

'''simple docstring'''




import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
_a			:	int								=  "stas/tiny-wmt19-en-ru"



class     lowercase_		(						SCREAMING_SNAKE_CASE__	,    unittest.TestCase	):

  '''simple docstring'''





  __lowerCAmelCase   :	Optional[int]      					=				FSMTTokenizer
  __lowerCAmelCase   :	Dict      					=				False
  def   snake_case_     (       self       )							->			int:


    """simple docstring"""
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    UpperCAmelCase  							=		[
        'l',
        'o',
        'w',
        'e',
        'r',
        's',
        't',
        'i',
        'd',
        'n',
        'w</w>',
        'r</w>',
        't</w>',
        'lo',
        'low',
        'er</w>',
        'low</w>',
        'lowest</w>',
        'newer</w>',
        'wider</w>',
        '<unk>',
    ]
    UpperCAmelCase  							=		dict(zip(a_				,	range(len(a_       )       )       )       )
    UpperCAmelCase  							=		['l o 123', 'lo w 1456', 'e r</w> 1789', '']

    UpperCAmelCase  							=		['en', 'ru']
    UpperCAmelCase  							=		{
        'langs': self.langs,
        'src_vocab_size': 1_0,
        'tgt_vocab_size': 2_0,
    }

    UpperCAmelCase  							=		os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['src_vocab_file']       )
    UpperCAmelCase  							=		os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['tgt_vocab_file']       )
    UpperCAmelCase  							=		os.path.join(self.tmpdirname				,	'tokenizer_config.json'       )
    UpperCAmelCase  							=		os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['merges_file']       )
    with open(self.src_vocab_file				,	'w'       ) as fp:
      fp.write(json.dumps(a_       )       )
    with open(self.tgt_vocab_file				,	'w'       ) as fp:
      fp.write(json.dumps(a_       )       )
    with open(self.merges_file				,	'w'       ) as fp:
      fp.write('\n'.join(a_       )       )
    with open(a_				,	'w'       ) as fp:
      fp.write(json.dumps(a_       )       )
  @cached_property
  def   snake_case_     (       self       )							->			Any:


    """simple docstring"""
    return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'       )
  @cached_property
  def   snake_case_     (       self       )							->			List[str]:


    """simple docstring"""
    return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'       )
  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		FSMTTokenizer.from_pretrained(a_       )
    self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]				,	['en', 'ru']       )
    self.assertEqual(tokenizer.src_vocab_size				,	2_1       )
    self.assertEqual(tokenizer.tgt_vocab_size				,	2_1       )
  def   snake_case_     (       self       )							->			Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase  							=		FSMTTokenizer(self.langs				,	self.src_vocab_file				,	self.tgt_vocab_file				,	self.merges_file       )

    UpperCAmelCase  							=		'lower'
    UpperCAmelCase  							=		['low', 'er</w>']
    UpperCAmelCase  							=		tokenizer.tokenize(a_       )
    self.assertListEqual(a_				,	a_       )

    UpperCAmelCase  							=		tokens + ['<unk>']
    UpperCAmelCase  							=		[1_4, 1_5, 2_0]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(a_       )				,	a_       )
  @slow
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		self.tokenizer_ru_en

    UpperCAmelCase  							=		tokenizer.encode('sequence builders'				,	add_special_tokens=a_       )
    UpperCAmelCase  							=		tokenizer.encode('multi-sequence build'				,	add_special_tokens=a_       )

    UpperCAmelCase  							=		tokenizer.build_inputs_with_special_tokens(a_       )
    UpperCAmelCase  							=		tokenizer.build_inputs_with_special_tokens(a_				,	a_       )

    assert encoded_sentence == text + [2]
    assert encoded_pair == text + [2] + text_a + [2]
  @slow
  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		self.tokenizer_en_ru
    UpperCAmelCase  							=		self.tokenizer_ru_en

    UpperCAmelCase  							=		[
        [
            'Here\'s a little song I wrote. Don\'t worry, be happy.',
            [2_4_7_0, 3_9, 1_1, 2_3_4_9, 7_2_2_2, 7_0, 5_9_7_9, 7, 8_4_5_0, 1_0_5_0, 1_3_1_6_0, 5, 2_6, 6_4_4_5, 7, 2],
        ],
        ['This is it. No more. I\'m done!', [1_3_2, 2_1, 3_7, 7, 1_4_3_4, 8_6, 7, 7_0, 6_4_7_6, 1_3_0_5, 4_2_7, 2]],
    ]

    # if data needs to be recreated or added, run:
    # import torch
    # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
    # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

    for src_text, tgt_input_ids in targets:
      UpperCAmelCase  							=		tokenizer_enc.encode(a_				,	return_tensors=a_       )
      self.assertListEqual(a_				,	a_       )

      # and decode backward, using the reversed languages model
      UpperCAmelCase  							=		tokenizer_dec.decode(a_				,	skip_special_tokens=a_       )
      self.assertEqual(a_				,	a_       )
  @slow
  def   snake_case_     (       self       )							->			Dict:


    """simple docstring"""
    UpperCAmelCase  							=		FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'				,	do_lower_case=a_       )
    UpperCAmelCase  							=		tokenizer.tokenize('USA is United States of America'       )
    UpperCAmelCase  							=		['us', 'a</w>', 'is</w>', 'un', 'i', 'ted</w>', 'st', 'ates</w>', 'of</w>', 'am', 'er', 'ica</w>']
    self.assertListEqual(a_				,	a_       )
  @unittest.skip('FSMTConfig.__init__  requires non-optional args'       )
  def   snake_case_     (       self       )							->			Dict:


    """simple docstring"""
    pass





  @unittest.skip('FSMTConfig.__init__  requires non-optional args'       )
  def   snake_case_     (       self       )							->			Optional[Any]:


    """simple docstring"""
    pass





'''simple docstring'''




import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
_a			:	Union[str, Any]								=  logging.getLogger(__name__)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple  ):
  np.random.seed(args.seed  )
  torch.manual_seed(args.seed  )
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed  )


def  lowerCamelCase__						(  ):
  UpperCAmelCase  							=		argparse.ArgumentParser()
  parser.add_argument(
      '--model_name_or_path'    ,				default=SCREAMING_SNAKE_CASE    ,				type=SCREAMING_SNAKE_CASE    ,				required=SCREAMING_SNAKE_CASE    ,				)
  parser.add_argument('--prompt'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''  )
  parser.add_argument('--length'    ,				type=SCREAMING_SNAKE_CASE    ,				default=20  )
  parser.add_argument('--stop_token'    ,				type=SCREAMING_SNAKE_CASE    ,				default=SCREAMING_SNAKE_CASE    ,				help='Token at which text generation is stopped'  )
  parser.add_argument(
      '--temperature'    ,				type=SCREAMING_SNAKE_CASE    ,				default=1.0    ,				help='temperature of 1.0 has no effect, lower tend toward greedy sampling'    ,				)
  parser.add_argument(
      '--repetition_penalty'    ,				type=SCREAMING_SNAKE_CASE    ,				default=1.0    ,				help='primarily useful for CTRL model; in that case, use 1.2'  )
  parser.add_argument('--k'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0  )
  parser.add_argument('--penalty_alpha'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0.0  )
  parser.add_argument('--p'    ,				type=SCREAMING_SNAKE_CASE    ,				default=0.9  )

  parser.add_argument('--prefix'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Text added prior to input.'  )
  parser.add_argument('--padding_text'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Deprecated, the use of `--prefix` is preferred.'  )
  parser.add_argument('--xlm_language'    ,				type=SCREAMING_SNAKE_CASE    ,				default=''    ,				help='Optional language when used with the XLM model.'  )

  parser.add_argument('--seed'    ,				type=SCREAMING_SNAKE_CASE    ,				default=42    ,				help='random seed for initialization'  )
  parser.add_argument('--no_cuda'    ,				action='store_true'    ,				help='Avoid using CUDA when available'  )
  parser.add_argument(
      '--fp16'    ,				action='store_true'    ,				help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'    ,				)
  UpperCAmelCase  							=		parser.parse_args()

  UpperCAmelCase  							=		torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'  )
  UpperCAmelCase  							=		0 if args.no_cuda else torch.cuda.device_count()

  logger.warning(f'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'''  )

  set_seed(SCREAMING_SNAKE_CASE  )

  # Initialize the model and tokenizer
  UpperCAmelCase  							=		AutoTokenizer.from_pretrained(args.model_name_or_path  )
  UpperCAmelCase  							=		AutoModelForCausalLM.from_pretrained(args.model_name_or_path  )

  # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
  # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
  model.to(args.device  )

  if args.fpaa:
    model.half()

  logger.info(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		args.prompt if args.prompt else input('Model prompt >>> '  )

  UpperCAmelCase  							=		tokenizer(SCREAMING_SNAKE_CASE    ,				return_tensors='pt'    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		{key: value.to(args.device  ) for key, value in inputs.items()}

  UpperCAmelCase  							=		model.generate(
      **SCREAMING_SNAKE_CASE    ,				max_length=args.length + len(inputs['input_ids'][0]  )    ,				penalty_alpha=args.penalty_alpha    ,				top_k=args.k    ,				)

  UpperCAmelCase  							=		[]
  for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE  ):
    print(f'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''  )
    UpperCAmelCase  							=		generated_sequence.tolist()

    # Decode text
    UpperCAmelCase  							=		tokenizer.decode(SCREAMING_SNAKE_CASE    ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )

    # Remove all text after the stop token
    UpperCAmelCase  							=		text[: text.find(args.stop_token  ) if args.stop_token else None]

    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
    UpperCAmelCase  							=		(
        prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]    ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE  )  ) :]
    )

    generated_sequences.append(SCREAMING_SNAKE_CASE  )
    print(SCREAMING_SNAKE_CASE  )

  return generated_sequences


if __name__ == "__main__":
  main()


