






'''simple docstring'''
import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
a_					=	logging.get_logger('transformers.models.encodec')

a_					=	{
    """quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
    """quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
    """quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
    """quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
a_					=	{
    """encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
    """encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
    """encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
    """encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
    """encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
    """encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
    """encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
    """encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
    """encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
    """encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
    """encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
    """encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
    """encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
    """encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
    """encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
    """encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
    """encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
    """encoder.model.13.lstm""": """encoder.layers.13.lstm""",
    """encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
a_					=	{
    """encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
    """encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
    """encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
    """encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
    """encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
    """encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
    """encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
    """encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
    """encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
    """encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
    """encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
    """encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
    """encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
    """encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
    """encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
    """encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
    """encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
    """encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
a_					=	{
    """decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
    """decoder.model.1.lstm""": """decoder.layers.1.lstm""",
    """decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
    """decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
    """decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
    """decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
    """decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
    """decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
    """decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
    """decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
    """decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
    """decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
    """decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
    """decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
    """decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
    """decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
    """decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
    """decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
    """decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
a_					=	{
    """decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
    """decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
    """decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
    """decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
    """decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
    """decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
    """decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
    """decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
    """decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
    """decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
    """decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
    """decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
    """decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
    """decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
    """decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
    """decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
    """decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
    """decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
a_					=	{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
a_					=	{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
a_					=	[]
a_					=	[]





def 	_a(					UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  Tuple,		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  Dict,		UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      for attribute in key.split('''.'''							):
            SCREAMING_SNAKE_CASE__       :			str													=getattr(__snake_case,		__snake_case							)

      if weight_type is not None:
            SCREAMING_SNAKE_CASE__       :			List[str]													=getattr(__snake_case,		__snake_case							).shape
      else:
            SCREAMING_SNAKE_CASE__       :			Tuple													=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":
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=value
      elif weight_type == "weight_g":
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=value
      elif weight_type == "weight_v":
            SCREAMING_SNAKE_CASE__       :			Any													=value
      elif weight_type == "bias":
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=value
      elif weight_type == "running_mean":
            SCREAMING_SNAKE_CASE__       :			List[str]													=value
      elif weight_type == "running_var":
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=value
      elif weight_type == "num_batches_tracked":
            SCREAMING_SNAKE_CASE__       :			str													=value
      elif weight_type == "weight_ih_l0":
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=value
      elif weight_type == "weight_hh_l0":
            SCREAMING_SNAKE_CASE__       :			int													=value
      elif weight_type == "bias_ih_l0":
            SCREAMING_SNAKE_CASE__       :			int													=value
      elif weight_type == "bias_hh_l0":
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=value
      elif weight_type == "weight_ih_l1":
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=value
      elif weight_type == "weight_hh_l1":
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=value
      elif weight_type == "bias_ih_l1":
            SCREAMING_SNAKE_CASE__       :			str													=value
      elif weight_type == "bias_hh_l1":
            SCREAMING_SNAKE_CASE__       :			Tuple													=value
      else:
            SCREAMING_SNAKE_CASE__       :			int													=value

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





def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  str							):




      '''simple docstring'''






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





def 	_a(					UpperCamelCase__			:  Optional[Any],		UpperCamelCase__			:  List[str],		UpperCamelCase__			:  Dict							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			int													=[]

      if model_name == "encodec_24khz" or "encodec_32khz":
            SCREAMING_SNAKE_CASE__       :			int													=MAPPING_24K
      elif model_name == "encodec_48khz":
            SCREAMING_SNAKE_CASE__       :			List[str]													=MAPPING_48K
      else:
            raise ValueError(f"Unsupported model: {model_name}"							)

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

            SCREAMING_SNAKE_CASE__       :			List[Any]													=False
            for key, mapped_key in MAPPING.items():
                  if "*" in key:
                        SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Any													=key.split('''.*.'''							)
                        if prefix in name and suffix in name:
                              SCREAMING_SNAKE_CASE__       :			List[str]													=suffix

                  if key in name:
                        # HACK otherwise .embed gets initialized with .embed_avg too
                        if key.endswith('''embed'''							) and name.endswith('''embed_avg'''							):
                              continue

                        SCREAMING_SNAKE_CASE__       :			List[Any]													=True
                        if "*" in mapped_key:
                              SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=name.split(__snake_case							)[0].split('''.'''							)[-2]
                              SCREAMING_SNAKE_CASE__       :			Tuple													=mapped_key.replace('''*''',		__snake_case							)
                        if "weight_g" in name:
                              SCREAMING_SNAKE_CASE__       :			List[Any]													='''weight_g'''
                        elif "weight_v" in name:
                              SCREAMING_SNAKE_CASE__       :			Optional[Any]													='''weight_v'''
                        elif "weight_ih_l0" in name:
                              SCREAMING_SNAKE_CASE__       :			int													='''weight_ih_l0'''
                        elif "weight_hh_l0" in name:
                              SCREAMING_SNAKE_CASE__       :			int													='''weight_hh_l0'''
                        elif "bias_ih_l0" in name:
                              SCREAMING_SNAKE_CASE__       :			Optional[Any]													='''bias_ih_l0'''
                        elif "bias_hh_l0" in name:
                              SCREAMING_SNAKE_CASE__       :			Any													='''bias_hh_l0'''
                        elif "weight_ih_l1" in name:
                              SCREAMING_SNAKE_CASE__       :			Any													='''weight_ih_l1'''
                        elif "weight_hh_l1" in name:
                              SCREAMING_SNAKE_CASE__       :			Tuple													='''weight_hh_l1'''
                        elif "bias_ih_l1" in name:
                              SCREAMING_SNAKE_CASE__       :			Any													='''bias_ih_l1'''
                        elif "bias_hh_l1" in name:
                              SCREAMING_SNAKE_CASE__       :			Dict													='''bias_hh_l1'''
                        elif "bias" in name:
                              SCREAMING_SNAKE_CASE__       :			List[Any]													='''bias'''
                        elif "weight" in name:
                              SCREAMING_SNAKE_CASE__       :			List[str]													='''weight'''
                        elif "running_mean" in name:
                              SCREAMING_SNAKE_CASE__       :			int													='''running_mean'''
                        elif "running_var" in name:
                              SCREAMING_SNAKE_CASE__       :			Optional[Any]													='''running_var'''
                        elif "num_batches_tracked" in name:
                              SCREAMING_SNAKE_CASE__       :			Dict													='''num_batches_tracked'''
                        else:
                              SCREAMING_SNAKE_CASE__       :			Dict													=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}"							)





@torch.no_grad()
def 	_a(					UpperCamelCase__			:  List[Any],		UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  Optional[Any],		UpperCamelCase__			:  Tuple=None,		UpperCamelCase__			:  List[str]=None,		):




      '''simple docstring'''






      if config_path is not None:
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=EncodecConfig.from_pretrained(__snake_case							)
      else:
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=EncodecConfig()

      if model_name == "encodec_24khz":
            pass  # config is already correct
      elif model_name == "encodec_32khz":
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=[8, 5, 4, 4]
            SCREAMING_SNAKE_CASE__       :			Tuple													=[2.2]
            SCREAMING_SNAKE_CASE__       :			Tuple													=6_4
            SCREAMING_SNAKE_CASE__       :			Dict													=3_2_0_0_0
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=2_0_4_8
            SCREAMING_SNAKE_CASE__       :			Tuple													=False
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=False
            SCREAMING_SNAKE_CASE__       :			Dict													=False
      elif model_name == "encodec_48khz":
            SCREAMING_SNAKE_CASE__       :			List[str]													=[8, 5, 4, 2]
            SCREAMING_SNAKE_CASE__       :			List[str]													=[3.0, 6.0, 1_2.0, 2_4.0]
            SCREAMING_SNAKE_CASE__       :			List[Any]													=4_8_0_0_0
            SCREAMING_SNAKE_CASE__       :			List[str]													=2
            SCREAMING_SNAKE_CASE__       :			List[Any]													=False
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													='''time_group_norm'''
            SCREAMING_SNAKE_CASE__       :			List[str]													=True
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=1.0
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=0.0_1
      else:
            raise ValueError(f"Unknown model name: {model_name}"							)

      SCREAMING_SNAKE_CASE__       :			List[Any]													=EncodecModel(__snake_case							)

      SCREAMING_SNAKE_CASE__       :			Any													=EncodecFeatureExtractor(
          feature_size=config.audio_channels,		sampling_rate=config.sampling_rate,		chunk_length_s=config.chunk_length_s,		overlap=config.overlap,		)
      feature_extractor.save_pretrained(__snake_case							)

      SCREAMING_SNAKE_CASE__       :			List[Any]													=torch.load(__snake_case							)
      if "best_state" in original_checkpoint:
            # we might have a training state saved, in which case discard the yaml results and just retain the weights
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=original_checkpoint['''best_state''']
      recursively_load_weights(__snake_case,		__snake_case,		__snake_case							)
      model.save_pretrained(__snake_case							)

      if repo_id:
            print('''Pushing to the hub...'''							)
            feature_extractor.push_to_hub(__snake_case							)
            model.push_to_hub(__snake_case							)


if __name__ == "__main__":
       a_					=	argparse.ArgumentParser()
       parser.add_argument(
           '--model',
           default='encodec_24khz',
           type=str,
           help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
       )
       parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
       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_checkpoint(
           args.model,
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.push_to_hub,
       )







'''simple docstring'''
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


a_					=	logging.get_logger(__name__)






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							["""pixel_values"""]




 def __init__( self		:							List[str]   , __lowercase		:							bool = True   , __lowercase		:							Optional[Dict[str, int]] = None   , __lowercase		:							PILImageResampling = PILImageResampling.BILINEAR   , __lowercase		:							bool = True   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							bool = True   , __lowercase		:							Union[int, float] = 1 / 2_55   , __lowercase		:							bool = True   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , **__lowercase		:							List[Any]   , )		->  None:
       super().__init__(**__lowercase )
       SCREAMING_SNAKE_CASE__       :			Any													=size if size is not None else {'''shortest_edge''': 2_56}
       SCREAMING_SNAKE_CASE__       :			str													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
       SCREAMING_SNAKE_CASE__       :			Tuple													=get_size_dict(__lowercase   , param_name='''crop_size''' )
       SCREAMING_SNAKE_CASE__       :			int													=do_resize
       SCREAMING_SNAKE_CASE__       :			Dict													=size
       SCREAMING_SNAKE_CASE__       :			List[str]													=resample
       SCREAMING_SNAKE_CASE__       :			List[Any]													=do_center_crop
       SCREAMING_SNAKE_CASE__       :			str													=crop_size
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_rescale
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=rescale_factor
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_normalize
       SCREAMING_SNAKE_CASE__       :			Any													=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=image_std if image_std is not None else IMAGENET_STANDARD_STD




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							np.ndarray   , __lowercase		:							Dict[str, int]   , __lowercase		:							PILImageResampling = PILImageResampling.BICUBIC   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Optional[Any]   , )		->  np.ndarray:
       SCREAMING_SNAKE_CASE__       :			str													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       if "shortest_edge" not in size:
             raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=get_resize_output_image_size(__lowercase   , size=size['''shortest_edge''']   , default_to_square=__lowercase )
       return resize(__lowercase   , size=__lowercase   , resample=__lowercase   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							int   , __lowercase		:							np.ndarray   , __lowercase		:							Dict[str, int]   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Any   , )		->  np.ndarray:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=get_size_dict(__lowercase )
       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(__lowercase   , size=(size['''height'''], size['''width'''])   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							Optional[Any]   , __lowercase		:							np.ndarray   , __lowercase		:							float   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Tuple )		->  np.ndarray:
       return rescale(__lowercase   , scale=__lowercase   , data_format=__lowercase   , **__lowercase )




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




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							ImageInput   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							PILImageResampling = None   , __lowercase		:							bool = None   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Optional[float] = None   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[str, TensorType]] = None   , __lowercase		:							Union[str, ChannelDimension] = ChannelDimension.FIRST   , **__lowercase		:							int   , )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__       :			int													=size if size is not None else self.size
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Any													=resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=do_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE__       :			List[str]													=crop_size if crop_size is not None else self.crop_size
       SCREAMING_SNAKE_CASE__       :			int													=get_size_dict(__lowercase   , param_name='''crop_size''' )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__       :			int													=rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE__       :			Any													=image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE__       :			str													=make_list_of_images(__lowercase )

       if not valid_images(__lowercase ):
             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.
       SCREAMING_SNAKE_CASE__       :			Dict													=[to_numpy_array(__lowercase ) for image in images]

       if do_resize:
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=[self.resize(image=__lowercase   , size=__lowercase   , resample=__lowercase ) for image in images]

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

       if do_rescale:
             SCREAMING_SNAKE_CASE__       :			List[Any]													=[self.rescale(image=__lowercase   , scale=__lowercase ) for image in images]

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

       SCREAMING_SNAKE_CASE__       :			str													=[to_channel_dimension_format(__lowercase   , __lowercase ) for image in images]

       SCREAMING_SNAKE_CASE__       :			Any													={'''pixel_values''': images}
       return BatchFeature(data=__lowercase   , tensor_type=__lowercase )




 def        __magic_name__		( self		:							Optional[int]   , __lowercase		:							Tuple   , __lowercase		:							List[Tuple] = None )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=outputs.logits

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

             if is_torch_tensor(__lowercase ):
                   SCREAMING_SNAKE_CASE__       :			int													=target_sizes.numpy()

             SCREAMING_SNAKE_CASE__       :			List[Any]													=[]

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

       return semantic_segmentation