import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
      from transformers import WhisperFeatureExtractor

if is_torch_available():
      import torch

UpperCamelCase  							=						random.Random()

def 			lowercase_				(			_lowerCamelCase							:       int		,	_lowerCamelCase							:       Optional[Any]=1.0		,	_lowerCamelCase							:       int=None		,	_lowerCamelCase							:       int=None):
    if rng is None:
        lowercase__ :   Optional[int]    					=				global_rng

    lowercase__ :   Tuple    					=				[]
    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_torchaudio
class   snake_case_       (		unittest.TestCase       ):






   def __init__(      self				:     Optional[Any]	,						lowercase_				:     Any	,						lowercase_				:     Any=7	,						lowercase_				:     List[str]=4_00	,						lowercase_				:     int=20_00	,						lowercase_				:     Union[str, Any]=10	,						lowercase_				:     Tuple=1_60	,						lowercase_				:     Dict=8	,						lowercase_				:     Dict=0.0	,						lowercase_				:     List[Any]=40_00	,						lowercase_				:     Optional[Any]=False	,						lowercase_				:     Tuple=True	,						)					->						List[Any]:
       lowercase__ :   Any    					=				parent
       lowercase__ :   Optional[Any]    					=				batch_size
       lowercase__ :   Dict    					=				min_seq_length
       lowercase__ :   Optional[Any]    					=				max_seq_length
       lowercase__ :   Optional[int]    					=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       lowercase__ :   Tuple    					=				padding_value
       lowercase__ :   Optional[Any]    					=				sampling_rate
       lowercase__ :   Optional[int]    					=				return_attention_mask
       lowercase__ :   Union[str, Any]    					=				do_normalize
       lowercase__ :   List[str]    					=				feature_size
       lowercase__ :   List[str]    					=				chunk_length
       lowercase__ :   List[Any]    					=				hop_length






   def       __UpperCamelCase     (      self				:     Dict  )					->						int:
       return {
           "feature_size": self.feature_size,
           "hop_length": self.hop_length,
           "chunk_length": self.chunk_length,
           "padding_value": self.padding_value,
           "sampling_rate": self.sampling_rate,
           "return_attention_mask": self.return_attention_mask,
           "do_normalize": self.do_normalize,
       }







   def       __UpperCamelCase     (      self				:     Union[str, Any]	,						lowercase_				:     str=False	,						lowercase_				:     Optional[int]=False  )					->						Union[str, Any]:
       def _flatten(lowercase_				:     List[str]  ):
           return list(itertools.chain(*lowercase_  )  )

       if equal_length:
           lowercase__ :   List[str]    					=				[floats_list((self.max_seq_length, self.feature_size)  ) for _ in range(self.batch_size  )]
       else:
           # make sure that inputs increase in size
           lowercase__ :   List[str]    					=				[
               floats_list((x, self.feature_size)  )
               for x in range(self.min_seq_length	,						self.max_seq_length	,						self.seq_length_diff  )
           ]
       if numpify:
           lowercase__ :   Tuple    					=				[np.asarray(lowercase_  ) for x in speech_inputs]
       return speech_inputs



@require_torch
@require_torchaudio
class   snake_case_       (		__A  ,unittest.TestCase       ):
   __A	:		Any         =				WhisperFeatureExtractor if is_speech_available() else None






   def       __UpperCamelCase     (      self				:     Dict  )					->						Dict:
       lowercase__ :   Tuple    					=				WhisperFeatureExtractionTester(self  )






   def       __UpperCamelCase     (      self				:     str  )					->						Optional[int]:
       lowercase__ :   Union[str, Any]    					=				self.feature_extraction_class(**self.feat_extract_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase__ :   Union[str, Any]    					=				feat_extract_first.save_pretrained(lowercase_  )[0]
           check_json_file_has_correct_format(lowercase_  )
           lowercase__ :   Optional[int]    					=				self.feature_extraction_class.from_pretrained(lowercase_  )

       lowercase__ :   Union[str, Any]    					=				feat_extract_first.to_dict()
       lowercase__ :   List[Any]    					=				feat_extract_second.to_dict()
       lowercase__ :   Optional[Any]    					=				feat_extract_first.mel_filters
       lowercase__ :   str    					=				feat_extract_second.mel_filters
       self.assertTrue(np.allclose(lowercase_	,						lowercase_  )  )
       self.assertEqual(lowercase_	,						lowercase_  )






   def       __UpperCamelCase     (      self				:     Tuple  )					->						Any:
       lowercase__ :   Tuple    					=				self.feature_extraction_class(**self.feat_extract_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase__ :   Optional[Any]    					=				os.path.join(lowercase_	,						"feat_extract.json"  )
           feat_extract_first.to_json_file(lowercase_  )
           lowercase__ :   Optional[Any]    					=				self.feature_extraction_class.from_json_file(lowercase_  )

       lowercase__ :   List[Any]    					=				feat_extract_first.to_dict()
       lowercase__ :   Tuple    					=				feat_extract_second.to_dict()
       lowercase__ :   int    					=				feat_extract_first.mel_filters
       lowercase__ :   str    					=				feat_extract_second.mel_filters
       self.assertTrue(np.allclose(lowercase_	,						lowercase_  )  )
       self.assertEqual(lowercase_	,						lowercase_  )






   def       __UpperCamelCase     (      self				:     Any  )					->						Optional[Any]:
       # Tests that all call wrap to encode_plus and batch_encode_plus
       lowercase__ :   Optional[int]    					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
       # create three inputs of length 800, 1000, and 1200
       lowercase__ :   Optional[Any]    					=				[floats_list((1, x)  )[0] for x in range(8_00	,						14_00	,						2_00  )]
       lowercase__ :   Tuple    					=				[np.asarray(lowercase_  ) for speech_input in speech_inputs]

       # Test feature size
       lowercase__ :   Dict    					=				feature_extractor(lowercase_	,						padding="max_length"	,						return_tensors="np"  ).input_features
       self.assertTrue(input_features.ndim == 3  )
       self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames  )
       self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size  )

       # Test not batched input
       lowercase__ :   str    					=				feature_extractor(speech_inputs[0]	,						return_tensors="np"  ).input_features
       lowercase__ :   Tuple    					=				feature_extractor(np_speech_inputs[0]	,						return_tensors="np"  ).input_features
       self.assertTrue(np.allclose(lowercase_	,						lowercase_	,						atol=1E-3  )  )

       # Test batched
       lowercase__ :   Union[str, Any]    					=				feature_extractor(lowercase_	,						return_tensors="np"  ).input_features
       lowercase__ :   Any    					=				feature_extractor(lowercase_	,						return_tensors="np"  ).input_features
       for enc_seq_a, enc_seq_a in zip(lowercase_	,						lowercase_  ):
           self.assertTrue(np.allclose(lowercase_	,						lowercase_	,						atol=1E-3  )  )

       # Test 2-D numpy arrays are batched.
       lowercase__ :   List[Any]    					=				[floats_list((1, x)  )[0] for x in (8_00, 8_00, 8_00)]
       lowercase__ :   Optional[Any]    					=				np.asarray(lowercase_  )
       lowercase__ :   Tuple    					=				feature_extractor(lowercase_	,						return_tensors="np"  ).input_features
       lowercase__ :   Tuple    					=				feature_extractor(lowercase_	,						return_tensors="np"  ).input_features
       for enc_seq_a, enc_seq_a in zip(lowercase_	,						lowercase_  ):
           self.assertTrue(np.allclose(lowercase_	,						lowercase_	,						atol=1E-3  )  )

       # Test truncation required
       lowercase__ :   Union[str, Any]    					=				[floats_list((1, x)  )[0] for x in range(2_00	,						(feature_extractor.n_samples + 5_00)	,						2_00  )]
       lowercase__ :   int    					=				[np.asarray(lowercase_  ) for speech_input in speech_inputs]

       lowercase__ :   Optional[int]    					=				[x[: feature_extractor.n_samples] for x in speech_inputs]
       lowercase__ :   Optional[int]    					=				[np.asarray(lowercase_  ) for speech_input in speech_inputs_truncated]

       lowercase__ :   List[str]    					=				feature_extractor(lowercase_	,						return_tensors="np"  ).input_features
       lowercase__ :   Tuple    					=				feature_extractor(lowercase_	,						return_tensors="np"  ).input_features
       for enc_seq_a, enc_seq_a in zip(lowercase_	,						lowercase_  ):
           self.assertTrue(np.allclose(lowercase_	,						lowercase_	,						atol=1E-3  )  )






   def       __UpperCamelCase     (      self				:     int  )					->						Dict:
       import torch

       lowercase__ :   Dict    					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
       lowercase__ :   Dict    					=				np.random.rand(1_00	,						32  ).astype(np.floataa  )
       lowercase__ :   Dict    					=				np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
           lowercase__ :   Tuple    					=				feature_extractor.pad([{"input_features": inputs}]	,						return_tensors="np"  )
           self.assertTrue(np_processed.input_features.dtype == np.floataa  )
           lowercase__ :   Dict    					=				feature_extractor.pad([{"input_features": inputs}]	,						return_tensors="pt"  )
           self.assertTrue(pt_processed.input_features.dtype == torch.floataa  )






   def       __UpperCamelCase     (      self				:     Optional[int]	,						lowercase_				:     Optional[int]  )					->						List[Any]:
       lowercase__ :   int    					=				load_dataset("hf-internal-testing/librispeech_asr_dummy"	,						"clean"	,						split="validation"  )
       # automatic decoding with librispeech
       lowercase__ :   Dict    					=				ds.sort("id"  ).select(range(lowercase_  )  )[:num_samples]["audio"]

       return [x["array"] for x in speech_samples]






   def       __UpperCamelCase     (      self				:     Tuple  )					->						Tuple:
       # fmt: off
       lowercase__ :   Any    					=				torch.tensor(
           [
               0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
               0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
               0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
               -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
           ]  )
       # fmt: on

       lowercase__ :   Dict    					=				self._load_datasamples(1  )
       lowercase__ :   Union[str, Any]    					=				WhisperFeatureExtractor()
       lowercase__ :   List[Any]    					=				feature_extractor(lowercase_	,						return_tensors="pt"  ).input_features
       self.assertEqual(input_features.shape	,						(1, 80, 30_00)  )
       self.assertTrue(torch.allclose(input_features[0, 0, :30]	,						lowercase_	,						atol=1E-4  )  )







   def       __UpperCamelCase     (      self				:     Any  )					->						str:
       lowercase__ :   Optional[int]    					=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
       lowercase__ :   Tuple    					=				self._load_datasamples(1  )[0]
       lowercase__ :   Optional[Any]    					=				((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
       lowercase__ :   str    					=				feat_extract.zero_mean_unit_var_norm([audio]	,						attention_mask=lowercase_  )[0]

       self.assertTrue(np.all(np.mean(lowercase_  ) < 1E-3  )  )
       self.assertTrue(np.all(np.abs(np.var(lowercase_  ) - 1  ) < 1E-3  )  )










'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_albert import AlbertTokenizer
else:
  __SCREAMING_SNAKE_CASE     :List[Any]       =				None

__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE     :List[str]       =				{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__SCREAMING_SNAKE_CASE     :List[Any]       =				{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

__SCREAMING_SNAKE_CASE     :Optional[Any]       =				{
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

__SCREAMING_SNAKE_CASE     :Optional[int]       =				'''▁'''







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[int]           =							VOCAB_FILES_NAMES
 _lowerCamelCase  : Any           =							PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase  : Tuple           =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase  : int           =							AlbertTokenizer




 def __init__(					self       :    Optional[Any]	,			snake_case_       :    Optional[Any]=None	,			snake_case_       :    Optional[Any]=None	,			snake_case_       :    Optional[Any]=True	,			snake_case_       :    str=True	,			snake_case_       :    Tuple=False	,			snake_case_       :    List[Any]="[CLS]"	,			snake_case_       :    Union[str, Any]="[SEP]"	,			snake_case_       :    str="<unk>"	,			snake_case_       :    Union[str, Any]="[SEP]"	,			snake_case_       :    List[Any]="<pad>"	,			snake_case_       :    List[str]="[CLS]"	,			snake_case_       :    int="[MASK]"	,			**snake_case_       :    Any	,			):
      # Mask token behave like a normal word, i.e. include the space before it and
      # is included in the raw text, there should be a match in a non-normalized sentence.
      _UpperCAmelCase     							=   (
          AddedToken(snake_case_	,			lstrip=snake_case_	,			rstrip=snake_case_	,			normalized=snake_case_    )
          if isinstance(snake_case_	,			snake_case_    )
          else mask_token
      )

      super().__init__(
          snake_case_	,			tokenizer_file=snake_case_	,			do_lower_case=snake_case_	,			remove_space=snake_case_	,			keep_accents=snake_case_	,			bos_token=snake_case_	,			eos_token=snake_case_	,			unk_token=snake_case_	,			sep_token=snake_case_	,			pad_token=snake_case_	,			cls_token=snake_case_	,			mask_token=snake_case_	,			**snake_case_	,			)

      _UpperCAmelCase     							=   do_lower_case
      _UpperCAmelCase     							=   remove_space
      _UpperCAmelCase     							=   keep_accents
      _UpperCAmelCase     							=   vocab_file
      _UpperCAmelCase     							=   False if not self.vocab_file else True




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    List[int]	,			snake_case_       :    Optional[List[int]] = None    ):
      _UpperCAmelCase     							=   [self.sep_token_id]
      _UpperCAmelCase     							=   [self.cls_token_id]
      if token_ids_a is None:
           return cls + token_ids_a + sep
      return cls + token_ids_a + sep + token_ids_a + sep




 def      lowercase	(					self       :    Dict	,			snake_case_       :    List[int]	,			snake_case_       :    Optional[List[int]] = None    ):
      _UpperCAmelCase     							=   [self.sep_token_id]
      _UpperCAmelCase     							=   [self.cls_token_id]

      if token_ids_a is None:
           return len(cls + token_ids_a + sep    ) * [0]
      return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    str	,			snake_case_       :    Optional[str] = None    ):
      if not self.can_save_slow_tokenizer:
           raise ValueError(
               "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
               "tokenizer."    )

      if not os.path.isdir(snake_case_    ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
           return
      _UpperCAmelCase     							=   os.path.join(
          snake_case_	,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

      if os.path.abspath(self.vocab_file    ) != os.path.abspath(snake_case_    ):
           copyfile(self.vocab_file	,			snake_case_    )

      return (out_vocab_file,)




