






import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
_a       =    logging.getLogger(__name__)

_a       =    int(10000)  # Hardcoded max length to avoid infinite loop

_a       =    {
    "gpt2": (GPTaLMHeadModel, GPTaTokenizer),
    "ctrl": (CTRLLMHeadModel, CTRLTokenizer),
    "openai-gpt": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    "xlnet": (XLNetLMHeadModel, XLNetTokenizer),
    "transfo-xl": (TransfoXLLMHeadModel, TransfoXLTokenizer),
    "xlm": (XLMWithLMHeadModel, XLMTokenizer),
    "gptj": (GPTJForCausalLM, AutoTokenizer),
    "bloom": (BloomForCausalLM, BloomTokenizerFast),
    "llama": (LlamaForCausalLM, LlamaTokenizer),
    "opt": (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
_a       =    "In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>"


def __A       ( __lowerCAmelCase      )->    Optional[Any]:





						"""simple docstring"""



						np.random.seed(args.seed      )
						torch.manual_seed(args.seed      )
						if args.n_gpu > 0:
												torch.cuda.manual_seed_all(args.seed      )


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    Dict:





						"""simple docstring"""



						if args.temperature > 0.7:
												logger.info('CTRL typically works better with lower temperatures (and lower top_k).'      )

						_UpperCAmelCase							   =     tokenizer.encode(UpperCamelCase__					,						add_special_tokens=UpperCamelCase__      )
						if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()      ):
												logger.info('WARNING! You are not starting your generation from a control code so you won\'t get good results'      )
						return prompt_text


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    List[Any]:





						"""simple docstring"""



						_UpperCAmelCase							   =     hasattr(model.config					,						'use_lang_emb'      ) and model.config.use_lang_emb
						if hasattr(model.config					,						'lang2id'      ) and use_lang_emb:
												_UpperCAmelCase							   =     model.config.langaid.keys()
												if args.xlm_language in available_languages:
																		_UpperCAmelCase							   =     args.xlm_language
												else:
																		_UpperCAmelCase							   =     None
																		while language not in available_languages:
																								_UpperCAmelCase							   =     input('Using XLM. Select language in ' + str(list(UpperCamelCase__      )      ) + ' >>> '      )

												_UpperCAmelCase							   =     model.config.langaid[language]
												# kwargs["language"] = tokenizer.lang2id[language]

						# TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
						# XLM masked-language modeling (MLM) models need masked token
						# is_xlm_mlm = "mlm" in args.model_name_or_path
						# if is_xlm_mlm:
						#     kwargs["mask_token_id"] = tokenizer.mask_token_id

						return prompt_text


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase							   =     args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
						_UpperCAmelCase							   =     prefix + prompt_text
						return prompt_text


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    str:





						"""simple docstring"""



						_UpperCAmelCase							   =     args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
						_UpperCAmelCase							   =     prefix + prompt_text
						return prompt_text


_a       =    {
    "ctrl": prepare_ctrl_input,
    "xlm": prepare_xlm_input,
    "xlnet": prepare_xlnet_input,
    "transfo-xl": prepare_transfoxl_input,
}


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    str:





						"""simple docstring"""



						if length < 0 and max_sequence_length > 0:
												_UpperCAmelCase							   =     max_sequence_length
						elif 0 < max_sequence_length < length:
												_UpperCAmelCase							   =     max_sequence_length  # No generation bigger than model size
						elif length < 0:
												_UpperCAmelCase							   =     MAX_LENGTH  # avoid infinite loop
						return length


def __A       ( __lowerCAmelCase      )->    List[str]:





						"""simple docstring"""



						_UpperCAmelCase							   =     None
						if hasattr(UpperCamelCase__					,						'hidden_size'      ):
												_UpperCAmelCase							   =     model_config.hidden_size
						elif hasattr(UpperCamelCase__					,						'n_embed'      ):
												_UpperCAmelCase							   =     model_config.n_embed
						elif hasattr(UpperCamelCase__					,						'n_embd'      ):
												_UpperCAmelCase							   =     model_config.n_embd

						_UpperCAmelCase							   =     None
						if hasattr(UpperCamelCase__					,						'num_attention_heads'      ):
												_UpperCAmelCase							   =     model_config.num_attention_heads
						elif hasattr(UpperCamelCase__					,						'n_head'      ):
												_UpperCAmelCase							   =     model_config.n_head

						if embedding_size is None or num_head is None or num_head == 0:
												raise ValueError('Check the model config'      )

						_UpperCAmelCase							   =     int(embedding_size / num_head      )
						if hasattr(UpperCamelCase__					,						'n_layer'      ):
												_UpperCAmelCase							   =     model_config.n_layer
						elif hasattr(UpperCamelCase__					,						'num_hidden_layers'      ):
												_UpperCAmelCase							   =     model_config.num_hidden_layers
						else:
												raise ValueError('Number of hidden layers couldn\'t be determined from the model config'      )

						return num_layer, num_head, num_embedding_size_per_head


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    int:





						"""simple docstring"""



						_UpperCAmelCase							   =     sparse_model_config(model.config      )
						if model.config.model_type == "bloom":
												_UpperCAmelCase							   =     tuple(
												    (
												        torch.empty(int(num_attention_heads * batch_size      )					,						UpperCamelCase__					,						UpperCamelCase__      )
												        .to(model.dtype      )
												        .to(model.device      ),
												        torch.empty(int(num_attention_heads * batch_size      )					,						UpperCamelCase__					,						UpperCamelCase__      )
												        .to(model.dtype      )
												        .to(model.device      ),
												    )
												    for _ in range(UpperCamelCase__      )      )
						else:
												_UpperCAmelCase							   =     tuple(
												    (
												        torch.empty(UpperCamelCase__					,						UpperCamelCase__					,						UpperCamelCase__					,						UpperCamelCase__      )
												        .to(model.dtype      )
												        .to(model.device      ),
												        torch.empty(UpperCamelCase__					,						UpperCamelCase__					,						UpperCamelCase__					,						UpperCamelCase__      )
												        .to(model.dtype      )
												        .to(model.device      ),
												    )
												    for _ in range(UpperCamelCase__      )      )
						return past_key_values


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase							   =     len(UpperCamelCase__      )
						_UpperCAmelCase							   =     tokenizer.batch_encode_plus(UpperCamelCase__					,						return_tensors='pt'      )
						_UpperCAmelCase							   =     dummy_input.to(model.device      )
						if model.config.use_cache:
												_UpperCAmelCase							   =     generate_past_key_values(UpperCamelCase__					,						UpperCamelCase__					,						1      )
						_UpperCAmelCase							   =     torch.cat(
						    [
						        torch.zeros(dummy_input['attention_mask'].shape[0]					,						1      )
						        .to(dummy_input['attention_mask'].dtype      )
						        .to(model.device      ),
						        dummy_input['attention_mask'],
						    ]					,						-1					,						)
						return dummy_input





class __lowerCamelCase					( lowercase__):





	"""simple docstring"""






	UpperCamelCase__								= ("_optimized", "_default")

	def __init__(				self			,       UpperCAmelCase			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     optimized
							_UpperCAmelCase							   =     default

	def __call__(				self			,       *UpperCAmelCase			,       **UpperCAmelCase       ):
							"""simple docstring"""







							if kwargs["past_key_values"] is None and self._default.config.use_cache:
													_UpperCAmelCase							   =     generate_past_key_values(self._default			,       kwargs['input_ids'].shape[0]			,       0       )
							kwargs.pop('position_ids'			,       __UpperCamelCase       )
							for k in list(kwargs.keys()       ):
													if kwargs[k] is None or isinstance(kwargs[k]			,       __UpperCamelCase       ):
																			kwargs.pop(__UpperCamelCase       )
							_UpperCAmelCase							   =     self._optimized(**__UpperCamelCase       )
							_UpperCAmelCase							   =     outputs[0]
							_UpperCAmelCase							   =     outputs[1]
							_UpperCAmelCase							   =     CausalLMOutputWithPast(
							    loss=__UpperCamelCase			,       logits=__UpperCamelCase			,       past_key_values=__UpperCamelCase			,       hidden_states=__UpperCamelCase			,       attentions=__UpperCamelCase			,       )
							return fixed_output

	def __getattr__(				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							return getattr(self._default			,       __UpperCamelCase       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase=None			,       UpperCAmelCase=None			,       UpperCAmelCase=None			,       **UpperCAmelCase       ):
							"""simple docstring"""







							return self._default.prepare_inputs_for_generation(
							    __UpperCamelCase			,       past_key_values=__UpperCamelCase			,       inputs_embeds=__UpperCamelCase			,       use_cache=__UpperCamelCase			,       **__UpperCamelCase       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase       ):
							"""simple docstring"""







							return self._default._reorder_cache(__UpperCamelCase			,       __UpperCamelCase       )



def __A       ( )->    str:





						"""simple docstring"""



						_UpperCAmelCase							   =     argparse.ArgumentParser()
						parser.add_argument(
						    '--model_type'					,						default=UpperCamelCase__					,						type=UpperCamelCase__					,						required=UpperCamelCase__					,						help='Model type selected in the list: ' + ', '.join(MODEL_CLASSES.keys()      )					,						)
						parser.add_argument(
						    '--model_name_or_path'					,						default=UpperCamelCase__					,						type=UpperCamelCase__					,						required=UpperCamelCase__					,						help='Path to pre-trained model or shortcut name selected in the list: ' + ', '.join(MODEL_CLASSES.keys()      )					,						)

						parser.add_argument('--prompt'					,						type=UpperCamelCase__					,						default=''      )
						parser.add_argument('--length'					,						type=UpperCamelCase__					,						default=20      )
						parser.add_argument('--stop_token'					,						type=UpperCamelCase__					,						default=UpperCamelCase__					,						help='Token at which text generation is stopped'      )

						parser.add_argument(
						    '--temperature'					,						type=UpperCamelCase__					,						default=1.0					,						help='temperature of 1.0 has no effect, lower tend toward greedy sampling'					,						)
						parser.add_argument(
						    '--repetition_penalty'					,						type=UpperCamelCase__					,						default=1.0					,						help='primarily useful for CTRL model; in that case, use 1.2'      )
						parser.add_argument('--k'					,						type=UpperCamelCase__					,						default=0      )
						parser.add_argument('--p'					,						type=UpperCamelCase__					,						default=0.9      )

						parser.add_argument('--prefix'					,						type=UpperCamelCase__					,						default=''					,						help='Text added prior to input.'      )
						parser.add_argument('--padding_text'					,						type=UpperCamelCase__					,						default=''					,						help='Deprecated, the use of `--prefix` is preferred.'      )
						parser.add_argument('--xlm_language'					,						type=UpperCamelCase__					,						default=''					,						help='Optional language when used with the XLM model.'      )

						parser.add_argument('--seed'					,						type=UpperCamelCase__					,						default=42					,						help='random seed for initialization'      )
						parser.add_argument('--no_cuda'					,						action='store_true'					,						help='Avoid using CUDA when available'      )
						parser.add_argument('--num_return_sequences'					,						type=UpperCamelCase__					,						default=1					,						help='The number of samples to generate.'      )
						parser.add_argument(
						    '--fp16'					,						action='store_true'					,						help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'					,						)
						parser.add_argument('--jit'					,						action='store_true'					,						help='Whether or not to use jit trace to accelerate inference'      )
						_UpperCAmelCase							   =     parser.parse_args()

						_UpperCAmelCase							   =     torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'      )
						_UpperCAmelCase							   =     0 if args.no_cuda else torch.cuda.device_count()

						logger.warning(F"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""      )

						set_seed(UpperCamelCase__      )

						# Initialize the model and tokenizer
						try:
												_UpperCAmelCase							   =     args.model_type.lower()
												_UpperCAmelCase							   =     MODEL_CLASSES[args.model_type]
						except KeyError:
												raise KeyError('the model {} you specified is not supported. You are welcome to add it and open a PR :)'      )

						_UpperCAmelCase							   =     tokenizer_class.from_pretrained(args.model_name_or_path      )
						if tokenizer.pad_token is None:
												_UpperCAmelCase							   =     tokenizer.eos_token
						_UpperCAmelCase							   =     model_class.from_pretrained(args.model_name_or_path      )
						model.to(args.device      )

						if args.fpaa:
												model.half()
						_UpperCAmelCase							   =     getattr(model.config					,						'max_position_embeddings'					,						0      )
						_UpperCAmelCase							   =     adjust_length_to_model(args.length					,						max_sequence_length=UpperCamelCase__      )
						logger.info(UpperCamelCase__      )

						_UpperCAmelCase							   =     args.prompt if args.prompt else input('Model prompt >>> '      )

						# Different models need different input formatting and/or extra arguments
						_UpperCAmelCase							   =     args.model_type in PREPROCESSING_FUNCTIONS.keys()
						if requires_preprocessing:
												_UpperCAmelCase							   =     PREPROCESSING_FUNCTIONS.get(args.model_type      )
												_UpperCAmelCase							   =     prepare_input(UpperCamelCase__					,						UpperCamelCase__					,						UpperCamelCase__					,						UpperCamelCase__      )

												if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
																		_UpperCAmelCase							   =     {'''add_space_before_punct_symbol''': True}
												else:
																		_UpperCAmelCase							   =     {}

												_UpperCAmelCase							   =     tokenizer.encode(
												    UpperCamelCase__					,						add_special_tokens=UpperCamelCase__					,						return_tensors='pt'					,						**UpperCamelCase__      )
						else:
												_UpperCAmelCase							   =     args.prefix if args.prefix else args.padding_text
												_UpperCAmelCase							   =     tokenizer.encode(prefix + prompt_text					,						add_special_tokens=UpperCamelCase__					,						return_tensors='pt'      )
						_UpperCAmelCase							   =     encoded_prompt.to(args.device      )

						if encoded_prompt.size()[-1] == 0:
												_UpperCAmelCase							   =     None
						else:
												_UpperCAmelCase							   =     encoded_prompt

						if args.jit:
												_UpperCAmelCase							   =     ['''enable jit''']
												_UpperCAmelCase							   =     prepare_jit_inputs(UpperCamelCase__					,						UpperCamelCase__					,						UpperCamelCase__      )
												torch._C._jit_set_texpr_fuser_enabled(UpperCamelCase__      )
												_UpperCAmelCase							   =     False
												if hasattr(UpperCamelCase__					,						'forward'      ):
																		_UpperCAmelCase							   =     inspect.signature(model.forward      )
												else:
																		_UpperCAmelCase							   =     inspect.signature(model.__call__      )
												_UpperCAmelCase							   =     tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(UpperCamelCase__					,						UpperCamelCase__      ) is not None      )
												_UpperCAmelCase							   =     torch.jit.trace(UpperCamelCase__					,						UpperCamelCase__					,						strict=UpperCamelCase__      )
												_UpperCAmelCase							   =     torch.jit.freeze(traced_model.eval()      )
												traced_model(*UpperCamelCase__      )
												traced_model(*UpperCamelCase__      )

												_UpperCAmelCase							   =     _ModelFallbackWrapper(UpperCamelCase__					,						UpperCamelCase__      )

						_UpperCAmelCase							   =     model.generate(
						    input_ids=UpperCamelCase__					,						max_length=args.length + len(encoded_prompt[0]      )					,						temperature=args.temperature					,						top_k=args.k					,						top_p=args.p					,						repetition_penalty=args.repetition_penalty					,						do_sample=UpperCamelCase__					,						num_return_sequences=args.num_return_sequences					,						)

						# Remove the batch dimension when returning multiple sequences
						if len(output_sequences.shape      ) > 2:
												output_sequences.squeeze_()

						_UpperCAmelCase							   =     []

						for generated_sequence_idx, generated_sequence in enumerate(UpperCamelCase__      ):
												print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""      )
												_UpperCAmelCase							   =     generated_sequence.tolist()

												# Decode text
												_UpperCAmelCase							   =     tokenizer.decode(UpperCamelCase__					,						clean_up_tokenization_spaces=UpperCamelCase__      )

												# Remove all text after the stop token
												_UpperCAmelCase							   =     text[: text.find(args.stop_token      ) if args.stop_token else None]

												# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
												_UpperCAmelCase							   =     (
												    prompt_text + text[len(tokenizer.decode(encoded_prompt[0]					,						clean_up_tokenization_spaces=UpperCamelCase__      )      ) :]
												)

												generated_sequences.append(UpperCamelCase__      )
												print(UpperCamelCase__      )

						return generated_sequences


if __name__ == "__main__":
		main()





'''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       :						Dict						=logging.get_logger(__name__)

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

if is_torch_available():
				import torch

if is_soundfile_availble():
				import soundfile as sf






class 			_SCREAMING_SNAKE_CASE				:



			def __init__(     self		:						List[str]    ,			__UpperCamelCase		:						Optional[int]			)      ->      List[str]:
							"""simple docstring"""





							snake_case__      :   int      =    value



			def __str__(     self		:						int			)      ->      str:
							"""simple docstring"""





							return self.to_string()



			def 				lowerCAmelCase (     self		:						List[Any]			)      ->      List[str]:
							"""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		:						Optional[Any]			)      ->      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 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__      ):



			def 				lowerCAmelCase (     self		:						str			)      ->      Tuple:
							"""simple docstring"""





							return self._value







			def 				lowerCAmelCase (     self		:						Optional[int]			)      ->      Union[str, Any]:
							"""simple docstring"""





							return self._value






class 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__      ):



			def __init__(     self		:						str    ,			__UpperCamelCase		:						List[Any]			)      ->      Dict:
							"""simple docstring"""





							super().__init__(__UpperCamelCase			)

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

							snake_case__      :   str      =    None
							snake_case__      :   List[Any]      =    None
							snake_case__      :   Tuple      =    None

							if isinstance(__UpperCamelCase    ,			__UpperCamelCase			):
											snake_case__      :   List[Any]      =    value
							elif isinstance(__UpperCamelCase    ,			(str, pathlib.Path)			):
											snake_case__      :   int      =    value
							elif isinstance(__UpperCamelCase    ,			torch.Tensor			):
											snake_case__      :   Union[str, Any]      =    value
							else:
											raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(__UpperCamelCase			)}'''			)



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						Dict=None    ,			__UpperCamelCase		:						Any=None			)      ->      List[Any]:
							"""simple docstring"""





							from IPython.display import Image, display

							display(Image(self.to_string()			)			)



			def 				lowerCAmelCase (     self		:						List[str]			)      ->      str:
							"""simple docstring"""





							if self._raw is not None:
											return self._raw

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







			def 				lowerCAmelCase (     self		:						Dict			)      ->      Optional[Any]:
							"""simple docstring"""





							if self._path is not None:
											return self._path

							if self._raw is not None:
											snake_case__      :   Any      =    tempfile.mkdtemp()
											snake_case__      :   Dict      =    os.path.join(__UpperCamelCase    ,			str(uuid.uuida()			) + '''.png'''			)
											self._raw.save(self._path			)

											return self._path

							if self._tensor is not None:
											snake_case__      :   List[str]      =    self._tensor.cpu().detach().numpy()

											# There is likely simpler than load into image into save
											snake_case__      :   List[Any]      =    Image.fromarray((array * 255).astype(np.uinta			)			)

											snake_case__      :   str      =    tempfile.mkdtemp()
											snake_case__      :   Dict      =    os.path.join(__UpperCamelCase    ,			str(uuid.uuida()			) + '''.png'''			)

											img.save(self._path			)

											return self._path






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):



			def __init__(     self		:						Optional[Any]    ,			__UpperCamelCase		:						List[Any]    ,			__UpperCamelCase		:						str=16000			)      ->      Union[str, Any]:
							"""simple docstring"""





							super().__init__(__UpperCamelCase			)

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

							snake_case__      :   List[str]      =    None
							snake_case__      :   Optional[int]      =    None

							snake_case__      :   Any      =    samplerate

							if isinstance(__UpperCamelCase    ,			(str, pathlib.Path)			):
											snake_case__      :   int      =    value
							elif isinstance(__UpperCamelCase    ,			torch.Tensor			):
											snake_case__      :   int      =    value
							else:
											raise ValueError(F'''Unsupported audio type: {type(__UpperCamelCase			)}'''			)



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						List[Any]=None    ,			__UpperCamelCase		:						Optional[Any]=None			)      ->      Any:
							"""simple docstring"""





							from IPython.display import Audio, display

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



			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





							if self._tensor is not None:
											return self._tensor

							if self._path is not None:
											snake_case__  ,      snake_case__      :   Tuple      =    sf.read(self._path			)
											snake_case__      :   Tuple      =    torch.tensor(__UpperCamelCase			)
											return self._tensor







			def 				lowerCAmelCase (     self		:						Any			)      ->      Dict:
							"""simple docstring"""





							if self._path is not None:
											return self._path

							if self._tensor is not None:
											snake_case__      :   Tuple      =    tempfile.mkdtemp()
											snake_case__      :   int      =    os.path.join(__UpperCamelCase    ,			str(uuid.uuida()			) + '''.wav'''			)
											sf.write(self._path    ,			self._tensor    ,			samplerate=self.samplerate			)
											return self._path



_lowercase       :						Union[str, Any]						={"text": AgentText, "image": AgentImage, "audio": AgentAudio}
_lowercase       :						List[Any]						={str: AgentText}

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


def 							__UpperCAmelCase				(				*UpperCamelCase__				:Optional[int]   ,				**UpperCamelCase__				:List[Any]			)		->						Optional[Any]:
				snake_case__      :   List[str]      =    [(arg.to_raw() if isinstance(UpperCamelCase__   ,				UpperCamelCase__			) else arg) for arg in args]
				snake_case__      :   Any      =    {k: (v.to_raw() if isinstance(UpperCamelCase__   ,				UpperCamelCase__			) else v) for k, v in kwargs.items()}
				return args, kwargs



def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[Any]   ,				UpperCamelCase__				:Tuple=None			)		->						Dict:
				if isinstance(UpperCamelCase__   ,				UpperCamelCase__			):
								snake_case__      :   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__      :   List[Any]      =    AGENT_TYPE_MAPPING[output_types[i]](UpperCamelCase__			)
																else:
																				snake_case__      :   Optional[int]      =    AgentType(UpperCamelCase__			)

												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(UpperCamelCase__   ,				_k			):
																								snake_case__      :   str      =    _v(UpperCamelCase__			)
																if k not in decoded_outputs:
																				snake_case__      :   Any      =    AgentType[v]

				elif isinstance(UpperCamelCase__   ,				(list, tuple)			):
								snake_case__      :   List[str]      =    type(UpperCamelCase__			)()
								for i, v in enumerate(UpperCamelCase__			):
												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]](UpperCamelCase__			)			)
																else:
																				decoded_outputs.append(AgentType(UpperCamelCase__			)			)
												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(UpperCamelCase__   ,				_k			):
																								decoded_outputs.append(_v(UpperCamelCase__			)			)
																								snake_case__      :   str      =    True

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

				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__      :   Tuple      =    AGENT_TYPE_MAPPING[output_types[0]](UpperCamelCase__			)

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

				return decoded_outputs




