



'''simple docstring'''

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


UpperCAmelCase      						=       logging.getLogger(__name__)






def 						_snake_case						(   _SCREAMING_SNAKE_CASE	:		int				,						_SCREAMING_SNAKE_CASE	:		Dict				)     ->		List[str]:







	"""simple docstring"""





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






def 						_snake_case						(   _SCREAMING_SNAKE_CASE	:		Optional[Any]				,						_SCREAMING_SNAKE_CASE	:		List[Any]=False				)     ->		Tuple:







	"""simple docstring"""
	lowerCAmelCase       =      2
	if unlogit:
		lowerCAmelCase       =      torch.pow(SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				)
	lowerCAmelCase       =      p * torch.log(SCREAMING_SNAKE_CASE__				)
	lowerCAmelCase       =      0
	return -plogp.sum(dim=-1				)






def 						_snake_case						(   _SCREAMING_SNAKE_CASE	:		Tuple				)     ->		int:







	"""simple docstring"""
	logger.info("""lv, h >\t""" + """\t""".join(f'{x + 1}' for x in range(len(SCREAMING_SNAKE_CASE__				)				)				)				)
	for row in range(len(SCREAMING_SNAKE_CASE__				)				):
		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 						_snake_case						(   _SCREAMING_SNAKE_CASE	:		int				,						_SCREAMING_SNAKE_CASE	:		Tuple				,						_SCREAMING_SNAKE_CASE	:		str				,						_SCREAMING_SNAKE_CASE	:		Any=True				,						_SCREAMING_SNAKE_CASE	:		Dict=True				,						_SCREAMING_SNAKE_CASE	:		str=None				,						_SCREAMING_SNAKE_CASE	:		Dict=False				)     ->		Tuple:







	"""simple docstring"""
	lowerCAmelCase       =      model.config.num_hidden_layers, model.config.num_attention_heads
	lowerCAmelCase       =      torch.zeros(SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				).to(args.device				)
	lowerCAmelCase       =      torch.zeros(SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				).to(args.device				)

	if head_mask is None:
		lowerCAmelCase       =      torch.ones(SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				).to(args.device				)

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

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

		# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
		lowerCAmelCase       =      model(SCREAMING_SNAKE_CASE__				,						labels=SCREAMING_SNAKE_CASE__				,						head_mask=SCREAMING_SNAKE_CASE__				)
		#  (loss), lm_logits, presents, (all hidden_states), (attentions)
		lowerCAmelCase       =      (
		    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(SCREAMING_SNAKE_CASE__				):
				lowerCAmelCase       =      entropy(attn.detach()				,						SCREAMING_SNAKE_CASE__				)
				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(SCREAMING_SNAKE_CASE__				).float().detach().sum().data

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

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

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






def 						_snake_case						(   _SCREAMING_SNAKE_CASE	:		str				,						_SCREAMING_SNAKE_CASE	:		Optional[int]				,						_SCREAMING_SNAKE_CASE	:		List[str]				)     ->		Any:







	"""simple docstring"""
	lowerCAmelCase       =      compute_heads_importance(SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				,						compute_entropy=SCREAMING_SNAKE_CASE__				)
	lowerCAmelCase       =      1 / loss  # instead of downsteam score use the LM loss
	logger.info("""Pruning: original score: %f, threshold: %f"""				,						SCREAMING_SNAKE_CASE__				,						original_score * args.masking_threshold				)

	lowerCAmelCase       =      torch.ones_like(SCREAMING_SNAKE_CASE__				)
	lowerCAmelCase       =      max(1				,						int(new_head_mask.numel() * args.masking_amount				)				)

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

		if len(SCREAMING_SNAKE_CASE__				) <= num_to_mask:
			print("""BREAK BY num_to_mask"""				)
			break

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

		# Compute metric and head importance again
		lowerCAmelCase       =      compute_heads_importance(
		    SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				,						compute_entropy=SCREAMING_SNAKE_CASE__				,						head_mask=SCREAMING_SNAKE_CASE__				)
		lowerCAmelCase       =      1 / loss
		logger.info(
		    """Masking: current score: %f, remaining heads %d (%.1f percents)"""				,						SCREAMING_SNAKE_CASE__				,						new_head_mask.sum()				,						new_head_mask.sum() / new_head_mask.numel() * 100				,						)

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

	return head_mask






def 						_snake_case						(   _SCREAMING_SNAKE_CASE	:		List[Any]				,						_SCREAMING_SNAKE_CASE	:		int				,						_SCREAMING_SNAKE_CASE	:		Optional[Any]				,						_SCREAMING_SNAKE_CASE	:		Union[str, Any]				)     ->		Optional[int]:







	"""simple docstring"""
	lowerCAmelCase       =      datetime.now()
	lowerCAmelCase       =      compute_heads_importance(
	    SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				,						compute_entropy=SCREAMING_SNAKE_CASE__				,						compute_importance=SCREAMING_SNAKE_CASE__				,						head_mask=SCREAMING_SNAKE_CASE__				)
	lowerCAmelCase       =      1 / loss
	lowerCAmelCase       =      datetime.now() - before_time

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

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

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

	lowerCAmelCase       =      datetime.now()
	lowerCAmelCase       =      compute_heads_importance(
	    SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				,						compute_entropy=SCREAMING_SNAKE_CASE__				,						compute_importance=SCREAMING_SNAKE_CASE__				,						head_mask=SCREAMING_SNAKE_CASE__				,						actually_pruned=SCREAMING_SNAKE_CASE__				,						)

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

	logger.info(
	    """Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"""				,						SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				,						pruned_num_params / original_num_params * 100				,						)
	logger.info("""Pruning: score with masking: %f score with pruning: %f"""				,						SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				)
	logger.info("""Pruning: speed ratio (original timing / new timing): %f percents"""				,						original_time / new_time * 100				)
	save_model(SCREAMING_SNAKE_CASE__				,						args.output_dir				)






def 						_snake_case						(   )     ->		int:







	"""simple docstring"""
	lowerCAmelCase       =      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--data_dir"""				,						default=SCREAMING_SNAKE_CASE__				,						type=SCREAMING_SNAKE_CASE__				,						required=SCREAMING_SNAKE_CASE__				,						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=SCREAMING_SNAKE_CASE__				,						type=SCREAMING_SNAKE_CASE__				,						required=SCREAMING_SNAKE_CASE__				,						help="""Path to pretrained model or model identifier from huggingface.co/models"""				,						)
	parser.add_argument(
	    """--output_dir"""				,						default=SCREAMING_SNAKE_CASE__				,						type=SCREAMING_SNAKE_CASE__				,						required=SCREAMING_SNAKE_CASE__				,						help="""The output directory where the model predictions and checkpoints will be written."""				,						)

	# Other parameters
	parser.add_argument(
	    """--config_name"""				,						default=""""""				,						type=SCREAMING_SNAKE_CASE__				,						help="""Pretrained config name or path if not the same as model_name_or_path"""				,						)
	parser.add_argument(
	    """--tokenizer_name"""				,						default=""""""				,						type=SCREAMING_SNAKE_CASE__				,						help="""Pretrained tokenizer name or path if not the same as model_name_or_path"""				,						)
	parser.add_argument(
	    """--cache_dir"""				,						default=SCREAMING_SNAKE_CASE__				,						type=SCREAMING_SNAKE_CASE__				,						help="""Where do you want to store the pre-trained models downloaded from s3"""				,						)
	parser.add_argument(
	    """--data_subset"""				,						type=SCREAMING_SNAKE_CASE__				,						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=SCREAMING_SNAKE_CASE__				,						help="""masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."""				,						)
	parser.add_argument(
	    """--masking_amount"""				,						default=0.1				,						type=SCREAMING_SNAKE_CASE__				,						help="""Amount to heads to masking at each masking step."""				)
	parser.add_argument("""--metric_name"""				,						default="""acc"""				,						type=SCREAMING_SNAKE_CASE__				,						help="""Metric to use for head masking."""				)

	parser.add_argument(
	    """--max_seq_length"""				,						default=128				,						type=SCREAMING_SNAKE_CASE__				,						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=SCREAMING_SNAKE_CASE__				,						help="""Batch size."""				)

	parser.add_argument("""--seed"""				,						type=SCREAMING_SNAKE_CASE__				,						default=42				)
	parser.add_argument("""--local_rank"""				,						type=SCREAMING_SNAKE_CASE__				,						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=SCREAMING_SNAKE_CASE__				,						default=""""""				,						help="""Can be used for distant debugging."""				)
	parser.add_argument("""--server_port"""				,						type=SCREAMING_SNAKE_CASE__				,						default=""""""				,						help="""Can be used for distant debugging."""				)
	lowerCAmelCase       =      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=SCREAMING_SNAKE_CASE__				)
		ptvsd.wait_for_attach()

	# Setup devices and distributed training
	if args.local_rank == -1 or args.no_cuda:
		lowerCAmelCase       =      torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""				)
		lowerCAmelCase       =      0 if args.no_cuda else torch.cuda.device_count()
	else:
		torch.cuda.set_device(args.local_rank				)
		lowerCAmelCase       =      torch.device("""cuda"""				,						args.local_rank				)
		lowerCAmelCase       =      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				)				)				)

	lowerCAmelCase       =      GPTaLMHeadModel.from_pretrained(args.model_name_or_path				)

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

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

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

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

	# 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:
		lowerCAmelCase       =      mask_heads(SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				)
		prune_heads(SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				)


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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase		   =			logging.get_logger(__name__)


if is_vision_available():
 import PIL




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    List[str] =['pixel_values']






      def __init__(   self		:	Any     ,  lowercase_		:	bool = True     ,  lowercase_		:	Dict[str, int] = None     ,  lowercase_		:	PILImageResampling = PILImageResampling.BICUBIC     ,  lowercase_		:	bool = True     ,  lowercase_		:	Dict[str, int] = None     ,  lowercase_		:	bool = True     ,  lowercase_		:	Union[int, float] = 1 / 255     ,  lowercase_		:	bool = True     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	bool = True     ,  **lowercase_		:	str     ,  )   ->		None:
             """simple docstring"""




             super().__init__(**lowercase_					)
             _lowerCamelCase			:						Optional[int]            =size if size is not None else {'shortest_edge': 224}
             _lowerCamelCase			:						List[Any]            =get_size_dict(lowercase_     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =crop_size if crop_size is not None else {'height': 224, 'width': 224}
             _lowerCamelCase			:						str            =get_size_dict(lowercase_     ,  default_to_square=lowercase_     ,  param_name='crop_size'					)

             _lowerCamelCase			:						Dict            =do_resize
             _lowerCamelCase			:						int            =size
             _lowerCamelCase			:						Optional[Any]            =resample
             _lowerCamelCase			:						Optional[int]            =do_center_crop
             _lowerCamelCase			:						Tuple            =crop_size
             _lowerCamelCase			:						Optional[int]            =do_rescale
             _lowerCamelCase			:						Optional[Any]            =rescale_factor
             _lowerCamelCase			:						Union[str, Any]            =do_normalize
             _lowerCamelCase			:						Optional[int]            =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
             _lowerCamelCase			:						Any            =image_std if image_std is not None else OPENAI_CLIP_STD
             _lowerCamelCase			:						Any            =do_convert_rgb






      def 			lowerCamelCase			(   self		:	int     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Dict[str, int]     ,  lowercase_		:	PILImageResampling = PILImageResampling.BICUBIC     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Any     ,  )   ->		np.ndarray:
             """simple docstring"""




             _lowerCamelCase			:						int            =get_size_dict(lowercase_     ,  default_to_square=lowercase_					)
             if "shortest_edge" not in size:
                    raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''					)
             _lowerCamelCase			:						Union[str, Any]            =get_resize_output_image_size(lowercase_     ,  size=size['shortest_edge']     ,  default_to_square=lowercase_					)
             return resize(lowercase_     ,  size=lowercase_     ,  resample=lowercase_     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Dict[str, int]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Tuple     ,  )   ->		np.ndarray:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =get_size_dict(lowercase_					)
             if "height" not in size or "width" not in size:
                    raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''					)
             return center_crop(lowercase_     ,  size=(size['height'], size['width'])     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Union[int, float]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	int     ,  )   ->		str:
             """simple docstring"""




             return rescale(lowercase_     ,  scale=lowercase_     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Union[float, List[float]]     ,  lowercase_		:	Union[float, List[float]]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Tuple     ,  )   ->		np.ndarray:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	List[str]     ,  lowercase_		:	ImageInput     ,  lowercase_		:	bool = None     ,  lowercase_		:	Dict[str, int] = None     ,  lowercase_		:	PILImageResampling = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	int = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	float = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	Optional[Union[str, TensorType]] = None     ,  lowercase_		:	Optional[ChannelDimension] = ChannelDimension.FIRST     ,  **lowercase_		:	Union[str, Any]     ,  )   ->		PIL.Image.Image:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =do_resize if do_resize is not None else self.do_resize
             _lowerCamelCase			:						List[str]            =size if size is not None else self.size
             _lowerCamelCase			:						Any            =get_size_dict(lowercase_     ,  param_name='size'     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =resample if resample is not None else self.resample
             _lowerCamelCase			:						List[Any]            =do_center_crop if do_center_crop is not None else self.do_center_crop
             _lowerCamelCase			:						Optional[Any]            =crop_size if crop_size is not None else self.crop_size
             _lowerCamelCase			:						Dict            =get_size_dict(lowercase_     ,  param_name='crop_size'     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =do_rescale if do_rescale is not None else self.do_rescale
             _lowerCamelCase			:						Tuple            =rescale_factor if rescale_factor is not None else self.rescale_factor
             _lowerCamelCase			:						List[str]            =do_normalize if do_normalize is not None else self.do_normalize
             _lowerCamelCase			:						Tuple            =image_mean if image_mean is not None else self.image_mean
             _lowerCamelCase			:						int            =image_std if image_std is not None else self.image_std
             _lowerCamelCase			:						Tuple            =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

             _lowerCamelCase			:						Any            =make_list_of_images(lowercase_					)

             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.'					)

             if do_resize and size is None:
                    raise ValueError('Size 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.'					)

             # PIL RGBA images are converted to RGB
             if do_convert_rgb:
                    _lowerCamelCase			:						Tuple            =[convert_to_rgb(lowercase_					) for image in images]

             # All transformations expect numpy arrays.
             _lowerCamelCase			:						Optional[Any]            =[to_numpy_array(lowercase_					) for image in images]

             if do_resize:
                    _lowerCamelCase			:						Optional[Any]            =[self.resize(image=lowercase_     ,  size=lowercase_     ,  resample=lowercase_					) for image in images]

             if do_center_crop:
                    _lowerCamelCase			:						Optional[int]            =[self.center_crop(image=lowercase_     ,  size=lowercase_					) for image in images]

             if do_rescale:
                    _lowerCamelCase			:						Optional[Any]            =[self.rescale(image=lowercase_     ,  scale=lowercase_					) for image in images]

             if do_normalize:
                    _lowerCamelCase			:						List[Any]            =[self.normalize(image=lowercase_     ,  mean=lowercase_     ,  std=lowercase_					) for image in images]

             _lowerCamelCase			:						List[str]            =[to_channel_dimension_format(lowercase_     ,  lowercase_					) for image in images]

             _lowerCamelCase			:						Tuple            ={'pixel_values': images}
             return BatchFeature(data=lowercase_     ,  tensor_type=lowercase_					)


