'''simple docstring'''

import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_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_torch_available():
 import torch

if is_datasets_available():
 from datasets import load_dataset

A_     :						Optional[int]    							=random.Random()



def snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							Optional[int]=1.0	,   __snake_case  :							Optional[Any]=None	,   __snake_case  :							int=None)			->					Dict:
   if rng is None:
      lowerCAmelCase_			=      global_rng

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

   return values


class     __UpperCAmelCase       ( unittest.TestCase		):





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase=7    ,     _lowerCamelCase=400    ,     _lowerCamelCase=2000    ,     _lowerCamelCase=2048    ,     _lowerCamelCase=128    ,     _lowerCamelCase=1    ,     _lowerCamelCase=512    ,     _lowerCamelCase=30    ,     _lowerCamelCase=4_4100    ,     ):
     lowerCAmelCase_			=      parent
     lowerCAmelCase_			=      batch_size
     lowerCAmelCase_			=      min_seq_length
     lowerCAmelCase_			=      max_seq_length
     lowerCAmelCase_			=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     lowerCAmelCase_			=      spectrogram_length
     lowerCAmelCase_			=      feature_size
     lowerCAmelCase_			=      num_audio_channels
     lowerCAmelCase_			=      hop_length
     lowerCAmelCase_			=      chunk_length
     lowerCAmelCase_			=      sampling_rate





  def 						UpperCAmelCase_	(	self ):
     return {
         "spectrogram_length": self.spectrogram_length,
         "feature_size": self.feature_size,
         "num_audio_channels": self.num_audio_channels,
         "hop_length": self.hop_length,
         "chunk_length": self.chunk_length,
         "sampling_rate": self.sampling_rate,
     }







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase=False    ,     _lowerCamelCase=False ):




     def _flatten(_lowerCamelCase ):
        return list(itertools.chain(*snake_case__ ) )

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






@require_torch
@require_torchaudio
class     __UpperCAmelCase       ( __a						,       unittest.TestCase		):
  __A :				Dict											= TvltFeatureExtractor





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      TvltFeatureExtractionTester(self )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.feature_extraction_class(**self.feat_extract_dict )
     self.assertTrue(hasattr(snake_case__    ,     '''spectrogram_length''' ) )
     self.assertTrue(hasattr(snake_case__    ,     '''feature_size''' ) )
     self.assertTrue(hasattr(snake_case__    ,     '''num_audio_channels''' ) )
     self.assertTrue(hasattr(snake_case__    ,     '''hop_length''' ) )
     self.assertTrue(hasattr(snake_case__    ,     '''chunk_length''' ) )
     self.assertTrue(hasattr(snake_case__    ,     '''sampling_rate''' ) )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.feature_extraction_class(**self.feat_extract_dict )

     with tempfile.TemporaryDirectory() as tmpdirname:
        lowerCAmelCase_			=      feat_extract_first.save_pretrained(snake_case__ )[0]
        check_json_file_has_correct_format(snake_case__ )
        lowerCAmelCase_			=      self.feature_extraction_class.from_pretrained(snake_case__ )

     lowerCAmelCase_			=      feat_extract_first.to_dict()
     lowerCAmelCase_			=      feat_extract_second.to_dict()
     lowerCAmelCase_			=      dict_first.pop('''mel_filters''' )
     lowerCAmelCase_			=      dict_second.pop('''mel_filters''' )
     self.assertTrue(np.allclose(snake_case__    ,     snake_case__ ) )
     self.assertEqual(snake_case__    ,     snake_case__ )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.feature_extraction_class(**self.feat_extract_dict )

     with tempfile.TemporaryDirectory() as tmpdirname:
        lowerCAmelCase_			=      os.path.join(snake_case__    ,     '''feat_extract.json''' )
        feat_extract_first.to_json_file(snake_case__ )
        lowerCAmelCase_			=      self.feature_extraction_class.from_json_file(snake_case__ )

     lowerCAmelCase_			=      feat_extract_first.to_dict()
     lowerCAmelCase_			=      feat_extract_second.to_dict()
     lowerCAmelCase_			=      dict_first.pop('''mel_filters''' )
     lowerCAmelCase_			=      dict_second.pop('''mel_filters''' )
     self.assertTrue(np.allclose(snake_case__    ,     snake_case__ ) )
     self.assertEqual(snake_case__    ,     snake_case__ )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.feature_extraction_class(**self.feat_extract_dict )

     # create three inputs of length 800, 1000, and 1200
     lowerCAmelCase_			=      [floats_list((1, x) )[0] for x in range(800    ,     1400    ,     200 )]
     lowerCAmelCase_			=      [np.asarray(snake_case__ ) for speech_input in speech_inputs]

     # Test not batched input
     lowerCAmelCase_			=      feature_extractor(np_speech_inputs[0]    ,     return_tensors='''np'''    ,     sampling_rate=4_4100 ).audio_values

     self.assertTrue(encoded_audios.ndim == 4 )
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )

     # Test batched
     lowerCAmelCase_			=      feature_extractor(snake_case__    ,     return_tensors='''np'''    ,     sampling_rate=4_4100 ).audio_values

     self.assertTrue(encoded_audios.ndim == 4 )
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )

     # Test audio masking
     lowerCAmelCase_			=      feature_extractor(
         snake_case__    ,     return_tensors='''np'''    ,     sampling_rate=4_4100    ,     mask_audio=snake_case__ ).audio_values

     self.assertTrue(encoded_audios.ndim == 4 )
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )

     # Test 2-D numpy arrays are batched.
     lowerCAmelCase_			=      [floats_list((1, x) )[0] for x in (800, 800, 800)]
     lowerCAmelCase_			=      np.asarray(snake_case__ )
     lowerCAmelCase_			=      feature_extractor(snake_case__    ,     return_tensors='''np'''    ,     sampling_rate=4_4100 ).audio_values
     self.assertTrue(encoded_audios.ndim == 4 )
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      load_dataset('''hf-internal-testing/librispeech_asr_dummy'''    ,     '''clean'''    ,     split='''validation''' )
     # automatic decoding with librispeech
     lowerCAmelCase_			=      ds.sort('''id''' ).select(range(snake_case__ ) )[:num_samples]["audio"]

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







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self._load_datasamples(1 )
     lowerCAmelCase_			=      TvltFeatureExtractor()
     lowerCAmelCase_			=      feature_extractor(snake_case__    ,     return_tensors='''pt''' ).audio_values

     self.assertEquals(audio_values.shape    ,     (1, 1, 192, 128) )

     lowerCAmelCase_			=      torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]] )
     self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]    ,     snake_case__    ,     atol=1E-4 ) )







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE     =       {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnnxConfig"""]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE     =       ["""DeiTFeatureExtractor"""]
      SCREAMING_SNAKE_CASE     =       ["""DeiTImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE     =       [
          """DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """DeiTForImageClassification""",
          """DeiTForImageClassificationWithTeacher""",
          """DeiTForMaskedImageModeling""",
          """DeiTModel""",
          """DeiTPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE     =       [
          """TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFDeiTForImageClassification""",
          """TFDeiTForImageClassificationWithTeacher""",
          """TFDeiTForMaskedImageModeling""",
          """TFDeiTModel""",
          """TFDeiTPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_deit import DeiTFeatureExtractor
            from .image_processing_deit import DeiTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_deit import (
                DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DeiTForImageClassification,
                DeiTForImageClassificationWithTeacher,
                DeiTForMaskedImageModeling,
                DeiTModel,
                DeiTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_deit import (
                TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDeiTForImageClassification,
                TFDeiTForImageClassificationWithTeacher,
                TFDeiTForMaskedImageModeling,
                TFDeiTModel,
                TFDeiTPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE     =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



