






import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
		import torch





@is_pipeline_test
class 	snake_case__ (							unittest.TestCase				):
	lowercase__						:					Optional[Any]											=   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
	lowercase__						:					int											=   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				List[Any]:
							__magic_name__      :       Dict       						= TextaTextGenerationPipeline(model=lowerCAmelCase__      ,       tokenizer=lowerCAmelCase__			)
							return generator, ["Something to write", "Something else"]
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				List[Any]:
							__magic_name__      :       List[str]       						= generator("""Something there"""			)
							self.assertEqual(lowerCAmelCase__      ,       [{"""generated_text""": ANY(lowerCAmelCase__			)}]			)
							# These are encoder decoder, they don't just append to incoming string
							self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""			)			)

							__magic_name__      :       str       						= generator(["""This is great !""", """Something else"""]      ,       num_return_sequences=2      ,       do_sample=lowerCAmelCase__			)
							self.assertEqual(
							    lowerCAmelCase__      ,       [
							        [{"""generated_text""": ANY(lowerCAmelCase__			)}, {"""generated_text""": ANY(lowerCAmelCase__			)}],
							        [{"""generated_text""": ANY(lowerCAmelCase__			)}, {"""generated_text""": ANY(lowerCAmelCase__			)}],
							    ]      ,       )

							__magic_name__      :       Any       						= generator(
							    ["""This is great !""", """Something else"""]      ,       num_return_sequences=2      ,       batch_size=2      ,       do_sample=lowerCAmelCase__			)
							self.assertEqual(
							    lowerCAmelCase__      ,       [
							        [{"""generated_text""": ANY(lowerCAmelCase__			)}, {"""generated_text""": ANY(lowerCAmelCase__			)}],
							        [{"""generated_text""": ANY(lowerCAmelCase__			)}, {"""generated_text""": ANY(lowerCAmelCase__			)}],
							    ]      ,       )

							with self.assertRaises(lowerCAmelCase__			):
													generator(4			)
	@require_torch
	def       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__      :       str       						= pipeline("""text2text-generation"""      ,       model="""patrickvonplaten/t5-tiny-random"""      ,       framework="""pt"""			)
							# do_sample=False necessary for reproducibility
							__magic_name__      :       int       						= generator("""Something there"""      ,       do_sample=lowerCAmelCase__			)
							self.assertEqual(lowerCAmelCase__      ,       [{"""generated_text""": """"""}]			)

							__magic_name__      :       List[Any]       						= 3
							__magic_name__      :       Any       						= generator(
							    """Something there"""      ,       num_return_sequences=lowerCAmelCase__      ,       num_beams=lowerCAmelCase__      ,       )
							__magic_name__      :       Union[str, Any]       						= [
							    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
							    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
							    {"""generated_text""": """"""},
							]
							self.assertEqual(lowerCAmelCase__      ,       lowerCAmelCase__			)

							__magic_name__      :       Tuple       						= generator("""This is a test"""      ,       do_sample=lowerCAmelCase__      ,       num_return_sequences=2      ,       return_tensors=lowerCAmelCase__			)
							self.assertEqual(
							    lowerCAmelCase__      ,       [
							        {"""generated_token_ids""": ANY(torch.Tensor			)},
							        {"""generated_token_ids""": ANY(torch.Tensor			)},
							    ]      ,       )
							__magic_name__      :       Optional[int]       						= generator.model.config.eos_token_id
							__magic_name__      :       str       						= """<pad>"""
							__magic_name__      :       List[Any]       						= generator(
							    ["""This is a test""", """This is a second test"""]      ,       do_sample=lowerCAmelCase__      ,       num_return_sequences=2      ,       batch_size=2      ,       return_tensors=lowerCAmelCase__      ,       )
							self.assertEqual(
							    lowerCAmelCase__      ,       [
							        [
							            {"""generated_token_ids""": ANY(torch.Tensor			)},
							            {"""generated_token_ids""": ANY(torch.Tensor			)},
							        ],
							        [
							            {"""generated_token_ids""": ANY(torch.Tensor			)},
							            {"""generated_token_ids""": ANY(torch.Tensor			)},
							        ],
							    ]      ,       )







	@require_tf
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       Optional[Any]       						= pipeline("""text2text-generation"""      ,       model="""patrickvonplaten/t5-tiny-random"""      ,       framework="""tf"""			)
							# do_sample=False necessary for reproducibility
							__magic_name__      :       List[Any]       						= generator("""Something there"""      ,       do_sample=lowerCAmelCase__			)
							self.assertEqual(lowerCAmelCase__      ,       [{"""generated_text""": """"""}]			)








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
)
__magic_name__:					str							=     logging.getLogger(__name__)

def    UpperCamelCase						(	):






						"""simple docstring"""







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

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

						logger.info("""Start encoding"""			)
						logger.info(f'{len(_A			)} examples to process.'			)

						__magic_name__      :       List[Any]       						= []
						__magic_name__      :       str       						= 0
						__magic_name__      :       str       						= 10000
						__magic_name__      :       Dict       						= time.time()
						for text in data:
												__magic_name__      :       Tuple       						= f'{bos} {text.strip()} {sep}'
												__magic_name__      :       Optional[int]       						= tokenizer.encode(_A,   add_special_tokens=_A			)
												rslt.append(_A			)

												iter += 1
												if iter % interval == 0:
																		__magic_name__      :       Union[str, Any]       						= time.time()
																		logger.info(f'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'			)
																		__magic_name__      :       Any       						= time.time()
						logger.info("""Finished binarization"""			)
						logger.info(f'{len(_A			)} examples processed.'			)

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


if __name__ == "__main__":
		main()
