torch.flatten(__lowercase ) ) / torch.norm(__lowercase ) / torch.norm(__lowercase ) )
return sin((1 - alpha) * theta ) * xa / sin(__lowercase ) + sin(alpha * theta ) * xa / sin(__lowercase )
| 665 | 0 |
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
return "".join(chr(ord(UpperCamelCase__ ) - 3_2 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 700 |
'''simple docstring'''
from math import isqrt
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =[True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2, UpperCamelCase__, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Any =False
return [i for i in range(2, UpperCamelCase__ ) if is_prime[i]]
def _a( UpperCamelCase__ : int = 1_0**8 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple =calculate_prime_numbers(max_number // 2 )
SCREAMING_SNAKE_CASE__ : int =0
SCREAMING_SNAKE_CASE__ : int =0
SCREAMING_SNAKE_CASE__ : Optional[int] =len(UpperCamelCase__ ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F'''{solution() = }''')
| 665 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ = {'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['EncoderDecoderModel']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['TFEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['FlaxEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_encoder_decoder import EncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encoder_decoder import EncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_encoder_decoder import TFEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 701 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
a_ = get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = SpeechTaTokenizer
snake_case_ = False
snake_case_ = True
def __magic_name__ ( self : int ) -> Any:
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : Optional[Any] =SpeechTaTokenizer(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =AddedToken('''<mask>''' , lstrip=__lowercase , rstrip=__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =mask_token
tokenizer.add_special_tokens({'''mask_token''': mask_token} )
tokenizer.add_tokens(['''<ctc_blank>'''] )
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__ ( self : Dict , __lowercase : int ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Optional[Any] ='''this is a test'''
SCREAMING_SNAKE_CASE__ : int ='''this is a test'''
return input_text, output_text
def __magic_name__ ( self : List[Any] , __lowercase : int , __lowercase : Optional[Any]=False , __lowercase : Union[str, Any]=20 , __lowercase : Any=5 ) -> Any:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int =self.get_input_output_texts(__lowercase )
SCREAMING_SNAKE_CASE__ : str =tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
SCREAMING_SNAKE_CASE__ : str =tokenizer.decode(__lowercase , clean_up_tokenization_spaces=__lowercase )
return text, ids
def __magic_name__ ( self : Dict ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] ='''<pad>'''
SCREAMING_SNAKE_CASE__ : Optional[int] =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowercase ) , __lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowercase ) , __lowercase )
def __magic_name__ ( self : Tuple ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-4] , '''œ''' )
self.assertEqual(vocab_keys[-2] , '''<mask>''' )
self.assertEqual(vocab_keys[-1] , '''<ctc_blank>''' )
self.assertEqual(len(__lowercase ) , 81 )
def __magic_name__ ( self : Dict ) -> List[str]:
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def __magic_name__ ( self : Optional[Any] ) -> str:
SCREAMING_SNAKE_CASE__ : str =self.get_tokenizers(do_lower_case=__lowercase )
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
SCREAMING_SNAKE_CASE__ : Tuple =tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : Any =len(__lowercase )
self.assertNotEqual(__lowercase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
SCREAMING_SNAKE_CASE__ : int =['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.add_tokens(__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] =len(__lowercase )
self.assertNotEqual(__lowercase , 0 )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , len(__lowercase ) )
self.assertEqual(__lowercase , all_size + len(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Tuple =tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l''' , add_special_tokens=__lowercase )
self.assertGreaterEqual(len(__lowercase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
SCREAMING_SNAKE_CASE__ : str ={'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
SCREAMING_SNAKE_CASE__ : int =tokenizer.add_special_tokens(__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : int =len(__lowercase )
self.assertNotEqual(__lowercase , 0 )
self.assertEqual(__lowercase , __lowercase )
self.assertEqual(__lowercase , len(__lowercase ) )
self.assertEqual(__lowercase , all_size_a + len(__lowercase ) )
SCREAMING_SNAKE_CASE__ : List[Any] =tokenizer.encode(
'''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l''' , add_special_tokens=__lowercase )
self.assertGreaterEqual(len(__lowercase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def __magic_name__ ( self : Optional[Any] ) -> Any:
pass
def __magic_name__ ( self : List[str] ) -> List[Any]:
pass
def __magic_name__ ( self : Dict ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Dict =self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer.tokenize('''This is a test''' )
# fmt: off
self.assertListEqual(__lowercase , [SPIECE_UNDERLINE, '''T''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''a''', SPIECE_UNDERLINE, '''t''', '''e''', '''s''', '''t'''] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowercase ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__lowercase , [SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''92000''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =tokenizer.convert_tokens_to_ids(__lowercase )
# fmt: off
self.assertListEqual(__lowercase , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer.convert_ids_to_tokens(__lowercase )
self.assertListEqual(
__lowercase , [SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''<unk>''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''] )
@slow
def __magic_name__ ( self : List[str] ) -> List[str]:
# Use custom sequence because this tokenizer does not handle numbers.
SCREAMING_SNAKE_CASE__ : List[Any] =[
'''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
'''general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
'''Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained '''
'''models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
'''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
'''conditioning on both left and right context in all layers.''',
'''The quick brown fox jumps over the lazy dog.''',
]
# fmt: off
SCREAMING_SNAKE_CASE__ : str ={
'''input_ids''': [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
],
'''attention_mask''': [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__lowercase , model_name='''microsoft/speecht5_asr''' , revision='''c5ef64c71905caeccde0e4462ef3f9077224c524''' , sequences=__lowercase , )
| 665 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : dict ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : set[int] =set()
# To detect a back edge, keep track of vertices currently in the recursion stack
SCREAMING_SNAKE_CASE__ : set[int] =set()
return any(
node not in visited and depth_first_search(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
for node in graph )
def _a( UpperCamelCase__ : dict, UpperCamelCase__ : int, UpperCamelCase__ : set, UpperCamelCase__ : set ) -> List[Any]:
'''simple docstring'''
visited.add(UpperCamelCase__ )
rec_stk.add(UpperCamelCase__ )
for node in graph[vertex]:
if node not in visited:
if depth_first_search(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(UpperCamelCase__ )
return False
if __name__ == "__main__":
from doctest import testmod
testmod()
| 702 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , __lowercase : Optional[int] , __lowercase : str=13 , __lowercase : int=10 , __lowercase : List[Any]=3 , __lowercase : List[str]=2 , __lowercase : int=2 , __lowercase : Dict=True , __lowercase : Optional[Any]=True , __lowercase : int=32 , __lowercase : List[Any]=5 , __lowercase : Union[str, Any]=4 , __lowercase : Any=37 , __lowercase : Optional[Any]="gelu" , __lowercase : Union[str, Any]=0.1 , __lowercase : Tuple=0.1 , __lowercase : Dict=10 , __lowercase : int=0.02 , __lowercase : str="divided_space_time" , __lowercase : Union[str, Any]=None , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : int =parent
SCREAMING_SNAKE_CASE__ : List[str] =batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =image_size
SCREAMING_SNAKE_CASE__ : List[Any] =num_channels
SCREAMING_SNAKE_CASE__ : int =patch_size
SCREAMING_SNAKE_CASE__ : Tuple =num_frames
SCREAMING_SNAKE_CASE__ : List[Any] =is_training
SCREAMING_SNAKE_CASE__ : List[str] =use_labels
SCREAMING_SNAKE_CASE__ : Optional[Any] =hidden_size
SCREAMING_SNAKE_CASE__ : str =num_hidden_layers
SCREAMING_SNAKE_CASE__ : int =num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple =intermediate_size
SCREAMING_SNAKE_CASE__ : List[str] =hidden_act
SCREAMING_SNAKE_CASE__ : Dict =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : List[Any] =attention_type
SCREAMING_SNAKE_CASE__ : Union[str, Any] =initializer_range
SCREAMING_SNAKE_CASE__ : Any =scope
SCREAMING_SNAKE_CASE__ : int =num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
SCREAMING_SNAKE_CASE__ : List[str] =(image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE__ : str =(num_frames) * self.num_patches_per_frame + 1
def __magic_name__ ( self : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[int] =floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : List[Any] =None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Any =ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE__ : int =self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self : int ) -> Any:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , 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 , initializer_range=self.initializer_range , attention_type=self.attention_type , )
SCREAMING_SNAKE_CASE__ : List[Any] =self.num_labels
return config
def __magic_name__ ( self : int , __lowercase : Optional[int] , __lowercase : Dict , __lowercase : List[Any] ) -> int:
SCREAMING_SNAKE_CASE__ : Tuple =TimesformerModel(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] =model(__lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self : List[str] , __lowercase : str , __lowercase : Tuple , __lowercase : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : str =TimesformerForVideoClassification(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(__lowercase )
# verify the logits shape
SCREAMING_SNAKE_CASE__ : Tuple =torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , __lowercase )
def __magic_name__ ( self : Any ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : int =self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str =config_and_inputs
SCREAMING_SNAKE_CASE__ : Any ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
snake_case_ = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
snake_case_ = (
{"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def __magic_name__ ( self : str ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Dict =TimesformerModelTester(self )
SCREAMING_SNAKE_CASE__ : List[Any] =ConfigTester(
self , config_class=__lowercase , has_text_modality=__lowercase , hidden_size=37 )
def __magic_name__ ( self : str , __lowercase : Dict , __lowercase : str , __lowercase : Optional[int]=False ) -> int:
SCREAMING_SNAKE_CASE__ : str =copy.deepcopy(__lowercase )
if return_labels:
if model_class in get_values(__lowercase ):
SCREAMING_SNAKE_CASE__ : Any =torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__lowercase )
return inputs_dict
def __magic_name__ ( self : List[Any] ) -> Any:
self.config_tester.run_common_tests()
@unittest.skip(reason='''TimeSformer does not use inputs_embeds''' )
def __magic_name__ ( self : List[Any] ) -> Optional[int]:
pass
def __magic_name__ ( self : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : str =model_class(__lowercase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
SCREAMING_SNAKE_CASE__ : Any =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__lowercase , nn.Linear ) )
def __magic_name__ ( self : Any ) -> Any:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Optional[Any] =model_class(__lowercase )
SCREAMING_SNAKE_CASE__ : str =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE__ : List[str] =[*signature.parameters.keys()]
SCREAMING_SNAKE_CASE__ : Dict =['''pixel_values''']
self.assertListEqual(arg_names[:1] , __lowercase )
def __magic_name__ ( self : int ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase )
def __magic_name__ ( self : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*__lowercase )
@slow
def __magic_name__ ( self : Optional[int] ) -> Optional[Any]:
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =TimesformerModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> List[str]:
if not self.has_attentions:
pass
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any =self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : List[Any] =True
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : List[Any] =self.model_tester.seq_length
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.model_tester.num_frames
SCREAMING_SNAKE_CASE__ : Optional[Any] =True
SCREAMING_SNAKE_CASE__ : str =False
SCREAMING_SNAKE_CASE__ : Tuple =True
SCREAMING_SNAKE_CASE__ : Dict =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(**self._prepare_for_class(__lowercase , __lowercase ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =outputs.attentions
self.assertEqual(len(__lowercase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
SCREAMING_SNAKE_CASE__ : List[Any] =True
SCREAMING_SNAKE_CASE__ : List[str] =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Any =model(**self._prepare_for_class(__lowercase , __lowercase ) )
SCREAMING_SNAKE_CASE__ : List[Any] =outputs.attentions
self.assertEqual(len(__lowercase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
SCREAMING_SNAKE_CASE__ : Optional[int] =len(__lowercase )
# Check attention is always last and order is fine
SCREAMING_SNAKE_CASE__ : Optional[int] =True
SCREAMING_SNAKE_CASE__ : Union[str, Any] =True
SCREAMING_SNAKE_CASE__ : Optional[Any] =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(**self._prepare_for_class(__lowercase , __lowercase ) )
self.assertEqual(out_len + 1 , len(__lowercase ) )
SCREAMING_SNAKE_CASE__ : List[str] =outputs.attentions
self.assertEqual(len(__lowercase ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def __magic_name__ ( self : Tuple ) -> List[Any]:
def check_hidden_states_output(__lowercase : Tuple , __lowercase : Dict , __lowercase : Optional[int] ):
SCREAMING_SNAKE_CASE__ : List[Any] =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : int =model(**self._prepare_for_class(__lowercase , __lowercase ) )
SCREAMING_SNAKE_CASE__ : List[Any] =outputs.hidden_states
SCREAMING_SNAKE_CASE__ : Tuple =self.model_tester.num_hidden_layers + 1
self.assertEqual(len(__lowercase ) , __lowercase )
SCREAMING_SNAKE_CASE__ : int =self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Tuple =True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE__ : List[str] =True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =hf_hub_download(
repo_id='''hf-internal-testing/spaghetti-video''', filename='''eating_spaghetti.npy''', repo_type='''dataset''' )
SCREAMING_SNAKE_CASE__ : Any =np.load(UpperCamelCase__ )
return list(UpperCamelCase__ )
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : Any ) -> List[str]:
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def __magic_name__ ( self : Any ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : int =TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400''' ).to(
__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =self.default_image_processor
SCREAMING_SNAKE_CASE__ : Tuple =prepare_video()
SCREAMING_SNAKE_CASE__ : Any =image_processor(video[:8] , return_tensors='''pt''' ).to(__lowercase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Optional[int] =model(**__lowercase )
# verify the logits
SCREAMING_SNAKE_CASE__ : List[str] =torch.Size((1, 4_00) )
self.assertEqual(outputs.logits.shape , __lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =torch.tensor([-0.3016, -0.7713, -0.4205] ).to(__lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowercase , atol=1e-4 ) )
| 665 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any =set()
# edges = list of graph's edges
SCREAMING_SNAKE_CASE__ : str =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:
SCREAMING_SNAKE_CASE__ : Optional[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 _a( UpperCamelCase__ : dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] =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)}")
| 703 |
'''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_bert import BertTokenizer
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
a_ = {
'vocab_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
),
'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
),
'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
),
'bert-base-multilingual-cased': (
'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
),
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-cased': (
'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
),
},
}
a_ = {
'bert-base-uncased': 5_1_2,
'bert-large-uncased': 5_1_2,
'bert-base-cased': 5_1_2,
'bert-large-cased': 5_1_2,
'bert-base-multilingual-uncased': 5_1_2,
'bert-base-multilingual-cased': 5_1_2,
'bert-base-chinese': 5_1_2,
'bert-base-german-cased': 5_1_2,
'bert-large-uncased-whole-word-masking': 5_1_2,
'bert-large-cased-whole-word-masking': 5_1_2,
'bert-large-uncased-whole-word-masking-finetuned-squad': 5_1_2,
'bert-large-cased-whole-word-masking-finetuned-squad': 5_1_2,
'bert-base-cased-finetuned-mrpc': 5_1_2,
'bert-base-german-dbmdz-cased': 5_1_2,
'bert-base-german-dbmdz-uncased': 5_1_2,
'TurkuNLP/bert-base-finnish-cased-v1': 5_1_2,
'TurkuNLP/bert-base-finnish-uncased-v1': 5_1_2,
'wietsedv/bert-base-dutch-cased': 5_1_2,
}
a_ = {
'bert-base-uncased': {'do_lower_case': True},
'bert-large-uncased': {'do_lower_case': True},
'bert-base-cased': {'do_lower_case': False},
'bert-large-cased': {'do_lower_case': False},
'bert-base-multilingual-uncased': {'do_lower_case': True},
'bert-base-multilingual-cased': {'do_lower_case': False},
'bert-base-chinese': {'do_lower_case': False},
'bert-base-german-cased': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
'bert-large-cased-whole-word-masking': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
'bert-base-german-dbmdz-cased': {'do_lower_case': False},
'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_INIT_CONFIGURATION
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = BertTokenizer
def __init__( self : int , __lowercase : Union[str, Any]=None , __lowercase : Tuple=None , __lowercase : str=True , __lowercase : Optional[Any]="[UNK]" , __lowercase : Tuple="[SEP]" , __lowercase : Any="[PAD]" , __lowercase : List[Any]="[CLS]" , __lowercase : Union[str, Any]="[MASK]" , __lowercase : Tuple=True , __lowercase : str=None , **__lowercase : Any , ) -> Optional[Any]:
super().__init__(
__lowercase , tokenizer_file=__lowercase , do_lower_case=__lowercase , unk_token=__lowercase , sep_token=__lowercase , pad_token=__lowercase , cls_token=__lowercase , mask_token=__lowercase , tokenize_chinese_chars=__lowercase , strip_accents=__lowercase , **__lowercase , )
SCREAMING_SNAKE_CASE__ : str =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __lowercase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __lowercase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __lowercase ) != tokenize_chinese_chars
):
SCREAMING_SNAKE_CASE__ : int =getattr(__lowercase , normalizer_state.pop('''type''' ) )
SCREAMING_SNAKE_CASE__ : Any =do_lower_case
SCREAMING_SNAKE_CASE__ : Any =strip_accents
SCREAMING_SNAKE_CASE__ : Dict =tokenize_chinese_chars
SCREAMING_SNAKE_CASE__ : Union[str, Any] =normalizer_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =do_lower_case
def __magic_name__ ( self : int , __lowercase : Optional[Any] , __lowercase : Union[str, Any]=None ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] =[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 __magic_name__ ( self : Union[str, Any] , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ) -> List[int]:
SCREAMING_SNAKE_CASE__ : List[str] =[self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] =[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 __magic_name__ ( self : List[Any] , __lowercase : str , __lowercase : Optional[str] = None ) -> Tuple[str]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self._tokenizer.model.save(__lowercase , name=__lowercase )
return tuple(__lowercase )
| 665 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel
def _a( UpperCamelCase__ : Optional[int], UpperCamelCase__ : Dict, UpperCamelCase__ : str=None, UpperCamelCase__ : List[Any]=None ):
'''simple docstring'''
if attention_mask is None:
SCREAMING_SNAKE_CASE__ : List[Any] =tf.cast(tf.math.not_equal(UpperCamelCase__, config.pad_token_id ), tf.inta )
return {"input_ids": input_ids, "attention_mask": attention_mask}
@require_tf
class __SCREAMING_SNAKE_CASE :
snake_case_ = OPTConfig
snake_case_ = {}
snake_case_ = """gelu"""
def __init__( self : Optional[Any] , __lowercase : str , __lowercase : Tuple=13 , __lowercase : Optional[Any]=7 , __lowercase : List[str]=True , __lowercase : Any=False , __lowercase : Union[str, Any]=99 , __lowercase : str=16 , __lowercase : Union[str, Any]=2 , __lowercase : Tuple=4 , __lowercase : Dict=4 , __lowercase : Dict="gelu" , __lowercase : Tuple=0.1 , __lowercase : Tuple=0.1 , __lowercase : Any=20 , __lowercase : Any=2 , __lowercase : List[Any]=1 , __lowercase : Any=0 , __lowercase : Any=16 , __lowercase : Dict=16 , ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : str =parent
SCREAMING_SNAKE_CASE__ : str =batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] =is_training
SCREAMING_SNAKE_CASE__ : str =use_labels
SCREAMING_SNAKE_CASE__ : Any =vocab_size
SCREAMING_SNAKE_CASE__ : str =hidden_size
SCREAMING_SNAKE_CASE__ : str =num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[int] =num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[Any] =intermediate_size
SCREAMING_SNAKE_CASE__ : int =hidden_act
SCREAMING_SNAKE_CASE__ : Dict =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[int] =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Any =max_position_embeddings
SCREAMING_SNAKE_CASE__ : Tuple =eos_token_id
SCREAMING_SNAKE_CASE__ : Tuple =pad_token_id
SCREAMING_SNAKE_CASE__ : Optional[int] =bos_token_id
SCREAMING_SNAKE_CASE__ : Union[str, Any] =embed_dim
SCREAMING_SNAKE_CASE__ : Dict =word_embed_proj_dim
SCREAMING_SNAKE_CASE__ : Union[str, Any] =False
def __magic_name__ ( self : Optional[int] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : int =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
SCREAMING_SNAKE_CASE__ : Any =tf.concat([input_ids, eos_tensor] , axis=1 )
SCREAMING_SNAKE_CASE__ : List[Any] =self.config_cls(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , embed_dim=self.embed_dim , word_embed_proj_dim=self.word_embed_proj_dim , is_encoder_decoder=__lowercase , **self.config_updates , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =prepare_opt_inputs_dict(__lowercase , __lowercase )
return config, inputs_dict
def __magic_name__ ( self : str , __lowercase : Optional[int] , __lowercase : Any ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[int] =TFOPTModel(config=__lowercase )
SCREAMING_SNAKE_CASE__ : int =inputs_dict['''input_ids''']
SCREAMING_SNAKE_CASE__ : Optional[Any] =input_ids[:1, :]
SCREAMING_SNAKE_CASE__ : Union[str, Any] =inputs_dict['''attention_mask'''][:1, :]
SCREAMING_SNAKE_CASE__ : Optional[int] =1
# first forward pass
SCREAMING_SNAKE_CASE__ : Optional[int] =model(__lowercase , attention_mask=__lowercase , use_cache=__lowercase )
SCREAMING_SNAKE_CASE__ : str =outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : Any =ids_tensor((self.batch_size, 3) , config.vocab_size )
SCREAMING_SNAKE_CASE__ : List[Any] =tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : List[str] =tf.concat([input_ids, next_tokens] , axis=-1 )
SCREAMING_SNAKE_CASE__ : Optional[int] =tf.concat([attention_mask, next_attn_mask] , axis=-1 )
SCREAMING_SNAKE_CASE__ : str =model(__lowercase , attention_mask=__lowercase )[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(__lowercase , attention_mask=__lowercase , past_key_values=__lowercase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
SCREAMING_SNAKE_CASE__ : List[Any] =int(ids_tensor((1,) , output_from_past.shape[-1] ) )
SCREAMING_SNAKE_CASE__ : str =output_from_no_past[:, -3:, random_slice_idx]
SCREAMING_SNAKE_CASE__ : int =output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__lowercase , __lowercase , rtol=1e-3 )
@require_tf
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
snake_case_ = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
snake_case_ = (TFOPTForCausalLM,) if is_tf_available() else ()
snake_case_ = (
{"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = 10
def __magic_name__ ( self : Optional[int] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =TFOPTModelTester(self )
SCREAMING_SNAKE_CASE__ : Any =ConfigTester(self , config_class=__lowercase )
def __magic_name__ ( self : str ) -> Tuple:
self.config_tester.run_common_tests()
def __magic_name__ ( self : Union[str, Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : str =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Tuple =self.model_tester.prepare_config_and_inputs_for_common()
def _get_word_embedding_weight(__lowercase : List[str] , __lowercase : Union[str, Any] ):
if hasattr(__lowercase , '''weight''' ):
return embedding_layer.weight
else:
# Here we build the word embeddings weights if not exists.
# And then we retry to get the attribute once built.
model.build()
if hasattr(__lowercase , '''weight''' ):
return embedding_layer.weight
else:
return None
for model_class in self.all_model_classes:
for size in [config.vocab_size - 10, config.vocab_size + 10]:
# build the embeddings
SCREAMING_SNAKE_CASE__ : str =model_class(config=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =_get_word_embedding_weight(__lowercase , model.get_input_embeddings() )
SCREAMING_SNAKE_CASE__ : int =_get_word_embedding_weight(__lowercase , model.get_output_embeddings() )
# reshape the embeddings
model.resize_token_embeddings(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =_get_word_embedding_weight(__lowercase , model.get_input_embeddings() )
SCREAMING_SNAKE_CASE__ : int =_get_word_embedding_weight(__lowercase , model.get_output_embeddings() )
# check that the resized embeddings size matches the desired size.
SCREAMING_SNAKE_CASE__ : List[Any] =size if size is not None else config.vocab_size
self.assertEqual(new_input_embeddings.shape[0] , __lowercase )
# check that weights remain the same after resizing
SCREAMING_SNAKE_CASE__ : Union[str, Any] =True
for pa, pa in zip(old_input_embeddings.value() , new_input_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
SCREAMING_SNAKE_CASE__ : List[str] =False
self.assertTrue(__lowercase )
if old_output_embeddings is not None and new_output_embeddings is not None:
self.assertEqual(new_output_embeddings.shape[0] , __lowercase )
SCREAMING_SNAKE_CASE__ : Any =True
for pa, pa in zip(old_output_embeddings.value() , new_output_embeddings.value() ):
if tf.math.reduce_sum(tf.math.abs(pa - pa ) ) > 0:
SCREAMING_SNAKE_CASE__ : Any =False
self.assertTrue(__lowercase )
def _a( UpperCamelCase__ : Dict ):
'''simple docstring'''
return tf.constant(UpperCamelCase__, dtype=tf.intaa )
@require_tf
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case_ = 99
def __magic_name__ ( self : Dict ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : str =tf.ones((4, 1) , dtype=tf.intaa ) * 2
SCREAMING_SNAKE_CASE__ : Any =tf.concat([ids_tensor((4, 6) , self.vocab_size - 3 ) + 3, eos_column_vector] , axis=1 )
SCREAMING_SNAKE_CASE__ : List[str] =input_ids.shape[0]
SCREAMING_SNAKE_CASE__ : Dict =OPTConfig(
vocab_size=self.vocab_size , hidden_size=24 , num_hidden_layers=2 , num_attention_heads=2 , ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
@require_sentencepiece
@require_tf
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@slow
def __magic_name__ ( self : Optional[Any] ) -> int:
SCREAMING_SNAKE_CASE__ : Tuple =TFOPTModel.from_pretrained('''facebook/opt-350m''' )
SCREAMING_SNAKE_CASE__ : Any =_long_tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
SCREAMING_SNAKE_CASE__ : Tuple =tf.not_equal(__lowercase , model.config.pad_token_id )
with tf.GradientTape():
SCREAMING_SNAKE_CASE__ : List[str] =model(input_ids=__lowercase , attention_mask=__lowercase ).last_hidden_state
SCREAMING_SNAKE_CASE__ : Optional[int] =(1, 11, 5_12)
self.assertEqual(output.shape , __lowercase )
SCREAMING_SNAKE_CASE__ : Any =tf.constant(
[[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]] )
self.assertTrue(np.allclose(output[:, :3, :3] , __lowercase , atol=4e-3 ) )
SCREAMING_SNAKE_CASE__ : str =tf.function(__lowercase , jit_compile=__lowercase )
SCREAMING_SNAKE_CASE__ : Any =xla_generate(__lowercase , __lowercase )[0]
self.assertTrue(np.allclose(output[:, :3, :3] , __lowercase , atol=4e-2 ) )
@require_tf
@slow
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : Union[str, Any] ) -> List[str]:
super().setUp()
SCREAMING_SNAKE_CASE__ : Union[str, Any] ='''facebook/opt-350m'''
def __magic_name__ ( self : Dict ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] =TFOPTForCausalLM.from_pretrained(self.path_model )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =GPTaTokenizer.from_pretrained(self.path_model )
SCREAMING_SNAKE_CASE__ : Dict =[
'''Today is a beautiful day and I want to''',
'''In the city of''',
'''Paris is the capital of France and''',
'''Computers and mobile phones have taken''',
]
# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
SCREAMING_SNAKE_CASE__ : List[Any] =tokenizer(__lowercase , return_tensors='''tf''' , padding=__lowercase , add_special_tokens=__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =tf.math.reduce_mean(model(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
SCREAMING_SNAKE_CASE__ : Tuple =tf.constant(
[
[1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
[-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
[0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
[6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
] )
self.assertTrue(np.allclose(__lowercase , __lowercase , atol=1e-4 ) )
SCREAMING_SNAKE_CASE__ : Tuple =tf.function(__lowercase , jit_compile=__lowercase )
SCREAMING_SNAKE_CASE__ : int =tf.math.reduce_mean(xla_generate(inputs.input_ids , attention_mask=inputs.attention_mask )[0] , axis=-1 )
self.assertTrue(np.allclose(__lowercase , __lowercase , atol=1e-4 ) )
@require_tf
@slow
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@property
def __magic_name__ ( self : List[str] ) -> Optional[int]:
return [
"Today is a beautiful day and I want",
"In the city of",
"Paris is the capital of France and",
"Computers and mobile phones have taken",
]
def __magic_name__ ( self : Union[str, Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : int ='''facebook/opt-125m'''
SCREAMING_SNAKE_CASE__ : Any =[
'''Today is a beautiful day and I want to''',
'''In the city of New York, the city''',
'''Paris is the capital of France and the capital''',
'''Computers and mobile phones have taken over the''',
]
SCREAMING_SNAKE_CASE__ : List[str] =[]
SCREAMING_SNAKE_CASE__ : Tuple =GPTaTokenizer.from_pretrained(__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =TFOPTForCausalLM.from_pretrained(__lowercase )
for prompt in self.prompts:
SCREAMING_SNAKE_CASE__ : Dict =tokenizer(__lowercase , return_tensors='''tf''' ).input_ids
SCREAMING_SNAKE_CASE__ : Dict =model.generate(__lowercase , max_length=10 )
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer.batch_decode(__lowercase , skip_special_tokens=__lowercase )
predicted_outputs += generated_string
self.assertListEqual(__lowercase , __lowercase )
def __magic_name__ ( self : Dict ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Any ='''facebook/opt-350m'''
SCREAMING_SNAKE_CASE__ : List[Any] =GPTaTokenizer.from_pretrained(__lowercase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =TFOPTForCausalLM.from_pretrained(__lowercase )
SCREAMING_SNAKE_CASE__ : Any ='''left'''
# use different length sentences to test batching
SCREAMING_SNAKE_CASE__ : Optional[int] =[
'''Hello, my dog is a little''',
'''Today, I''',
]
SCREAMING_SNAKE_CASE__ : int =tokenizer(__lowercase , return_tensors='''tf''' , padding=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =inputs['''input_ids''']
SCREAMING_SNAKE_CASE__ : Dict =model.generate(input_ids=__lowercase , attention_mask=inputs['''attention_mask'''] )
SCREAMING_SNAKE_CASE__ : Any =tokenizer(sentences[0] , return_tensors='''tf''' ).input_ids
SCREAMING_SNAKE_CASE__ : List[str] =model.generate(input_ids=__lowercase )
SCREAMING_SNAKE_CASE__ : int =inputs_non_padded.shape[-1] - tf.math.reduce_sum(
tf.cast(inputs['''attention_mask'''][-1] , tf.intaa ) )
SCREAMING_SNAKE_CASE__ : int =tokenizer(sentences[1] , return_tensors='''tf''' ).input_ids
SCREAMING_SNAKE_CASE__ : Any =model.generate(input_ids=__lowercase , max_length=model.config.max_length - num_paddings )
SCREAMING_SNAKE_CASE__ : Any =tokenizer.batch_decode(__lowercase , skip_special_tokens=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =tokenizer.decode(output_non_padded[0] , skip_special_tokens=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =tokenizer.decode(output_padded[0] , skip_special_tokens=__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =[
'''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
'''Today, I was in the middle of a conversation with a friend about the''',
]
self.assertListEqual(__lowercase , __lowercase )
self.assertListEqual(__lowercase , [non_padded_sentence, padded_sentence] )
def __magic_name__ ( self : Tuple ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[str] ='''facebook/opt-350m'''
SCREAMING_SNAKE_CASE__ : List[str] =[
'''Today is a beautiful day and I want to''',
'''In the city of San Francisco, the city''',
'''Paris is the capital of France and the capital''',
'''Computers and mobile phones have taken over the''',
]
SCREAMING_SNAKE_CASE__ : int =[]
SCREAMING_SNAKE_CASE__ : Union[str, Any] =GPTaTokenizer.from_pretrained(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =TFOPTForCausalLM.from_pretrained(__lowercase )
for prompt in self.prompts:
SCREAMING_SNAKE_CASE__ : str =tokenizer(__lowercase , return_tensors='''tf''' ).input_ids
SCREAMING_SNAKE_CASE__ : int =model.generate(__lowercase , max_length=10 )
SCREAMING_SNAKE_CASE__ : int =tokenizer.batch_decode(__lowercase , skip_special_tokens=__lowercase )
predicted_outputs += generated_string
self.assertListEqual(__lowercase , __lowercase )
| 704 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')
# TF training parameters
a_ = False
a_ = False
def _a( UpperCamelCase__ : Namespace ):
'''simple docstring'''
return TrainCommand(UpperCamelCase__ )
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
@staticmethod
def __magic_name__ ( __lowercase : ArgumentParser ) -> Any:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =parser.add_parser('''train''' , help='''CLI tool to train a model on a task.''' )
train_parser.add_argument(
'''--train_data''' , type=__lowercase , required=__lowercase , help='''path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.''' , )
train_parser.add_argument(
'''--column_label''' , type=__lowercase , default=0 , help='''Column of the dataset csv file with example labels.''' )
train_parser.add_argument(
'''--column_text''' , type=__lowercase , default=1 , help='''Column of the dataset csv file with example texts.''' )
train_parser.add_argument(
'''--column_id''' , type=__lowercase , default=2 , help='''Column of the dataset csv file with example ids.''' )
train_parser.add_argument(
'''--skip_first_row''' , action='''store_true''' , help='''Skip the first row of the csv file (headers).''' )
train_parser.add_argument('''--validation_data''' , type=__lowercase , default='''''' , help='''path to validation dataset.''' )
train_parser.add_argument(
'''--validation_split''' , type=__lowercase , default=0.1 , help='''if validation dataset is not provided, fraction of train dataset to use as validation dataset.''' , )
train_parser.add_argument('''--output''' , type=__lowercase , default='''./''' , help='''path to saved the trained model.''' )
train_parser.add_argument(
'''--task''' , type=__lowercase , default='''text_classification''' , help='''Task to train the model on.''' )
train_parser.add_argument(
'''--model''' , type=__lowercase , default='''bert-base-uncased''' , help='''Model\'s name or path to stored model.''' )
train_parser.add_argument('''--train_batch_size''' , type=__lowercase , default=32 , help='''Batch size for training.''' )
train_parser.add_argument('''--valid_batch_size''' , type=__lowercase , default=64 , help='''Batch size for validation.''' )
train_parser.add_argument('''--learning_rate''' , type=__lowercase , default=3e-5 , help='''Learning rate.''' )
train_parser.add_argument('''--adam_epsilon''' , type=__lowercase , default=1e-08 , help='''Epsilon for Adam optimizer.''' )
train_parser.set_defaults(func=__lowercase )
def __init__( self : Tuple , __lowercase : Namespace ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Tuple =logging.get_logger('''transformers-cli/training''' )
SCREAMING_SNAKE_CASE__ : int ='''tf''' if is_tf_available() else '''torch'''
os.makedirs(args.output , exist_ok=__lowercase )
SCREAMING_SNAKE_CASE__ : Any =args.output
SCREAMING_SNAKE_CASE__ : str =args.column_label
SCREAMING_SNAKE_CASE__ : List[Any] =args.column_text
SCREAMING_SNAKE_CASE__ : Tuple =args.column_id
self.logger.info(F"Loading {args.task} pipeline for {args.model}" )
if args.task == "text_classification":
SCREAMING_SNAKE_CASE__ : List[str] =TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(F"Loading dataset from {args.train_data}" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =None
if args.validation_data:
self.logger.info(F"Loading validation dataset from {args.validation_data}" )
SCREAMING_SNAKE_CASE__ : List[Any] =Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =args.validation_split
SCREAMING_SNAKE_CASE__ : List[Any] =args.train_batch_size
SCREAMING_SNAKE_CASE__ : Any =args.valid_batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =args.learning_rate
SCREAMING_SNAKE_CASE__ : int =args.adam_epsilon
def __magic_name__ ( self : Any ) -> str:
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def __magic_name__ ( self : Optional[int] ) -> Tuple:
raise NotImplementedError
def __magic_name__ ( self : Dict ) -> List[Any]:
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 665 | 0 |
'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def _a( UpperCamelCase__ : Features ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] =np.inf
def set_batch_size(UpperCamelCase__ : FeatureType ) -> None:
nonlocal batch_size
if isinstance(UpperCamelCase__, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : str =min(UpperCamelCase__, config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(UpperCamelCase__, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : int =min(UpperCamelCase__, config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(UpperCamelCase__, UpperCamelCase__ ) and feature.dtype == "binary":
SCREAMING_SNAKE_CASE__ : Dict =min(UpperCamelCase__, config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(UpperCamelCase__, UpperCamelCase__ )
return None if batch_size is np.inf else batch_size
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __init__( self : Any , __lowercase : NestedDataStructureLike[PathLike] , __lowercase : Optional[NamedSplit] = None , __lowercase : Optional[Features] = None , __lowercase : str = None , __lowercase : bool = False , __lowercase : bool = False , __lowercase : Optional[int] = None , **__lowercase : str , ) -> Optional[int]:
super().__init__(
__lowercase , split=__lowercase , features=__lowercase , cache_dir=__lowercase , keep_in_memory=__lowercase , streaming=__lowercase , num_proc=__lowercase , **__lowercase , )
SCREAMING_SNAKE_CASE__ : Dict =path_or_paths if isinstance(__lowercase , __lowercase ) else {self.split: path_or_paths}
SCREAMING_SNAKE_CASE__ : Union[str, Any] =_PACKAGED_DATASETS_MODULES['''parquet'''][1]
SCREAMING_SNAKE_CASE__ : List[str] =Parquet(
cache_dir=__lowercase , data_files=__lowercase , features=__lowercase , hash=__lowercase , **__lowercase , )
def __magic_name__ ( self : Optional[Any] ) -> List[Any]:
# Build iterable dataset
if self.streaming:
SCREAMING_SNAKE_CASE__ : Tuple =self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
SCREAMING_SNAKE_CASE__ : Any =None
SCREAMING_SNAKE_CASE__ : Any =None
SCREAMING_SNAKE_CASE__ : Tuple =None
SCREAMING_SNAKE_CASE__ : Tuple =None
self.builder.download_and_prepare(
download_config=__lowercase , download_mode=__lowercase , verification_mode=__lowercase , base_path=__lowercase , num_proc=self.num_proc , )
SCREAMING_SNAKE_CASE__ : Dict =self.builder.as_dataset(
split=self.split , verification_mode=__lowercase , in_memory=self.keep_in_memory )
return dataset
class __SCREAMING_SNAKE_CASE :
def __init__( self : Dict , __lowercase : Dataset , __lowercase : Union[PathLike, BinaryIO] , __lowercase : Optional[int] = None , **__lowercase : int , ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[Any] =dataset
SCREAMING_SNAKE_CASE__ : str =path_or_buf
SCREAMING_SNAKE_CASE__ : int =batch_size or get_writer_batch_size(dataset.features )
SCREAMING_SNAKE_CASE__ : Optional[Any] =parquet_writer_kwargs
def __magic_name__ ( self : Union[str, Any] ) -> int:
SCREAMING_SNAKE_CASE__ : Tuple =self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with open(self.path_or_buf , '''wb+''' ) as buffer:
SCREAMING_SNAKE_CASE__ : Optional[int] =self._write(file_obj=__lowercase , batch_size=__lowercase , **self.parquet_writer_kwargs )
else:
SCREAMING_SNAKE_CASE__ : int =self._write(file_obj=self.path_or_buf , batch_size=__lowercase , **self.parquet_writer_kwargs )
return written
def __magic_name__ ( self : List[Any] , __lowercase : BinaryIO , __lowercase : int , **__lowercase : Tuple ) -> int:
SCREAMING_SNAKE_CASE__ : int =0
SCREAMING_SNAKE_CASE__ : Optional[int] =parquet_writer_kwargs.pop('''path_or_buf''' , __lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dataset.features.arrow_schema
SCREAMING_SNAKE_CASE__ : List[Any] =pq.ParquetWriter(__lowercase , schema=__lowercase , **__lowercase )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , __lowercase ) , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating parquet from Arrow format''' , ):
SCREAMING_SNAKE_CASE__ : Dict =query_table(
table=self.dataset._data , key=slice(__lowercase , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(__lowercase )
written += batch.nbytes
writer.close()
return written
| 705 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = KandinskyVaaImgaImgPipeline
snake_case_ = ["""image_embeds""", """negative_image_embeds""", """image"""]
snake_case_ = [
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
snake_case_ = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
snake_case_ = False
@property
def __magic_name__ ( self : List[str] ) -> Tuple:
return 32
@property
def __magic_name__ ( self : List[str] ) -> str:
return 32
@property
def __magic_name__ ( self : Any ) -> Optional[int]:
return self.time_input_dim
@property
def __magic_name__ ( self : List[Any] ) -> int:
return self.time_input_dim * 4
@property
def __magic_name__ ( self : Tuple ) -> Optional[int]:
return 1_00
@property
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] ={
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
SCREAMING_SNAKE_CASE__ : Optional[int] =UNetaDConditionModel(**__lowercase )
return model
@property
def __magic_name__ ( self : Dict ) -> Any:
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __magic_name__ ( self : Tuple ) -> Optional[Any]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] =VQModel(**self.dummy_movq_kwargs )
return model
def __magic_name__ ( self : str ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[str] =self.dummy_unet
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dummy_movq
SCREAMING_SNAKE_CASE__ : Optional[Any] ={
'''num_train_timesteps''': 10_00,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
SCREAMING_SNAKE_CASE__ : str =DDIMScheduler(**__lowercase )
SCREAMING_SNAKE_CASE__ : Any ={
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __magic_name__ ( self : str , __lowercase : Optional[Any] , __lowercase : Any=0 ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[int] =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__lowercase ) ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__lowercase )
# create init_image
SCREAMING_SNAKE_CASE__ : Optional[Any] =floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowercase ) ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE__ : Any =Image.fromarray(np.uinta(__lowercase ) ).convert('''RGB''' ).resize((2_56, 2_56) )
if str(__lowercase ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE__ : Dict =torch.manual_seed(__lowercase )
else:
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device=__lowercase ).manual_seed(__lowercase )
SCREAMING_SNAKE_CASE__ : str ={
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def __magic_name__ ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[Any] ='''cpu'''
SCREAMING_SNAKE_CASE__ : Tuple =self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Dict =self.pipeline_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[Any] =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =pipe(**self.get_dummy_inputs(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Tuple =output.images
SCREAMING_SNAKE_CASE__ : Union[str, Any] =pipe(
**self.get_dummy_inputs(__lowercase ) , return_dict=__lowercase , )[0]
SCREAMING_SNAKE_CASE__ : List[Any] =image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : List[str] =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE__ : Tuple =np.array(
[0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : int ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : str =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
SCREAMING_SNAKE_CASE__ : List[Any] ='''A red cartoon frog, 4k'''
SCREAMING_SNAKE_CASE__ : Optional[int] =KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =KandinskyVaaImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : Dict =pipeline.to(__lowercase )
pipeline.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device='''cpu''' ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] =pipe_prior(
__lowercase , generator=__lowercase , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
image=__lowercase , image_embeds=__lowercase , negative_image_embeds=__lowercase , generator=__lowercase , num_inference_steps=1_00 , height=7_68 , width=7_68 , strength=0.2 , output_type='''np''' , )
SCREAMING_SNAKE_CASE__ : int =output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(__lowercase , __lowercase )
| 665 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """pegasus"""
snake_case_ = ["""past_key_values"""]
snake_case_ = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : Optional[Any] , __lowercase : List[Any]=5_02_65 , __lowercase : Union[str, Any]=10_24 , __lowercase : Optional[Any]=12 , __lowercase : List[str]=40_96 , __lowercase : int=16 , __lowercase : List[Any]=12 , __lowercase : Any=40_96 , __lowercase : List[str]=16 , __lowercase : Any=0.0 , __lowercase : Union[str, Any]=0.0 , __lowercase : str=True , __lowercase : Dict=True , __lowercase : Optional[Any]="gelu" , __lowercase : Union[str, Any]=10_24 , __lowercase : Optional[int]=0.1 , __lowercase : List[Any]=0.0 , __lowercase : Any=0.0 , __lowercase : Tuple=0.02 , __lowercase : Union[str, Any]=0 , __lowercase : Tuple=False , __lowercase : str=0 , __lowercase : Optional[int]=1 , __lowercase : List[Any]=1 , **__lowercase : Tuple , ) -> Dict:
SCREAMING_SNAKE_CASE__ : int =vocab_size
SCREAMING_SNAKE_CASE__ : Optional[Any] =max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[str] =d_model
SCREAMING_SNAKE_CASE__ : Tuple =encoder_ffn_dim
SCREAMING_SNAKE_CASE__ : str =encoder_layers
SCREAMING_SNAKE_CASE__ : List[str] =encoder_attention_heads
SCREAMING_SNAKE_CASE__ : Any =decoder_ffn_dim
SCREAMING_SNAKE_CASE__ : Optional[Any] =decoder_layers
SCREAMING_SNAKE_CASE__ : Any =decoder_attention_heads
SCREAMING_SNAKE_CASE__ : List[Any] =dropout
SCREAMING_SNAKE_CASE__ : Optional[Any] =attention_dropout
SCREAMING_SNAKE_CASE__ : Any =activation_dropout
SCREAMING_SNAKE_CASE__ : List[Any] =activation_function
SCREAMING_SNAKE_CASE__ : str =init_std
SCREAMING_SNAKE_CASE__ : Optional[int] =encoder_layerdrop
SCREAMING_SNAKE_CASE__ : Optional[int] =decoder_layerdrop
SCREAMING_SNAKE_CASE__ : Union[str, Any] =use_cache
SCREAMING_SNAKE_CASE__ : Union[str, Any] =encoder_layers
SCREAMING_SNAKE_CASE__ : Optional[Any] =scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=__lowercase , eos_token_id=__lowercase , is_encoder_decoder=__lowercase , decoder_start_token_id=__lowercase , forced_eos_token_id=__lowercase , **__lowercase , )
@property
def __magic_name__ ( self : List[Any] ) -> int:
return self.encoder_attention_heads
@property
def __magic_name__ ( self : List[str] ) -> int:
return self.d_model
| 706 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
a_ = TypeVar('T')
class __SCREAMING_SNAKE_CASE ( Generic[T] ):
snake_case_ = 42 # Cache store of keys
snake_case_ = 42 # References of the keys in cache
snake_case_ = 10 # Maximum capacity of cache
def __init__( self : Dict , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : Any =deque()
SCREAMING_SNAKE_CASE__ : str =set()
if not n:
SCREAMING_SNAKE_CASE__ : Optional[Any] =sys.maxsize
elif n < 0:
raise ValueError('''n should be an integer greater than 0.''' )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =n
def __magic_name__ ( self : List[str] , __lowercase : T ) -> None:
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
SCREAMING_SNAKE_CASE__ : int =self.dq_store.pop()
self.key_reference.remove(__lowercase )
else:
self.dq_store.remove(__lowercase )
self.dq_store.appendleft(__lowercase )
self.key_reference.add(__lowercase )
def __magic_name__ ( self : Union[str, Any] ) -> None:
for k in self.dq_store:
print(__lowercase )
def __repr__( self : List[Any] ) -> str:
return F"LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}"
if __name__ == "__main__":
import doctest
doctest.testmod()
a_ = LRUCache(4)
lru_cache.refer('A')
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer('A')
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 665 | 0 |
'''simple docstring'''
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =ArgumentParser('''Accelerate CLI tool''', usage='''accelerate <command> [<args>]''', allow_abbrev=UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : str =parser.add_subparsers(help='''accelerate command helpers''' )
# Register commands
get_config_parser(subparsers=UpperCamelCase__ )
env_command_parser(subparsers=UpperCamelCase__ )
launch_command_parser(subparsers=UpperCamelCase__ )
tpu_command_parser(subparsers=UpperCamelCase__ )
test_command_parser(subparsers=UpperCamelCase__ )
# Let's go
SCREAMING_SNAKE_CASE__ : Dict =parser.parse_args()
if not hasattr(UpperCamelCase__, '''func''' ):
parser.print_help()
exit(1 )
# Run
args.func(UpperCamelCase__ )
if __name__ == "__main__":
main()
| 707 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
a_ = list[list[float | int]]
def _a( UpperCamelCase__ : Matrix, UpperCamelCase__ : Matrix ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Matrix =[[0 for _ in range(size + 1 )] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : float
for row in range(UpperCamelCase__ ):
for col in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Tuple =matrix[row][col]
SCREAMING_SNAKE_CASE__ : Optional[int] =vector[row][0]
SCREAMING_SNAKE_CASE__ : Any =0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =0
while row < size and col < size:
# pivoting
SCREAMING_SNAKE_CASE__ : Any =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__ : Optional[Any] =augmented[pivot_row], augmented[row]
for rowa in range(row + 1, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] =augmented[rowa][col] / augmented[row][col]
SCREAMING_SNAKE_CASE__ : Tuple =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[Any] =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], 1_0 )] for row in range(UpperCamelCase__ )
]
def _a( UpperCamelCase__ : list[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Matrix =[[0 for _ in range(UpperCamelCase__ )] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Matrix =[[0] for _ in range(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Matrix
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
for x_val, y_val in enumerate(UpperCamelCase__ ):
for col in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[int] =(x_val + 1) ** (size - col - 1)
SCREAMING_SNAKE_CASE__ : Dict =y_val
SCREAMING_SNAKE_CASE__ : Optional[int] =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 ):
'''simple docstring'''
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**1_0
)
def _a( UpperCamelCase__ : Callable[[int], int] = question_function, UpperCamelCase__ : int = 1_0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : list[int] =[func(UpperCamelCase__ ) for x_val in range(1, order + 1 )]
SCREAMING_SNAKE_CASE__ : list[Callable[[int], int]] =[
interpolate(data_points[:max_coeff] ) for max_coeff in range(1, order + 1 )
]
SCREAMING_SNAKE_CASE__ : int =0
SCREAMING_SNAKE_CASE__ : Callable[[int], int]
SCREAMING_SNAKE_CASE__ : int
for poly in polynomials:
SCREAMING_SNAKE_CASE__ : Any =1
while func(UpperCamelCase__ ) == poly(UpperCamelCase__ ):
x_val += 1
ret += poly(UpperCamelCase__ )
return ret
if __name__ == "__main__":
print(F'''{solution() = }''')
| 665 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
a_ = logging.getLogger(__name__)
@dataclass
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
@dataclass
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
snake_case_ = 42
snake_case_ = 42
snake_case_ = None
snake_case_ = None
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
'''simple docstring'''
snake_case_ = """train"""
snake_case_ = """dev"""
snake_case_ = """test"""
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
@staticmethod
def __magic_name__ ( __lowercase : Union[str, Any] , __lowercase : Union[Split, str] ) -> List[InputExample]:
raise NotImplementedError
@staticmethod
def __magic_name__ ( __lowercase : str ) -> List[str]:
raise NotImplementedError
@staticmethod
def __magic_name__ ( __lowercase : List[InputExample] , __lowercase : List[str] , __lowercase : int , __lowercase : PreTrainedTokenizer , __lowercase : Union[str, Any]=False , __lowercase : str="[CLS]" , __lowercase : List[str]=1 , __lowercase : Any="[SEP]" , __lowercase : Optional[Any]=False , __lowercase : Optional[Any]=False , __lowercase : Tuple=0 , __lowercase : Optional[int]=0 , __lowercase : List[str]=-1_00 , __lowercase : List[Any]=0 , __lowercase : List[Any]=True , ) -> List[InputFeatures]:
SCREAMING_SNAKE_CASE__ : Any ={label: i for i, label in enumerate(__lowercase )}
SCREAMING_SNAKE_CASE__ : Any =[]
for ex_index, example in enumerate(__lowercase ):
if ex_index % 1_00_00 == 0:
logger.info('''Writing example %d of %d''' , __lowercase , len(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Optional[int] =[]
SCREAMING_SNAKE_CASE__ : str =[]
for word, label in zip(example.words , example.labels ):
SCREAMING_SNAKE_CASE__ : int =tokenizer.tokenize(__lowercase )
# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
if len(__lowercase ) > 0:
tokens.extend(__lowercase )
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__lowercase ) - 1) )
# Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
SCREAMING_SNAKE_CASE__ : Union[str, Any] =tokenizer.num_special_tokens_to_add()
if len(__lowercase ) > max_seq_length - special_tokens_count:
SCREAMING_SNAKE_CASE__ : List[Any] =tokens[: (max_seq_length - special_tokens_count)]
SCREAMING_SNAKE_CASE__ : Tuple =label_ids[: (max_seq_length - special_tokens_count)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambiguously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens += [sep_token]
label_ids += [pad_token_label_id]
if sep_token_extra:
# roberta uses an extra separator b/w pairs of sentences
tokens += [sep_token]
label_ids += [pad_token_label_id]
SCREAMING_SNAKE_CASE__ : List[str] =[sequence_a_segment_id] * len(__lowercase )
if cls_token_at_end:
tokens += [cls_token]
label_ids += [pad_token_label_id]
segment_ids += [cls_token_segment_id]
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =[cls_token] + tokens
SCREAMING_SNAKE_CASE__ : List[Any] =[pad_token_label_id] + label_ids
SCREAMING_SNAKE_CASE__ : Union[str, Any] =[cls_token_segment_id] + segment_ids
SCREAMING_SNAKE_CASE__ : int =tokenizer.convert_tokens_to_ids(__lowercase )
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
SCREAMING_SNAKE_CASE__ : Tuple =[1 if mask_padding_with_zero else 0] * len(__lowercase )
# Zero-pad up to the sequence length.
SCREAMING_SNAKE_CASE__ : List[Any] =max_seq_length - len(__lowercase )
if pad_on_left:
SCREAMING_SNAKE_CASE__ : Any =([pad_token] * padding_length) + input_ids
SCREAMING_SNAKE_CASE__ : Tuple =([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
SCREAMING_SNAKE_CASE__ : Union[str, Any] =([pad_token_segment_id] * padding_length) + segment_ids
SCREAMING_SNAKE_CASE__ : List[Any] =([pad_token_label_id] * padding_length) + label_ids
else:
input_ids += [pad_token] * padding_length
input_mask += [0 if mask_padding_with_zero else 1] * padding_length
segment_ids += [pad_token_segment_id] * padding_length
label_ids += [pad_token_label_id] * padding_length
assert len(__lowercase ) == max_seq_length
assert len(__lowercase ) == max_seq_length
assert len(__lowercase ) == max_seq_length
assert len(__lowercase ) == max_seq_length
if ex_index < 5:
logger.info('''*** Example ***''' )
logger.info('''guid: %s''' , example.guid )
logger.info('''tokens: %s''' , ''' '''.join([str(__lowercase ) for x in tokens] ) )
logger.info('''input_ids: %s''' , ''' '''.join([str(__lowercase ) for x in input_ids] ) )
logger.info('''input_mask: %s''' , ''' '''.join([str(__lowercase ) for x in input_mask] ) )
logger.info('''segment_ids: %s''' , ''' '''.join([str(__lowercase ) for x in segment_ids] ) )
logger.info('''label_ids: %s''' , ''' '''.join([str(__lowercase ) for x in label_ids] ) )
if "token_type_ids" not in tokenizer.model_input_names:
SCREAMING_SNAKE_CASE__ : Optional[Any] =None
features.append(
InputFeatures(
input_ids=__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase , label_ids=__lowercase ) )
return features
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
'''simple docstring'''
snake_case_ = 42
snake_case_ = nn.CrossEntropyLoss().ignore_index
def __init__( self : Union[str, Any] , __lowercase : TokenClassificationTask , __lowercase : str , __lowercase : PreTrainedTokenizer , __lowercase : List[str] , __lowercase : str , __lowercase : Optional[int] = None , __lowercase : Union[str, Any]=False , __lowercase : Split = Split.train , ) -> Dict:
# Load data features from cache or dataset file
SCREAMING_SNAKE_CASE__ : str =os.path.join(
__lowercase , '''cached_{}_{}_{}'''.format(mode.value , tokenizer.__class__.__name__ , str(__lowercase ) ) , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
SCREAMING_SNAKE_CASE__ : List[str] =cached_features_file + '''.lock'''
with FileLock(__lowercase ):
if os.path.exists(__lowercase ) and not overwrite_cache:
logger.info(F"Loading features from cached file {cached_features_file}" )
SCREAMING_SNAKE_CASE__ : Dict =torch.load(__lowercase )
else:
logger.info(F"Creating features from dataset file at {data_dir}" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =token_classification_task.read_examples_from_file(__lowercase , __lowercase )
# TODO clean up all this to leverage built-in features of tokenizers
SCREAMING_SNAKE_CASE__ : Tuple =token_classification_task.convert_examples_to_features(
__lowercase , __lowercase , __lowercase , __lowercase , cls_token_at_end=bool(model_type in ['''xlnet'''] ) , cls_token=tokenizer.cls_token , cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0 , sep_token=tokenizer.sep_token , sep_token_extra=__lowercase , pad_on_left=bool(tokenizer.padding_side == '''left''' ) , pad_token=tokenizer.pad_token_id , pad_token_segment_id=tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info(F"Saving features into cached file {cached_features_file}" )
torch.save(self.features , __lowercase )
def __len__( self : Optional[Any] ) -> Tuple:
return len(self.features )
def __getitem__( self : str , __lowercase : List[str] ) -> InputFeatures:
return self.features[i]
if is_tf_available():
import tensorflow as tf
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
snake_case_ = 42
snake_case_ = -100
def __init__( self : Union[str, Any] , __lowercase : TokenClassificationTask , __lowercase : str , __lowercase : PreTrainedTokenizer , __lowercase : List[str] , __lowercase : str , __lowercase : Optional[int] = None , __lowercase : List[str]=False , __lowercase : Split = Split.train , ) -> str:
SCREAMING_SNAKE_CASE__ : Any =token_classification_task.read_examples_from_file(__lowercase , __lowercase )
# TODO clean up all this to leverage built-in features of tokenizers
SCREAMING_SNAKE_CASE__ : Optional[int] =token_classification_task.convert_examples_to_features(
__lowercase , __lowercase , __lowercase , __lowercase , cls_token_at_end=bool(model_type in ['''xlnet'''] ) , cls_token=tokenizer.cls_token , cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0 , sep_token=tokenizer.sep_token , sep_token_extra=__lowercase , pad_on_left=bool(tokenizer.padding_side == '''left''' ) , pad_token=tokenizer.pad_token_id , pad_token_segment_id=tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
def gen():
for ex in self.features:
if ex.token_type_ids is None:
yield (
{"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
ex.label_ids,
)
else:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label_ids,
)
if "token_type_ids" not in tokenizer.model_input_names:
SCREAMING_SNAKE_CASE__ : Optional[int] =tf.data.Dataset.from_generator(
__lowercase , ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa) , (
{'''input_ids''': tf.TensorShape([None] ), '''attention_mask''': tf.TensorShape([None] )},
tf.TensorShape([None] ),
) , )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =tf.data.Dataset.from_generator(
__lowercase , ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa) , (
{
'''input_ids''': tf.TensorShape([None] ),
'''attention_mask''': tf.TensorShape([None] ),
'''token_type_ids''': tf.TensorShape([None] ),
},
tf.TensorShape([None] ),
) , )
def __magic_name__ ( self : Optional[int] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Any =self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )
return self.dataset
def __len__( self : List[str] ) -> Tuple:
return len(self.features )
def __getitem__( self : List[Any] , __lowercase : Any ) -> InputFeatures:
return self.features[i]
| 708 |
'''simple docstring'''
def _a( UpperCamelCase__ : Optional[int], UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =0
SCREAMING_SNAKE_CASE__ : Union[str, Any] =len(UpperCamelCase__ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
SCREAMING_SNAKE_CASE__ : Union[str, Any] =left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCamelCase__ ):
return None
SCREAMING_SNAKE_CASE__ : Optional[int] =sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =left
SCREAMING_SNAKE_CASE__ : Optional[Any] =point
elif point > right:
SCREAMING_SNAKE_CASE__ : Optional[int] =right
SCREAMING_SNAKE_CASE__ : Tuple =point
else:
if item < current_item:
SCREAMING_SNAKE_CASE__ : str =point - 1
else:
SCREAMING_SNAKE_CASE__ : Tuple =point + 1
return None
def _a( UpperCamelCase__ : List[str], UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
SCREAMING_SNAKE_CASE__ : Dict =left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(UpperCamelCase__ ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
elif point > right:
return interpolation_search_by_recursion(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, point - 1 )
else:
return interpolation_search_by_recursion(
UpperCamelCase__, UpperCamelCase__, point + 1, UpperCamelCase__ )
def _a( UpperCamelCase__ : Dict ):
'''simple docstring'''
if collection != sorted(UpperCamelCase__ ):
raise ValueError('''Collection must be ascending sorted''' )
return True
if __name__ == "__main__":
import sys
a_ = 0
if debug == 1:
a_ = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
try:
__assert_sorted(collection)
except ValueError:
sys.exit('Sequence must be ascending sorted to apply interpolation search')
a_ = 6_7
a_ = interpolation_search(collection, target)
if result is not None:
print(F'''{target} found at positions: {result}''')
else:
print('Not found')
| 665 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a_ = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['SpeechEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['FlaxSpeechEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 709 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __SCREAMING_SNAKE_CASE :
@staticmethod
def __magic_name__ ( *__lowercase : int , **__lowercase : Optional[Any] ) -> Optional[Any]:
pass
@is_pipeline_test
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@require_torch
def __magic_name__ ( self : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , )
SCREAMING_SNAKE_CASE__ : List[str] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Any =image_classifier(__lowercase , candidate_labels=['''a''', '''b''', '''c'''] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(__lowercase ) , [
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
] , )
SCREAMING_SNAKE_CASE__ : int =image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
] , )
@require_tf
def __magic_name__ ( self : Union[str, Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , framework='''tf''' )
SCREAMING_SNAKE_CASE__ : Dict =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Tuple =image_classifier(__lowercase , candidate_labels=['''a''', '''b''', '''c'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}] , )
SCREAMING_SNAKE_CASE__ : List[Any] =image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
] , )
@slow
@require_torch
def __magic_name__ ( self : List[Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE__ : Any =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : List[str] =image_classifier(__lowercase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
@slow
@require_tf
def __magic_name__ ( self : Union[str, Any] ) -> int:
SCREAMING_SNAKE_CASE__ : str =pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , framework='''tf''' )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE__ : Optional[Any] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Any =image_classifier(__lowercase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
SCREAMING_SNAKE_CASE__ : List[Any] =image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
| 665 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
a_ = None
a_ = logging.get_logger(__name__)
a_ = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
a_ = {
'vocab_file': {
'facebook/nllb-200-distilled-600M': (
'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
),
},
'tokenizer_file': {
'facebook/nllb-200-distilled-600M': (
'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
),
},
}
a_ = {
'facebook/nllb-large-en-ro': 1_0_2_4,
'facebook/nllb-200-distilled-600M': 1_0_2_4,
}
# fmt: off
a_ = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = ["""input_ids""", """attention_mask"""]
snake_case_ = NllbTokenizer
snake_case_ = []
snake_case_ = []
def __init__( self : Any , __lowercase : int=None , __lowercase : str=None , __lowercase : str="<s>" , __lowercase : Any="</s>" , __lowercase : List[str]="</s>" , __lowercase : Any="<s>" , __lowercase : Optional[int]="<unk>" , __lowercase : List[Any]="<pad>" , __lowercase : Any="<mask>" , __lowercase : Any=None , __lowercase : str=None , __lowercase : Union[str, Any]=None , __lowercase : str=False , **__lowercase : List[str] , ) -> Optional[Any]:
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE__ : List[Any] =AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else mask_token
SCREAMING_SNAKE_CASE__ : Union[str, Any] =legacy_behaviour
super().__init__(
vocab_file=__lowercase , tokenizer_file=__lowercase , bos_token=__lowercase , eos_token=__lowercase , sep_token=__lowercase , cls_token=__lowercase , unk_token=__lowercase , pad_token=__lowercase , mask_token=__lowercase , src_lang=__lowercase , tgt_lang=__lowercase , additional_special_tokens=__lowercase , legacy_behaviour=__lowercase , **__lowercase , )
SCREAMING_SNAKE_CASE__ : List[str] =vocab_file
SCREAMING_SNAKE_CASE__ : str =False if not self.vocab_file else True
SCREAMING_SNAKE_CASE__ : Union[str, Any] =FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens} )
SCREAMING_SNAKE_CASE__ : List[Any] ={
lang_code: self.convert_tokens_to_ids(__lowercase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE__ : Optional[int] =src_lang if src_lang is not None else '''eng_Latn'''
SCREAMING_SNAKE_CASE__ : Dict =self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE__ : List[str] =tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def __magic_name__ ( self : Optional[int] ) -> str:
return self._src_lang
@src_lang.setter
def __magic_name__ ( self : List[Any] , __lowercase : str ) -> None:
SCREAMING_SNAKE_CASE__ : List[Any] =new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __magic_name__ ( self : Optional[int] , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __magic_name__ ( self : Dict , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ) -> List[int]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =[self.sep_token_id]
SCREAMING_SNAKE_CASE__ : List[Any] =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __magic_name__ ( self : Any , __lowercase : str , __lowercase : str , __lowercase : Optional[str] , __lowercase : Optional[str] , **__lowercase : Dict ) -> List[Any]:
if src_lang is None or tgt_lang is None:
raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model''' )
SCREAMING_SNAKE_CASE__ : Optional[int] =src_lang
SCREAMING_SNAKE_CASE__ : Any =self(__lowercase , add_special_tokens=__lowercase , return_tensors=__lowercase , **__lowercase )
SCREAMING_SNAKE_CASE__ : int =self.convert_tokens_to_ids(__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =tgt_lang_id
return inputs
def __magic_name__ ( self : int , __lowercase : List[str] , __lowercase : str = "eng_Latn" , __lowercase : Optional[List[str]] = None , __lowercase : str = "fra_Latn" , **__lowercase : Optional[Any] , ) -> BatchEncoding:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =src_lang
SCREAMING_SNAKE_CASE__ : str =tgt_lang
return super().prepare_seqaseq_batch(__lowercase , __lowercase , **__lowercase )
def __magic_name__ ( self : int ) -> Any:
return self.set_src_lang_special_tokens(self.src_lang )
def __magic_name__ ( self : List[str] ) -> Union[str, Any]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def __magic_name__ ( self : str , __lowercase : int ) -> None:
SCREAMING_SNAKE_CASE__ : List[str] =self.convert_tokens_to_ids(__lowercase )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : List[Any] =[]
SCREAMING_SNAKE_CASE__ : Optional[int] =[self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =[self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Tuple =[self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Tuple =self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : List[Any] =self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : Dict =processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def __magic_name__ ( self : Optional[Any] , __lowercase : str ) -> None:
SCREAMING_SNAKE_CASE__ : List[str] =self.convert_tokens_to_ids(__lowercase )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : Any =[]
SCREAMING_SNAKE_CASE__ : Optional[int] =[self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =[self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Optional[Any] =[self.eos_token_id]
SCREAMING_SNAKE_CASE__ : List[Any] =self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : Dict =self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : Optional[int] =processors.TemplateProcessing(
single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def __magic_name__ ( self : Dict , __lowercase : str , __lowercase : Optional[str] = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(__lowercase ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory." )
return
SCREAMING_SNAKE_CASE__ : Tuple =os.path.join(
__lowercase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowercase ):
copyfile(self.vocab_file , __lowercase )
return (out_vocab_file,)
| 710 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case_ = JukeboxTokenizer
snake_case_ = {
"""artist""": """Zac Brown Band""",
"""genres""": """Country""",
"""lyrics""": """I met a traveller from an antique land,
Who said \"Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
""",
}
@require_torch
def __magic_name__ ( self : Optional[int] ) -> str:
import torch
SCREAMING_SNAKE_CASE__ : List[str] =JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics''' )
SCREAMING_SNAKE_CASE__ : str =tokenizer(**self.metas )['''input_ids''']
# fmt: off
SCREAMING_SNAKE_CASE__ : str =[
torch.tensor([[
0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
76, 76]] ),
torch.tensor([[0, 0, 0, 10_69, 11]] ),
torch.tensor([[0, 0, 0, 10_69, 11]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
@require_torch
def __magic_name__ ( self : Any ) -> List[str]:
import torch
SCREAMING_SNAKE_CASE__ : int =JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics''' )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer(**self.metas )['''input_ids''']
# fmt: off
SCREAMING_SNAKE_CASE__ : Optional[int] =[
torch.tensor([[
0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
77, 77, 77, 77, 77, 77]] ),
torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]] ),
torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]] ),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0] , EXPECTED_OUTPUT[0] ) )
self.assertTrue(torch.allclose(tokens[1] , EXPECTED_OUTPUT[1] ) )
self.assertTrue(torch.allclose(tokens[2] , EXPECTED_OUTPUT[2] ) )
| 665 | 0 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
a_ = 1_6
a_ = 3_2
def _a( UpperCamelCase__ : Accelerator, UpperCamelCase__ : int = 1_6, UpperCamelCase__ : str = "bert-base-cased" ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any =AutoTokenizer.from_pretrained(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =load_dataset('''glue''', '''mrpc''' )
def tokenize_function(UpperCamelCase__ : Union[str, Any] ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE__ : int =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
SCREAMING_SNAKE_CASE__ : str =datasets.map(
UpperCamelCase__, batched=UpperCamelCase__, remove_columns=['''idx''', '''sentence1''', '''sentence2'''], load_from_cache_file=UpperCamelCase__ )
# 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__ : Tuple =tokenized_datasets.rename_column('''label''', '''labels''' )
def collate_fn(UpperCamelCase__ : List[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(UpperCamelCase__, padding='''max_length''', max_length=1_2_8, return_tensors='''pt''' )
return tokenizer.pad(UpperCamelCase__, padding='''longest''', return_tensors='''pt''' )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE__ : Any =DataLoader(
tokenized_datasets['''train'''], shuffle=UpperCamelCase__, collate_fn=UpperCamelCase__, batch_size=UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =DataLoader(
tokenized_datasets['''validation'''], shuffle=UpperCamelCase__, collate_fn=UpperCamelCase__, batch_size=UpperCamelCase__ )
return train_dataloader, eval_dataloader
def _a( UpperCamelCase__ : Tuple, UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE__ : Any =config['''lr''']
SCREAMING_SNAKE_CASE__ : int =int(config['''num_epochs'''] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =int(config['''seed'''] )
SCREAMING_SNAKE_CASE__ : str =int(config['''batch_size'''] )
SCREAMING_SNAKE_CASE__ : List[Any] =args.model_name_or_path
set_seed(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Tuple =get_dataloaders(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE__ : Any =AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__, return_dict=UpperCamelCase__ )
# Instantiate optimizer
SCREAMING_SNAKE_CASE__ : List[Any] =(
AdamW
if accelerator.state.deepspeed_plugin is None
or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
SCREAMING_SNAKE_CASE__ : int =optimizer_cls(params=model.parameters(), lr=UpperCamelCase__ )
if accelerator.state.deepspeed_plugin is not None:
SCREAMING_SNAKE_CASE__ : Tuple =accelerator.state.deepspeed_plugin.deepspeed_config[
'''gradient_accumulation_steps'''
]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] =1
SCREAMING_SNAKE_CASE__ : Any =(len(UpperCamelCase__ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
SCREAMING_SNAKE_CASE__ : Union[str, Any] =get_linear_schedule_with_warmup(
optimizer=UpperCamelCase__, num_warmup_steps=0, num_training_steps=UpperCamelCase__, )
else:
SCREAMING_SNAKE_CASE__ : List[str] =DummyScheduler(UpperCamelCase__, total_num_steps=UpperCamelCase__, warmup_num_steps=0 )
# 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__ : List[Any] =accelerator.prepare(
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
# We need to keep track of how many total steps we have iterated over
SCREAMING_SNAKE_CASE__ : int =0
# We also need to keep track of the stating epoch so files are named properly
SCREAMING_SNAKE_CASE__ : Optional[Any] =0
# Now we train the model
SCREAMING_SNAKE_CASE__ : List[Any] =evaluate.load('''glue''', '''mrpc''' )
SCREAMING_SNAKE_CASE__ : Optional[int] =0
SCREAMING_SNAKE_CASE__ : Dict ={}
for epoch in range(UpperCamelCase__, UpperCamelCase__ ):
model.train()
for step, batch in enumerate(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : List[str] =model(**UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =outputs.loss
SCREAMING_SNAKE_CASE__ : Any =loss / gradient_accumulation_steps
accelerator.backward(UpperCamelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
SCREAMING_SNAKE_CASE__ : Any =0
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__ : Tuple =model(**UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] =outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
SCREAMING_SNAKE_CASE__ : str =accelerator.gather(
(predictions, batch['''labels''']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(UpperCamelCase__ ) - 1:
SCREAMING_SNAKE_CASE__ : Any =predictions[: len(eval_dataloader.dataset ) - samples_seen]
SCREAMING_SNAKE_CASE__ : Optional[Any] =references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=UpperCamelCase__, references=UpperCamelCase__, )
SCREAMING_SNAKE_CASE__ : List[str] =metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"epoch {epoch}:", UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[Any] =eval_metric['''accuracy''']
if best_performance < eval_metric["accuracy"]:
SCREAMING_SNAKE_CASE__ : Dict =eval_metric['''accuracy''']
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir, '''all_results.json''' ), '''w''' ) as f:
json.dump(UpperCamelCase__, UpperCamelCase__ )
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' )
parser.add_argument(
'''--model_name_or_path''', type=UpperCamelCase__, default='''bert-base-cased''', help='''Path to pretrained model or model identifier from huggingface.co/models.''', required=UpperCamelCase__, )
parser.add_argument(
'''--output_dir''', type=UpperCamelCase__, default='''.''', help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''', )
parser.add_argument(
'''--performance_lower_bound''', type=UpperCamelCase__, default=UpperCamelCase__, help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.''', )
parser.add_argument(
'''--num_epochs''', type=UpperCamelCase__, default=3, help='''Number of train epochs.''', )
SCREAMING_SNAKE_CASE__ : Any =parser.parse_args()
SCREAMING_SNAKE_CASE__ : str ={'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
training_function(UpperCamelCase__, UpperCamelCase__ )
if __name__ == "__main__":
main()
| 711 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = """gpt_neox"""
def __init__( self : List[Any] , __lowercase : Union[str, Any]=5_04_32 , __lowercase : int=61_44 , __lowercase : Tuple=44 , __lowercase : List[str]=64 , __lowercase : str=2_45_76 , __lowercase : Dict="gelu" , __lowercase : Tuple=0.25 , __lowercase : Tuple=1_00_00 , __lowercase : Tuple=0.0 , __lowercase : str=0.0 , __lowercase : List[Any]=0.1 , __lowercase : Dict=20_48 , __lowercase : Any=0.02 , __lowercase : Dict=1e-5 , __lowercase : List[Any]=True , __lowercase : str=0 , __lowercase : Optional[Any]=2 , __lowercase : Tuple=False , __lowercase : List[Any]=True , __lowercase : Optional[Any]=None , **__lowercase : Any , ) -> Dict:
super().__init__(bos_token_id=__lowercase , eos_token_id=__lowercase , **__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] =max_position_embeddings
SCREAMING_SNAKE_CASE__ : Any =hidden_size
SCREAMING_SNAKE_CASE__ : str =num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any =num_attention_heads
SCREAMING_SNAKE_CASE__ : Union[str, Any] =intermediate_size
SCREAMING_SNAKE_CASE__ : Dict =hidden_act
SCREAMING_SNAKE_CASE__ : str =rotary_pct
SCREAMING_SNAKE_CASE__ : Optional[Any] =rotary_emb_base
SCREAMING_SNAKE_CASE__ : List[Any] =attention_dropout
SCREAMING_SNAKE_CASE__ : List[Any] =hidden_dropout
SCREAMING_SNAKE_CASE__ : str =classifier_dropout
SCREAMING_SNAKE_CASE__ : Any =initializer_range
SCREAMING_SNAKE_CASE__ : Dict =layer_norm_eps
SCREAMING_SNAKE_CASE__ : Any =use_cache
SCREAMING_SNAKE_CASE__ : Tuple =tie_word_embeddings
SCREAMING_SNAKE_CASE__ : Tuple =use_parallel_residual
SCREAMING_SNAKE_CASE__ : Union[str, Any] =rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )
def __magic_name__ ( self : Optional[Any] ) -> Optional[Any]:
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __lowercase ) 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}" )
SCREAMING_SNAKE_CASE__ : int =self.rope_scaling.get('''type''' , __lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =self.rope_scaling.get('''factor''' , __lowercase )
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(__lowercase , __lowercase ) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 665 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
a_ = logging.getLogger(__name__)
def _a( UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : List[Any] ):
'''simple docstring'''
return (preds == labels).mean()
@dataclass
class __SCREAMING_SNAKE_CASE :
snake_case_ = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
snake_case_ = field(
default=lowerCamelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
snake_case_ = field(
default=lowerCamelCase , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
snake_case_ = field(
default=lowerCamelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
@dataclass
class __SCREAMING_SNAKE_CASE :
snake_case_ = field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys() )} )
snake_case_ = field(metadata={"""help""": """Should contain the data files for the task."""} )
snake_case_ = field(
default=128 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
snake_case_ = field(
default=lowerCamelCase , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE__ : List[str] =parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''', training_args.local_rank, training_args.device, training_args.n_gpu, bool(training_args.local_rank != -1 ), training_args.fpaa, )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''', UpperCamelCase__ )
# Set seed
set_seed(training_args.seed )
try:
SCREAMING_SNAKE_CASE__ : List[Any] =processors[data_args.task_name]()
SCREAMING_SNAKE_CASE__ : int =processor.get_labels()
SCREAMING_SNAKE_CASE__ : Tuple =len(UpperCamelCase__ )
except KeyError:
raise ValueError('''Task not found: %s''' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE__ : Any =AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=UpperCamelCase__, finetuning_task=data_args.task_name, cache_dir=model_args.cache_dir, )
SCREAMING_SNAKE_CASE__ : Optional[Any] =AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, )
SCREAMING_SNAKE_CASE__ : Tuple =AutoModelForMultipleChoice.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, )
# Get datasets
SCREAMING_SNAKE_CASE__ : Tuple =(
MultipleChoiceDataset(
data_dir=data_args.data_dir, tokenizer=UpperCamelCase__, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.train, )
if training_args.do_train
else None
)
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(
MultipleChoiceDataset(
data_dir=data_args.data_dir, tokenizer=UpperCamelCase__, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.dev, )
if training_args.do_eval
else None
)
def compute_metrics(UpperCamelCase__ : EvalPrediction ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[int] =np.argmax(p.predictions, axis=1 )
return {"acc": simple_accuracy(UpperCamelCase__, p.label_ids )}
# Data collator
SCREAMING_SNAKE_CASE__ : Optional[Any] =DataCollatorWithPadding(UpperCamelCase__, pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
SCREAMING_SNAKE_CASE__ : Dict =Trainer(
model=UpperCamelCase__, args=UpperCamelCase__, train_dataset=UpperCamelCase__, eval_dataset=UpperCamelCase__, compute_metrics=UpperCamelCase__, data_collator=UpperCamelCase__, )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
SCREAMING_SNAKE_CASE__ : Dict ={}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
SCREAMING_SNAKE_CASE__ : List[str] =trainer.evaluate()
SCREAMING_SNAKE_CASE__ : str =os.path.join(training_args.output_dir, '''eval_results.txt''' )
if trainer.is_world_master():
with open(UpperCamelCase__, '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''', UpperCamelCase__, UpperCamelCase__ )
writer.write('''%s = %s\n''' % (key, value) )
results.update(UpperCamelCase__ )
return results
def _a( UpperCamelCase__ : Optional[int] ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 712 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
'tokenization_deberta': ['DebertaTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['DebertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'DebertaForMaskedLM',
'DebertaForQuestionAnswering',
'DebertaForSequenceClassification',
'DebertaForTokenClassification',
'DebertaModel',
'DebertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDebertaForMaskedLM',
'TFDebertaForQuestionAnswering',
'TFDebertaForSequenceClassification',
'TFDebertaForTokenClassification',
'TFDebertaModel',
'TFDebertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 665 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
def __magic_name__ ( self : Tuple ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Dict =self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__lowercase , '''hidden_sizes''' ) )
self.parent.assertTrue(hasattr(__lowercase , '''neck_hidden_sizes''' ) )
self.parent.assertTrue(hasattr(__lowercase , '''num_attention_heads''' ) )
class __SCREAMING_SNAKE_CASE :
def __init__( self : int , __lowercase : List[Any] , __lowercase : Optional[Any]=13 , __lowercase : Any=32 , __lowercase : Union[str, Any]=2 , __lowercase : Any=3 , __lowercase : Tuple=6_40 , __lowercase : Optional[Any]=4 , __lowercase : Optional[int]="silu" , __lowercase : List[str]=3 , __lowercase : int=32 , __lowercase : Tuple=0.1 , __lowercase : List[Any]=0.1 , __lowercase : Union[str, Any]=0.1 , __lowercase : Optional[Any]=0.02 , __lowercase : int=True , __lowercase : Optional[Any]=True , __lowercase : Optional[int]=10 , __lowercase : Optional[Any]=None , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : str =parent
SCREAMING_SNAKE_CASE__ : List[Any] =batch_size
SCREAMING_SNAKE_CASE__ : Optional[Any] =image_size
SCREAMING_SNAKE_CASE__ : List[Any] =patch_size
SCREAMING_SNAKE_CASE__ : Tuple =num_channels
SCREAMING_SNAKE_CASE__ : Tuple =last_hidden_size
SCREAMING_SNAKE_CASE__ : Tuple =num_attention_heads
SCREAMING_SNAKE_CASE__ : List[Any] =hidden_act
SCREAMING_SNAKE_CASE__ : Tuple =conv_kernel_size
SCREAMING_SNAKE_CASE__ : Any =output_stride
SCREAMING_SNAKE_CASE__ : str =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : List[str] =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Any =classifier_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] =use_labels
SCREAMING_SNAKE_CASE__ : Union[str, Any] =is_training
SCREAMING_SNAKE_CASE__ : List[str] =num_labels
SCREAMING_SNAKE_CASE__ : Tuple =initializer_range
SCREAMING_SNAKE_CASE__ : Dict =scope
def __magic_name__ ( self : Union[str, Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : str =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : Any =None
SCREAMING_SNAKE_CASE__ : Any =None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Optional[int] =ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE__ : Optional[Any] =ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
SCREAMING_SNAKE_CASE__ : List[str] =self.get_config()
return config, pixel_values, labels, pixel_labels
def __magic_name__ ( self : int ) -> List[Any]:
return MobileViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def __magic_name__ ( self : Optional[Any] , __lowercase : Optional[Any] , __lowercase : List[str] , __lowercase : Union[str, Any] , __lowercase : Optional[Any] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Dict =MobileViTModel(config=__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : str =model(__lowercase )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __magic_name__ ( self : Any , __lowercase : str , __lowercase : Optional[Any] , __lowercase : List[Any] , __lowercase : str ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Dict =self.num_labels
SCREAMING_SNAKE_CASE__ : Tuple =MobileViTForImageClassification(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] =model(__lowercase , labels=__lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __magic_name__ ( self : Dict , __lowercase : List[Any] , __lowercase : str , __lowercase : Optional[Any] , __lowercase : str ) -> Any:
SCREAMING_SNAKE_CASE__ : List[Any] =self.num_labels
SCREAMING_SNAKE_CASE__ : Optional[int] =MobileViTForSemanticSegmentation(__lowercase )
model.to(__lowercase )
model.eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model(__lowercase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
SCREAMING_SNAKE_CASE__ : str =model(__lowercase , labels=__lowercase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __magic_name__ ( self : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : List[str] =self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] =config_and_inputs
SCREAMING_SNAKE_CASE__ : Optional[Any] ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
snake_case_ = (
{
"""feature-extraction""": MobileViTModel,
"""image-classification""": MobileViTForImageClassification,
"""image-segmentation""": MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
snake_case_ = False
def __magic_name__ ( self : Tuple ) -> Dict:
SCREAMING_SNAKE_CASE__ : Tuple =MobileViTModelTester(self )
SCREAMING_SNAKE_CASE__ : List[str] =MobileViTConfigTester(self , config_class=__lowercase , has_text_modality=__lowercase )
def __magic_name__ ( self : Any ) -> Any:
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileViT does not use inputs_embeds''' )
def __magic_name__ ( self : Union[str, Any] ) -> Any:
pass
@unittest.skip(reason='''MobileViT does not support input and output embeddings''' )
def __magic_name__ ( self : Optional[Any] ) -> Optional[int]:
pass
@unittest.skip(reason='''MobileViT does not output attentions''' )
def __magic_name__ ( self : Union[str, Any] ) -> Optional[int]:
pass
def __magic_name__ ( self : Any ) -> Any:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : List[Any] =model_class(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE__ : int =[*signature.parameters.keys()]
SCREAMING_SNAKE_CASE__ : Optional[int] =['''pixel_values''']
self.assertListEqual(arg_names[:1] , __lowercase )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __magic_name__ ( self : List[str] ) -> int:
pass
def __magic_name__ ( self : Dict ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Optional[int] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowercase )
def __magic_name__ ( self : Optional[Any] ) -> List[Any]:
def check_hidden_states_output(__lowercase : Optional[Any] , __lowercase : str , __lowercase : str ):
SCREAMING_SNAKE_CASE__ : List[str] =model_class(__lowercase )
model.to(__lowercase )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Dict =model(**self._prepare_for_class(__lowercase , __lowercase ) )
SCREAMING_SNAKE_CASE__ : Any =outputs.hidden_states
SCREAMING_SNAKE_CASE__ : Dict =5
self.assertEqual(len(__lowercase ) , __lowercase )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
SCREAMING_SNAKE_CASE__ : Tuple =2
for i in range(len(__lowercase ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
SCREAMING_SNAKE_CASE__ : List[str] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : str =True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE__ : Tuple =True
check_hidden_states_output(__lowercase , __lowercase , __lowercase )
def __magic_name__ ( self : Any ) -> str:
SCREAMING_SNAKE_CASE__ : List[str] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowercase )
def __magic_name__ ( self : Optional[Any] ) -> Any:
SCREAMING_SNAKE_CASE__ : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__lowercase )
@slow
def __magic_name__ ( self : Optional[int] ) -> Optional[Any]:
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Dict =MobileViTModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self : int ) -> Union[str, Any]:
return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small''' ) if is_vision_available() else None
@slow
def __magic_name__ ( self : Dict ) -> Tuple:
SCREAMING_SNAKE_CASE__ : int =MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small''' ).to(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =self.default_image_processor
SCREAMING_SNAKE_CASE__ : int =prepare_img()
SCREAMING_SNAKE_CASE__ : List[Any] =image_processor(images=__lowercase , return_tensors='''pt''' ).to(__lowercase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[Any] =model(**__lowercase )
# verify the logits
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.tensor([-1.9364, -1.2327, -0.4653] ).to(__lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowercase , atol=1e-4 ) )
@slow
def __magic_name__ ( self : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Tuple =MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] =model.to(__lowercase )
SCREAMING_SNAKE_CASE__ : int =MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
SCREAMING_SNAKE_CASE__ : int =prepare_img()
SCREAMING_SNAKE_CASE__ : Optional[Any] =image_processor(images=__lowercase , return_tensors='''pt''' ).to(__lowercase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[str] =model(**__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE__ : Any =torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , __lowercase )
SCREAMING_SNAKE_CASE__ : Any =torch.tensor(
[
[[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
[[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
[[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
] , device=__lowercase , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __lowercase , atol=1e-4 ) )
@slow
def __magic_name__ ( self : int ) -> Any:
SCREAMING_SNAKE_CASE__ : List[Any] =MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model.to(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =prepare_img()
SCREAMING_SNAKE_CASE__ : Any =image_processor(images=__lowercase , return_tensors='''pt''' ).to(__lowercase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : List[Any] =model(**__lowercase )
SCREAMING_SNAKE_CASE__ : int =outputs.logits.detach().cpu()
SCREAMING_SNAKE_CASE__ : Any =image_processor.post_process_semantic_segmentation(outputs=__lowercase , target_sizes=[(50, 60)] )
SCREAMING_SNAKE_CASE__ : List[Any] =torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , __lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =image_processor.post_process_semantic_segmentation(outputs=__lowercase )
SCREAMING_SNAKE_CASE__ : int =torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , __lowercase )
| 713 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _a( UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : NDArray[floataa], UpperCamelCase__ : list[int], UpperCamelCase__ : int, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple =coefficient_matrix.shape
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple =constant_matrix.shape
if rowsa != colsa:
SCREAMING_SNAKE_CASE__ : Any =f"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
raise ValueError(UpperCamelCase__ )
if colsa != 1:
SCREAMING_SNAKE_CASE__ : str =f"Constant matrix must be nx1 but received {rowsa}x{colsa}"
raise ValueError(UpperCamelCase__ )
if rowsa != rowsa:
SCREAMING_SNAKE_CASE__ : str =(
'''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:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(
'''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''' )
SCREAMING_SNAKE_CASE__ : NDArray[floataa] =np.concatenate(
(coefficient_matrix, constant_matrix), axis=1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any =table.shape
strictly_diagonally_dominant(UpperCamelCase__ )
# Iterates the whole matrix for given number of times
for _ in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : List[str] =[]
for row in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =0
for col in range(UpperCamelCase__ ):
if col == row:
SCREAMING_SNAKE_CASE__ : int =table[row][col]
elif col == cols - 1:
SCREAMING_SNAKE_CASE__ : Any =table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
SCREAMING_SNAKE_CASE__ : int =(temp + val) / denom
new_val.append(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =new_val
return [float(UpperCamelCase__ ) for i in new_val]
def _a( UpperCamelCase__ : NDArray[floataa] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] =table.shape
SCREAMING_SNAKE_CASE__ : Any =True
for i in range(0, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : int =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()
| 665 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = GPTaTokenizer
snake_case_ = GPTaTokenizerFast
snake_case_ = True
snake_case_ = {"""add_prefix_space""": True}
snake_case_ = False
def __magic_name__ ( self : Any ) -> Tuple:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
SCREAMING_SNAKE_CASE__ : List[Any] =[
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
'''<|endoftext|>''',
]
SCREAMING_SNAKE_CASE__ : Any =dict(zip(__lowercase , range(len(__lowercase ) ) ) )
SCREAMING_SNAKE_CASE__ : Any =['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
SCREAMING_SNAKE_CASE__ : Tuple ={'''unk_token''': '''<unk>'''}
SCREAMING_SNAKE_CASE__ : Optional[Any] =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
SCREAMING_SNAKE_CASE__ : List[Any] =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 ) )
def __magic_name__ ( self : Any , **__lowercase : Optional[Any] ) -> str:
kwargs.update(self.special_tokens_map )
return GPTaTokenizer.from_pretrained(self.tmpdirname , **__lowercase )
def __magic_name__ ( self : int , **__lowercase : Optional[Any] ) -> Tuple:
kwargs.update(self.special_tokens_map )
return GPTaTokenizerFast.from_pretrained(self.tmpdirname , **__lowercase )
def __magic_name__ ( self : List[str] , __lowercase : Tuple ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Any ='''lower newer'''
SCREAMING_SNAKE_CASE__ : List[str] ='''lower newer'''
return input_text, output_text
def __magic_name__ ( self : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =GPTaTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
SCREAMING_SNAKE_CASE__ : Tuple ='''lower newer'''
SCREAMING_SNAKE_CASE__ : List[str] =['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
SCREAMING_SNAKE_CASE__ : List[Any] =tokenizer.tokenize(__lowercase , add_prefix_space=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
SCREAMING_SNAKE_CASE__ : Dict =tokens + [tokenizer.unk_token]
SCREAMING_SNAKE_CASE__ : Optional[Any] =[14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase ) , __lowercase )
def __magic_name__ ( self : Tuple ) -> Union[str, Any]:
if not self.test_rust_tokenizer:
return
SCREAMING_SNAKE_CASE__ : List[str] =self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Tuple =self.get_rust_tokenizer(add_prefix_space=__lowercase )
SCREAMING_SNAKE_CASE__ : int ='''lower newer'''
# Testing tokenization
SCREAMING_SNAKE_CASE__ : Union[str, Any] =tokenizer.tokenize(__lowercase , add_prefix_space=__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =rust_tokenizer.tokenize(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# Testing conversion to ids without special tokens
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.encode(__lowercase , add_special_tokens=__lowercase , add_prefix_space=__lowercase )
SCREAMING_SNAKE_CASE__ : List[Any] =rust_tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# Testing conversion to ids with special tokens
SCREAMING_SNAKE_CASE__ : Optional[int] =self.get_rust_tokenizer(add_prefix_space=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =tokenizer.encode(__lowercase , add_prefix_space=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =rust_tokenizer.encode(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
# Testing the unknown token
SCREAMING_SNAKE_CASE__ : Dict =tokens + [rust_tokenizer.unk_token]
SCREAMING_SNAKE_CASE__ : Union[str, Any] =[14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__lowercase ) , __lowercase )
def __magic_name__ ( self : Union[str, Any] , *__lowercase : Tuple , **__lowercase : Dict ) -> Tuple:
# It's very difficult to mix/test pretokenization with byte-level
# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def __magic_name__ ( self : Optional[int] , __lowercase : List[str]=15 ) -> Any:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.rust_tokenizer_class.from_pretrained(__lowercase , **__lowercase )
# Simple input
SCREAMING_SNAKE_CASE__ : Dict ='''This is a simple input'''
SCREAMING_SNAKE_CASE__ : List[Any] =['''This is a simple input 1''', '''This is a simple input 2''']
SCREAMING_SNAKE_CASE__ : Optional[Any] =('''This is a simple input''', '''This is a pair''')
SCREAMING_SNAKE_CASE__ : List[str] =[
('''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(__lowercase , tokenizer_r.encode , __lowercase , max_length=__lowercase , padding='''max_length''' )
# Simple input
self.assertRaises(__lowercase , tokenizer_r.encode_plus , __lowercase , max_length=__lowercase , padding='''max_length''' )
# Simple input
self.assertRaises(
__lowercase , tokenizer_r.batch_encode_plus , __lowercase , max_length=__lowercase , padding='''max_length''' , )
# Pair input
self.assertRaises(__lowercase , tokenizer_r.encode , __lowercase , max_length=__lowercase , padding='''max_length''' )
# Pair input
self.assertRaises(__lowercase , tokenizer_r.encode_plus , __lowercase , max_length=__lowercase , padding='''max_length''' )
# Pair input
self.assertRaises(
__lowercase , tokenizer_r.batch_encode_plus , __lowercase , max_length=__lowercase , padding='''max_length''' , )
def __magic_name__ ( self : Union[str, Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[str] =GPTaTokenizer.from_pretrained(self.tmpdirname , pad_token='''<pad>''' )
# Simple input
SCREAMING_SNAKE_CASE__ : Optional[int] ='''This is a simple input'''
SCREAMING_SNAKE_CASE__ : str =['''This is a simple input looooooooong''', '''This is a simple input''']
SCREAMING_SNAKE_CASE__ : List[Any] =('''This is a simple input''', '''This is a pair''')
SCREAMING_SNAKE_CASE__ : Optional[int] =[
('''This is a simple input loooooong''', '''This is a simple input'''),
('''This is a simple pair loooooong''', '''This is a simple pair'''),
]
SCREAMING_SNAKE_CASE__ : Dict =tokenizer.pad_token_id
SCREAMING_SNAKE_CASE__ : Union[str, Any] =tokenizer(__lowercase , padding='''max_length''' , max_length=30 , return_tensors='''np''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =tokenizer(__lowercase , padding=__lowercase , truncate=__lowercase , return_tensors='''np''' )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer(*__lowercase , padding='''max_length''' , max_length=60 , return_tensors='''np''' )
SCREAMING_SNAKE_CASE__ : Any =tokenizer(__lowercase , padding=__lowercase , truncate=__lowercase , return_tensors='''np''' )
# s
# test single string max_length padding
self.assertEqual(out_s['''input_ids'''].shape[-1] , 30 )
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] , 33 )
# 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] , 60 )
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] , 52 )
# 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 __magic_name__ ( self : Union[str, Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Dict ='''$$$'''
SCREAMING_SNAKE_CASE__ : List[Any] =GPTaTokenizer.from_pretrained(self.tmpdirname , bos_token=__lowercase , add_bos_token=__lowercase )
SCREAMING_SNAKE_CASE__ : int ='''This is a simple input'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =['''This is a simple input 1''', '''This is a simple input 2''']
SCREAMING_SNAKE_CASE__ : List[Any] =tokenizer.bos_token_id
SCREAMING_SNAKE_CASE__ : Union[str, Any] =tokenizer(__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =tokenizer(__lowercase )
self.assertEqual(out_s.input_ids[0] , __lowercase )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
SCREAMING_SNAKE_CASE__ : Any =tokenizer.decode(out_s.input_ids )
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , __lowercase )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
def __magic_name__ ( self : Tuple ) -> List[Any]:
pass
def __magic_name__ ( self : Any ) -> int:
# TODO: change to self.get_tokenizers() when the fast version is implemented
SCREAMING_SNAKE_CASE__ : Dict =[self.get_tokenizer(do_lower_case=__lowercase , add_bos_token=__lowercase )]
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
SCREAMING_SNAKE_CASE__ : List[str] ='''Encode this.'''
SCREAMING_SNAKE_CASE__ : List[Any] ='''This one too please.'''
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
encoded_sequence += tokenizer.encode(__lowercase , add_special_tokens=__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.encode_plus(
__lowercase , __lowercase , add_special_tokens=__lowercase , return_special_tokens_mask=__lowercase , )
SCREAMING_SNAKE_CASE__ : Any =encoded_sequence_dict['''input_ids''']
SCREAMING_SNAKE_CASE__ : Tuple =encoded_sequence_dict['''special_tokens_mask''']
self.assertEqual(len(__lowercase ) , len(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] =[
(x if not special_tokens_mask[i] else None) for i, x in enumerate(__lowercase )
]
SCREAMING_SNAKE_CASE__ : int =[x for x in filtered_sequence if x is not None]
self.assertEqual(__lowercase , __lowercase )
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : List[Any] ) -> Tuple:
# More context:
# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439
SCREAMING_SNAKE_CASE__ : List[str] =AutoTokenizer.from_pretrained('''facebook/opt-350m''' , from_slow=__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] ='''A photo of a cat'''
SCREAMING_SNAKE_CASE__ : Dict =tokenizer.encode(
__lowercase , )
self.assertEqual(__lowercase , [2, 2_50, 13_45, 9, 10, 47_58] )
tokenizer.save_pretrained('''test_opt''' )
SCREAMING_SNAKE_CASE__ : List[Any] =AutoTokenizer.from_pretrained('''./test_opt''' )
SCREAMING_SNAKE_CASE__ : List[Any] =tokenizer.encode(
__lowercase , )
self.assertEqual(__lowercase , [2, 2_50, 13_45, 9, 10, 47_58] )
def __magic_name__ ( self : Dict ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[str] =AutoTokenizer.from_pretrained('''facebook/opt-350m''' , use_slow=__lowercase )
SCREAMING_SNAKE_CASE__ : int ='''A photo of a cat'''
SCREAMING_SNAKE_CASE__ : Any =tokenizer.encode(
__lowercase , )
# Same as above
self.assertEqual(__lowercase , [2, 2_50, 13_45, 9, 10, 47_58] )
@unittest.skip('''This test is failing because of a bug in the fast tokenizer''' )
def __magic_name__ ( self : List[str] ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[Any] =AutoTokenizer.from_pretrained('''facebook/opt-350m''' , from_slow=__lowercase )
SCREAMING_SNAKE_CASE__ : str ='''bos'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer.get_vocab()['''bos''']
SCREAMING_SNAKE_CASE__ : Any ='''A photo of a cat'''
SCREAMING_SNAKE_CASE__ : Any =tokenizer.encode(
__lowercase , )
# We changed the bos token
self.assertEqual(__lowercase , [3_19_57, 2_50, 13_45, 9, 10, 47_58] )
tokenizer.save_pretrained('''./tok''' )
SCREAMING_SNAKE_CASE__ : List[Any] =AutoTokenizer.from_pretrained('''./tok''' )
self.assertTrue(tokenizer.is_fast )
SCREAMING_SNAKE_CASE__ : str =tokenizer.encode(
__lowercase , )
self.assertEqual(__lowercase , [3_19_57, 2_50, 13_45, 9, 10, 47_58] )
| 714 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def _a( UpperCamelCase__ : str, UpperCamelCase__ : Tuple, UpperCamelCase__ : Any, UpperCamelCase__ : List[str], UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
with open(UpperCamelCase__ ) as metadata_file:
SCREAMING_SNAKE_CASE__ : Optional[int] =json.load(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =LukeConfig(use_entity_aware_attention=UpperCamelCase__, **metadata['''model_config'''] )
# Load in the weights from the checkpoint_path
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch.load(UpperCamelCase__, map_location='''cpu''' )['''module''']
# Load the entity vocab file
SCREAMING_SNAKE_CASE__ : List[str] =load_original_entity_vocab(UpperCamelCase__ )
# add an entry for [MASK2]
SCREAMING_SNAKE_CASE__ : Optional[int] =max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
SCREAMING_SNAKE_CASE__ : Optional[int] =XLMRobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''] )
# Add special tokens to the token vocabulary for downstream tasks
SCREAMING_SNAKE_CASE__ : List[Any] =AddedToken('''<ent>''', lstrip=UpperCamelCase__, rstrip=UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Any =AddedToken('''<ent2>''', lstrip=UpperCamelCase__, rstrip=UpperCamelCase__ )
tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f"Saving tokenizer to {pytorch_dump_folder_path}" )
tokenizer.save_pretrained(UpperCamelCase__ )
with open(os.path.join(UpperCamelCase__, '''tokenizer_config.json''' ), '''r''' ) as f:
SCREAMING_SNAKE_CASE__ : Optional[Any] =json.load(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : int ='''MLukeTokenizer'''
with open(os.path.join(UpperCamelCase__, '''tokenizer_config.json''' ), '''w''' ) as f:
json.dump(UpperCamelCase__, UpperCamelCase__ )
with open(os.path.join(UpperCamelCase__, MLukeTokenizer.vocab_files_names['''entity_vocab_file'''] ), '''w''' ) as f:
json.dump(UpperCamelCase__, UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =MLukeTokenizer.from_pretrained(UpperCamelCase__ )
# Initialize the embeddings of the special tokens
SCREAMING_SNAKE_CASE__ : str =tokenizer.convert_tokens_to_ids(['''@'''] )[0]
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer.convert_tokens_to_ids(['''#'''] )[0]
SCREAMING_SNAKE_CASE__ : Dict =state_dict['''embeddings.word_embeddings.weight''']
SCREAMING_SNAKE_CASE__ : List[str] =word_emb[ent_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : Tuple =word_emb[enta_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : str =torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =state_dict[bias_name]
SCREAMING_SNAKE_CASE__ : List[Any] =decoder_bias[ent_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : str =decoder_bias[enta_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : List[str] =torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
SCREAMING_SNAKE_CASE__ : Tuple =f"encoder.layer.{layer_index}.attention.self."
SCREAMING_SNAKE_CASE__ : Optional[int] =state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE__ : Optional[int] =state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE__ : List[Any] =state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
SCREAMING_SNAKE_CASE__ : Any =state_dict['''entity_embeddings.entity_embeddings.weight''']
SCREAMING_SNAKE_CASE__ : Any =entity_emb[entity_vocab['''[MASK]''']].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : Any =torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
SCREAMING_SNAKE_CASE__ : Optional[int] =state_dict['''entity_predictions.bias''']
SCREAMING_SNAKE_CASE__ : Tuple =entity_prediction_bias[entity_vocab['''[MASK]''']].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ : Any =torch.cat([entity_prediction_bias, entity_mask_bias] )
SCREAMING_SNAKE_CASE__ : int =LukeForMaskedLM(config=UpperCamelCase__ ).eval()
state_dict.pop('''entity_predictions.decoder.weight''' )
state_dict.pop('''lm_head.decoder.weight''' )
state_dict.pop('''lm_head.decoder.bias''' )
SCREAMING_SNAKE_CASE__ : Tuple =OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('''lm_head''' ) or key.startswith('''entity_predictions''' )):
SCREAMING_SNAKE_CASE__ : Optional[Any] =state_dict[key]
else:
SCREAMING_SNAKE_CASE__ : Any =state_dict[key]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] =model.load_state_dict(UpperCamelCase__, strict=UpperCamelCase__ )
if set(UpperCamelCase__ ) != {"luke.embeddings.position_ids"}:
raise ValueError(f"Unexpected unexpected_keys: {unexpected_keys}" )
if set(UpperCamelCase__ ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(f"Unexpected missing_keys: {missing_keys}" )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
SCREAMING_SNAKE_CASE__ : Any =MLukeTokenizer.from_pretrained(UpperCamelCase__, task='''entity_classification''' )
SCREAMING_SNAKE_CASE__ : str ='''ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] =(0, 9)
SCREAMING_SNAKE_CASE__ : str =tokenizer(UpperCamelCase__, entity_spans=[span], return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : List[Any] =model(**UpperCamelCase__ )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
SCREAMING_SNAKE_CASE__ : str =torch.Size((1, 3_3, 7_6_8) )
SCREAMING_SNAKE_CASE__ : int =torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f"Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3], UpperCamelCase__, atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
SCREAMING_SNAKE_CASE__ : Any =torch.Size((1, 1, 7_6_8) )
SCREAMING_SNAKE_CASE__ : int =torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
f"Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"
f" {expected_shape}" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3], UpperCamelCase__, atol=1e-4 ):
raise ValueError
# Verify masked word/entity prediction
SCREAMING_SNAKE_CASE__ : str =MLukeTokenizer.from_pretrained(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] ='''Tokyo is the capital of <mask>.'''
SCREAMING_SNAKE_CASE__ : Dict =(2_4, 3_0)
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer(UpperCamelCase__, entity_spans=[span], return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : List[str] =model(**UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[Any] =encoding['''input_ids'''][0].tolist()
SCREAMING_SNAKE_CASE__ : Any =input_ids.index(tokenizer.convert_tokens_to_ids('''<mask>''' ) )
SCREAMING_SNAKE_CASE__ : List[Any] =outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =outputs.entity_logits[0][0].argmax().item()
SCREAMING_SNAKE_CASE__ : Dict =[
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('''en:''' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('''Saving PyTorch model to {}'''.format(UpperCamelCase__ ) )
model.save_pretrained(UpperCamelCase__ )
def _a( UpperCamelCase__ : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] =['''[MASK]''', '''[PAD]''', '''[UNK]''']
SCREAMING_SNAKE_CASE__ : List[str] =[json.loads(UpperCamelCase__ ) for line in open(UpperCamelCase__ )]
SCREAMING_SNAKE_CASE__ : Optional[int] ={}
for entry in data:
SCREAMING_SNAKE_CASE__ : Tuple =entry['''id''']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
SCREAMING_SNAKE_CASE__ : str =entity_id
break
SCREAMING_SNAKE_CASE__ : Union[str, Any] =f"{language}:{entity_name}"
SCREAMING_SNAKE_CASE__ : Union[str, Any] =entity_id
return new_mapping
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
parser.add_argument(
'--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
)
parser.add_argument(
'--entity_vocab_path',
default=None,
type=str,
help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
)
parser.add_argument(
'--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
)
a_ = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 665 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] =generate_pascal_triangle(UpperCamelCase__ )
for row_idx in range(UpperCamelCase__ ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=''' ''' )
# Print row values
for col_idx in range(row_idx + 1 ):
if col_idx != row_idx:
print(triangle[row_idx][col_idx], end=''' ''' )
else:
print(triangle[row_idx][col_idx], end='''''' )
print()
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
if not isinstance(UpperCamelCase__, UpperCamelCase__ ):
raise TypeError('''The input value of \'num_rows\' should be \'int\'''' )
if num_rows == 0:
return []
elif num_rows < 0:
raise ValueError(
'''The input value of \'num_rows\' should be greater than or equal to 0''' )
SCREAMING_SNAKE_CASE__ : list[list[int]] =[]
for current_row_idx in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[Any] =populate_current_row(UpperCamelCase__, UpperCamelCase__ )
triangle.append(UpperCamelCase__ )
return triangle
def _a( UpperCamelCase__ : list[list[int]], UpperCamelCase__ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =[-1] * (current_row_idx + 1)
# first and last elements of current row are equal to 1
SCREAMING_SNAKE_CASE__ : List[str] =1, 1
for current_col_idx in range(1, UpperCamelCase__ ):
calculate_current_element(
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
return current_row
def _a( UpperCamelCase__ : list[list[int]], UpperCamelCase__ : list[int], UpperCamelCase__ : int, UpperCamelCase__ : int, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict =triangle[current_row_idx - 1][current_col_idx - 1]
SCREAMING_SNAKE_CASE__ : str =triangle[current_row_idx - 1][current_col_idx]
SCREAMING_SNAKE_CASE__ : str =above_to_left_elt + above_to_right_elt
def _a( UpperCamelCase__ : int ):
'''simple docstring'''
if not isinstance(UpperCamelCase__, UpperCamelCase__ ):
raise TypeError('''The input value of \'num_rows\' should be \'int\'''' )
if num_rows == 0:
return []
elif num_rows < 0:
raise ValueError(
'''The input value of \'num_rows\' should be greater than or equal to 0''' )
SCREAMING_SNAKE_CASE__ : list[list[int]] =[[1]]
for row_index in range(1, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Tuple =[0] + result[-1] + [0]
SCREAMING_SNAKE_CASE__ : List[Any] =row_index + 1
# Calculate the number of distinct elements in a row
SCREAMING_SNAKE_CASE__ : int =sum(divmod(UpperCamelCase__, 2 ) )
SCREAMING_SNAKE_CASE__ : str =[
temp_row[i - 1] + temp_row[i] for i in range(1, distinct_elements + 1 )
]
SCREAMING_SNAKE_CASE__ : int =row_first_half[: (row_index + 1) // 2]
row_second_half.reverse()
SCREAMING_SNAKE_CASE__ : Optional[int] =row_first_half + row_second_half
result.append(UpperCamelCase__ )
return result
def _a( ):
'''simple docstring'''
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(UpperCamelCase__ : Callable, UpperCamelCase__ : int ) -> None:
SCREAMING_SNAKE_CASE__ : List[Any] =f"{func.__name__}({value})"
SCREAMING_SNAKE_CASE__ : Optional[int] =timeit(f"__main__.{call}", setup='''import __main__''' )
# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
print(f"{call:38} -- {timing:.4f} seconds" )
for value in range(1_5 ): # (1, 7, 14):
for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
benchmark_a_function(UpperCamelCase__, UpperCamelCase__ )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 715 |
'''simple docstring'''
def _a( UpperCamelCase__ : str, UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] =len(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Tuple =[[False for _ in range(m + 1 )] for _ in range(n + 1 )]
SCREAMING_SNAKE_CASE__ : List[Any] =True
for i in range(UpperCamelCase__ ):
for j in range(m + 1 ):
if dp[i][j]:
if j < m and a[i].upper() == b[j]:
SCREAMING_SNAKE_CASE__ : Optional[int] =True
if a[i].islower():
SCREAMING_SNAKE_CASE__ : List[Any] =True
return dp[n][m]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665 | 0 |
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