import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
	import numpy as np
	import torch

	from . import (
	    AlbertForPreTraining,
	    BartForConditionalGeneration,
	    BertForPreTraining,
	    BertForQuestionAnswering,
	    BertForSequenceClassification,
	    CamembertForMaskedLM,
	    CTRLLMHeadModel,
	    DistilBertForMaskedLM,
	    DistilBertForQuestionAnswering,
	    DPRContextEncoder,
	    DPRQuestionEncoder,
	    DPRReader,
	    ElectraForPreTraining,
	    FlaubertWithLMHeadModel,
	    GPTaLMHeadModel,
	    LayoutLMForMaskedLM,
	    LxmertForPreTraining,
	    LxmertVisualFeatureEncoder,
	    OpenAIGPTLMHeadModel,
	    RobertaForMaskedLM,
	    RobertaForSequenceClassification,
	    TaForConditionalGeneration,
	    TransfoXLLMHeadModel,
	    XLMRobertaForMaskedLM,
	    XLMWithLMHeadModel,
	    XLNetLMHeadModel,
	)


logging.set_verbosity_info()

lowerCamelCase_							:						str					=			{
    """bart""": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """bert""": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-base-cased-finetuned-mrpc""": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """dpr""": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """gpt2""": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlnet""": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm""": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm-roberta""": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """transfo-xl""": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """openai-gpt""": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """roberta""": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """layoutlm""": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """roberta-large-mnli""": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """camembert""": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """flaubert""": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert""": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert-base-distilled-squad""": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert""": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert-visual-feature-encoder""": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """ctrl""": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """albert""": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """t5""": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """electra""": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """wav2vec2""": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}
def 				lowerCAmelCase(      __lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=False ,							__lowerCamelCase=True		):

					if model_type not in MODEL_CLASSES:
										raise ValueError(f'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()		)}.'''		)

					__a						   =  MODEL_CLASSES[model_type]

					# Initialise TF model
					if config_file in aws_config_map:
										__a						   =  cached_file(lowercase_ ,							lowercase_ ,							force_download=not use_cached_models		)
					__a						   =  config_class.from_json_file(lowercase_		)
					__a						   =  True
					__a						   =  True
					print(f'''Building TensorFlow model from configuration: {config}'''		)
					__a						   =  model_class(lowercase_		)

					# Load weights from tf checkpoint
					if pytorch_checkpoint_path in aws_config_map.keys():
										__a						   =  cached_file(
										    lowercase_ ,							lowercase_ ,							force_download=not use_cached_models		)
					# Load PyTorch checkpoint in tf2 model:
					__a						   =  load_pytorch_checkpoint_in_tfa_model(lowercase_ ,							lowercase_		)

					if compare_with_pt_model:
										__a						   =  tf_model(tf_model.dummy_inputs ,							training=lowercase_		)  # build the network

										__a						   =  torch.load(lowercase_ ,							map_location='cpu'		)
										__a						   =  pt_model_class.from_pretrained(
										    pretrained_model_name_or_path=lowercase_ ,							config=lowercase_ ,							state_dict=lowercase_		)

										with torch.no_grad():
															__a						   =  pt_model(**pt_model.dummy_inputs		)

										__a						   =  pto[0].numpy()
										__a						   =  tfo[0].numpy()
										__a						   =  np.amax(np.abs(np_pt - np_tf		)		)
										print(f'''Max absolute difference between models outputs {diff}'''		)
										assert diff <= 2e-2, f'''Error, model absolute difference is >2e-2: {diff}'''

					# Save pytorch-model
					print(f'''Save TensorFlow model to {tf_dump_path}'''		)
					tf_model.save_weights(lowercase_ ,							save_format='h5'		)
def 				lowerCAmelCase(      __lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=None ,							__lowerCamelCase=None ,							__lowerCamelCase=False ,							__lowerCamelCase=False ,							__lowerCamelCase=False ,							__lowerCamelCase=False ,							):

					if args_model_type is None:
										__a						   =  list(MODEL_CLASSES.keys()		)
					else:
										__a						   =  [args_model_type]

					for j, model_type in enumerate(lowercase_ ,							start=1		):
										print('=' * 100		)
										print(f''' Converting model type {j}/{len(lowercase_		)}: {model_type}'''		)
										print('=' * 100		)
										if model_type not in MODEL_CLASSES:
															raise ValueError(f'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()		)}.'''		)

										__a						   =  MODEL_CLASSES[model_type]

										if model_shortcut_names_or_path is None:
															__a						   =  list(aws_model_maps.keys()		)
										if config_shortcut_names_or_path is None:
															__a						   =  model_shortcut_names_or_path

										for i, (model_shortcut_name, config_shortcut_name) in enumerate(
										    zip(lowercase_ ,							lowercase_		) ,							start=1		):
															print('-' * 100		)
															if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
																				if not only_convert_finetuned_models:
																									print(f'''    Skipping finetuned checkpoint {model_shortcut_name}'''		)
																									continue
																				__a						   =  model_shortcut_name
															elif only_convert_finetuned_models:
																				print(f'''    Skipping not finetuned checkpoint {model_shortcut_name}'''		)
																				continue
															print(
															    f'''    Converting checkpoint {i}/{len(lowercase_		)}: {model_shortcut_name} - model_type {model_type}'''		)
															print('-' * 100		)

															if config_shortcut_name in aws_config_map:
																				__a						   =  cached_file(lowercase_ ,							lowercase_ ,							force_download=not use_cached_models		)
															else:
																				__a						   =  config_shortcut_name

															if model_shortcut_name in aws_model_maps:
																				__a						   =  cached_file(lowercase_ ,							lowercase_ ,							force_download=not use_cached_models		)
															else:
																				__a						   =  model_shortcut_name

															if os.path.isfile(lowercase_		):
																				__a						   =  "converted_model"

															convert_pt_checkpoint_to_tf(
															    model_type=lowercase_ ,							pytorch_checkpoint_path=lowercase_ ,							config_file=lowercase_ ,							tf_dump_path=os.path.join(lowercase_ ,							model_shortcut_name + '-tf_model.h5'		) ,							compare_with_pt_model=lowercase_ ,							)
															if remove_cached_files:
																				os.remove(lowercase_		)
																				os.remove(lowercase_		)


if __name__ == "__main__":
	lowerCamelCase_							:						List[Any]					=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--tf_dump_path""", default=None, type=str, required=True, help="""Path to the output Tensorflow dump file."""
	)
	parser.add_argument(
	    """--model_type""",
	    default=None,
	    type=str,
	    help=(
	        F'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
	        """convert all the models from AWS."""
	    ),
	)
	parser.add_argument(
	    """--pytorch_checkpoint_path""",
	    default=None,
	    type=str,
	    help=(
	        """Path to the PyTorch checkpoint path or shortcut name to download from AWS. """
	        """If not given, will download and convert all the checkpoints from AWS."""
	    ),
	)
	parser.add_argument(
	    """--config_file""",
	    default=None,
	    type=str,
	    help=(
	        """The config json file corresponding to the pre-trained model. \n"""
	        """This specifies the model architecture. If not given and """
	        """--pytorch_checkpoint_path is not given or is a shortcut name """
	        """use the configuration associated to the shortcut name on the AWS"""
	    ),
	)
	parser.add_argument(
	    """--compare_with_pt_model""", action="""store_true""", help="""Compare Tensorflow and PyTorch model predictions."""
	)
	parser.add_argument(
	    """--use_cached_models""",
	    action="""store_true""",
	    help="""Use cached models if possible instead of updating to latest checkpoint versions.""",
	)
	parser.add_argument(
	    """--remove_cached_files""",
	    action="""store_true""",
	    help="""Remove pytorch models after conversion (save memory when converting in batches).""",
	)
	parser.add_argument("""--only_convert_finetuned_models""", action="""store_true""", help="""Only convert finetuned models.""")
	lowerCamelCase_							:						str					=			parser.parse_args()

	# if args.pytorch_checkpoint_path is not None:
	#     convert_pt_checkpoint_to_tf(args.model_type.lower(),
	#                                 args.pytorch_checkpoint_path,
	#                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
	#                                 args.tf_dump_path,
	#                                 compare_with_pt_model=args.compare_with_pt_model,
	#                                 use_cached_models=args.use_cached_models)
	# else:
	convert_all_pt_checkpoints_to_tf(
	    args.model_type.lower() if args.model_type is not None else None,
	    args.tf_dump_path,
	    model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
	    if args.pytorch_checkpoint_path is not None
	    else None,
	    config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
	    compare_with_pt_model=args.compare_with_pt_model,
	    use_cached_models=args.use_cached_models,
	    remove_cached_files=args.remove_cached_files,
	    only_convert_finetuned_models=args.only_convert_finetuned_models,
	)











"""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


lowerCamelCase__						= logging.get_logger(__name__)


if is_vision_available():
  import PIL



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[int]						  =       ["pixel_values"]

  def __init__(		self :							Tuple       , __a :							bool = True       , __a :							Dict[str, int] = None       , __a :							PILImageResampling = PILImageResampling.BICUBIC       , __a :							bool = True       , __a :							Dict[str, int] = None       , __a :							bool = True       , __a :							Union[int, float] = 1 / 255       , __a :							bool = True       , __a :							Optional[Union[float, List[float]]] = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							bool = True       , **__a :							List[Any]       , )		->					None:
        super().__init__(**__a						)
        _UpperCamelCase   :						Optional[int]    =							size if size is not None else {"shortest_edge": 224}
        _UpperCamelCase   :						Optional[int]    =							get_size_dict(__a       , default_to_square=__a						)
        _UpperCamelCase   :						Union[str, Any]    =							crop_size if crop_size is not None else {"height": 224, "width": 224}
        _UpperCamelCase   :						Tuple    =							get_size_dict(__a       , default_to_square=__a       , param_name="crop_size"						)

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

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							np.ndarray       , __a :							Dict[str, int]       , __a :							PILImageResampling = PILImageResampling.BICUBIC       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							str       , )		->					np.ndarray:
        _UpperCamelCase   :						Any    =							get_size_dict(__a       , default_to_square=__a						)
        if "shortest_edge" not in size:
              raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''						)
        _UpperCamelCase   :						Dict    =							get_resize_output_image_size(__a       , size=size["shortest_edge"]       , default_to_square=__a						)
        return resize(__a       , size=__a       , resample=__a       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							np.ndarray       , __a :							Dict[str, int]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Tuple       , )		->					np.ndarray:
        _UpperCamelCase   :						List[str]    =							get_size_dict(__a						)
        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(__a       , size=(size["height"], size["width"])       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]       , __a :							np.ndarray       , __a :							Union[int, float]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Optional[Any]       , )		->					List[str]:
        return rescale(__a       , scale=__a       , data_format=__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple       , __a :							np.ndarray       , __a :							Union[float, List[float]]       , __a :							Union[float, List[float]]       , __a :							Optional[Union[str, ChannelDimension]] = None       , **__a :							Tuple       , )		->					np.ndarray:
        return normalize(__a       , mean=__a       , std=__a       , data_format=__a       , **__a						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							ImageInput       , __a :							bool = None       , __a :							Dict[str, int] = None       , __a :							PILImageResampling = None       , __a :							bool = None       , __a :							int = None       , __a :							bool = None       , __a :							float = None       , __a :							bool = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							Optional[Union[float, List[float]]] = None       , __a :							bool = None       , __a :							Optional[Union[str, TensorType]] = None       , __a :							Optional[ChannelDimension] = ChannelDimension.FIRST       , **__a :							Union[str, Any]       , )		->					PIL.Image.Image:
        _UpperCamelCase   :						Tuple    =							do_resize if do_resize is not None else self.do_resize
        _UpperCamelCase   :						int    =							size if size is not None else self.size
        _UpperCamelCase   :						int    =							get_size_dict(__a       , param_name="size"       , default_to_square=__a						)
        _UpperCamelCase   :						List[Any]    =							resample if resample is not None else self.resample
        _UpperCamelCase   :						List[Any]    =							do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCamelCase   :						Union[str, Any]    =							crop_size if crop_size is not None else self.crop_size
        _UpperCamelCase   :						List[Any]    =							get_size_dict(__a       , param_name="crop_size"       , default_to_square=__a						)
        _UpperCamelCase   :						Dict    =							do_rescale if do_rescale is not None else self.do_rescale
        _UpperCamelCase   :						Union[str, Any]    =							rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCamelCase   :						Union[str, Any]    =							do_normalize if do_normalize is not None else self.do_normalize
        _UpperCamelCase   :						List[Any]    =							image_mean if image_mean is not None else self.image_mean
        _UpperCamelCase   :						List[Any]    =							image_std if image_std is not None else self.image_std
        _UpperCamelCase   :						List[str]    =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        _UpperCamelCase   :						Any    =							make_list_of_images(__a						)

        if not valid_images(__a						):
              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   :						int    =							[convert_to_rgb(__a						) for image in images]

        # All transformations expect numpy arrays.
        _UpperCamelCase   :						List[Any]    =							[to_numpy_array(__a						) for image in images]

        if do_resize:
              _UpperCamelCase   :						Union[str, Any]    =							[self.resize(image=__a       , size=__a       , resample=__a						) for image in images]

        if do_center_crop:
              _UpperCamelCase   :						Optional[Any]    =							[self.center_crop(image=__a       , size=__a						) for image in images]

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

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

        _UpperCamelCase   :						List[Any]    =							[to_channel_dimension_format(__a       , __a						) for image in images]

        _UpperCamelCase   :						str    =							{"pixel_values": images}
        return BatchFeature(data=__a       , tensor_type=__a						)
