


"""simple docstring"""


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

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


A     :   Tuple			    =			logging.get_logger(__name__)


A     :   Optional[Any]			    =			{
    "vocab_file": "vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
    "merges_file": "merges.txt",
}

A     :   str			    =			{
    "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"
        ),
    },
}

A     :   str			    =			"</w>"
A     :   Dict			    =			"@@ "


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''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
A     :   Any			    =			{"facebook/s2t-wav2vec2-large-en-de": 1_0_2_4}


class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[int]				      =VOCAB_FILES_NAMES
							__UpperCAmelCase						:    str				      =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase						:    Any				      =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase						:    Dict				      =["""input_ids""", """attention_mask"""]

							def __init__(     self    ,   __a    ,   __a="<s>"    ,   __a="<pad>"    ,   __a="</s>"    ,   __a="<unk>"    ,   __a=False    ,   __a=None    ,   **__a    ,   ):
										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	):
										return len(self.decoder	)

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

							def 			snake_case				(     self    ,   __a	):
										__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	):

										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	):
										return self.encoder.get(__a    ,   self.encoder.get(self.unk_token	)	)

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			self.decoder.get(__a    ,   self.unk_token	)
										return result

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			" ".join(__a	)

										# make sure @@ tokens are concatenated
										__lowerCAmelCase				    =			"".join(string.split(__a	)	)

										return string



							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										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'''





import numpy as np

from transformers import Pipeline





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    Optional[Any]    						=      np.max(_lowerCAmelCase     ,   axis=-1     ,   keepdims=_lowerCAmelCase		)
      snake_case__  :    List[str]    						=      np.exp(outputs - maxes		)
      return shifted_exp / shifted_exp.sum(axis=-1     ,   keepdims=_lowerCAmelCase		)







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    def lowerCamelCase       (    self					:     Optional[Any] ,     **snake_case_					:     int						):
          snake_case__  :    Optional[int]    						=      {}
          if "second_text" in kwargs:
                snake_case__  :    Union[str, Any]    						=      kwargs["""second_text"""]
          return preprocess_kwargs, {}, {}




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     Tuple ,     snake_case_					:     Union[str, Any]=None						):
          return self.tokenizer(snake_case_ ,     text_pair=snake_case_ ,     return_tensors=self.framework						)




    def lowerCamelCase       (    self					:     List[Any] ,     snake_case_					:     Dict						):
          return self.model(**snake_case_						)




    def lowerCamelCase       (    self					:     int ,     snake_case_					:     List[Any]						):
          snake_case__  :    Union[str, Any]    						=      model_outputs.logits[0].numpy()
          snake_case__  :    List[str]    						=      softmax(snake_case_						)

          snake_case__  :    List[str]    						=      np.argmax(snake_case_						)
          snake_case__  :    List[str]    						=      self.model.config.idalabel[best_class]
          snake_case__  :    Optional[int]    						=      probabilities[best_class].item()
          snake_case__  :    str    						=      logits.tolist()
          return {"label": label, "score": score, "logits": logits}
