





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


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ChineseCLIPImageProcessor





class  A__    (							unittest.TestCase	):




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=7					,  lowercase=3					,  lowercase=18					,  lowercase=30					,  lowercase=400					,  lowercase=True					,  lowercase=None					,  lowercase=True					,  lowercase=None					,  lowercase=True					,  lowercase=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]					,  lowercase=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]					,  lowercase=True					,  )			->						List[Any]:







          '''simple docstring'''

          a__						:	str =				size if size is not None else {'height': 224, 'width': 224}
          a__						:	Tuple =				crop_size if crop_size is not None else {'height': 18, 'width': 18}
          a__						:	Dict =				parent
          a__						:	Dict =				batch_size
          a__						:	Any =				num_channels
          a__						:	Any =				image_size
          a__						:	Optional[int] =				min_resolution
          a__						:	Tuple =				max_resolution
          a__						:	int =				do_resize
          a__						:	Union[str, Any] =				size
          a__						:	Optional[int] =				do_center_crop
          a__						:	Optional[int] =				crop_size
          a__						:	str =				do_normalize
          a__						:	List[str] =				image_mean
          a__						:	str =				image_std
          a__						:	Union[str, Any] =				do_convert_rgb

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          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,
          }

   def 							__lowercase		(					self					,  lowercase=False					,  lowercase=False					,  lowercase=False)			->						Any:







          '''simple docstring'''

          assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

          if equal_resolution:
                 a__						:	Optional[int] =				[]
                 for i in range(self.batch_size):
                        image_inputs.append(
                            np.random.randint(
                                255					,  size=(self.num_channels, self.max_resolution, self.max_resolution)					,  dtype=np.uinta))
          else:
                 a__						:	Tuple =				[]
                 for i in range(self.batch_size):
                        a__ ,							a__						:	Tuple =				np.random.choice(np.arange(self.min_resolution					,  self.max_resolution)					,  2)
                        image_inputs.append(np.random.randint(255					,  size=(self.num_channels, width, height)					,  dtype=np.uinta))

          if not numpify and not torchify:
                 # PIL expects the channel dimension as last dimension
                 a__						:	List[Any] =				[Image.fromarray(np.moveaxis(lowercase					,  0					,  -1)) for x in image_inputs]

          if torchify:
                 a__						:	Any =				[torch.from_numpy(lowercase) for x in image_inputs]

          return image_inputs





@require_torch
@require_vision
class  A__    (							__UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Tuple         =  ChineseCLIPImageProcessor if is_vision_available() else None

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Union[str, Any] =				ChineseCLIPImageProcessingTester(self					,  do_center_crop=lowercase)

   @property
   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          return self.image_processor_tester.prepare_image_processor_dict()

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          a__						:	Dict =				self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(lowercase					,  'do_resize'))
          self.assertTrue(hasattr(lowercase					,  'size'))
          self.assertTrue(hasattr(lowercase					,  'do_center_crop'))
          self.assertTrue(hasattr(lowercase					,  'center_crop'))
          self.assertTrue(hasattr(lowercase					,  'do_normalize'))
          self.assertTrue(hasattr(lowercase					,  'image_mean'))
          self.assertTrue(hasattr(lowercase					,  'image_std'))
          self.assertTrue(hasattr(lowercase					,  'do_convert_rgb'))

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size					,  {'height': 224, 'width': 224})
          self.assertEqual(image_processor.crop_size					,  {'height': 18, 'width': 18})

          a__						:	List[str] =				self.image_processing_class.from_dict(self.image_processor_dict					,  size=42					,  crop_size=84)
          self.assertEqual(image_processor.size					,  {'shortest_edge': 42})
          self.assertEqual(image_processor.crop_size					,  {'height': 84, 'width': 84})

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	str =				self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          a__						:	Optional[Any] =				self.image_processor_tester.prepare_inputs(equal_resolution=lowercase)
          for image in image_inputs:
                 self.assertIsInstance(lowercase					,  Image.Image)

          # Test not batched input
          a__						:	Dict =				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
          a__						:	str =				image_processing(lowercase					,  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:







          '''simple docstring'''

          a__						:	Tuple =				self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          a__						:	Tuple =				self.image_processor_tester.prepare_inputs(equal_resolution=lowercase					,  numpify=lowercase)
          for image in image_inputs:
                 self.assertIsInstance(lowercase					,  np.ndarray)

          # Test not batched input
          a__						:	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
          a__						:	List[Any] =				image_processing(lowercase					,  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[int]:







          '''simple docstring'''

          a__						:	int =				self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          a__						:	List[str] =				self.image_processor_tester.prepare_inputs(equal_resolution=lowercase					,  torchify=lowercase)
          for image in image_inputs:
                 self.assertIsInstance(lowercase					,  torch.Tensor)

          # Test not batched input
          a__						:	List[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
          a__						:	str =				image_processing(lowercase					,  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'],
              )					,  )





@require_torch
@require_vision
class  A__    (							__UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Optional[int]         =  ChineseCLIPImageProcessor if is_vision_available() else None

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Union[str, Any] =				ChineseCLIPImageProcessingTester(self					,  num_channels=4					,  do_center_crop=lowercase)
          a__						:	List[Any] =				3

   @property
   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          return self.image_processor_tester.prepare_image_processor_dict()

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Dict =				self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(lowercase					,  'do_resize'))
          self.assertTrue(hasattr(lowercase					,  'size'))
          self.assertTrue(hasattr(lowercase					,  'do_center_crop'))
          self.assertTrue(hasattr(lowercase					,  'center_crop'))
          self.assertTrue(hasattr(lowercase					,  'do_normalize'))
          self.assertTrue(hasattr(lowercase					,  'image_mean'))
          self.assertTrue(hasattr(lowercase					,  'image_std'))
          self.assertTrue(hasattr(lowercase					,  'do_convert_rgb'))

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Optional[int] =				self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          a__						:	Optional[int] =				self.image_processor_tester.prepare_inputs(equal_resolution=lowercase)
          for image in image_inputs:
                 self.assertIsInstance(lowercase					,  Image.Image)

          # Test not batched input
          a__						:	List[str] =				image_processing(image_inputs[0]					,  return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape					,  (
                  1,
                  self.expected_encoded_image_num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )					,  )

          # Test batched
          a__						:	Optional[Any] =				image_processing(lowercase					,  return_tensors='pt').pixel_values
          self.assertEqual(
              encoded_images.shape					,  (
                  self.image_processor_tester.batch_size,
                  self.expected_encoded_image_num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )					,  )









from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


A    :					str =      logging.get_logger(__name__)

A    :					Union[str, Any] =      {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Optional[Any]							=    '''layoutlmv3'''






			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    Optional[int]=5_02_65							,	__lowerCAmelCase   :    Tuple=7_68							,	__lowerCAmelCase   :    Union[str, Any]=12							,	__lowerCAmelCase   :    Any=12							,	__lowerCAmelCase   :    List[Any]=30_72							,	__lowerCAmelCase   :    Optional[int]="gelu"							,	__lowerCAmelCase   :    Dict=0.1							,	__lowerCAmelCase   :    List[Any]=0.1							,	__lowerCAmelCase   :    Dict=5_12							,	__lowerCAmelCase   :    Any=2							,	__lowerCAmelCase   :    Dict=0.0_2							,	__lowerCAmelCase   :    List[str]=1e-5							,	__lowerCAmelCase   :    List[str]=1							,	__lowerCAmelCase   :    int=0							,	__lowerCAmelCase   :    Dict=2							,	__lowerCAmelCase   :    Tuple=10_24							,	__lowerCAmelCase   :    List[str]=1_28							,	__lowerCAmelCase   :    Optional[int]=1_28							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Optional[Any]=32							,	__lowerCAmelCase   :    Any=1_28							,	__lowerCAmelCase   :    str=64							,	__lowerCAmelCase   :    Optional[int]=2_56							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    List[str]=True							,	__lowerCAmelCase   :    int=2_24							,	__lowerCAmelCase   :    Dict=3							,	__lowerCAmelCase   :    List[Any]=16							,	__lowerCAmelCase   :    Dict=None							,	**__lowerCAmelCase   :    Optional[Any]							,	)	->	Dict:







					"""simple docstring"""






					super().__init__(
					    vocab_size=__lowerCAmelCase							,	hidden_size=__lowerCAmelCase							,	num_hidden_layers=__lowerCAmelCase							,	num_attention_heads=__lowerCAmelCase							,	intermediate_size=__lowerCAmelCase							,	hidden_act=__lowerCAmelCase							,	hidden_dropout_prob=__lowerCAmelCase							,	attention_probs_dropout_prob=__lowerCAmelCase							,	max_position_embeddings=__lowerCAmelCase							,	type_vocab_size=__lowerCAmelCase							,	initializer_range=__lowerCAmelCase							,	layer_norm_eps=__lowerCAmelCase							,	pad_token_id=__lowerCAmelCase							,	bos_token_id=__lowerCAmelCase							,	eos_token_id=__lowerCAmelCase							,	**__lowerCAmelCase							,	)
					A__        =   max_ad_position_embeddings
					A__        =   coordinate_size
					A__        =   shape_size
					A__        =   has_relative_attention_bias
					A__        =   rel_pos_bins
					A__        =   max_rel_pos
					A__        =   has_spatial_attention_bias
					A__        =   rel_ad_pos_bins
					A__        =   max_rel_ad_pos
					A__        =   text_embed
					A__        =   visual_embed
					A__        =   input_size
					A__        =   num_channels
					A__        =   patch_size
					A__        =   classifier_dropout


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    version.parse('''1.12'''    )






			@property
			def    a_       (			self   :    int   )	->	Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					if self.task in ["question-answering", "sequence-classification"]:
							return OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
							        ("""bbox""", {0: """batch""", 1: """sequence"""}),
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							    ]   )
					else:
							return OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
							        ("""bbox""", {0: """batch""", 1: """sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
							        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
							    ]   )






			@property
			def    a_       (			self   :    Optional[int]   )	->	float:







					"""simple docstring"""






					return 1e-5






			@property
			def    a_       (			self   :    Tuple   )	->	int:







					"""simple docstring"""






					return 12







			def    a_       (			self   :    str							,	__lowerCAmelCase   :    "ProcessorMixin"							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    bool = False							,	__lowerCAmelCase   :    Optional["TensorType"] = None							,	__lowerCAmelCase   :    int = 3							,	__lowerCAmelCase   :    int = 40							,	__lowerCAmelCase   :    int = 40							,	)	->	Mapping[str, Any]:







					"""simple docstring"""






					setattr(processor.image_processor							,	"""apply_ocr"""							,	__lowerCAmelCase   )

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					A__        =   compute_effective_axis_dimension(
					    __lowerCAmelCase							,	fixed_dimension=OnnxConfig.default_fixed_batch							,	num_token_to_add=0   )
					# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
					A__        =   processor.tokenizer.num_special_tokens_to_add(__lowerCAmelCase   )
					A__        =   compute_effective_axis_dimension(
					    __lowerCAmelCase							,	fixed_dimension=OnnxConfig.default_fixed_sequence							,	num_token_to_add=__lowerCAmelCase   )
					# Generate dummy inputs according to compute batch and sequence
					A__        =   [[""" """.join([processor.tokenizer.unk_token]   ) * seq_length]] * batch_size

					# Generate dummy bounding boxes
					A__        =   [[[48, 84, 73, 1_28]]] * batch_size

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
					A__        =   self._generate_dummy_images(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )

					A__        =   dict(
					    processor(
					        __lowerCAmelCase							,	text=__lowerCAmelCase							,	boxes=__lowerCAmelCase							,	return_tensors=__lowerCAmelCase							,	)   )

					return inputs



