
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     :					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




'''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)


