


'''simple docstring'''

import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class 	snake_case       (       lowercase     ,		unittest.TestCase     ):





  """simple docstring"""




  _lowerCamelCase						       =    TransfoXLTokenizer
  _lowerCamelCase						       =    False
  _lowerCamelCase						       =    False






  def     snake_case						(       self	):







   """simple docstring"""
   super().setUp()

   lowerCamelCase_						=    [
       "<unk>",
       "[CLS]",
       "[SEP]",
       "want",
       "unwanted",
       "wa",
       "un",
       "running",
       ",",
       "low",
       "l",
   ]
   lowerCamelCase_						=    os.path.join(self.tmpdirname     ,    VOCAB_FILES_NAMES["vocab_file"]	)
   with open(self.vocab_file     ,    "w"     ,    encoding="utf-8"	) as vocab_writer:
    vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)






  def     snake_case						(       self     ,    **UpperCamelCase	):







   """simple docstring"""
   lowerCamelCase_						=    True
   return TransfoXLTokenizer.from_pretrained(self.tmpdirname     ,    **A_	)






  def     snake_case						(       self     ,    UpperCamelCase	):







   """simple docstring"""
   lowerCamelCase_						=    "<unk> UNwanted , running"
   lowerCamelCase_						=    "<unk> unwanted, running"
   return input_text, output_text






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    TransfoXLTokenizer(vocab_file=self.vocab_file     ,    lower_case=A_	)

   lowerCamelCase_						=    tokenizer.tokenize("<unk> UNwanted , running"	)
   self.assertListEqual(A_     ,    ["<unk>", "unwanted", ",", "running"]	)

   self.assertListEqual(tokenizer.convert_tokens_to_ids(A_	)     ,    [0, 4, 8, 7]	)






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    TransfoXLTokenizer(lower_case=A_	)

   self.assertListEqual(
       tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "	)     ,    ["hello", "!", "how", "are", "you", "?"]	)






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    TransfoXLTokenizer(lower_case=A_	)

   self.assertListEqual(
       tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "	)     ,    ["HeLLo", "!", "how", "Are", "yoU", "?"]	)






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    TransfoXLTokenizer(lower_case=A_	)
   lowerCamelCase_						=    "Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"
   lowerCamelCase_						=    [
       "Hello",
       "(",
       "bracket",
       ")",
       "and",
       "side",
       "@-@",
       "scrolled",
       "[",
       "and",
       "]",
       "Henry",
       "'s",
       "$",
       "5",
       "@,@",
       "000",
       "with",
       "3",
       "@.@",
       "34",
       "m",
       ".",
       "What",
       "'s",
       "up",
       "!",
       "?",
   ]

   self.assertListEqual(tokenizer.tokenize(A_	)     ,    A_	)

   self.assertEqual(tokenizer.convert_tokens_to_string(A_	)     ,    A_	)







  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    self.get_tokenizer()
   lowerCamelCase_						=    len(A_	)

   tokenizer.add_tokens(["new1", "new2"]	)
   tokenizer.move_added_token("new1"     ,    1	)

   # Check that moved token is not copied (duplicate)
   self.assertEqual(len(A_	)     ,    original_len + 2	)
   # Check that token is moved to specified id
   self.assertEqual(tokenizer.encode("new1"	)     ,    [1]	)
   self.assertEqual(tokenizer.decode([1]	)     ,    "new1"	)










from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'EncodecFeatureExtractor'
 __UpperCamelCase       =      ('T5Tokenizer', 'T5TokenizerFast')

 def __init__(  self,		A_,		A_		)	->       Optional[int]:
    super().__init__(A_,		A_		)
    UpperCAmelCase__						=self.feature_extractor
    UpperCAmelCase__						=False

 def  __UpperCAmelCase						(  self,		A_=None,		A_=None,		A_=True		)	->       Union[str, Any]:
    return self.tokenizer.get_decoder_prompt_ids(task=A_,		language=A_,		no_timestamps=A_		)

 def __call__(  self,		*A_,		**A_		)	->       Any:
    # For backward compatibility
    if self._in_target_context_manager:
       return self.current_processor(*A_,		**A_		)

    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("sampling_rate",		A_		)
    UpperCAmelCase__						=kwargs.pop("text",		A_		)
    if len(A_		) > 0:
       UpperCAmelCase__						=args[0]
       UpperCAmelCase__						=args[1:]

    if audio is None and text is None:
       raise ValueError("You need to specify either an `audio` or `text` input to process."		)

    if text is not None:
       UpperCAmelCase__						=self.tokenizer(A_,		**A_		)

    if audio is not None:
       UpperCAmelCase__						=self.feature_extractor(A_,		*A_,		sampling_rate=A_,		**A_		)

    if audio is None:
       return inputs

    elif text is None:
       return audio_inputs

    else:
       UpperCAmelCase__						=audio_inputs["input_values"]
       if "padding_mask" in audio_inputs:
          UpperCAmelCase__						=audio_inputs["padding_mask"]
       return inputs

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       Dict:
    UpperCAmelCase__						=kwargs.pop("audio",		A_		)
    UpperCAmelCase__						=kwargs.pop("padding_mask",		A_		)

    if len(A_		) > 0:
       UpperCAmelCase__						=args[0]
       UpperCAmelCase__						=args[1:]

    if audio_values is not None:
       return self._decode_audio(A_,		padding_mask=A_		)
    else:
       return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)






 def  __UpperCAmelCase						(  self,		A_,		A_ = None		)	->       List[np.ndarray]:
    UpperCAmelCase__						=to_numpy(A_		)
    UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=audio_values.shape

    if padding_mask is None:
       return list(A_		)

    UpperCAmelCase__						=to_numpy(A_		)

    # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
    # token (so that the generated audio values are **not** treated as padded tokens)
    UpperCAmelCase__						=seq_len - padding_mask.shape[-1]
    UpperCAmelCase__						=1 - self.feature_extractor.padding_value
    UpperCAmelCase__						=np.pad(A_,		((0, 0), (0, difference)),		"constant",		constant_values=A_		)

    UpperCAmelCase__						=audio_values.tolist()
    for i in range(A_		):
       UpperCAmelCase__						=np.asarray(audio_values[i]		)[
           padding_mask[i][None, :] != self.feature_extractor.padding_value
       ]
       UpperCAmelCase__						=sliced_audio.reshape(A_,		-1		)

    return audio_values



