





import os

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


_lowerCamelCase							=     logging.get_logger(__name__)




class 					UpperCamelCase_    (       UpperCamelCase__      ):


							def __init__(							self      :Union[str, Any]							,			__A      :Union[str, Any]							,			__A      :Union[str, Any]=None						)				->				List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= language
									SCREAMING_SNAKE_CASE__								= train_language


							def        _snake_case					(							self      :int							,			__A      :Tuple						)				->				Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.language if self.train_language is None else self.train_language
									SCREAMING_SNAKE_CASE__								= self._read_tsv(os.path.join(__A							,			f'''XNLI-MT-1.0/multinli/multinli.train.{lg}.tsv'''						)						)
									SCREAMING_SNAKE_CASE__								= []
									for i, line in enumerate(__A						):
											if i == 0:
													continue
											SCREAMING_SNAKE_CASE__								= f'''train-{i}'''
											SCREAMING_SNAKE_CASE__								= line[0]
											SCREAMING_SNAKE_CASE__								= line[1]
											SCREAMING_SNAKE_CASE__								= """contradiction""" if line[2] == """contradictory""" else line[2]
											if not isinstance(__A							,			__A						):
													raise ValueError(f'''Training input {text_a} is not a string'''						)
											if not isinstance(__A							,			__A						):
													raise ValueError(f'''Training input {text_b} is not a string'''						)
											if not isinstance(__A							,			__A						):
													raise ValueError(f'''Training label {label} is not a string'''						)
											examples.append(InputExample(guid=__A							,			text_a=__A							,			text_b=__A							,			label=__A						)						)
									return examples


							def        _snake_case					(							self      :Optional[Any]							,			__A      :Optional[int]						)				->				Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self._read_tsv(os.path.join(__A							,			"""XNLI-1.0/xnli.test.tsv"""						)						)
									SCREAMING_SNAKE_CASE__								= []
									for i, line in enumerate(__A						):
											if i == 0:
													continue
											SCREAMING_SNAKE_CASE__								= line[0]
											if language != self.language:
													continue
											SCREAMING_SNAKE_CASE__								= f'''test-{i}'''
											SCREAMING_SNAKE_CASE__								= line[6]
											SCREAMING_SNAKE_CASE__								= line[7]
											SCREAMING_SNAKE_CASE__								= line[1]
											if not isinstance(__A							,			__A						):
													raise ValueError(f'''Training input {text_a} is not a string'''						)
											if not isinstance(__A							,			__A						):
													raise ValueError(f'''Training input {text_b} is not a string'''						)
											if not isinstance(__A							,			__A						):
													raise ValueError(f'''Training label {label} is not a string'''						)
											examples.append(InputExample(guid=__A							,			text_a=__A							,			text_b=__A							,			label=__A						)						)
									return examples


							def        _snake_case					(							self      :Optional[int]						)				->				Optional[int]:



									"""simple docstring"""



									return ["contradiction", "entailment", "neutral"]



_lowerCamelCase							=     {
    'xnli': XnliProcessor,
}

_lowerCamelCase							=     {
    'xnli': 'classification',
}

_lowerCamelCase							=     {
    'xnli': 3,
}






import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Any   ,				UpperCamelCase__:							List[Any]   ,				UpperCamelCase__:							Union[str, Any]      ):
		# Construct configuration depending on size
		SCREAMING_SNAKE_CASE__								= {"""small""": (512, 8, 24), """medium""": (1_024, 8, 36), """large""": (1_536, 16, 48)}
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= MODELS[model_size]  # set model hyperparameters
		SCREAMING_SNAKE_CASE__								= ImageGPTConfig(n_embd=UpperCamelCase__   ,				n_layer=UpperCamelCase__   ,				n_head=UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= ImageGPTForCausalLM(UpperCamelCase__      )

		# Load weights from numpy
		load_tf_weights_in_imagegpt(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__      )

		# Save pytorch-model
		SCREAMING_SNAKE_CASE__								= pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
		SCREAMING_SNAKE_CASE__								= pytorch_dump_folder_path + """/""" + CONFIG_NAME
		print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''      )
		torch.save(model.state_dict()   ,				UpperCamelCase__      )
		print(f'''Save configuration file to {pytorch_config_dump_path}'''      )
		with open(UpperCamelCase__   ,				"""w"""   ,				encoding="""utf-8"""      ) as f:
				f.write(config.to_json_string()      )


if __name__ == "__main__":
	_lowerCamelCase							=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--imagegpt_checkpoint_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Path to the TensorFlow checkpoint path.',
	)
	parser.add_argument(
	    '--model_size',
	    default=None,
	    type=str,
	    required=True,
	    help='Size of the model (can be either \'small\', \'medium\' or \'large\').',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	_lowerCamelCase							=     parser.parse_args()
	convert_imagegpt_checkpoint_to_pytorch(
	    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
	)