

"""simple docstring"""




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


_lowerCamelCase    :       Optional[Any]        =							logging.get_logger(__name__)

_lowerCamelCase    :       List[str]        =							{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}





class  __UpperCAmelCase					(	__lowerCAmelCase						):
   UpperCamelCase       					=					"layoutlmv3"



   def __init__(							self							:					List[Any],		__A							:					Union[str, Any]=5_0_2_6_5,		__A							:					Tuple=7_6_8,		__A							:					Optional[Any]=1_2,		__A							:					Tuple=1_2,		__A							:					Optional[int]=3_0_7_2,		__A							:					Dict="gelu",		__A							:					Optional[Any]=0.1,		__A							:					Any=0.1,		__A							:					List[Any]=5_1_2,		__A							:					str=2,		__A							:					Union[str, Any]=0.0_2,		__A							:					Tuple=1E-5,		__A							:					Union[str, Any]=1,		__A							:					List[str]=0,		__A							:					Optional[int]=2,		__A							:					str=1_0_2_4,		__A							:					Dict=1_2_8,		__A							:					Union[str, Any]=1_2_8,		__A							:					Any=True,		__A							:					Optional[int]=3_2,		__A							:					List[Any]=1_2_8,		__A							:					Optional[int]=6_4,		__A							:					List[Any]=2_5_6,		__A							:					Optional[int]=True,		__A							:					int=True,		__A							:					Optional[Any]=True,		__A							:					Dict=2_2_4,		__A							:					Optional[Any]=3,		__A							:					List[Any]=1_6,		__A							:					Dict=None,		**__A							:					List[Any],		):
     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__,		)
     UpperCAmelCase	:							List[Any]						=			max_ad_position_embeddings
     UpperCAmelCase	:							Tuple						=			coordinate_size
     UpperCAmelCase	:							Union[str, Any]						=			shape_size
     UpperCAmelCase	:							int						=			has_relative_attention_bias
     UpperCAmelCase	:							Any						=			rel_pos_bins
     UpperCAmelCase	:							List[Any]						=			max_rel_pos
     UpperCAmelCase	:							Union[str, Any]						=			has_spatial_attention_bias
     UpperCAmelCase	:							str						=			rel_ad_pos_bins
     UpperCAmelCase	:							Any						=			max_rel_ad_pos
     UpperCAmelCase	:							str						=			text_embed
     UpperCAmelCase	:							Optional[Any]						=			visual_embed
     UpperCAmelCase	:							str						=			input_size
     UpperCAmelCase	:							Any						=			num_channels
     UpperCAmelCase	:							Union[str, Any]						=			patch_size
     UpperCAmelCase	:							Optional[Any]						=			classifier_dropout






class  __UpperCAmelCase					(	__lowerCAmelCase						):
   UpperCamelCase       					=					version.parse("""1.12"""						)



   @property
   def 	__magic_name__   (							self							:					Tuple						):






     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 	__magic_name__   (							self							:					Union[str, Any]						):
     return 1E-5



   @property
   def 	__magic_name__   (							self							:					Tuple						):
     return 1_2





   def 	__magic_name__   (							self							:					Any,		__A							:					"ProcessorMixin",		__A							:					int = -1,		__A							:					int = -1,		__A							:					bool = False,		__A							:					Optional["TensorType"] = None,		__A							:					int = 3,		__A							:					int = 4_0,		__A							:					int = 4_0,		):
     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
     UpperCAmelCase	:							Union[str, Any]						=			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
     UpperCAmelCase	:							List[str]						=			processor.tokenizer.num_special_tokens_to_add(lowerCamelCase__						)
     UpperCAmelCase	:							Any						=			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
     UpperCAmelCase	:							Optional[Any]						=			[[''' '''.join([processor.tokenizer.unk_token]						) * seq_length]] * batch_size

     # Generate dummy bounding boxes
     UpperCAmelCase	:							Dict						=			[[[4_8, 8_4, 7_3, 1_2_8]]] * 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)
     UpperCAmelCase	:							List[str]						=			self._generate_dummy_images(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__						)

     UpperCAmelCase	:							Optional[int]						=			dict(
         processor(
             lowerCamelCase__,		text=lowerCamelCase__,		boxes=lowerCamelCase__,		return_tensors=lowerCamelCase__,		)						)

     return inputs








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  __UpperCAmelCase					(	unittest.TestCase						):



   def __init__(							self							:					List[Any],		__A							:					str,		__A							:					List[str]=7,		__A							:					List[str]=3,		__A							:					Optional[int]=1_8,		__A							:					List[Any]=3_0,		__A							:					Tuple=4_0_0,		__A							:					Tuple=True,		__A							:					List[Any]=None,		__A							:					str=True,		__A							:					int=None,		__A							:					Optional[Any]=True,		__A							:					List[Any]=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],		__A							:					List[str]=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],		__A							:					Tuple=True,		):
     UpperCAmelCase	:							int						=			size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
     UpperCAmelCase	:							Optional[Any]						=			crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
     UpperCAmelCase	:							int						=			parent
     UpperCAmelCase	:							Union[str, Any]						=			batch_size
     UpperCAmelCase	:							List[Any]						=			num_channels
     UpperCAmelCase	:							Dict						=			image_size
     UpperCAmelCase	:							List[str]						=			min_resolution
     UpperCAmelCase	:							Optional[Any]						=			max_resolution
     UpperCAmelCase	:							Union[str, Any]						=			do_resize
     UpperCAmelCase	:							Dict						=			size
     UpperCAmelCase	:							Any						=			do_center_crop
     UpperCAmelCase	:							Union[str, Any]						=			crop_size
     UpperCAmelCase	:							List[str]						=			do_normalize
     UpperCAmelCase	:							Optional[Any]						=			image_mean
     UpperCAmelCase	:							Optional[Any]						=			image_std
     UpperCAmelCase	:							List[Any]						=			do_convert_rgb



   def 	__magic_name__   (							self							:					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,
     }





   def 	__magic_name__   (							self							:					Optional[Any],		__A							:					Any=False,		__A							:					str=False,		__A							:					List[Any]=False						):
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     if equal_resolution:
       UpperCAmelCase	:							Dict						=			[]
       for i in range(self.batch_size						):
         image_inputs.append(
             np.random.randint(
                 2_5_5,		size=(self.num_channels, self.max_resolution, self.max_resolution),		dtype=np.uinta						)						)
     else:
       UpperCAmelCase	:							Tuple						=			[]
       for i in range(self.batch_size						):
         UpperCAmelCase			,						UpperCAmelCase	:							Tuple						=			np.random.choice(np.arange(self.min_resolution,		self.max_resolution						),		2						)
         image_inputs.append(np.random.randint(2_5_5,		size=(self.num_channels, width, height),		dtype=np.uinta						)						)

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

     if torchify:
       UpperCAmelCase	:							str						=			[torch.from_numpy(__A						) for x in image_inputs]

     return image_inputs






@require_torch
@require_vision
class  __UpperCAmelCase					(	lowerCamelCase__      ,  unittest.TestCase						):
   UpperCamelCase       					=					ChineseCLIPImageProcessor if is_vision_available() else None



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							Optional[Any]						=			ChineseCLIPImageProcessingTester(self,		do_center_crop=__A						)



   @property
   def 	__magic_name__   (							self							:					Tuple						):
     return self.image_processor_tester.prepare_image_processor_dict()



   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							List[Any]						=			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'''						)						)
     self.assertTrue(hasattr(__A,		'''do_normalize'''						)						)
     self.assertTrue(hasattr(__A,		'''image_mean'''						)						)
     self.assertTrue(hasattr(__A,		'''image_std'''						)						)
     self.assertTrue(hasattr(__A,		'''do_convert_rgb'''						)						)



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							Union[str, Any]						=			self.image_processing_class.from_dict(self.image_processor_dict						)
     self.assertEqual(image_processor.size,		{'''height''': 2_2_4, '''width''': 2_2_4}						)
     self.assertEqual(image_processor.crop_size,		{'''height''': 1_8, '''width''': 1_8}						)

     UpperCAmelCase	:							int						=			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 	__magic_name__   (							self							:					Union[str, Any]						):
     pass



   def 	__magic_name__   (							self							:					Tuple						):
     # Initialize image_processing
     UpperCAmelCase	:							Dict						=			self.image_processing_class(**self.image_processor_dict						)
     # create random PIL images
     UpperCAmelCase	:							Optional[int]						=			self.image_processor_tester.prepare_inputs(equal_resolution=__A						)
     for image in image_inputs:
       self.assertIsInstance(__A,		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	:							Any						=			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 	__magic_name__   (							self							:					Optional[Any]						):
     # Initialize image_processing
     UpperCAmelCase	:							List[str]						=			self.image_processing_class(**self.image_processor_dict						)
     # create random numpy tensors
     UpperCAmelCase	:							List[Any]						=			self.image_processor_tester.prepare_inputs(equal_resolution=__A,		numpify=__A						)
     for image in image_inputs:
       self.assertIsInstance(__A,		np.ndarray						)

     # Test not batched input
     UpperCAmelCase	:							List[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	:							Dict						=			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 	__magic_name__   (							self							:					Any						):
     # Initialize image_processing
     UpperCAmelCase	:							str						=			self.image_processing_class(**self.image_processor_dict						)
     # create random PyTorch tensors
     UpperCAmelCase	:							Optional[Any]						=			self.image_processor_tester.prepare_inputs(equal_resolution=__A,		torchify=__A						)
     for image in image_inputs:
       self.assertIsInstance(__A,		torch.Tensor						)

     # 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	:							List[str]						=			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'''],
         ),		)






@require_torch
@require_vision
class  __UpperCAmelCase					(	lowerCamelCase__      ,  unittest.TestCase						):
   UpperCamelCase       					=					ChineseCLIPImageProcessor if is_vision_available() else None



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							str						=			ChineseCLIPImageProcessingTester(self,		num_channels=4,		do_center_crop=__A						)
     UpperCAmelCase	:							Dict						=			3



   @property
   def 	__magic_name__   (							self							:					List[Any]						):
     return self.image_processor_tester.prepare_image_processor_dict()



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							List[Any]						=			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'''						)						)
     self.assertTrue(hasattr(__A,		'''do_normalize'''						)						)
     self.assertTrue(hasattr(__A,		'''image_mean'''						)						)
     self.assertTrue(hasattr(__A,		'''image_std'''						)						)
     self.assertTrue(hasattr(__A,		'''do_convert_rgb'''						)						)



   def 	__magic_name__   (							self							:					List[Any]						):
     pass





   def 	__magic_name__   (							self							:					Tuple						):
     # Initialize image_processing
     UpperCAmelCase	:							Optional[Any]						=			self.image_processing_class(**self.image_processor_dict						)
     # create random PIL images
     UpperCAmelCase	:							Dict						=			self.image_processor_tester.prepare_inputs(equal_resolution=__A						)
     for image in image_inputs:
       self.assertIsInstance(__A,		Image.Image						)

     # Test not batched input
     UpperCAmelCase	:							Tuple						=			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
     UpperCAmelCase	:							Tuple						=			image_processing(__A,		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'''],
         ),		)





