

import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
a__				=		logging.get_logger('''transformers.models.speecht5''')

a__				=		{
    '''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
    '''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
    '''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
    '''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
a__				=		{
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
a__				=		{
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
    '''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
    '''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
    '''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
a__				=		{
    '''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
    '''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
    '''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
    '''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
    '''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
    '''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
    '''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
    '''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
a__				=		{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
a__				=		{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
a__				=		{
    '''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
    '''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
    '''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
    '''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
    '''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
    '''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
    '''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
    '''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
    '''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
a__				=		{
    '''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
    '''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
    '''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
    '''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
    '''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
    '''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
    '''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
    '''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
    '''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
    '''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
    '''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
    '''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
    '''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
a__				=		{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
a__				=		{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a__				=		{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a__				=		[]
a__				=		[
    '''encoder.version''',
    '''encoder.layers.*.norm_k.weight''',
    '''encoder.layers.*.norm_k.bias''',
    '''decoder.version''',
    '''decoder.layers.*.norm_k.weight''',
    '''decoder.layers.*.norm_k.bias''',
    '''decoder.pos_emb.pe_k''',
    '''speech_encoder_prenet.embed_positions._float_tensor''',
    '''text_decoder_prenet.embed_positions._float_tensor''',
]
a__				=		IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
a__				=		IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
a__				=		IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]



def    __UpperCAmelCase (	__a      :			Dict		,__a      :			Dict		,__a      :			Optional[Any]		,__a      :			int		,__a      :			List[str]	)			->     Union[str, Any]:

					"""simple docstring"""

					for attribute in key.split('''.'''	):
										_a	:    Optional[Any]         =		getattr(__a		,__a	)

					if weight_type is not None:
										_a	:    int         =		getattr(__a		,__a	).shape
					else:
										_a	:    Any         =		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":
										_a	:    Tuple         =		value
					elif weight_type == "weight_g":
										_a	:    Optional[Any]         =		value
					elif weight_type == "weight_v":
										_a	:    Any         =		value
					elif weight_type == "bias":
										_a	:    Any         =		value
					elif weight_type == "running_mean":
										_a	:    Optional[int]         =		value
					elif weight_type == "running_var":
										_a	:    Tuple         =		value
					elif weight_type == "num_batches_tracked":
										_a	:    Union[str, Any]         =		value
					else:
										_a	:    Tuple         =		value

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



def    __UpperCAmelCase (	__a      :			str		,__a      :			str	)			->     str:

					"""simple docstring"""

					for key in ignore_keys:
										if key.endswith('''.*'''	):
															if name.startswith(key[:-1]	):
																				return True
										elif ".*." in key:
															_a      ,					_a	:    Dict         =		key.split('''.*.'''	)
															if prefix in name and suffix in name:
																				return True
										elif key in name:
															return True
					return False



def    __UpperCAmelCase (	__a      :			str		,__a      :			Optional[int]		,__a      :			Dict	)			->     str:

					"""simple docstring"""

					_a	:    int         =		[]

					if task == "s2t":
										_a	:    Any         =		hf_model.speechta.encoder.prenet.feature_encoder
										_a	:    List[Any]         =		MAPPING_S2T
										_a	:    Optional[Any]         =		IGNORE_KEYS_S2T
					elif task == "t2s":
										_a	:    List[Any]         =		None
										_a	:    Optional[Any]         =		MAPPING_T2S
										_a	:    Optional[int]         =		IGNORE_KEYS_T2S
					elif task == "s2s":
										_a	:    List[str]         =		hf_model.speechta.encoder.prenet.feature_encoder
										_a	:    Dict         =		MAPPING_S2S
										_a	:    str         =		IGNORE_KEYS_S2S
					else:
										raise ValueError(F"""Unsupported task: {task}"""	)

					for name, value in fairseq_dict.items():
										if should_ignore(__a		,__a	):
															logger.info(F"""{name} was ignored"""	)
															continue

										_a	:    List[Any]         =		False
										if "conv_layers" in name:
															load_conv_layer(
															    __a		,__a		,__a		,__a		,hf_model.config.feat_extract_norm == '''group'''		,)
															_a	:    Any         =		True
										else:
															for key, mapped_key in MAPPING.items():
																				# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

																				if "*" in key:
																									_a      ,					_a	:    List[str]         =		key.split('''.*.'''	)
																									if prefix in name and suffix in name:
																														_a	:    List[str]         =		suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																				if key in name:
																									_a	:    List[str]         =		True
																									if "*" in mapped_key:
																														_a	:    Any         =		name.split(__a	)[0].split('''.'''	)[-2]
																														_a	:    Optional[Any]         =		mapped_key.replace('''*'''		,__a	)
																									if "weight_g" in name:
																														_a	:    List[Any]         =		'''weight_g'''
																									elif "weight_v" in name:
																														_a	:    Any         =		'''weight_v'''
																									elif "bias" in name:
																														_a	:    List[str]         =		'''bias'''
																									elif "weight" in name:
																														_a	:    str         =		'''weight'''
																									elif "running_mean" in name:
																														_a	:    Dict         =		'''running_mean'''
																									elif "running_var" in name:
																														_a	:    Optional[int]         =		'''running_var'''
																									elif "num_batches_tracked" in name:
																														_a	:    Tuple         =		'''num_batches_tracked'''
																									else:
																														_a	:    Tuple         =		None
																									set_recursively(__a		,__a		,__a		,__a		,__a	)
																				continue
										if not is_used:
															unused_weights.append(__a	)

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



def    __UpperCAmelCase (	__a      :			Dict		,__a      :			Optional[Any]		,__a      :			Tuple		,__a      :			Optional[int]		,__a      :			Union[str, Any]	)			->     Union[str, Any]:

					"""simple docstring"""

					_a	:    int         =		full_name.split('''conv_layers.'''	)[-1]
					_a	:    List[str]         =		name.split('''.'''	)
					_a	:    int         =		int(items[0]	)
					_a	:    Dict         =		int(items[1]	)

					if type_id == 0:
										if "bias" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																				raise ValueError(
																				    F"""{full_name} has size {value.shape}, but"""
																				    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""	)
															_a	:    Optional[Any]         =		value
															logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
										elif "weight" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																				raise ValueError(
																				    F"""{full_name} has size {value.shape}, but"""
																				    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""	)
															_a	:    Union[str, Any]         =		value
															logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
					elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
										if "bias" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																				raise ValueError(
																				    F"""{full_name} has size {value.shape}, but"""
																				    F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""	)
															_a	:    Tuple         =		value
															logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
										elif "weight" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																				raise ValueError(
																				    F"""{full_name} has size {value.shape}, but"""
																				    F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""	)
															_a	:    Tuple         =		value
															logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
					else:
										unused_weights.append(__a	)



@torch.no_grad()
def    __UpperCAmelCase (	__a      :			Union[str, Any]		,__a      :			Optional[Any]		,__a      :			Tuple		,__a      :			Tuple=None		,__a      :			List[Any]=None		,__a      :			str=None		,)			->     Any:

					"""simple docstring"""

					if config_path is not None:
										_a	:    str         =		SpeechTaConfig.from_pretrained(__a	)
					else:
										_a	:    str         =		SpeechTaConfig()

					if task == "s2t":
										_a	:    List[Any]         =		config.max_text_positions
										_a	:    Union[str, Any]         =		SpeechTaForSpeechToText(__a	)
					elif task == "t2s":
										_a	:    Optional[Any]         =		1_876
										_a	:    Dict         =		600
										_a	:    Tuple         =		config.max_speech_positions
										_a	:    Union[str, Any]         =		SpeechTaForTextToSpeech(__a	)
					elif task == "s2s":
										_a	:    Union[str, Any]         =		1_876
										_a	:    Tuple         =		config.max_speech_positions
										_a	:    List[Any]         =		SpeechTaForSpeechToSpeech(__a	)
					else:
										raise ValueError(F"""Unknown task name: {task}"""	)

					if vocab_path:
										_a	:    Union[str, Any]         =		SpeechTaTokenizer(__a		,model_max_length=config.max_text_positions	)

										# Mask token behaves like a normal word, i.e. include the space before it
										_a	:    Optional[int]         =		AddedToken('''<mask>'''		,lstrip=__a		,rstrip=__a	)
										_a	:    int         =		mask_token
										tokenizer.add_special_tokens({'''mask_token''': mask_token}	)
										tokenizer.add_tokens(['''<ctc_blank>''']	)

					_a	:    Union[str, Any]         =		SpeechTaFeatureExtractor()
					_a	:    Union[str, Any]         =		SpeechTaProcessor(tokenizer=__a		,feature_extractor=__a	)
					processor.save_pretrained(__a	)

					_a	:    Optional[int]         =		torch.load(__a	)
					recursively_load_weights(fairseq_checkpoint['''model''']		,__a		,__a	)

					model.save_pretrained(__a	)

					if repo_id:
										print('''Pushing to the hub...'''	)
										processor.push_to_hub(__a	)
										model.push_to_hub(__a	)


if __name__ == "__main__":
			a__				=		argparse.ArgumentParser()
			parser.add_argument(
			    '''--task''',
			    default='''s2t''',
			    type=str,
			    help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
			)
			parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
			parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
			parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
			)
			parser.add_argument(
			    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
			)

			a__				=		parser.parse_args()
			convert_speechta_checkpoint(
			    args.task,
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.config_path,
			    args.vocab_path,
			    args.push_to_hub,
			)






import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
			import torch

if is_vision_available():
			import PIL


a__				=		logging.get_logger(__name__)



def    __UpperCAmelCase (	__a      :			np.ndarray		,__a      :			Union[int, Iterable[int]]		,__a      :			bool		,__a      :			int	)			->     Tuple[int, int]:

					"""simple docstring"""

					def constraint_to_multiple_of(__a      :			List[str]		,__a      :			Dict		,__a      :			Any=0		,__a      :			int=None	):
										_a	:    Dict         =		round(val / multiple	) * multiple

										if max_val is not None and x > max_val:
															_a	:    Any         =		math.floor(val / multiple	) * multiple

										if x < min_val:
															_a	:    Dict         =		math.ceil(val / multiple	) * multiple

										return x

					_a	:    Union[str, Any]         =		(output_size, output_size) if isinstance(__a		,__a	) else output_size

					_a      ,					_a	:    List[Any]         =		get_image_size(__a	)
					_a      ,					_a	:    Any         =		output_size

					# determine new height and width
					_a	:    Union[str, Any]         =		output_height / input_height
					_a	:    Tuple         =		output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width	) < abs(1 - scale_height	):
															# fit width
															_a	:    Optional[Any]         =		scale_width
										else:
															# fit height
															_a	:    Tuple         =		scale_height

					_a	:    Optional[Any]         =		constraint_to_multiple_of(scale_height * input_height		,multiple=__a	)
					_a	:    int         =		constraint_to_multiple_of(scale_width * input_width		,multiple=__a	)

					return (new_height, new_width)







class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    ["pixel_values"]


							def __init__(			self       ,							_a = True       ,							_a = None       ,							_a = PILImageResampling.BILINEAR       ,							_a = False       ,							_a = 1       ,							_a = True       ,							_a = 1 / 2_5_5       ,							_a = True       ,							_a = None       ,							_a = None       ,							**_a       ,							)    ->    None:
												super().__init__(**_a       )
												_a	:    Optional[int]         =		size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
												_a	:    Optional[Any]         =		get_size_dict(_a       )
												_a	:    Any         =		do_resize
												_a	:    Dict         =		size
												_a	:    str         =		keep_aspect_ratio
												_a	:    Any         =		ensure_multiple_of
												_a	:    Optional[Any]         =		resample
												_a	:    List[Any]         =		do_rescale
												_a	:    int         =		rescale_factor
												_a	:    Any         =		do_normalize
												_a	:    Union[str, Any]         =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												_a	:    List[str]         =		image_std if image_std is not None else IMAGENET_STANDARD_STD


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = False       ,							_a = 1       ,							_a = PILImageResampling.BICUBIC       ,							_a = None       ,							**_a       ,							)    ->    np.ndarray:
												_a	:    str         =		get_size_dict(_a       )
												if "height" not in size or "width" not in size:
																	raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""       )
												_a	:    Optional[Any]         =		get_resize_output_image_size(
												    _a       ,							output_size=(size['''height'''], size['''width'''])       ,							keep_aspect_ratio=_a       ,							multiple=_a       ,							)
												return resize(_a       ,							size=_a       ,							resample=_a       ,							data_format=_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = None       ,							**_a       ,							)    ->    int:
												return rescale(_a       ,							scale=_a       ,							data_format=_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a = None       ,							**_a       ,							)    ->    np.ndarray:
												return normalize(_a       ,							mean=_a       ,							std=_a       ,							data_format=_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = ChannelDimension.FIRST       ,							**_a       ,							)    ->    PIL.Image.Image:
												_a	:    Optional[int]         =		do_resize if do_resize is not None else self.do_resize
												_a	:    Union[str, Any]         =		size if size is not None else self.size
												_a	:    str         =		get_size_dict(_a       )
												_a	:    List[Any]         =		keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
												_a	:    Optional[Any]         =		ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
												_a	:    str         =		resample if resample is not None else self.resample
												_a	:    str         =		do_rescale if do_rescale is not None else self.do_rescale
												_a	:    Union[str, Any]         =		rescale_factor if rescale_factor is not None else self.rescale_factor
												_a	:    List[Any]         =		do_normalize if do_normalize is not None else self.do_normalize
												_a	:    str         =		image_mean if image_mean is not None else self.image_mean
												_a	:    Tuple         =		image_std if image_std is not None else self.image_std

												_a	:    Dict         =		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 or resample is None:
																	raise ValueError('''Size and resample must be specified if do_resize is True.'''       )

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

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

												# All transformations expect numpy arrays.
												_a	:    Dict         =		[to_numpy_array(_a       ) for image in images]

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

												if do_rescale:
																	_a	:    Optional[Any]         =		[self.rescale(image=_a       ,							scale=_a       ) for image in images]

												if do_normalize:
																	_a	:    Optional[int]         =		[self.normalize(image=_a       ,							mean=_a       ,							std=_a       ) for image in images]

												_a	:    int         =		[to_channel_dimension_format(_a       ,							_a       ) for image in images]

												_a	:    Tuple         =		{'''pixel_values''': images}
												return BatchFeature(data=_a       ,							tensor_type=_a       )




							def 				__lowercase				(			self       ,							_a       ,							_a = None       )    ->    Any:
												_a	:    Optional[Any]         =		outputs.logits

												# Resize logits and compute semantic segmentation maps
												if target_sizes is not None:
																	if len(_a       ) != len(_a       ):
																						raise ValueError(
																						    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''       )

																	if is_torch_tensor(_a       ):
																						_a	:    List[Any]         =		target_sizes.numpy()

																	_a	:    str         =		[]

																	for idx in range(len(_a       )       ):
																						_a	:    str         =		torch.nn.functional.interpolate(
																						    logits[idx].unsqueeze(dim=0       )       ,							size=target_sizes[idx]       ,							mode='''bilinear'''       ,							align_corners=_a       )
																						_a	:    Union[str, Any]         =		resized_logits[0].argmax(dim=0       )
																						semantic_segmentation.append(_a       )
												else:
																	_a	:    Tuple         =		logits.argmax(dim=1       )
																	_a	:    Union[str, Any]         =		[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

												return semantic_segmentation



