


'''simple docstring'''





import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
UpperCAmelCase__  			=				data_utils.TransfoXLTokenizer
UpperCAmelCase__  			=				data_utils.TransfoXLCorpus
UpperCAmelCase__  			=				data_utils
UpperCAmelCase__  			=				data_utils






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Optional[int],_SCREAMING_SNAKE_CASE					: Optional[Any],_SCREAMING_SNAKE_CASE					: Tuple,_SCREAMING_SNAKE_CASE					: List[Any] ):






			"""simple docstring"""




			if transfo_xl_dataset_file:
						# Convert a pre-processed corpus (see original TensorFlow repo)
						with open(_SCREAMING_SNAKE_CASE,'rb' ) as fp:
									__A=	pickle.load(_SCREAMING_SNAKE_CASE,encoding='latin1' )
						# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
						__A=	pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
						print(f"""Save vocabulary to {pytorch_vocab_dump_path}""" )
						__A=	corpus.vocab.__dict__
						torch.save(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )

						__A=	corpus.__dict__
						corpus_dict_no_vocab.pop('vocab',_SCREAMING_SNAKE_CASE )
						__A=	pytorch_dump_folder_path + '/' + CORPUS_NAME
						print(f"""Save dataset to {pytorch_dataset_dump_path}""" )
						torch.save(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )

			if tf_checkpoint_path:
						# Convert a pre-trained TensorFlow model
						__A=	os.path.abspath(_SCREAMING_SNAKE_CASE )
						__A=	os.path.abspath(_SCREAMING_SNAKE_CASE )

						print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.""" )
						# Initialise PyTorch model
						if transfo_xl_config_file == "":
									__A=	TransfoXLConfig()
						else:
									__A=	TransfoXLConfig.from_json_file(_SCREAMING_SNAKE_CASE )
						print(f"""Building PyTorch model from configuration: {config}""" )
						__A=	TransfoXLLMHeadModel(_SCREAMING_SNAKE_CASE )

						__A=	load_tf_weights_in_transfo_xl(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
						# Save pytorch-model
						__A=	os.path.join(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
						__A=	os.path.join(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
						print(f"""Save PyTorch model to {os.path.abspath(_SCREAMING_SNAKE_CASE )}""" )
						torch.save(model.state_dict(),_SCREAMING_SNAKE_CASE )
						print(f"""Save configuration file to {os.path.abspath(_SCREAMING_SNAKE_CASE )}""" )
						with open(_SCREAMING_SNAKE_CASE,'w',encoding='utf-8' ) as f:
									f.write(config.to_json_string() )


if __name__ == "__main__":
					UpperCAmelCase__  			=				argparse.ArgumentParser()
					parser.add_argument(
					    '''--pytorch_dump_folder_path''',
					    default=None,
					    type=str,
					    required=True,
					    help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
					)
					parser.add_argument(
					    '''--tf_checkpoint_path''',
					    default='''''',
					    type=str,
					    help='''An optional path to a TensorFlow checkpoint path to be converted.''',
					)
					parser.add_argument(
					    '''--transfo_xl_config_file''',
					    default='''''',
					    type=str,
					    help=(
					        '''An optional config json file corresponding to the pre-trained BERT model. \n'''
					        '''This specifies the model architecture.'''
					    ),
					)
					parser.add_argument(
					    '''--transfo_xl_dataset_file''',
					    default='''''',
					    type=str,
					    help='''An optional dataset file to be converted in a vocabulary.''',
					)
					UpperCAmelCase__  			=				parser.parse_args()
					convert_transfo_xl_checkpoint_to_pytorch(
					    args.tf_checkpoint_path,
					    args.transfo_xl_config_file,
					    args.pytorch_dump_folder_path,
					    args.transfo_xl_dataset_file,
					)







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__  			=				logging.get_logger(__name__)

UpperCAmelCase__  			=				{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}
class 						a__					(					a_   ):





			'''simple docstring'''

			A						:				Union[str, Any]					      =			'''roberta'''
			def __init__(  self							:			Any			,    lowerCAmelCase_							:			int=50_265			,    lowerCAmelCase_							:			int=768			,    lowerCAmelCase_							:			Optional[Any]=12			,    lowerCAmelCase_							:			Tuple=12			,    lowerCAmelCase_							:			Optional[int]=3_072			,    lowerCAmelCase_							:			List[str]="gelu"			,    lowerCAmelCase_							:			List[str]=0.1			,    lowerCAmelCase_							:			Tuple=0.1			,    lowerCAmelCase_							:			List[Any]=512			,    lowerCAmelCase_							:			Union[str, Any]=2			,    lowerCAmelCase_							:			List[Any]=0.02			,    lowerCAmelCase_							:			Optional[int]=1E-12			,    lowerCAmelCase_							:			str=1			,    lowerCAmelCase_							:			Dict=0			,    lowerCAmelCase_							:			Any=2			,    lowerCAmelCase_							:			List[str]="absolute"			,    lowerCAmelCase_							:			Any=True			,    lowerCAmelCase_							:			Dict=None			,    **lowerCAmelCase_							:			Any			,    )      ->		int:
						super().__init__(pad_token_id=lowerCAmelCase_			,    bos_token_id=lowerCAmelCase_			,    eos_token_id=lowerCAmelCase_			,    **lowerCAmelCase_	)

						__A=	vocab_size
						__A=	hidden_size
						__A=	num_hidden_layers
						__A=	num_attention_heads
						__A=	hidden_act
						__A=	intermediate_size
						__A=	hidden_dropout_prob
						__A=	attention_probs_dropout_prob
						__A=	max_position_embeddings
						__A=	type_vocab_size
						__A=	initializer_range
						__A=	layer_norm_eps
						__A=	position_embedding_type
						__A=	use_cache
						__A=	classifier_dropout


class 						a__					(					a_   ):





			'''simple docstring'''

			@property
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									__A=	{0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
									__A=	{0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]	)



