
"""simple docstring"""


import random
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():
			import PIL

			from transformers import FlavaImageProcessor
			from transformers.image_utils import PILImageResampling
			from transformers.models.flava.image_processing_flava import (
			    FLAVA_CODEBOOK_MEAN,
			    FLAVA_CODEBOOK_STD,
			    FLAVA_IMAGE_MEAN,
			    FLAVA_IMAGE_STD,
			)
else:
			__A 		= __A 		= __A 		= __A 		= None







class 	lowerCamelCase__      (      unittest.TestCase       ):





			'''simple docstring'''



			def __init__(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase=7  ,  __UpperCAmelCase=3  ,  __UpperCAmelCase=30  ,  __UpperCAmelCase=4_00  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase=None  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase=None  ,  __UpperCAmelCase=None  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase=1 / 2_55  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase=FLAVA_IMAGE_MEAN  ,  __UpperCAmelCase=FLAVA_IMAGE_STD  ,  __UpperCAmelCase=14  ,  __UpperCAmelCase=75  ,  __UpperCAmelCase=None  ,  __UpperCAmelCase=16  ,  __UpperCAmelCase=0.3  ,  __UpperCAmelCase=None  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase=None  ,  __UpperCAmelCase=None  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase=None  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase=FLAVA_CODEBOOK_MEAN  ,  __UpperCAmelCase=FLAVA_CODEBOOK_STD  ,  )				->  Optional[int]:
				_lowerCAmelCase			=size if size is not None else {"""height""": 2_24, """width""": 2_24}
				_lowerCAmelCase			=crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
				_lowerCAmelCase			=codebook_size if codebook_size is not None else {"""height""": 1_12, """width""": 1_12}
				_lowerCAmelCase			=codebook_crop_size if codebook_crop_size is not None else {"""height""": 1_12, """width""": 1_12}

				_lowerCAmelCase			=parent
				_lowerCAmelCase			=batch_size
				_lowerCAmelCase			=num_channels
				_lowerCAmelCase			=do_resize
				_lowerCAmelCase			=do_rescale
				_lowerCAmelCase			=rescale_factor
				_lowerCAmelCase			=min_resolution
				_lowerCAmelCase			=max_resolution
				_lowerCAmelCase			=size
				_lowerCAmelCase			=resample if resample is not None else PILImageResampling.BICUBIC
				_lowerCAmelCase			=do_normalize
				_lowerCAmelCase			=image_mean
				_lowerCAmelCase			=image_std
				_lowerCAmelCase			=do_center_crop
				_lowerCAmelCase			=crop_size

				_lowerCAmelCase			=input_size_patches
				_lowerCAmelCase			=total_mask_patches
				_lowerCAmelCase			=mask_group_max_patches
				_lowerCAmelCase			=mask_group_min_patches
				_lowerCAmelCase			=mask_group_min_aspect_ratio
				_lowerCAmelCase			=mask_group_max_aspect_ratio

				_lowerCAmelCase			=codebook_do_resize
				_lowerCAmelCase			=codebook_size
				_lowerCAmelCase			=codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
				_lowerCAmelCase			=codebook_do_center_crop
				_lowerCAmelCase			=codebook_crop_size
				_lowerCAmelCase			=codebook_do_map_pixels
				_lowerCAmelCase			=codebook_do_normalize
				_lowerCAmelCase			=codebook_image_mean
				_lowerCAmelCase			=codebook_image_std



			def       _lowerCAmelCase	(							self						)				->  List[str]:
				return {
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_normalize": self.do_normalize,
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "resample": self.resample,
				    "do_rescale": self.do_rescale,
				    "rescale_factor": self.rescale_factor,
				    "do_center_crop": self.do_center_crop,
				    "crop_size": self.crop_size,
				    "input_size_patches": self.input_size_patches,
				    "total_mask_patches": self.total_mask_patches,
				    "mask_group_max_patches": self.mask_group_max_patches,
				    "mask_group_min_patches": self.mask_group_min_patches,
				    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
				    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
				    "codebook_do_resize": self.codebook_do_resize,
				    "codebook_size": self.codebook_size,
				    "codebook_resample": self.codebook_resample,
				    "codebook_do_center_crop": self.codebook_do_center_crop,
				    "codebook_crop_size": self.codebook_crop_size,
				    "codebook_do_map_pixels": self.codebook_do_map_pixels,
				    "codebook_do_normalize": self.codebook_do_normalize,
				    "codebook_image_mean": self.codebook_image_mean,
				    "codebook_image_std": self.codebook_image_std,
				}



			def       _lowerCAmelCase	(							self						)				->  Tuple:
				return (self.size["height"], self.size["width"])



			def       _lowerCAmelCase	(							self						)				->  List[str]:
				return (
				    (self.input_size_patches, self.input_size_patches)
				    if not isinstance(self.input_size_patches  ,  __UpperCAmelCase						)
				    else self.input_size_patches
				)



			def       _lowerCAmelCase	(							self						)				->  List[Any]:
				return (self.codebook_size["height"], self.codebook_size["width"])







@require_torch
@require_vision
class 	lowerCamelCase__      (      __magic_name__				,			unittest.TestCase       ):





			'''simple docstring'''
			lowerCamelCase						      =	FlavaImageProcessor if is_vision_available() else None
			lowerCamelCase						      =	None



			def       _lowerCAmelCase	(							self						)				->  int:
				_lowerCAmelCase			=FlavaImageProcessingTester(self						)



			@property
			def       _lowerCAmelCase	(							self						)				->  Optional[Any]:
				return self.image_processor_tester.prepare_image_processor_dict()



			def       _lowerCAmelCase	(							self						)				->  Any:
				_lowerCAmelCase			=self.image_processing_class(**self.image_processor_dict						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """image_mean"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """image_std"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """do_normalize"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """do_resize"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """resample"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """crop_size"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """do_center_crop"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """do_rescale"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """rescale_factor"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """masking_generator"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """codebook_do_resize"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """codebook_size"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """codebook_resample"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """codebook_do_center_crop"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """codebook_crop_size"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """codebook_do_map_pixels"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """codebook_do_normalize"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """codebook_image_mean"""						)						)
				self.assertTrue(hasattr(__UpperCAmelCase  ,  """codebook_image_std"""						)						)



			def       _lowerCAmelCase	(							self						)				->  Dict:
				_lowerCAmelCase			=self.image_processing_class.from_dict(self.image_processor_dict						)
				self.assertEqual(image_processor.size  ,  {"""height""": 2_24, """width""": 2_24}						)
				self.assertEqual(image_processor.crop_size  ,  {"""height""": 2_24, """width""": 2_24}						)
				self.assertEqual(image_processor.codebook_size  ,  {"""height""": 1_12, """width""": 1_12}						)
				self.assertEqual(image_processor.codebook_crop_size  ,  {"""height""": 1_12, """width""": 1_12}						)

				_lowerCAmelCase			=self.image_processing_class.from_dict(
				    self.image_processor_dict  ,  size=42  ,  crop_size=84  ,  codebook_size=33  ,  codebook_crop_size=66						)
				self.assertEqual(image_processor.size  ,  {"""height""": 42, """width""": 42}						)
				self.assertEqual(image_processor.crop_size  ,  {"""height""": 84, """width""": 84}						)
				self.assertEqual(image_processor.codebook_size  ,  {"""height""": 33, """width""": 33}						)
				self.assertEqual(image_processor.codebook_crop_size  ,  {"""height""": 66, """width""": 66}						)



			def       _lowerCAmelCase	(							self						)				->  Dict:
				pass



			def       _lowerCAmelCase	(							self						)				->  Union[str, Any]:
				# 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=__UpperCAmelCase						)
				for image in image_inputs:
					self.assertIsInstance(__UpperCAmelCase  ,  PIL.Image.Image						)

				# Test not batched input
				_lowerCAmelCase			=image_processing(image_inputs[0]  ,  return_tensors="""pt"""						)

				# Test no bool masked pos
				self.assertFalse("""bool_masked_pos""" in encoded_images						)

				_lowerCAmelCase		,							_lowerCAmelCase			=self.image_processor_tester.get_expected_image_size()

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

				# Test batched
				_lowerCAmelCase			=image_processing(__UpperCAmelCase  ,  return_tensors="""pt"""						)
				_lowerCAmelCase		,							_lowerCAmelCase			=self.image_processor_tester.get_expected_image_size()

				# Test no bool masked pos
				self.assertFalse("""bool_masked_pos""" in encoded_images						)

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



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  Optional[int]:
				# Initialize image_processing
				_lowerCAmelCase			=self.image_processing_class(**self.image_processor_dict						)
				# create random tensors
				_lowerCAmelCase			=prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=__UpperCAmelCase  ,  **__UpperCAmelCase						)
				for image in image_inputs:
					self.assertIsInstance(__UpperCAmelCase  ,  __UpperCAmelCase						)

				# Test not batched input
				_lowerCAmelCase			=image_processing(image_inputs[0]  ,  return_tensors="""pt"""						)

				_lowerCAmelCase		,							_lowerCAmelCase			=self.image_processor_tester.get_expected_image_size()
				self.assertEqual(
				    encoded_images.pixel_values.shape  ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,  )

				_lowerCAmelCase			=image_processing(__UpperCAmelCase  ,  return_image_mask=__UpperCAmelCase  ,  return_tensors="""pt"""						)

				_lowerCAmelCase		,							_lowerCAmelCase			=self.image_processor_tester.get_expected_image_size()
				self.assertEqual(
				    encoded_images.pixel_values.shape  ,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )  ,  )

				_lowerCAmelCase		,							_lowerCAmelCase			=self.image_processor_tester.get_expected_mask_size()
				self.assertEqual(
				    encoded_images.bool_masked_pos.shape  ,  (
				        self.image_processor_tester.batch_size,
				        expected_height,
				        expected_width,
				    )  ,  )

				# Test batched
				_lowerCAmelCase			=image_processing(__UpperCAmelCase  ,  return_tensors="""pt"""						).pixel_values

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

				# Test masking
				_lowerCAmelCase			=image_processing(__UpperCAmelCase  ,  return_image_mask=__UpperCAmelCase  ,  return_tensors="""pt"""						)

				_lowerCAmelCase		,							_lowerCAmelCase			=self.image_processor_tester.get_expected_image_size()
				self.assertEqual(
				    encoded_images.pixel_values.shape  ,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )  ,  )

				_lowerCAmelCase		,							_lowerCAmelCase			=self.image_processor_tester.get_expected_mask_size()
				self.assertEqual(
				    encoded_images.bool_masked_pos.shape  ,  (
				        self.image_processor_tester.batch_size,
				        expected_height,
				        expected_width,
				    )  ,  )



			def       _lowerCAmelCase	(							self						)				->  Tuple:
				self._test_call_framework(np.ndarray  ,  prepare_kwargs={"""numpify""": True}						)



			def       _lowerCAmelCase	(							self						)				->  Any:
				self._test_call_framework(torch.Tensor  ,  prepare_kwargs={"""torchify""": True}						)



			def       _lowerCAmelCase	(							self						)				->  Optional[int]:
				# Initialize image_processing
				random.seed(12_34						)
				_lowerCAmelCase			=self.image_processing_class(**self.image_processor_dict						)
				_lowerCAmelCase			=prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=__UpperCAmelCase  ,  torchify=__UpperCAmelCase						)

				# Test not batched input
				_lowerCAmelCase			=image_processing(image_inputs[0]  ,  return_image_mask=__UpperCAmelCase  ,  return_tensors="""pt"""						)
				self.assertEqual(encoded_images.bool_masked_pos.sum().item()  ,  75						)



			def       _lowerCAmelCase	(							self						)				->  Dict:
				# 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=__UpperCAmelCase						)
				for image in image_inputs:
					self.assertIsInstance(__UpperCAmelCase  ,  PIL.Image.Image						)

				# Test not batched input
				_lowerCAmelCase			=image_processing(image_inputs[0]  ,  return_codebook_pixels=__UpperCAmelCase  ,  return_tensors="""pt"""						)
				_lowerCAmelCase		,							_lowerCAmelCase			=self.image_processor_tester.get_expected_codebook_image_size()
				self.assertEqual(
				    encoded_images.codebook_pixel_values.shape  ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,  )

				# Test batched
				_lowerCAmelCase			=image_processing(__UpperCAmelCase  ,  return_codebook_pixels=__UpperCAmelCase  ,  return_tensors="""pt"""						)
				_lowerCAmelCase		,							_lowerCAmelCase			=self.image_processor_tester.get_expected_codebook_image_size()
				self.assertEqual(
				    encoded_images.codebook_pixel_values.shape  ,  (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )  ,  )





"""simple docstring"""


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A 		= {
    'configuration_convnextv2': [
        'CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConvNextV2Config',
    ]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= [
			    'CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'ConvNextV2ForImageClassification',
			    'ConvNextV2Model',
			    'ConvNextV2PreTrainedModel',
			    'ConvNextV2Backbone',
			]


if TYPE_CHECKING:
			from .configuration_convnextva import (
			    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    ConvNextVaConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_convnextva import (
						    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ConvNextVaBackbone,
						    ConvNextVaForImageClassification,
						    ConvNextVaModel,
						    ConvNextVaPreTrainedModel,
						)

else:
			import sys

			__A 		= _LazyModule(__name__, globals()['__file__'], _import_structure)



