

import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
   pass

if is_speech_available():
   from transformers import EncodecFeatureExtractor, MusicgenProcessor


__UpperCAmelCase      = random.Random()





def 	snake_case_      (__A     :						List[Any]					,   __A     :						List[Any]=1.0					,   __A     :						Any=None					,   __A     :						List[str]=None     )			->	Any:
   if rng is None:
      __lowerCAmelCase  :			List[str]					    =						global_rng

   __lowerCAmelCase  :			str					    =						[]
   for batch_idx in range(shape[0]     ):
      values.append([]     )
      for _ in range(shape[1]     ):
         values[-1].append(rng.random() * scale     )

   return values



@require_torch
@require_sentencepiece
class SCREAMING_SNAKE_CASE	(  unittest.TestCase ):
  """simple docstring"""






  def        SCREAMING_SNAKE_CASE    (			self :    int    )							->	List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[int]					    =						"""facebook/musicgen-small"""
     __lowerCAmelCase  :			str					    =						tempfile.mkdtemp()







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[int]       ,							**lowerCAmelCase :    Tuple    )							->	Any:






     """simple docstring"""







     return TaTokenizer.from_pretrained(self.checkpoint       ,							**__snake_case    )







  def        SCREAMING_SNAKE_CASE    (			self :    List[str]       ,							**lowerCAmelCase :    List[Any]    )							->	Optional[Any]:






     """simple docstring"""







     return EncodecFeatureExtractor.from_pretrained(self.checkpoint       ,							**__snake_case    )







  def        SCREAMING_SNAKE_CASE    (			self :    Tuple    )							->	List[str]:






     """simple docstring"""







     shutil.rmtree(self.tmpdirname    )







  def        SCREAMING_SNAKE_CASE    (			self :    int    )							->	Dict:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[Any]					    =						self.get_tokenizer()
     __lowerCAmelCase  :			Any					    =						self.get_feature_extractor()

     __lowerCAmelCase  :			int					    =						MusicgenProcessor(tokenizer=__snake_case       ,							feature_extractor=__snake_case    )

     processor.save_pretrained(self.tmpdirname    )
     __lowerCAmelCase  :			str					    =						MusicgenProcessor.from_pretrained(self.tmpdirname    )

     self.assertEqual(processor.tokenizer.get_vocab()       ,							tokenizer.get_vocab()    )
     self.assertIsInstance(processor.tokenizer       ,							__snake_case    )

     self.assertEqual(processor.feature_extractor.to_json_string()       ,							feature_extractor.to_json_string()    )
     self.assertIsInstance(processor.feature_extractor       ,							__snake_case    )







  def        SCREAMING_SNAKE_CASE    (			self :    Any    )							->	int:






     """simple docstring"""







     __lowerCAmelCase  :			Tuple					    =						MusicgenProcessor(tokenizer=self.get_tokenizer()       ,							feature_extractor=self.get_feature_extractor()    )
     processor.save_pretrained(self.tmpdirname    )

     __lowerCAmelCase  :			List[str]					    =						self.get_tokenizer(bos_token="""(BOS)"""       ,							eos_token="""(EOS)"""    )
     __lowerCAmelCase  :			str					    =						self.get_feature_extractor(do_normalize=__snake_case       ,							padding_value=1.0    )

     __lowerCAmelCase  :			Union[str, Any]					    =						MusicgenProcessor.from_pretrained(
         self.tmpdirname       ,							bos_token="""(BOS)"""       ,							eos_token="""(EOS)"""       ,							do_normalize=__snake_case       ,							padding_value=1.0    )

     self.assertEqual(processor.tokenizer.get_vocab()       ,							tokenizer_add_kwargs.get_vocab()    )
     self.assertIsInstance(processor.tokenizer       ,							__snake_case    )

     self.assertEqual(processor.feature_extractor.to_json_string()       ,							feature_extractor_add_kwargs.to_json_string()    )
     self.assertIsInstance(processor.feature_extractor       ,							__snake_case    )







  def        SCREAMING_SNAKE_CASE    (			self :    Tuple    )							->	Tuple:






     """simple docstring"""







     __lowerCAmelCase  :			Any					    =						self.get_feature_extractor()
     __lowerCAmelCase  :			Any					    =						self.get_tokenizer()

     __lowerCAmelCase  :			Optional[int]					    =						MusicgenProcessor(tokenizer=__snake_case       ,							feature_extractor=__snake_case    )

     __lowerCAmelCase  :			Optional[int]					    =						floats_list((3, 10_00)    )

     __lowerCAmelCase  :			Dict					    =						feature_extractor(__snake_case       ,							return_tensors="""np"""    )
     __lowerCAmelCase  :			Optional[Any]					    =						processor(__snake_case       ,							return_tensors="""np"""    )

     for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()       ,							input_processor[key].sum()       ,							delta=1e-2    )







  def        SCREAMING_SNAKE_CASE    (			self :    List[Any]    )							->	int:






     """simple docstring"""







     __lowerCAmelCase  :			Union[str, Any]					    =						self.get_feature_extractor()
     __lowerCAmelCase  :			Union[str, Any]					    =						self.get_tokenizer()

     __lowerCAmelCase  :			Any					    =						MusicgenProcessor(tokenizer=__snake_case       ,							feature_extractor=__snake_case    )

     __lowerCAmelCase  :			str					    =						"""This is a test string"""

     __lowerCAmelCase  :			List[Any]					    =						processor(text=__snake_case    )

     __lowerCAmelCase  :			Optional[Any]					    =						tokenizer(__snake_case    )

     for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]       ,							encoded_processor[key]    )







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[Any]    )							->	Tuple:






     """simple docstring"""







     __lowerCAmelCase  :			Dict					    =						self.get_feature_extractor()
     __lowerCAmelCase  :			Dict					    =						self.get_tokenizer()

     __lowerCAmelCase  :			int					    =						MusicgenProcessor(tokenizer=__snake_case       ,							feature_extractor=__snake_case    )

     __lowerCAmelCase  :			str					    =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     __lowerCAmelCase  :			str					    =						processor.batch_decode(sequences=__snake_case    )
     __lowerCAmelCase  :			Any					    =						tokenizer.batch_decode(__snake_case    )

     self.assertListEqual(__snake_case       ,							__snake_case    )







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[Any]    )							->	Tuple:






     """simple docstring"""







     __lowerCAmelCase  :			Any					    =						self.get_feature_extractor()
     __lowerCAmelCase  :			Union[str, Any]					    =						self.get_tokenizer()

     __lowerCAmelCase  :			List[Any]					    =						MusicgenProcessor(tokenizer=__snake_case       ,							feature_extractor=__snake_case    )

     self.assertListEqual(
         processor.model_input_names       ,							feature_extractor.model_input_names       ,							msg="""`processor` and `feature_extractor` model input names do not match"""       ,							)







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[int]    )							->	Tuple:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[int]					    =						self.get_feature_extractor(padding_side="""left"""    )
     __lowerCAmelCase  :			Optional[Any]					    =						self.get_tokenizer()

     __lowerCAmelCase  :			Optional[int]					    =						MusicgenProcessor(tokenizer=__snake_case       ,							feature_extractor=__snake_case    )

     __lowerCAmelCase  :			Dict					    =						[floats_list((1, x)    )[0] for x in range(5       ,							20       ,							5    )]
     __lowerCAmelCase  :			int					    =						processor(__snake_case    ).padding_mask

     __lowerCAmelCase  :			str					    =						np.asarray(floats_list((3, 20)    )    )[:, None, :]
     __lowerCAmelCase  :			Union[str, Any]					    =						processor.batch_decode(__snake_case       ,							padding_mask=__snake_case    )

     self.assertIsInstance(__snake_case       ,							__snake_case    )

     for audio in decoded_audios:
        self.assertIsInstance(__snake_case       ,							np.ndarray    )

     self.assertTrue(decoded_audios[0].shape == (1, 10)    )
     self.assertTrue(decoded_audios[1].shape == (1, 15)    )
     self.assertTrue(decoded_audios[2].shape == (1, 20)    )








from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy



class 			a     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       Any   		=  'MarkupLMFeatureExtractor'
     lowerCAmelCase				:       Optional[Any]   		=  ('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
     lowerCAmelCase				:       List[Any]   		=  True
     def __call__(	self   :			Dict					,    __snake_case   :			Tuple=None					,    __snake_case   :			List[Any]=None					,    __snake_case   :			Tuple=None					,    __snake_case   :			Tuple=None					,    __snake_case   :			Any=None					,    __snake_case   :			bool = True					,    __snake_case   :			Union[bool, str, PaddingStrategy] = False					,    __snake_case   :			Union[bool, str, TruncationStrategy] = None					,    __snake_case   :			Optional[int] = None					,    __snake_case   :			int = 0					,    __snake_case   :			Optional[int] = None					,    __snake_case   :			Optional[bool] = None					,    __snake_case   :			Optional[bool] = None					,    __snake_case   :			bool = False					,    __snake_case   :			bool = False					,    __snake_case   :			bool = False					,    __snake_case   :			bool = False					,    __snake_case   :			bool = True					,    __snake_case   :			Optional[Union[str, TensorType]] = None					,    **__snake_case   :			Any					,    ):
         # first, create nodes and xpaths
         if self.parse_html:
             if html_strings is None:
                 raise ValueError('''Make sure to pass HTML strings in case `parse_html` is set to `True`'''				)

             if nodes is not None or xpaths is not None or node_labels is not None:
                 raise ValueError(
                     '''Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'''				)

             UpperCAmelCase_							 =     self.feature_extractor(__snake_case				)
             UpperCAmelCase_							 =     features['''nodes''']
             UpperCAmelCase_							 =     features['''xpaths''']
         else:
             if html_strings is not None:
                 raise ValueError('''You have passed HTML strings but `parse_html` is set to `False`.'''				)
             if nodes is None or xpaths is None:
                 raise ValueError('''Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'''				)

        # # second, apply the tokenizer
         if questions is not None and self.parse_html:
             if isinstance(__snake_case					,    __snake_case				):
                 UpperCAmelCase_							 =     [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

         UpperCAmelCase_							 =     self.tokenizer(
             text=questions if questions is not None else nodes					,    text_pair=nodes if questions is not None else None					,    xpaths=__snake_case					,    node_labels=__snake_case					,    add_special_tokens=__snake_case					,    padding=__snake_case					,    truncation=__snake_case					,    max_length=__snake_case					,    stride=__snake_case					,    pad_to_multiple_of=__snake_case					,    return_token_type_ids=__snake_case					,    return_attention_mask=__snake_case					,    return_overflowing_tokens=__snake_case					,    return_special_tokens_mask=__snake_case					,    return_offsets_mapping=__snake_case					,    return_length=__snake_case					,    verbose=__snake_case					,    return_tensors=__snake_case					,    **__snake_case					,    )

         return encoded_inputs
     def   lowerCamelCase_   (	self   :			List[Any]					,    *__snake_case   :			Dict					,    **__snake_case   :			Optional[int]				):
         return self.tokenizer.batch_decode(*__snake_case					,    **__snake_case				)
     def   lowerCamelCase_   (	self   :			Optional[Any]					,    *__snake_case   :			Any					,    **__snake_case   :			Dict				):
         return self.tokenizer.decode(*__snake_case					,    **__snake_case				)




     @property
     def   lowerCamelCase_   (	self   :			Dict				):
         UpperCAmelCase_							 =     self.tokenizer.model_input_names
         return tokenizer_input_names





