

'''simple docstring'''





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()
__UpperCAmelCase    				=     logging.get_logger('transformers.models.speecht5')

__UpperCAmelCase    				=     {
    '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',
}
__UpperCAmelCase    				=     {
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
__UpperCAmelCase    				=     {
    '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',
}
__UpperCAmelCase    				=     {
    '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',
}
__UpperCAmelCase    				=     {
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
__UpperCAmelCase    				=     {
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
__UpperCAmelCase    				=     {
    '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',
}
__UpperCAmelCase    				=     {
    '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',
}
__UpperCAmelCase    				=     {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__UpperCAmelCase    				=     {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__UpperCAmelCase    				=     {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__UpperCAmelCase    				=     []
__UpperCAmelCase    				=     [
    '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',
]
__UpperCAmelCase    				=     IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
__UpperCAmelCase    				=     IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
__UpperCAmelCase    				=     IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Optional[int]:

     """simple docstring"""







     for attribute in key.split("."				):
          _lowerCAmelCase   :					Optional[Any]        =    getattr(__snake_case		,							__snake_case				)

     if weight_type is not None:
          _lowerCAmelCase   :					Dict        =    getattr(__snake_case		,							__snake_case				).shape
     else:
          _lowerCAmelCase   :					Dict        =    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   :					List[Any]        =    value
     elif weight_type == "weight_v":
          _lowerCAmelCase   :					Dict        =    value
     elif weight_type == "bias":
          _lowerCAmelCase   :					List[Any]        =    value
     elif weight_type == "running_mean":
          _lowerCAmelCase   :					List[Any]        =    value
     elif weight_type == "running_var":
          _lowerCAmelCase   :					Optional[Any]        =    value
     elif weight_type == "num_batches_tracked":
          _lowerCAmelCase   :					str        =    value
     else:
          _lowerCAmelCase   :					int        =    value

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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Optional[Any]:

     """simple docstring"""







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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      List[str]:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[int]        =    []

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

     for name, value in fairseq_dict.items():
          if should_ignore(__snake_case		,							__snake_case				):
               logger.info(f'''{name} was ignored'''				)
               continue

          _lowerCAmelCase   :					str        =    False
          if "conv_layers" in name:
               load_conv_layer(
                   __snake_case		,							__snake_case		,							__snake_case		,							__snake_case		,							hf_model.config.feat_extract_norm == "group"		,							)
               _lowerCAmelCase   :					Optional[int]        =    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:
                         _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[int]        =    key.split(".*."				)
                         if prefix in name and suffix in name:
                              _lowerCAmelCase   :					Optional[Any]        =    suffix

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

     logger.warning(f'''Unused weights: {unused_weights}'''				)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Tuple:

     """simple docstring"""







     _lowerCAmelCase   :					List[str]        =    full_name.split("conv_layers."				)[-1]
     _lowerCAmelCase   :					Any        =    name.split("."				)
     _lowerCAmelCase   :					int        =    int(items[0]				)
     _lowerCAmelCase   :					List[str]        =    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.'''				)
               _lowerCAmelCase   :					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.'''				)
               _lowerCAmelCase   :					Optional[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.'''				)
               _lowerCAmelCase   :					Union[str, Any]        =    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.'''				)
               _lowerCAmelCase   :					Tuple        =    value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
     else:
          unused_weights.append(__snake_case				)







@torch.no_grad()
def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE=None		,							)       ->      str:

     """simple docstring"""







     if config_path is not None:
          _lowerCAmelCase   :					int        =    SpeechTaConfig.from_pretrained(__snake_case				)
     else:
          _lowerCAmelCase   :					str        =    SpeechTaConfig()

     if task == "s2t":
          _lowerCAmelCase   :					int        =    config.max_text_positions
          _lowerCAmelCase   :					Union[str, Any]        =    SpeechTaForSpeechToText(__snake_case				)
     elif task == "t2s":
          _lowerCAmelCase   :					Optional[int]        =    1876
          _lowerCAmelCase   :					Optional[Any]        =    600
          _lowerCAmelCase   :					Any        =    config.max_speech_positions
          _lowerCAmelCase   :					Dict        =    SpeechTaForTextToSpeech(__snake_case				)
     elif task == "s2s":
          _lowerCAmelCase   :					List[Any]        =    1876
          _lowerCAmelCase   :					str        =    config.max_speech_positions
          _lowerCAmelCase   :					Tuple        =    SpeechTaForSpeechToSpeech(__snake_case				)
     else:
          raise ValueError(f'''Unknown task name: {task}'''				)

     if vocab_path:
          _lowerCAmelCase   :					str        =    SpeechTaTokenizer(__snake_case		,							model_max_length=config.max_text_positions				)

          # Mask token behaves like a normal word, i.e. include the space before it
          _lowerCAmelCase   :					List[str]        =    AddedToken("<mask>"		,							lstrip=__snake_case		,							rstrip=__snake_case				)
          _lowerCAmelCase   :					Any        =    mask_token
          tokenizer.add_special_tokens({"mask_token": mask_token}				)
          tokenizer.add_tokens(["<ctc_blank>"]				)

     _lowerCAmelCase   :					Tuple        =    SpeechTaFeatureExtractor()
     _lowerCAmelCase   :					List[Any]        =    SpeechTaProcessor(tokenizer=__snake_case		,							feature_extractor=__snake_case				)
     processor.save_pretrained(__snake_case				)

     _lowerCAmelCase   :					str        =    torch.load(__snake_case				)
     recursively_load_weights(fairseq_checkpoint["model"]		,							__snake_case		,							__snake_case				)

     model.save_pretrained(__snake_case				)

     if repo_id:
          print("Pushing to the hub..."				)
          processor.push_to_hub(__snake_case				)
          model.push_to_hub(__snake_case				)


if __name__ == "__main__":
  __UpperCAmelCase    				=     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.'
  )

  __UpperCAmelCase    				=     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,
  )









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

import numpy as np

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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
  import torch


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					int      =						['''pixel_values''']

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






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					Optional[Any]        =    size if size is not None else {"shortest_edge": 2_5_6}
      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Dict        =    crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Tuple        =    get_size_dict(A_     ,						param_name="crop_size"       )
      _lowerCAmelCase   :					Dict        =    do_resize
      _lowerCAmelCase   :					Optional[Any]        =    size
      _lowerCAmelCase   :					Union[str, Any]        =    resample
      _lowerCAmelCase   :					List[Any]        =    do_center_crop
      _lowerCAmelCase   :					str        =    crop_size
      _lowerCAmelCase   :					int        =    do_rescale
      _lowerCAmelCase   :					Tuple        =    rescale_factor
      _lowerCAmelCase   :					str        =    do_normalize
      _lowerCAmelCase   :					List[Any]        =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _lowerCAmelCase   :					str        =    image_std if image_std is not None else IMAGENET_STANDARD_STD

 def    __magic_name__       (		self :	Dict     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PILImageResampling.BICUBIC     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Tuple     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_     ,						default_to_square=A_       )
      if "shortest_edge" not in size:
           raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
      _lowerCAmelCase   :					Optional[Any]        =    get_resize_output_image_size(A_     ,						size=size["shortest_edge"]     ,						default_to_square=A_       )
      return resize(A_     ,						size=A_     ,						resample=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''       )
      return center_crop(A_     ,						size=(size["height"], size["width"])     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	int     ,						A_ :	np.ndarray     ,						A_ :	float     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[Any]       ):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	Tuple     ,						A_ :	np.ndarray     ,						A_ :	Union[float, List[float]]     ,						A_ :	Union[float, List[float]]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[Any]     ,						):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	List[str]     ,						A_ :	ImageInput     ,						A_ :	Optional[bool] = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = None     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	Optional[bool] = None     ,						A_ :	Optional[float] = None     ,						A_ :	Optional[bool] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	Union[str, ChannelDimension] = ChannelDimension.FIRST     ,						**A_ :	int     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					Optional[Any]        =    size if size is not None else self.size
      _lowerCAmelCase   :					Optional[Any]        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Tuple        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					str        =    do_center_crop if do_center_crop is not None else self.do_center_crop
      _lowerCAmelCase   :					List[Any]        =    crop_size if crop_size is not None else self.crop_size
      _lowerCAmelCase   :					str        =    get_size_dict(A_     ,						param_name="crop_size"       )
      _lowerCAmelCase   :					Union[str, Any]        =    do_rescale if do_rescale is not None else self.do_rescale
      _lowerCAmelCase   :					List[str]        =    rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowerCAmelCase   :					int        =    do_normalize if do_normalize is not None else self.do_normalize
      _lowerCAmelCase   :					Optional[Any]        =    image_mean if image_mean is not None else self.image_mean
      _lowerCAmelCase   :					Any        =    image_std if image_std is not None else self.image_std

      _lowerCAmelCase   :					str        =    make_list_of_images(A_       )

      if not valid_images(A_       ):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."       )

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

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

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

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

      # All transformations expect numpy arrays.
      _lowerCAmelCase   :					Any        =    [to_numpy_array(A_       ) for image in images]

      if do_resize:
           _lowerCAmelCase   :					str        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      if do_center_crop:
           _lowerCAmelCase   :					List[Any]        =    [self.center_crop(image=A_     ,						size=A_       ) for image in images]

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

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

      _lowerCAmelCase   :					List[Any]        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    {"pixel_values": images}
      return BatchFeature(data=A_     ,						tensor_type=A_       )


 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	Tuple     ,						A_ :	List[Tuple] = None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    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_       ):
                _lowerCAmelCase   :					List[str]        =    target_sizes.numpy()

           _lowerCAmelCase   :					Tuple        =    []

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

      return semantic_segmentation






