






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

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


A        =			logging.get_logger(__name__)


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

A        =			{
    '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        =			'</w>'
A        =			'@@ '



def 					_lowerCamelCase(			lowerCAmelCase__				:		Tuple      ):





				'''simple docstring'''







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


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


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				Tuple				    =						VOCAB_FILES_NAMES
		UpperCAmelCase__      :				List[Any]				    =						PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase__      :				Optional[Any]				    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase__      :				Tuple				    =						["""input_ids""", """attention_mask"""]


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

						SCREAMING_SNAKE_CASE_   :	Dict													=   do_lower_case

						with open(UpperCamelCase__ ,    encoding='utf-8'       ) as vocab_handle:
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   json.load(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   {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.'''       )

										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   None
										SCREAMING_SNAKE_CASE_   :	Any													=   None
						else:
										with open(UpperCamelCase__ ,    encoding='utf-8'       ) as merges_handle:
														SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   merges_handle.read().split('\n'       )[:-1]

										SCREAMING_SNAKE_CASE_   :	Dict													=   [tuple(merge.split()[:2]       ) for merge in merges]
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   dict(zip(UpperCamelCase__ ,    range(len(UpperCamelCase__       )       )       )       )
										SCREAMING_SNAKE_CASE_   :	str													=   {}


		@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 ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   tuple(token[:-1]       ) + (token[-1] + BPE_TOKEN_MERGES,)
						if token in self.cache:
										return self.cache[token]
						SCREAMING_SNAKE_CASE_   :	List[str]													=   get_pairs(UpperCamelCase__       )

						if not pairs:
										return token

						while True:
										SCREAMING_SNAKE_CASE_   :	List[str]													=   min(UpperCamelCase__ ,    key=lambda UpperCamelCase__       : self.bpe_ranks.get(UpperCamelCase__ ,    float('inf'       )       )       )
										if bigram not in self.bpe_ranks:
														break
										SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Any													=   bigram
										SCREAMING_SNAKE_CASE_   :	str													=   []
										SCREAMING_SNAKE_CASE_   :	Any													=   0
										while i < len(UpperCamelCase__       ):
														try:
																		SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   word.index(UpperCamelCase__ ,    UpperCamelCase__       )
														except ValueError:
																		new_word.extend(word[i:]       )
																		break
														else:
																		new_word.extend(word[i:j]       )
																		SCREAMING_SNAKE_CASE_   :	Tuple													=   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
										SCREAMING_SNAKE_CASE_   :	int													=   tuple(UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   new_word
										if len(UpperCamelCase__       ) == 1:
														break
										else:
														SCREAMING_SNAKE_CASE_   :	Dict													=   get_pairs(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   ' '.join(UpperCamelCase__       )
						if word == "\n  " + BPE_TOKEN_MERGES:
										SCREAMING_SNAKE_CASE_   :	int													=   '\n' + BPE_TOKEN_MERGES

						if word.endswith(UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   word.replace(UpperCamelCase__ ,    ''       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   word.replace(' ' ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   word
						return word


		def   __snake_case			(						self ,    UpperCamelCase__       ):

						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:
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   text.lower()

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   text.split()

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   []
						for token in text:
										if token:
														split_tokens.extend(list(self.bpe(UpperCamelCase__       ).split(' '       )       )       )

						return split_tokens


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						return self.encoder.get(UpperCamelCase__ ,    self.encoder.get(self.unk_token       )       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.decoder.get(UpperCamelCase__ ,    self.unk_token       )
						return result


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   ' '.join(UpperCamelCase__       )

						# make sure @@ tokens are concatenated
						SCREAMING_SNAKE_CASE_   :	List[str]													=   ''.join(string.split(UpperCamelCase__       )       )

						return string






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						if not os.path.isdir(UpperCamelCase__       ):
										logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
										return
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   os.path.join(
						    UpperCamelCase__ ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )
						SCREAMING_SNAKE_CASE_   :	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'       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   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!'       )
																		SCREAMING_SNAKE_CASE_   :	str													=   token_index
														writer.write(' '.join(UpperCamelCase__       ) + '\n'       )
														index += 1

						return (vocab_file, merges_file)







from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch


@require_torch
@require_tf
class 							__a							(  __A						):





		'''simple docstring'''



		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   SMALL_MODEL_IDENTIFIER
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   'pt'
						SCREAMING_SNAKE_CASE_   :	Tuple													=   'tf'


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   AutoModel.from_pretrained(self.test_model       )
						model_pt.save_pretrained(UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   TFAutoModel.from_pretrained(self.test_model ,    from_pt=UpperCamelCase__       )
						model_tf.save_pretrained(UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   'mock_framework'

						# Framework provided - return whatever the user provides
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   FeaturesManager.determine_framework(self.test_model ,    UpperCamelCase__       )
						self.assertEqual(UpperCamelCase__ ,    UpperCamelCase__       )

						# Local checkpoint and framework provided - return provided framework
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
										self._setup_pt_ckpt(UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Dict													=   FeaturesManager.determine_framework(UpperCamelCase__ ,    UpperCamelCase__       )
										self.assertEqual(UpperCamelCase__ ,    UpperCamelCase__       )

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
										self._setup_tf_ckpt(UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	List[str]													=   FeaturesManager.determine_framework(UpperCamelCase__ ,    UpperCamelCase__       )
										self.assertEqual(UpperCamelCase__ ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
										self._setup_pt_ckpt(UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   FeaturesManager.determine_framework(UpperCamelCase__       )
										self.assertEqual(UpperCamelCase__ ,    self.framework_pt       )

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
										self._setup_tf_ckpt(UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   FeaturesManager.determine_framework(UpperCamelCase__       )
										self.assertEqual(UpperCamelCase__ ,    self.framework_tf       )

						# Invalid local checkpoint
						with TemporaryDirectory() as local_invalid_ckpt:
										with self.assertRaises(UpperCamelCase__       ):
														SCREAMING_SNAKE_CASE_   :	Any													=   FeaturesManager.determine_framework(UpperCamelCase__       )






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   MagicMock(return_value=UpperCamelCase__       )
						with patch('transformers.onnx.features.is_tf_available' ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	str													=   FeaturesManager.determine_framework(self.test_model       )
										self.assertEqual(UpperCamelCase__ ,    self.framework_pt       )

						# PyTorch not in environment -> use TensorFlow
						SCREAMING_SNAKE_CASE_   :	List[str]													=   MagicMock(return_value=UpperCamelCase__       )
						with patch('transformers.onnx.features.is_torch_available' ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   FeaturesManager.determine_framework(self.test_model       )
										self.assertEqual(UpperCamelCase__ ,    self.framework_tf       )

						# Both in environment -> use PyTorch
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   MagicMock(return_value=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   MagicMock(return_value=UpperCamelCase__       )
						with patch('transformers.onnx.features.is_tf_available' ,    UpperCamelCase__       ), patch(
						    'transformers.onnx.features.is_torch_available' ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   FeaturesManager.determine_framework(self.test_model       )
										self.assertEqual(UpperCamelCase__ ,    self.framework_pt       )

						# Both not in environment -> raise error
						SCREAMING_SNAKE_CASE_   :	Tuple													=   MagicMock(return_value=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   MagicMock(return_value=UpperCamelCase__       )
						with patch('transformers.onnx.features.is_tf_available' ,    UpperCamelCase__       ), patch(
						    'transformers.onnx.features.is_torch_available' ,    UpperCamelCase__       ):
										with self.assertRaises(UpperCamelCase__       ):
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   FeaturesManager.determine_framework(self.test_model       )