code: 
  '''simple docstring'''
  
  
  
  
  
  from typing import List, Optional, Union
  
  import numpy as np
  import torch
  import torchaudio.compliance.kaldi as ta_kaldi
  
  from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
  from ...feature_extraction_utils import BatchFeature
  from ...utils import TensorType, logging
  
  
  A           =   logging.get_logger(__name__)
  
  
  
  class  lowerCAmelCase__   (       __SCREAMING_SNAKE_CASE       ):
  
  
  
   '''simple docstring'''
  
  
  
  
  
  
   _UpperCamelCase						= ['input_values', 'attention_mask']
  
   def __init__(self  : Any	, lowerCAmelCase_  : Optional[Any]=1	, lowerCAmelCase_  : List[str]=1_60_00	, lowerCAmelCase_  : List[Any]=1_28	, lowerCAmelCase_  : int=10_24	, lowerCAmelCase_  : int=0.0	, lowerCAmelCase_  : Tuple=True	, lowerCAmelCase_  : List[str]=-4.2_677_393	, lowerCAmelCase_  : Tuple=4.5_689_974	, lowerCAmelCase_  : Optional[Any]=False	, **lowerCAmelCase_  : Any	, )   ->       Any:
         super().__init__(feature_size=UpperCamelCase__	, sampling_rate=UpperCamelCase__	, padding_value=UpperCamelCase__	, **UpperCamelCase__    )
         snake_case_						       =							num_mel_bins
         snake_case_						       =							max_length
         snake_case_						       =							do_normalize
         snake_case_						       =							mean
         snake_case_						       =							std
         snake_case_						       =							return_attention_mask
  
   def 		_lowerCamelCase	(self  : Optional[int]	, lowerCAmelCase_  : np.ndarray	, lowerCAmelCase_  : int	, )   ->       np.ndarray:
         snake_case_						       =							torch.from_numpy(UpperCamelCase__    ).unsqueeze(0    )
         snake_case_						       =							ta_kaldi.fbank(
             UpperCamelCase__	, htk_compat=UpperCamelCase__	, sample_frequency=self.sampling_rate	, use_energy=UpperCamelCase__	, window_type="hanning"	, num_mel_bins=self.num_mel_bins	, dither=0.0	, frame_shift=10	, )
  
         snake_case_						       =							fbank.shape[0]
         snake_case_						       =							max_length - n_frames
  
         # pad or truncate, depending on difference
         if difference > 0:
               snake_case_						       =							torch.nn.ZeroPadad((0, 0, 0, difference)    )
               snake_case_						       =							pad_module(UpperCamelCase__    )
         elif difference < 0:
               snake_case_						       =							fbank[0:max_length, :]
  
         snake_case_						       =							fbank.numpy()
  
         return fbank
  
   def 		_lowerCamelCase	(self  : Any	, lowerCAmelCase_  : np.ndarray    )   ->       np.ndarray:
         return (input_values - (self.mean)) / (self.std * 2)
  
  
   def __call__(self  : Union[str, Any]	, lowerCAmelCase_  : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	, lowerCAmelCase_  : Optional[int] = None	, lowerCAmelCase_  : Optional[Union[str, TensorType]] = None	, **lowerCAmelCase_  : str	, )   ->       BatchFeature:
  
  
  
  
  
  
  
         if sampling_rate is not None:
               if sampling_rate != self.sampling_rate:
                     raise ValueError(
                         f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                         f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
                         f''' {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."    )
  
         snake_case_						       =							isinstance(UpperCamelCase__	, 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}'''    )
         snake_case_						       =							is_batched_numpy or (
             isinstance(UpperCamelCase__	, (list, tuple)    ) and (isinstance(raw_speech[0]	, (np.ndarray, tuple, list)    ))
         )
  
         if is_batched:
               snake_case_						       =							[np.asarray(UpperCamelCase__	, dtype=np.floataa    ) for speech in raw_speech]
         elif not is_batched and not isinstance(UpperCamelCase__	, np.ndarray    ):
               snake_case_						       =							np.asarray(UpperCamelCase__	, dtype=np.floataa    )
         elif isinstance(UpperCamelCase__	, np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
               snake_case_						       =							raw_speech.astype(np.floataa    )
  
         # always return batch
         if not is_batched:
               snake_case_						       =							[raw_speech]
  
         # extract fbank features and pad/truncate to max_length
         snake_case_						       =							[self._extract_fbank_features(UpperCamelCase__	, max_length=self.max_length    ) for waveform in raw_speech]
  
         # convert into BatchFeature
         snake_case_						       =							BatchFeature({"input_values": features}    )
  
         # make sure list is in array format
         snake_case_						       =							padded_inputs.get("input_values"    )
         if isinstance(input_values[0]	, UpperCamelCase__    ):
               snake_case_						       =							[np.asarray(UpperCamelCase__	, dtype=np.floataa    ) for feature in input_values]
  
         # normalization
         if self.do_normalize:
               snake_case_						       =							[self.normalize(UpperCamelCase__    ) for feature in input_values]
  
         if return_tensors is not None:
               snake_case_						       =							padded_inputs.convert_to_tensors(UpperCamelCase__    )
  
         return padded_inputs
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  from __future__ import annotations
  
  import inspect
  import unittest
  
  import numpy as np
  
  from transformers import SwinConfig
  from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
  from transformers.utils import cached_property, is_tf_available, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
       import tensorflow as tf
  
       from transformers.models.swin.modeling_tf_swin import (
           TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFSwinForImageClassification,
           TFSwinForMaskedImageModeling,
           TFSwinModel,
       )
  
  
  if is_vision_available():
       from PIL import Image
  
       from transformers import AutoImageProcessor
  
  
  
  class  lowerCAmelCase__   :
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def __init__(self  : Optional[int]	, lowerCAmelCase_  : List[Any]	, lowerCAmelCase_  : Any=13	, lowerCAmelCase_  : int=32	, lowerCAmelCase_  : int=2	, lowerCAmelCase_  : List[str]=3	, lowerCAmelCase_  : Any=16	, lowerCAmelCase_  : Tuple=[1, 2, 1]	, lowerCAmelCase_  : List[str]=[2, 2, 4]	, lowerCAmelCase_  : Dict=2	, lowerCAmelCase_  : str=2.0	, lowerCAmelCase_  : int=True	, lowerCAmelCase_  : List[Any]=0.0	, lowerCAmelCase_  : List[Any]=0.0	, lowerCAmelCase_  : List[Any]=0.1	, lowerCAmelCase_  : Optional[int]="gelu"	, lowerCAmelCase_  : Dict=False	, lowerCAmelCase_  : str=True	, lowerCAmelCase_  : Optional[int]=0.02	, lowerCAmelCase_  : List[str]=1E-5	, lowerCAmelCase_  : Dict=True	, lowerCAmelCase_  : Tuple=None	, lowerCAmelCase_  : List[str]=True	, lowerCAmelCase_  : str=10	, lowerCAmelCase_  : Tuple=8	, )   ->       None:
          snake_case_						       =							parent
          snake_case_						       =							batch_size
          snake_case_						       =							image_size
          snake_case_						       =							patch_size
          snake_case_						       =							num_channels
          snake_case_						       =							embed_dim
          snake_case_						       =							depths
          snake_case_						       =							num_heads
          snake_case_						       =							window_size
          snake_case_						       =							mlp_ratio
          snake_case_						       =							qkv_bias
          snake_case_						       =							hidden_dropout_prob
          snake_case_						       =							attention_probs_dropout_prob
          snake_case_						       =							drop_path_rate
          snake_case_						       =							hidden_act
          snake_case_						       =							use_absolute_embeddings
          snake_case_						       =							patch_norm
          snake_case_						       =							layer_norm_eps
          snake_case_						       =							initializer_range
          snake_case_						       =							is_training
          snake_case_						       =							scope
          snake_case_						       =							use_labels
          snake_case_						       =							type_sequence_label_size
          snake_case_						       =							encoder_stride
  
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Dict:
          snake_case_						       =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )
  
          snake_case_						       =							None
          if self.use_labels:
                snake_case_						       =							ids_tensor([self.batch_size]	, self.type_sequence_label_size    )
  
          snake_case_						       =							self.get_config()
  
          return config, pixel_values, labels
  
    def 		_lowerCamelCase	(self  : str    )   ->       Tuple:
          return SwinConfig(
              image_size=self.image_size	, patch_size=self.patch_size	, num_channels=self.num_channels	, embed_dim=self.embed_dim	, depths=self.depths	, num_heads=self.num_heads	, window_size=self.window_size	, mlp_ratio=self.mlp_ratio	, qkv_bias=self.qkv_bias	, hidden_dropout_prob=self.hidden_dropout_prob	, attention_probs_dropout_prob=self.attention_probs_dropout_prob	, drop_path_rate=self.drop_path_rate	, hidden_act=self.hidden_act	, use_absolute_embeddings=self.use_absolute_embeddings	, path_norm=self.patch_norm	, layer_norm_eps=self.layer_norm_eps	, initializer_range=self.initializer_range	, encoder_stride=self.encoder_stride	, )
  
    def 		_lowerCamelCase	(self  : Optional[int]	, lowerCAmelCase_  : int	, lowerCAmelCase_  : List[Any]	, lowerCAmelCase_  : Optional[int]    )   ->       Any:
          snake_case_						       =							TFSwinModel(config=lowerCAmelCase_    )
          snake_case_						       =							model(lowerCAmelCase_    )
  
          snake_case_						       =							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
          snake_case_						       =							int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )
  
          self.parent.assertEqual(result.last_hidden_state.shape	, (self.batch_size, expected_seq_len, expected_dim)    )
  
    def 		_lowerCamelCase	(self  : Optional[Any]	, lowerCAmelCase_  : Optional[int]	, lowerCAmelCase_  : Dict	, lowerCAmelCase_  : Optional[int]    )   ->       Optional[int]:
          snake_case_						       =							TFSwinForMaskedImageModeling(config=lowerCAmelCase_    )
          snake_case_						       =							model(lowerCAmelCase_    )
          self.parent.assertEqual(
              result.logits.shape	, (self.batch_size, self.num_channels, self.image_size, self.image_size)    )
  
          # test greyscale images
          snake_case_						       =							1
          snake_case_						       =							TFSwinForMaskedImageModeling(lowerCAmelCase_    )
  
          snake_case_						       =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
          snake_case_						       =							model(lowerCAmelCase_    )
          self.parent.assertEqual(result.logits.shape	, (self.batch_size, 1, self.image_size, self.image_size)    )
  
    def 		_lowerCamelCase	(self  : Dict	, lowerCAmelCase_  : Tuple	, lowerCAmelCase_  : List[str]	, lowerCAmelCase_  : Optional[Any]    )   ->       Tuple:
          snake_case_						       =							self.type_sequence_label_size
          snake_case_						       =							TFSwinForImageClassification(lowerCAmelCase_    )
          snake_case_						       =							model(lowerCAmelCase_	, labels=lowerCAmelCase_    )
          self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.type_sequence_label_size)    )
  
          # test greyscale images
          snake_case_						       =							1
          snake_case_						       =							TFSwinForImageClassification(lowerCAmelCase_    )
          snake_case_						       =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
          snake_case_						       =							model(lowerCAmelCase_    )
          self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.type_sequence_label_size)    )
  
  
    def 		_lowerCamelCase	(self  : Optional[Any]    )   ->       str:
          snake_case_						       =							self.prepare_config_and_inputs()
          snake_case_       ,	snake_case_       ,	snake_case_						       =							config_and_inputs
          snake_case_						       =							{"pixel_values": pixel_values}
          return config, inputs_dict
  
  
  
  @require_tf
  class  lowerCAmelCase__   (       UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    _UpperCamelCase						= (
        (
            TFSwinModel,
            TFSwinForImageClassification,
            TFSwinForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    _UpperCamelCase						= (
        {'feature-extraction': TFSwinModel, 'image-classification': TFSwinForImageClassification}
        if is_tf_available()
        else {}
    )
  
    _UpperCamelCase						= False
    _UpperCamelCase						= False
    _UpperCamelCase						= False
    _UpperCamelCase						= False
  
    def 		_lowerCamelCase	(self  : Any    )   ->       List[Any]:
          snake_case_						       =							TFSwinModelTester(self    )
          snake_case_						       =							ConfigTester(self	, config_class=lowerCAmelCase_	, embed_dim=37    )
  
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Optional[Any]:
          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 		_lowerCamelCase	(self  : List[Any]    )   ->       List[str]:
          return
  
    def 		_lowerCamelCase	(self  : Any    )   ->       Dict:
          snake_case_						       =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : int    )   ->       Any:
          snake_case_						       =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : Tuple    )   ->       Optional[Any]:
          snake_case_						       =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_    )
  
    @unittest.skip(reason="Swin does not use inputs_embeds"    )
    def 		_lowerCamelCase	(self  : Optional[Any]    )   ->       Union[str, Any]:
          pass
  
    def 		_lowerCamelCase	(self  : List[str]    )   ->       Any:
          snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
                snake_case_						       =							model_class(lowerCAmelCase_    )
                self.assertIsInstance(model.get_input_embeddings()	, tf.keras.layers.Layer    )
                snake_case_						       =							model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(lowerCAmelCase_	, tf.keras.layers.Dense    )    )
  
    def 		_lowerCamelCase	(self  : Dict    )   ->       Dict:
          snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
                snake_case_						       =							model_class(lowerCAmelCase_    )
                snake_case_						       =							inspect.signature(model.call    )
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                snake_case_						       =							[*signature.parameters.keys()]
  
                snake_case_						       =							["pixel_values"]
                self.assertListEqual(arg_names[:1]	, lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : Dict    )   ->       Any:
          snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
          snake_case_						       =							True
  
          for model_class in self.all_model_classes:
                snake_case_						       =							True
                snake_case_						       =							False
                snake_case_						       =							True
                snake_case_						       =							model_class(lowerCAmelCase_    )
                snake_case_						       =							model(**self._prepare_for_class(lowerCAmelCase_	, lowerCAmelCase_    )    )
                snake_case_						       =							outputs.attentions
                snake_case_						       =							len(self.model_tester.depths    )
                self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )
  
                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                snake_case_						       =							True
                snake_case_						       =							config.window_size**2
                snake_case_						       =							model_class(lowerCAmelCase_    )
                snake_case_						       =							model(**self._prepare_for_class(lowerCAmelCase_	, lowerCAmelCase_    )    )
                snake_case_						       =							outputs.attentions
                self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )
  
                self.assertListEqual(
                    list(attentions[0].shape[-3:]    )	, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	, )
                snake_case_						       =							len(lowerCAmelCase_    )
  
                # Check attention is always last and order is fine
                snake_case_						       =							True
                snake_case_						       =							True
                snake_case_						       =							model_class(lowerCAmelCase_    )
                snake_case_						       =							model(**self._prepare_for_class(lowerCAmelCase_	, lowerCAmelCase_    )    )
  
                if hasattr(self.model_tester	, "num_hidden_states_types"    ):
                      snake_case_						       =							self.model_tester.num_hidden_states_types
                else:
                      # also another +1 for reshaped_hidden_states
                      snake_case_						       =							2
                self.assertEqual(out_len + added_hidden_states	, len(lowerCAmelCase_    )    )
  
                snake_case_						       =							outputs.attentions
  
                self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )
  
                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]    )	, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]	, )
  
    def 		_lowerCamelCase	(self  : Optional[int]	, lowerCAmelCase_  : Union[str, Any]	, lowerCAmelCase_  : int	, lowerCAmelCase_  : Dict	, lowerCAmelCase_  : List[Any]    )   ->       Optional[int]:
          snake_case_						       =							model_class(lowerCAmelCase_    )
          snake_case_						       =							model(**self._prepare_for_class(lowerCAmelCase_	, lowerCAmelCase_    )    )
          snake_case_						       =							outputs.hidden_states
  
          snake_case_						       =							getattr(
              self.model_tester	, "expected_num_hidden_layers"	, len(self.model_tester.depths    ) + 1    )
          self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )
  
          # Swin has a different seq_length
          snake_case_						       =							to_atuple(config.patch_size    )
  
          snake_case_						       =							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
  
          self.assertListEqual(
              list(hidden_states[0].shape[-2:]    )	, [num_patches, self.model_tester.embed_dim]	, )
  
          snake_case_						       =							outputs.reshaped_hidden_states
          self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )
  
          snake_case_       ,	snake_case_       ,	snake_case_       ,	snake_case_						       =							reshaped_hidden_states[0].shape
  
          snake_case_						       =							tf.reshape(reshaped_hidden_states[0]	, (batch_size, num_channels, height * width)    )
          snake_case_						       =							tf.transpose(lowerCAmelCase_	, (0, 2, 1)    )
  
          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]    )	, [num_patches, self.model_tester.embed_dim]	, )
  
    def 		_lowerCamelCase	(self  : Dict    )   ->       str:
          snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
  
          snake_case_						       =							to_atuple(self.model_tester.image_size    )
  
          for model_class in self.all_model_classes:
                snake_case_						       =							True
                self.check_hidden_states_output(lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_    )
  
                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                snake_case_						       =							True
  
                self.check_hidden_states_output(lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : Tuple    )   ->       List[Any]:
          snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
          snake_case_						       =							3
  
          snake_case_						       =							to_atuple(self.model_tester.image_size    )
          snake_case_						       =							to_atuple(config.patch_size    )
  
          snake_case_						       =							image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          snake_case_						       =							image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
  
          for model_class in self.all_model_classes:
                snake_case_						       =							True
                self.check_hidden_states_output(lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_	, (padded_height, padded_width)    )
  
                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                snake_case_						       =							True
                self.check_hidden_states_output(lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_	, (padded_height, padded_width)    )
  
  
    @slow
    def 		_lowerCamelCase	(self  : Any    )   ->       List[str]:
          for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case_						       =							TFSwinModel.from_pretrained(lowerCAmelCase_    )
                self.assertIsNotNone(lowerCAmelCase_    )
  
  
  
  @require_vision
  @require_tf
  class  lowerCAmelCase__   (       unittest.TestCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    @cached_property
    def 		_lowerCamelCase	(self  : Dict    )   ->       str:
          return (
              AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"    )
              if is_vision_available()
              else None
          )
  
  
    @slow
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       str:
          snake_case_						       =							TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"    )
          snake_case_						       =							self.default_image_processor
  
          snake_case_						       =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
          snake_case_						       =							image_processor(images=lowerCAmelCase_	, return_tensors="tf"    )
  
          # forward pass
          snake_case_						       =							model(lowerCAmelCase_    )
  
          # verify the logits
          snake_case_						       =							tf.TensorShape((1, 10_00)    )
          self.assertEqual(outputs.logits.shape	, lowerCAmelCase_    )
          snake_case_						       =							tf.constant([-0.0_948, -0.6_454, -0.0_921]    )
          self.assertTrue(np.allclose(outputs.logits[0, :3]	, lowerCAmelCase_	, atol=1E-4    )    )
  
  