


import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


UpperCAmelCase_ :					Tuple       =			random.Random()

if is_torch_available():
   import torch







def 			SCREAMING_SNAKE_CASE_		(     __A					:     Tuple    ,       __A					:     Any=1.0    ,       __A					:     Optional[int]=None    ,       __A					:     Optional[Any]=None							)      ->						List[Any]:

       """simple docstring"""

       if rng is None:
              a_   :						List[Any]				=    global_rng

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

       return values
class 					SCREAMING_SNAKE_CASE__    (							unittest.TestCase     ):
      def __init__(			self							:			Dict	,				SCREAMING_SNAKE_CASE__							:			Any	,				SCREAMING_SNAKE_CASE__							:			Dict=7	,				SCREAMING_SNAKE_CASE__							:			Optional[int]=4_0_0	,				SCREAMING_SNAKE_CASE__							:			Optional[Any]=2_0_0_0	,				SCREAMING_SNAKE_CASE__							:			Optional[int]=1	,				SCREAMING_SNAKE_CASE__							:			Tuple=0.0	,				SCREAMING_SNAKE_CASE__							:			int=1_6_0_0_0	,				SCREAMING_SNAKE_CASE__							:			str=True	,				SCREAMING_SNAKE_CASE__							:			Dict=True	,				)						->     Optional[Any]:
             a_   :						str				=    parent
             a_   :						Dict				=    batch_size
             a_   :						List[Any]				=    min_seq_length
             a_   :						int				=    max_seq_length
             a_   :						str				=    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
             a_   :						Optional[int]				=    feature_size
             a_   :						Any				=    padding_value
             a_   :						Tuple				=    sampling_rate
             a_   :						Any				=    return_attention_mask
             a_   :						int				=    do_normalize
      def 			SCREAMING_SNAKE_CASE				(			self							:			Union[str, Any]	)						->     List[str]:
             return {
                 "feature_size": self.feature_size,
                 "padding_value": self.padding_value,
                 "sampling_rate": self.sampling_rate,
                 "return_attention_mask": self.return_attention_mask,
                 "do_normalize": self.do_normalize,
             }

      def 			SCREAMING_SNAKE_CASE				(			self							:			str	,				SCREAMING_SNAKE_CASE__							:			Dict=False	,				SCREAMING_SNAKE_CASE__							:			Optional[int]=False	)						->     Optional[Any]:



             def _flatten(SCREAMING_SNAKE_CASE__							:			List[str]	):
                    return list(itertools.chain(*SCREAMING_SNAKE_CASE__	)	)

             if equal_length:
                    a_   :						Dict				=    floats_list((self.batch_size, self.max_seq_length)	)
             else:
                    # make sure that inputs increase in size
                    a_   :						Optional[Any]				=    [
                        _flatten(floats_list((x, self.feature_size)	)	)
                        for x in range(self.min_seq_length	,				self.max_seq_length	,				self.seq_length_diff	)
                    ]

             if numpify:
                    a_   :						Optional[int]				=    [np.asarray(SCREAMING_SNAKE_CASE__	) for x in speech_inputs]

             return speech_inputs
@require_torch
@require_torchaudio
class 					SCREAMING_SNAKE_CASE__    (							lowercase__		,       unittest.TestCase     ):
      snake_case__							:	int	  =		ASTFeatureExtractor
      def 			SCREAMING_SNAKE_CASE				(			self							:			Tuple	)						->     List[str]:
             a_   :						str				=    ASTFeatureExtractionTester(self	)
      def 			SCREAMING_SNAKE_CASE				(			self							:			Tuple	)						->     Dict:
             a_   :						Union[str, Any]				=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
             # create three inputs of length 800, 1000, and 1200
             a_   :						int				=    [floats_list((1, x)	)[0] for x in range(8_0_0	,				1_4_0_0	,				2_0_0	)]
             a_   :						Any				=    [np.asarray(SCREAMING_SNAKE_CASE__	) for speech_input in speech_inputs]

             # Test not batched input
             a_   :						List[Any]				=    feat_extract(speech_inputs[0]	,				return_tensors='np'	).input_values
             a_   :						int				=    feat_extract(np_speech_inputs[0]	,				return_tensors='np'	).input_values
             self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__	,				SCREAMING_SNAKE_CASE__	,				atol=1E-3	)	)

             # Test batched
             a_   :						Optional[int]				=    feat_extract(SCREAMING_SNAKE_CASE__	,				padding=SCREAMING_SNAKE_CASE__	,				return_tensors='np'	).input_values
             a_   :						int				=    feat_extract(SCREAMING_SNAKE_CASE__	,				padding=SCREAMING_SNAKE_CASE__	,				return_tensors='np'	).input_values
             for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE__	,				SCREAMING_SNAKE_CASE__	):
                    self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__	,				SCREAMING_SNAKE_CASE__	,				atol=1E-3	)	)

             # Test 2-D numpy arrays are batched.
             a_   :						Union[str, Any]				=    [floats_list((1, x)	)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
             a_   :						Union[str, Any]				=    np.asarray(SCREAMING_SNAKE_CASE__	)
             a_   :						str				=    feat_extract(SCREAMING_SNAKE_CASE__	,				return_tensors='np'	).input_values
             a_   :						Tuple				=    feat_extract(SCREAMING_SNAKE_CASE__	,				return_tensors='np'	).input_values
             for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE__	,				SCREAMING_SNAKE_CASE__	):
                    self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__	,				SCREAMING_SNAKE_CASE__	,				atol=1E-3	)	)
      @require_torch
      def 			SCREAMING_SNAKE_CASE				(			self							:			Optional[Any]	)						->     Union[str, Any]:
             import torch

             a_   :						Dict				=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
             a_   :						Optional[Any]				=    np.random.rand(1_0_0	).astype(np.floataa	)
             a_   :						Optional[int]				=    np_speech_inputs.tolist()

             for inputs in [py_speech_inputs, np_speech_inputs]:
                    a_   :						List[Any]				=    feature_extractor.pad([{'input_values': inputs}]	,				return_tensors='np'	)
                    self.assertTrue(np_processed.input_values.dtype == np.floataa	)
                    a_   :						Union[str, Any]				=    feature_extractor.pad([{'input_values': inputs}]	,				return_tensors='pt'	)
                    self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)
      def 			SCREAMING_SNAKE_CASE				(			self							:			Dict	,				SCREAMING_SNAKE_CASE__							:			Any	)						->     Optional[Any]:
             from datasets import load_dataset

             a_   :						Tuple				=    load_dataset('hf-internal-testing/librispeech_asr_dummy'	,				'clean'	,				split='validation'	)
             # automatic decoding with librispeech
             a_   :						List[Any]				=    ds.sort('id'	).select(range(SCREAMING_SNAKE_CASE__	)	)[:num_samples]['audio']

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

      @require_torch
      def 			SCREAMING_SNAKE_CASE				(			self							:			Tuple	)						->     Union[str, Any]:
             a_   :						Optional[int]				=    torch.tensor(
                 [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
                  -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
                  -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
                  -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]	)
             # fmt: on

             a_   :						int				=    self._load_datasamples(1	)
             a_   :						List[Any]				=    ASTFeatureExtractor()
             a_   :						str				=    feature_extractor(SCREAMING_SNAKE_CASE__	,				return_tensors='pt'	).input_values
             self.assertEquals(input_values.shape	,				(1, 1_0_2_4, 1_2_8)	)
             self.assertTrue(torch.allclose(input_values[0, 0, :3_0]	,				SCREAMING_SNAKE_CASE__	,				atol=1E-4	)	)







'''simple docstring'''





import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DPTImageProcessor






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def __init__(self					,  A					,  A=7					,  A=3					,  A=18					,  A=30					,  A=400					,  A=True					,  A=None					,  A=True					,  A=[0.5, 0.5, 0.5]					,  A=[0.5, 0.5, 0.5]					,  )       ->	str:


      """simple docstring"""



      _a           =    size if size is not None else {'''height''': 18, '''width''': 18}
      _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



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }






@require_torch
@require_vision
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			str		=			DPTImageProcessor if is_vision_available() else None



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    DPTImageProcessingTester(self			)



 @property
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(A					,  '''image_mean'''			)			)
      self.assertTrue(hasattr(A					,  '''image_std'''			)			)
      self.assertTrue(hasattr(A					,  '''do_normalize'''			)			)
      self.assertTrue(hasattr(A					,  '''do_resize'''			)			)
      self.assertTrue(hasattr(A					,  '''size'''			)			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size					,  {'''height''': 18, '''width''': 18}			)

      _a           =    self.image_processing_class.from_dict(self.image_processor_dict					,  size=42			)
      self.assertEqual(image_processor.size					,  {'''height''': 42, '''width''': 42}			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  Image.Image			)

      # Test not batched input
      _a           =    image_processing(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_processing(A					,  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    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  numpify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  np.ndarray			)

      # Test not batched input
      _a           =    image_processing(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_processing(A					,  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    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  torchify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  torch.Tensor			)

      # Test not batched input
      _a           =    image_processing(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_processing(A					,  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'''],
          )					,  )



