

"""simple docstring"""


import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


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

__A		     =    {
    "post_extract_proj": "feature_projection.projection",
    "models.0.layer_norm": "feature_projection.layer_norm",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
__A		     =    [
    "lm_head",
]






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		for attribute in key.split("."				):
				__lowerCAmelCase: Optional[Any]					   =    getattr(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)

		if weight_type is not None:
				__lowerCAmelCase: int					   =    getattr(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				).shape
		else:
				__lowerCAmelCase: Optional[int]					   =    hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
				    F" {value.shape} for {full_name}"				)

		if weight_type == "weight":
				__lowerCAmelCase: List[str]					   =    value
		elif weight_type == "weight_g":
				__lowerCAmelCase: int					   =    value
		elif weight_type == "weight_v":
				__lowerCAmelCase: Optional[Any]					   =    value
		elif weight_type == "bias":
				__lowerCAmelCase: Dict					   =    value
		else:
				__lowerCAmelCase: Dict					   =    value

		logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			Union[str, Any]:
		__lowerCAmelCase: List[str]					   =    []
		__lowerCAmelCase: int					   =    fairseq_model.state_dict()

		if not is_headless:
				__lowerCAmelCase: Tuple					   =    hf_model.dataavec_audio.feature_extractor
				__lowerCAmelCase: Union[str, Any]					   =    hf_model.dataavec_audio.encoder.pos_conv_embed

		else:
				__lowerCAmelCase: Optional[int]					   =    hf_model.feature_extractor
				__lowerCAmelCase: List[str]					   =    hf_model.encoder.pos_conv_embed

		for name, value in fairseq_dict.items():
				__lowerCAmelCase: Tuple					   =    False
				if "conv_layers" in name:
						load_conv_layer(
						    __SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				)
						__lowerCAmelCase: Optional[int]					   =    True
				elif "pos_conv" in name:
						load_pos_conv_layer(
						    __SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				)
						__lowerCAmelCase: List[Any]					   =    True
				else:
						for key, mapped_key in MAPPING.items():
								if not is_headless:
										__lowerCAmelCase: str					   =    "data2vec_audio." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split("w2v_model."				)[-1] == name.split("."				)[0]:
										__lowerCAmelCase: str					   =    True
										if "*" in mapped_key:
												__lowerCAmelCase: Union[str, Any]					   =    name.split(__SCREAMING_SNAKE_CASE				)[0].split("."				)[-2]
												__lowerCAmelCase: Union[str, Any]					   =    mapped_key.replace("*"      ,				__SCREAMING_SNAKE_CASE				)
										if "weight_g" in name:
												__lowerCAmelCase: Union[str, Any]					   =    "weight_g"
										elif "weight_v" in name:
												__lowerCAmelCase: Optional[Any]					   =    "weight_v"
										elif "bias" in name:
												__lowerCAmelCase: List[str]					   =    "bias"
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												__lowerCAmelCase: Tuple					   =    "weight"
										else:
												__lowerCAmelCase: str					   =    None
										set_recursively(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
								continue
				if not is_used:
						unused_weights.append(__SCREAMING_SNAKE_CASE				)

		logger.warning(F"Unused weights: {unused_weights}"				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			int:
		__lowerCAmelCase: Tuple					   =    path.split("."				)
		return reduce(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			List[str]:
		__lowerCAmelCase: Any					   =    access_by_string(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: str					   =    hf_weight.data

		if fsq_value.shape != hf_value.shape:
				raise ValueError(F"{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."				)
		__lowerCAmelCase: Union[str, Any]					   =    fsq_value
		logger.info(F"{full_name} was correctly initialized from {hf_weight_path}."				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			Any:
		__lowerCAmelCase: List[str]					   =    full_name.split("conv_layers."				)[-1]
		__lowerCAmelCase: Union[str, Any]					   =    name.split("."				)
		__lowerCAmelCase: Tuple					   =    int(items[0]				)
		__lowerCAmelCase: Tuple					   =    int(items[1]				)

		__lowerCAmelCase: Dict					   =    name.split("."				)[-1]
		if type_id == 0:
				__lowerCAmelCase: Any					   =    "conv"
		elif type_id == 2:
				__lowerCAmelCase: str					   =    "layer_norm"
		else:
				unused_weights.append(__SCREAMING_SNAKE_CASE				)
				return

		set_weights(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				F"conv_layers.{layer_id}.{layer_type}.{weight_type}"				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			Any:
		__lowerCAmelCase: Union[str, Any]					   =    full_name.split("pos_conv."				)[-1]
		__lowerCAmelCase: Optional[Any]					   =    name.split("."				)
		__lowerCAmelCase: Dict					   =    int(items[0]				)
		__lowerCAmelCase: List[Any]					   =    int(items[1]				)

		__lowerCAmelCase: Any					   =    name.split("."				)[-1]
		if type_id != 0:
				unused_weights.append(__SCREAMING_SNAKE_CASE				)
				return
		else:
				__lowerCAmelCase: str					   =    "conv"

		set_weights(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				F"layers.{layer_id}.{layer_type}.{weight_type}"				)






@torch.no_grad()
def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE=None      ,				__SCREAMING_SNAKE_CASE=None      ,				__SCREAMING_SNAKE_CASE=True				)			->			Optional[int]:
		if config_path is not None:
				__lowerCAmelCase: List[Any]					   =    DataaVecAudioConfig.from_pretrained(__SCREAMING_SNAKE_CASE				)
		else:
				__lowerCAmelCase: List[Any]					   =    DataaVecAudioConfig()

		if not is_finetuned:
				# Modify final_proj layer name
				__lowerCAmelCase: str					   =    DataaVecAudioModel(__SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase: Dict					   =    os.path.dirname(__SCREAMING_SNAKE_CASE				)

				__lowerCAmelCase: List[Any]					   =    torch.load(__SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase: Union[str, Any]					   =    state_dict["model"].pop("final_proj.0.weight"				)
				__lowerCAmelCase: str					   =    state_dict["model"].pop("final_proj.0.bias"				)
				__lowerCAmelCase: List[str]					   =    os.path.join(__SCREAMING_SNAKE_CASE      ,				"converted.pt"				)
				torch.save(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		else:
				__lowerCAmelCase: List[Any]					   =    DataaVecAudioForCTC(__SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase: Any					   =    checkpoint_path

		def load_dataavec(__SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: Tuple					   =    fairseq.checkpoint_utils.load_model_ensemble_and_task([path]				)
				return model[0].eval()

		__lowerCAmelCase: int					   =    load_dataavec(__SCREAMING_SNAKE_CASE				)

		recursively_load_weights(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				not is_finetuned				)

		__lowerCAmelCase: List[Any]					   =    WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"				)

		__lowerCAmelCase: Dict					   =    load_dataset("patrickvonplaten/librispeech_asr_dummy"      ,				"clean"      ,				split="validation"				)
		__lowerCAmelCase: List[Any]					   =    [x["array"] for x in ds[:4]["audio"]]

		__lowerCAmelCase: List[str]					   =    processor(__SCREAMING_SNAKE_CASE      ,				return_tensors="pt"      ,				padding=__SCREAMING_SNAKE_CASE				)

		__lowerCAmelCase: Union[str, Any]					   =    inputs.input_values
		__lowerCAmelCase: Any					   =    inputs.attention_mask
		#    input_values = inputs.input_values[:, :-1]
		#    attention_mask = inputs.attention_mask[:, :-1]

		hf_wavavec.eval()
		model.eval()
		if is_finetuned:
				__lowerCAmelCase: List[Any]					   =    model(source=__SCREAMING_SNAKE_CASE      ,				padding_mask=(1 - attention_mask)      ,				mask=__SCREAMING_SNAKE_CASE      ,				features_only=__SCREAMING_SNAKE_CASE				)[
				    "encoder_out"
				].transpose(0      ,				1				)
				__lowerCAmelCase: Optional[Any]					   =    hf_wavavec(__SCREAMING_SNAKE_CASE      ,				attention_mask=__SCREAMING_SNAKE_CASE				)["logits"]

				__lowerCAmelCase: int					   =    torch.argmax(__SCREAMING_SNAKE_CASE      ,				dim=-1				)
				__lowerCAmelCase: Optional[Any]					   =    processor.batch_decode(__SCREAMING_SNAKE_CASE				)

				print(F"Expected Output: {ds[:4]['text']}, Pred: {output_string}"				)
		else:
				__lowerCAmelCase: List[str]					   =    model(source=__SCREAMING_SNAKE_CASE      ,				padding_mask=(1 - attention_mask)      ,				mask=__SCREAMING_SNAKE_CASE      ,				features_only=__SCREAMING_SNAKE_CASE				)[
				    "layer_results"
				][-1][0].transpose(0      ,				1				)
				__lowerCAmelCase: str					   =    hf_wavavec(__SCREAMING_SNAKE_CASE      ,				attention_mask=__SCREAMING_SNAKE_CASE				)["last_hidden_state"]

		print(our_output.shape      ,				their_output.shape				)
		__lowerCAmelCase: int					   =    torch.max(torch.abs(our_output - their_output				)				).item()
		print(F"max_absolute_diff = {max_absolute_diff}"				)  # ~ 1e-7
		__lowerCAmelCase: int					   =    torch.allclose(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				atol=1E-3				)
		print("Do both models output the same tensors?"      ,				"🔥" if success else "💩"				)
		if not success:
				raise Exception("Something went wRoNg"				)

		hf_wavavec.save_pretrained(__SCREAMING_SNAKE_CASE				)

		if is_finetuned:
				processor.save_pretrained(__SCREAMING_SNAKE_CASE				)
		else:
				processor.feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
			__A		     =    argparse.ArgumentParser()
			parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
			parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
			parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
			parser.add_argument(
			    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
			)
			__A		     =    parser.parse_args()
			convert_wavaveca_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)





"""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 (
    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


__A		     =    logging.get_logger(__name__)


if is_vision_available():
			import PIL





class 					snake_case			(       __snake_case						):
				SCREAMING_SNAKE_CASE_     :   Union[str, Any]        =						["""pixel_values"""]

				def __init__(			self							:       int  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       Dict[str, int] = None  ,						UpperCamelCase__							:       PILImageResampling = PILImageResampling.BILINEAR  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       bool = False  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       Union[int, float] = 1 / 2_5_5  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						**UpperCamelCase__							:       Tuple  ,						)->     None:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)

						__lowerCAmelCase: List[str]					   =    size if size is not None else {"height": 2_5_6_0, "width": 1_9_2_0}
						if isinstance(UpperCamelCase__  ,						(tuple, list)):
								# The previous feature extractor size parameter was in (width, height) format
								__lowerCAmelCase: str					   =    size[::-1]
						__lowerCAmelCase: int					   =    get_size_dict(UpperCamelCase__)

						__lowerCAmelCase: Union[str, Any]					   =    do_resize
						__lowerCAmelCase: Optional[Any]					   =    size
						__lowerCAmelCase: List[str]					   =    resample
						__lowerCAmelCase: Tuple					   =    do_thumbnail
						__lowerCAmelCase: int					   =    do_align_long_axis
						__lowerCAmelCase: Any					   =    do_pad
						__lowerCAmelCase: Any					   =    do_rescale
						__lowerCAmelCase: int					   =    rescale_factor
						__lowerCAmelCase: str					   =    do_normalize
						__lowerCAmelCase: List[str]					   =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__lowerCAmelCase: List[Any]					   =    image_std if image_std is not None else IMAGENET_STANDARD_STD

				def 							lowercase_  (			self							:       Optional[int]  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Dict[str, int]  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None)->     np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase   ,				__lowerCAmelCase: Optional[int]					   =    get_image_size(UpperCamelCase__)
						__lowerCAmelCase   ,				__lowerCAmelCase: Union[str, Any]					   =    size["height"], size["width"]

						if (output_width < output_height and input_width > input_height) or (
						    output_width > output_height and input_width < input_height
						):
								__lowerCAmelCase: List[Any]					   =    np.rotaa(UpperCamelCase__  ,						3)

						if data_format is not None:
								__lowerCAmelCase: Dict					   =    to_channel_dimension_format(UpperCamelCase__  ,						UpperCamelCase__)

						return image

				def 							lowercase_  (			self							:       Optional[Any]  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Dict[str, int]  ,						UpperCamelCase__							:       bool = False  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None  ,						)->     np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase   ,				__lowerCAmelCase: List[str]					   =    size["height"], size["width"]
						__lowerCAmelCase   ,				__lowerCAmelCase: Optional[Any]					   =    get_image_size(UpperCamelCase__)

						__lowerCAmelCase: List[Any]					   =    output_width - input_width
						__lowerCAmelCase: str					   =    output_height - input_height

						if random_padding:
								__lowerCAmelCase: Tuple					   =    np.random.randint(low=0  ,						high=delta_height + 1)
								__lowerCAmelCase: Union[str, Any]					   =    np.random.randint(low=0  ,						high=delta_width + 1)
						else:
								__lowerCAmelCase: str					   =    delta_height // 2
								__lowerCAmelCase: Optional[Any]					   =    delta_width // 2

						__lowerCAmelCase: List[Any]					   =    delta_height - pad_top
						__lowerCAmelCase: Dict					   =    delta_width - pad_left

						__lowerCAmelCase: Optional[Any]					   =    ((pad_top, pad_bottom), (pad_left, pad_right))
						return pad(UpperCamelCase__  ,						UpperCamelCase__  ,						data_format=UpperCamelCase__)

				def 							lowercase_  (			self							:       Union[str, Any]  ,						*UpperCamelCase__							:       str  ,						**UpperCamelCase__							:       Optional[int])->     Optional[int]:



						'''simple docstring'''


						logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead.")
						return self.pad_image(*UpperCamelCase__  ,						**UpperCamelCase__)

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



						'''simple docstring'''


						__lowerCAmelCase   ,				__lowerCAmelCase: List[str]					   =    get_image_size(UpperCamelCase__)
						__lowerCAmelCase   ,				__lowerCAmelCase: Tuple					   =    size["height"], size["width"]

						# We always resize to the smallest of either the input or output size.
						__lowerCAmelCase: Union[str, Any]					   =    min(UpperCamelCase__  ,						UpperCamelCase__)
						__lowerCAmelCase: List[Any]					   =    min(UpperCamelCase__  ,						UpperCamelCase__)

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

						if input_height > input_width:
								__lowerCAmelCase: Dict					   =    int(input_width * height / input_height)
						elif input_width > input_height:
								__lowerCAmelCase: Any					   =    int(input_height * width / input_width)

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

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



						'''simple docstring'''


						__lowerCAmelCase: Tuple					   =    get_size_dict(UpperCamelCase__)
						__lowerCAmelCase: Dict					   =    min(size["height"]  ,						size["width"])
						__lowerCAmelCase: List[str]					   =    get_resize_output_image_size(UpperCamelCase__  ,						size=UpperCamelCase__  ,						default_to_square=UpperCamelCase__)
						__lowerCAmelCase: Optional[int]					   =    resize(UpperCamelCase__  ,						size=UpperCamelCase__  ,						resample=UpperCamelCase__  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__)
						return resized_image

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



						'''simple docstring'''


						return rescale(UpperCamelCase__  ,						scale=UpperCamelCase__  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__)

				def 							lowercase_  (			self							:       Any  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Union[float, List[float]]  ,						UpperCamelCase__							:       Union[float, List[float]]  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__							:       List[str]  ,						)->     np.ndarray:



						'''simple docstring'''


						return normalize(UpperCamelCase__  ,						mean=UpperCamelCase__  ,						std=UpperCamelCase__  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__)




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



						'''simple docstring'''


						__lowerCAmelCase: Optional[int]					   =    do_resize if do_resize is not None else self.do_resize
						__lowerCAmelCase: Any					   =    size if size is not None else self.size
						if isinstance(UpperCamelCase__  ,						(tuple, list)):
								# Previous feature extractor had size in (width, height) format
								__lowerCAmelCase: int					   =    size[::-1]
						__lowerCAmelCase: Dict					   =    get_size_dict(UpperCamelCase__)
						__lowerCAmelCase: Optional[Any]					   =    resample if resample is not None else self.resample
						__lowerCAmelCase: int					   =    do_thumbnail if do_thumbnail is not None else self.do_thumbnail
						__lowerCAmelCase: Any					   =    do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
						__lowerCAmelCase: str					   =    do_pad if do_pad is not None else self.do_pad
						__lowerCAmelCase: List[Any]					   =    do_rescale if do_rescale is not None else self.do_rescale
						__lowerCAmelCase: List[Any]					   =    rescale_factor if rescale_factor is not None else self.rescale_factor
						__lowerCAmelCase: List[Any]					   =    do_normalize if do_normalize is not None else self.do_normalize
						__lowerCAmelCase: Union[str, Any]					   =    image_mean if image_mean is not None else self.image_mean
						__lowerCAmelCase: List[str]					   =    image_std if image_std is not None else self.image_std

						__lowerCAmelCase: Union[str, Any]					   =    make_list_of_images(UpperCamelCase__)

						if not valid_images(UpperCamelCase__):
								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.
						__lowerCAmelCase: int					   =    [to_numpy_array(UpperCamelCase__) for image in images]

						if do_align_long_axis:
								__lowerCAmelCase: Any					   =    [self.align_long_axis(UpperCamelCase__  ,						size=UpperCamelCase__) for image in images]

						if do_resize:
								__lowerCAmelCase: Tuple					   =    [self.resize(image=UpperCamelCase__  ,						size=UpperCamelCase__  ,						resample=UpperCamelCase__) for image in images]

						if do_thumbnail:
								__lowerCAmelCase: List[Any]					   =    [self.thumbnail(image=UpperCamelCase__  ,						size=UpperCamelCase__) for image in images]

						if do_pad:
								__lowerCAmelCase: Tuple					   =    [self.pad_image(image=UpperCamelCase__  ,						size=UpperCamelCase__  ,						random_padding=UpperCamelCase__) for image in images]

						if do_rescale:
								__lowerCAmelCase: Optional[int]					   =    [self.rescale(image=UpperCamelCase__  ,						scale=UpperCamelCase__) for image in images]

						if do_normalize:
								__lowerCAmelCase: Tuple					   =    [self.normalize(image=UpperCamelCase__  ,						mean=UpperCamelCase__  ,						std=UpperCamelCase__) for image in images]

						__lowerCAmelCase: Union[str, Any]					   =    [to_channel_dimension_format(UpperCamelCase__  ,						UpperCamelCase__) for image in images]

						__lowerCAmelCase: Optional[Any]					   =    {"pixel_values": images}
						return BatchFeature(data=UpperCamelCase__  ,						tensor_type=UpperCamelCase__)


