"""simple docstring"""





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

							logger.info(F"""Loading Tokenizer ({args.tokenizer_name})"""	)
							if args.tokenizer_type == "bert":
														_SCREAMING_SNAKE_CASE		:    List[Any]      	=     BertTokenizer.from_pretrained(args.tokenizer_name	)
														_SCREAMING_SNAKE_CASE		:    int      	=     tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
														_SCREAMING_SNAKE_CASE		:    str      	=     tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
							elif args.tokenizer_type == "roberta":
														_SCREAMING_SNAKE_CASE		:    int      	=     RobertaTokenizer.from_pretrained(args.tokenizer_name	)
														_SCREAMING_SNAKE_CASE		:    List[str]      	=     tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
														_SCREAMING_SNAKE_CASE		:    int      	=     tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
							elif args.tokenizer_type == "gpt2":
														_SCREAMING_SNAKE_CASE		:    Any      	=     GPTaTokenizer.from_pretrained(args.tokenizer_name	)
														_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
														_SCREAMING_SNAKE_CASE		:    List[str]      	=     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:
														_SCREAMING_SNAKE_CASE		:    List[str]      	=     fp.readlines()

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

							_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     []
							_SCREAMING_SNAKE_CASE		:    List[Any]      	=     0
							_SCREAMING_SNAKE_CASE		:    Tuple      	=     10_000
							_SCREAMING_SNAKE_CASE		:    Dict      	=     time.time()
							for text in data:
														_SCREAMING_SNAKE_CASE		:    Union[str, Any]      	=     F"""{bos} {text.strip()} {sep}"""
														_SCREAMING_SNAKE_CASE		:    List[str]      	=     tokenizer.encode(lowerCAmelCase__  ,						add_special_tokens=lowerCAmelCase__	)
														rslt.append(lowerCAmelCase__	)

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

							_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
							_SCREAMING_SNAKE_CASE		:    Dict      	=     tokenizer.vocab_size
							if vocab_size < (1 << 16):
														_SCREAMING_SNAKE_CASE		:    Any      	=     [np.uintaa(lowerCAmelCase__	) for d in rslt]
							else:
														_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     [np.intaa(lowerCAmelCase__	) for d in rslt]
							random.shuffle(rslt_	)
							logger.info(F"""Dump to {dp_file}"""	)
							with open(lowerCAmelCase__  ,						"""wb"""	) as handle:
														pickle.dump(rslt_  ,						lowerCAmelCase__  ,						protocol=pickle.HIGHEST_PROTOCOL	)


if __name__ == "__main__":
			main()




"""simple docstring"""


import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
						import optax
						from flax.training.common_utils import onehot

						from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
						from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class  __lowerCamelCase  ( unittest.TestCase			):




				@slow
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''   )
							lowerCamelCase_					=  AutoTokenizer.from_pretrained('''google/mt5-small'''   )

							lowerCamelCase_					=  tokenizer('''Hello there'''							,						return_tensors='''np'''   ).input_ids
							lowerCamelCase_					=  tokenizer('''Hi I am'''							,						return_tensors='''np'''   ).input_ids

							lowerCamelCase_					=  shift_tokens_right(UpperCAmelCase							,						model.config.pad_token_id							,						model.config.decoder_start_token_id   )

							lowerCamelCase_					=  model(UpperCAmelCase							,						decoder_input_ids=UpperCAmelCase   ).logits
							lowerCamelCase_					=  optax.softmax_cross_entropy(UpperCAmelCase							,						onehot(UpperCAmelCase							,						logits.shape[-1]   )   ).mean()

							lowerCamelCase_					=  -(labels.shape[-1] * loss.item())

							lowerCamelCase_					=  -8_4.9_1_2_7
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1e-4   )



