





'''simple docstring'''

import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_MAPPING,
        LevitForImageClassification,
        LevitForImageClassificationWithTeacher,
        LevitModel,
    )
    from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import LevitImageProcessor







class __a			( _snake_case      ):





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.config_class(**self.inputs_dict     )
      self.parent.assertTrue(hasattr(lowerCamelCase					,"""hidden_sizes"""     )     )
      self.parent.assertTrue(hasattr(lowerCamelCase					,"""num_attention_heads"""     )     )







class __a			:





   def __init__(       self					:      Dict					,lowerCamelCase					:      str					,lowerCamelCase					:      str=13					,lowerCamelCase					:      str=64					,lowerCamelCase					:      int=3					,lowerCamelCase					:      Dict=3					,lowerCamelCase					:      List[str]=2					,lowerCamelCase					:      Optional[int]=1					,lowerCamelCase					:      int=16					,lowerCamelCase					:      Any=[128, 256, 384]					,lowerCamelCase					:      str=[4, 6, 8]					,lowerCamelCase					:      Optional[int]=[2, 3, 4]					,lowerCamelCase					:      Tuple=[16, 16, 16]					,lowerCamelCase					:      Optional[Any]=0					,lowerCamelCase					:      List[str]=[2, 2, 2]					,lowerCamelCase					:      int=[2, 2, 2]					,lowerCamelCase					:      str=0.02					,lowerCamelCase					:      List[str]=True					,lowerCamelCase					:      str=True					,lowerCamelCase					:      Union[str, Any]=2					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							parent
      __SCREAMING_SNAKE_CASE   				=							batch_size
      __SCREAMING_SNAKE_CASE   				=							image_size
      __SCREAMING_SNAKE_CASE   				=							num_channels
      __SCREAMING_SNAKE_CASE   				=							kernel_size
      __SCREAMING_SNAKE_CASE   				=							stride
      __SCREAMING_SNAKE_CASE   				=							padding
      __SCREAMING_SNAKE_CASE   				=							hidden_sizes
      __SCREAMING_SNAKE_CASE   				=							num_attention_heads
      __SCREAMING_SNAKE_CASE   				=							depths
      __SCREAMING_SNAKE_CASE   				=							key_dim
      __SCREAMING_SNAKE_CASE   				=							drop_path_rate
      __SCREAMING_SNAKE_CASE   				=							patch_size
      __SCREAMING_SNAKE_CASE   				=							attention_ratio
      __SCREAMING_SNAKE_CASE   				=							mlp_ratio
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							[
          ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
          ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
      ]
      __SCREAMING_SNAKE_CASE   				=							is_training
      __SCREAMING_SNAKE_CASE   				=							use_labels
      __SCREAMING_SNAKE_CASE   				=							num_labels
      __SCREAMING_SNAKE_CASE   				=							initializer_range





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_labels:
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size]					,self.num_labels     )

      __SCREAMING_SNAKE_CASE   				=							self.get_config()
      return config, pixel_values, labels





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      return LevitConfig(
          image_size=self.image_size					,num_channels=self.num_channels					,kernel_size=self.kernel_size					,stride=self.stride					,padding=self.padding					,patch_size=self.patch_size					,hidden_sizes=self.hidden_sizes					,num_attention_heads=self.num_attention_heads					,depths=self.depths					,key_dim=self.key_dim					,drop_path_rate=self.drop_path_rate					,mlp_ratio=self.mlp_ratio					,attention_ratio=self.attention_ratio					,initializer_range=self.initializer_range					,down_ops=self.down_ops					,)





   def   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							LevitModel(config=lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							(self.image_size, self.image_size)
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							image_size[0], image_size[1]
      for _ in range(4     ):
         __SCREAMING_SNAKE_CASE   				=							floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1     )
         __SCREAMING_SNAKE_CASE   				=							floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1     )
      self.parent.assertEqual(
          result.last_hidden_state.shape					,(self.batch_size, ceil(height / 4     ) * ceil(width / 4     ), self.hidden_sizes[-1])					,)





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.num_labels
      __SCREAMING_SNAKE_CASE   				=							LevitForImageClassification(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,labels=lowerCamelCase     )
      self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							config_and_inputs
      __SCREAMING_SNAKE_CASE   				=							{"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_torch
class __a			( _snake_case, _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			Union[str, Any]						   =						(
       (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
       if is_torch_available()
       else ()
   )
   __UpperCamelCase :			Dict						   =						(
       {
           'feature-extraction': LevitModel,
           'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
       }
       if is_torch_available()
       else {}
   )

   __UpperCamelCase :			List[str]						   =						False
   __UpperCamelCase :			Optional[int]						   =						False
   __UpperCamelCase :			str						   =						False
   __UpperCamelCase :			Optional[int]						   =						False
   __UpperCamelCase :			Optional[Any]						   =						False





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							LevitModelTester(self     )
      __SCREAMING_SNAKE_CASE   				=							ConfigTester(self					,config_class=lowerCamelCase					,has_text_modality=lowerCamelCase					,hidden_size=37     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      return





   @unittest.skip(reason="""Levit does not use inputs_embeds"""     )
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      pass





   @unittest.skip(reason="""Levit does not support input and output embeddings"""     )
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      pass





   @unittest.skip(reason="""Levit does not output attentions"""     )
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      pass





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()

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

         __SCREAMING_SNAKE_CASE   				=							["""pixel_values"""]
         self.assertListEqual(arg_names[:1]					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      def check_hidden_states_output(lowerCamelCase					:      Any					,lowerCamelCase					:      Any					,lowerCamelCase					:      Tuple     ):
         __SCREAMING_SNAKE_CASE   				=							model_class(lowerCamelCase     )
         model.to(lowerCamelCase     )
         model.eval()

         with torch.no_grad():
            __SCREAMING_SNAKE_CASE   				=							model(**self._prepare_for_class(lowerCamelCase					,lowerCamelCase     )     )

         __SCREAMING_SNAKE_CASE   				=							outputs.hidden_states

         __SCREAMING_SNAKE_CASE   				=							len(self.model_tester.depths     ) + 1
         self.assertEqual(len(lowerCamelCase     )					,lowerCamelCase     )

         __SCREAMING_SNAKE_CASE   				=							(self.model_tester.image_size, self.model_tester.image_size)
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							image_size[0], image_size[1]
         for _ in range(4     ):
            __SCREAMING_SNAKE_CASE   				=							floor(
                (
                    (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                    / self.model_tester.stride
                )
                + 1     )
            __SCREAMING_SNAKE_CASE   				=							floor(
                (
                    (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                    / self.model_tester.stride
                )
                + 1     )
         # verify the first hidden states (first block)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:]     )					,[
                 height * width,
                 self.model_tester.hidden_sizes[0],
             ]					,)

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __SCREAMING_SNAKE_CASE   				=							True
         check_hidden_states_output(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         __SCREAMING_SNAKE_CASE   				=							True

         check_hidden_states_output(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )





   @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""     )
   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      pass





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      Optional[int]					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Any=False     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							super()._prepare_for_class(lowerCamelCase					,lowerCamelCase					,return_labels=lowerCamelCase     )

      if return_labels:
         if model_class.__name__ == "LevitForImageClassificationWithTeacher":
            del inputs_dict["labels"]

      return inputs_dict





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      if not self.model_tester.is_training:
         return

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
      __SCREAMING_SNAKE_CASE   				=							True

      for model_class in self.all_model_classes:
         # LevitForImageClassificationWithTeacher supports inference-only
         if (
             model_class in get_values(lowerCamelCase     )
             or model_class.__name__ == "LevitForImageClassificationWithTeacher"
         ):
            continue
         __SCREAMING_SNAKE_CASE   				=							model_class(lowerCamelCase     )
         model.to(lowerCamelCase     )
         model.train()
         __SCREAMING_SNAKE_CASE   				=							self._prepare_for_class(lowerCamelCase					,lowerCamelCase					,return_labels=lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							model(**lowerCamelCase     ).loss
         loss.backward()





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()
      if not self.model_tester.is_training:
         return

      __SCREAMING_SNAKE_CASE   				=							False
      __SCREAMING_SNAKE_CASE   				=							True

      for model_class in self.all_model_classes:
         if model_class in get_values(lowerCamelCase     ) or not model_class.supports_gradient_checkpointing:
            continue
         # LevitForImageClassificationWithTeacher supports inference-only
         if model_class.__name__ == "LevitForImageClassificationWithTeacher":
            continue
         __SCREAMING_SNAKE_CASE   				=							model_class(lowerCamelCase     )
         model.gradient_checkpointing_enable()
         model.to(lowerCamelCase     )
         model.train()
         __SCREAMING_SNAKE_CASE   				=							self._prepare_for_class(lowerCamelCase					,lowerCamelCase					,return_labels=lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							model(**lowerCamelCase     ).loss
         loss.backward()





   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs_for_common()

      __SCREAMING_SNAKE_CASE   				=							[
          {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
          {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
          {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
      ]

      for model_class in self.all_model_classes:
         if (
             model_class
             not in [
                 *get_values(lowerCamelCase     ),
             ]
             or model_class.__name__ == "LevitForImageClassificationWithTeacher"
         ):
            continue

         for problem_type in problem_types:
            with self.subTest(msg=f"""Testing {model_class} with {problem_type['title']}"""     ):
               __SCREAMING_SNAKE_CASE   				=							problem_type["""title"""]
               __SCREAMING_SNAKE_CASE   				=							problem_type["""num_labels"""]

               __SCREAMING_SNAKE_CASE   				=							model_class(lowerCamelCase     )
               model.to(lowerCamelCase     )
               model.train()

               __SCREAMING_SNAKE_CASE   				=							self._prepare_for_class(lowerCamelCase					,lowerCamelCase					,return_labels=lowerCamelCase     )

               if problem_type["num_labels"] > 1:
                  __SCREAMING_SNAKE_CASE   				=							inputs["""labels"""].unsqueeze(1     ).repeat(1					,problem_type["""num_labels"""]     )

               __SCREAMING_SNAKE_CASE   				=							inputs["""labels"""].to(problem_type["""dtype"""]     )

               # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
               # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
               # they have the same size." which is a symptom something in wrong for the regression problem.
               # See https://github.com/huggingface/transformers/issues/11780
               with warnings.catch_warnings(record=lowerCamelCase     ) as warning_list:
                  __SCREAMING_SNAKE_CASE   				=							model(**lowerCamelCase     ).loss
               for w in warning_list:
                  if "Using a target size that is different to the input size" in str(w.message     ):
                     raise ValueError(
                         f"""Something is going wrong in the regression problem: intercepted {w.message}"""     )

               loss.backward()





   @slow
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __SCREAMING_SNAKE_CASE   				=							LevitModel.from_pretrained(lowerCamelCase     )
         self.assertIsNotNone(lowerCamelCase     )





def     __magic_name__							(				)    ->       Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
   return image







@require_torch
@require_vision
class __a			( unittest.TestCase      ):





   @cached_property
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )





   @slow
   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(
          lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							self.default_image_processor
      __SCREAMING_SNAKE_CASE   				=							prepare_img()
      __SCREAMING_SNAKE_CASE   				=							image_processor(images=lowerCamelCase					,return_tensors="""pt"""     ).to(lowerCamelCase     )

      # forward pass
      with torch.no_grad():
         __SCREAMING_SNAKE_CASE   				=							model(**lowerCamelCase     )

      # verify the logits
      __SCREAMING_SNAKE_CASE   				=							torch.Size((1, 1000)     )
      self.assertEqual(outputs.logits.shape					,lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							torch.tensor([1.0_448, -0.3_745, -1.8_317]     ).to(lowerCamelCase     )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]					,lowerCamelCase					,atol=1E-4     )     )












'''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


a   	=    logging.get_logger(__name__)







class __a			( _snake_case      ):
   __UpperCamelCase :			List[str]						   =						['input_features', 'is_longer']





   def __init__(       self					:      Union[str, Any]					,lowerCamelCase					:      Union[str, Any]=64					,lowerCamelCase					:      Any=4_8000					,lowerCamelCase					:      int=480					,lowerCamelCase					:      Any=10					,lowerCamelCase					:      Dict=1024					,lowerCamelCase					:      Union[str, Any]=0.0					,lowerCamelCase					:      int=False					,lowerCamelCase					:      float = 0					,lowerCamelCase					:      float = 1_4000					,lowerCamelCase					:      int = None					,lowerCamelCase					:      str = "fusion"					,lowerCamelCase					:      str = "repeatpad"					,**lowerCamelCase					:      Dict					,):

      '''simple docstring'''

      super().__init__(
          feature_size=lowerCamelCase					,sampling_rate=lowerCamelCase					,padding_value=lowerCamelCase					,return_attention_mask=lowerCamelCase					,**lowerCamelCase					,)
      __SCREAMING_SNAKE_CASE   				=							top_db
      __SCREAMING_SNAKE_CASE   				=							truncation
      __SCREAMING_SNAKE_CASE   				=							padding
      __SCREAMING_SNAKE_CASE   				=							fft_window_size
      __SCREAMING_SNAKE_CASE   				=							(fft_window_size >> 1) + 1
      __SCREAMING_SNAKE_CASE   				=							hop_length
      __SCREAMING_SNAKE_CASE   				=							max_length_s
      __SCREAMING_SNAKE_CASE   				=							max_length_s * sampling_rate
      __SCREAMING_SNAKE_CASE   				=							sampling_rate
      __SCREAMING_SNAKE_CASE   				=							frequency_min
      __SCREAMING_SNAKE_CASE   				=							frequency_max
      __SCREAMING_SNAKE_CASE   				=							mel_filter_bank(
          num_frequency_bins=self.nb_frequency_bins					,num_mel_filters=lowerCamelCase					,min_frequency=lowerCamelCase					,max_frequency=lowerCamelCase					,sampling_rate=lowerCamelCase					,norm=lowerCamelCase					,mel_scale="""htk"""					,)
      __SCREAMING_SNAKE_CASE   				=							mel_filter_bank(
          num_frequency_bins=self.nb_frequency_bins					,num_mel_filters=lowerCamelCase					,min_frequency=lowerCamelCase					,max_frequency=lowerCamelCase					,sampling_rate=lowerCamelCase					,norm="""slaney"""					,mel_scale="""slaney"""					,)





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							copy.deepcopy(self.__dict__     )
      __SCREAMING_SNAKE_CASE   				=							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   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      np.array					,lowerCamelCase					:      Optional[np.array] = None     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							spectrogram(
          lowerCamelCase					,window_function(self.fft_window_size					,"""hann"""     )					,frame_length=self.fft_window_size					,hop_length=self.hop_length					,power=2.0					,mel_filters=lowerCamelCase					,log_mel="""dB"""					,)
      return log_mel_spectrogram.T





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      int					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							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
         __SCREAMING_SNAKE_CASE   				=							[0]
      if len(ranges[2]     ) == 0:
         # if the audio is too short, we just use the first chunk
         __SCREAMING_SNAKE_CASE   				=							[0]
      # randomly choose index for each part
      __SCREAMING_SNAKE_CASE   				=							np.random.choice(ranges[0]     )
      __SCREAMING_SNAKE_CASE   				=							np.random.choice(ranges[1]     )
      __SCREAMING_SNAKE_CASE   				=							np.random.choice(ranges[2]     )

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

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





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      np.array					,lowerCamelCase					:      str					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      if waveform.shape[0] > max_length:
         if truncation == "rand_trunc":
            __SCREAMING_SNAKE_CASE   				=							True
            # random crop to max_length (for compatibility) -> this should be handled by self.pad
            __SCREAMING_SNAKE_CASE   				=							len(lowerCamelCase     ) - max_length
            __SCREAMING_SNAKE_CASE   				=							np.random.randint(0					,overflow + 1     )
            __SCREAMING_SNAKE_CASE   				=							waveform[idx : idx + max_length]
            __SCREAMING_SNAKE_CASE   				=							self._np_extract_fbank_features(lowerCamelCase					,self.mel_filters_slaney     )[None, :]
         elif truncation == "fusion":
            __SCREAMING_SNAKE_CASE   				=							self._np_extract_fbank_features(lowerCamelCase					,self.mel_filters     )
            __SCREAMING_SNAKE_CASE   				=							max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
            __SCREAMING_SNAKE_CASE   				=							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.
               __SCREAMING_SNAKE_CASE   				=							np.stack([mel, mel, mel, mel]					,axis=0     )
               __SCREAMING_SNAKE_CASE   				=							False
            else:
               __SCREAMING_SNAKE_CASE   				=							self._random_mel_fusion(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )
               __SCREAMING_SNAKE_CASE   				=							True
         else:
            raise NotImplementedError(f"""data_truncating {truncation} not implemented"""     )

      else:
         __SCREAMING_SNAKE_CASE   				=							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":
               __SCREAMING_SNAKE_CASE   				=							int(max_length / len(lowerCamelCase     )     )
               __SCREAMING_SNAKE_CASE   				=							np.stack(np.tile(lowerCamelCase					,n_repeat + 1     )     )[:max_length]
            if padding == "repeatpad":
               __SCREAMING_SNAKE_CASE   				=							int(max_length / len(lowerCamelCase     )     )
               __SCREAMING_SNAKE_CASE   				=							np.stack(np.tile(lowerCamelCase					,lowerCamelCase     )     )
            __SCREAMING_SNAKE_CASE   				=							np.pad(lowerCamelCase					,(0, max_length - waveform.shape[0])					,mode="""constant"""					,constant_values=0     )

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

      return input_mel, longer





   def __call__(       self					:      Optional[Any]					,lowerCamelCase					:      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,lowerCamelCase					:      str = None					,lowerCamelCase					:      Optional[str] = None					,lowerCamelCase					:      Optional[int] = None					,lowerCamelCase					:      Optional[int] = None					,lowerCamelCase					:      Optional[Union[str, TensorType]] = None					,**lowerCamelCase					:      str					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							truncation if truncation is not None else self.truncation
      __SCREAMING_SNAKE_CASE   				=							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."""     )

      __SCREAMING_SNAKE_CASE   				=							isinstance(lowerCamelCase					,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}"""     )
      __SCREAMING_SNAKE_CASE   				=							is_batched_numpy or (
          isinstance(lowerCamelCase					,(list, tuple)     ) and (isinstance(raw_speech[0]					,(np.ndarray, tuple, list)     ))
      )

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

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

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

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

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

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

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

      __SCREAMING_SNAKE_CASE   				=							{"""input_features""": input_mel, """is_longer""": is_longer}
      __SCREAMING_SNAKE_CASE   				=							BatchFeature(lowerCamelCase     )

      if return_tensors is not None:
         __SCREAMING_SNAKE_CASE   				=							input_features.convert_to_tensors(lowerCamelCase     )

      return input_features





