


import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case             =     get_tests_dir('''fixtures/test_sentencepiece.model''')

if is_sentencepiece_available():
   import sentencepiece as sp


_snake_case             =     5
_snake_case             =     10
@require_sentencepiece
@require_tokenizers
class 	UpperCAmelCase_					(					UpperCamelCase		, unittest.TestCase    ):

   '''simple docstring'''




   __A :      List[str]     		=		SpeechaTextTokenizer
   __A :      Any     		=		False
   __A :      List[str]     		=		True






   def _snake_case    (		self ):






     """simple docstring"""





     super().setUp()

     lowerCamelCase :					Optional[int]    =				sp.SentencePieceProcessor()
     spm_model.Load(__A )
     lowerCamelCase :					List[Any]    =				["<s>", "<pad>", "</s>", "<unk>"]

     vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(__A ) )]
     lowerCamelCase :					Optional[Any]    =				dict(zip(__A						,				range(len(__A ) ) ) )

     lowerCamelCase :					str    =				Path(self.tmpdirname )
     save_json(__A						,				save_dir / VOCAB_FILES_NAMES["vocab_file"] )
     if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
       copyfile(__A						,				save_dir / VOCAB_FILES_NAMES["spm_file"] )

     lowerCamelCase :					Union[str, Any]    =				SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
     tokenizer.save_pretrained(self.tmpdirname )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				"<pad>"
     lowerCamelCase :					int    =				1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A )						,				__A )
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A )						,				__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					int    =				list(self.get_tokenizer().get_vocab().keys() )

     self.assertEqual(vocab_keys[0]						,				"<s>" )
     self.assertEqual(vocab_keys[1]						,				"<pad>" )
     self.assertEqual(vocab_keys[-1]						,				"j" )
     self.assertEqual(len(__A )						,				1001 )






   def _snake_case    (		self ):






     """simple docstring"""





     self.assertEqual(self.get_tokenizer().vocab_size						,				1001 )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				SpeechaTextTokenizer.from_pretrained(self.tmpdirname )

     lowerCamelCase :					int    =				tokenizer.tokenize("This is a test" )
     self.assertListEqual(__A						,				["▁This", "▁is", "▁a", "▁t", "est"] )

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(__A )						,				[289, 50, 14, 174, 386]						,				)

     lowerCamelCase :					Optional[Any]    =				tokenizer.tokenize("I was born in 92000, and this is falsé." )
     self.assertListEqual(
         __A						,				[SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."]						,				)
     lowerCamelCase :					int    =				tokenizer.convert_tokens_to_ids(__A )
     self.assertListEqual(__A						,				[12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8] )

     lowerCamelCase :					List[str]    =				tokenizer.convert_ids_to_tokens(__A )
     self.assertListEqual(
         __A						,				[SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", "."]						,				)






   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Any    =				{"input_ids": [[3791, 797, 31, 11, 64, 797, 31, 2429, 433, 12, 1176, 12, 20, 786, 915, 142, 2413, 240, 37, 3238, 797, 31, 11, 35, 93, 915, 142, 2413, 240, 37, 5540, 567, 1276, 93, 37, 610, 40, 62, 455, 657, 1042, 123, 780, 177, 37, 309, 241, 1298, 514, 20, 292, 2737, 114, 2469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3388, 511, 459, 4, 3555, 40, 321, 302, 705, 4, 3388, 511, 583, 326, 5, 5, 5, 62, 3310, 560, 177, 2680, 217, 1508, 32, 31, 853, 418, 64, 583, 511, 1605, 62, 35, 93, 560, 177, 2680, 217, 1508, 1521, 64, 583, 511, 519, 62, 20, 1515, 764, 20, 149, 261, 5625, 7972, 20, 5540, 567, 1276, 93, 3925, 1675, 11, 15, 802, 7972, 576, 217, 1508, 11, 35, 93, 1253, 2441, 15, 289, 652, 31, 416, 321, 3842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2681, 1153, 3434, 20, 5540, 37, 567, 126, 1253, 2441, 3376, 449, 210, 431, 1563, 177, 767, 5540, 11, 1203, 472, 11, 2953, 685, 285, 364, 706, 1153, 20, 6799, 20, 2869, 20, 4464, 126, 40, 2429, 20, 1040, 866, 2664, 418, 20, 318, 20, 1726, 186, 20, 265, 522, 35, 93, 2191, 4634, 20, 1040, 12, 6799, 15, 228, 2356, 142, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2575, 2666, 684, 1582, 1176, 12, 627, 149, 619, 20, 4902, 563, 11, 20, 149, 261, 3420, 2356, 174, 142, 4714, 131, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=__A						,				model_name="facebook/s2t-small-mustc-en-de-st"						,				revision="a14f04cf0776c02f62a8cb800cf7909e15ea23ad"						,				)



@require_sentencepiece
class 	UpperCAmelCase_					(					unittest.TestCase    ):

   '''simple docstring'''




   __A :      List[str]     		=		"valhalla/s2t_mustc_multilinguial_medium"

   __A :      Any     		=		"C'est trop cool"
   __A :      Union[str, Any]     		=		"Esto es genial"






   @classmethod
   def _snake_case    (		cls ):






     """simple docstring"""





     lowerCamelCase :					SpeechaTextTokenizer    =				SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name )
     return cls






   def _snake_case    (		self ):






     """simple docstring"""





     self.assertEqual(self.tokenizer.lang_code_to_id["pt"]						,				4 )
     self.assertEqual(self.tokenizer.lang_code_to_id["ru"]						,				6 )
     self.assertEqual(self.tokenizer.lang_code_to_id["it"]						,				9 )
     self.assertEqual(self.tokenizer.lang_code_to_id["de"]						,				11 )






   def _snake_case    (		self ):






     """simple docstring"""





     self.assertEqual(self.tokenizer.vocab_size						,				1_0000 )






   def _snake_case    (		self ):






     """simple docstring"""





     self.assertIn(__A						,				self.tokenizer.all_special_ids )
     lowerCamelCase :					Any    =				[ES_CODE, 4, 1601, 47, 7647, 2]
     lowerCamelCase :					List[Any]    =				self.tokenizer.decode(__A						,				skip_special_tokens=__A )
     lowerCamelCase :					Union[str, Any]    =				self.tokenizer.decode(generated_ids[1:]						,				skip_special_tokens=__A )
     self.assertEqual(__A						,				__A )
     self.assertNotIn(self.tokenizer.eos_token						,				__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				"fr"
     lowerCamelCase :					Tuple    =				self.tokenizer(self.french_text ).input_ids
     self.assertEqual(encoded[0]						,				__A )
     self.assertEqual(encoded[-1]						,				self.tokenizer.eos_token_id )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				"fr"
     self.assertListEqual(self.tokenizer.prefix_tokens						,				[FR_CODE] )

     lowerCamelCase :					Tuple    =				"es"
     self.assertListEqual(self.tokenizer.prefix_tokens						,				[ES_CODE] )







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if "model" in orig_key:
    lowerCamelCase :					Dict    =				orig_key.replace("model."      ,						""	)
  if "norm1" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("norm1"      ,						"attention.output.LayerNorm"	)
  if "norm2" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("norm2"      ,						"output.LayerNorm"	)
  if "norm" in orig_key:
    lowerCamelCase :					Optional[Any]    =				orig_key.replace("norm"      ,						"LayerNorm"	)
  if "transformer" in orig_key:
    lowerCamelCase :					int    =				orig_key.split("."	)[0].split("_"	)[-1]
    lowerCamelCase :					Dict    =				orig_key.replace(f"""transformer_{layer_num}"""      ,						f"""encoder.layer.{layer_num}"""	)
  if "mha.attn" in orig_key:
    lowerCamelCase :					List[str]    =				orig_key.replace("mha.attn"      ,						"attention.self"	)
  if "mha" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("mha"      ,						"attention"	)
  if "W_q" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("W_q"      ,						"self.query"	)
  if "W_k" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("W_k"      ,						"self.key"	)
  if "W_v" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("W_v"      ,						"self.value"	)
  if "ff1" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("ff1"      ,						"intermediate.dense"	)
  if "ff2" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("ff2"      ,						"output.dense"	)
  if "ff" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("ff"      ,						"output.dense"	)
  if "mlm_class" in orig_key:
    lowerCamelCase :					Dict    =				orig_key.replace("mlm.mlm_class"      ,						"cls.predictions.decoder"	)
  if "mlm" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("mlm"      ,						"cls.predictions.transform"	)
  if "cls" not in orig_key:
    lowerCamelCase :					int    =				"yoso." + orig_key

  return orig_key






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for key in orig_state_dict.copy().keys():
    lowerCamelCase :					List[str]    =				orig_state_dict.pop(SCREAMING_SNAKE_CASE_	)

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      lowerCamelCase :					Dict    =				val

  lowerCamelCase :					Dict    =				orig_state_dict["cls.predictions.decoder.bias"]
  lowerCamelCase :					Dict    =				torch.arange(SCREAMING_SNAKE_CASE_	).expand((1, -1)	) + 2

  return orig_state_dict






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				torch.load(SCREAMING_SNAKE_CASE_      ,						map_location="cpu"	)["model_state_dict"]
  lowerCamelCase :					List[str]    =				YosoConfig.from_json_file(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Any    =				YosoForMaskedLM(SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					List[Any]    =				convert_checkpoint_helper(config.max_position_embeddings      ,						SCREAMING_SNAKE_CASE_	)

  print(model.load_state_dict(SCREAMING_SNAKE_CASE_	)	)
  model.eval()
  model.save_pretrained(SCREAMING_SNAKE_CASE_	)

  print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)


if __name__ == "__main__":
   _snake_case             =     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The json file for YOSO model config.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   _snake_case             =     parser.parse_args()
   convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



