


'''simple docstring'''


import torch

from transformers import AutoTokenizer





class 					lowercase__						(		_snake_case      ):





	'''simple docstring'''


	def __init__(					self					,      __snake_case						):
					_SCREAMING_SNAKE_CASE				: List[Any]  	=   AutoTokenizer.from_pretrained(UpperCAmelCase_						)






	def UpperCAmelCase_			(					self					,      __snake_case						):

					if isinstance(UpperCAmelCase_					,      UpperCAmelCase_						) and all(isinstance(_x					,      UpperCAmelCase_						) for _x in x						):
									_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   None
									for l in x:
													_SCREAMING_SNAKE_CASE				: Any  	=   self.tokenizer(
													    UpperCAmelCase_					,      padding="""max_length"""					,      max_length=384					,      truncation=UpperCAmelCase_					,      return_tensors="""pt"""					,      )
													_SCREAMING_SNAKE_CASE				: List[Any]  	=   torch.tensor([len(UpperCAmelCase_						)]						)
													if d is not None:
																	for k in d.keys():
																					_SCREAMING_SNAKE_CASE				: Dict  	=   torch.cat((d[k], t[k])					,      0						)
													else:
																	_SCREAMING_SNAKE_CASE				: Optional[int]  	=   t

									_SCREAMING_SNAKE_CASE				: str  	=   torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""						)						)
									_SCREAMING_SNAKE_CASE				: str  	=   torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""						)						)

					elif isinstance(UpperCAmelCase_					,      UpperCAmelCase_						) and all(isinstance(_x					,      UpperCAmelCase_						) for _x in x						):
									_SCREAMING_SNAKE_CASE				: int  	=   self.tokenizer(
									    UpperCAmelCase_					,      padding="""max_length"""					,      max_length=384					,      truncation=UpperCAmelCase_					,      return_tensors="""pt"""					,      )

					else:
									raise Exception(
									    """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
									    """ strings` for supports are supported."""						)

					return d






	def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case=0.70						):
					_SCREAMING_SNAKE_CASE				: str  	=   []
					for idx in range(len(W_query["""input_ids"""]						)						):
									_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   range(p_start.shape[1]						)

									_SCREAMING_SNAKE_CASE				: str  	=   []
									for start_id in start_indexes:
													for end_id in end_indexes:
																	if start_id < end_id:
																					output.append(
																					    (
																					        start_id,
																					        end_id,
																					        p_start[idx][start_id].item(),
																					        p_end[idx][end_id].item(),
																					    )						)

									output.sort(key=lambda __snake_case						: (tup[2] * tup[3])					,      reverse=UpperCAmelCase_						)
									_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   []
									for k in range(len(UpperCAmelCase_						)						):
													if output[k][2] * output[k][3] >= thresh:
																	_SCREAMING_SNAKE_CASE				: int  	=   output[k][0], output[k][1]
																	_SCREAMING_SNAKE_CASE				: List[Any]  	=   self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]						)
																	temp.append((decoded, output[k][2] * output[k][3])						)

									final_outputs.append(UpperCAmelCase_						)

					return final_outputs








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_	:     Any							     =	{
    'configuration_musicgen': [
        'MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MusicgenConfig',
        'MusicgenDecoderConfig',
    ],
    'processing_musicgen': ['MusicgenProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Any							     =	[
				    'MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MusicgenForConditionalGeneration',
				    'MusicgenForCausalLM',
				    'MusicgenModel',
				    'MusicgenPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_musicgen import (
				    MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MusicgenConfig,
				    MusicgenDecoderConfig,
				)
				from .processing_musicgen import MusicgenProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_musicgen import (
								    MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MusicgenForCausalLM,
								    MusicgenForConditionalGeneration,
								    MusicgenModel,
								    MusicgenPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_	:     Tuple							     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




