'''simple docstring'''





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_:     Union[str, Any]  =logging.get_logger(__name__)




class        __A      ( UpperCamelCase__	):
    a__       :						Optional[int]          =  ["""input_features""", """is_longer"""]



    def __init__(self    :    Tuple   ,			__a    :    Optional[int]=64   ,			__a    :    Union[str, Any]=48000   ,			__a    :    str=480   ,			__a    :    Optional[int]=10   ,			__a    :    List[Any]=1024   ,			__a    :    Optional[Any]=0.0   ,			__a    :    Tuple=False   ,			__a    :    float = 0   ,			__a    :    float = 14000   ,			__a    :    int = None   ,			__a    :    str = "fusion"   ,			__a    :    str = "repeatpad"   ,			**__a    :    Any   ,			):
         super().__init__(
             feature_size=__a   ,			sampling_rate=__a   ,			padding_value=__a   ,			return_attention_mask=__a   ,			**__a   ,			)
         UpperCAmelCase_           =   top_db
         UpperCAmelCase_           =   truncation
         UpperCAmelCase_           =   padding
         UpperCAmelCase_           =   fft_window_size
         UpperCAmelCase_           =   (fft_window_size >> 1) + 1
         UpperCAmelCase_           =   hop_length
         UpperCAmelCase_           =   max_length_s
         UpperCAmelCase_           =   max_length_s * sampling_rate
         UpperCAmelCase_           =   sampling_rate
         UpperCAmelCase_           =   frequency_min
         UpperCAmelCase_           =   frequency_max
         UpperCAmelCase_           =   mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins   ,			num_mel_filters=__a   ,			min_frequency=__a   ,			max_frequency=__a   ,			sampling_rate=__a   ,			norm=__a   ,			mel_scale="htk"   ,			)
         UpperCAmelCase_           =   mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins   ,			num_mel_filters=__a   ,			min_frequency=__a   ,			max_frequency=__a   ,			sampling_rate=__a   ,			norm="slaney"   ,			mel_scale="slaney"   ,			)



    def 					_lowercase						(self    :    List[str] ):
         UpperCAmelCase_           =   copy.deepcopy(self.__dict__ )
         UpperCAmelCase_           =   self.__class__.__name__
         if "mel_filters" in output:
              del output["mel_filters"]
         if "mel_filters_slaney" in output:
              del output["mel_filters_slaney"]
         return output



    def 					_lowercase						(self    :    str   ,			__a    :    np.array   ,			__a    :    Optional[np.array] = None ):
         UpperCAmelCase_           =   spectrogram(
             __a   ,			window_function(self.fft_window_size   ,			"hann" )   ,			frame_length=self.fft_window_size   ,			hop_length=self.hop_length   ,			power=2.0   ,			mel_filters=__a   ,			log_mel="dB"   ,			)
         return log_mel_spectrogram.T



    def 					_lowercase						(self    :    List[Any]   ,			__a    :    Dict   ,			__a    :    Optional[Any]   ,			__a    :    Tuple ):
         UpperCAmelCase_           =   np.array_split(list(range(0   ,			total_frames - chunk_frames + 1 ) )   ,			3 )
         if len(ranges[1] ) == 0:
              # if the audio is too short, we just use the first chunk
              UpperCAmelCase_           =   [0]
         if len(ranges[2] ) == 0:
              # if the audio is too short, we just use the first chunk
              UpperCAmelCase_           =   [0]
         # randomly choose index for each part
         UpperCAmelCase_           =   np.random.choice(ranges[0] )
         UpperCAmelCase_           =   np.random.choice(ranges[1] )
         UpperCAmelCase_           =   np.random.choice(ranges[2] )

         UpperCAmelCase_           =   mel[idx_front : idx_front + chunk_frames, :]
         UpperCAmelCase_           =   mel[idx_middle : idx_middle + chunk_frames, :]
         UpperCAmelCase_           =   mel[idx_back : idx_back + chunk_frames, :]

         UpperCAmelCase_           =   torch.tensor(mel[None, None, :] )
         UpperCAmelCase_           =   torch.nn.functional.interpolate(
             __a   ,			size=[chunk_frames, 64]   ,			mode="bilinear"   ,			align_corners=__a )
         UpperCAmelCase_           =   mel_shrink[0][0].numpy()
         UpperCAmelCase_           =   np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]   ,			axis=0 )
         return mel_fusion



    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    np.array   ,			__a    :    Any   ,			__a    :    List[str]   ,			__a    :    Optional[Any] ):
         if waveform.shape[0] > max_length:
              if truncation == "rand_trunc":
                   UpperCAmelCase_           =   True
                   # random crop to max_length (for compatibility) -> this should be handled by self.pad
                   UpperCAmelCase_           =   len(__a ) - max_length
                   UpperCAmelCase_           =   np.random.randint(0   ,			overflow + 1 )
                   UpperCAmelCase_           =   waveform[idx : idx + max_length]
                   UpperCAmelCase_           =   self._np_extract_fbank_features(__a   ,			self.mel_filters_slaney )[None, :]
              elif truncation == "fusion":
                   UpperCAmelCase_           =   self._np_extract_fbank_features(__a   ,			self.mel_filters )
                   UpperCAmelCase_           =   max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                   UpperCAmelCase_           =   mel.shape[0]
                   if chunk_frames == total_frames:
                        # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                        # In this case, we just use the whole audio.
                        UpperCAmelCase_           =   np.stack([mel, mel, mel, mel]   ,			axis=0 )
                        UpperCAmelCase_           =   False
                   else:
                        UpperCAmelCase_           =   self._random_mel_fusion(__a   ,			__a   ,			__a )
                        UpperCAmelCase_           =   True
              else:
                   raise NotImplementedError(f"""data_truncating {truncation} not implemented""" )

         else:
              UpperCAmelCase_           =   False
              # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
              if waveform.shape[0] < max_length:
                   if padding == "repeat":
                        UpperCAmelCase_           =   int(max_length / len(__a ) )
                        UpperCAmelCase_           =   np.stack(np.tile(__a   ,			n_repeat + 1 ) )[:max_length]
                   if padding == "repeatpad":
                        UpperCAmelCase_           =   int(max_length / len(__a ) )
                        UpperCAmelCase_           =   np.stack(np.tile(__a   ,			__a ) )
                   UpperCAmelCase_           =   np.pad(__a   ,			(0, max_length - waveform.shape[0])   ,			mode="constant"   ,			constant_values=0 )

              if truncation == "fusion":
                   UpperCAmelCase_           =   self._np_extract_fbank_features(__a   ,			self.mel_filters )
                   UpperCAmelCase_           =   np.stack([input_mel, input_mel, input_mel, input_mel]   ,			axis=0 )
              else:
                   UpperCAmelCase_           =   self._np_extract_fbank_features(__a   ,			self.mel_filters_slaney )[None, :]

         return input_mel, longer






    def __call__(self    :    Any   ,			__a    :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,			__a    :    str = None   ,			__a    :    Optional[str] = None   ,			__a    :    Optional[int] = None   ,			__a    :    Optional[int] = None   ,			__a    :    Optional[Union[str, TensorType]] = None   ,			**__a    :    int   ,			):
         UpperCAmelCase_           =   truncation if truncation is not None else self.truncation
         UpperCAmelCase_           =   padding if padding else self.padding

         if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                   raise ValueError(
                       f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
                       f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
                       f""" was sampled with {self.sampling_rate} and not {sampling_rate}.""" )
         else:
              logger.warning(
                  "It is strongly recommended to pass the `sampling_rate` argument to this function. "
                  "Failing to do so can result in silent errors that might be hard to debug." )

         UpperCAmelCase_           =   isinstance(__a   ,			np.ndarray ) and len(raw_speech.shape ) > 1
         if is_batched_numpy and len(raw_speech.shape ) > 2:
              raise ValueError(f"""Only mono-channel audio is supported for input to {self}""" )
         UpperCAmelCase_           =   is_batched_numpy or (
             isinstance(__a   ,			(list, tuple) ) and (isinstance(raw_speech[0]   ,			(np.ndarray, tuple, list) ))
         )

         if is_batched:
              UpperCAmelCase_           =   [np.asarray(__a   ,			dtype=np.floataa ) for speech in raw_speech]
         elif not is_batched and not isinstance(__a   ,			np.ndarray ):
              UpperCAmelCase_           =   np.asarray(__a   ,			dtype=np.floataa )
         elif isinstance(__a   ,			np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
              UpperCAmelCase_           =   raw_speech.astype(np.floataa )

         # always return batch
         if not is_batched:
              UpperCAmelCase_           =   [np.asarray(__a )]

         # convert to mel spectrogram, truncate and pad if needed.
         UpperCAmelCase_           =   [
             self._get_input_mel(__a   ,			max_length if max_length else self.nb_max_samples   ,			__a   ,			__a )
             for waveform in raw_speech
         ]

         UpperCAmelCase_           =   []
         UpperCAmelCase_           =   []
         for mel, longer in padded_inputs:
              input_mel.append(__a )
              is_longer.append(__a )

         if truncation == "fusion" and sum(__a ) == 0:
              # if no audio is longer than 10s, then randomly select one audio to be longer
              UpperCAmelCase_           =   np.random.randint(0   ,			len(__a ) )
              UpperCAmelCase_           =   True

         if isinstance(input_mel[0]   ,			__a ):
              UpperCAmelCase_           =   [np.asarray(__a   ,			dtype=np.floataa ) for feature in input_mel]

         # is_longer is a list of bool
         UpperCAmelCase_           =   [[longer] for longer in is_longer]

         UpperCAmelCase_           =   {"input_features": input_mel, "is_longer": is_longer}
         UpperCAmelCase_           =   BatchFeature(__a )

         if return_tensors is not None:
              UpperCAmelCase_           =   input_features.convert_to_tensors(__a )

         return input_features





'''simple docstring'''





import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax

    from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class        __A      ( unittest.TestCase	):



    def __init__(self    :    str   ,			__a    :    Optional[Any]   ,			__a    :    Optional[Any]=13   ,			__a    :    int=30   ,			__a    :    Union[str, Any]=2   ,			__a    :    Dict=3   ,			__a    :    List[Any]=True   ,			__a    :    Optional[Any]=True   ,			__a    :    List[Any]=32   ,			__a    :    Any=5   ,			__a    :    str=4   ,			__a    :    Optional[int]=37   ,			__a    :    Optional[int]="gelu"   ,			__a    :    List[str]=0.1   ,			__a    :    Tuple=0.1   ,			__a    :    List[str]=10   ,			__a    :    Optional[int]=0.02   ,			):
         UpperCAmelCase_           =   parent
         UpperCAmelCase_           =   batch_size
         UpperCAmelCase_           =   image_size
         UpperCAmelCase_           =   patch_size
         UpperCAmelCase_           =   num_channels
         UpperCAmelCase_           =   is_training
         UpperCAmelCase_           =   use_labels
         UpperCAmelCase_           =   hidden_size
         UpperCAmelCase_           =   num_hidden_layers
         UpperCAmelCase_           =   num_attention_heads
         UpperCAmelCase_           =   intermediate_size
         UpperCAmelCase_           =   hidden_act
         UpperCAmelCase_           =   hidden_dropout_prob
         UpperCAmelCase_           =   attention_probs_dropout_prob
         UpperCAmelCase_           =   type_sequence_label_size
         UpperCAmelCase_           =   initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         UpperCAmelCase_           =   (image_size // patch_size) ** 2
         UpperCAmelCase_           =   num_patches + 1



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         UpperCAmelCase_           =   ViTConfig(
             image_size=self.image_size   ,			patch_size=self.patch_size   ,			num_channels=self.num_channels   ,			hidden_size=self.hidden_size   ,			num_hidden_layers=self.num_hidden_layers   ,			num_attention_heads=self.num_attention_heads   ,			intermediate_size=self.intermediate_size   ,			hidden_act=self.hidden_act   ,			hidden_dropout_prob=self.hidden_dropout_prob   ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,			is_decoder=__a   ,			initializer_range=self.initializer_range   ,			)

         return config, pixel_values



    def 					_lowercase						(self    :    Dict   ,			__a    :    Any   ,			__a    :    List[Any] ):
         UpperCAmelCase_           =   FlaxViTModel(config=__a )
         UpperCAmelCase_           =   model(__a )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         UpperCAmelCase_           =   (self.image_size, self.image_size)
         UpperCAmelCase_           =   (self.patch_size, self.patch_size)
         UpperCAmelCase_           =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape   ,			(self.batch_size, num_patches + 1, self.hidden_size) )



    def 					_lowercase						(self    :    Tuple   ,			__a    :    str   ,			__a    :    Any ):
         UpperCAmelCase_           =   self.type_sequence_label_size
         UpperCAmelCase_           =   FlaxViTForImageClassification(config=__a )
         UpperCAmelCase_           =   model(__a )
         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.type_sequence_label_size) )

         # test greyscale images
         UpperCAmelCase_           =   1
         UpperCAmelCase_           =   FlaxViTForImageClassification(__a )

         UpperCAmelCase_           =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
         UpperCAmelCase_           =   model(__a )






    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , 
         )           =   config_and_inputs
         UpperCAmelCase_           =   {"pixel_values": pixel_values}
         return config, inputs_dict




@require_flax
class        __A      ( UpperCamelCase__		,	unittest.TestCase	):
    a__       :						Tuple          =  (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   FlaxViTModelTester(self )
         UpperCAmelCase_           =   ConfigTester(self   ,			config_class=__a   ,			has_text_modality=__a   ,			hidden_size=37 )



    def 					_lowercase						(self    :    Tuple ):
         self.config_tester.run_common_tests()



    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a )



    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__a )



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_    , UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_           =   model_class(__a )
              UpperCAmelCase_           =   inspect.signature(model.__call__ )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase_           =   [*signature.parameters.keys()]

              UpperCAmelCase_           =   ["pixel_values"]
              self.assertListEqual(arg_names[:1]   ,			__a )



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_    , UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__ ):
                   UpperCAmelCase_           =   self._prepare_for_class(__a   ,			__a )
                   UpperCAmelCase_           =   model_class(__a )

                   @jax.jit
                   def model_jitted(__a    :    Tuple   ,			**__a    :    List[Any] ):
                        return model(pixel_values=__a   ,			**__a )

                   with self.subTest("JIT Enabled" ):
                        UpperCAmelCase_           =   model_jitted(**__a ).to_tuple()

                   with self.subTest("JIT Disabled" ):
                        with jax.disable_jit():
                             UpperCAmelCase_           =   model_jitted(**__a ).to_tuple()

                   self.assertEqual(len(__a )   ,			len(__a ) )
                   for jitted_output, output in zip(__a   ,			__a ):
                        self.assertEqual(jitted_output.shape   ,			output.shape )






    @slow
    def 					_lowercase						(self    :    Tuple ):
         for model_class_name in self.all_model_classes:
              UpperCAmelCase_           =   model_class_name.from_pretrained("google/vit-base-patch16-224" )
              UpperCAmelCase_           =   model(np.ones((1, 3, 224, 224) ) )
              self.assertIsNotNone(__a )




