






'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import BlipImageProcessor






class        a__     (      unittest.TestCase    ):
     '''simple docstring'''







     def __init__(self     :						List[Any]					,					lowercase__     :						Dict					,					lowercase__     :						Dict=7					,					lowercase__     :						List[Any]=3					,					lowercase__     :						List[Any]=18					,					lowercase__     :						Union[str, Any]=30					,					lowercase__     :						Any=400					,					lowercase__     :						Any=True					,					lowercase__     :						str=None					,					lowercase__     :						int=True					,					lowercase__     :						Any=False					,					lowercase__     :						List[Any]=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]					,					lowercase__     :						Any=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]					,					lowercase__     :						Optional[int]=True					,					)	->							Optional[int]:
           __A		 =   size if size is not None else {"height": 20, "width": 20}
           __A		 =   parent
           __A		 =   batch_size
           __A		 =   num_channels
           __A		 =   image_size
           __A		 =   min_resolution
           __A		 =   max_resolution
           __A		 =   do_resize
           __A		 =   size
           __A		 =   do_normalize
           __A		 =   image_mean
           __A		 =   image_std
           __A		 =   do_pad
           __A		 =   do_convert_rgb






     def      _UpperCamelCase (self     :						Tuple       )	->							Tuple:
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_convert_rgb": self.do_convert_rgb,
               "do_pad": self.do_pad,
           }






     def      _UpperCamelCase (self     :						str					,					lowercase__     :						int=False					,					lowercase__     :						List[str]=False					,					lowercase__     :						Tuple=False       )	->							Optional[Any]:
           assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

           if equal_resolution:
                 __A		 =   []
                 for i in range(self.batch_size       ):
                       image_inputs.append(
                           np.random.randint(
                               255					,					size=(self.num_channels, self.max_resolution, self.max_resolution)					,					dtype=np.uinta       )       )
           else:
                 __A		 =   []
                 for i in range(self.batch_size       ):
                       __A ,					__A		 =   np.random.choice(np.arange(self.min_resolution					,					self.max_resolution       )					,					2       )
                       image_inputs.append(np.random.randint(255					,					size=(self.num_channels, width, height)					,					dtype=np.uinta       )       )

           if not numpify and not torchify:
                 # PIL expects the channel dimension as last dimension
                 __A		 =   [Image.fromarray(np.moveaxis(lowercase__					,					0					,					-1       )       ) for x in image_inputs]

           if torchify:
                 __A		 =   [torch.from_numpy(lowercase__       ) for x in image_inputs]

           return image_inputs







@require_torch
@require_vision
class        a__     (      lowerCamelCase			,    unittest.TestCase    ):
     '''simple docstring'''







     _snake_case							   =	BlipImageProcessor if is_vision_available() else None






     def      _UpperCamelCase (self     :						str       )	->							Any:
           __A		 =   BlipImageProcessingTester(self       )






     @property
     def      _UpperCamelCase (self     :						List[Any]       )	->							int:
           return self.image_processor_tester.prepare_image_processor_dict()






     def      _UpperCamelCase (self     :						List[Any]       )	->							str:
           __A		 =   self.image_processing_class(**self.image_processor_dict       )
           self.assertTrue(hasattr(lowercase__					,					"do_resize"       )       )
           self.assertTrue(hasattr(lowercase__					,					"size"       )       )
           self.assertTrue(hasattr(lowercase__					,					"do_normalize"       )       )
           self.assertTrue(hasattr(lowercase__					,					"image_mean"       )       )
           self.assertTrue(hasattr(lowercase__					,					"image_std"       )       )
           self.assertTrue(hasattr(lowercase__					,					"do_convert_rgb"       )       )






     def      _UpperCamelCase (self     :						Any       )	->							Any:
           pass






     def      _UpperCamelCase (self     :						Union[str, Any]       )	->							str:
           # Initialize image_processor
           __A		 =   self.image_processing_class(**self.image_processor_dict       )
           # create random PIL images
           __A		 =   self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__       )
           for image in image_inputs:
                 self.assertIsInstance(lowercase__					,					Image.Image       )

           # Test not batched input
           __A		 =   image_processor(image_inputs[0]					,					return_tensors="pt"       ).pixel_values
           self.assertEqual(
               encoded_images.shape					,					(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )					,					)

           # Test batched
           __A		 =   image_processor(lowercase__					,					return_tensors="pt"       ).pixel_values
           self.assertEqual(
               encoded_images.shape					,					(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )					,					)






     def      _UpperCamelCase (self     :						Dict       )	->							str:
           # Initialize image_processor
           __A		 =   self.image_processing_class(**self.image_processor_dict       )
           # create random numpy tensors
           __A		 =   self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__					,					numpify=lowercase__       )
           for image in image_inputs:
                 self.assertIsInstance(lowercase__					,					np.ndarray       )

           # Test not batched input
           __A		 =   image_processor(image_inputs[0]					,					return_tensors="pt"       ).pixel_values
           self.assertEqual(
               encoded_images.shape					,					(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )					,					)

           # Test batched
           __A		 =   image_processor(lowercase__					,					return_tensors="pt"       ).pixel_values
           self.assertEqual(
               encoded_images.shape					,					(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )					,					)






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							List[str]:
           # Initialize image_processor
           __A		 =   self.image_processing_class(**self.image_processor_dict       )
           # create random PyTorch tensors
           __A		 =   self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__					,					torchify=lowercase__       )
           for image in image_inputs:
                 self.assertIsInstance(lowercase__					,					torch.Tensor       )

           # Test not batched input
           __A		 =   image_processor(image_inputs[0]					,					return_tensors="pt"       ).pixel_values
           self.assertEqual(
               encoded_images.shape					,					(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )					,					)

           # Test batched
           __A		 =   image_processor(lowercase__					,					return_tensors="pt"       ).pixel_values
           self.assertEqual(
               encoded_images.shape					,					(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )					,					)







@require_torch
@require_vision
class        a__     (      lowerCamelCase			,    unittest.TestCase    ):
     '''simple docstring'''







     _snake_case							   =	BlipImageProcessor if is_vision_available() else None






     def      _UpperCamelCase (self     :						str       )	->							Optional[int]:
           __A		 =   BlipImageProcessingTester(self					,					num_channels=4       )
           __A		 =   3






     @property
     def      _UpperCamelCase (self     :						Any       )	->							List[Any]:
           return self.image_processor_tester.prepare_image_processor_dict()






     def      _UpperCamelCase (self     :						Optional[int]       )	->							Union[str, Any]:
           __A		 =   self.image_processing_class(**self.image_processor_dict       )
           self.assertTrue(hasattr(lowercase__					,					"do_resize"       )       )
           self.assertTrue(hasattr(lowercase__					,					"size"       )       )
           self.assertTrue(hasattr(lowercase__					,					"do_normalize"       )       )
           self.assertTrue(hasattr(lowercase__					,					"image_mean"       )       )
           self.assertTrue(hasattr(lowercase__					,					"image_std"       )       )
           self.assertTrue(hasattr(lowercase__					,					"do_convert_rgb"       )       )






     def      _UpperCamelCase (self     :						Tuple       )	->							Optional[Any]:
           pass






     def      _UpperCamelCase (self     :						Optional[int]       )	->							int:
           # Initialize image_processor
           __A		 =   self.image_processing_class(**self.image_processor_dict       )
           # create random PIL images
           __A		 =   self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__       )
           for image in image_inputs:
                 self.assertIsInstance(lowercase__					,					Image.Image       )

           # Test not batched input
           __A		 =   image_processor(image_inputs[0]					,					return_tensors="pt"       ).pixel_values
           self.assertEqual(
               encoded_images.shape					,					(
                   1,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )					,					)

           # Test batched
           __A		 =   image_processor(lowercase__					,					return_tensors="pt"       ).pixel_values
           self.assertEqual(
               encoded_images.shape					,					(
                   self.image_processor_tester.batch_size,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )					,					)










'''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   :							int     	=						logging.get_logger(__name__)






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     _snake_case							   =	['input_values', 'attention_mask']






     def __init__(self     :						Optional[int]					,					lowercase__     :						Tuple=1					,					lowercase__     :						Optional[int]=16000					,					lowercase__     :						Any=128					,					lowercase__     :						Optional[Any]=1024					,					lowercase__     :						int=0.0					,					lowercase__     :						List[str]=True					,					lowercase__     :						Dict=-4.2_67_73_93					,					lowercase__     :						Tuple=4.5_68_99_74					,					lowercase__     :						Optional[int]=False					,					**lowercase__     :						Optional[int]					,					)	->							Tuple:
           super().__init__(feature_size=lowercase__					,					sampling_rate=lowercase__					,					padding_value=lowercase__					,					**lowercase__       )
           __A		 =   num_mel_bins
           __A		 =   max_length
           __A		 =   do_normalize
           __A		 =   mean
           __A		 =   std
           __A		 =   return_attention_mask






     def      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						np.ndarray					,					lowercase__     :						int					,					)	->							np.ndarray:
           __A		 =   torch.from_numpy(lowercase__       ).unsqueeze(0       )
           __A		 =   ta_kaldi.fbank(
               lowercase__					,					htk_compat=lowercase__					,					sample_frequency=self.sampling_rate					,					use_energy=lowercase__					,					window_type="hanning"					,					num_mel_bins=self.num_mel_bins					,					dither=0.0					,					frame_shift=10					,					)

           __A		 =   fbank.shape[0]
           __A		 =   max_length - n_frames

           # pad or truncate, depending on difference
           if difference > 0:
                 __A		 =   torch.nn.ZeroPadad((0, 0, 0, difference)       )
                 __A		 =   pad_module(lowercase__       )
           elif difference < 0:
                 __A		 =   fbank[0:max_length, :]

           __A		 =   fbank.numpy()

           return fbank






     def      _UpperCamelCase (self     :						int					,					lowercase__     :						np.ndarray       )	->							np.ndarray:
           return (input_values - (self.mean)) / (self.std * 2)






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

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

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

           # always return batch
           if not is_batched:
                 __A		 =   [raw_speech]

           # extract fbank features and pad/truncate to max_length
           __A		 =   [self._extract_fbank_features(lowercase__					,					max_length=self.max_length       ) for waveform in raw_speech]

           # convert into BatchFeature
           __A		 =   BatchFeature({"input_values": features}       )

           # make sure list is in array format
           __A		 =   padded_inputs.get("input_values"       )
           if isinstance(input_values[0]					,					lowercase__       ):
                 __A		 =   [np.asarray(lowercase__					,					dtype=np.floataa       ) for feature in input_values]

           # normalization
           if self.do_normalize:
                 __A		 =   [self.normalize(lowercase__       ) for feature in input_values]

           if return_tensors is not None:
                 __A		 =   padded_inputs.convert_to_tensors(lowercase__       )

           return padded_inputs


