






import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


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

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Tuple   )						->     Optional[int]:






	"""simple docstring"""



	UpperCamelCase	:List[str]		=      MobileNetVaConfig(layer_norm_eps=0.001   )

	if "quant" in model_name:
		raise ValueError("""Quantized models are not supported."""   )

	UpperCamelCase	:str		=      re.match(R"""^.*mobilenet_v2_([^_]*)_([^_]*)$"""      ,     __magic_name__   )
	if matches:
		UpperCamelCase	:int		=      float(matches[1]   )
		UpperCamelCase	:Optional[int]		=      int(matches[2]   )

	if model_name.startswith("""deeplabv3_"""   ):
		UpperCamelCase	:Optional[int]		=      8
		UpperCamelCase	:List[str]		=      21
		UpperCamelCase	:List[Any]		=      """pascal-voc-id2label.json"""
	else:
		# The TensorFlow version of MobileNetV2 predicts 1001 classes instead
		# of the usual 1000. The first class (index 0) is "background".
		UpperCamelCase	:str		=      1001
		UpperCamelCase	:List[str]		=      """imagenet-1k-id2label.json"""

	UpperCamelCase	:Dict		=      """huggingface/label-files"""
	UpperCamelCase	:int		=      json.load(open(hf_hub_download(__magic_name__      ,     __magic_name__      ,     repo_type="""dataset"""   )      ,     """r"""   )   )

	if config.num_labels == 1001:
		UpperCamelCase	:Dict		=      {int(__magic_name__   ) + 1: v for k, v in idalabel.items()}
		UpperCamelCase	:Optional[int]		=      """background"""
	else:
		UpperCamelCase	:List[Any]		=      {int(__magic_name__   ): v for k, v in idalabel.items()}

	UpperCamelCase	:List[str]		=      idalabel
	UpperCamelCase	:Dict		=      {v: k for k, v in idalabel.items()}

	return config

def   SCREAMING_SNAKE_CASE_				(				)						->     List[str]:






	"""simple docstring"""



	UpperCamelCase	:List[Any]		=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
	UpperCamelCase	:Dict		=      Image.open(requests.get(__magic_name__      ,     stream=__magic_name__   ).raw   )
	return im

@torch.no_grad()
def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Tuple      ,     __magic_name__   :    Tuple      ,     __magic_name__   :    List[str]      ,     __magic_name__   :    Dict=False   )						->     List[str]:






	"""simple docstring"""



	UpperCamelCase	:Tuple		=      get_mobilenet_va_config(__magic_name__   )

	# Load 🤗 model
	if model_name.startswith("""deeplabv3_"""   ):
		UpperCamelCase	:str		=      MobileNetVaForSemanticSegmentation(__magic_name__   ).eval()
	else:
		UpperCamelCase	:Optional[Any]		=      MobileNetVaForImageClassification(__magic_name__   ).eval()

	# Load weights from TensorFlow checkpoint
	load_tf_weights_in_mobilenet_va(__magic_name__      ,     __magic_name__      ,     __magic_name__   )

	# Check outputs on an image, prepared by MobileNetV2ImageProcessor
	UpperCamelCase	:Dict		=      MobileNetVaImageProcessor(
	    crop_size={"""width""": config.image_size, """height""": config.image_size}      ,     size={"""shortest_edge""": config.image_size + 32}      ,     )
	UpperCamelCase	:int		=      image_processor(images=prepare_img()      ,     return_tensors="""pt"""   )
	UpperCamelCase	:str		=      model(**__magic_name__   )
	UpperCamelCase	:int		=      outputs.logits

	if model_name.startswith("""deeplabv3_"""   ):
		assert logits.shape == (1, 21, 65, 65)

		if model_name == "deeplabv3_mobilenet_v2_1.0_513":
			UpperCamelCase	:int		=      torch.tensor(
			    [
			        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
			        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
			        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
			    ]   )

		else:
			raise ValueError(f"""Unknown model name: {model_name}"""   )

		assert torch.allclose(logits[0, :3, :3, :3]      ,     __magic_name__      ,     atol=1E-4   )
	else:
		assert logits.shape == (1, 1001)

		if model_name == "mobilenet_v2_1.4_224":
			UpperCamelCase	:Dict		=      torch.tensor([0.0181, -1.0015, 0.4688]   )
		elif model_name == "mobilenet_v2_1.0_224":
			UpperCamelCase	:Union[str, Any]		=      torch.tensor([0.2445, -1.1993, 0.1905]   )
		elif model_name == "mobilenet_v2_0.75_160":
			UpperCamelCase	:Dict		=      torch.tensor([0.2482, 0.4136, 0.6669]   )
		elif model_name == "mobilenet_v2_0.35_96":
			UpperCamelCase	:Union[str, Any]		=      torch.tensor([0.1451, -0.4624, 0.7192]   )
		else:
			UpperCamelCase	:Union[str, Any]		=      None

		if expected_logits is not None:
			assert torch.allclose(logits[0, :3]      ,     __magic_name__      ,     atol=1E-4   )

	Path(__magic_name__   ).mkdir(exist_ok=__magic_name__   )
	print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""   )
	model.save_pretrained(__magic_name__   )
	print(f"""Saving image processor to {pytorch_dump_folder_path}"""   )
	image_processor.save_pretrained(__magic_name__   )

	if push_to_hub:
		print("""Pushing to the hub..."""   )
		UpperCamelCase	:str		=      """google/""" + model_name
		image_processor.push_to_hub(__magic_name__   )
		model.push_to_hub(__magic_name__   )


if __name__ == "__main__":
	UpperCAmelCase_					:		int           = argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--model_name''',
	    default='''mobilenet_v2_1.0_224''',
	    type=str,
	    help='''Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<depth>_<size>\'.''',
	)
	parser.add_argument(
	    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
	)
	parser.add_argument(
	    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
	)

	UpperCAmelCase_					:		Optional[int]           = parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)








import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


UpperCAmelCase_					:		Dict           = logging.get_logger(__name__)


UpperCAmelCase_					:		Dict           = {
    '''suno/bark-small''': '''https://huggingface.co/suno/bark-small/resolve/main/config.json''',
    '''suno/bark''': '''https://huggingface.co/suno/bark/resolve/main/config.json''',
}

UpperCAmelCase_					:		Any           = '''
    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        block_size (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        input_vocab_size (`int`, *optional*, defaults to 10_048):
            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
            regards to the chosen sub-model.
        output_vocab_size (`int`, *optional*, defaults to 10_048):
            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
            with regards to the chosen sub-model.
        num_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the given sub-model.
        num_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer architecture.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use bias in the linear layers and layer norm layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
'''

class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	str	      =    """bark_module"""
	snake_case__				:	int	      =    ["""past_key_values"""]

	snake_case__				:	Optional[int]	      =    {
	    """num_attention_heads""": """num_heads""",
	    """num_hidden_layers""": """num_layers""",
	    """vocab_size""": """input_vocab_size""",
	    """window_size""": """block_size""",
	}





	def __init__(						self							:   List[Any]					,    __lowerCamelCase							:   List[Any]=1_024					,    __lowerCamelCase							:   int=10_048					,    __lowerCamelCase							:   List[Any]=10_048					,    __lowerCamelCase							:   str=12					,    __lowerCamelCase							:   Dict=12					,    __lowerCamelCase							:   Tuple=768					,    __lowerCamelCase							:   Dict=0.0					,    __lowerCamelCase							:   List[str]=True					,    __lowerCamelCase							:   Tuple=0.02					,    __lowerCamelCase							:   List[str]=True					,    **__lowerCamelCase							:   str					,    ):
		UpperCamelCase	:Optional[int]		=      block_size
		UpperCamelCase	:List[Any]		=      input_vocab_size
		UpperCamelCase	:int		=      output_vocab_size
		UpperCamelCase	:int		=      num_layers
		UpperCamelCase	:Union[str, Any]		=      num_heads
		UpperCamelCase	:Dict		=      hidden_size
		UpperCamelCase	:Any		=      dropout
		UpperCamelCase	:List[str]		=      bias
		UpperCamelCase	:Tuple		=      use_cache
		UpperCamelCase	:Any		=      initializer_range

		super().__init__(**__lowerCamelCase      )





	@classmethod
	def 		_A					(						cls							:   int					,    __lowerCamelCase							:   Union[str, os.PathLike]					,    __lowerCamelCase							:   Optional[Union[str, os.PathLike]] = None					,    __lowerCamelCase							:   bool = False					,    __lowerCamelCase							:   bool = False					,    __lowerCamelCase							:   Optional[Union[str, bool]] = None					,    __lowerCamelCase							:   str = "main"					,    **__lowerCamelCase							:   List[Any]					,    ):
		UpperCamelCase	:Dict		=      cache_dir
		UpperCamelCase	:List[Any]		=      force_download
		UpperCamelCase	:List[Any]		=      local_files_only
		UpperCamelCase	:int		=      revision

		cls._set_token_in_kwargs(__lowerCamelCase					,    __lowerCamelCase      )

		UpperCamelCase	, UpperCamelCase	:Dict		=      cls.get_config_dict(__lowerCamelCase					,    **__lowerCamelCase      )

		# get the config dict if we are loading from Bark
		if config_dict.get("""model_type"""      ) == "bark":
			UpperCamelCase	:List[str]		=      config_dict[F"""{cls.model_type}_config"""]

		if "model_type" in config_dict and hasattr(cls					,    """model_type"""      ) and config_dict["model_type"] != cls.model_type:
			logger.warning(
			    F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
			    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""      )

		return cls.from_dict(__lowerCamelCase					,    **__lowerCamelCase      )

@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkSemanticConfig"""						,  model="""BarkSemanticModel""" )						,  """
    Example:

    ```python
    >>> from transformers import BarkSemanticConfig, BarkSemanticModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkSemanticConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkSemanticModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""						,  )
class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	Optional[int]	      =    """semantic"""

@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkCoarseConfig"""						,  model="""BarkCoarseModel""" )						,  """
    Example:

    ```python
    >>> from transformers import BarkCoarseConfig, BarkCoarseModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkCoarseConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkCoarseModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""						,  )
class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	int	      =    """coarse_acoustics"""

@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="""BarkFineConfig"""						,  model="""BarkFineModel""" )						,  """
        n_codes_total (`int`, *optional*, defaults to 8):
            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
        n_codes_given (`int`, *optional*, defaults to 1):
            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
            sub-models.
    Example:

    ```python
    >>> from transformers import BarkFineConfig, BarkFineModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkFineConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkFineModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""						,  )
class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	List[Any]	      =    """fine_acoustics"""





	def __init__(						self							:   Any					,    __lowerCamelCase							:   str=True					,    __lowerCamelCase							:   Optional[int]=8					,    __lowerCamelCase							:   Dict=1					,    **__lowerCamelCase							:   str      ):
		UpperCamelCase	:List[str]		=      n_codes_total
		UpperCamelCase	:str		=      n_codes_given

		super().__init__(tie_word_embeddings=__lowerCamelCase					,    **__lowerCamelCase      )

class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	Union[str, Any]	      =    """bark"""
	snake_case__				:	Union[str, Any]	      =    True





	def __init__(						self							:   int					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   Dict = None					,    __lowerCamelCase							:   str=0.02					,    **__lowerCamelCase							:   List[str]					,    ):
		if semantic_config is None:
			UpperCamelCase	:Union[str, Any]		=      {}
			logger.info("""semantic_config is None. initializing the semantic model with default values."""      )

		if coarse_acoustics_config is None:
			UpperCamelCase	:Any		=      {}
			logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""      )

		if fine_acoustics_config is None:
			UpperCamelCase	:Any		=      {}
			logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""      )

		if codec_config is None:
			UpperCamelCase	:Dict		=      {}
			logger.info("""codec_config is None. initializing the codec model with default values."""      )

		UpperCamelCase	:Tuple		=      BarkSemanticConfig(**__lowerCamelCase      )
		UpperCamelCase	:str		=      BarkCoarseConfig(**__lowerCamelCase      )
		UpperCamelCase	:List[Any]		=      BarkFineConfig(**__lowerCamelCase      )
		UpperCamelCase	:List[Any]		=      AutoConfig.for_model(**__lowerCamelCase      )

		UpperCamelCase	:Tuple		=      initializer_range

		super().__init__(**__lowerCamelCase      )





	@classmethod
	def 		_A					(						cls							:   int					,    __lowerCamelCase							:   BarkSemanticConfig					,    __lowerCamelCase							:   BarkCoarseConfig					,    __lowerCamelCase							:   BarkFineConfig					,    __lowerCamelCase							:   AutoConfig					,    **__lowerCamelCase							:   int					,    ):
		return cls(
		    semantic_config=semantic_config.to_dict()					,    coarse_acoustics_config=coarse_acoustics_config.to_dict()					,    fine_acoustics_config=fine_acoustics_config.to_dict()					,    codec_config=codec_config.to_dict()					,    **__lowerCamelCase					,    )





	def 		_A					(						self							:   Dict      ):
		UpperCamelCase	:List[Any]		=      copy.deepcopy(self.__dict__      )

		UpperCamelCase	:Any		=      self.semantic_config.to_dict()
		UpperCamelCase	:Optional[int]		=      self.coarse_acoustics_config.to_dict()
		UpperCamelCase	:Tuple		=      self.fine_acoustics_config.to_dict()
		UpperCamelCase	:List[str]		=      self.codec_config.to_dict()

		UpperCamelCase	:List[str]		=      self.__class__.model_type
		return output
