


"""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 					__lowerCAmelCase					(   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]							, ):
				__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 			__UpperCAmelCase       (  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 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE     ,			unittest.TestCase       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Dict										=				DPTImageProcessor if is_vision_available() else None

		def 			__UpperCAmelCase       (  self      ):
				__a           =			DPTImageProcessingTester(self      )

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

		def 			__UpperCAmelCase       (  self      ):
				__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 			__UpperCAmelCase       (  self      ):
				__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 			__UpperCAmelCase       (  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=_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 			__UpperCAmelCase       (  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=_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 			__UpperCAmelCase       (  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=_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'''],
				    )							, )










"""simple docstring"""




from typing import TYPE_CHECKING

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


lowercase_			     =  {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "UniSpeechForCTC",
				    "UniSpeechForPreTraining",
				    "UniSpeechForSequenceClassification",
				    "UniSpeechModel",
				    "UniSpeechPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_unispeech import (
								    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
								    UniSpeechForCTC,
								    UniSpeechForPreTraining,
								    UniSpeechForSequenceClassification,
								    UniSpeechModel,
								    UniSpeechPreTrainedModel,
								)

else:
				import sys

				lowercase_			     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






