
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 OwlViTImageProcessor

class  UpperCamelCase__ ( unittest.TestCase			):






      def __init__(     self					:   Optional[int],		__lowerCamelCase					:   List[Any],		__lowerCamelCase					:   str=7,		__lowerCamelCase					:   str=3,		__lowerCamelCase					:   int=18,		__lowerCamelCase					:   List[Any]=30,		__lowerCamelCase					:   Optional[int]=4_00,		__lowerCamelCase					:   Any=True,		__lowerCamelCase					:   List[Any]=None,		__lowerCamelCase					:   Any=True,		__lowerCamelCase					:   Tuple=None,		__lowerCamelCase					:   Union[str, Any]=True,		__lowerCamelCase					:   Any=[0.4814_5466, 0.457_8275, 0.4082_1073],		__lowerCamelCase					:   str=[0.2686_2954, 0.2613_0258, 0.2757_7711],		__lowerCamelCase					:   Optional[int]=True,		)  -> List[str]:
           UpperCamelCase__							:  Optional[int]								=     parent
           UpperCamelCase__							:  Optional[Any]								=     batch_size
           UpperCamelCase__							:  Tuple								=     num_channels
           UpperCamelCase__							:  List[Any]								=     image_size
           UpperCamelCase__							:  Any								=     min_resolution
           UpperCamelCase__							:  Dict								=     max_resolution
           UpperCamelCase__							:  Optional[Any]								=     do_resize
           UpperCamelCase__							:  str								=     size if size is not None else {'height': 18, 'width': 18}
           UpperCamelCase__							:  List[str]								=     do_center_crop
           UpperCamelCase__							:  List[Any]								=     crop_size if crop_size is not None else {'height': 18, 'width': 18}
           UpperCamelCase__							:  int								=     do_normalize
           UpperCamelCase__							:  int								=     image_mean
           UpperCamelCase__							:  Optional[int]								=     image_std
           UpperCamelCase__							:  Union[str, Any]								=     do_convert_rgb






      def 	__lowercase(     self					:   Any   )  -> int:
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_convert_rgb": self.do_convert_rgb,
           }

@require_torch
@require_vision
class  UpperCamelCase__ ( a__							,						unittest.TestCase			):
      a__   :     List[Any]								=						OwlViTImageProcessor if is_vision_available() else None






      def 	__lowercase(     self					:   List[str]   )  -> Union[str, Any]:
           UpperCamelCase__							:  Union[str, Any]								=     OwlViTImageProcessingTester(self   )






      @property
      def 	__lowercase(     self					:   Any   )  -> int:
           return self.image_processor_tester.prepare_image_processor_dict()






      def 	__lowercase(     self					:   int   )  -> Union[str, Any]:
           UpperCamelCase__							:  Optional[Any]								=     self.image_processing_class(**self.image_processor_dict   )
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,		'''do_resize'''   )   )
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,		'''size'''   )   )
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,		'''do_center_crop'''   )   )
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,		'''center_crop'''   )   )
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,		'''do_normalize'''   )   )
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,		'''image_mean'''   )   )
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,		'''image_std'''   )   )
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,		'''do_convert_rgb'''   )   )






      def 	__lowercase(     self					:   str   )  -> str:
           UpperCamelCase__							:  Tuple								=     self.image_processing_class.from_dict(self.image_processor_dict   )
           self.assertEqual(image_processor.size,		{'''height''': 18, '''width''': 18}   )
           self.assertEqual(image_processor.crop_size,		{'''height''': 18, '''width''': 18}   )

           UpperCamelCase__							:  Optional[Any]								=     self.image_processing_class.from_dict(self.image_processor_dict,		size=42,		crop_size=84   )
           self.assertEqual(image_processor.size,		{'''height''': 42, '''width''': 42}   )
           self.assertEqual(image_processor.crop_size,		{'''height''': 84, '''width''': 84}   )






      def 	__lowercase(     self					:   List[str]   )  -> str:
           # Initialize image_processing
           UpperCamelCase__							:  Optional[int]								=     self.image_processing_class(**self.image_processor_dict   )
           # create random PIL images
           UpperCamelCase__							:  int								=     prepare_image_inputs(self.image_processor_tester,		equal_resolution=SCREAMING_SNAKE_CASE_   )

           for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE_,		Image.Image   )

           # Test not batched input
           UpperCamelCase__							:  str								=     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__							:  Optional[int]								=     image_processing(SCREAMING_SNAKE_CASE_,		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 	__lowercase(     self					:   Tuple   )  -> str:
           # Initialize image_processing
           UpperCamelCase__							:  Optional[Any]								=     self.image_processing_class(**self.image_processor_dict   )
           # create random numpy tensors
           UpperCamelCase__							:  Optional[Any]								=     prepare_image_inputs(self.image_processor_tester,		equal_resolution=SCREAMING_SNAKE_CASE_,		numpify=SCREAMING_SNAKE_CASE_   )
           for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE_,		np.ndarray   )

           # Test not batched input
           UpperCamelCase__							:  Optional[Any]								=     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__							:  Union[str, Any]								=     image_processing(SCREAMING_SNAKE_CASE_,		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 	__lowercase(     self					:   Optional[Any]   )  -> List[Any]:
           # Initialize image_processing
           UpperCamelCase__							:  str								=     self.image_processing_class(**self.image_processor_dict   )
           # create random PyTorch tensors
           UpperCamelCase__							:  int								=     prepare_image_inputs(self.image_processor_tester,		equal_resolution=SCREAMING_SNAKE_CASE_,		torchify=SCREAMING_SNAKE_CASE_   )
           for image in image_inputs:
                self.assertIsInstance(SCREAMING_SNAKE_CASE_,		torch.Tensor   )

           # Test not batched input
           UpperCamelCase__							:  Any								=     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__							:  Optional[Any]								=     image_processing(SCREAMING_SNAKE_CASE_,		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'''],
               ),		)








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE :					Optional[Any]			=	{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					Dict			=	["""LayoutLMTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					List[str]			=	[
       """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LayoutLMForMaskedLM""",
       """LayoutLMForSequenceClassification""",
       """LayoutLMForTokenClassification""",
       """LayoutLMForQuestionAnswering""",
       """LayoutLMModel""",
       """LayoutLMPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					str			=	[
       """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFLayoutLMForMaskedLM""",
       """TFLayoutLMForSequenceClassification""",
       """TFLayoutLMForTokenClassification""",
       """TFLayoutLMForQuestionAnswering""",
       """TFLayoutLMMainLayer""",
       """TFLayoutLMModel""",
       """TFLayoutLMPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )

else:
   import sys

   _SCREAMING_SNAKE_CASE :					Any			=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




