



"""simple docstring"""







import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a_							=    logging.get_logger(__name__)

a_							=    {
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

a_							=    {
    """b0""": {
        """hidden_dim""": 12_80,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 2_24,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 12_80,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 2_40,
        """dropout_rate""": 0.2,
        """dw_padding""": [16],
    },
    """b2""": {
        """hidden_dim""": 14_08,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 2_60,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 16],
    },
    """b3""": {
        """hidden_dim""": 15_36,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 3_00,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 18],
    },
    """b4""": {
        """hidden_dim""": 17_92,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 3_80,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 20_48,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 4_56,
        """dropout_rate""": 0.4,
        """dw_padding""": [13, 27],
    },
    """b6""": {
        """hidden_dim""": 23_04,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 5_28,
        """dropout_rate""": 0.5,
        """dw_padding""": [31],
    },
    """b7""": {
        """hidden_dim""": 25_60,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 6_00,
        """dropout_rate""": 0.5,
        """dw_padding""": [18],
    },
}

def     UpperCAmelCase_					(				__a   :			int						):




			'''simple docstring'''



			_lowerCamelCase					:					List[str]	       =    EfficientNetConfig()
			_lowerCamelCase					:					Any	       =    CONFIG_MAP[model_name]["""hidden_dim"""]
			_lowerCamelCase					:					List[str]	       =    CONFIG_MAP[model_name]["""width_coef"""]
			_lowerCamelCase					:					str	       =    CONFIG_MAP[model_name]["""depth_coef"""]
			_lowerCamelCase					:					int	       =    CONFIG_MAP[model_name]["""image_size"""]
			_lowerCamelCase					:					List[Any]	       =    CONFIG_MAP[model_name]["""dropout_rate"""]
			_lowerCamelCase					:					int	       =    CONFIG_MAP[model_name]["""dw_padding"""]

			_lowerCamelCase					:					Optional[int]	       =    """huggingface/label-files"""
			_lowerCamelCase					:					int	       =    """imagenet-1k-id2label.json"""
			_lowerCamelCase					:					Any	       =    10_00
			_lowerCamelCase					:					Any	       =    json.load(open(hf_hub_download(_A      ,							_A      ,							repo_type='dataset'						)      ,							'r'						)						)
			_lowerCamelCase					:					Optional[int]	       =    {int(_A						): v for k, v in idalabel.items()}

			_lowerCamelCase					:					int	       =    idalabel
			_lowerCamelCase					:					Optional[Any]	       =    {v: k for k, v in idalabel.items()}
			return config

def     UpperCAmelCase_					(				):




			'''simple docstring'''



			_lowerCamelCase					:					Any	       =    """http://images.cocodataset.org/val2017/000000039769.jpg"""
			_lowerCamelCase					:					Optional[int]	       =    Image.open(requests.get(_A      ,							stream=_A						).raw						)
			return im

def     UpperCAmelCase_					(				__a   :			Optional[int]						):




			'''simple docstring'''



			_lowerCamelCase					:					str	       =    CONFIG_MAP[model_name]["""image_size"""]
			_lowerCamelCase					:					Optional[int]	       =    EfficientNetImageProcessor(
			    size={'height': size, 'width': size}      ,							image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]      ,							image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3]      ,							do_center_crop=_A      ,							)
			return preprocessor

def     UpperCAmelCase_					(				__a   :			Optional[int]						):




			'''simple docstring'''



			_lowerCamelCase					:					Union[str, Any]	       =    [v.split('_'						)[0].split('block'						)[1] for v in original_param_names if v.startswith('block'						)]
			_lowerCamelCase					:					Optional[Any]	       =    sorted(set(_A						)						)
			_lowerCamelCase					:					Dict	       =    len(_A						)
			_lowerCamelCase					:					List[str]	       =    {b: str(_A						) for b, i in zip(_A      ,							range(_A						)						)}

			_lowerCamelCase					:					Union[str, Any]	       =    []
			rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')						)
			rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')						)
			rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')						)
			rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')						)
			rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')						)

			for b in block_names:
						_lowerCamelCase					:					str	       =    block_name_mapping[b]
						rename_keys.append((f"block{b}_expand_conv/kernel:0", f"encoder.blocks.{hf_b}.expansion.expand_conv.weight")						)
						rename_keys.append((f"block{b}_expand_bn/gamma:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.weight")						)
						rename_keys.append((f"block{b}_expand_bn/beta:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.bias")						)
						rename_keys.append(
						    (f"block{b}_expand_bn/moving_mean:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean")						)
						rename_keys.append(
						    (f"block{b}_expand_bn/moving_variance:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_var")						)
						rename_keys.append(
						    (f"block{b}_dwconv/depthwise_kernel:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight")						)
						rename_keys.append((f"block{b}_bn/gamma:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight")						)
						rename_keys.append((f"block{b}_bn/beta:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias")						)
						rename_keys.append(
						    (f"block{b}_bn/moving_mean:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean")						)
						rename_keys.append(
						    (f"block{b}_bn/moving_variance:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var")						)

						rename_keys.append((f"block{b}_se_reduce/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight")						)
						rename_keys.append((f"block{b}_se_reduce/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias")						)
						rename_keys.append((f"block{b}_se_expand/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.weight")						)
						rename_keys.append((f"block{b}_se_expand/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.bias")						)
						rename_keys.append(
						    (f"block{b}_project_conv/kernel:0", f"encoder.blocks.{hf_b}.projection.project_conv.weight")						)
						rename_keys.append((f"block{b}_project_bn/gamma:0", f"encoder.blocks.{hf_b}.projection.project_bn.weight")						)
						rename_keys.append((f"block{b}_project_bn/beta:0", f"encoder.blocks.{hf_b}.projection.project_bn.bias")						)
						rename_keys.append(
						    (f"block{b}_project_bn/moving_mean:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_mean")						)
						rename_keys.append(
						    (f"block{b}_project_bn/moving_variance:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_var")						)

			rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight')						)
			rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight')						)
			rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias')						)
			rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean')						)
			rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var')						)

			_lowerCamelCase					:					Union[str, Any]	       =    {}
			for item in rename_keys:
						if item[0] in original_param_names:
									_lowerCamelCase					:					Optional[int]	       =    """efficientnet.""" + item[1]

			_lowerCamelCase					:					Any	       =    """classifier.weight"""
			_lowerCamelCase					:					Tuple	       =    """classifier.bias"""
			return key_mapping

def     UpperCAmelCase_					(				__a   :			List[Any]      ,							__a   :			str      ,							__a   :			Dict						):




			'''simple docstring'''



			for key, value in tf_params.items():
						if "normalization" in key:
									continue

						_lowerCamelCase					:					List[Any]	       =    key_mapping[key]
						if "_conv" in key and "kernel" in key:
									_lowerCamelCase					:					str	       =    torch.from_numpy(_A						).permute(3      ,							2      ,							0      ,							1						)
						elif "depthwise_kernel" in key:
									_lowerCamelCase					:					Optional[int]	       =    torch.from_numpy(_A						).permute(2      ,							3      ,							0      ,							1						)
						elif "kernel" in key:
									_lowerCamelCase					:					List[Any]	       =    torch.from_numpy(np.transpose(_A						)						)
						else:
									_lowerCamelCase					:					Optional[int]	       =    torch.from_numpy(_A						)

						# Replace HF parameters with original TF model parameters
						assert hf_params[hf_key].shape == new_hf_value.shape
						hf_params[hf_key].copy_(_A						)

@torch.no_grad()
def     UpperCAmelCase_					(				__a   :			str      ,							__a   :			str      ,							__a   :			Optional[int]      ,							__a   :			List[str]						):




			'''simple docstring'''



			_lowerCamelCase					:					Any	       =    model_classes[model_name](
			    include_top=_A      ,							weights='imagenet'      ,							input_tensor=_A      ,							input_shape=_A      ,							pooling=_A      ,							classes=10_00      ,							classifier_activation='softmax'      ,							)

			_lowerCamelCase					:					Dict	       =    original_model.trainable_variables
			_lowerCamelCase					:					Any	       =    original_model.non_trainable_variables
			_lowerCamelCase					:					Any	       =    {param.name: param.numpy() for param in tf_params}
			for param in tf_non_train_params:
						_lowerCamelCase					:					Dict	       =    param.numpy()
			_lowerCamelCase					:					List[str]	       =    list(tf_params.keys()						)

			# Load HuggingFace model
			_lowerCamelCase					:					str	       =    get_efficientnet_config(_A						)
			_lowerCamelCase					:					List[Any]	       =    EfficientNetForImageClassification(_A						).eval()
			_lowerCamelCase					:					Optional[int]	       =    hf_model.state_dict()

			# Create src-to-dst parameter name mapping dictionary
			print('Converting parameters...'						)
			_lowerCamelCase					:					int	       =    rename_keys(_A						)
			replace_params(_A      ,							_A      ,							_A						)

			# Initialize preprocessor and preprocess input image
			_lowerCamelCase					:					Optional[int]	       =    convert_image_processor(_A						)
			_lowerCamelCase					:					Any	       =    preprocessor(images=prepare_img()      ,							return_tensors='pt'						)

			# HF model inference
			hf_model.eval()
			with torch.no_grad():
						_lowerCamelCase					:					Union[str, Any]	       =    hf_model(**_A						)
			_lowerCamelCase					:					List[Any]	       =    outputs.logits.detach().numpy()

			# Original model inference
			_lowerCamelCase					:					Optional[Any]	       =    False
			_lowerCamelCase					:					str	       =    CONFIG_MAP[model_name]["""image_size"""]
			_lowerCamelCase					:					Optional[Any]	       =    prepare_img().resize((image_size, image_size)      ,							resample=PIL.Image.NEAREST						)
			_lowerCamelCase					:					Optional[Any]	       =    image.img_to_array(_A						)
			_lowerCamelCase					:					Dict	       =    np.expand_dims(_A      ,							axis=0						)
			_lowerCamelCase					:					List[str]	       =    original_model.predict(_A						)

			# Check whether original and HF model outputs match  -> np.allclose
			assert np.allclose(_A      ,							_A      ,							atol=1E-3						), "The predicted logits are not the same."
			print('Model outputs match!'						)

			if save_model:
						# Create folder to save model
						if not os.path.isdir(_A						):
									os.mkdir(_A						)
						# Save converted model and image processor
						hf_model.save_pretrained(_A						)
						preprocessor.save_pretrained(_A						)

			if push_to_hub:
						# Push model and image processor to hub
						print(f"Pushing converted {model_name} to the hub..."						)
						_lowerCamelCase					:					Dict	       =    f"efficientnet-{model_name}"
						preprocessor.push_to_hub(_A						)
						hf_model.push_to_hub(_A						)


if __name__ == "__main__":
	a_							=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""b0""",
	    type=str,
	    help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default="""hf_model""",
	    type=str,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
	parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")

	a_							=    parser.parse_args()
	convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)













"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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


if is_vision_available():
				import PIL

_lowerCAmelCase            =	logging.get_logger(__name__)


def      UpperCamelCase       ( _A )     ->       List[List[ImageInput]]:
				if isinstance(_A  ,				(list, tuple) ) and isinstance(videos[0]  ,				(list, tuple) ) and is_valid_image(videos[0][0] ):
								return videos

				elif isinstance(_A  ,				(list, tuple) ) and is_valid_image(videos[0] ):
								return [videos]

				elif is_valid_image(_A ):
								return [[videos]]

				raise ValueError(F"""Could not make batched video from {videos}""" )


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : str												=		["""pixel_values"""]






						def __init__(		self  :str			, __magic_name__  :bool = True			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :PILImageResampling = PILImageResampling.BILINEAR			, __magic_name__  :bool = True			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :bool = True			, __magic_name__  :Union[int, float] = 1 / 255			, __magic_name__  :bool = True			, __magic_name__  :bool = True			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, **__magic_name__  :List[str]			, )      ->None:
										super().__init__(**__magic_name__				)
										lowercase    :      Union[str, Any]       = size if size is not None else {"""shortest_edge""": 256}
										lowercase    :      Tuple       = get_size_dict(__magic_name__			, default_to_square=__magic_name__				)
										lowercase    :      List[Any]       = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										lowercase    :      List[str]       = get_size_dict(__magic_name__			, param_name="""crop_size"""				)

										lowercase    :      Tuple       = do_resize
										lowercase    :      Optional[Any]       = size
										lowercase    :      Union[str, Any]       = do_center_crop
										lowercase    :      List[Any]       = crop_size
										lowercase    :      Union[str, Any]       = resample
										lowercase    :      Optional[Any]       = do_rescale
										lowercase    :      int       = rescale_factor
										lowercase    :      Dict       = offset
										lowercase    :      Optional[int]       = do_normalize
										lowercase    :      Optional[Any]       = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowercase    :      Tuple       = image_std if image_std is not None else IMAGENET_STANDARD_STD






						def      __snake_case      (		self  :Any			, __magic_name__  :np.ndarray			, __magic_name__  :Dict[str, int]			, __magic_name__  :PILImageResampling = PILImageResampling.BILINEAR			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :Optional[Any]			, )      ->np.ndarray:
										lowercase    :      Optional[int]       = get_size_dict(__magic_name__			, default_to_square=__magic_name__				)
										if "shortest_edge" in size:
														lowercase    :      int       = get_resize_output_image_size(__magic_name__			, size["""shortest_edge"""]			, default_to_square=__magic_name__				)
										elif "height" in size and "width" in size:
														lowercase    :      Tuple       = (size["""height"""], size["""width"""])
										else:
														raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""				)
										return resize(__magic_name__			, size=__magic_name__			, resample=__magic_name__			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :np.ndarray			, __magic_name__  :Dict[str, int]			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :str			, )      ->np.ndarray:
										lowercase    :      Any       = get_size_dict(__magic_name__				)
										if "height" not in size or "width" not in size:
														raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""				)
										return center_crop(__magic_name__			, size=(size["""height"""], size["""width"""])			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :np.ndarray			, __magic_name__  :Union[int, float]			, __magic_name__  :bool = True			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :Optional[Any]			, )      ->Any:
										lowercase    :      Optional[int]       = image.astype(np.floataa				)
										if offset:
														lowercase    :      Optional[int]       = image - (scale / 2)
										return rescale(__magic_name__			, scale=__magic_name__			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :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__  :Union[str, Any]			, )      ->np.ndarray:
										return normalize(__magic_name__			, mean=__magic_name__			, std=__magic_name__			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :List[Any]			, __magic_name__  :ImageInput			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :PILImageResampling = None			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :bool = None			, __magic_name__  :float = None			, __magic_name__  :bool = None			, __magic_name__  :bool = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[ChannelDimension] = ChannelDimension.FIRST			, )      ->np.ndarray:
										if do_resize and size is None or resample is None:
														raise ValueError("""Size and resample must be specified if do_resize is True."""				)

										if do_center_crop and crop_size is None:
														raise ValueError("""Crop size must be specified if do_center_crop is True."""				)

										if do_rescale and rescale_factor is None:
														raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

										if do_normalize and (image_mean is None or image_std is None):
														raise ValueError("""Image mean and std must be specified if do_normalize is True."""				)

										if offset and not do_rescale:
														raise ValueError("""For offset, do_rescale must also be set to True."""				)

										# All transformations expect numpy arrays.
										lowercase    :      Optional[Any]       = to_numpy_array(__magic_name__				)

										if do_resize:
														lowercase    :      Any       = self.resize(image=__magic_name__			, size=__magic_name__			, resample=__magic_name__				)

										if do_center_crop:
														lowercase    :      Any       = self.center_crop(__magic_name__			, size=__magic_name__				)

										if do_rescale:
														lowercase    :      Optional[int]       = self.rescale(image=__magic_name__			, scale=__magic_name__			, offset=__magic_name__				)

										if do_normalize:
														lowercase    :      Union[str, Any]       = self.normalize(image=__magic_name__			, mean=__magic_name__			, std=__magic_name__				)

										lowercase    :      Optional[Any]       = to_channel_dimension_format(__magic_name__			, __magic_name__				)
										return image






						def      __snake_case      (		self  :Union[str, Any]			, __magic_name__  :ImageInput			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :PILImageResampling = None			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :bool = None			, __magic_name__  :float = None			, __magic_name__  :bool = None			, __magic_name__  :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__  :List[str]			, )      ->PIL.Image.Image:
										lowercase    :      List[Any]       = do_resize if do_resize is not None else self.do_resize
										lowercase    :      Dict       = resample if resample is not None else self.resample
										lowercase    :      Optional[int]       = do_center_crop if do_center_crop is not None else self.do_center_crop
										lowercase    :      List[Any]       = do_rescale if do_rescale is not None else self.do_rescale
										lowercase    :      Tuple       = rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase    :      Optional[Any]       = offset if offset is not None else self.offset
										lowercase    :      str       = do_normalize if do_normalize is not None else self.do_normalize
										lowercase    :      List[str]       = image_mean if image_mean is not None else self.image_mean
										lowercase    :      Union[str, Any]       = image_std if image_std is not None else self.image_std

										lowercase    :      Optional[int]       = size if size is not None else self.size
										lowercase    :      Optional[Any]       = get_size_dict(__magic_name__			, default_to_square=__magic_name__				)
										lowercase    :      Tuple       = crop_size if crop_size is not None else self.crop_size
										lowercase    :      List[str]       = get_size_dict(__magic_name__			, param_name="""crop_size"""				)

										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."""				)

										lowercase    :      Tuple       = make_batched(__magic_name__				)

										lowercase    :      List[str]       = [
										    [
										        self._preprocess_image(
										            image=__magic_name__			, do_resize=__magic_name__			, size=__magic_name__			, resample=__magic_name__			, do_center_crop=__magic_name__			, crop_size=__magic_name__			, do_rescale=__magic_name__			, rescale_factor=__magic_name__			, offset=__magic_name__			, do_normalize=__magic_name__			, image_mean=__magic_name__			, image_std=__magic_name__			, data_format=__magic_name__			, )
										        for img in video
										    ]
										    for video in videos
										]

										lowercase    :      Dict       = {"""pixel_values""": videos}
										return BatchFeature(data=__magic_name__			, tensor_type=__magic_name__				)
