





'''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__ :     str,     SCREAMING_SNAKE_CASE__ :     int,     SCREAMING_SNAKE_CASE__ :     Union[str, Any]=0				)	->		str:
						# Format the message.
						if name is None:
												UpperCAmelCase_     : List[Any]         =					None
						else:
												UpperCAmelCase_     : Optional[Any]         =					'''.''' * max(0,     spaces - 2				) + '''# {:''' + str(50 - spaces				) + '''s}'''
												UpperCAmelCase_     : Union[str, Any]         =					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__ :     Union[str, Any],     SCREAMING_SNAKE_CASE__ :     Union[str, Any],     SCREAMING_SNAKE_CASE__ :     Any,     SCREAMING_SNAKE_CASE__ :     Union[str, Any],     SCREAMING_SNAKE_CASE__ :     Dict				)	->		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_     : List[Any]         =					param.size()
						if checkpoint_version == 1.0:
												# version 1.0 stores [num_heads * hidden_size * num_splits, :]
												UpperCAmelCase_     : Optional[int]         =					(num_heads, hidden_size, num_splits) + input_shape[1:]
												UpperCAmelCase_     : Optional[Any]         =					param.view(*SCREAMING_SNAKE_CASE__				)
												UpperCAmelCase_     : Any         =					param.transpose(0,     2				)
												UpperCAmelCase_     : Dict         =					param.transpose(1,     2				).contiguous()
						elif checkpoint_version >= 2.0:
												# other versions store [num_heads * num_splits * hidden_size, :]
												UpperCAmelCase_     : Union[str, Any]         =					(num_heads, num_splits, hidden_size) + input_shape[1:]
												UpperCAmelCase_     : Optional[int]         =					param.view(*SCREAMING_SNAKE_CASE__				)
												UpperCAmelCase_     : Dict         =					param.transpose(0,     1				).contiguous()
						UpperCAmelCase_     : List[str]         =					param.view(*SCREAMING_SNAKE_CASE__				)
						return param





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     Optional[Any],     SCREAMING_SNAKE_CASE__ :     List[Any],     SCREAMING_SNAKE_CASE__ :     Any				)	->		Union[str, Any]:
						# The converted output model.
						UpperCAmelCase_     : Union[str, Any]         =					{}

						# old versions did not store training args
						UpperCAmelCase_     : List[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_     : Optional[Any]         =					ds_args.tokenizer_type
												UpperCAmelCase_     : Union[str, Any]         =					ds_args.padded_vocab_size
												UpperCAmelCase_     : List[str]         =					ds_args.max_position_embeddings
												UpperCAmelCase_     : List[str]         =					ds_args.hidden_size
												UpperCAmelCase_     : List[str]         =					ds_args.num_layers
												UpperCAmelCase_     : Optional[Any]         =					ds_args.num_attention_heads
												UpperCAmelCase_     : Dict         =					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_     : int         =					config.num_attention_heads
						# The hidden_size per head.
						UpperCAmelCase_     : str         =					config.hidden_size // heads
						# Megatron-LM checkpoint version
						if "checkpoint_version" in input_state_dict.keys():
												UpperCAmelCase_     : int         =					input_state_dict['''checkpoint_version''']
						else:
												UpperCAmelCase_     : Optional[Any]         =					0.0

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

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

						# The position embeddings.
						UpperCAmelCase_     : List[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_     : Dict         =					pos_embeddings

						# The token-type embeddings.
						UpperCAmelCase_     : Optional[Any]         =					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_     : Union[str, Any]         =					re.compile(R'''layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'''				)

						# The simple map of names for "automated" rules.
						UpperCAmelCase_     : Any         =					{
						    '''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_     : Any         =					None

						# Extract the layers.
						for key, val in transformer.items():
												# Match the name.
												UpperCAmelCase_     : List[str]         =					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[Any]         =					int(m.group(1				)				)
												# The name of the operation.
												UpperCAmelCase_     : Dict         =					m.group(2				)
												# Is it a weight or a bias?
												UpperCAmelCase_     : str         =					m.group(3				)

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

												# For layernorm(s), simply store the layer norm.
												if op_name.endswith('''layernorm'''				):
																		UpperCAmelCase_     : Union[str, Any]         =					'''attention.ln''' if op_name.startswith('''input'''				) else '''ln'''
																		UpperCAmelCase_     : Optional[int]         =					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[Any]         =					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_     : int         =					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_     : List[str]         =					attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
																		UpperCAmelCase_     : Tuple         =					attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
																		UpperCAmelCase_     : List[Any]         =					attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

																		# Store.
																		UpperCAmelCase_     : List[Any]         =					q
																		UpperCAmelCase_     : Union[str, Any]         =					q_bias
																		UpperCAmelCase_     : Optional[int]         =					k
																		UpperCAmelCase_     : Tuple         =					k_bias
																		UpperCAmelCase_     : Any         =					v
																		UpperCAmelCase_     : Any         =					v_bias

																		# Clear the stored tensor.
																		UpperCAmelCase_     : Optional[Any]         =					None

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

    # The final layernorm.
						UpperCAmelCase_     : Optional[Any]         =					transformer['''final_layernorm.weight''']
						UpperCAmelCase_     : Any         =					transformer['''final_layernorm.bias''']

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

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

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

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

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

						# For the decoder, we replicate the weights.
						UpperCAmelCase_     : int         =					word_embeddings
						UpperCAmelCase_     : Dict         =					lm_head['''bias''']

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

						# Store the classifier.
						UpperCAmelCase_     : List[str]         =					binary_head['''weight''']
						UpperCAmelCase_     : int         =					binary_head['''bias''']

						# It should be done!
						return output_state_dict





def     lowerCamelCase_ (					)	->		Any:
						# Create the argument parser.
						UpperCAmelCase_     : str         =					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_     : Optional[int]         =					parser.parse_args()

						# Extract the basename.
						UpperCAmelCase_     : Union[str, 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_     : Optional[int]         =					torch.load(SCREAMING_SNAKE_CASE__,     map_location='''cpu'''				)
						else:
												UpperCAmelCase_     : Union[str, Any]         =					torch.load(args.path_to_checkpoint,     map_location='''cpu'''				)

						if args.config_file == "":
												# Default config of megatron-bert 345m
												UpperCAmelCase_     : Union[str, Any]         =					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_     : List[Any]         =					input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
						else:
												UpperCAmelCase_     : Dict         =					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_     : Tuple         =					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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


snake_case_       :    int          =					logging.get_logger(__name__)


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

							def __init__(							self							:       str    ,       __magic_name__							:       bool = True    ,       __magic_name__							:       Dict[str, int] = None    ,       __magic_name__							:       bool = True    ,       __magic_name__							:       Dict[str, int] = None    ,       __magic_name__							:       PILImageResampling = PILImageResampling.BICUBIC    ,       __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__							:       List[Any]    ,       )   ->    None:







													"""simple docstring"""
													super().__init__(**__magic_name__				)
													UpperCAmelCase_     : Tuple         =					crop_size if crop_size is not None else {'''height''': 2_56, '''width''': 2_56}
													UpperCAmelCase_     : str         =					get_size_dict(__magic_name__    ,       param_name='''crop_size'''				)
													UpperCAmelCase_     : List[Any]         =					size if size is not None else {'''height''': 2_24, '''width''': 2_24}
													UpperCAmelCase_     : int         =					get_size_dict(__magic_name__				)

													UpperCAmelCase_     : Optional[int]         =					do_center_crop
													UpperCAmelCase_     : List[str]         =					crop_size
													UpperCAmelCase_     : Dict         =					do_resize
													UpperCAmelCase_     : str         =					size
													UpperCAmelCase_     : int         =					resample
													UpperCAmelCase_     : Optional[int]         =					do_rescale
													UpperCAmelCase_     : List[str]         =					rescale_factor
													UpperCAmelCase_     : int         =					do_normalize
													UpperCAmelCase_     : Any         =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
													UpperCAmelCase_     : str         =					image_std if image_std is not None else IMAGENET_DEFAULT_STD

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







													"""simple docstring"""
													UpperCAmelCase_     : List[Any]         =					self.size if size is None else size
													UpperCAmelCase_     : str         =					get_size_dict(__magic_name__				)
													UpperCAmelCase_     : Union[str, Any]         =					get_size_dict(__magic_name__    ,       param_name='''crop_size'''				)

													UpperCAmelCase_  ,    UpperCAmelCase_     : Any         =					get_image_size(__magic_name__				)
													UpperCAmelCase_     : List[str]         =					min(__magic_name__    ,       __magic_name__				)
													UpperCAmelCase_     : Dict         =					(size['''height'''] / crop_size['''height''']) * min_dim
													UpperCAmelCase_     : List[str]         =					(size['''width'''] / crop_size['''width''']) * min_dim
													return center_crop(__magic_name__    ,       size=(cropped_height, cropped_width)    ,       data_format=__magic_name__    ,       **__magic_name__				)

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







													"""simple docstring"""
													UpperCAmelCase_     : str         =					get_size_dict(__magic_name__				)
													if "height" not in size or "width" not in size:
																			raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""				)
													return resize(
													    __magic_name__    ,       size=(size['''height'''], size['''width'''])    ,       resample=__magic_name__    ,       data_format=__magic_name__    ,       **__magic_name__				)

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







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

							def       UpperCAmelCase__		(							self							:       Optional[int]    ,       __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__							:       Dict    ,       )   ->    np.ndarray:







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



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







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

													UpperCAmelCase_     : Optional[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_center_crop and crop_size is None:
																			raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''				)

													if do_resize and size is None:
																			raise ValueError('''Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True.'''				)

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

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

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

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

													UpperCAmelCase_     : Union[str, Any]         =					{'''pixel_values''': images}
													return BatchFeature(data=__magic_name__    ,       tensor_type=__magic_name__				)





