code: 
  
  
  
  
  
  import os
  
  from ...utils import logging
  from .utils import DataProcessor, InputExample
  
  
  _lowercase		:     List[str] 					=logging.get_logger(__name__)
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					def __init__(		self    :    Tuple					,			lowerCamelCase    :    Optional[int]					,			lowerCamelCase    :    Optional[int]=None	):
  							lowerCamelCase_  :    Dict   	=			language
  							lowerCamelCase_  :    Optional[int]   	=			train_language
  
  
  
  					def      __a    (		self    :    int					,			lowerCamelCase    :    Optional[Any]	):
  							lowerCamelCase_  :    Optional[int]   	=			self.language if self.train_language is None else self.train_language
  							lowerCamelCase_  :    Tuple   	=			self._read_tsv(os.path.join(lowerCamelCase					,			F"XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv"	)	)
  							lowerCamelCase_  :    Tuple   	=			[]
  							for i, line in enumerate(lowerCamelCase	):
  									if i == 0:
  											continue
  									lowerCamelCase_  :    Any   	=			F"train-{i}"
  									lowerCamelCase_  :    Union[str, Any]   	=			line[0]
  									lowerCamelCase_  :    Optional[int]   	=			line[1]
  									lowerCamelCase_  :    str   	=			'contradiction' if line[2] == 'contradictory' else line[2]
  									if not isinstance(lowerCamelCase					,			lowerCamelCase	):
  											raise ValueError(F"Training input {text_a} is not a string"	)
  									if not isinstance(lowerCamelCase					,			lowerCamelCase	):
  											raise ValueError(F"Training input {text_b} is not a string"	)
  									if not isinstance(lowerCamelCase					,			lowerCamelCase	):
  											raise ValueError(F"Training label {label} is not a string"	)
  									examples.append(InputExample(guid=lowerCamelCase					,			text_a=lowerCamelCase					,			text_b=lowerCamelCase					,			label=lowerCamelCase	)	)
  							return examples
  
  
  
  					def      __a    (		self    :    Dict					,			lowerCamelCase    :    Optional[Any]	):
  							lowerCamelCase_  :    List[Any]   	=			self._read_tsv(os.path.join(lowerCamelCase					,			'XNLI-1.0/xnli.test.tsv'	)	)
  							lowerCamelCase_  :    Tuple   	=			[]
  							for i, line in enumerate(lowerCamelCase	):
  									if i == 0:
  											continue
  									lowerCamelCase_  :    Tuple   	=			line[0]
  									if language != self.language:
  											continue
  									lowerCamelCase_  :    Optional[int]   	=			F"test-{i}"
  									lowerCamelCase_  :    List[Any]   	=			line[6]
  									lowerCamelCase_  :    str   	=			line[7]
  									lowerCamelCase_  :    Dict   	=			line[1]
  									if not isinstance(lowerCamelCase					,			lowerCamelCase	):
  											raise ValueError(F"Training input {text_a} is not a string"	)
  									if not isinstance(lowerCamelCase					,			lowerCamelCase	):
  											raise ValueError(F"Training input {text_b} is not a string"	)
  									if not isinstance(lowerCamelCase					,			lowerCamelCase	):
  											raise ValueError(F"Training label {label} is not a string"	)
  									examples.append(InputExample(guid=lowerCamelCase					,			text_a=lowerCamelCase					,			text_b=lowerCamelCase					,			label=lowerCamelCase	)	)
  							return examples
  
  
  
  
  
  					def      __a    (		self    :    Union[str, Any]	):
  							return ["contradiction", "entailment", "neutral"]
  _lowercase		:     int 					={
      """xnli""": XnliProcessor,
  }
  
  _lowercase		:     Union[str, Any] 					={
      """xnli""": """classification""",
  }
  
  _lowercase		:     int 					={
      """xnli""": 3,
  }
  
style_context: 
  
  
  
  
  
  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,
  )
  
  
  _lowercase		:     int 					={
      """configuration_marian""": ["""MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarianConfig""", """MarianOnnxConfig"""],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Any 					=["""MarianTokenizer"""]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Any 					=[
  		    """MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
  		    """MarianForCausalLM""",
  		    """MarianModel""",
  		    """MarianMTModel""",
  		    """MarianPreTrainedModel""",
  		]
  
  try:
  		if not is_tf_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Dict 					=["""TFMarianModel""", """TFMarianMTModel""", """TFMarianPreTrainedModel"""]
  
  try:
  		if not is_flax_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Optional[Any] 					=["""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
  
  		_lowercase		:     List[str] 					=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  