code: 
  '''simple docstring'''
  import os
  import pathlib
  import tempfile
  import uuid
  
  import numpy as np
  
  from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging
  
  
  lowercase__						=			logging.get_logger(__name__)
  
  if is_vision_available():
  						import PIL.Image
  						from PIL import Image
  						from PIL.Image import Image as ImageType
  else:
  						lowercase__						=			object
  
  if is_torch_available():
  						import torch
  
  if is_soundfile_availble():
  						import soundfile as sf
  
  
  
  
  
  
  class   snake_case__					:
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: Optional[Any]     ,  UpperCamelCase__							: Optional[int]			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	value
  
  
  
  		def __str__(			self							: Optional[int]			)	-> Any:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return self.to_string()
  
  
  
  		def 			lowerCAmelCase							(			self							: str			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				logger.error(
  				    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''			)
  				return self._value
  
  
  
  		def 			lowerCAmelCase							(			self							: str			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				logger.error(
  				    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''			)
  				return str(self._value			)
  
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> int:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return self._value
  
  
  
  		def 			lowerCAmelCase							(			self							: Optional[Any]			)	-> int:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return self._value
  
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: List[Any]     ,  UpperCamelCase__							: Optional[Any]			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				super().__init__(UpperCamelCase__			)
  
  				if not is_vision_available():
  						raise ImportError('''PIL must be installed in order to handle images.'''			)
  
  				snake_case     :					Optional[int]     =	None
  				snake_case     :					Any     =	None
  				snake_case     :					Union[str, Any]     =	None
  
  				if isinstance(UpperCamelCase__     ,  UpperCamelCase__			):
  						snake_case     :					List[str]     =	value
  				elif isinstance(UpperCamelCase__     ,  (str, pathlib.Path)			):
  						snake_case     :					List[str]     =	value
  				elif isinstance(UpperCamelCase__     ,  torch.Tensor			):
  						snake_case     :					str     =	value
  				else:
  						raise ValueError(f'Unsupported type for {self.__class__.__name__}: {type(UpperCamelCase__			)}'			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Optional[Any]     ,  UpperCamelCase__							: Optional[Any]=None     ,  UpperCamelCase__							: Optional[int]=None			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				from IPython.display import Image, display
  
  				display(Image(self.to_string()			)			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Tuple			)	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if self._raw is not None:
  						return self._raw
  
  				if self._path is not None:
  						snake_case     :					Any     =	Image.open(self._path			)
  						return self._raw
  
  
  
  		def 			lowerCAmelCase							(			self							: str			)	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if self._path is not None:
  						return self._path
  
  				if self._raw is not None:
  						snake_case     :					int     =	tempfile.mkdtemp()
  						snake_case     :					str     =	os.path.join(UpperCamelCase__     ,  str(uuid.uuida()			) + '''.png'''			)
  						self._raw.save(self._path			)
  
  						return self._path
  
  				if self._tensor is not None:
  						snake_case     :					Union[str, Any]     =	self._tensor.cpu().detach().numpy()
  
  						# There is likely simpler than load into image into save
  						snake_case     :					Optional[int]     =	Image.fromarray((array * 255).astype(np.uinta			)			)
  
  						snake_case     :					Union[str, Any]     =	tempfile.mkdtemp()
  						snake_case     :					Optional[Any]     =	os.path.join(UpperCamelCase__     ,  str(uuid.uuida()			) + '''.png'''			)
  
  						img.save(self._path			)
  
  						return self._path
  
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: int     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: Optional[int]=1_6000			)	-> Any:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				super().__init__(UpperCamelCase__			)
  
  				if not is_soundfile_availble():
  						raise ImportError('''soundfile must be installed in order to handle audio.'''			)
  
  				snake_case     :					Any     =	None
  				snake_case     :					int     =	None
  
  				snake_case     :					int     =	samplerate
  
  				if isinstance(UpperCamelCase__     ,  (str, pathlib.Path)			):
  						snake_case     :					Dict     =	value
  				elif isinstance(UpperCamelCase__     ,  torch.Tensor			):
  						snake_case     :					Union[str, Any]     =	value
  				else:
  						raise ValueError(f'Unsupported audio type: {type(UpperCamelCase__			)}'			)
  
  
  
  		def 			lowerCAmelCase							(			self							: str     ,  UpperCamelCase__							: Optional[int]=None     ,  UpperCamelCase__							: Union[str, Any]=None			)	-> Tuple:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				from IPython.display import Audio, display
  
  				display(Audio(self.to_string()     ,  rate=self.samplerate			)			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Optional[Any]			)	-> Tuple:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if self._tensor is not None:
  						return self._tensor
  
  				if self._path is not None:
  						snake_case ,snake_case     :					Optional[Any]     =	sf.read(self._path			)
  						snake_case     :					List[Any]     =	torch.tensor(UpperCamelCase__			)
  						return self._tensor
  
  
  
  		def 			lowerCAmelCase							(			self							: Any			)	-> Any:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if self._path is not None:
  						return self._path
  
  				if self._tensor is not None:
  						snake_case     :					Any     =	tempfile.mkdtemp()
  						snake_case     :					Optional[Any]     =	os.path.join(UpperCamelCase__     ,  str(uuid.uuida()			) + '''.wav'''			)
  						sf.write(self._path     ,  self._tensor     ,  samplerate=self.samplerate			)
  						return self._path
  
  
  
  
  
  
  
  lowercase__						=			{"text": AgentText, "image": AgentImage, "audio": AgentAudio}
  lowercase__						=			{str: AgentText}
  
  if is_vision_available():
  						lowercase__						=			AgentImage
  
  
  
  
  def       _UpperCamelCase	(					*SCREAMING_SNAKE_CASE__   ,					**SCREAMING_SNAKE_CASE__ )     ->       Dict:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[Any]     =	[(arg.to_raw() if isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ) else arg) for arg in args]
  		snake_case     :					Any     =	{k: (v.to_raw() if isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ) else v) for k, v in kwargs.items()}
  		return args, kwargs
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__=None )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		if isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ):
  				snake_case     :					Union[str, Any]     =	{}
  				for i, (k, v) in enumerate(outputs.items() ):
  						if output_types is not None:
  								# If the class has defined outputs, we can map directly according to the class definition
  								if output_types[i] in AGENT_TYPE_MAPPING:
  										snake_case     :					Any     =	AGENT_TYPE_MAPPING[output_types[i]](SCREAMING_SNAKE_CASE__ )
  								else:
  										snake_case     :					List[str]     =	AgentType(SCREAMING_SNAKE_CASE__ )
  
  						else:
  								# If the class does not have defined output, then we map according to the type
  								for _k, _v in INSTANCE_TYPE_MAPPING.items():
  										if isinstance(SCREAMING_SNAKE_CASE__   ,					_k ):
  												snake_case     :					List[Any]     =	_v(SCREAMING_SNAKE_CASE__ )
  								if k not in decoded_outputs:
  										snake_case     :					Dict     =	AgentType[v]
  
  		elif isinstance(SCREAMING_SNAKE_CASE__   ,					(list, tuple) ):
  				snake_case     :					Any     =	type(SCREAMING_SNAKE_CASE__ )()
  				for i, v in enumerate(SCREAMING_SNAKE_CASE__ ):
  						if output_types is not None:
  								# If the class has defined outputs, we can map directly according to the class definition
  								if output_types[i] in AGENT_TYPE_MAPPING:
  										decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](SCREAMING_SNAKE_CASE__ ) )
  								else:
  										decoded_outputs.append(AgentType(SCREAMING_SNAKE_CASE__ ) )
  						else:
  								# If the class does not have defined output, then we map according to the type
  								snake_case     :					Any     =	False
  								for _k, _v in INSTANCE_TYPE_MAPPING.items():
  										if isinstance(SCREAMING_SNAKE_CASE__   ,					_k ):
  												decoded_outputs.append(_v(SCREAMING_SNAKE_CASE__ ) )
  												snake_case     :					List[str]     =	True
  
  								if not found:
  										decoded_outputs.append(AgentType(SCREAMING_SNAKE_CASE__ ) )
  
  		else:
  				if output_types[0] in AGENT_TYPE_MAPPING:
  						# If the class has defined outputs, we can map directly according to the class definition
  						snake_case     :					int     =	AGENT_TYPE_MAPPING[output_types[0]](SCREAMING_SNAKE_CASE__ )
  
  				else:
  						# If the class does not have defined output, then we map according to the type
  						for _k, _v in INSTANCE_TYPE_MAPPING.items():
  								if isinstance(SCREAMING_SNAKE_CASE__   ,					_k ):
  										return _v(SCREAMING_SNAKE_CASE__ )
  						return AgentType(SCREAMING_SNAKE_CASE__ )
  
  		return decoded_outputs
  
  
  
style_context: 
  '''simple docstring'''
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Tuple     =	json.load(open(SCREAMING_SNAKE_CASE__   ,					'''r''' ) )
  		snake_case     :					Any     =	GPTNeoConfig(
  		    hidden_size=config_json['''n_embd''']   ,					num_layers=config_json['''n_layer''']   ,					num_heads=config_json['''n_head''']   ,					attention_types=config_json['''attention_types''']   ,					max_position_embeddings=config_json['''n_positions''']   ,					resid_dropout=config_json['''res_dropout''']   ,					embed_dropout=config_json['''embed_dropout''']   ,					attention_dropout=config_json['''attn_dropout''']   ,					)
  		print(F'Building PyTorch model from configuration: {config}' )
  		snake_case     :					Union[str, Any]     =	GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__ )
  
  		# Load weights from tf checkpoint
  		load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  
  		# Save pytorch-model
  		print(F'Save PyTorch model to {pytorch_dump_path}' )
  		model.save_pretrained(SCREAMING_SNAKE_CASE__ )
  
  
  if __name__ == "__main__":
  						lowercase__						=			argparse.ArgumentParser()
  						# Required parameters
  						parser.add_argument(
  						    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  						)
  						parser.add_argument(
  						    "--config_file",
  						    default=None,
  						    type=str,
  						    required=True,
  						    help=(
  						        "The config json file corresponding to the pre-trained mesh-tf model. \n"
  						        "This specifies the model architecture."
  						    ),
  						)
  						parser.add_argument(
  						    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  						)
  						lowercase__						=			parser.parse_args()
  						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  