

import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowerCAmelCase__	   = logging.getLogger(__name__)



def 					lowerCAmelCase__							(	)			->      Union[str, Any]:




				'''simple docstring'''





				A__        =			argparse.ArgumentParser(
				    description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."      )
				parser.add_argument("--file_path"	,       type=SCREAMING_SNAKE_CASE_	,       default="data/dump.txt"	,       help="The path to the data."      )
				parser.add_argument("--tokenizer_type"	,       type=SCREAMING_SNAKE_CASE_	,       default="bert"	,       choices=["bert", "roberta", "gpt2"]      )
				parser.add_argument("--tokenizer_name"	,       type=SCREAMING_SNAKE_CASE_	,       default="bert-base-uncased"	,       help="The tokenizer to use."      )
				parser.add_argument("--dump_file"	,       type=SCREAMING_SNAKE_CASE_	,       default="data/dump"	,       help="The dump file prefix."      )
				A__        =			parser.parse_args()

				logger.info(F'Loading Tokenizer ({args.tokenizer_name})'      )
				if args.tokenizer_type == "bert":
								A__        =			BertTokenizer.from_pretrained(args.tokenizer_name      )
								A__        =			tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
								A__        =			tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
				elif args.tokenizer_type == "roberta":
								A__        =			RobertaTokenizer.from_pretrained(args.tokenizer_name      )
								A__        =			tokenizer.special_tokens_map["cls_token"]  # `<s>`
								A__        =			tokenizer.special_tokens_map["sep_token"]  # `</s>`
				elif args.tokenizer_type == "gpt2":
								A__        =			GPTaTokenizer.from_pretrained(args.tokenizer_name      )
								A__        =			tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
								A__        =			tokenizer.special_tokens_map["eos_token"]  # `<|endoftext|>`

				logger.info(F'Loading text from {args.file_path}'      )
				with open(args.file_path	,       "r"	,       encoding="utf8"      ) as fp:
								A__        =			fp.readlines()

				logger.info("Start encoding"      )
				logger.info(F'{len(SCREAMING_SNAKE_CASE_      )} examples to process.'      )

				A__        =			[]
				A__        =			0
				A__        =			1_0_0_0_0
				A__        =			time.time()
				for text in data:
								A__        =			F'{bos} {text.strip()} {sep}'
								A__        =			tokenizer.encode(SCREAMING_SNAKE_CASE_	,       add_special_tokens=SCREAMING_SNAKE_CASE_      )
								rslt.append(SCREAMING_SNAKE_CASE_      )

								iter += 1
								if iter % interval == 0:
												A__        =			time.time()
												logger.info(F'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'      )
												A__        =			time.time()
				logger.info("Finished binarization"      )
				logger.info(F'{len(SCREAMING_SNAKE_CASE_      )} examples processed.'      )

				A__        =			F'{args.dump_file}.{args.tokenizer_name}.pickle'
				A__        =			tokenizer.vocab_size
				if vocab_size < (1 << 1_6):
								A__        =			[np.uintaa(SCREAMING_SNAKE_CASE_      ) for d in rslt]
				else:
								A__        =			[np.intaa(SCREAMING_SNAKE_CASE_      ) for d in rslt]
				random.shuffle(rslt_      )
				logger.info(F'Dump to {dp_file}'      )
				with open(SCREAMING_SNAKE_CASE_	,       "wb"      ) as handle:
								pickle.dump(rslt_	,       SCREAMING_SNAKE_CASE_	,       protocol=pickle.HIGHEST_PROTOCOL      )


if __name__ == "__main__":
	main()




from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Namespace      )			->      Tuple:




				'''simple docstring'''





				return ConvertCommand(
				    args.model_type	,       args.tf_checkpoint	,       args.pytorch_dump_output	,       args.config	,       args.finetuning_task_name      )


lowerCAmelCase__	   = """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""





class 					a__						(					snake_case			):
			"""simple docstring"""







			@staticmethod
			def      UpperCamelCase		(    lowercase       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			parser.add_parser(
							    "convert"      ,      help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."      ,      )
							train_parser.add_argument("--model_type"      ,      type=lowercase      ,      required=lowercase      ,      help="Model's type."       )
							train_parser.add_argument(
							    "--tf_checkpoint"      ,      type=lowercase      ,      required=lowercase      ,      help="TensorFlow checkpoint path or folder."       )
							train_parser.add_argument(
							    "--pytorch_dump_output"      ,      type=lowercase      ,      required=lowercase      ,      help="Path to the PyTorch saved model output."       )
							train_parser.add_argument("--config"      ,      type=lowercase      ,      default=""      ,      help="Configuration file path or folder."       )
							train_parser.add_argument(
							    "--finetuning_task_name"      ,      type=lowercase      ,      default=lowercase      ,      help="Optional fine-tuning task name if the TF model was a finetuned model."      ,      )
							train_parser.set_defaults(func=lowercase       )


			def __init__(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      *lowercase      ,      )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			logging.get_logger("transformers-cli/converting"       )

							self._logger.info(F'Loading model {model_type}'       )
							A__        =			model_type
							A__        =			tf_checkpoint
							A__        =			pytorch_dump_output
							A__        =			config
							A__        =			finetuning_task_name


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							if self._model_type == "albert":
											try:
															from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
															    convert_tf_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "bert":
											try:
															from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
															    convert_tf_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "funnel":
											try:
															from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
															    convert_tf_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "t5":
											try:
															from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
											except ImportError:
															raise ImportError(lowercase       )

											convert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "gpt":
											from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
											    convert_openai_checkpoint_to_pytorch,
											)

											convert_openai_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "transfo_xl":
											try:
															from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
															    convert_transfo_xl_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											if "ckpt" in self._tf_checkpoint.lower():
															A__        =			self._tf_checkpoint
															A__        =			""
											else:
															A__        =			self._tf_checkpoint
															A__        =			""
											convert_transfo_xl_checkpoint_to_pytorch(
											    lowercase      ,      self._config      ,      self._pytorch_dump_output      ,      lowercase       )
							elif self._model_type == "gpt2":
											try:
															from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
															    convert_gpta_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							elif self._model_type == "xlnet":
											try:
															from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
															    convert_xlnet_checkpoint_to_pytorch,
															)
											except ImportError:
															raise ImportError(lowercase       )

											convert_xlnet_checkpoint_to_pytorch(
											    self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output      ,      self._finetuning_task_name       )
							elif self._model_type == "xlm":
											from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
											    convert_xlm_checkpoint_to_pytorch,
											)

											convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._pytorch_dump_output       )
							elif self._model_type == "lxmert":
											from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
											    convert_lxmert_checkpoint_to_pytorch,
											)

											convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._pytorch_dump_output       )
							elif self._model_type == "rembert":
											from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
											    convert_rembert_tf_checkpoint_to_pytorch,
											)

											convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint      ,      self._config      ,      self._pytorch_dump_output       )
							else:
											raise ValueError(
											    "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"       )

