




"""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


UpperCamelCase__			:     Tuple			       =				logging.get_logger(__name__)

if is_vision_available():
		import PIL.Image
		from PIL import Image
		from PIL.Image import Image as ImageType
else:
		UpperCamelCase__			:     List[Any]			       =				object

if is_torch_available():
		import torch

if is_soundfile_availble():
		import soundfile as sf
class      A__   :




	def __init__( self	:						Union[str, Any]   ,     lowercase	:						str						)       ->			Optional[Any]:
						__snake_case        =						value




	def __str__( self	:						str						)       ->			Optional[int]:
						return self.to_string()




	def 				a_		( self	:						List[Any]						)       ->			str:
						logger.error(
						    """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"""						)
						return self._value




	def 				a_		( self	:						int						)       ->			str:
						logger.error(
						    """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"""						)
						return str(self._value						)

class      A__   (   lowercase_  ,						lowercase_  ):




	def 				a_		( self	:						int						)       ->			Tuple:
						return self._value




	def 				a_		( self	:						Optional[Any]						)       ->			Tuple:
						return self._value

class      A__   (   lowercase_  ,						lowercase_  ):




	def __init__( self	:						List[str]   ,     lowercase	:						Union[str, Any]						)       ->			Any:
						super().__init__(lowercase						)

						if not is_vision_available():
											raise ImportError("""PIL must be installed in order to handle images."""						)

						__snake_case        =						None
						__snake_case        =						None
						__snake_case        =						None

						if isinstance(lowercase   ,     lowercase						):
											__snake_case        =						value
						elif isinstance(lowercase   ,     (str, pathlib.Path)						):
											__snake_case        =						value
						elif isinstance(lowercase   ,     torch.Tensor						):
											__snake_case        =						value
						else:
											raise ValueError(f'''Unsupported type for {self.__class__.__name__}: {type(lowercase						)}'''						)




	def 				a_		( self	:						List[str]   ,     lowercase	:						Optional[int]=None   ,     lowercase	:						Optional[Any]=None						)       ->			str:
						from IPython.display import Image, display

						display(Image(self.to_string()						)						)




	def 				a_		( self	:						Dict						)       ->			Union[str, Any]:
						if self._raw is not None:
											return self._raw

						if self._path is not None:
											__snake_case        =						Image.open(self._path						)
											return self._raw




	def 				a_		( self	:						Optional[Any]						)       ->			Dict:
						if self._path is not None:
											return self._path

						if self._raw is not None:
											__snake_case        =						tempfile.mkdtemp()
											__snake_case        =						os.path.join(lowercase   ,     str(uuid.uuida()						) + """.png"""						)
											self._raw.save(self._path						)

											return self._path

						if self._tensor is not None:
											__snake_case        =						self._tensor.cpu().detach().numpy()

											# There is likely simpler than load into image into save
											__snake_case        =						Image.fromarray((array * 255).astype(np.uinta						)						)

											__snake_case        =						tempfile.mkdtemp()
											__snake_case        =						os.path.join(lowercase   ,     str(uuid.uuida()						) + """.png"""						)

											img.save(self._path						)

											return self._path

class      A__   (   lowercase_  ):




	def __init__( self	:						List[str]   ,     lowercase	:						Dict   ,     lowercase	:						Dict=16_000						)       ->			Tuple:
						super().__init__(lowercase						)

						if not is_soundfile_availble():
											raise ImportError("""soundfile must be installed in order to handle audio."""						)

						__snake_case        =						None
						__snake_case        =						None

						__snake_case        =						samplerate

						if isinstance(lowercase   ,     (str, pathlib.Path)						):
											__snake_case        =						value
						elif isinstance(lowercase   ,     torch.Tensor						):
											__snake_case        =						value
						else:
											raise ValueError(f'''Unsupported audio type: {type(lowercase						)}'''						)




	def 				a_		( self	:						Any   ,     lowercase	:						Any=None   ,     lowercase	:						Optional[int]=None						)       ->			Optional[int]:
						from IPython.display import Audio, display

						display(Audio(self.to_string()   ,     rate=self.samplerate						)						)




	def 				a_		( self	:						Dict						)       ->			int:
						if self._tensor is not None:
											return self._tensor

						if self._path is not None:
											__snake_case   ,   __snake_case        =						sf.read(self._path						)
											__snake_case        =						torch.tensor(lowercase						)
											return self._tensor




	def 				a_		( self	:						Any						)       ->			List[str]:
						if self._path is not None:
											return self._path

						if self._tensor is not None:
											__snake_case        =						tempfile.mkdtemp()
											__snake_case        =						os.path.join(lowercase   ,     str(uuid.uuida()						) + """.wav"""						)
											sf.write(self._path   ,     self._tensor   ,     samplerate=self.samplerate						)
											return self._path

UpperCamelCase__			:     Tuple			       =				{"text": AgentText, "image": AgentImage, "audio": AgentAudio}
UpperCamelCase__			:     Dict			       =				{str: AgentText}

if is_vision_available():
		UpperCamelCase__			:     Optional[Any]			       =				AgentImage




def    snake_case__						(							*lowerCAmelCase :		int					,       **lowerCAmelCase :		int		):
					__snake_case        =						[(arg.to_raw() if isinstance(lowerCAmelCase					,       lowerCAmelCase		) else arg) for arg in args]
					__snake_case        =						{k: (v.to_raw() if isinstance(lowerCAmelCase					,       lowerCAmelCase		) else v) for k, v in kwargs.items()}
					return args, kwargs




def    snake_case__						(							lowerCAmelCase :		Dict					,       lowerCAmelCase :		Dict=None		):
					if isinstance(lowerCAmelCase					,       lowerCAmelCase		):
										__snake_case        =						{}
										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        =						AGENT_TYPE_MAPPING[output_types[i]](lowerCAmelCase		)
																				else:
																									__snake_case        =						AgentType(lowerCAmelCase		)

															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(lowerCAmelCase					,       _k		):
																														__snake_case        =						_v(lowerCAmelCase		)
																				if k not in decoded_outputs:
																									__snake_case        =						AgentType[v]

					elif isinstance(lowerCAmelCase					,       (list, tuple)		):
										__snake_case        =						type(lowerCAmelCase		)()
										for i, v in enumerate(lowerCAmelCase		):
															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]](lowerCAmelCase		)		)
																				else:
																									decoded_outputs.append(AgentType(lowerCAmelCase		)		)
															else:
																				# If the class does not have defined output, then we map according to the type
																				__snake_case        =						False
																				for _k, _v in INSTANCE_TYPE_MAPPING.items():
																									if isinstance(lowerCAmelCase					,       _k		):
																														decoded_outputs.append(_v(lowerCAmelCase		)		)
																														__snake_case        =						True

																				if not found:
																									decoded_outputs.append(AgentType(lowerCAmelCase		)		)

					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        =						AGENT_TYPE_MAPPING[output_types[0]](lowerCAmelCase		)

										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(lowerCAmelCase					,       _k		):
																									return _v(lowerCAmelCase		)
															return AgentType(lowerCAmelCase		)

					return decoded_outputs








"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__			:     List[Any]			       =				logging.get_logger(__name__)

UpperCamelCase__			:     List[str]			       =				{}
class      A__   (   lowercase_  ):
	A_				:					Dict      =   """fsmt_decoder"""




	def __init__( self	:						List[Any]   ,     lowercase	:						Any=0   ,     lowercase	:						Dict=0						)       ->			str:
						super().__init__()
						__snake_case        =						vocab_size
						__snake_case        =						bos_token_id

class      A__   (   lowercase_  ):
	A_				:					Union[str, Any]      =   """fsmt"""
	A_				:					int      =   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




	def __init__( self	:						Optional[Any]   ,     lowercase	:						Any=["en", "de"]   ,     lowercase	:						Optional[int]=42_024   ,     lowercase	:						Optional[int]=42_024   ,     lowercase	:						Tuple="relu"   ,     lowercase	:						Tuple=1_024   ,     lowercase	:						Union[str, Any]=200   ,     lowercase	:						List[str]=1_024   ,     lowercase	:						Tuple=4_096   ,     lowercase	:						int=12   ,     lowercase	:						Any=16   ,     lowercase	:						List[Any]=0.0   ,     lowercase	:						str=4_096   ,     lowercase	:						str=12   ,     lowercase	:						Optional[int]=16   ,     lowercase	:						Dict=0.0   ,     lowercase	:						Any=0.0   ,     lowercase	:						Union[str, Any]=0.1   ,     lowercase	:						Tuple=0.0   ,     lowercase	:						List[str]=0.02   ,     lowercase	:						int=2   ,     lowercase	:						Any=True   ,     lowercase	:						int=True   ,     lowercase	:						int=False   ,     lowercase	:						List[str]=5   ,     lowercase	:						List[Any]=1.0   ,     lowercase	:						Union[str, Any]=False   ,     lowercase	:						Optional[Any]=True   ,     lowercase	:						int=1   ,     lowercase	:						Any=0   ,     lowercase	:						Optional[Any]=2   ,     lowercase	:						Any=2   ,     **lowercase	:						List[Any]   ,     )       ->			Union[str, Any]:
						__snake_case        =						langs
						__snake_case        =						src_vocab_size
						__snake_case        =						tgt_vocab_size
						__snake_case        =						d_model  # encoder_embed_dim and decoder_embed_dim

						__snake_case        =						encoder_ffn_dim
						__snake_case        =						__snake_case        =						encoder_layers
						__snake_case        =						encoder_attention_heads
						__snake_case        =						encoder_layerdrop
						__snake_case        =						decoder_layerdrop
						__snake_case        =						decoder_ffn_dim
						__snake_case        =						decoder_layers
						__snake_case        =						decoder_attention_heads
						__snake_case        =						max_position_embeddings
						__snake_case        =						init_std  # Normal(0, this parameter)
						__snake_case        =						activation_function

						__snake_case        =						DecoderConfig(vocab_size=lowercase   ,     bos_token_id=lowercase						)
						if "decoder" in common_kwargs:
											del common_kwargs["decoder"]

						__snake_case        =						scale_embedding  # scale factor will be sqrt(d_model) if True

						# 3 Types of Dropout
						__snake_case        =						attention_dropout
						__snake_case        =						activation_dropout
						__snake_case        =						dropout

						__snake_case        =						use_cache
						super().__init__(
						    pad_token_id=lowercase   ,     bos_token_id=lowercase   ,     eos_token_id=lowercase   ,     decoder_start_token_id=lowercase   ,     is_encoder_decoder=lowercase   ,     tie_word_embeddings=lowercase   ,     forced_eos_token_id=lowercase   ,     max_length=lowercase   ,     num_beams=lowercase   ,     length_penalty=lowercase   ,     early_stopping=lowercase   ,     **lowercase   ,     )




	def 				a_		( self	:						Union[str, Any]						)       ->			Dict:
						__snake_case        =						copy.deepcopy(self.__dict__						)
						__snake_case        =						self.decoder.to_dict()
						__snake_case        =						self.__class__.model_type
						return output


