






"""simple docstring"""



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCamelCase		:  List[str]						=		{
    'configuration_retribert': ['RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RetriBertConfig'],
    'tokenization_retribert': ['RetriBertTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  str						=		['RetriBertTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  Any						=		[
        'RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'RetriBertModel',
        'RetriBertPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
    from .tokenization_retribert import RetriBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_retribert_fast import RetriBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_retribert import (
            RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            RetriBertModel,
            RetriBertPreTrainedModel,
        )

else:
    import sys

    _UpperCamelCase		:  str						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










"""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 ViTImageProcessor

class 	a					(       unittest.TestCase     ):







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase=7							,		_lowerCamelCase=3							,		_lowerCamelCase=1_8							,		_lowerCamelCase=3_0							,		_lowerCamelCase=4_0_0							,		_lowerCamelCase=True							,		_lowerCamelCase=None							,		_lowerCamelCase=True							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		):
      lowercase							   =	size if size is not None else {'height': 1_8, 'width': 1_8}
      lowercase							   =	parent
      lowercase							   =	batch_size
      lowercase							   =	num_channels
      lowercase							   =	image_size
      lowercase							   =	min_resolution
      lowercase							   =	max_resolution
      lowercase							   =	do_resize
      lowercase							   =	size
      lowercase							   =	do_normalize
      lowercase							   =	image_mean
      lowercase							   =	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 	a					(       a_,    unittest.TestCase     ):
    UpperCAmelCase_			:					int     					=ViTImageProcessor if is_vision_available() else None







    def       UpperCamelCase_				(      self	):
      lowercase							   =	ViTImageProcessingTester(self	)







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







    def       UpperCamelCase_				(      self	):
      lowercase							   =	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_resize'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'size'	)	)







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

      lowercase							   =	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       UpperCamelCase_				(      self	):
      pass







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

      # Test not batched input
      lowercase							   =	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
      lowercase							   =	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,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      lowercase							   =	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
      lowercase							   =	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
      lowercase							   =	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,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      lowercase							   =	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
      lowercase							   =	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
      lowercase							   =	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,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)


