


"""simple docstring"""







import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowercase    					=      logging.get_logger(__name__)


__lowercase    					=      {
    '''vocab_file''': '''vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
    '''merges_file''': '''merges.txt''',
}

__lowercase    					=      {
    '''vocab_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
        ),
    },
    '''tokenizer_config_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
        ),
    },
    '''merges_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
        ),
    },
}

__lowercase    					=      '''</w>'''
__lowercase    					=      '''@@ '''


def      lowercase			(				A_       )->      Dict:
  '''simple docstring'''







  a						:		List[str]      							=			set()
  a						:		List[Any]      							=			word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)       )
    a						:		int      							=			char
  return pairs


# Speech2Text2 has no max input length
__lowercase    					=      {'''facebook/s2t-wav2vec2-large-en-de''': 1024}







class 						_A  (  _lowerCAmelCase  ):







    """simple docstring"""




    UpperCAmelCase       :							Tuple	=       VOCAB_FILES_NAMES
    UpperCAmelCase       :							int	=       PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase       :							Optional[int]	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase       :							int	=       ["""input_ids""", """attention_mask"""]






    def __init__(					self				:    Dict				, __UpperCAmelCase				:    List[Any]				, __UpperCAmelCase				:    Tuple="<s>"				, __UpperCAmelCase				:    Union[str, Any]="<pad>"				, __UpperCAmelCase				:    Optional[int]="</s>"				, __UpperCAmelCase				:    Tuple="<unk>"				, __UpperCAmelCase				:    int=False				, __UpperCAmelCase				:    Tuple=None				, **__UpperCAmelCase				:    Dict				, ):
      super().__init__(
          unk_token=__UpperCAmelCase				, bos_token=__UpperCAmelCase				, eos_token=__UpperCAmelCase				, pad_token=__UpperCAmelCase				, do_lower_case=__UpperCAmelCase				, **__UpperCAmelCase				, )

      a						:		Any      							=			do_lower_case

      with open(__UpperCAmelCase				, encoding="utf-8") as vocab_handle:
        a						:		Dict      							=			json.load(__UpperCAmelCase)
      a						:		Tuple      							=			{v: k for k, v in self.encoder.items()}

      if merges_file is None:
        logger.info(f'''No merges files provided. {self.__class__.__name__} can only be used for decoding.''')

        a						:		Any      							=			None
        a						:		int      							=			None
      else:
        with open(__UpperCAmelCase				, encoding="utf-8") as merges_handle:
          a						:		List[str]      							=			merges_handle.read().split("\n")[:-1]

        a						:		Optional[int]      							=			[tuple(merge.split()[:2]) for merge in merges]
        a						:		List[str]      							=			dict(zip(__UpperCAmelCase				, range(len(__UpperCAmelCase))))
        a						:		str      							=			{}






    @property
    def 	__snake_case							(					self				:    List[Any]):
      return len(self.decoder)






    def 	__snake_case							(					self				:    int):
      return dict(self.encoder				, **self.added_tokens_encoder)






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    Tuple):
      a						:		List[str]      							=			tuple(token[:-1]) + (token[-1] + BPE_TOKEN_MERGES,)
      if token in self.cache:
        return self.cache[token]
      a						:		Dict      							=			get_pairs(__UpperCAmelCase)

      if not pairs:
        return token

      while True:
        a						:		Any      							=			min(__UpperCAmelCase				, key=lambda __UpperCAmelCase: self.bpe_ranks.get(__UpperCAmelCase				, float("inf")))
        if bigram not in self.bpe_ranks:
          break
        a						:		Optional[int]      							=			bigram
        a						:		Tuple      							=			[]
        a						:		Optional[int]      							=			0
        while i < len(__UpperCAmelCase):
          try:
            a						:		List[Any]      							=			word.index(__UpperCAmelCase				, __UpperCAmelCase)
          except ValueError:
            new_word.extend(word[i:])
            break
          else:
            new_word.extend(word[i:j])
            a						:		Union[str, Any]      							=			j

          if word[i] == first and i < len(__UpperCAmelCase) - 1 and word[i + 1] == second:
            new_word.append(first + second)
            i += 2
          else:
            new_word.append(word[i])
            i += 1
        a						:		Optional[Any]      							=			tuple(__UpperCAmelCase)
        a						:		Tuple      							=			new_word
        if len(__UpperCAmelCase) == 1:
          break
        else:
          a						:		Tuple      							=			get_pairs(__UpperCAmelCase)
      a						:		Any      							=			" ".join(__UpperCAmelCase)
      if word == "\n  " + BPE_TOKEN_MERGES:
        a						:		int      							=			"\n" + BPE_TOKEN_MERGES

      if word.endswith(__UpperCAmelCase):
        a						:		int      							=			word.replace(__UpperCAmelCase				, "")

      a						:		Dict      							=			word.replace(" "				, __UpperCAmelCase)
      a						:		int      							=			word
      return word






    def 	__snake_case							(					self				:    Optional[int]				, __UpperCAmelCase				:    List[str]):




      if self.bpe_ranks is None:
        raise ValueError(
            "This tokenizer was instantiated without a `merges.txt` file, so"
            " that it can only be used for decoding, not for encoding."
            "Make sure to provide `merges.txt` file at instantiation to enable "
            "encoding.")

      if self.do_lower_case:
        a						:		List[Any]      							=			text.lower()

      a						:		Tuple      							=			text.split()

      a						:		int      							=			[]
      for token in text:
        if token:
          split_tokens.extend(list(self.bpe(__UpperCAmelCase).split(" ")))

      return split_tokens






    def 	__snake_case							(					self				:    Any				, __UpperCAmelCase				:    str):
      return self.encoder.get(__UpperCAmelCase				, self.encoder.get(self.unk_token))






    def 	__snake_case							(					self				:    Tuple				, __UpperCAmelCase				:    int):
      a						:		Any      							=			self.decoder.get(__UpperCAmelCase				, self.unk_token)
      return result






    def 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    List[str]):
      a						:		Optional[Any]      							=			" ".join(__UpperCAmelCase)

      # make sure @@ tokens are concatenated
      a						:		int      							=			"".join(string.split(__UpperCAmelCase))

      return string






    def 	__snake_case							(					self				:    Tuple				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    Optional[str] = None):




      if not os.path.isdir(__UpperCAmelCase):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
        return
      a						:		str      							=			os.path.join(
          __UpperCAmelCase				, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
      a						:		List[Any]      							=			os.path.join(
          __UpperCAmelCase				, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"])

      with open(__UpperCAmelCase				, "w"				, encoding="utf-8") as f:
        f.write(json.dumps(self.encoder				, indent=2				, sort_keys=__UpperCAmelCase				, ensure_ascii=__UpperCAmelCase) + "\n")

      a						:		Tuple      							=			0
      if self.bpe_ranks is None:
        return (vocab_file,)

      with open(__UpperCAmelCase				, "w"				, encoding="utf-8") as writer:
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				, key=lambda __UpperCAmelCase: kv[1]):
          if index != token_index:
            logger.warning(
                f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
                " Please check that the tokenizer is not corrupted!")
            a						:		Optional[Any]      							=			token_index
          writer.write(" ".join(__UpperCAmelCase) + "\n")
          index += 1

      return (vocab_file, merges_file)













'''simple docstring'''


from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase					:     Optional[int]												=logging.get_logger(__name__)

lowerCAmelCase					:     Tuple												={
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}
class       a_   (				_lowerCAmelCase							):
     __A      =			"autoformer"
     __A      =			{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
         "num_hidden_layers": "encoder_layers",
     }





     def __init__( self		:					Any	,      lowercase		:					Optional[int] = None	,      lowercase		:					Optional[int] = None	,      lowercase		:					str = "student_t"	,      lowercase		:					str = "nll"	,      lowercase		:					int = 1	,      lowercase		:					List[int] = [1, 2, 3, 4, 5, 6, 7]	,      lowercase		:					bool = True	,      lowercase		:					int = 0	,      lowercase		:					int = 0	,      lowercase		:					int = 0	,      lowercase		:					int = 0	,      lowercase		:					Optional[List[int]] = None	,      lowercase		:					Optional[List[int]] = None	,      lowercase		:					int = 64	,      lowercase		:					int = 2	,      lowercase		:					int = 2	,      lowercase		:					int = 2	,      lowercase		:					int = 2	,      lowercase		:					int = 32	,      lowercase		:					int = 32	,      lowercase		:					str = "gelu"	,      lowercase		:					float = 0.1	,      lowercase		:					float = 0.1	,      lowercase		:					float = 0.1	,      lowercase		:					float = 0.1	,      lowercase		:					float = 0.1	,      lowercase		:					int = 100	,      lowercase		:					float = 0.02	,      lowercase		:					bool = True	,      lowercase		:					int=True	,      lowercase		:					int = 10	,      lowercase		:					int = 25	,      lowercase		:					int = 3	,      **lowercase		:					Dict	,      ):





        """simple docstring"""




        lowercase_			:str    			= prediction_length
        lowercase_			:Dict    			= context_length if context_length is not None else prediction_length
        lowercase_			:Any    			= distribution_output
        lowercase_			:Tuple    			= loss
        lowercase_			:Dict    			= input_size
        lowercase_			:Tuple    			= num_time_features
        lowercase_			:int    			= lags_sequence
        lowercase_			:Tuple    			= scaling
        lowercase_			:List[Any]    			= num_dynamic_real_features
        lowercase_			:Union[str, Any]    			= num_static_real_features
        lowercase_			:str    			= num_static_categorical_features
        if cardinality is not None and num_static_categorical_features > 0:
           if len(lowercase						) != num_static_categorical_features:
              raise ValueError(
                  "The cardinality should be a list of the same length as `num_static_categorical_features`"						)
           lowercase_			:Optional[int]    			= cardinality
        else:
           lowercase_			:Optional[int]    			= [0]
        if embedding_dimension is not None and num_static_categorical_features > 0:
           if len(lowercase						) != num_static_categorical_features:
              raise ValueError(
                  "The embedding dimension should be a list of the same length as `num_static_categorical_features`"						)
           lowercase_			:Tuple    			= embedding_dimension
        else:
           lowercase_			:Dict    			= [min(50	,      (cat + 1) // 2						) for cat in self.cardinality]
        lowercase_			:Any    			= num_parallel_samples

        # Transformer architecture configuration
        lowercase_			:Tuple    			= input_size * len(self.lags_sequence						) + self._number_of_features
        lowercase_			:Union[str, Any]    			= d_model
        lowercase_			:Optional[Any]    			= encoder_attention_heads
        lowercase_			:Optional[Any]    			= decoder_attention_heads
        lowercase_			:Optional[int]    			= encoder_ffn_dim
        lowercase_			:int    			= decoder_ffn_dim
        lowercase_			:Any    			= encoder_layers
        lowercase_			:Optional[Any]    			= decoder_layers

        lowercase_			:Dict    			= dropout
        lowercase_			:Dict    			= attention_dropout
        lowercase_			:str    			= activation_dropout
        lowercase_			:int    			= encoder_layerdrop
        lowercase_			:Dict    			= decoder_layerdrop

        lowercase_			:List[str]    			= activation_function
        lowercase_			:int    			= init_std

        lowercase_			:Optional[int]    			= use_cache

        # Autoformer
        lowercase_			:List[str]    			= label_length
        lowercase_			:List[str]    			= moving_average
        lowercase_			:Optional[int]    			= autocorrelation_factor

        super().__init__(is_encoder_decoder=lowercase	,      **lowercase						)







     @property
     def 							lowercase__      ( self		:					int						):





        """simple docstring"""




        return (
            sum(self.embedding_dimension						)
            + self.num_dynamic_real_features
            + self.num_time_features
            + self.num_static_real_features
            + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
        )


