'''simple docstring'''
import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						CTRLTokenizer
     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						False







     def lowerCAmelCase     (		self    ):
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __UpperCamelCase	:	Tuple		    =  ['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
         __UpperCamelCase	:	Optional[Any]		    =  dict(zip(_lowerCamelCase     ,			range(len(_lowerCamelCase    )    )    )    )
         __UpperCamelCase	:	List[Any]		    =  ['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
         __UpperCamelCase	:	List[str]		    =  {'unk_token': '<unk>'}

         __UpperCamelCase	:	List[Any]		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase	:	List[str]		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['merges_file']    )
         with open(self.vocab_file     ,			'w'     ,			encoding='utf-8'    ) as fp:
             fp.write(json.dumps(_lowerCamelCase    ) + '\n'    )
         with open(self.merges_file     ,			'w'     ,			encoding='utf-8'    ) as fp:
             fp.write('\n'.join(_lowerCamelCase    )    )







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         kwargs.update(self.special_tokens_map    )
         return CTRLTokenizer.from_pretrained(self.tmpdirname     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  'adapt react readapt apt'
         __UpperCamelCase	:	List[Any]		    =  'adapt react readapt apt'
         return input_text, output_text







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  CTRLTokenizer(self.vocab_file     ,			self.merges_file     ,			**self.special_tokens_map    )
         __UpperCamelCase	:	List[Any]		    =  'adapt react readapt apt'
         __UpperCamelCase	:	int		    =  'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
         __UpperCamelCase	:	List[str]		    =  tokenizer.tokenize(_lowerCamelCase    )
         self.assertListEqual(_lowerCamelCase     ,			_lowerCamelCase    )

         __UpperCamelCase	:	Union[str, Any]		    =  tokens + [tokenizer.unk_token]

         __UpperCamelCase	:	List[str]		    =  [0, 1, 2, 4, 5, 1, 0, 3, 6]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase    )     ,			_lowerCamelCase    )




'''simple docstring'''
from ...processing_utils import ProcessorMixin






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''SpeechT5FeatureExtractor'''
     SCREAMING_SNAKE_CASE__		   =						'''SpeechT5Tokenizer'''







     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         super().__init__(_lowerCamelCase     ,			_lowerCamelCase    )







     def __call__(		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Union[str, Any]		    =  kwargs.pop('audio'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  kwargs.pop('text'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  kwargs.pop('text_target'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  kwargs.pop('audio_target'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Any		    =  kwargs.pop('sampling_rate'     ,			_lowerCamelCase    )

         if audio is not None and text is not None:
             raise ValueError(
                 'Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'    )
         if audio_target is not None and text_target is not None:
             raise ValueError(
                 'Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'    )
         if audio is None and audio_target is None and text is None and text_target is None:
             raise ValueError(
                 'You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'    )

         if audio is not None:
             __UpperCamelCase	:	Dict		    =  self.feature_extractor(_lowerCamelCase     ,			*_lowerCamelCase     ,			sampling_rate=_lowerCamelCase     ,			**_lowerCamelCase    )
         elif text is not None:
             __UpperCamelCase	:	int		    =  self.tokenizer(_lowerCamelCase     ,			**_lowerCamelCase    )
         else:
             __UpperCamelCase	:	List[str]		    =  None

         if audio_target is not None:
             __UpperCamelCase	:	Dict		    =  self.feature_extractor(audio_target=_lowerCamelCase     ,			*_lowerCamelCase     ,			sampling_rate=_lowerCamelCase     ,			**_lowerCamelCase    )
             __UpperCamelCase	:	Optional[int]		    =  targets['input_values']
         elif text_target is not None:
             __UpperCamelCase	:	Any		    =  self.tokenizer(_lowerCamelCase     ,			**_lowerCamelCase    )
             __UpperCamelCase	:	Optional[int]		    =  targets['input_ids']
         else:
             __UpperCamelCase	:	List[str]		    =  None

         if inputs is None:
             return targets

         if targets is not None:
             __UpperCamelCase	:	List[Any]		    =  labels

             __UpperCamelCase	:	Any		    =  targets.get('attention_mask'    )
             if decoder_attention_mask is not None:
                 __UpperCamelCase	:	int		    =  decoder_attention_mask

         return inputs







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[Any]		    =  kwargs.pop('input_values'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  kwargs.pop('input_ids'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  kwargs.pop('labels'     ,			_lowerCamelCase    )

         if input_values is not None and input_ids is not None:
             raise ValueError('Cannot process both `input_values` and `input_ids` inputs.'    )
         if input_values is None and input_ids is None and labels is None:
             raise ValueError(
                 'You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'    )

         if input_values is not None:
             __UpperCamelCase	:	Optional[int]		    =  self.feature_extractor.pad(_lowerCamelCase     ,			*_lowerCamelCase     ,			**_lowerCamelCase    )
         elif input_ids is not None:
             __UpperCamelCase	:	int		    =  self.tokenizer.pad(_lowerCamelCase     ,			**_lowerCamelCase    )
         else:
             __UpperCamelCase	:	str		    =  None

         if labels is not None:
             if "input_ids" in labels or (isinstance(_lowerCamelCase     ,			_lowerCamelCase    ) and "input_ids" in labels[0]):
                 __UpperCamelCase	:	Dict		    =  self.tokenizer.pad(_lowerCamelCase     ,			**_lowerCamelCase    )
                 __UpperCamelCase	:	str		    =  targets['input_ids']
             else:
                 __UpperCamelCase	:	int		    =  self.feature_extractor.feature_size
                 __UpperCamelCase	:	Any		    =  self.feature_extractor.num_mel_bins
                 __UpperCamelCase	:	Dict		    =  self.feature_extractor.pad(_lowerCamelCase     ,			*_lowerCamelCase     ,			**_lowerCamelCase    )
                 __UpperCamelCase	:	List[str]		    =  feature_size_hack
                 __UpperCamelCase	:	Tuple		    =  targets['input_values']
         else:
             __UpperCamelCase	:	List[str]		    =  None

         if inputs is None:
             return targets

         if targets is not None:
             __UpperCamelCase	:	Optional[int]		    =  labels

             __UpperCamelCase	:	Union[str, Any]		    =  targets.get('attention_mask'    )
             if decoder_attention_mask is not None:
                 __UpperCamelCase	:	Optional[Any]		    =  decoder_attention_mask

         return inputs







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         return self.tokenizer.batch_decode(*_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         return self.tokenizer.decode(*_lowerCamelCase     ,			**_lowerCamelCase    )



