



'''simple docstring'''

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

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


UpperCAmelCase     		=   logging.get_logger(__name__)


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

UpperCAmelCase     		=   {
    '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'
        ),
    },
}

UpperCAmelCase     		=   '</w>'
UpperCAmelCase     		=   '@@ '






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						str:







	"""simple docstring"""
	lowerCAmelCase   =     set()
	lowerCAmelCase   =     word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)							)
		lowerCAmelCase   =     char
	return pairs


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





class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Tuple									=		VOCAB_FILES_NAMES
						UpperCAmelCase							:   Tuple									=		PRETRAINED_VOCAB_FILES_MAP
						UpperCAmelCase							:   Dict									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCAmelCase							:   Any									=		["input_ids", "attention_mask"]


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

							lowerCAmelCase   =     do_lower_case

							with open(A_     ,	encoding="""utf-8"""				) as vocab_handle:
								lowerCAmelCase   =     json.load(A_				)
							lowerCAmelCase   =     {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.'				)

								lowerCAmelCase   =     None
								lowerCAmelCase   =     None
							else:
								with open(A_     ,	encoding="""utf-8"""				) as merges_handle:
									lowerCAmelCase   =     merges_handle.read().split("""\n"""				)[:-1]

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


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


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


						def     __snake_case   ( self     ,	A_				)     ->     Optional[Any]:
							lowerCAmelCase   =     tuple(token[:-1]				) + (token[-1] + BPE_TOKEN_MERGES,)
							if token in self.cache:
								return self.cache[token]
							lowerCAmelCase   =     get_pairs(A_				)

							if not pairs:
								return token

							while True:
								lowerCAmelCase   =     min(A_     ,	key=lambda A_				: self.bpe_ranks.get(A_     ,	float("""inf"""				)				)				)
								if bigram not in self.bpe_ranks:
									break
								lowerCAmelCase,       lowerCAmelCase   =     bigram
								lowerCAmelCase   =     []
								lowerCAmelCase   =     0
								while i < len(A_				):
									try:
										lowerCAmelCase   =     word.index(A_     ,	A_				)
									except ValueError:
										new_word.extend(word[i:]				)
										break
									else:
										new_word.extend(word[i:j]				)
										lowerCAmelCase   =     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
								lowerCAmelCase   =     tuple(A_				)
								lowerCAmelCase   =     new_word
								if len(A_				) == 1:
									break
								else:
									lowerCAmelCase   =     get_pairs(A_				)
							lowerCAmelCase   =     """ """.join(A_				)
							if word == "\n  " + BPE_TOKEN_MERGES:
								lowerCAmelCase   =     """\n""" + BPE_TOKEN_MERGES

							if word.endswith(A_				):
								lowerCAmelCase   =     word.replace(A_     ,	""""""				)

							lowerCAmelCase   =     word.replace(""" """     ,	A_				)
							lowerCAmelCase   =     word
							return word


						def     __snake_case   ( self     ,	A_				)     ->     List[Any]:

							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:
								lowerCAmelCase   =     text.lower()

							lowerCAmelCase   =     text.split()

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

							return split_tokens


						def     __snake_case   ( self     ,	A_				)     ->     int:
							return self.encoder.get(A_     ,	self.encoder.get(self.unk_token				)				)


						def     __snake_case   ( self     ,	A_				)     ->     str:
							lowerCAmelCase   =     self.decoder.get(A_     ,	self.unk_token				)
							return result


						def     __snake_case   ( self     ,	A_				)     ->     str:
							lowerCAmelCase   =     """ """.join(A_				)

							# make sure @@ tokens are concatenated
							lowerCAmelCase   =     """""".join(string.split(A_				)				)

							return string




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

							lowerCAmelCase   =     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!"""				)
										lowerCAmelCase   =     token_index
									writer.write(""" """.join(A_				) + """\n"""				)
									index += 1

							return (vocab_file, merges_file)




'''simple docstring'''

from typing import List, Optional, Union

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


UpperCAmelCase     		=   logging.get_logger(__name__)

UpperCAmelCase     		=   {
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   List[Any]									=		"time_series_transformer"
						UpperCAmelCase							:   int									=		{
						    "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_ = [1, 2, 3, 4, 5, 6, 7]     ,	A_ = "mean"     ,	A_ = 0     ,	A_ = 0     ,	A_ = 0     ,	A_ = 0     ,	A_ = None     ,	A_ = None     ,	A_ = 32     ,	A_ = 32     ,	A_ = 2     ,	A_ = 2     ,	A_ = 2     ,	A_ = 2     ,	A_ = True     ,	A_ = "gelu"     ,	A_ = 64     ,	A_ = 0.1     ,	A_ = 0.1     ,	A_ = 0.1     ,	A_ = 0.1     ,	A_ = 0.1     ,	A_ = 100     ,	A_ = 0.0_2     ,	A_=True     ,	**A_     ,	)     ->     Optional[Any]:
							# time series specific configuration
							lowerCAmelCase   =     prediction_length
							lowerCAmelCase   =     context_length or prediction_length
							lowerCAmelCase   =     distribution_output
							lowerCAmelCase   =     loss
							lowerCAmelCase   =     input_size
							lowerCAmelCase   =     num_time_features
							lowerCAmelCase   =     lags_sequence
							lowerCAmelCase   =     scaling
							lowerCAmelCase   =     num_dynamic_real_features
							lowerCAmelCase   =     num_static_real_features
							lowerCAmelCase   =     num_static_categorical_features
							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`"""				)
								lowerCAmelCase   =     cardinality
							else:
								lowerCAmelCase   =     [0]
							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`"""				)
								lowerCAmelCase   =     embedding_dimension
							else:
								lowerCAmelCase   =     [min(50     ,	(cat + 1) // 2				) for cat in self.cardinality]
							lowerCAmelCase   =     num_parallel_samples

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

							lowerCAmelCase   =     dropout
							lowerCAmelCase   =     attention_dropout
							lowerCAmelCase   =     activation_dropout
							lowerCAmelCase   =     encoder_layerdrop
							lowerCAmelCase   =     decoder_layerdrop

							lowerCAmelCase   =     activation_function
							lowerCAmelCase   =     init_std

							lowerCAmelCase   =     use_cache

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




						@property
						def     __snake_case   ( 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
							)