import os

from ...utils import logging
from .utils import DataProcessor, InputExample


lowerCAmelCase__  	=      logging.get_logger(__name__)
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=None       ):
		_lowercase   =language
		_lowercase   =train_language


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =self.language if self.train_language is None else self.train_language
		_lowercase   =self._read_tsv(os.path.join(lowerCAmelCase_       ,  F'''XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv'''       )       )
		_lowercase   =[]
		for i, line in enumerate(lowerCAmelCase_       ):
			if i == 0:
				continue
			_lowercase   =F'''train-{i}'''
			_lowercase   =line[0]
			_lowercase   =line[1]
			_lowercase   ="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 __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =self._read_tsv(os.path.join(lowerCAmelCase_       ,  "XNLI-1.0/xnli.test.tsv"       )       )
		_lowercase   =[]
		for i, line in enumerate(lowerCAmelCase_       ):
			if i == 0:
				continue
			_lowercase   =line[0]
			if language != self.language:
				continue
			_lowercase   =F'''test-{i}'''
			_lowercase   =line[6]
			_lowercase   =line[7]
			_lowercase   =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 __lowerCAmelCase      (   self       ):
		return ["contradiction", "entailment", "neutral"]

lowerCAmelCase__  	=      {
    "xnli": XnliProcessor,
}

lowerCAmelCase__  	=      {
    "xnli": "classification",
}

lowerCAmelCase__  	=      {
    "xnli": 3,
}





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__  	=      {
    "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  	=      ["ImageGPTFeatureExtractor"]
						lowerCAmelCase__  	=      ["ImageGPTImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  	=      [
						    "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ImageGPTForCausalImageModeling",
						    "ImageGPTForImageClassification",
						    "ImageGPTModel",
						    "ImageGPTPreTrainedModel",
						    "load_tf_weights_in_imagegpt",
						]


if TYPE_CHECKING:
						from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
												from .image_processing_imagegpt import ImageGPTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_imagegpt import (
												    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ImageGPTForCausalImageModeling,
												    ImageGPTForImageClassification,
												    ImageGPTModel,
												    ImageGPTPreTrainedModel,
												    load_tf_weights_in_imagegpt,
												)

else:
						import sys

						lowerCAmelCase__  	=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




