
import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case			:       List[str]      =						logging.get_logger(__name__)

def    __snake_case       (  __magic_name__	):







				'''simple docstring'''


				lowercase						    =			tf.image.resize(__magic_name__		,						(346, 346)	)
				lowercase						    =			tf.image.crop_to_bounding_box(__magic_name__		,						(346 - 289) // 2		,						(346 - 289) // 2		,						289		,						289	)
				return image

def    __snake_case       (  ):







				'''simple docstring'''


				lowercase						    =			EfficientNetConfig.from_pretrained("google/efficientnet-b7"	)
				lowercase						    =			289
				lowercase						    =			640
				lowercase						    =			{"0": "LABEL_0", "1": "LABEL_1"}
				lowercase						    =			{"LABEL_0": 0, "LABEL_1": 1}
				lowercase						    =			[]

				lowercase						    =			BertConfig()
				lowercase						    =			AlignConfig.from_text_vision_configs(
				    text_config=__magic_name__		,						vision_config=__magic_name__		,						projection_dim=640	)
				return config

def    __snake_case       (  ):







				'''simple docstring'''


				lowercase						    =			"http://images.cocodataset.org/val2017/000000039769.jpg"
				lowercase						    =			Image.open(requests.get(__magic_name__		,						stream=__magic_name__	).raw	)
				return im

def    __snake_case       (  ):







				'''simple docstring'''


				lowercase						    =			EfficientNetImageProcessor(
				    do_center_crop=__magic_name__		,						rescale_factor=1 / 1_2_7.5		,						rescale_offset=__magic_name__		,						do_normalize=__magic_name__		,						include_top=__magic_name__		,						resample=Image.BILINEAR		,						)
				lowercase						    =			BertTokenizer.from_pretrained("bert-base-uncased"	)
				lowercase						    =			64
				lowercase						    =			AlignProcessor(image_processor=__magic_name__		,						tokenizer=__magic_name__	)
				return processor

def    __snake_case       (  __magic_name__	):







				'''simple docstring'''


				lowercase						    =			[v.split("_"	)[0].split("block"	)[1] for v in original_param_names if v.startswith("block"	)]
				lowercase						    =			list(set(__magic_name__	)	)
				lowercase						    =			sorted(__magic_name__	)
				lowercase						    =			len(__magic_name__	)
				lowercase						    =			{b: str(__magic_name__	) for b, i in zip(__magic_name__		,						range(__magic_name__	)	)}

				lowercase						    =			[]
				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:
								lowercase						    =			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''')	)

				lowercase						    =			{}
				for item in rename_keys:
								if item[0] in original_param_names:
												lowercase						    =			"vision_model." + item[1]

    # BERT text encoder
				lowercase						    =			[]
				lowercase						    =			"tf_bert_model/bert"
				lowercase						    =			"text_model"
				for i in range(12	):
								rename_keys.append(
								    (
								        F'''{old}/encoder/layer_._{i}/attention/self/query/kernel:0''',
								        F'''{new}.encoder.layer.{i}.attention.self.query.weight''',
								    )	)
								rename_keys.append(
								    (
								        F'''{old}/encoder/layer_._{i}/attention/self/query/bias:0''',
								        F'''{new}.encoder.layer.{i}.attention.self.query.bias''',
								    )	)
								rename_keys.append(
								    (
								        F'''{old}/encoder/layer_._{i}/attention/self/key/kernel:0''',
								        F'''{new}.encoder.layer.{i}.attention.self.key.weight''',
								    )	)
								rename_keys.append(
								    (
								        F'''{old}/encoder/layer_._{i}/attention/self/key/bias:0''',
								        F'''{new}.encoder.layer.{i}.attention.self.key.bias''',
								    )	)
								rename_keys.append(
								    (
								        F'''{old}/encoder/layer_._{i}/attention/self/value/kernel:0''',
								        F'''{new}.encoder.layer.{i}.attention.self.value.weight''',
								    )	)
								rename_keys.append(
								    (
								        F'''{old}/encoder/layer_._{i}/attention/self/value/bias:0''',
								        F'''{new}.encoder.layer.{i}.attention.self.value.bias''',
								    )	)
								rename_keys.append(
								    (
								        F'''{old}/encoder/layer_._{i}/attention/output/dense/kernel:0''',
								        F'''{new}.encoder.layer.{i}.attention.output.dense.weight''',
								    )	)
								rename_keys.append(
								    (
								        F'''{old}/encoder/layer_._{i}/attention/output/dense/bias:0''',
								        F'''{new}.encoder.layer.{i}.attention.output.dense.bias''',
								    )	)
								rename_keys.append(
								    (
								        F'''{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0''',
								        F'''{new}.encoder.layer.{i}.attention.output.LayerNorm.weight''',
								    )	)
								rename_keys.append(
								    (
								        F'''{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0''',
								        F'''{new}.encoder.layer.{i}.attention.output.LayerNorm.bias''',
								    )	)
								rename_keys.append(
								    (
								        F'''{old}/encoder/layer_._{i}/intermediate/dense/kernel:0''',
								        F'''{new}.encoder.layer.{i}.intermediate.dense.weight''',
								    )	)
								rename_keys.append(
								    (
								        F'''{old}/encoder/layer_._{i}/intermediate/dense/bias:0''',
								        F'''{new}.encoder.layer.{i}.intermediate.dense.bias''',
								    )	)
								rename_keys.append(
								    (F'''{old}/encoder/layer_._{i}/output/dense/kernel:0''', F'''{new}.encoder.layer.{i}.output.dense.weight''')	)
								rename_keys.append(
								    (F'''{old}/encoder/layer_._{i}/output/dense/bias:0''', F'''{new}.encoder.layer.{i}.output.dense.bias''')	)
								rename_keys.append(
								    (F'''{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0''', F'''{new}.encoder.layer.{i}.output.LayerNorm.weight''')	)
								rename_keys.append(
								    (F'''{old}/encoder/layer_._{i}/output/LayerNorm/beta:0''', F'''{new}.encoder.layer.{i}.output.LayerNorm.bias''')	)

				rename_keys.append((F'''{old}/embeddings/word_embeddings/weight:0''', F'''{new}.embeddings.word_embeddings.weight''')	)
				rename_keys.append(
				    (F'''{old}/embeddings/position_embeddings/embeddings:0''', F'''{new}.embeddings.position_embeddings.weight''')	)
				rename_keys.append(
				    (F'''{old}/embeddings/token_type_embeddings/embeddings:0''', F'''{new}.embeddings.token_type_embeddings.weight''')	)
				rename_keys.append((F'''{old}/embeddings/LayerNorm/gamma:0''', F'''{new}.embeddings.LayerNorm.weight''')	)
				rename_keys.append((F'''{old}/embeddings/LayerNorm/beta:0''', F'''{new}.embeddings.LayerNorm.bias''')	)

				rename_keys.append((F'''{old}/pooler/dense/kernel:0''', F'''{new}.pooler.dense.weight''')	)
				rename_keys.append((F'''{old}/pooler/dense/bias:0''', F'''{new}.pooler.dense.bias''')	)
				rename_keys.append(("dense/kernel:0", "text_projection.weight")	)
				rename_keys.append(("dense/bias:0", "text_projection.bias")	)
				rename_keys.append(("dense/bias:0", "text_projection.bias")	)
				rename_keys.append(("temperature:0", "temperature")	)

				for item in rename_keys:
								if item[0] in original_param_names:
												lowercase						    =			item[1]
				return key_mapping

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				list(hf_params.keys()	)

				for key, value in tf_params.items():
								if key not in key_mapping:
												continue

								lowercase						    =			key_mapping[key]
								if "_conv" in key and "kernel" in key:
												lowercase						    =			torch.from_numpy(__magic_name__	).permute(3		,						2		,						0		,						1	)
								elif "embeddings" in key:
												lowercase						    =			torch.from_numpy(__magic_name__	)
								elif "depthwise_kernel" in key:
												lowercase						    =			torch.from_numpy(__magic_name__	).permute(2		,						3		,						0		,						1	)
								elif "kernel" in key:
												lowercase						    =			torch.from_numpy(np.transpose(__magic_name__	)	)
								elif "temperature" in key:
												lowercase						    =			value
								elif "bn/gamma" or "bn/beta" in key:
												lowercase						    =			torch.from_numpy(np.transpose(__magic_name__	)	).squeeze()
								else:
												lowercase						    =			torch.from_numpy(__magic_name__	)

								# Replace HF parameters with original TF model parameters
								hf_params[hf_key].copy_(__magic_name__	)

@torch.no_grad()
def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			64
				lowercase						    =			Tokenizer(__magic_name__	)
				lowercase						    =			align.Align("efficientnet-b7"		,						"bert-base"		,						640		,						__magic_name__		,						tok.get_vocab_size()	)
				original_model.compile()
				original_model.load_weights(__magic_name__	)

				lowercase						    =			original_model.trainable_variables
				lowercase						    =			original_model.non_trainable_variables
				lowercase						    =			{param.name: param.numpy() for param in tf_params}
				for param in tf_non_train_params:
								lowercase						    =			param.numpy()
				lowercase						    =			list(tf_params.keys()	)

				# Load HuggingFace model
				lowercase						    =			get_align_config()
				lowercase						    =			AlignModel(__magic_name__	).eval()
				lowercase						    =			hf_model.state_dict()

				# Create src-to-dst parameter name mapping dictionary
				print("Converting parameters..."	)
				lowercase						    =			rename_keys(__magic_name__	)
				replace_params(__magic_name__		,						__magic_name__		,						__magic_name__	)

				# Initialize processor
				lowercase						    =			get_processor()
				lowercase						    =			processor(
				    images=prepare_img()		,						text="A picture of a cat"		,						padding="max_length"		,						max_length=64		,						return_tensors="pt"	)

				# HF model inference
				hf_model.eval()
				with torch.no_grad():
								lowercase						    =			hf_model(**__magic_name__	)

				lowercase						    =			outputs.image_embeds.detach().numpy()
				lowercase						    =			outputs.text_embeds.detach().numpy()

				# Original model inference
				lowercase						    =			False
				lowercase						    =			EfficientNetImageProcessor(
				    do_center_crop=__magic_name__		,						do_rescale=__magic_name__		,						do_normalize=__magic_name__		,						include_top=__magic_name__		,						resample=Image.BILINEAR		,						)
				lowercase						    =			tf_image_processor(images=prepare_img()		,						return_tensors="tf"		,						data_format="channels_last"	)["pixel_values"]
				lowercase						    =			tok(tf.constant(["A picture of a cat"]	)	)

				lowercase						    =			original_model.image_encoder(__magic_name__		,						training=__magic_name__	)
				lowercase						    =			original_model.text_encoder(__magic_name__		,						training=__magic_name__	)

				lowercase						    =			tf.nn.la_normalize(__magic_name__		,						axis=-1	)
				lowercase						    =			tf.nn.la_normalize(__magic_name__		,						axis=-1	)

				# Check whether original and HF model outputs match  -> np.allclose
				if not np.allclose(__magic_name__		,						__magic_name__		,						atol=1e-3	):
								raise ValueError("The predicted image features are not the same."	)
				if not np.allclose(__magic_name__		,						__magic_name__		,						atol=1e-3	):
								raise ValueError("The predicted text features are not the same."	)
				print("Model outputs match!"	)

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

				if push_to_hub:
								# Push model and image processor to hub
								print("Pushing converted ALIGN to the hub..."	)
								processor.push_to_hub("align-base"	)
								hf_model.push_to_hub("align-base"	)


if __name__ == "__main__":
						_snake_case			:       List[str]      =						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint_path",
						    default="./weights/model-weights",
						    type=str,
						    help="Path to the pretrained TF ALIGN checkpoint.",
						)
						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")

						_snake_case			:       List[str]      =						parser.parse_args()
						convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)


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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_snake_case			:       Tuple      =						logging.get_logger(__name__)


if is_vision_available():
						import PIL





class      UpperCamelCase_			(			__a		):







						'''simple docstring'''



						UpperCamelCase			:     Optional[int]							      =				['''pixel_values''']





						def __init__( self       :List[str]		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Dict[str, int] = None		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BILINEAR		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Union[int, float] = 1 / 255		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						**lowerCAmelCase__       :int		,						)     ->None:
										super().__init__(**lowerCAmelCase__    )

										lowercase						    =			size if size is not None else {"height": 2560, "width": 1920}
										if isinstance(lowerCAmelCase__		,						(tuple, list)    ):
														# The previous feature extractor size parameter was in (width, height) format
														lowercase						    =			size[::-1]
										lowercase						    =			get_size_dict(lowerCAmelCase__    )

										lowercase						    =			do_resize
										lowercase						    =			size
										lowercase						    =			resample
										lowercase						    =			do_thumbnail
										lowercase						    =			do_align_long_axis
										lowercase						    =			do_pad
										lowercase						    =			do_rescale
										lowercase						    =			rescale_factor
										lowercase						    =			do_normalize
										lowercase						    =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowercase						    =			image_std if image_std is not None else IMAGENET_STANDARD_STD





						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None    )     ->np.ndarray:
										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )
										lowercase    ,  lowercase						    =			size["height"], size["width"]

										if (output_width < output_height and input_width > input_height) or (
										    output_width > output_height and input_width < input_height
										):
														lowercase						    =			np.rotaa(lowerCAmelCase__		,						3    )

										if data_format is not None:
														lowercase						    =			to_channel_dimension_format(lowerCAmelCase__		,						lowerCAmelCase__    )

										return image





						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						)     ->np.ndarray:
										lowercase    ,  lowercase						    =			size["height"], size["width"]
										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )

										lowercase						    =			output_width - input_width
										lowercase						    =			output_height - input_height

										if random_padding:
														lowercase						    =			np.random.randint(low=0		,						high=delta_height + 1    )
														lowercase						    =			np.random.randint(low=0		,						high=delta_width + 1    )
										else:
														lowercase						    =			delta_height // 2
														lowercase						    =			delta_width // 2

										lowercase						    =			delta_height - pad_top
										lowercase						    =			delta_width - pad_left

										lowercase						    =			((pad_top, pad_bottom), (pad_left, pad_right))
										return pad(lowerCAmelCase__		,						lowerCAmelCase__		,						data_format=lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Dict		,						*lowerCAmelCase__       :Union[str, Any]		,						**lowerCAmelCase__       :str    )     ->List[str]:
										logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."    )
										return self.pad_image(*lowerCAmelCase__		,						**lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BICUBIC		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :Dict		,						)     ->np.ndarray:
										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )
										lowercase    ,  lowercase						    =			size["height"], size["width"]

										# We always resize to the smallest of either the input or output size.
										lowercase						    =			min(lowerCAmelCase__		,						lowerCAmelCase__    )
										lowercase						    =			min(lowerCAmelCase__		,						lowerCAmelCase__    )

										if height == input_height and width == input_width:
														return image

										if input_height > input_width:
														lowercase						    =			int(input_width * height / input_height    )
										elif input_width > input_height:
														lowercase						    =			int(input_height * width / input_width    )

										return resize(
										    lowerCAmelCase__		,						size=(height, width)		,						resample=lowerCAmelCase__		,						reducing_gap=2.0		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :List[str]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BICUBIC		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :List[str]		,						)     ->np.ndarray:
										lowercase						    =			get_size_dict(lowerCAmelCase__    )
										lowercase						    =			min(size["height"]		,						size["width"]    )
										lowercase						    =			get_resize_output_image_size(lowerCAmelCase__		,						size=lowerCAmelCase__		,						default_to_square=lowerCAmelCase__    )
										lowercase						    =			resize(lowerCAmelCase__		,						size=lowerCAmelCase__		,						resample=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )
										return resized_image





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





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





						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]		,						lowerCAmelCase__       :ImageInput		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :Dict[str, int] = None		,						lowerCAmelCase__       :PILImageResampling = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :float = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[str, TensorType]] = None		,						lowerCAmelCase__       :Optional[ChannelDimension] = ChannelDimension.FIRST		,						**lowerCAmelCase__       :Tuple		,						)     ->PIL.Image.Image:
										lowercase						    =			do_resize if do_resize is not None else self.do_resize
										lowercase						    =			size if size is not None else self.size
										if isinstance(lowerCAmelCase__		,						(tuple, list)    ):
														# Previous feature extractor had size in (width, height) format
														lowercase						    =			size[::-1]
										lowercase						    =			get_size_dict(lowerCAmelCase__    )
										lowercase						    =			resample if resample is not None else self.resample
										lowercase						    =			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
										lowercase						    =			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
										lowercase						    =			do_pad if do_pad is not None else self.do_pad
										lowercase						    =			do_rescale if do_rescale is not None else self.do_rescale
										lowercase						    =			rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase						    =			do_normalize if do_normalize is not None else self.do_normalize
										lowercase						    =			image_mean if image_mean is not None else self.image_mean
										lowercase						    =			image_std if image_std is not None else self.image_std

										lowercase						    =			make_list_of_images(lowerCAmelCase__    )

										if not valid_images(lowerCAmelCase__    ):
														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_rescale and rescale_factor is None:
														raise ValueError("Rescale factor must be specified if do_rescale is True."    )

										if do_pad and size is None:
														raise ValueError("Size must be specified if do_pad 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."    )

										# All transformations expect numpy arrays.
										lowercase						    =			[to_numpy_array(lowerCAmelCase__    ) for image in images]

										if do_align_long_axis:
														lowercase						    =			[self.align_long_axis(lowerCAmelCase__		,						size=lowerCAmelCase__    ) for image in images]

										if do_resize:
														lowercase						    =			[self.resize(image=lowerCAmelCase__		,						size=lowerCAmelCase__		,						resample=lowerCAmelCase__    ) for image in images]

										if do_thumbnail:
														lowercase						    =			[self.thumbnail(image=lowerCAmelCase__		,						size=lowerCAmelCase__    ) for image in images]

										if do_pad:
														lowercase						    =			[self.pad_image(image=lowerCAmelCase__		,						size=lowerCAmelCase__		,						random_padding=lowerCAmelCase__    ) for image in images]

										if do_rescale:
														lowercase						    =			[self.rescale(image=lowerCAmelCase__		,						scale=lowerCAmelCase__    ) for image in images]

										if do_normalize:
														lowercase						    =			[self.normalize(image=lowerCAmelCase__		,						mean=lowerCAmelCase__		,						std=lowerCAmelCase__    ) for image in images]

										lowercase						    =			[to_channel_dimension_format(lowerCAmelCase__		,						lowerCAmelCase__    ) for image in images]

										lowercase						    =			{"pixel_values": images}
										return BatchFeature(data=lowerCAmelCase__		,						tensor_type=lowerCAmelCase__    )
