


"""simple docstring"""






import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__snake_case				=				logging.get_logger(__name__)

__snake_case				=				"""▁"""

__snake_case				=				{
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__snake_case				=				{
    """vocab_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json""",
    },
    """spm_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_config_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json""",
    },
}

__snake_case				=				{
    """facebook/m2m100_418M""": 1024,
}

# fmt: off
__snake_case				=				{
    """m2m100""": ["""af""", """am""", """ar""", """ast""", """az""", """ba""", """be""", """bg""", """bn""", """br""", """bs""", """ca""", """ceb""", """cs""", """cy""", """da""", """de""", """el""", """en""", """es""", """et""", """fa""", """ff""", """fi""", """fr""", """fy""", """ga""", """gd""", """gl""", """gu""", """ha""", """he""", """hi""", """hr""", """ht""", """hu""", """hy""", """id""", """ig""", """ilo""", """is""", """it""", """ja""", """jv""", """ka""", """kk""", """km""", """kn""", """ko""", """lb""", """lg""", """ln""", """lo""", """lt""", """lv""", """mg""", """mk""", """ml""", """mn""", """mr""", """ms""", """my""", """ne""", """nl""", """no""", """ns""", """oc""", """or""", """pa""", """pl""", """ps""", """pt""", """ro""", """ru""", """sd""", """si""", """sk""", """sl""", """so""", """sq""", """sr""", """ss""", """su""", """sv""", """sw""", """ta""", """th""", """tl""", """tn""", """tr""", """uk""", """ur""", """uz""", """vi""", """wo""", """xh""", """yi""", """yo""", """zh""", """zu"""],
    """wmt21""": ["""en""", """ha""", """is""", """ja""", """cs""", """ru""", """zh""", """de"""]
}


class 			__lowerCamelCase     (    UpperCamelCase__ ):

     '''simple docstring'''
     A_				:		Union[str, Any]			=						VOCAB_FILES_NAMES
     A_				:		Optional[Any]			=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_				:		int			=						PRETRAINED_VOCAB_FILES_MAP
     A_				:		Dict			=						["""input_ids""", """attention_mask"""]

     A_				:		List[int]			=						[]
     A_				:		List[int]			=						[]


     def __init__(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase=None			, __UpperCAmelCase=None			, __UpperCAmelCase="<s>"			, __UpperCAmelCase="</s>"			, __UpperCAmelCase="</s>"			, __UpperCAmelCase="<pad>"			, __UpperCAmelCase="<unk>"			, __UpperCAmelCase="m2m100"			, __UpperCAmelCase = None			, __UpperCAmelCase=8			, **__UpperCAmelCase			, )				->							None:
          _a					= {} if sp_model_kwargs is None else sp_model_kwargs

          _a					= language_codes
          _a					= FAIRSEQ_LANGUAGE_CODES[language_codes]
          _a					= {lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

          _a					= kwargs.get('''additional_special_tokens'''			, []							)
          kwargs["additional_special_tokens"] += [
              self.get_lang_token(lowerCAmelCase__							)
              for lang_code in fairseq_language_code
              if self.get_lang_token(lowerCAmelCase__							) not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              src_lang=lowerCAmelCase__			, tgt_lang=lowerCAmelCase__			, bos_token=lowerCAmelCase__			, eos_token=lowerCAmelCase__			, sep_token=lowerCAmelCase__			, unk_token=lowerCAmelCase__			, pad_token=lowerCAmelCase__			, language_codes=lowerCAmelCase__			, sp_model_kwargs=self.sp_model_kwargs			, num_madeup_words=lowerCAmelCase__			, **lowerCAmelCase__			, )

          _a					= vocab_file
          _a					= load_json(lowerCAmelCase__							)
          _a					= {v: k for k, v in self.encoder.items()}
          _a					= spm_file
          _a					= load_spm(lowerCAmelCase__			, self.sp_model_kwargs							)

          _a					= len(self.encoder							)

          _a					= {
              self.get_lang_token(lowerCAmelCase__							): self.encoder_size + i for i, lang_code in enumerate(lowerCAmelCase__							)
          }
          _a					= {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowerCAmelCase__							)}
          _a					= {v: k for k, v in self.lang_token_to_id.items()}

          _a					= src_lang if src_lang is not None else "en"
          _a					= tgt_lang
          _a					= self.get_lang_id(self._src_lang							)
          self.set_src_lang_special_tokens(self._src_lang							)

          _a					= num_madeup_words


     @property
     def 							_UpperCAmelCase					(					self							)				->							int:
          return len(self.encoder							) + len(self.lang_token_to_id							)


     @property
     def 							_UpperCAmelCase					(					self							)				->							str:
          return self._src_lang


     @src_lang.setter
     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							None:
          _a					= new_src_lang
          self.set_src_lang_special_tokens(self._src_lang							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							List[str]:
          return self.sp_model.encode(lowerCAmelCase__			, out_type=lowerCAmelCase__							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							Tuple:




          if token in self.lang_token_to_id:
               return self.lang_token_to_id[token]
          return self.encoder.get(lowerCAmelCase__			, self.encoder[self.unk_token]							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							str:




          if index in self.id_to_lang_token:
               return self.id_to_lang_token[index]
          return self.decoder.get(lowerCAmelCase__			, self.unk_token							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							Any:
          _a					= []
          _a					= ""
          for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    out_string += self.sp_model.decode(lowerCAmelCase__							) + token
                    _a					= []
               else:
                    current_sub_tokens.append(lowerCAmelCase__							)
          out_string += self.sp_model.decode(lowerCAmelCase__							)
          return out_string.strip()


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase = None			, __UpperCAmelCase = False							)				->							List[int]:




          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCAmelCase__			, token_ids_a=lowerCAmelCase__			, already_has_special_tokens=lowerCAmelCase__							)

          _a					= [1] * len(self.prefix_tokens							)
          _a					= [1] * len(self.suffix_tokens							)
          if token_ids_a is None:
               return prefix_ones + ([0] * len(lowerCAmelCase__							)) + suffix_ones
          return prefix_ones + ([0] * len(lowerCAmelCase__							)) + ([0] * len(lowerCAmelCase__							)) + suffix_ones


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase = None							)				->							List[int]:




          if token_ids_a is None:
               return self.prefix_tokens + token_ids_a + self.suffix_tokens
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


     def 							_UpperCAmelCase					(					self							)				->							Dict:
          _a					= {self.convert_ids_to_tokens(lowerCAmelCase__							): i for i in range(self.vocab_size							)}
          vocab.update(self.added_tokens_encoder							)
          return vocab


     def __getstate__(					self							)				->							Dict:
          _a					= self.__dict__.copy()
          _a					= None
          return state


     def __setstate__(					self			, __UpperCAmelCase							)				->							None:
          _a					= d

          # for backward compatibility
          if not hasattr(self			, '''sp_model_kwargs'''							):
               _a					= {}

          _a					= load_spm(self.spm_file			, self.sp_model_kwargs							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase = None							)				->							Tuple[str]:
          _a					= Path(lowerCAmelCase__							)
          if not save_dir.is_dir():
               raise OSError(F'{save_directory} should be a directory'							)
          _a					= save_dir / (
              (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
          )
          _a					= save_dir / (
              (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
          )

          save_json(self.encoder			, lowerCAmelCase__							)

          if os.path.abspath(self.spm_file							) != os.path.abspath(lowerCAmelCase__							) and os.path.isfile(self.spm_file							):
               copyfile(self.spm_file			, lowerCAmelCase__							)
          elif not os.path.isfile(self.spm_file							):
               with open(lowerCAmelCase__			, '''wb'''							) as fi:
                    _a					= self.sp_model.serialized_model_proto()
                    fi.write(lowerCAmelCase__							)

          return (str(lowerCAmelCase__							), str(lowerCAmelCase__							))


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase = "en"			, __UpperCAmelCase = None			, __UpperCAmelCase = "ro"			, **__UpperCAmelCase			, )				->							BatchEncoding:
          _a					= src_lang
          _a					= tgt_lang
          self.set_src_lang_special_tokens(self.src_lang							)
          return super().prepare_seqaseq_batch(lowerCAmelCase__			, lowerCAmelCase__			, **lowerCAmelCase__							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, **__UpperCAmelCase							)				->							str:




          if src_lang is None or tgt_lang is None:
               raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''							)
          _a					= src_lang
          _a					= self(lowerCAmelCase__			, add_special_tokens=lowerCAmelCase__			, **lowerCAmelCase__							)
          _a					= self.get_lang_id(lowerCAmelCase__							)
          _a					= tgt_lang_id
          return inputs


     def 							_UpperCAmelCase					(					self							)				->							Tuple:
          self.set_src_lang_special_tokens(self.src_lang							)


     def 							_UpperCAmelCase					(					self							)				->							List[Any]:
          self.set_tgt_lang_special_tokens(self.tgt_lang							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							None:
          _a					= self.get_lang_token(lowerCAmelCase__							)
          _a					= self.lang_token_to_id[lang_token]
          _a					= [self.cur_lang_id]
          _a					= [self.eos_token_id]


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							None:
          _a					= self.get_lang_token(lowerCAmelCase__							)
          _a					= self.lang_token_to_id[lang_token]
          _a					= [self.cur_lang_id]
          _a					= [self.eos_token_id]


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							str:
          return self.lang_code_to_token[lang]





     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							int:
          _a					= self.get_lang_token(lowerCAmelCase__							)
          return self.lang_token_to_id[lang_token]







def 					A_      (      _lowerCAmelCase   :    str,   _lowerCAmelCase   :    Dict[str, Any]				):






     """simple docstring"""

     _a					= sentencepiece.SentencePieceProcessor(**_lowerCAmelCase				)
     spm.Load(str(_lowerCAmelCase				)				)
     return spm
def 					A_      (      _lowerCAmelCase   :    str				):






     """simple docstring"""

     with open(_lowerCAmelCase,   '''r'''				) as f:
          return json.load(_lowerCAmelCase				)

def 					A_      (      _lowerCAmelCase   :    int,   _lowerCAmelCase   :    str				):






     """simple docstring"""

     with open(_lowerCAmelCase,   '''w'''				) as f:
          json.dump(_lowerCAmelCase,   _lowerCAmelCase,   indent=2				)






import numpy

# List of input, output pairs
UpperCAmelCase	:	str		     =       (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCAmelCase	:	Optional[int]		     =       (((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase	:	str		     =       [2, 4, 1, 5]
UpperCAmelCase	:	List[str]		     =       len(train_data)
UpperCAmelCase	:	Dict		     =       0.0_0_9
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   Tuple="train"						):





  """simple docstring"""



  return calculate_hypothesis_value(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						) - output(
      SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple						):





  """simple docstring"""



  a__	:					Tuple										=0
  for i in range(len(SCREAMING_SNAKE_CASE						) - 1						):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val
def      _A       (		SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Dict						):





  """simple docstring"""



  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None
def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[Any]       ,       SCREAMING_SNAKE_CASE     :   Tuple						):





  """simple docstring"""



  if data_set == "train":
    return _hypothesis_value(train_data[example_no][0]						)
  elif data_set == "test":
    return _hypothesis_value(test_data[example_no][0]						)
  return None
def      _A       (		SCREAMING_SNAKE_CASE     :   Union[str, Any]       ,       SCREAMING_SNAKE_CASE     :   int=m						):





  """simple docstring"""



  a__	:					Any										=0
  for i in range(SCREAMING_SNAKE_CASE						):
    if index == -1:
      summation_value += _error(SCREAMING_SNAKE_CASE						)
    else:
      summation_value += _error(SCREAMING_SNAKE_CASE						) * train_data[i][0][index]
  return summation_value
def      _A       (		SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  a__	:					Any										=summation_of_cost_derivative(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						) / m
  return cost_derivative_value
def      _A       (		):





  """simple docstring"""



  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  a__	:					Dict										=0.0_0_0_0_0_2
  a__	:					Union[str, Any]										=0
  a__	:					Any										=0
  while True:
    j += 1
    a__	:					Any										=[0, 0, 0, 0]
    for i in range(0       ,       len(SCREAMING_SNAKE_CASE						)						):
      a__	:					Tuple										=get_cost_derivative(i - 1						)
      a__	:					List[Any]										=(
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       atol=SCREAMING_SNAKE_CASE       ,       rtol=SCREAMING_SNAKE_CASE       ,       ):
      break
    a__	:					Optional[Any]										=temp_parameter_vector
  print(("Number of iterations:", j)						)
def      _A       (		):





  """simple docstring"""



  for i in range(len(SCREAMING_SNAKE_CASE						)						):
    print(("Actual output value:", output(SCREAMING_SNAKE_CASE       ,       "test"						))						)
    print(("Hypothesis output:", calculate_hypothesis_value(SCREAMING_SNAKE_CASE       ,       "test"						))						)


if __name__ == "__main__":
     run_gradient_descent()
     print("""\nTesting gradient descent for a linear hypothesis function.\n""")
     test_gradient_descent()






