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       lowercase__		(     unittest.TestCase):


						def __init__( self			:       Union[str, Any]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       List[str]=13					,	UpperCamelCase__			:       Tuple=3					,	UpperCamelCase__			:       Optional[int]=224					,	UpperCamelCase__			:       List[Any]=30					,	UpperCamelCase__			:       Union[str, Any]=400					,	UpperCamelCase__			:       List[str]=True					,	UpperCamelCase__			:       List[str]=None					,	UpperCamelCase__			:       Any=True					,	UpperCamelCase__			:       Any=[0.5, 0.5, 0.5]					,	UpperCamelCase__			:       Tuple=[0.5, 0.5, 0.5]					,	):





												'''simple docstring'''





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



						def  __A ( self			:       Any		):





												'''simple docstring'''





												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       lowercase__		(     lowerCamelCase__			,							unittest.TestCase):
						UpperCamelCase_  =	ViTImageProcessor if is_vision_available() else None


						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							EfficientFormerImageProcessorTester(self		)


						@property
						def  __A ( self			:       str		):





												'''simple docstring'''





												return self.image_proc_tester.prepare_image_processor_dict()


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							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  __A ( self			:       List[str]		):





												'''simple docstring'''





												pass


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							self.image_processing_class(**self.image_processor_dict		)
												# create random PIL images
												SCREAMING_SNAKE_CASE							:					List[str]           =							prepare_image_inputs(self.image_proc_tester					,	equal_resolution=__lowerCamelCase		)
												for image in image_inputs:
																		self.assertIsInstance(__lowerCamelCase					,	Image.Image		)

												# Test not batched input
												SCREAMING_SNAKE_CASE							:					Tuple           =							image_processor(image_inputs[0]					,	return_tensors='''pt'''		).pixel_values
												self.assertEqual(
												    encoded_images.shape					,	(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['''height'''],
												        self.image_proc_tester.size['''width'''],
												    )					,	)

												# Test batched
												SCREAMING_SNAKE_CASE							:					List[Any]           =							image_processor(__lowerCamelCase					,	return_tensors='''pt'''		).pixel_values
												self.assertEqual(
												    encoded_images.shape					,	(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['''height'''],
												        self.image_proc_tester.size['''width'''],
												    )					,	)


						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.image_processing_class(**self.image_processor_dict		)
												# create random numpy tensors
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							prepare_image_inputs(self.image_proc_tester					,	equal_resolution=__lowerCamelCase					,	numpify=__lowerCamelCase		)
												for image in image_inputs:
																		self.assertIsInstance(__lowerCamelCase					,	np.ndarray		)

												# Test not batched input
												SCREAMING_SNAKE_CASE							:					int           =							image_processor(image_inputs[0]					,	return_tensors='''pt'''		).pixel_values
												self.assertEqual(
												    encoded_images.shape					,	(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['''height'''],
												        self.image_proc_tester.size['''width'''],
												    )					,	)

												# Test batched
												SCREAMING_SNAKE_CASE							:					int           =							image_processor(__lowerCamelCase					,	return_tensors='''pt'''		).pixel_values
												self.assertEqual(
												    encoded_images.shape					,	(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['''height'''],
												        self.image_proc_tester.size['''width'''],
												    )					,	)



						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.image_processing_class(**self.image_processor_dict		)
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE							:					List[str]           =							prepare_image_inputs(self.image_proc_tester					,	equal_resolution=__lowerCamelCase					,	torchify=__lowerCamelCase		)
												for image in image_inputs:
																		self.assertIsInstance(__lowerCamelCase					,	torch.Tensor		)

												# Test not batched input
												SCREAMING_SNAKE_CASE							:					Tuple           =							image_processor(image_inputs[0]					,	return_tensors='''pt'''		).pixel_values
												self.assertEqual(
												    encoded_images.shape					,	(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['''height'''],
												        self.image_proc_tester.size['''width'''],
												    )					,	)

												# Test batched
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							image_processor(__lowerCamelCase					,	return_tensors='''pt'''		).pixel_values
												self.assertEqual(
												    encoded_images.shape					,	(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['''height'''],
												        self.image_proc_tester.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


__UpperCamelCase	: str    		=  logging.get_logger(__name__)

__UpperCamelCase	: str    		=  {
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""layoutlmv3"""


						def __init__( self			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]=5_0265					,	UpperCamelCase__			:       List[Any]=768					,	UpperCamelCase__			:       Union[str, Any]=12					,	UpperCamelCase__			:       Union[str, Any]=12					,	UpperCamelCase__			:       Tuple=3072					,	UpperCamelCase__			:       List[Any]="gelu"					,	UpperCamelCase__			:       List[Any]=0.1					,	UpperCamelCase__			:       List[str]=0.1					,	UpperCamelCase__			:       Any=512					,	UpperCamelCase__			:       List[Any]=2					,	UpperCamelCase__			:       List[Any]=0.02					,	UpperCamelCase__			:       Optional[Any]=1E-5					,	UpperCamelCase__			:       List[str]=1					,	UpperCamelCase__			:       int=0					,	UpperCamelCase__			:       str=2					,	UpperCamelCase__			:       List[str]=1024					,	UpperCamelCase__			:       str=128					,	UpperCamelCase__			:       str=128					,	UpperCamelCase__			:       Tuple=True					,	UpperCamelCase__			:       Optional[int]=32					,	UpperCamelCase__			:       Any=128					,	UpperCamelCase__			:       Optional[Any]=64					,	UpperCamelCase__			:       Dict=256					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       str=True					,	UpperCamelCase__			:       Dict=True					,	UpperCamelCase__			:       Dict=224					,	UpperCamelCase__			:       Optional[Any]=3					,	UpperCamelCase__			:       Any=16					,	UpperCamelCase__			:       Any=None					,	**UpperCamelCase__			:       Optional[Any]					,	):





												'''simple docstring'''





												super().__init__(
												    vocab_size=UpperCamelCase__					,	hidden_size=UpperCamelCase__					,	num_hidden_layers=UpperCamelCase__					,	num_attention_heads=UpperCamelCase__					,	intermediate_size=UpperCamelCase__					,	hidden_act=UpperCamelCase__					,	hidden_dropout_prob=UpperCamelCase__					,	attention_probs_dropout_prob=UpperCamelCase__					,	max_position_embeddings=UpperCamelCase__					,	type_vocab_size=UpperCamelCase__					,	initializer_range=UpperCamelCase__					,	layer_norm_eps=UpperCamelCase__					,	pad_token_id=UpperCamelCase__					,	bos_token_id=UpperCamelCase__					,	eos_token_id=UpperCamelCase__					,	**UpperCamelCase__					,	)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							max_ad_position_embeddings
												SCREAMING_SNAKE_CASE							:					List[Any]           =							coordinate_size
												SCREAMING_SNAKE_CASE							:					List[str]           =							shape_size
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							has_relative_attention_bias
												SCREAMING_SNAKE_CASE							:					List[Any]           =							rel_pos_bins
												SCREAMING_SNAKE_CASE							:					str           =							max_rel_pos
												SCREAMING_SNAKE_CASE							:					Any           =							has_spatial_attention_bias
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							rel_ad_pos_bins
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							max_rel_ad_pos
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							text_embed
												SCREAMING_SNAKE_CASE							:					List[str]           =							visual_embed
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input_size
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							num_channels
												SCREAMING_SNAKE_CASE							:					List[Any]           =							patch_size
												SCREAMING_SNAKE_CASE							:					List[Any]           =							classifier_dropout






class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	version.parse("""1.12""")


						@property
						def  __A ( self			:       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			:       int		):





												'''simple docstring'''





												return 1E-5


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return 12



						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       "ProcessorMixin"					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional["TensorType"] = None					,	UpperCamelCase__			:       int = 3					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       int = 40					,	):





												'''simple docstring'''





												setattr(processor.image_processor					,	'''apply_ocr'''					,	UpperCamelCase__		)

												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												SCREAMING_SNAKE_CASE							:					Any           =							compute_effective_axis_dimension(
												    UpperCamelCase__					,	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
												SCREAMING_SNAKE_CASE							:					List[Any]           =							processor.tokenizer.num_special_tokens_to_add(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							compute_effective_axis_dimension(
												    UpperCamelCase__					,	fixed_dimension=OnnxConfig.default_fixed_sequence					,	num_token_to_add=UpperCamelCase__		)
												# Generate dummy inputs according to compute batch and sequence
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[[''' '''.join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

												# Generate dummy bounding boxes
												SCREAMING_SNAKE_CASE							:					Any           =							[[[48, 84, 73, 128]]] * 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)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self._generate_dummy_images(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							dict(
												    processor(
												        UpperCamelCase__					,	text=UpperCamelCase__					,	boxes=UpperCamelCase__					,	return_tensors=UpperCamelCase__					,	)		)

												return inputs





