import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


UpperCAmelCase_								=							random.Random()

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_=1.0       ,					lowerCAmelCase_=None       ,					lowerCAmelCase_=None   )		->						str:




							'''simple docstring'''






							if rng is None:
														_A=	global_rng

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

							return values





class      lowerCAmelCase						(					unittest.TestCase			):



					def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__=7				,      lowerCAmelCase__=400				,      lowerCAmelCase__=2000				,      lowerCAmelCase__=1				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=16000				,      lowerCAmelCase__=True				,      lowerCAmelCase__=True				,      ):
												_A=	parent
												_A=	batch_size
												_A=	min_seq_length
												_A=	max_seq_length
												_A=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												_A=	feature_size
												_A=	padding_value
												_A=	sampling_rate
												_A=	return_attention_mask
												_A=	do_normalize



					def 			a__  (					self       ):
												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 			a__  (					self				,      lowerCAmelCase__=False				,      lowerCAmelCase__=False       ):
												def _flatten(lowerCAmelCase__       ):
																			return list(itertools.chain(*__UpperCamelCase       )       )

												if equal_length:
																			_A=	floats_list((self.batch_size, self.max_seq_length)       )
												else:
																			# make sure that inputs increase in size
																			_A=	[
																			    _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=	[np.asarray(__UpperCamelCase       ) for x in speech_inputs]

												return speech_inputs





class      lowerCAmelCase						(					_a			,		unittest.TestCase			):
					_SCREAMING_SNAKE_CASE     :     Any		=WavaVecaFeatureExtractor



					def 			a__  (					self       ):
												_A=	WavaVecaFeatureExtractionTester(self       )



					def 			a__  (					self				,      lowerCAmelCase__       ):
												self.assertTrue(np.all(np.mean(__UpperCamelCase				,      axis=0       ) < 1E-3       )       )
												self.assertTrue(np.all(np.abs(np.var(__UpperCamelCase				,      axis=0       ) - 1       ) < 1E-3       )       )



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

												# Test not batched input
												_A=	feat_extract(speech_inputs[0]				,      return_tensors='np'       ).input_values
												_A=	feat_extract(np_speech_inputs[0]				,      return_tensors='np'       ).input_values
												self.assertTrue(np.allclose(__UpperCamelCase				,      __UpperCamelCase				,      atol=1E-3       )       )

												# Test batched
												_A=	feat_extract(__UpperCamelCase				,      return_tensors='np'       ).input_values
												_A=	feat_extract(__UpperCamelCase				,      return_tensors='np'       ).input_values
												for enc_seq_a, enc_seq_a in zip(__UpperCamelCase				,      __UpperCamelCase       ):
																			self.assertTrue(np.allclose(__UpperCamelCase				,      __UpperCamelCase				,      atol=1E-3       )       )

												# Test 2-D numpy arrays are batched.
												_A=	[floats_list((1, x)       )[0] for x in (800, 800, 800)]
												_A=	np.asarray(__UpperCamelCase       )
												_A=	feat_extract(__UpperCamelCase				,      return_tensors='np'       ).input_values
												_A=	feat_extract(__UpperCamelCase				,      return_tensors='np'       ).input_values
												for enc_seq_a, enc_seq_a in zip(__UpperCamelCase				,      __UpperCamelCase       ):
																			self.assertTrue(np.allclose(__UpperCamelCase				,      __UpperCamelCase				,      atol=1E-3       )       )



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

												_A=	['longest', 'max_length', 'do_not_pad']
												_A=	[None, 1600, None]
												for max_length, padding in zip(__UpperCamelCase				,      __UpperCamelCase       ):
																			_A=	feat_extract(__UpperCamelCase				,      padding=__UpperCamelCase				,      max_length=__UpperCamelCase				,      return_tensors='np'       )
																			_A=	processed.input_values

																			self._check_zero_mean_unit_variance(input_values[0][:800]       )
																			self.assertTrue(input_values[0][800:].sum() < 1E-6       )
																			self._check_zero_mean_unit_variance(input_values[1][:1000]       )
																			self.assertTrue(input_values[0][1000:].sum() < 1E-6       )
																			self._check_zero_mean_unit_variance(input_values[2][:1200]       )



					def 			a__  (					self       ):
												_A=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
												_A=	range(800				,      1400				,      200       )
												_A=	[floats_list((1, x)       )[0] for x in lengths]

												_A=	['longest', 'max_length', 'do_not_pad']
												_A=	[None, 1600, None]

												for max_length, padding in zip(__UpperCamelCase				,      __UpperCamelCase       ):
																			_A=	feat_extract(__UpperCamelCase				,      max_length=__UpperCamelCase				,      padding=__UpperCamelCase       )
																			_A=	processed.input_values

																			self._check_zero_mean_unit_variance(input_values[0][:800]       )
																			self._check_zero_mean_unit_variance(input_values[1][:1000]       )
																			self._check_zero_mean_unit_variance(input_values[2][:1200]       )



					def 			a__  (					self       ):
												_A=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
												_A=	[floats_list((1, x)       )[0] for x in range(800				,      1400				,      200       )]
												_A=	feat_extract(
												    __UpperCamelCase				,      truncation=__UpperCamelCase				,      max_length=1000				,      padding='max_length'				,      return_tensors='np'       )
												_A=	processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :800]       )
												self._check_zero_mean_unit_variance(input_values[1]       )
												self._check_zero_mean_unit_variance(input_values[2]       )



					def 			a__  (					self       ):
												_A=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
												_A=	[floats_list((1, x)       )[0] for x in range(800				,      1400				,      200       )]
												_A=	feat_extract(
												    __UpperCamelCase				,      truncation=__UpperCamelCase				,      max_length=1000				,      padding='longest'				,      return_tensors='np'       )
												_A=	processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :800]       )
												self._check_zero_mean_unit_variance(input_values[1, :1000]       )
												self._check_zero_mean_unit_variance(input_values[2]       )

												# make sure that if max_length < longest -> then pad to max_length
												self.assertTrue(input_values.shape == (3, 1000)       )

												_A=	[floats_list((1, x)       )[0] for x in range(800				,      1400				,      200       )]
												_A=	feat_extract(
												    __UpperCamelCase				,      truncation=__UpperCamelCase				,      max_length=2000				,      padding='longest'				,      return_tensors='np'       )
												_A=	processed.input_values

												self._check_zero_mean_unit_variance(input_values[0, :800]       )
												self._check_zero_mean_unit_variance(input_values[1, :1000]       )
												self._check_zero_mean_unit_variance(input_values[2]       )

												# make sure that if max_length > longest -> then pad to longest
												self.assertTrue(input_values.shape == (3, 1200)       )



					@require_torch
					def 			a__  (					self       ):
												import torch

												_A=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
												_A=	np.random.rand(100       ).astype(np.floataa       )
												_A=	np_speech_inputs.tolist()

												for inputs in [py_speech_inputs, np_speech_inputs]:
																			_A=	feature_extractor.pad([{'input_values': inputs}]				,      return_tensors='np'       )
																			self.assertTrue(np_processed.input_values.dtype == np.floataa       )
																			_A=	feature_extractor.pad([{'input_values': inputs}]				,      return_tensors='pt'       )
																			self.assertTrue(pt_processed.input_values.dtype == torch.floataa       )





					@slow
					@require_torch
					def 			a__  (					self       ):
												# this test makes sure that models that are using
												# group norm don't have their feature extractor return the
												# attention_mask
												for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
																			_A=	WavaVecaConfig.from_pretrained(__UpperCamelCase       )
																			_A=	WavaVecaFeatureExtractor.from_pretrained(__UpperCamelCase       )

																			# only "layer" feature extraction norm should make use of
																			# attention_mask
																			self.assertEqual(feat_extract.return_attention_mask				,      config.feat_extract_norm == 'layer'       )
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetrImageProcessor





class      lowerCAmelCase						(					unittest.TestCase			):



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__=7				,      lowerCAmelCase__=3				,      lowerCAmelCase__=30				,      lowerCAmelCase__=400				,      lowerCAmelCase__=True				,      lowerCAmelCase__=None				,      lowerCAmelCase__=True				,      lowerCAmelCase__=1 / 255				,      lowerCAmelCase__=True				,      lowerCAmelCase__=[0.5, 0.5, 0.5]				,      lowerCAmelCase__=[0.5, 0.5, 0.5]				,      lowerCAmelCase__=True				,      ):
            # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
            _A=	size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
            _A=	parent
            _A=	batch_size
            _A=	num_channels
            _A=	min_resolution
            _A=	max_resolution
            _A=	do_resize
            _A=	size
            _A=	do_rescale
            _A=	rescale_factor
            _A=	do_normalize
            _A=	image_mean
            _A=	image_std
            _A=	do_pad



     def 			a__  (					self       ):
            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_rescale": self.do_rescale,
                "rescale_factor": self.rescale_factor,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_pad": self.do_pad,
            }





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=False       ):
            if not batched:
                   _A=	image_inputs[0]
                   if isinstance(lowerCAmelCase__				,      Image.Image       ):
                          _A,			_A=	image.size
                   else:
                          _A,			_A=	image.shape[1], image.shape[2]
                   if w < h:
                          _A=	int(self.size['shortest_edge'] * h / w       )
                          _A=	self.size['shortest_edge']
                   elif w > h:
                          _A=	self.size['shortest_edge']
                          _A=	int(self.size['shortest_edge'] * w / h       )
                   else:
                          _A=	self.size['shortest_edge']
                          _A=	self.size['shortest_edge']

            else:
                   _A=	[]
                   for image in image_inputs:
                          _A,			_A=	self.get_expected_values([image]       )
                          expected_values.append((expected_height, expected_width)       )
                   _A=	max(lowerCAmelCase__				,      key=lambda lowerCAmelCase__       : item[0]       )[0]
                   _A=	max(lowerCAmelCase__				,      key=lambda lowerCAmelCase__       : item[1]       )[1]

            return expected_height, expected_width





@require_torch
@require_vision
class      lowerCAmelCase						(					_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     List[str]		=DetrImageProcessor if is_vision_available() else None



     def 			a__  (					self       ):
            _A=	DetrImageProcessingTester(self       )



     @property
     def 			a__  (					self       ):
            return self.image_processor_tester.prepare_image_processor_dict()



     def 			a__  (					self       ):
            _A=	self.image_processing_class(**self.image_processor_dict       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'image_mean'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'image_std'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'do_normalize'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'do_rescale'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'rescale_factor'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'do_resize'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'size'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'do_pad'       )       )



     def 			a__  (					self       ):
            _A=	self.image_processing_class.from_dict(self.image_processor_dict       )
            self.assertEqual(image_processor.size				,      {'shortest_edge': 18, 'longest_edge': 1333}       )
            self.assertEqual(image_processor.do_pad				,      lowerCAmelCase__       )

            _A=	self.image_processing_class.from_dict(
                self.image_processor_dict				,      size=42				,      max_size=84				,      pad_and_return_pixel_mask=lowerCAmelCase__       )
            self.assertEqual(image_processor.size				,      {'shortest_edge': 42, 'longest_edge': 84}       )
            self.assertEqual(image_processor.do_pad				,      lowerCAmelCase__       )



     def 			a__  (					self       ):
            pass



     def 			a__  (					self       ):
            # Initialize image_processing
            _A=	self.image_processing_class(**self.image_processor_dict       )
            # create random PIL images
            _A=	prepare_image_inputs(self.image_processor_tester				,      equal_resolution=lowerCAmelCase__       )
            for image in image_inputs:
                   self.assertIsInstance(lowerCAmelCase__				,      Image.Image       )

            # Test not batched input
            _A=	image_processing(image_inputs[0]				,      return_tensors='pt'       ).pixel_values

            _A,			_A=	self.image_processor_tester.get_expected_values(lowerCAmelCase__       )

            self.assertEqual(
                encoded_images.shape				,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,      )

            # Test batched
            _A,			_A=	self.image_processor_tester.get_expected_values(lowerCAmelCase__				,      batched=lowerCAmelCase__       )

            _A=	image_processing(lowerCAmelCase__				,      return_tensors='pt'       ).pixel_values
            self.assertEqual(
                encoded_images.shape				,      (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )				,      )



     def 			a__  (					self       ):
            # Initialize image_processing
            _A=	self.image_processing_class(**self.image_processor_dict       )
            # create random numpy tensors
            _A=	prepare_image_inputs(self.image_processor_tester				,      equal_resolution=lowerCAmelCase__				,      numpify=lowerCAmelCase__       )
            for image in image_inputs:
                   self.assertIsInstance(lowerCAmelCase__				,      np.ndarray       )

            # Test not batched input
            _A=	image_processing(image_inputs[0]				,      return_tensors='pt'       ).pixel_values

            _A,			_A=	self.image_processor_tester.get_expected_values(lowerCAmelCase__       )

            self.assertEqual(
                encoded_images.shape				,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,      )

            # Test batched
            _A=	image_processing(lowerCAmelCase__				,      return_tensors='pt'       ).pixel_values

            _A,			_A=	self.image_processor_tester.get_expected_values(lowerCAmelCase__				,      batched=lowerCAmelCase__       )

            self.assertEqual(
                encoded_images.shape				,      (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )				,      )



     def 			a__  (					self       ):
            # Initialize image_processing
            _A=	self.image_processing_class(**self.image_processor_dict       )
            # create random PyTorch tensors
            _A=	prepare_image_inputs(self.image_processor_tester				,      equal_resolution=lowerCAmelCase__				,      torchify=lowerCAmelCase__       )
            for image in image_inputs:
                   self.assertIsInstance(lowerCAmelCase__				,      torch.Tensor       )

            # Test not batched input
            _A=	image_processing(image_inputs[0]				,      return_tensors='pt'       ).pixel_values

            _A,			_A=	self.image_processor_tester.get_expected_values(lowerCAmelCase__       )

            self.assertEqual(
                encoded_images.shape				,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,      )

            # Test batched
            _A=	image_processing(lowerCAmelCase__				,      return_tensors='pt'       ).pixel_values

            _A,			_A=	self.image_processor_tester.get_expected_values(lowerCAmelCase__				,      batched=lowerCAmelCase__       )

            self.assertEqual(
                encoded_images.shape				,      (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )				,      )



     @slow
     def 			a__  (					self       ):
            # prepare image and target
            _A=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
            with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'				,      'r'       ) as f:
                   _A=	json.loads(f.read()       )

            _A=	{'image_id': 39769, 'annotations': target}

            # encode them
            _A=	DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'       )
            _A=	image_processing(images=lowerCAmelCase__				,      annotations=lowerCAmelCase__				,      return_tensors='pt'       )

            # verify pixel values
            _A=	torch.Size([1, 3, 800, 1066]       )
            self.assertEqual(encoding['pixel_values'].shape				,      lowerCAmelCase__       )

            _A=	torch.tensor([0.2796, 0.3138, 0.3481]       )
            self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,      lowerCAmelCase__				,      atol=1E-4       )       )

            # verify area
            _A=	torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,      lowerCAmelCase__       )       )
            # verify boxes
            _A=	torch.Size([6, 4]       )
            self.assertEqual(encoding['labels'][0]['boxes'].shape				,      lowerCAmelCase__       )
            _A=	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,      lowerCAmelCase__				,      atol=1E-3       )       )
            # verify image_id
            _A=	torch.tensor([39769]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,      lowerCAmelCase__       )       )
            # verify is_crowd
            _A=	torch.tensor([0, 0, 0, 0, 0, 0]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,      lowerCAmelCase__       )       )
            # verify class_labels
            _A=	torch.tensor([75, 75, 63, 65, 17, 17]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,      lowerCAmelCase__       )       )
            # verify orig_size
            _A=	torch.tensor([480, 640]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,      lowerCAmelCase__       )       )
            # verify size
            _A=	torch.tensor([800, 1066]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,      lowerCAmelCase__       )       )





     @slow
     def 			a__  (					self       ):
            # prepare image, target and masks_path
            _A=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
            with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'				,      'r'       ) as f:
                   _A=	json.loads(f.read()       )

            _A=	{'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

            _A=	pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'       )

            # encode them
            _A=	DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'       )
            _A=	image_processing(images=lowerCAmelCase__				,      annotations=lowerCAmelCase__				,      masks_path=lowerCAmelCase__				,      return_tensors='pt'       )

            # verify pixel values
            _A=	torch.Size([1, 3, 800, 1066]       )
            self.assertEqual(encoding['pixel_values'].shape				,      lowerCAmelCase__       )

            _A=	torch.tensor([0.2796, 0.3138, 0.3481]       )
            self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,      lowerCAmelCase__				,      atol=1E-4       )       )

            # verify area
            _A=	torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,      lowerCAmelCase__       )       )
            # verify boxes
            _A=	torch.Size([6, 4]       )
            self.assertEqual(encoding['labels'][0]['boxes'].shape				,      lowerCAmelCase__       )
            _A=	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,      lowerCAmelCase__				,      atol=1E-3       )       )
            # verify image_id
            _A=	torch.tensor([39769]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,      lowerCAmelCase__       )       )
            # verify is_crowd
            _A=	torch.tensor([0, 0, 0, 0, 0, 0]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,      lowerCAmelCase__       )       )
            # verify class_labels
            _A=	torch.tensor([17, 17, 63, 75, 75, 93]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,      lowerCAmelCase__       )       )
            # verify masks
            _A=	822873
            self.assertEqual(encoding['labels'][0]['masks'].sum().item()				,      lowerCAmelCase__       )
            # verify orig_size
            _A=	torch.tensor([480, 640]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,      lowerCAmelCase__       )       )
            # verify size
            _A=	torch.tensor([800, 1066]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,      lowerCAmelCase__       )       )