

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a				: int 							=						[
      '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				: List[Any] 							=						_LazyModule(__name__, globals()['__file__'], _import_structure)





'''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 MobileNetVaImageProcessor



class        lowercase_	(		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_=None   , )							->				List[str]:


    """simple docstring"""
    UpperCAmelCase													=	size if size is not None else {'shortest_edge': 2_0}
    UpperCAmelCase													=	crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
    UpperCAmelCase													=	parent
    UpperCAmelCase													=	batch_size
    UpperCAmelCase													=	num_channels
    UpperCAmelCase													=	image_size
    UpperCAmelCase													=	min_resolution
    UpperCAmelCase													=	max_resolution
    UpperCAmelCase													=	do_resize
    UpperCAmelCase													=	size
    UpperCAmelCase													=	do_center_crop
    UpperCAmelCase													=	crop_size





  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
    }




@require_torch
@require_vision
class        lowercase_	(		a ,       unittest.TestCase ):

  '''simple docstring'''





  __lowerCAmelCase					: Union[str, Any]      =   MobileNetVaImageProcessor if is_vision_available() else None
  def 						snake_case_			(     self     )							->				Dict:


    """simple docstring"""
    UpperCAmelCase													=	MobileNetVaImageProcessingTester(self     )
  @property
  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    return self.image_processor_tester.prepare_image_processor_dict()
  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    UpperCAmelCase													=	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'     )     )
  def 						snake_case_			(     self     )							->				str:


    """simple docstring"""
    UpperCAmelCase													=	self.image_processing_class.from_dict(self.image_processor_dict     )
    self.assertEqual(image_processor.size   , {'shortest_edge': 2_0}     )
    self.assertEqual(image_processor.crop_size   , {'height': 1_8, 'width': 1_8}     )

    UpperCAmelCase													=	self.image_processing_class.from_dict(self.image_processor_dict   , size=4_2   , crop_size=8_4     )
    self.assertEqual(image_processor.size   , {'shortest_edge': 4_2}     )
    self.assertEqual(image_processor.crop_size   , {'height': 8_4, 'width': 8_4}     )
  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    pass
  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    # Initialize image_processing
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    # create random PIL images
    UpperCAmelCase													=	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													=	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													=	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 						snake_case_			(     self     )							->				str:


    """simple docstring"""
    # Initialize image_processing
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    # create random numpy tensors
    UpperCAmelCase													=	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													=	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													=	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 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    # Initialize image_processing
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    # create random PyTorch tensors
    UpperCAmelCase													=	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													=	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													=	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'],
        )   , )


