'''simple docstring'''




import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
  from transformers import SpeechaTextFeatureExtractor

__SCREAMING_SNAKE_CASE       :     Tuple     =  random.Random()

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Union[str, Any]			,	_UpperCAmelCase  :      Any=1.0			,	_UpperCAmelCase  :      Optional[Any]=None			,	_UpperCAmelCase  :      List[str]=None    )    ->    str:


       """simple docstring"""







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

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

       return values

@require_torch
@require_torchaudio
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def __init__(       self						:				int						,					A						:				str						,					A						:				Dict=7						,					A						:				List[Any]=400						,					A						:				Union[str, Any]=2000						,					A						:				str=24						,					A						:				Optional[Any]=24						,					A						:				Optional[Any]=0.0						,					A						:				Optional[int]=16000						,					A						:				str=True						,					A						:				Optional[Any]=True						,					):
             _UpperCAmelCase     :  Optional[int]							      =    parent
             _UpperCAmelCase     :  int							      =    batch_size
             _UpperCAmelCase     :  Tuple							      =    min_seq_length
             _UpperCAmelCase     :  List[str]							      =    max_seq_length
             _UpperCAmelCase     :  Tuple							      =    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
             _UpperCAmelCase     :  Dict							      =    feature_size
             _UpperCAmelCase     :  List[Any]							      =    num_mel_bins
             _UpperCAmelCase     :  Union[str, Any]							      =    padding_value
             _UpperCAmelCase     :  Optional[Any]							      =    sampling_rate
             _UpperCAmelCase     :  Optional[int]							      =    return_attention_mask
             _UpperCAmelCase     :  Tuple							      =    do_normalize




      def      _A	(       self						:				Union[str, Any]       ):
             return {
                 "feature_size": self.feature_size,
                 "num_mel_bins": self.num_mel_bins,
                 "padding_value": self.padding_value,
                 "sampling_rate": self.sampling_rate,
                 "return_attention_mask": self.return_attention_mask,
                 "do_normalize": self.do_normalize,
             }





      def      _A	(       self						:				str						,					A						:				Tuple=False						,					A						:				Any=False       ):
             def _flatten(A						:				Optional[Any]       ):
                    return list(itertools.chain(*A       )       )

             if equal_length:
                    _UpperCAmelCase     :  List[Any]							      =    [floats_list((self.max_seq_length, self.feature_size)       ) for _ in range(self.batch_size       )]
             else:
                    # make sure that inputs increase in size
                    _UpperCAmelCase     :  Dict							      =    [
                        floats_list((x, self.feature_size)       )
                        for x in range(self.min_seq_length						,					self.max_seq_length						,					self.seq_length_diff       )
                    ]
             if numpify:
                    _UpperCAmelCase     :  Optional[int]							      =    [np.asarray(A       ) for x in speech_inputs]
             return speech_inputs

@require_torch
@require_torchaudio
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  int						=						SpeechaTextFeatureExtractor if is_speech_available() else None




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Optional[Any]							      =    SpeechaTextFeatureExtractionTester(self       )




      def      _A	(       self						:				Optional[Any]						,					A						:				Any       ):
             self.assertTrue(np.all(np.mean(A						,					axis=0       ) < 1E-3       )       )
             self.assertTrue(np.all(np.abs(np.var(A						,					axis=0       ) - 1       ) < 1E-3       )       )




      def      _A	(       self						:				Any       ):
             # Tests that all call wrap to encode_plus and batch_encode_plus
             _UpperCAmelCase     :  str							      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
             # create three inputs of length 800, 1000, and 1200
             _UpperCAmelCase     :  Optional[Any]							      =    [floats_list((1, x)       )[0] for x in range(800						,					1400						,					200       )]
             _UpperCAmelCase     :  int							      =    [np.asarray(A       ) for speech_input in speech_inputs]

             # Test feature size
             _UpperCAmelCase     :  Tuple							      =    feature_extractor(A						,					padding=A						,					return_tensors="np"       ).input_features
             self.assertTrue(input_features.ndim == 3       )
             self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size       )

             # Test not batched input
             _UpperCAmelCase     :  Optional[Any]							      =    feature_extractor(speech_inputs[0]						,					return_tensors="np"       ).input_features
             _UpperCAmelCase     :  str							      =    feature_extractor(np_speech_inputs[0]						,					return_tensors="np"       ).input_features
             self.assertTrue(np.allclose(A						,					A						,					atol=1E-3       )       )

             # Test batched
             _UpperCAmelCase     :  List[str]							      =    feature_extractor(A						,					return_tensors="np"       ).input_features
             _UpperCAmelCase     :  List[str]							      =    feature_extractor(A						,					return_tensors="np"       ).input_features
             for enc_seq_a, enc_seq_a in zip(A						,					A       ):
                    self.assertTrue(np.allclose(A						,					A						,					atol=1E-3       )       )

             # Test 2-D numpy arrays are batched.
             _UpperCAmelCase     :  str							      =    [floats_list((1, x)       )[0] for x in (800, 800, 800)]
             _UpperCAmelCase     :  Tuple							      =    np.asarray(A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    feature_extractor(A						,					return_tensors="np"       ).input_features
             _UpperCAmelCase     :  Any							      =    feature_extractor(A						,					return_tensors="np"       ).input_features
             for enc_seq_a, enc_seq_a in zip(A						,					A       ):
                    self.assertTrue(np.allclose(A						,					A						,					atol=1E-3       )       )




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
             _UpperCAmelCase     :  str							      =    [floats_list((1, x)       )[0] for x in range(800						,					1400						,					200       )]

             _UpperCAmelCase     :  Tuple							      =    ["longest", "max_length", "do_not_pad"]
             _UpperCAmelCase     :  int							      =    [None, 16, None]
             for max_length, padding in zip(A						,					A       ):
                    _UpperCAmelCase     :  str							      =    feature_extractor(
                        A						,					padding=A						,					max_length=A						,					return_attention_mask=A       )
                    _UpperCAmelCase     :  int							      =    inputs.input_features
                    _UpperCAmelCase     :  Any							      =    inputs.attention_mask
                    _UpperCAmelCase     :  Dict							      =    [np.sum(A       ) for x in attention_mask]

                    self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]       )
                    self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]       )
                    self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]       )




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  List[Any]							      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
             _UpperCAmelCase     :  Dict							      =    [floats_list((1, x)       )[0] for x in range(800						,					1400						,					200       )]

             _UpperCAmelCase     :  Optional[int]							      =    ["longest", "max_length", "do_not_pad"]
             _UpperCAmelCase     :  Optional[int]							      =    [None, 16, None]
             for max_length, padding in zip(A						,					A       ):
                    _UpperCAmelCase     :  List[Any]							      =    feature_extractor(
                        A						,					max_length=A						,					padding=A						,					return_tensors="np"						,					return_attention_mask=A       )
                    _UpperCAmelCase     :  Dict							      =    inputs.input_features
                    _UpperCAmelCase     :  Dict							      =    inputs.attention_mask
                    _UpperCAmelCase     :  str							      =    [np.sum(A       ) for x in attention_mask]

                    self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]       )
                    self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6       )
                    self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]       )
                    self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6       )
                    self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]       )




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  int							      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
             _UpperCAmelCase     :  Any							      =    [floats_list((1, x)       )[0] for x in range(800						,					1400						,					200       )]
             _UpperCAmelCase     :  Optional[int]							      =    feature_extractor(
                 A						,					padding="max_length"						,					max_length=4						,					truncation=A						,					return_tensors="np"						,					return_attention_mask=A						,					)
             _UpperCAmelCase     :  List[Any]							      =    inputs.input_features
             _UpperCAmelCase     :  List[Any]							      =    inputs.attention_mask
             _UpperCAmelCase     :  List[str]							      =    np.sum(attention_mask == 1						,					axis=1       )

             self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]       )
             self._check_zero_mean_unit_variance(input_features[1]       )
             self._check_zero_mean_unit_variance(input_features[2]       )




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  int							      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
             _UpperCAmelCase     :  Union[str, Any]							      =    [floats_list((1, x)       )[0] for x in range(800						,					1400						,					200       )]
             _UpperCAmelCase     :  Optional[int]							      =    feature_extractor(
                 A						,					padding="longest"						,					max_length=4						,					truncation=A						,					return_tensors="np"						,					return_attention_mask=A						,					)
             _UpperCAmelCase     :  Dict							      =    inputs.input_features
             _UpperCAmelCase     :  Tuple							      =    inputs.attention_mask
             _UpperCAmelCase     :  List[Any]							      =    np.sum(attention_mask == 1						,					axis=1       )

             self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]       )
             self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]       )
             self._check_zero_mean_unit_variance(input_features[2]       )

             # make sure that if max_length < longest -> then pad to max_length
             self.assertEqual(input_features.shape						,					(3, 4, 24)       )

             _UpperCAmelCase     :  Tuple							      =    [floats_list((1, x)       )[0] for x in range(800						,					1400						,					200       )]
             _UpperCAmelCase     :  Dict							      =    feature_extractor(
                 A						,					padding="longest"						,					max_length=16						,					truncation=A						,					return_tensors="np"						,					return_attention_mask=A						,					)
             _UpperCAmelCase     :  int							      =    inputs.input_features
             _UpperCAmelCase     :  Tuple							      =    inputs.attention_mask
             _UpperCAmelCase     :  Any							      =    np.sum(attention_mask == 1						,					axis=1       )

             self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]       )
             self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]       )
             self._check_zero_mean_unit_variance(input_features[2]       )

             # make sure that if max_length < longest -> then pad to max_length
             self.assertEqual(input_features.shape						,					(3, 6, 24)       )




      def      _A	(       self						:				Optional[int]       ):
             import torch

             _UpperCAmelCase     :  List[str]							      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
             _UpperCAmelCase     :  str							      =    np.random.rand(100						,					32       ).astype(np.floataa       )
             _UpperCAmelCase     :  Dict							      =    np_speech_inputs.tolist()

             for inputs in [py_speech_inputs, np_speech_inputs]:
                    _UpperCAmelCase     :  Tuple							      =    feature_extractor.pad([{"input_features": inputs}]						,					return_tensors="np"       )
                    self.assertTrue(np_processed.input_features.dtype == np.floataa       )
                    _UpperCAmelCase     :  List[str]							      =    feature_extractor.pad([{"input_features": inputs}]						,					return_tensors="pt"       )
                    self.assertTrue(pt_processed.input_features.dtype == torch.floataa       )




      def      _A	(       self						:				Dict						,					A						:				List[str]       ):
             from datasets import load_dataset

             _UpperCAmelCase     :  List[str]							      =    load_dataset("hf-internal-testing/librispeech_asr_dummy"						,					"clean"						,					split="validation"       )
             # automatic decoding with librispeech
             _UpperCAmelCase     :  Dict							      =    ds.sort("id"       ).select(range(A       )       )[:num_samples]["audio"]

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





      def      _A	(       self						:				str       ):
             # fmt: off
             _UpperCAmelCase     :  str							      =    np.array([
                 -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
                 -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
                 -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
             ]       )
             # fmt: on

             _UpperCAmelCase     :  int							      =    self._load_datasamples(1       )
             _UpperCAmelCase     :  Tuple							      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
             _UpperCAmelCase     :  Optional[int]							      =    feature_extractor(A						,					return_tensors="pt"       ).input_features
             self.assertEquals(input_features.shape						,					(1, 584, 24)       )
             self.assertTrue(np.allclose(input_features[0, 0, :30]						,					A						,					atol=1E-4       )       )


'''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, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import MobileViTImageProcessor

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

      def __init__(       self						:				Optional[Any]						,					A						:				Dict						,					A						:				Dict=7						,					A						:				Optional[int]=3						,					A						:				Optional[int]=18						,					A						:				Dict=30						,					A						:				List[Any]=400						,					A						:				Union[str, Any]=True						,					A						:				Tuple=None						,					A						:				List[Any]=True						,					A						:				int=None						,					A						:				Optional[int]=True						,					):
             _UpperCAmelCase     :  Optional[int]							      =    size if size is not None else {"shortest_edge": 20}
             _UpperCAmelCase     :  Optional[Any]							      =    crop_size if crop_size is not None else {"height": 18, "width": 18}
             _UpperCAmelCase     :  List[Any]							      =    parent
             _UpperCAmelCase     :  Union[str, Any]							      =    batch_size
             _UpperCAmelCase     :  Optional[Any]							      =    num_channels
             _UpperCAmelCase     :  Union[str, Any]							      =    image_size
             _UpperCAmelCase     :  int							      =    min_resolution
             _UpperCAmelCase     :  Optional[int]							      =    max_resolution
             _UpperCAmelCase     :  List[str]							      =    do_resize
             _UpperCAmelCase     :  Optional[Any]							      =    size
             _UpperCAmelCase     :  Tuple							      =    do_center_crop
             _UpperCAmelCase     :  Optional[int]							      =    crop_size
             _UpperCAmelCase     :  Optional[Any]							      =    do_flip_channel_order





      def      _A	(       self						:				Dict       ):
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_flip_channel_order": self.do_flip_channel_order,
             }

@require_torch
@require_vision
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Tuple						=						MobileViTImageProcessor if is_vision_available() else None




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Any							      =    MobileViTImageProcessingTester(self       )




      @property
      def      _A	(       self						:				int       ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  int							      =    self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(A						,					"do_resize"       )       )
             self.assertTrue(hasattr(A						,					"size"       )       )
             self.assertTrue(hasattr(A						,					"do_center_crop"       )       )
             self.assertTrue(hasattr(A						,					"center_crop"       )       )
             self.assertTrue(hasattr(A						,					"do_flip_channel_order"       )       )




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 20}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 18, "width": 18}       )

             _UpperCAmelCase     :  Dict							      =    self.image_processing_class.from_dict(self.image_processor_dict						,					size=42						,					crop_size=84       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 42}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 84, "width": 84}       )




      def      _A	(       self						:				Any       ):
             pass




      def      _A	(       self						:				Dict       ):
             # Initialize image_processing
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             _UpperCAmelCase     :  Union[str, Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					Image.Image       )

             # Test not batched input
             _UpperCAmelCase     :  List[Any]							      =    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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Optional[Any]							      =    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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)




      def      _A	(       self						:				Union[str, Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Dict							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             _UpperCAmelCase     :  Optional[Any]							      =    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
             _UpperCAmelCase     :  Optional[int]							      =    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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Optional[int]							      =    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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)





      def      _A	(       self						:				Any       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Any							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             _UpperCAmelCase     :  Optional[Any]							      =    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
             _UpperCAmelCase     :  List[str]							      =    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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Any							      =    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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

