
"""simple docstring"""






import torch

from transformers import AutoTokenizer




class      _SCREAMING_SNAKE_CASE   (     A__				):






			def __init__(   self			,  __A			)     ->	Tuple:
						lowerCAmelCase_       :int     	=    AutoTokenizer.from_pretrained(__A			)






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	Tuple:

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

									lowerCAmelCase_       :List[Any]     	=    torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""			)			)
									lowerCAmelCase_       :Optional[int]     	=    torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""			)			)

						elif isinstance(__A			,  __A			) and all(isinstance(_x			,  __A			) for _x in x			):
									lowerCAmelCase_       :Tuple     	=    self.tokenizer(
									    __A			,  padding="""max_length"""			,  max_length=384			,  truncation=__A			,  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 					__lowerCAmelCase					(   self			,  __A			,  __A			,  __A			,  __A			,  __A=0.7_0			)     ->	str:
						lowerCAmelCase_       :str     	=    []
						for idx in range(len(W_query["""input_ids"""]			)			):
									lowerCAmelCase_       :Any     	=    range(p_start.shape[1]			)

									lowerCAmelCase_       :Tuple     	=    []
									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 __A			: (tup[2] * tup[3])			,  reverse=__A			)
									lowerCAmelCase_       :int     	=    []
									for k in range(len(__A			)			):
												if output[k][2] * output[k][3] >= thresh:
															lowerCAmelCase_	,			lowerCAmelCase_       :int     	=    output[k][0], output[k][1]
															lowerCAmelCase_       :List[str]     	=    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(__A			)

						return final_outputs








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase 					=    {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    ['MarianTokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    [
				    'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MarianForCausalLM',
				    'MarianModel',
				    'MarianMTModel',
				    'MarianPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    ['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    ['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
				from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_marian import MarianTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_marian import (
								    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MarianForCausalLM,
								    MarianModel,
								    MarianMTModel,
								    MarianPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
				import sys

				__UpperCAmelCase 					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






