


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

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


_lowercase       :      str	=	logging.get_logger(__name__)


_lowercase       :      Dict	=	{
    "vocab_file": "vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
    "merges_file": "merges.txt",
}

_lowercase       :      Tuple	=	{
    "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       :      List[Any]	=	"</w>"
_lowercase       :      Tuple	=	"@@ "





def   _lowerCAmelCase				(   UpperCamelCase__:     Any						) ->   List[Any]:





	"""simple docstring"""







	A								=				set()
	A								=				word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)						)
		A								=				char
	return pairs


# Speech2Text2 has no max input length
_lowercase       :      Union[str, Any]	=	{"facebook/s2t-wav2vec2-large-en-de": 1024}






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= VOCAB_FILES_NAMES
				lowerCAmelCase 		= PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase 		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase 		= ['input_ids', 'attention_mask']

				def __init__(						self				,   a__				,   a__="<s>"				,   a__="<pad>"				,   a__="</s>"				,   a__="<unk>"				,   a__=False				,   a__=None				,   **a__				,   )	->				int:
					super().__init__(
					    unk_token=a__				,   bos_token=a__				,   eos_token=a__				,   pad_token=a__				,   do_lower_case=a__				,   **a__				,   )

					A								=				do_lower_case

					with open(a__				,   encoding="""utf-8"""   ) as vocab_handle:
						A								=				json.load(a__   )
					A								=				{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								=				None
						A								=				None
					else:
						with open(a__				,   encoding="""utf-8"""   ) as merges_handle:
							A								=				merges_handle.read().split("""\n"""   )[:-1]

						A								=				[tuple(merge.split()[:2]   ) for merge in merges]
						A								=				dict(zip(a__				,   range(len(a__   )   )   )   )
						A								=				{}

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					return len(self.decoder   )

				def      _UpperCAmelCase					(						self   )	->				Dict:
					return dict(self.encoder				,   **self.added_tokens_encoder   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				List[str]:
					A								=				tuple(token[:-1]   ) + (token[-1] + BPE_TOKEN_MERGES,)
					if token in self.cache:
						return self.cache[token]
					A								=				get_pairs(a__   )

					if not pairs:
						return token

					while True:
						A								=				min(a__				,   key=lambda a__   : self.bpe_ranks.get(a__				,   float("""inf"""   )   )   )
						if bigram not in self.bpe_ranks:
							break
						A					,			A								=				bigram
						A								=				[]
						A								=				0
						while i < len(a__   ):
							try:
								A								=				word.index(a__				,   a__   )
							except ValueError:
								new_word.extend(word[i:]   )
								break
							else:
								new_word.extend(word[i:j]   )
								A								=				j

							if word[i] == first and i < len(a__   ) - 1 and word[i + 1] == second:
								new_word.append(first + second   )
								i += 2
							else:
								new_word.append(word[i]   )
								i += 1
						A								=				tuple(a__   )
						A								=				new_word
						if len(a__   ) == 1:
							break
						else:
							A								=				get_pairs(a__   )
					A								=				""" """.join(a__   )
					if word == "\n  " + BPE_TOKEN_MERGES:
						A								=				"""\n""" + BPE_TOKEN_MERGES

					if word.endswith(a__   ):
						A								=				word.replace(a__				,   """"""   )

					A								=				word.replace(""" """				,   a__   )
					A								=				word
					return word

				def      _UpperCAmelCase					(						self				,   a__   )	->				Tuple:

					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								=				text.lower()

					A								=				text.split()

					A								=				[]
					for token in text:
						if token:
							split_tokens.extend(list(self.bpe(a__   ).split(""" """   )   )   )

					return split_tokens

				def      _UpperCAmelCase					(						self				,   a__   )	->				int:
					return self.encoder.get(a__				,   self.encoder.get(self.unk_token   )   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				str:
					A								=				self.decoder.get(a__				,   self.unk_token   )
					return result

				def      _UpperCAmelCase					(						self				,   a__   )	->				str:
					A								=				""" """.join(a__   )

					# make sure @@ tokens are concatenated
					A								=				"""""".join(string.split(a__   )   )

					return string

				def      _UpperCAmelCase					(						self				,   a__				,   a__ = None   )	->				Tuple[str]:
					if not os.path.isdir(a__   ):
						logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
						return
					A								=				os.path.join(
					    a__				,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
					A								=				os.path.join(
					    a__				,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]   )

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

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

					with open(a__				,   """w"""				,   encoding="""utf-8"""   ) as writer:
						for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,   key=lambda a__   : 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								=				token_index
							writer.write(""" """.join(a__   ) + """\n"""   )
							index += 1

					return (vocab_file, merges_file)







from typing import List, Optional, Union

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


_lowercase       :      Optional[Any]	=	logging.get_logger(__name__)

_lowercase       :      List[str]	=	{
    "huggingface/informer-tourism-monthly": (
        "https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 'informer'
				lowerCAmelCase 		= {
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				    'num_hidden_layers': 'encoder_layers',
				}

				def __init__(						self				,   a__ = None				,   a__ = None				,   a__ = "student_t"				,   a__ = "nll"				,   a__ = 1				,   a__ = None				,   a__ = "mean"				,   a__ = 0				,   a__ = 0				,   a__ = 0				,   a__ = 0				,   a__ = None				,   a__ = None				,   a__ = 64				,   a__ = 32				,   a__ = 32				,   a__ = 2				,   a__ = 2				,   a__ = 2				,   a__ = 2				,   a__ = True				,   a__ = "gelu"				,   a__ = 0.05				,   a__ = 0.1				,   a__ = 0.1				,   a__ = 0.1				,   a__ = 0.1				,   a__ = 100				,   a__ = 0.02				,   a__=True				,   a__ = "prob"				,   a__ = 5				,   a__ = True				,   **a__				,   )	->				Union[str, Any]:
					# time series specific configuration
					A								=				prediction_length
					A								=				context_length or prediction_length
					A								=				distribution_output
					A								=				loss
					A								=				input_size
					A								=				num_time_features
					A								=				lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
					A								=				scaling
					A								=				num_dynamic_real_features
					A								=				num_static_real_features
					A								=				num_static_categorical_features

					# set cardinality
					if cardinality and num_static_categorical_features > 0:
						if len(a__   ) != num_static_categorical_features:
							raise ValueError(
							    """The cardinality should be a list of the same length as `num_static_categorical_features`"""   )
						A								=				cardinality
					else:
						A								=				[0]

					# set embedding_dimension
					if embedding_dimension and num_static_categorical_features > 0:
						if len(a__   ) != num_static_categorical_features:
							raise ValueError(
							    """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""   )
						A								=				embedding_dimension
					else:
						A								=				[min(50				,   (cat + 1) // 2   ) for cat in self.cardinality]

					A								=				num_parallel_samples

					# Transformer architecture configuration
					A								=				input_size * len(self.lags_sequence   ) + self._number_of_features
					A								=				d_model
					A								=				encoder_attention_heads
					A								=				decoder_attention_heads
					A								=				encoder_ffn_dim
					A								=				decoder_ffn_dim
					A								=				encoder_layers
					A								=				decoder_layers

					A								=				dropout
					A								=				attention_dropout
					A								=				activation_dropout
					A								=				encoder_layerdrop
					A								=				decoder_layerdrop

					A								=				activation_function
					A								=				init_std

					A								=				use_cache

					# Informer
					A								=				attention_type
					A								=				sampling_factor
					A								=				distil

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

				@property
				def      _UpperCAmelCase					(						self   )	->				int:
					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
					)



