

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 ViTImageProcessor





class      snake_case_      (					unittest.TestCase				):


							"""simple docstring"""





							def __init__(			self       ,    _A       ,    _A=7       ,    _A=3       ,    _A=1_8       ,    _A=3_0       ,    _A=4_0_0       ,    _A=True       ,    _A=None       ,    _A=True       ,    _A=[0.5, 0.5, 0.5]       ,    _A=[0.5, 0.5, 0.5]       ,    ):
												__lowerCAmelCase       =				size if size is not None else {'''height''': 1_8, '''width''': 1_8}
												__lowerCAmelCase       =				parent
												__lowerCAmelCase       =				batch_size
												__lowerCAmelCase       =				num_channels
												__lowerCAmelCase       =				image_size
												__lowerCAmelCase       =				min_resolution
												__lowerCAmelCase       =				max_resolution
												__lowerCAmelCase       =				do_resize
												__lowerCAmelCase       =				size
												__lowerCAmelCase       =				do_normalize
												__lowerCAmelCase       =				image_mean
												__lowerCAmelCase       =				image_std




							def 							A__	(			self				):
												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      snake_case_      (					lowerCAmelCase_      ,			unittest.TestCase				):


							"""simple docstring"""





							__UpperCAmelCase      =ViTImageProcessor if is_vision_available() else None




							def 							A__	(			self				):
												__lowerCAmelCase       =				ViTImageProcessingTester(self				)




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




							def 							A__	(			self				):
												__lowerCAmelCase       =				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				):
												__lowerCAmelCase       =				self.image_processing_class.from_dict(self.image_processor_dict				)
												self.assertEqual(image_processor.size       ,    {'height': 1_8, 'width': 1_8}				)

												__lowerCAmelCase       =				self.image_processing_class.from_dict(self.image_processor_dict       ,    size=4_2				)
												self.assertEqual(image_processor.size       ,    {'height': 4_2, 'width': 4_2}				)




							def 							A__	(			self				):
												pass




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

												# Test not batched input
												__lowerCAmelCase       =				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
												__lowerCAmelCase       =				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				):
												# Initialize image_processing
												__lowerCAmelCase       =				self.image_processing_class(**self.image_processor_dict				)
												# create random numpy tensors
												__lowerCAmelCase       =				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
												__lowerCAmelCase       =				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
												__lowerCAmelCase       =				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				):
												# Initialize image_processing
												__lowerCAmelCase       =				self.image_processing_class(**self.image_processor_dict				)
												# create random PyTorch tensors
												__lowerCAmelCase       =				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
												__lowerCAmelCase       =				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
												__lowerCAmelCase       =				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'],
												    )       ,    )











"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase					:					Any 		=		{
    "configuration_unispeech_sat": ["UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechSatConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					Any 		=		[
		    "UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "UniSpeechSatForAudioFrameClassification",
		    "UniSpeechSatForCTC",
		    "UniSpeechSatForPreTraining",
		    "UniSpeechSatForSequenceClassification",
		    "UniSpeechSatForXVector",
		    "UniSpeechSatModel",
		    "UniSpeechSatPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech_sat import (
				    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechSatForAudioFrameClassification,
				    UniSpeechSatForCTC,
				    UniSpeechSatForPreTraining,
				    UniSpeechSatForSequenceClassification,
				    UniSpeechSatForXVector,
				    UniSpeechSatModel,
				    UniSpeechSatPreTrainedModel,
				)

else:
		import sys

		_lowercase					:					int 		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




