


import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class 					SCREAMING_SNAKE_CASE__    (							lowercase__     ):
      snake_case__							:	str	  =		None
      snake_case__							:	Optional[Any]	  =		None
      @property
      def 			SCREAMING_SNAKE_CASE				(			self							:			Optional[int]	)						->     List[Any]:
             return self.feat_extract_tester.prepare_feat_extract_dict()
      def 			SCREAMING_SNAKE_CASE				(			self							:			int	)						->     Optional[int]:
             a_   :						Union[str, Any]				=    self.feature_extraction_class(**self.feat_extract_dict	)
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__	,				'feature_size'	)	)
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__	,				'sampling_rate'	)	)
             self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__	,				'padding_value'	)	)
      def 			SCREAMING_SNAKE_CASE				(			self							:			Tuple	)						->     str:
             a_   :						Optional[Any]				=    self.feat_extract_tester.prepare_inputs_for_common()
             a_   :						Union[str, Any]				=    self.feature_extraction_class(**self.feat_extract_dict	)
             a_   :						int				=    feat_extract.model_input_names[0]

             a_   :						str				=    BatchFeature({input_name: speech_inputs}	)

             self.assertTrue(all(len(SCREAMING_SNAKE_CASE__	) == len(SCREAMING_SNAKE_CASE__	) for x, y in zip(SCREAMING_SNAKE_CASE__	,				processed_features[input_name]	)	)	)

             a_   :						int				=    self.feat_extract_tester.prepare_inputs_for_common(equal_length=SCREAMING_SNAKE_CASE__	)
             a_   :						List[Any]				=    BatchFeature({input_name: speech_inputs}	,				tensor_type='np'	)

             a_   :						str				=    processed_features[input_name]

             if len(batch_features_input.shape	) < 3:
                    a_   :						Optional[Any]				=    batch_features_input[:, :, None]

             self.assertTrue(
                 batch_features_input.shape
                 == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.feature_size)	)
      @require_torch
      def 			SCREAMING_SNAKE_CASE				(			self							:			int	)						->     Dict:
             a_   :						Tuple				=    self.feat_extract_tester.prepare_inputs_for_common(equal_length=SCREAMING_SNAKE_CASE__	)
             a_   :						Optional[Any]				=    self.feature_extraction_class(**self.feat_extract_dict	)
             a_   :						str				=    feat_extract.model_input_names[0]

             a_   :						Optional[Any]				=    BatchFeature({input_name: speech_inputs}	,				tensor_type='pt'	)

             a_   :						Dict				=    processed_features[input_name]

             if len(batch_features_input.shape	) < 3:
                    a_   :						Optional[Any]				=    batch_features_input[:, :, None]

             self.assertTrue(
                 batch_features_input.shape
                 == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.feature_size)	)
      @require_tf
      def 			SCREAMING_SNAKE_CASE				(			self							:			Optional[Any]	)						->     str:
             a_   :						Any				=    self.feat_extract_tester.prepare_inputs_for_common(equal_length=SCREAMING_SNAKE_CASE__	)
             a_   :						str				=    self.feature_extraction_class(**self.feat_extract_dict	)
             a_   :						Any				=    feat_extract.model_input_names[0]

             a_   :						Dict				=    BatchFeature({input_name: speech_inputs}	,				tensor_type='tf'	)

             a_   :						List[str]				=    processed_features[input_name]

             if len(batch_features_input.shape	) < 3:
                    a_   :						int				=    batch_features_input[:, :, None]

             self.assertTrue(
                 batch_features_input.shape
                 == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.feature_size)	)
      def 			SCREAMING_SNAKE_CASE				(			self							:			Dict	,				SCREAMING_SNAKE_CASE__							:			Optional[int]=False	)						->     Any:



             def _inputs_have_equal_length(SCREAMING_SNAKE_CASE__							:			Optional[int]	):
                    a_   :						Dict				=    len(input[0]	)
                    for input_slice in input[1:]:
                           if len(SCREAMING_SNAKE_CASE__	) != length:
                                  return False
                    return True

             def _inputs_are_equal(SCREAMING_SNAKE_CASE__							:			List[Any]	,				SCREAMING_SNAKE_CASE__							:			str	):
                    if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
                           return False

                    for input_slice_a, input_slice_a in zip(SCREAMING_SNAKE_CASE__	,				SCREAMING_SNAKE_CASE__	):
                           if not np.allclose(np.asarray(SCREAMING_SNAKE_CASE__	)	,				np.asarray(SCREAMING_SNAKE_CASE__	)	,				atol=1E-3	):
                                  return False
                    return True

             a_   :						Optional[int]				=    self.feature_extraction_class(**self.feat_extract_dict	)
             a_   :						int				=    self.feat_extract_tester.prepare_inputs_for_common(numpify=SCREAMING_SNAKE_CASE__	)
             a_   :						Tuple				=    feat_extract.model_input_names[0]

             a_   :						List[Any]				=    BatchFeature({input_name: speech_inputs}	)

             a_   :						Optional[Any]				=    self.feat_extract_tester.seq_length_diff
             a_   :						Union[str, Any]				=    self.feat_extract_tester.max_seq_length + pad_diff
             a_   :						List[str]				=    self.feat_extract_tester.min_seq_length
             a_   :						Union[str, Any]				=    self.feat_extract_tester.batch_size
             a_   :						Optional[int]				=    self.feat_extract_tester.feature_size

             # test padding for List[int] + numpy
             a_   :						Optional[Any]				=    feat_extract.pad(SCREAMING_SNAKE_CASE__	,				padding=SCREAMING_SNAKE_CASE__	)
             a_   :						List[Any]				=    input_a[input_name]

             a_   :						int				=    feat_extract.pad(SCREAMING_SNAKE_CASE__	,				padding='longest'	)
             a_   :						str				=    input_a[input_name]

             a_   :						Optional[int]				=    feat_extract.pad(SCREAMING_SNAKE_CASE__	,				padding='max_length'	,				max_length=len(speech_inputs[-1]	)	)
             a_   :						List[Any]				=    input_a[input_name]

             a_   :						Tuple				=    feat_extract.pad(SCREAMING_SNAKE_CASE__	,				padding='longest'	,				return_tensors='np'	)
             a_   :						Union[str, Any]				=    input_a[input_name]

             # max_length parameter has to be provided when setting `padding="max_length"`
             with self.assertRaises(SCREAMING_SNAKE_CASE__	):
                    feat_extract.pad(SCREAMING_SNAKE_CASE__	,				padding='max_length'	)[input_name]

             a_   :						List[str]				=    feat_extract.pad(
                 SCREAMING_SNAKE_CASE__	,				padding='max_length'	,				max_length=SCREAMING_SNAKE_CASE__	,				return_tensors='np'	)
             a_   :						Optional[Any]				=    input_a[input_name]

             self.assertFalse(_inputs_have_equal_length(SCREAMING_SNAKE_CASE__	)	)
             self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE__	)	)
             self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE__	)	)
             self.assertTrue(_inputs_are_equal(SCREAMING_SNAKE_CASE__	,				SCREAMING_SNAKE_CASE__	)	)
             self.assertTrue(len(input_a[0]	) == pad_min_length	)
             self.assertTrue(len(input_a[1]	) == pad_min_length + pad_diff	)
             self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]	))	)
             self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)	)

             if feature_size > 1:
                    self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size	)

             # test padding for `pad_to_multiple_of` for List[int] + numpy
             a_   :						Optional[Any]				=    feat_extract.pad(SCREAMING_SNAKE_CASE__	,				pad_to_multiple_of=1_0	)
             a_   :						int				=    input_a[input_name]

             a_   :						Tuple				=    feat_extract.pad(SCREAMING_SNAKE_CASE__	,				padding='longest'	,				pad_to_multiple_of=1_0	)
             a_   :						Tuple				=    input_a[input_name]

             a_   :						Optional[int]				=    feat_extract.pad(
                 SCREAMING_SNAKE_CASE__	,				padding='max_length'	,				pad_to_multiple_of=1_0	,				max_length=SCREAMING_SNAKE_CASE__	)
             a_   :						List[Any]				=    input_a[input_name]

             a_   :						Union[str, Any]				=    feat_extract.pad(
                 SCREAMING_SNAKE_CASE__	,				padding='max_length'	,				pad_to_multiple_of=1_0	,				max_length=SCREAMING_SNAKE_CASE__	,				return_tensors='np'	,				)
             a_   :						Any				=    input_a[input_name]

             self.assertTrue(all(len(SCREAMING_SNAKE_CASE__	) % 1_0 == 0 for x in input_a	)	)
             self.assertTrue(_inputs_are_equal(SCREAMING_SNAKE_CASE__	,				SCREAMING_SNAKE_CASE__	)	)

             a_   :						int				=    pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
             self.assertTrue(all(len(SCREAMING_SNAKE_CASE__	) == expected_mult_pad_length for x in input_a	)	)
             self.assertEqual(input_a.shape[:2]	,				(batch_size, expected_mult_pad_length)	)

             if feature_size > 1:
                    self.assertTrue(input_a.shape[2] == feature_size	)

             # Check padding value is correct
             a_   :						int				=    (np.ones(self.feat_extract_tester.feature_size	) * feat_extract.padding_value).sum()
             self.assertTrue(
                 abs(np.asarray(input_a[0]	)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)	)
                 < 1E-3	)
             self.assertTrue(
                 abs(
                     np.asarray(input_a[1]	)[pad_min_length + pad_diff :].sum()
                     - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)	)
                 < 1E-3	)
             self.assertTrue(
                 abs(
                     np.asarray(input_a[2]	)[pad_min_length + 2 * pad_diff :].sum()
                     - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)	)
                 < 1E-3	)
             self.assertTrue(
                 abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)	) < 1E-3	)
             self.assertTrue(
                 abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)	)
                 < 1E-3	)
      def 			SCREAMING_SNAKE_CASE				(			self							:			Any	,				SCREAMING_SNAKE_CASE__							:			str=False	)						->     List[Any]:



             def _inputs_have_equal_length(SCREAMING_SNAKE_CASE__							:			List[Any]	):
                    a_   :						int				=    len(input[0]	)
                    for input_slice in input[1:]:
                           if len(SCREAMING_SNAKE_CASE__	) != length:
                                  return False
                    return True

             def _inputs_are_equal(SCREAMING_SNAKE_CASE__							:			List[Any]	,				SCREAMING_SNAKE_CASE__							:			List[str]	):
                    if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
                           return False

                    for input_slice_a, input_slice_a in zip(SCREAMING_SNAKE_CASE__	,				SCREAMING_SNAKE_CASE__	):
                           if not np.allclose(np.asarray(SCREAMING_SNAKE_CASE__	)	,				np.asarray(SCREAMING_SNAKE_CASE__	)	,				atol=1E-3	):
                                  return False
                    return True

             a_   :						int				=    self.feature_extraction_class(**self.feat_extract_dict	)
             a_   :						Optional[Any]				=    self.feat_extract_tester.prepare_inputs_for_common(numpify=SCREAMING_SNAKE_CASE__	)
             a_   :						Any				=    feat_extract.model_input_names[0]

             a_   :						Optional[Any]				=    BatchFeature({input_name: speech_inputs}	)

             # truncate to smallest
             a_   :						Union[str, Any]				=    feat_extract.pad(
                 SCREAMING_SNAKE_CASE__	,				padding='max_length'	,				max_length=len(speech_inputs[0]	)	,				truncation=SCREAMING_SNAKE_CASE__	)
             a_   :						int				=    input_a[input_name]

             a_   :						Any				=    feat_extract.pad(SCREAMING_SNAKE_CASE__	,				padding='max_length'	,				max_length=len(speech_inputs[0]	)	)
             a_   :						str				=    input_a[input_name]

             self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE__	)	)
             self.assertFalse(_inputs_have_equal_length(SCREAMING_SNAKE_CASE__	)	)

             # truncate to smallest with np
             a_   :						Optional[Any]				=    feat_extract.pad(
                 SCREAMING_SNAKE_CASE__	,				padding='max_length'	,				max_length=len(speech_inputs[0]	)	,				return_tensors='np'	,				truncation=SCREAMING_SNAKE_CASE__	,				)
             a_   :						Tuple				=    input_a[input_name]

             a_   :						List[Any]				=    feat_extract.pad(
                 SCREAMING_SNAKE_CASE__	,				padding='max_length'	,				max_length=len(speech_inputs[0]	)	,				return_tensors='np'	)
             a_   :						Optional[Any]				=    input_a[input_name]

             self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE__	)	)
             self.assertTrue(input_a.shape[1] == len(speech_inputs[0]	)	)

             # since truncation forces padding to be smaller than longest input
             # function can't return `np.ndarray`, but has to return list
             self.assertFalse(_inputs_have_equal_length(SCREAMING_SNAKE_CASE__	)	)

             # truncate to middle
             a_   :						List[Any]				=    feat_extract.pad(
                 SCREAMING_SNAKE_CASE__	,				padding='max_length'	,				max_length=len(speech_inputs[1]	)	,				truncation=SCREAMING_SNAKE_CASE__	,				return_tensors='np'	,				)
             a_   :						Dict				=    input_a[input_name]

             a_   :						List[Any]				=    feat_extract.pad(
                 SCREAMING_SNAKE_CASE__	,				padding='max_length'	,				max_length=len(speech_inputs[1]	)	,				truncation=SCREAMING_SNAKE_CASE__	)
             a_   :						Any				=    input_a[input_name]

             a_   :						Tuple				=    feat_extract.pad(
                 SCREAMING_SNAKE_CASE__	,				padding='max_length'	,				max_length=len(speech_inputs[1]	)	,				return_tensors='np'	)
             a_   :						List[Any]				=    input_a[input_name]

             self.assertTrue(input_a.shape[1] == len(speech_inputs[1]	)	)
             self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE__	)	)
             self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE__	)	)
             self.assertTrue(_inputs_are_equal(SCREAMING_SNAKE_CASE__	,				SCREAMING_SNAKE_CASE__	)	)

             # since truncation forces padding to be smaller than longest input
             # function can't return `np.ndarray`, but has to return list
             self.assertFalse(_inputs_have_equal_length(SCREAMING_SNAKE_CASE__	)	)
             self.assertTrue(len(input_a[-1]	) == len(speech_inputs[-1]	)	)

             # padding has to be max_length when setting `truncation=True`
             with self.assertRaises(SCREAMING_SNAKE_CASE__	):
                    feat_extract.pad(SCREAMING_SNAKE_CASE__	,				truncation=SCREAMING_SNAKE_CASE__	)[input_name]

             # padding has to be max_length when setting `truncation=True`
             with self.assertRaises(SCREAMING_SNAKE_CASE__	):
                    feat_extract.pad(SCREAMING_SNAKE_CASE__	,				padding='longest'	,				truncation=SCREAMING_SNAKE_CASE__	)[input_name]

             # padding has to be max_length when setting `truncation=True`
             with self.assertRaises(SCREAMING_SNAKE_CASE__	):
                    feat_extract.pad(SCREAMING_SNAKE_CASE__	,				padding='longest'	,				truncation=SCREAMING_SNAKE_CASE__	)[input_name]

             # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
             with self.assertRaises(SCREAMING_SNAKE_CASE__	):
                    feat_extract.pad(SCREAMING_SNAKE_CASE__	,				padding='max_length'	,				truncation=SCREAMING_SNAKE_CASE__	)[input_name]

             # test truncation for `pad_to_multiple_of` for List[int] + numpy
             a_   :						int				=    1_2
             a_   :						str				=    feat_extract.pad(
                 SCREAMING_SNAKE_CASE__	,				padding='max_length'	,				max_length=len(speech_inputs[0]	)	,				pad_to_multiple_of=SCREAMING_SNAKE_CASE__	,				truncation=SCREAMING_SNAKE_CASE__	,				)
             a_   :						Union[str, Any]				=    input_a[input_name]

             a_   :						Optional[int]				=    feat_extract.pad(
                 SCREAMING_SNAKE_CASE__	,				padding='max_length'	,				max_length=len(speech_inputs[0]	)	,				pad_to_multiple_of=SCREAMING_SNAKE_CASE__	,				)
             a_   :						Any				=    input_a[input_name]

             # retrieve expected_length as multiple of pad_to_multiple_of
             a_   :						Union[str, Any]				=    len(speech_inputs[0]	)
             if expected_length % pad_to_multiple_of != 0:
                    a_   :						Any				=    ((len(speech_inputs[0]	) // pad_to_multiple_of) + 1) * pad_to_multiple_of

             self.assertTrue(len(input_a[0]	) == expected_length	)
             self.assertTrue(_inputs_have_equal_length(SCREAMING_SNAKE_CASE__	)	)
             self.assertFalse(_inputs_have_equal_length(SCREAMING_SNAKE_CASE__	)	)
      def 			SCREAMING_SNAKE_CASE				(			self							:			Union[str, Any]	)						->     List[Any]:
             self._check_padding(numpify=SCREAMING_SNAKE_CASE__	)
      def 			SCREAMING_SNAKE_CASE				(			self							:			int	)						->     Optional[Any]:
             self._check_padding(numpify=SCREAMING_SNAKE_CASE__	)
      def 			SCREAMING_SNAKE_CASE				(			self							:			Union[str, Any]	)						->     Optional[Any]:
             self._check_truncation(numpify=SCREAMING_SNAKE_CASE__	)
      def 			SCREAMING_SNAKE_CASE				(			self							:			Any	)						->     Union[str, Any]:
             self._check_truncation(numpify=SCREAMING_SNAKE_CASE__	)
      @require_torch
      def 			SCREAMING_SNAKE_CASE				(			self							:			Union[str, Any]	)						->     Optional[Any]:
             a_   :						Optional[Any]				=    self.feature_extraction_class(**self.feat_extract_dict	)
             a_   :						str				=    self.feat_extract_tester.prepare_inputs_for_common()
             a_   :						Optional[int]				=    feat_extract.model_input_names[0]

             a_   :						Tuple				=    BatchFeature({input_name: speech_inputs}	)

             a_   :						Tuple				=    feat_extract.pad(SCREAMING_SNAKE_CASE__	,				padding='longest'	,				return_tensors='np'	)[input_name]
             a_   :						List[str]				=    feat_extract.pad(SCREAMING_SNAKE_CASE__	,				padding='longest'	,				return_tensors='pt'	)[input_name]

             self.assertTrue(abs(input_np.astype(np.floataa	).sum() - input_pt.numpy().astype(np.floataa	).sum()	) < 1E-2	)
      @require_tf
      def 			SCREAMING_SNAKE_CASE				(			self							:			Tuple	)						->     Any:
             a_   :						Any				=    self.feature_extraction_class(**self.feat_extract_dict	)
             a_   :						Union[str, Any]				=    self.feat_extract_tester.prepare_inputs_for_common()
             a_   :						Tuple				=    feat_extract.model_input_names[0]

             a_   :						str				=    BatchFeature({input_name: speech_inputs}	)

             a_   :						str				=    feat_extract.pad(SCREAMING_SNAKE_CASE__	,				padding='longest'	,				return_tensors='np'	)[input_name]
             a_   :						str				=    feat_extract.pad(SCREAMING_SNAKE_CASE__	,				padding='longest'	,				return_tensors='tf'	)[input_name]

             self.assertTrue(abs(input_np.astype(np.floataa	).sum() - input_tf.numpy().astype(np.floataa	).sum()	) < 1E-2	)
      def 			SCREAMING_SNAKE_CASE				(			self							:			Dict	)						->     Dict:
             a_   :						int				=    self.feat_extract_dict
             a_   :						int				=    True
             a_   :						List[Any]				=    self.feature_extraction_class(**SCREAMING_SNAKE_CASE__	)
             a_   :						Optional[Any]				=    self.feat_extract_tester.prepare_inputs_for_common()
             a_   :						Optional[int]				=    [len(SCREAMING_SNAKE_CASE__	) for x in speech_inputs]
             a_   :						List[Any]				=    feat_extract.model_input_names[0]

             a_   :						Optional[Any]				=    BatchFeature({input_name: speech_inputs}	)

             a_   :						Optional[Any]				=    feat_extract.pad(SCREAMING_SNAKE_CASE__	,				padding='longest'	,				return_tensors='np'	)
             self.assertIn('attention_mask'	,				SCREAMING_SNAKE_CASE__	)
             self.assertListEqual(list(processed.attention_mask.shape	)	,				list(processed[input_name].shape[:2]	)	)
             self.assertListEqual(processed.attention_mask.sum(-1	).tolist()	,				SCREAMING_SNAKE_CASE__	)

      def 			SCREAMING_SNAKE_CASE				(			self							:			List[Any]	)						->     List[str]:
             a_   :						Optional[Any]				=    self.feat_extract_dict
             a_   :						Dict				=    True
             a_   :						Optional[int]				=    self.feature_extraction_class(**SCREAMING_SNAKE_CASE__	)
             a_   :						Dict				=    self.feat_extract_tester.prepare_inputs_for_common()
             a_   :						int				=    [len(SCREAMING_SNAKE_CASE__	) for x in speech_inputs]
             a_   :						Union[str, Any]				=    feat_extract.model_input_names[0]

             a_   :						Union[str, Any]				=    BatchFeature({input_name: speech_inputs}	)
             a_   :						Union[str, Any]				=    min(SCREAMING_SNAKE_CASE__	)

             a_   :						List[str]				=    feat_extract.pad(
                 SCREAMING_SNAKE_CASE__	,				padding='max_length'	,				max_length=SCREAMING_SNAKE_CASE__	,				truncation=SCREAMING_SNAKE_CASE__	,				return_tensors='np'	)
             self.assertIn('attention_mask'	,				SCREAMING_SNAKE_CASE__	)
             self.assertListEqual(
                 list(processed_pad.attention_mask.shape	)	,				[processed_pad[input_name].shape[0], max_length]	)
             self.assertListEqual(
                 processed_pad.attention_mask[:, :max_length].sum(-1	).tolist()	,				[max_length for x in speech_inputs]	)







'''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'''],
          )					,  )



