


import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


lowerCAmelCase__  :List[Any]             =							logging.getLogger(__name__)

def 	lowerCAmelCase__			(      a__:		int      ,  a__:		str	)  ->		Any:


							'''simple docstring'''





							if os.path.exists(a__	):
														if os.path.exists(os.path.join(a__      ,  'config.json'	)	) and os.path.isfile(
														    os.path.join(a__      ,  'config.json'	)	):
																					os.remove(os.path.join(a__      ,  'config.json'	)	)
														if os.path.exists(os.path.join(a__      ,  'pytorch_model.bin'	)	) and os.path.isfile(
														    os.path.join(a__      ,  'pytorch_model.bin'	)	):
																					os.remove(os.path.join(a__      ,  'pytorch_model.bin'	)	)
							else:
														os.makedirs(a__	)
							model.save_pretrained(a__	)

def 	lowerCAmelCase__			(      a__:		int      ,  a__:		Union[str, Any]=False	)  ->		List[str]:


							'''simple docstring'''





							_UpperCAmelCase					=      2
							if unlogit:
														_UpperCAmelCase					=      torch.pow(a__      ,  a__	)
							_UpperCAmelCase					=      p * torch.log(a__	)
							_UpperCAmelCase					=      0
							return -plogp.sum(dim=-1	)

def 	lowerCAmelCase__			(      a__:		str	)  ->		Union[str, Any]:


							'''simple docstring'''





							logger.info('lv, h >\t' + '\t'.join(F'''{x + 1}''' for x in range(len(a__	)	)	)	)
							for row in range(len(a__	)	):
														if tensor.dtype != torch.long:
																					logger.info(F'''layer {row + 1}:\t''' + '\t'.join(F'''{x:.5f}''' for x in tensor[row].cpu().data	)	)
														else:
																					logger.info(F'''layer {row + 1}:\t''' + '\t'.join(F'''{x:d}''' for x in tensor[row].cpu().data	)	)

def 	lowerCAmelCase__			(      a__:		Tuple      ,  a__:		Optional[int]      ,  a__:		str      ,  a__:		Optional[Any]=True      ,  a__:		Tuple=True      ,  a__:		int=None      ,  a__:		int=False	)  ->		str:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      model.config.num_hidden_layers, model.config.num_attention_heads
							_UpperCAmelCase					=      torch.zeros(a__      ,  a__	).to(args.device	)
							_UpperCAmelCase					=      torch.zeros(a__      ,  a__	).to(args.device	)

							if head_mask is None:
														_UpperCAmelCase					=      torch.ones(a__      ,  a__	).to(args.device	)

							head_mask.requires_grad_(requires_grad=a__	)
							# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
							if actually_pruned:
														_UpperCAmelCase					=      None

							_UpperCAmelCase					=      0.0
							_UpperCAmelCase					=      0.0
							for step, inputs in enumerate(tqdm(a__      ,  desc='Iteration'      ,  disable=args.local_rank not in [-1, 0]	)	):
														_UpperCAmelCase					=      tuple(t.to(args.device	) for t in inputs	)
														((_UpperCAmelCase)		,	)					=      inputs

														# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
														_UpperCAmelCase					=      model(a__      ,  labels=a__      ,  head_mask=a__	)
														#  (loss), lm_logits, presents, (all hidden_states), (attentions)
														_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      (
														    outputs[0],
														    outputs[1],
														    outputs[-1],
														)  # Loss and logits are the first, attention the last
														loss.backward()  # Backpropagate to populate the gradients in the head mask
														total_loss += loss.detach().cpu().numpy()
														if compute_entropy:
																					for layer, attn in enumerate(a__	):
																												_UpperCAmelCase					=      entropy(attn.detach()      ,  a__	)
																												attn_entropy[layer] += masked_entropy.sum(-1	).sum(0	).sum(0	).detach()

														if compute_importance:
																					head_importance += head_mask.grad.abs().detach()
														tot_tokens += torch.ones_like(a__	).float().detach().sum().data

							# Normalize
							attn_entropy /= tot_tokens
							head_importance /= tot_tokens
							# Layerwise importance normalization
							if not args.dont_normalize_importance_by_layer:
														_UpperCAmelCase					=      2
														_UpperCAmelCase					=      torch.pow(torch.pow(a__      ,  a__	).sum(-1	)      ,  1 / exponent	)
														head_importance /= norm_by_layer.unsqueeze(-1	) + 1e-20

							if not args.dont_normalize_global_importance:
														_UpperCAmelCase					=      (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

							# Print matrices
							if compute_entropy:
														logger.info('Attention entropies'	)
														print_ad_tensor(a__	)
							if compute_importance:
														logger.info('Head importance scores'	)
														print_ad_tensor(a__	)
							logger.info('Head ranked by importance scores'	)
							_UpperCAmelCase					=      torch.zeros(head_importance.numel()      ,  dtype=torch.long      ,  device=args.device	)
							_UpperCAmelCase					=      torch.arange(
							    head_importance.numel()      ,  device=args.device	)
							_UpperCAmelCase					=      head_ranks.view_as(a__	)
							print_ad_tensor(a__	)
							return attn_entropy, head_importance, total_loss

def 	lowerCAmelCase__			(      a__:		Any      ,  a__:		Dict      ,  a__:		int	)  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      compute_heads_importance(a__      ,  a__      ,  a__      ,  compute_entropy=a__	)
							_UpperCAmelCase					=      1 / loss  # instead of downsteam score use the LM loss
							logger.info('Pruning: original score: %f, threshold: %f'      ,  a__      ,  original_score * args.masking_threshold	)

							_UpperCAmelCase					=      torch.ones_like(a__	)
							_UpperCAmelCase					=      max(1      ,  int(new_head_mask.numel() * args.masking_amount	)	)

							_UpperCAmelCase					=      original_score
							while current_score >= original_score * args.masking_threshold:
														_UpperCAmelCase					=      new_head_mask.clone().detach()  # save current head mask
														# heads from least important to most - keep only not-masked heads
														_UpperCAmelCase					=      float('Inf'	)
														_UpperCAmelCase					=      head_importance.view(-1	).sort()[1]

														if len(a__	) <= num_to_mask:
																					print('BREAK BY num_to_mask'	)
																					break

														# mask heads
														_UpperCAmelCase					=      current_heads_to_mask[:num_to_mask]
														logger.info('Heads to mask: %s'      ,  str(current_heads_to_mask.tolist()	)	)
														_UpperCAmelCase					=      new_head_mask.view(-1	)
														_UpperCAmelCase					=      0.0
														_UpperCAmelCase					=      new_head_mask.view_as(a__	)
														_UpperCAmelCase					=      new_head_mask.clone().detach()
														print_ad_tensor(a__	)

														# Compute metric and head importance again
														_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      compute_heads_importance(
														    a__      ,  a__      ,  a__      ,  compute_entropy=a__      ,  head_mask=a__	)
														_UpperCAmelCase					=      1 / loss
														logger.info(
														    'Masking: current score: %f, remaining heads %d (%.1f percents)'      ,  a__      ,  new_head_mask.sum()      ,  new_head_mask.sum() / new_head_mask.numel() * 1_0_0      ,  )

							logger.info('Final head mask'	)
							print_ad_tensor(a__	)
							np.save(os.path.join(args.output_dir      ,  'head_mask.npy'	)      ,  head_mask.detach().cpu().numpy()	)

							return head_mask

def 	lowerCAmelCase__			(      a__:		List[str]      ,  a__:		List[str]      ,  a__:		Dict      ,  a__:		List[Any]	)  ->		int:


							'''simple docstring'''





							_UpperCAmelCase					=      datetime.now()
							_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      compute_heads_importance(
							    a__      ,  a__      ,  a__      ,  compute_entropy=a__      ,  compute_importance=a__      ,  head_mask=a__	)
							_UpperCAmelCase					=      1 / loss
							_UpperCAmelCase					=      datetime.now() - before_time

							_UpperCAmelCase					=      sum(p.numel() for p in model.parameters()	)
							_UpperCAmelCase					=      {
							    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(a__	)	)
							}

							for k, v in heads_to_prune.items():
														if isinstance(a__      ,  a__	):
																					_UpperCAmelCase					=      [
																					    v,
																					]

							assert sum(len(a__	) for h in heads_to_prune.values()	) == (1 - head_mask.long()).sum().item()
							model.prune_heads(a__	)
							_UpperCAmelCase					=      sum(p.numel() for p in model.parameters()	)

							_UpperCAmelCase					=      datetime.now()
							_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      compute_heads_importance(
							    a__      ,  a__      ,  a__      ,  compute_entropy=a__      ,  compute_importance=a__      ,  head_mask=a__      ,  actually_pruned=a__      ,  )

							_UpperCAmelCase					=      1 / loss
							_UpperCAmelCase					=      datetime.now() - before_time

							logger.info(
							    'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'      ,  a__      ,  a__      ,  pruned_num_params / original_num_params * 1_0_0      ,  )
							logger.info('Pruning: score with masking: %f score with pruning: %f'      ,  a__      ,  a__	)
							logger.info('Pruning: speed ratio (original timing / new timing): %f percents'      ,  original_time / new_time * 1_0_0	)
							save_model(a__      ,  args.output_dir	)

def 	lowerCAmelCase__			(      )  ->		Dict:


							'''simple docstring'''





							_UpperCAmelCase					=      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--data_dir'      ,  default=a__      ,  type=a__      ,  required=a__      ,  help='The input data dir. Should contain the .tsv files (or other data files) for the task.'      ,  )
							parser.add_argument(
							    '--model_name_or_path'      ,  default=a__      ,  type=a__      ,  required=a__      ,  help='Path to pretrained model or model identifier from huggingface.co/models'      ,  )
							parser.add_argument(
							    '--output_dir'      ,  default=a__      ,  type=a__      ,  required=a__      ,  help='The output directory where the model predictions and checkpoints will be written.'      ,  )

							# Other parameters
							parser.add_argument(
							    '--config_name'      ,  default=''      ,  type=a__      ,  help='Pretrained config name or path if not the same as model_name_or_path'      ,  )
							parser.add_argument(
							    '--tokenizer_name'      ,  default=''      ,  type=a__      ,  help='Pretrained tokenizer name or path if not the same as model_name_or_path'      ,  )
							parser.add_argument(
							    '--cache_dir'      ,  default=a__      ,  type=a__      ,  help='Where do you want to store the pre-trained models downloaded from s3'      ,  )
							parser.add_argument(
							    '--data_subset'      ,  type=a__      ,  default=-1      ,  help='If > 0: limit the data to a subset of data_subset instances.'	)
							parser.add_argument(
							    '--overwrite_output_dir'      ,  action='store_true'      ,  help='Whether to overwrite data in output directory'	)
							parser.add_argument(
							    '--overwrite_cache'      ,  action='store_true'      ,  help='Overwrite the cached training and evaluation sets'	)

							parser.add_argument(
							    '--dont_normalize_importance_by_layer'      ,  action='store_true'      ,  help='Don\'t normalize importance score by layers'	)
							parser.add_argument(
							    '--dont_normalize_global_importance'      ,  action='store_true'      ,  help='Don\'t normalize all importance scores between 0 and 1'      ,  )

							parser.add_argument(
							    '--try_masking'      ,  action='store_true'      ,  help='Whether to try to mask head until a threshold of accuracy.'	)
							parser.add_argument(
							    '--masking_threshold'      ,  default=0.9      ,  type=a__      ,  help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'      ,  )
							parser.add_argument(
							    '--masking_amount'      ,  default=0.1      ,  type=a__      ,  help='Amount to heads to masking at each masking step.'	)
							parser.add_argument('--metric_name'      ,  default='acc'      ,  type=a__      ,  help='Metric to use for head masking.'	)

							parser.add_argument(
							    '--max_seq_length'      ,  default=1_2_8      ,  type=a__      ,  help=(
							        'The maximum total input sequence length after WordPiece tokenization. \n'
							        'Sequences longer than this will be truncated, sequences shorter padded.'
							    )      ,  )
							parser.add_argument('--batch_size'      ,  default=1      ,  type=a__      ,  help='Batch size.'	)

							parser.add_argument('--seed'      ,  type=a__      ,  default=4_2	)
							parser.add_argument('--local_rank'      ,  type=a__      ,  default=-1      ,  help='local_rank for distributed training on gpus'	)
							parser.add_argument('--no_cuda'      ,  action='store_true'      ,  help='Whether not to use CUDA when available'	)
							parser.add_argument('--server_ip'      ,  type=a__      ,  default=''      ,  help='Can be used for distant debugging.'	)
							parser.add_argument('--server_port'      ,  type=a__      ,  default=''      ,  help='Can be used for distant debugging.'	)
							_UpperCAmelCase					=      parser.parse_args()

							if args.server_ip and args.server_port:
														# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
														import ptvsd

														print('Waiting for debugger attach'	)
														ptvsd.enable_attach(address=(args.server_ip, args.server_port)      ,  redirect_output=a__	)
														ptvsd.wait_for_attach()

							# Setup devices and distributed training
							if args.local_rank == -1 or args.no_cuda:
														_UpperCAmelCase					=      torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'	)
														_UpperCAmelCase					=      0 if args.no_cuda else torch.cuda.device_count()
							else:
														torch.cuda.set_device(args.local_rank	)
														_UpperCAmelCase					=      torch.device('cuda'      ,  args.local_rank	)
														_UpperCAmelCase					=      1
														torch.distributed.init_process_group(backend='nccl'	)  # Initializes the distributed backend

							# Setup logging
							logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN	)
							logger.info('device: {} n_gpu: {}, distributed: {}'.format(args.device      ,  args.n_gpu      ,  bool(args.local_rank != -1	)	)	)

							_UpperCAmelCase					=      GPTaLMHeadModel.from_pretrained(args.model_name_or_path	)

							# Distributed and parallel training
							model.to(args.device	)
							if args.local_rank != -1:
														_UpperCAmelCase					=      nn.parallel.DistributedDataParallel(
														    a__      ,  device_ids=[args.local_rank]      ,  output_device=args.local_rank      ,  find_unused_parameters=a__	)
							elif args.n_gpu > 1:
														_UpperCAmelCase					=      nn.DataParallel(a__	)

							# Print/save training arguments
							os.makedirs(args.output_dir      ,  exist_ok=a__	)
							torch.save(a__      ,  os.path.join(args.output_dir      ,  'run_args.bin'	)	)
							logger.info('Training/evaluation parameters %s'      ,  a__	)

							# Prepare dataset
							_UpperCAmelCase					=      np.concatenate(
							    [
							        np.loadtxt(args.data_dir      ,  dtype=np.intaa	),
							    ]	)
							_UpperCAmelCase					=      (torch.from_numpy(a__	),)
							_UpperCAmelCase					=      TensorDataset(*a__	)
							_UpperCAmelCase					=      RandomSampler(a__	)
							_UpperCAmelCase					=      DataLoader(a__      ,  sampler=a__      ,  batch_size=args.batch_size	)

							# Compute head entropy and importance score
							compute_heads_importance(a__      ,  a__      ,  a__	)

							# Try head masking (set heads to zero until the score goes under a threshole)
							# and head pruning (remove masked heads and see the effect on the network)
							if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
														_UpperCAmelCase					=      mask_heads(a__      ,  a__      ,  a__	)
														prune_heads(a__      ,  a__      ,  a__      ,  a__	)


if __name__ == "__main__":
		main()




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCAmelCase__  :List[str]             =							logging.get_logger(__name__)

class 		__a				(    UpperCAmelCase      ):
		_a     :  Optional[int]           =     ['pixel_values']
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = 1 / 255      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	None:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      size if size is not None else {'height': 256, 'width': 256}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      crop_size if crop_size is not None else {'height': 224, 'width': 224}
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'						)

									_UpperCAmelCase					=      do_resize
									_UpperCAmelCase					=      size
									_UpperCAmelCase					=      resample
									_UpperCAmelCase					=      do_center_crop
									_UpperCAmelCase					=      crop_size
									_UpperCAmelCase					=      do_rescale
									_UpperCAmelCase					=      rescale_factor
									_UpperCAmelCase					=      do_normalize
									_UpperCAmelCase					=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									_UpperCAmelCase					=      image_std if image_std is not None else IMAGENET_STANDARD_STD
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									if "height" not in size or "width" not in size:
																raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
									return resize(
									    _SCREAMING_SNAKE_CASE      ,   size=(size['height'], size['width'])      ,   resample=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									if "height" not in size or "width" not in size:
																raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
									return center_crop(_SCREAMING_SNAKE_CASE      ,   size=(size['height'], size['width'])      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	str:




									"""simple docstring"""





									return rescale(_SCREAMING_SNAKE_CASE      ,   scale=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	np.ndarray:




									"""simple docstring"""





									return normalize(_SCREAMING_SNAKE_CASE      ,   mean=_SCREAMING_SNAKE_CASE      ,   std=_SCREAMING_SNAKE_CASE      ,   data_format=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	PIL.Image.Image:




									"""simple docstring"""





									_UpperCAmelCase					=      do_resize if do_resize is not None else self.do_resize
									_UpperCAmelCase					=      resample if resample is not None else self.resample
									_UpperCAmelCase					=      do_center_crop if do_center_crop is not None else self.do_center_crop
									_UpperCAmelCase					=      do_rescale if do_rescale is not None else self.do_rescale
									_UpperCAmelCase					=      rescale_factor if rescale_factor is not None else self.rescale_factor
									_UpperCAmelCase					=      do_normalize if do_normalize is not None else self.do_normalize
									_UpperCAmelCase					=      image_mean if image_mean is not None else self.image_mean
									_UpperCAmelCase					=      image_std if image_std is not None else self.image_std

									_UpperCAmelCase					=      size if size is not None else self.size
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      crop_size if crop_size is not None else self.crop_size
									_UpperCAmelCase					=      get_size_dict(_SCREAMING_SNAKE_CASE      ,   param_name='crop_size'						)

									_UpperCAmelCase					=      make_list_of_images(_SCREAMING_SNAKE_CASE						)

									if not valid_images(_SCREAMING_SNAKE_CASE						):
																raise ValueError(
																    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																    'torch.Tensor, tf.Tensor or jax.ndarray.'						)

									if do_resize and size is None or resample is None:
																raise ValueError('Size and resample must be specified if do_resize is True.'						)

									if do_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.'						)

									# All transformations expect numpy arrays.
									_UpperCAmelCase					=      [to_numpy_array(_SCREAMING_SNAKE_CASE						) for image in images]

									if do_resize:
																_UpperCAmelCase					=      [self.resize(image=_SCREAMING_SNAKE_CASE      ,   size=_SCREAMING_SNAKE_CASE      ,   resample=_SCREAMING_SNAKE_CASE						) for image in images]

									if do_center_crop:
																_UpperCAmelCase					=      [self.center_crop(image=_SCREAMING_SNAKE_CASE      ,   size=_SCREAMING_SNAKE_CASE						) for image in images]

									if do_rescale:
																_UpperCAmelCase					=      [self.rescale(image=_SCREAMING_SNAKE_CASE      ,   scale=_SCREAMING_SNAKE_CASE						) for image in images]

									if do_normalize:
																_UpperCAmelCase					=      [self.normalize(image=_SCREAMING_SNAKE_CASE      ,   mean=_SCREAMING_SNAKE_CASE      ,   std=_SCREAMING_SNAKE_CASE						) for image in images]

									_UpperCAmelCase					=      [to_channel_dimension_format(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) for image in images]

									_UpperCAmelCase					=      {'pixel_values': images}
									return BatchFeature(data=_SCREAMING_SNAKE_CASE      ,   tensor_type=_SCREAMING_SNAKE_CASE						)
