





'''simple docstring'''







# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      Dict,      SCREAMING_SNAKE_CASE__   :      Tuple=0 )			->						Any:
						# Format the message.
						if name is None:
												UpperCAmelCase_      :   Tuple      =	None
						else:
												UpperCAmelCase_      :   Dict      =	'''.''' * max(0,      spaces - 2 ) + '''# {:''' + str(50 - spaces ) + '''s}'''
												UpperCAmelCase_      :   List[str]      =	fmt.format(SCREAMING_SNAKE_CASE__ )

						# Print and recurse (if needed).
						if isinstance(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ ):
												if msg is not None:
																		print(SCREAMING_SNAKE_CASE__ )
												for k in val.keys():
																		recursive_print(SCREAMING_SNAKE_CASE__,      val[k],      spaces + 2 )
						elif isinstance(SCREAMING_SNAKE_CASE__,      torch.Tensor ):
												print(SCREAMING_SNAKE_CASE__,      ''':''',      val.size() )
						else:
												print(SCREAMING_SNAKE_CASE__,      ''':''',      SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Any,      SCREAMING_SNAKE_CASE__   :      Any,      SCREAMING_SNAKE_CASE__   :      Tuple,      SCREAMING_SNAKE_CASE__   :      Any,      SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						List[Any]:
						# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
						# for compatibility with later versions of NVIDIA Megatron-LM.
						# The inverse operation is performed inside Megatron-LM to read checkpoints:
						# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
						# If param is the weight tensor of the self-attention block, the returned tensor
						# will have to be transposed one more time to be read by HuggingFace BERT.
						UpperCAmelCase_      :   Optional[Any]      =	param.size()
						if checkpoint_version == 1.0:
												# version 1.0 stores [num_heads * hidden_size * num_splits, :]
												UpperCAmelCase_      :   Tuple      =	(num_heads, hidden_size, num_splits) + input_shape[1:]
												UpperCAmelCase_      :   Any      =	param.view(*SCREAMING_SNAKE_CASE__ )
												UpperCAmelCase_      :   Optional[int]      =	param.transpose(0,      2 )
												UpperCAmelCase_      :   str      =	param.transpose(1,      2 ).contiguous()
						elif checkpoint_version >= 2.0:
												# other versions store [num_heads * num_splits * hidden_size, :]
												UpperCAmelCase_      :   str      =	(num_heads, num_splits, hidden_size) + input_shape[1:]
												UpperCAmelCase_      :   Any      =	param.view(*SCREAMING_SNAKE_CASE__ )
												UpperCAmelCase_      :   Any      =	param.transpose(0,      1 ).contiguous()
						UpperCAmelCase_      :   Optional[int]      =	param.view(*SCREAMING_SNAKE_CASE__ )
						return param





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      int,      SCREAMING_SNAKE_CASE__   :      int,      SCREAMING_SNAKE_CASE__   :      Dict )			->						Tuple:
						# The converted output model.
						UpperCAmelCase_      :   List[Any]      =	{}

						# old versions did not store training args
						UpperCAmelCase_      :   Optional[Any]      =	input_state_dict.get('''args''',      SCREAMING_SNAKE_CASE__ )
						if ds_args is not None:
												# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
												# from pprint import pprint
												# pprint(vars(ds_args))

												UpperCAmelCase_      :   List[str]      =	ds_args.tokenizer_type
												UpperCAmelCase_      :   Any      =	ds_args.padded_vocab_size
												UpperCAmelCase_      :   List[str]      =	ds_args.max_position_embeddings
												UpperCAmelCase_      :   int      =	ds_args.hidden_size
												UpperCAmelCase_      :   Optional[Any]      =	ds_args.num_layers
												UpperCAmelCase_      :   Union[str, Any]      =	ds_args.num_attention_heads
												UpperCAmelCase_      :   Optional[Any]      =	ds_args.ffn_hidden_size if '''ffn_hidden_size''' in ds_args else 4 * ds_args.hidden_size
												# pprint(config)

						# The number of heads.
						UpperCAmelCase_      :   Tuple      =	config.num_attention_heads
						# The hidden_size per head.
						UpperCAmelCase_      :   Dict      =	config.hidden_size // heads
						# Megatron-LM checkpoint version
						if "checkpoint_version" in input_state_dict.keys():
												UpperCAmelCase_      :   Optional[Any]      =	input_state_dict['''checkpoint_version''']
						else:
												UpperCAmelCase_      :   Dict      =	0.0

						# The model.
						UpperCAmelCase_      :   str      =	input_state_dict['''model''']
						# The language model.
						UpperCAmelCase_      :   str      =	model['''language_model''']
						# The embeddings.
						UpperCAmelCase_      :   Any      =	lm['''embedding''']

						# The word embeddings.
						UpperCAmelCase_      :   Dict      =	embeddings['''word_embeddings''']['''weight''']
						# Truncate the embedding table to vocab_size rows.
						UpperCAmelCase_      :   List[str]      =	word_embeddings[: config.vocab_size, :]
						# Store the word embeddings.
						UpperCAmelCase_      :   Union[str, Any]      =	word_embeddings

						# The position embeddings.
						UpperCAmelCase_      :   Any      =	embeddings['''position_embeddings''']['''weight''']
						assert pos_embeddings.size(0 ) == config.max_position_embeddings and pos_embeddings.size(1 ) == config.hidden_size
						# Store the position embeddings.
						UpperCAmelCase_      :   List[Any]      =	pos_embeddings

						# The token-type embeddings.
						UpperCAmelCase_      :   Tuple      =	embeddings['''tokentype_embeddings''']['''weight''']
						# Store the position embeddings.
						UpperCAmelCase_      :   Union[str, Any]      =	tokentype_embeddings

						# The transformer.
						UpperCAmelCase_      :   Optional[Any]      =	lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

						# The regex to extract layer names.
						UpperCAmelCase_      :   str      =	re.compile(R'''layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)''' )

						# The simple map of names for "automated" rules.
						UpperCAmelCase_      :   List[str]      =	{
						    '''attention.dense''': '''.attention.output.dense.''',
						    '''self_attention.dense''': '''.attention.output.dense.''',
						    '''mlp.dense_h_to_4h''': '''.intermediate.dense.''',
						    '''mlp.dense_4h_to_h''': '''.output.dense.''',
						}

						# Keep track of the attention/query/value tensor.
						UpperCAmelCase_      :   str      =	None

						# Extract the layers.
						for key, val in transformer.items():
												# Match the name.
												UpperCAmelCase_      :   int      =	layer_re.match(SCREAMING_SNAKE_CASE__ )

												# Stop if that's not a layer
												if m is None:
																		break

												# The index of the layer.
												UpperCAmelCase_      :   List[str]      =	int(m.group(1 ) )
												# The name of the operation.
												UpperCAmelCase_      :   str      =	m.group(2 )
												# Is it a weight or a bias?
												UpperCAmelCase_      :   Any      =	m.group(3 )

												# The name of the layer.
												UpperCAmelCase_      :   Any      =	F"""bert.encoder.layer.{layer_idx}"""

												# For layernorm(s), simply store the layer norm.
												if op_name.endswith('''layernorm''' ):
																		UpperCAmelCase_      :   Dict      =	'''attention.ln''' if op_name.startswith('''input''' ) else '''ln'''
																		UpperCAmelCase_      :   List[Any]      =	val

												# Transpose the QKV matrix.
												elif (
												    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
												) and weight_or_bias == "weight":
																		# Make sure the QKV pointer is nil.
																		assert attention_qkv_weight is None, ""

																		UpperCAmelCase_      :   List[str]      =	fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      3,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
																		# Store the tensor as we need the bias as well to interleave QKV and biases.
																		UpperCAmelCase_      :   Union[str, Any]      =	out_val

												# Transpose the bias.
												elif (
												    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
												) and weight_or_bias == "bias":
																		# Make sure we read the weight tensor.
																		assert attention_qkv_weight is not None, ""

																		# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
																		UpperCAmelCase_      :   Tuple      =	attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
																		UpperCAmelCase_      :   List[str]      =	attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
																		UpperCAmelCase_      :   Optional[int]      =	attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

																		UpperCAmelCase_      :   str      =	fix_query_key_value_ordering(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      3,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
																		# Split the bias.
																		UpperCAmelCase_      :   Dict      =	out_val[0 * config.hidden_size : 1 * config.hidden_size]
																		UpperCAmelCase_      :   str      =	out_val[1 * config.hidden_size : 2 * config.hidden_size]
																		UpperCAmelCase_      :   Optional[Any]      =	out_val[2 * config.hidden_size : 3 * config.hidden_size]

																		# Store.
																		UpperCAmelCase_      :   Any      =	q
																		UpperCAmelCase_      :   Optional[int]      =	q_bias
																		UpperCAmelCase_      :   int      =	k
																		UpperCAmelCase_      :   Dict      =	k_bias
																		UpperCAmelCase_      :   Any      =	v
																		UpperCAmelCase_      :   Optional[Any]      =	v_bias

																		# Clear the stored tensor.
																		UpperCAmelCase_      :   Tuple      =	None

												# Copy weights and biases as is.
												elif weight_or_bias in ["weight", "bias"]:
																		UpperCAmelCase_      :   Tuple      =	megatron_to_transformers[op_name]
																		UpperCAmelCase_      :   Dict      =	val

    # The final layernorm.
						UpperCAmelCase_      :   Union[str, Any]      =	transformer['''final_layernorm.weight''']
						UpperCAmelCase_      :   Tuple      =	transformer['''final_layernorm.bias''']

						# The pooler.
						UpperCAmelCase_      :   int      =	lm['''pooler''']

						# Store the matrix and the bias.
						UpperCAmelCase_      :   Any      =	pooler['''dense.weight''']
						UpperCAmelCase_      :   List[str]      =	pooler['''dense.bias''']

						# The LM head from Megatron (for RACE).
						UpperCAmelCase_      :   Optional[int]      =	model['''lm_head''']

						# The transform matrix.
						UpperCAmelCase_      :   List[str]      =	lm_head['''dense.weight''']
						UpperCAmelCase_      :   Any      =	lm_head['''dense.bias''']

						# The transform LN.
						UpperCAmelCase_      :   str      =	lm_head['''layernorm.weight''']
						UpperCAmelCase_      :   Optional[Any]      =	lm_head['''layernorm.bias''']

						# For the decoder, we replicate the weights.
						UpperCAmelCase_      :   List[Any]      =	word_embeddings
						UpperCAmelCase_      :   List[str]      =	lm_head['''bias''']

						# The classifier from Megatron (for MLNI).
						UpperCAmelCase_      :   str      =	model['''binary_head''']

						# Store the classifier.
						UpperCAmelCase_      :   Optional[int]      =	binary_head['''weight''']
						UpperCAmelCase_      :   Tuple      =	binary_head['''bias''']

						# It should be done!
						return output_state_dict





def 							lowerCamelCase_   (	)			->						int:
						# Create the argument parser.
						UpperCAmelCase_      :   List[Any]      =	argparse.ArgumentParser()
						parser.add_argument('''--print-checkpoint-structure''',      action='''store_true''' )
						parser.add_argument('''path_to_checkpoint''',      type=SCREAMING_SNAKE_CASE__,      help='''Path to the ZIP file containing the checkpoint''' )
						parser.add_argument(
						    '''--config_file''',      default='''''',      type=SCREAMING_SNAKE_CASE__,      help='''An optional config json file describing the pre-trained model.''',      )
						UpperCAmelCase_      :   Any      =	parser.parse_args()

						# Extract the basename.
						UpperCAmelCase_      :   Optional[Any]      =	os.path.dirname(args.path_to_checkpoint )

						# Load the model.
						# the .zip is very optional, let's keep it for backward compatibility
						print(F"""Extracting PyTorch state dictionary from \"{args.path_to_checkpoint}\"""" )
						if args.path_to_checkpoint.endswith('''.zip''' ):
												with zipfile.ZipFile(args.path_to_checkpoint,      '''r''' ) as checkpoint:
																		with checkpoint.open('''release/mp_rank_00/model_optim_rng.pt''' ) as pytorch_dict:
																								UpperCAmelCase_      :   List[Any]      =	torch.load(SCREAMING_SNAKE_CASE__,      map_location='''cpu''' )
						else:
												UpperCAmelCase_      :   List[Any]      =	torch.load(args.path_to_checkpoint,      map_location='''cpu''' )

						if args.config_file == "":
												# Default config of megatron-bert 345m
												UpperCAmelCase_      :   str      =	MegatronBertConfig()

												# different megatron-bert-*-345m models have different vocab sizes, so override the default
												# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
												UpperCAmelCase_      :   Any      =	input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
						else:
												UpperCAmelCase_      :   Union[str, Any]      =	MegatronBertConfig.from_json_file(args.config_file )

						# Convert.
						print('''Converting''' )
						UpperCAmelCase_      :   Optional[Any]      =	convert_megatron_checkpoint(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )

						# Print the structure of converted state dict.
						if args.print_checkpoint_structure:
												recursive_print(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )

						# Store the config to file.
						print('''Saving config''' )
						config.save_pretrained(SCREAMING_SNAKE_CASE__ )

						# Store the state_dict to file.
						UpperCAmelCase_      :   List[str]      =	os.path.join(SCREAMING_SNAKE_CASE__,      '''pytorch_model.bin''' )
						print(F"""Saving checkpoint to \"{output_checkpoint_file}\"""" )
						torch.save(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )


####################################################################################################

if __name__ == "__main__":
					main()

####################################################################################################












'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    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


snake_case_  :							Any            =	logging.get_logger(__name__)


if is_vision_available():
					import PIL


class        __a   (lowerCamelCase  ):
							__a       : Any          =							["pixel_values"]

							def __init__(				self				:  Optional[int]							,  __magic_name__				:  bool = True							,  __magic_name__				:  Dict[str, int] = None							,  __magic_name__				:  PILImageResampling = PILImageResampling.BICUBIC							,  __magic_name__				:  bool = True							,  __magic_name__				:  Dict[str, int] = None							,  __magic_name__				:  bool = True							,  __magic_name__				:  Union[int, float] = 1 / 2_55							,  __magic_name__				:  bool = True							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  bool = True							,  **__magic_name__				:  Union[str, Any]							,  )       ->							None:







													"""simple docstring"""
													super().__init__(**__magic_name__ )
													UpperCAmelCase_      :   List[Any]      =	size if size is not None else {'''shortest_edge''': 2_24}
													UpperCAmelCase_      :   List[str]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   int      =	crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
													UpperCAmelCase_      :   Union[str, Any]      =	get_size_dict(__magic_name__ )

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

							def      UpperCAmelCase__						(				self				:  int							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Dict[str, int]							,  __magic_name__				:  PILImageResampling = PILImageResampling.BICUBIC							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Optional[int]							,  )       ->							np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   Any      =	get_resize_output_image_size(
													    __magic_name__							,  size=(size['''height'''], size['''width'''])							,  default_to_square=__magic_name__ )
													return resize(__magic_name__							,  size=__magic_name__							,  resample=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  List[Any]							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Dict[str, int]							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Any							,  )       ->							np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	get_size_dict(__magic_name__ )
													return center_crop(__magic_name__							,  size=(size['''height'''], size['''width'''])							,  data_format=__magic_name__							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  str							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Union[int, float]							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Any							,  )       ->							Union[str, Any]:







													"""simple docstring"""
													return rescale(__magic_name__							,  scale=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  str							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Union[float, List[float]]							,  __magic_name__				:  Union[float, List[float]]							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Optional[int]							,  )       ->							np.ndarray:







													"""simple docstring"""
													return normalize(__magic_name__							,  mean=__magic_name__							,  std=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )



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







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase_      :   Dict      =	size if size is not None else self.size
													UpperCAmelCase_      :   Union[str, Any]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   str      =	resample if resample is not None else self.resample
													UpperCAmelCase_      :   Tuple      =	do_center_crop if do_center_crop is not None else self.do_center_crop
													UpperCAmelCase_      :   List[str]      =	crop_size if crop_size is not None else self.crop_size
													UpperCAmelCase_      :   Optional[Any]      =	get_size_dict(__magic_name__ )
													UpperCAmelCase_      :   str      =	do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_      :   Tuple      =	rescale_factor if rescale_factor is not None else self.rescale_factor
													UpperCAmelCase_      :   List[Any]      =	do_normalize if do_normalize is not None else self.do_normalize
													UpperCAmelCase_      :   Tuple      =	image_mean if image_mean is not None else self.image_mean
													UpperCAmelCase_      :   int      =	image_std if image_std is not None else self.image_std
													UpperCAmelCase_      :   Optional[Any]      =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													UpperCAmelCase_      :   Any      =	make_list_of_images(__magic_name__ )

													if not valid_images(__magic_name__ ):
																			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:
																			UpperCAmelCase_      :   Optional[int]      =	[convert_to_rgb(__magic_name__ ) for image in images]

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

													if do_resize:
																			UpperCAmelCase_      :   Dict      =	[self.resize(image=__magic_name__							,  size=__magic_name__							,  resample=__magic_name__ ) for image in images]

													if do_center_crop:
																			UpperCAmelCase_      :   Union[str, Any]      =	[self.center_crop(image=__magic_name__							,  size=__magic_name__ ) for image in images]

													if do_rescale:
																			UpperCAmelCase_      :   Dict      =	[self.rescale(image=__magic_name__							,  scale=__magic_name__ ) for image in images]

													if do_normalize:
																			UpperCAmelCase_      :   Optional[int]      =	[self.normalize(image=__magic_name__							,  mean=__magic_name__							,  std=__magic_name__ ) for image in images]

													UpperCAmelCase_      :   Optional[Any]      =	[to_channel_dimension_format(__magic_name__							,  __magic_name__ ) for image in images]

													UpperCAmelCase_      :   int      =	{'''pixel_values''': images}
													return BatchFeature(data=__magic_name__							,  tensor_type=__magic_name__ )





