

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


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						       =	{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     Any		     =      'layoutlmv3'






     def __init__(							self						:   Optional[Any]						,lowerCamelCase__						:   Tuple=50265						,lowerCamelCase__						:   Union[str, Any]=768						,lowerCamelCase__						:   str=12						,lowerCamelCase__						:   int=12						,lowerCamelCase__						:   Any=3072						,lowerCamelCase__						:   Any="gelu"						,lowerCamelCase__						:   Any=0.1						,lowerCamelCase__						:   Tuple=0.1						,lowerCamelCase__						:   Any=512						,lowerCamelCase__						:   List[str]=2						,lowerCamelCase__						:   Optional[Any]=0.02						,lowerCamelCase__						:   Optional[int]=1e-5						,lowerCamelCase__						:   int=1						,lowerCamelCase__						:   Optional[Any]=0						,lowerCamelCase__						:   Optional[Any]=2						,lowerCamelCase__						:   List[str]=1024						,lowerCamelCase__						:   Any=128						,lowerCamelCase__						:   int=128						,lowerCamelCase__						:   Tuple=True						,lowerCamelCase__						:   List[Any]=32						,lowerCamelCase__						:   Any=128						,lowerCamelCase__						:   int=64						,lowerCamelCase__						:   List[str]=256						,lowerCamelCase__						:   Union[str, Any]=True						,lowerCamelCase__						:   Any=True						,lowerCamelCase__						:   List[str]=True						,lowerCamelCase__						:   Optional[Any]=224						,lowerCamelCase__						:   List[Any]=3						,lowerCamelCase__						:   int=16						,lowerCamelCase__						:   Optional[Any]=None						,**lowerCamelCase__						:   Union[str, Any]						,)					->		List[str]:




           '''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						,)
           SCREAMING_SNAKE_CASE		=						max_ad_position_embeddings
           SCREAMING_SNAKE_CASE		=						coordinate_size
           SCREAMING_SNAKE_CASE		=						shape_size
           SCREAMING_SNAKE_CASE		=						has_relative_attention_bias
           SCREAMING_SNAKE_CASE		=						rel_pos_bins
           SCREAMING_SNAKE_CASE		=						max_rel_pos
           SCREAMING_SNAKE_CASE		=						has_spatial_attention_bias
           SCREAMING_SNAKE_CASE		=						rel_ad_pos_bins
           SCREAMING_SNAKE_CASE		=						max_rel_ad_pos
           SCREAMING_SNAKE_CASE		=						text_embed
           SCREAMING_SNAKE_CASE		=						visual_embed
           SCREAMING_SNAKE_CASE		=						input_size
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						patch_size
           SCREAMING_SNAKE_CASE		=						classifier_dropout

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     str		     =      version.parse("1.12"       )






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Tuple:




           '''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     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		List[Any]:




           '''simple docstring'''


           return 1e-5






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		Any:




           '''simple docstring'''


           return 12






     def     SCREAMING_SNAKE_CASE__  (							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						,)					->		Union[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
           SCREAMING_SNAKE_CASE		=						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
           SCREAMING_SNAKE_CASE		=						processor.tokenizer.num_special_tokens_to_add(__lowerCAmelCase	)
           SCREAMING_SNAKE_CASE		=						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
           SCREAMING_SNAKE_CASE		=						[[""" """.join([processor.tokenizer.unk_token]	) * seq_length]] * batch_size

           # Generate dummy bounding boxes
           SCREAMING_SNAKE_CASE		=						[[[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		=						self._generate_dummy_images(__lowerCAmelCase						,__lowerCAmelCase						,__lowerCAmelCase						,__lowerCAmelCase	)

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

           return inputs









import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor

class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     def __init__(							self						:   Optional[Any]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Optional[Any]=7						,lowerCamelCase__						:   Optional[Any]=3						,lowerCamelCase__						:   Union[str, Any]=18						,lowerCamelCase__						:   Optional[int]=30						,lowerCamelCase__						:   int=400						,lowerCamelCase__						:   List[str]=True						,lowerCamelCase__						:   Union[str, Any]=None						,lowerCamelCase__						:   Union[str, Any]=True						,lowerCamelCase__						:   Optional[int]=[0.5, 0.5, 0.5]						,lowerCamelCase__						:   Any=[0.5, 0.5, 0.5]						,)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						size if size is not None else {"""height""": 18, """width""": 18}
           SCREAMING_SNAKE_CASE		=						parent
           SCREAMING_SNAKE_CASE		=						batch_size
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						image_size
           SCREAMING_SNAKE_CASE		=						min_resolution
           SCREAMING_SNAKE_CASE		=						max_resolution
           SCREAMING_SNAKE_CASE		=						do_resize
           SCREAMING_SNAKE_CASE		=						size
           SCREAMING_SNAKE_CASE		=						do_normalize
           SCREAMING_SNAKE_CASE		=						image_mean
           SCREAMING_SNAKE_CASE		=						image_std






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Union[str, 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 		UpperCamelCase__			(				lowerCAmelCase_							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     Tuple		     =      DPTImageProcessor if is_vision_available() else None






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						DPTImageProcessingTester(self	)






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		int:




           '''simple docstring'''


           return self.image_processor_tester.prepare_image_processor_dict()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class.from_dict(self.image_processor_dict	)
           self.assertEqual(image_processor.size						,{"""height""": 18, """width""": 18}	)

           SCREAMING_SNAKE_CASE		=						self.image_processing_class.from_dict(self.image_processor_dict						,size=42	)
           self.assertEqual(image_processor.size						,{"""height""": 42, """width""": 42}	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class(**self.image_processor_dict	)
           # create random PIL images
           SCREAMING_SNAKE_CASE		=						prepare_image_inputs(self.image_processor_tester						,equal_resolution=lowerCamelCase__	)
           for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase__						,Image.Image	)

           # Test not batched input
           SCREAMING_SNAKE_CASE		=						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
           SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class(**self.image_processor_dict	)
           # create random numpy tensors
           SCREAMING_SNAKE_CASE		=						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
           SCREAMING_SNAKE_CASE		=						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
           SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.image_processing_class(**self.image_processor_dict	)
           # create random PyTorch tensors
           SCREAMING_SNAKE_CASE		=						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
           SCREAMING_SNAKE_CASE		=						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
           SCREAMING_SNAKE_CASE		=						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"""],
               )						,)


