import torch

from transformers import AutoTokenizer





class 	_lowercase		(      snake_case_ ):

 def __init__(				self			:							Optional[int]     ,   snake_case			:							str    )     ->		Any:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      AutoTokenizer.from_pretrained(snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict     ,   snake_case			:							Any    )     ->		List[str]:

  """simple docstring"""





  if isinstance(snake_case     ,   snake_case    ) and all(isinstance(_x     ,   snake_case    ) for _x in x    ):
   UpperCamelCase_      :		Optional[int]				=      None
   for l in x:
    UpperCamelCase_      :		Dict				=      self.tokenizer(
        snake_case     ,   padding='max_length'     ,   max_length=3_8_4     ,   truncation=snake_case     ,   return_tensors='pt'     ,   )
    UpperCamelCase_      :		Dict				=      torch.tensor([len(snake_case    )]    )
    if d is not None:
     for k in d.keys():
      UpperCamelCase_      :		Dict				=      torch.cat((d[k], t[k])     ,   0    )
    else:
     UpperCamelCase_      :		Tuple				=      t

   UpperCamelCase_      :		Union[str, Any]				=      torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'    )    )
   UpperCamelCase_      :		str				=      torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'    )    )

  elif isinstance(snake_case     ,   snake_case    ) and all(isinstance(_x     ,   snake_case    ) for _x in x    ):
   UpperCamelCase_      :		str				=      self.tokenizer(
       snake_case     ,   padding='max_length'     ,   max_length=3_8_4     ,   truncation=snake_case     ,   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 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple     ,   snake_case			:							Union[str, Any]     ,   snake_case			:							Tuple     ,   snake_case			:							Optional[Any]     ,   snake_case			:							Optional[Any]     ,   snake_case			:							Tuple=0.70    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		int				=      []
  for idx in range(len(W_query['input_ids']    )    ):
   UpperCamelCase_      :		Any				=      range(p_start.shape[1]    )

   UpperCamelCase_      :		Union[str, Any]				=      []
   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=snake_case    )
   UpperCamelCase_      :		Optional[int]				=      []
   for k in range(len(snake_case    )    ):
    if output[k][2] * output[k][3] >= thresh:
     UpperCamelCase_,				UpperCamelCase_      :		List[str]				=      output[k][0], output[k][1]
     UpperCamelCase_      :		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(snake_case    )

  return final_outputs




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,
)


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

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

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

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

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = ['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

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



