




"""simple docstring"""

import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
           MODEL_FOR_QUESTION_ANSWERING_MAPPING,
           MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
           MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
           LayoutLMvaConfig,
           LayoutLMvaForQuestionAnswering,
           LayoutLMvaForSequenceClassification,
           LayoutLMvaForTokenClassification,
           LayoutLMvaModel,
       )
       from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
       from PIL import Image

       from transformers import LayoutLMvaImageProcessor





class 				lowercase_       :



 '''simple docstring'''

 def __init__( self       :       str    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       Union[str, Any]=2    ,							_UpperCAmelCase       :       Dict=3    ,							_UpperCAmelCase       :       Dict=4    ,							_UpperCAmelCase       :       Optional[Any]=2    ,							_UpperCAmelCase       :       Optional[Any]=7    ,							_UpperCAmelCase       :       List[str]=True    ,							_UpperCAmelCase       :       List[str]=True    ,							_UpperCAmelCase       :       Any=True    ,							_UpperCAmelCase       :       Union[str, Any]=True    ,							_UpperCAmelCase       :       Any=99    ,							_UpperCAmelCase       :       int=36    ,							_UpperCAmelCase       :       Tuple=3    ,							_UpperCAmelCase       :       int=4    ,							_UpperCAmelCase       :       Tuple=37    ,							_UpperCAmelCase       :       Optional[int]="gelu"    ,							_UpperCAmelCase       :       str=0.1    ,							_UpperCAmelCase       :       Any=0.1    ,							_UpperCAmelCase       :       Dict=512    ,							_UpperCAmelCase       :       List[str]=16    ,							_UpperCAmelCase       :       Union[str, Any]=2    ,							_UpperCAmelCase       :       str=0.02    ,							_UpperCAmelCase       :       Union[str, Any]=6    ,							_UpperCAmelCase       :       Union[str, Any]=6    ,							_UpperCAmelCase       :       Optional[Any]=3    ,							_UpperCAmelCase       :       Any=4    ,							_UpperCAmelCase       :       Optional[int]=None    ,							_UpperCAmelCase       :       Dict=1_000    ,							):
       _A					   =   parent
       _A					   =   batch_size
       _A					   =   num_channels
       _A					   =   image_size
       _A					   =   patch_size
       _A					   =   text_seq_length
       _A					   =   is_training
       _A					   =   use_input_mask
       _A					   =   use_token_type_ids
       _A					   =   use_labels
       _A					   =   vocab_size
       _A					   =   hidden_size
       _A					   =   num_hidden_layers
       _A					   =   num_attention_heads
       _A					   =   intermediate_size
       _A					   =   hidden_act
       _A					   =   hidden_dropout_prob
       _A					   =   attention_probs_dropout_prob
       _A					   =   max_position_embeddings
       _A					   =   type_vocab_size
       _A					   =   type_sequence_label_size
       _A					   =   initializer_range
       _A					   =   coordinate_size
       _A					   =   shape_size
       _A					   =   num_labels
       _A					   =   num_choices
       _A					   =   scope
       _A					   =   range_bbox

       # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
       _A					   =   text_seq_length
       _A					   =   (image_size // patch_size) ** 2 + 1
       _A					   =   self.text_seq_length + self.image_seq_length

 def 			lowerCAmelCase_							( self       :       int		):
       _A					   =   ids_tensor([self.batch_size, self.text_seq_length]    ,							self.vocab_size		)

       _A					   =   ids_tensor([self.batch_size, self.text_seq_length, 4]    ,							self.range_bbox		)
       # Ensure that bbox is legal
       for i in range(bbox.shape[0]		):
             for j in range(bbox.shape[1]		):
                   if bbox[i, j, 3] < bbox[i, j, 1]:
                         _A					   =   bbox[i, j, 3]
                         _A					   =   bbox[i, j, 1]
                         _A					   =   t
                   if bbox[i, j, 2] < bbox[i, j, 0]:
                         _A					   =   bbox[i, j, 2]
                         _A					   =   bbox[i, j, 0]
                         _A					   =   t

       _A					   =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

       _A					   =   None
       if self.use_input_mask:
             _A					   =   random_attention_mask([self.batch_size, self.text_seq_length]		)

       _A					   =   None
       if self.use_token_type_ids:
             _A					   =   ids_tensor([self.batch_size, self.text_seq_length]    ,							self.type_vocab_size		)

       _A					   =   None
       _A					   =   None
       if self.use_labels:
             _A					   =   ids_tensor([self.batch_size]    ,							self.type_sequence_label_size		)
             _A					   =   ids_tensor([self.batch_size, self.text_seq_length]    ,							self.num_labels		)

       _A					   =   LayoutLMvaConfig(
           vocab_size=self.vocab_size    ,							hidden_size=self.hidden_size    ,							num_hidden_layers=self.num_hidden_layers    ,							num_attention_heads=self.num_attention_heads    ,							intermediate_size=self.intermediate_size    ,							hidden_act=self.hidden_act    ,							hidden_dropout_prob=self.hidden_dropout_prob    ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,							max_position_embeddings=self.max_position_embeddings    ,							type_vocab_size=self.type_vocab_size    ,							initializer_range=self.initializer_range    ,							coordinate_size=self.coordinate_size    ,							shape_size=self.shape_size    ,							input_size=self.image_size    ,							patch_size=self.patch_size    ,							)

       return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels

 def 			lowerCAmelCase_							( self       :       Optional[int]    ,							_UpperCAmelCase       :       int    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       List[str]    ,							_UpperCAmelCase       :       Optional[int]    ,							_UpperCAmelCase       :       List[str]    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       List[str]		):
       _A					   =   LayoutLMvaModel(config=_a		)
       model.to(_a		)
       model.eval()

       # text + image
       _A					   =   model(_a    ,							pixel_values=_a		)
       _A					   =   model(
           _a    ,							bbox=_a    ,							pixel_values=_a    ,							attention_mask=_a    ,							token_type_ids=_a		)
       _A					   =   model(_a    ,							bbox=_a    ,							pixel_values=_a    ,							token_type_ids=_a		)
       _A					   =   model(_a    ,							bbox=_a    ,							pixel_values=_a		)

       self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size)		)

       # text only
       _A					   =   model(_a		)
       self.parent.assertEqual(
           result.last_hidden_state.shape    ,							(self.batch_size, self.text_seq_length, self.hidden_size)		)

       # image only
       _A					   =   model(pixel_values=_a		)
       self.parent.assertEqual(
           result.last_hidden_state.shape    ,							(self.batch_size, self.image_seq_length, self.hidden_size)		)

 def 			lowerCAmelCase_							( self       :       Union[str, Any]    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       List[str]    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       Optional[int]    ,							_UpperCAmelCase       :       List[str]    ,							_UpperCAmelCase       :       Optional[int]    ,							_UpperCAmelCase       :       Optional[int]    ,							_UpperCAmelCase       :       Tuple		):
       _A					   =   self.num_labels
       _A					   =   LayoutLMvaForSequenceClassification(_a		)
       model.to(_a		)
       model.eval()
       _A					   =   model(
           _a    ,							bbox=_a    ,							pixel_values=_a    ,							attention_mask=_a    ,							token_type_ids=_a    ,							labels=_a    ,							)
       self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels)		)

 def 			lowerCAmelCase_							( self       :       Tuple    ,							_UpperCAmelCase       :       Optional[Any]    ,							_UpperCAmelCase       :       List[str]    ,							_UpperCAmelCase       :       Dict    ,							_UpperCAmelCase       :       int    ,							_UpperCAmelCase       :       Union[str, Any]    ,							_UpperCAmelCase       :       Dict    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       Optional[int]		):
       _A					   =   self.num_labels
       _A					   =   LayoutLMvaForTokenClassification(config=_a		)
       model.to(_a		)
       model.eval()
       _A					   =   model(
           _a    ,							bbox=_a    ,							pixel_values=_a    ,							attention_mask=_a    ,							token_type_ids=_a    ,							labels=_a    ,							)
       self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.text_seq_length, self.num_labels)		)

 def 			lowerCAmelCase_							( self       :       Optional[int]    ,							_UpperCAmelCase       :       str    ,							_UpperCAmelCase       :       Dict    ,							_UpperCAmelCase       :       Optional[int]    ,							_UpperCAmelCase       :       Tuple    ,							_UpperCAmelCase       :       Tuple    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       int		):
       _A					   =   LayoutLMvaForQuestionAnswering(config=_a		)
       model.to(_a		)
       model.eval()
       _A					   =   model(
           _a    ,							bbox=_a    ,							pixel_values=_a    ,							attention_mask=_a    ,							token_type_ids=_a    ,							start_positions=_a    ,							end_positions=_a    ,							)
       self.parent.assertEqual(result.start_logits.shape    ,							(self.batch_size, self.seq_length)		)
       self.parent.assertEqual(result.end_logits.shape    ,							(self.batch_size, self.seq_length)		)



 def 			lowerCAmelCase_							( self       :       List[str]		):
       _A					   =   self.prepare_config_and_inputs()
       (
           _A					
       )					   =   config_and_inputs
       _A					   =   {
           '''input_ids''': input_ids,
           '''bbox''': bbox,
           '''pixel_values''': pixel_values,
           '''token_type_ids''': token_type_ids,
           '''attention_mask''': input_mask,
       }
       return config, inputs_dict





@require_torch
class 				lowercase_       (     __lowercase			,	__lowercase			,	unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase    :     str        =	False
 UpperCAmelCase    :     Any        =	False
 UpperCAmelCase    :     int        =	False

 UpperCAmelCase    :     Optional[Any]        =	(
     (
         LayoutLMvaModel,
         LayoutLMvaForSequenceClassification,
         LayoutLMvaForTokenClassification,
         LayoutLMvaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 UpperCAmelCase    :     Any        =	(
     {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
     if is_torch_available()
     else {}
 )

 def 			lowerCAmelCase_							( self       :       Tuple    ,							_UpperCAmelCase       :       Dict    ,							_UpperCAmelCase       :       Union[str, Any]    ,							_UpperCAmelCase       :       Optional[int]    ,							_UpperCAmelCase       :       Dict    ,							_UpperCAmelCase       :       Dict		):
       # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
       # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
       # the sequence dimension of the text embedding only.
       # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
       return True

 def 			lowerCAmelCase_							( self       :       int		):
       _A					   =   LayoutLMvaModelTester(self		)
       _A					   =   ConfigTester(self    ,							config_class=_a    ,							hidden_size=37		)

 def 			lowerCAmelCase_							( self       :       Dict    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       Dict    ,							_UpperCAmelCase       :       Tuple=False		):
       _A					   =   copy.deepcopy(_a		)
       if model_class in get_values(_a		):
             _A					   =   {
                 k: v.unsqueeze(1		).expand(-1    ,							self.model_tester.num_choices    ,							-1		).contiguous()
                 if isinstance(_a    ,							torch.Tensor		) and v.ndim > 1
                 else v
                 for k, v in inputs_dict.items()
             }
       if return_labels:
             if model_class in get_values(_a		):
                   _A					   =   torch.ones(self.model_tester.batch_size    ,							dtype=torch.long    ,							device=_a		)
             elif model_class in get_values(_a		):
                   _A					   =   torch.zeros(
                       self.model_tester.batch_size    ,							dtype=torch.long    ,							device=_a		)
                   _A					   =   torch.zeros(
                       self.model_tester.batch_size    ,							dtype=torch.long    ,							device=_a		)
             elif model_class in [
                 *get_values(_a		),
             ]:
                   _A					   =   torch.zeros(
                       self.model_tester.batch_size    ,							dtype=torch.long    ,							device=_a		)
             elif model_class in [
                 *get_values(_a		),
             ]:
                   _A					   =   torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.text_seq_length)    ,							dtype=torch.long    ,							device=_a    ,							)

       return inputs_dict

 def 			lowerCAmelCase_							( self       :       List[Any]		):
       self.config_tester.run_common_tests()

 def 			lowerCAmelCase_							( self       :       str		):
       _A					   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_a		)

 def 			lowerCAmelCase_							( self       :       Dict		):
       _A					   =   self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
             _A					   =   type
             self.model_tester.create_and_check_model(*_a		)

 def 			lowerCAmelCase_							( self       :       List[Any]		):
       _A					   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_a		)

 def 			lowerCAmelCase_							( self       :       str		):
       _A					   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_a		)

 def 			lowerCAmelCase_							( self       :       Optional[Any]		):
       _A					   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_a		)



 @slow
 def 			lowerCAmelCase_							( self       :       Dict		):
       for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A					   =   LayoutLMvaModel.from_pretrained(_a		)
             self.assertIsNotNone(_a		)






def 						_snake_case     (					)    ->					int:






      '''simple docstring'''


      _A					   =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
      return image







@require_torch
class 				lowercase_       (     unittest.TestCase							):



 '''simple docstring'''

 @cached_property
 def 			lowerCAmelCase_							( self       :       Tuple		):
       return LayoutLMvaImageProcessor(apply_ocr=_a		) if is_vision_available() else None



 @slow
 def 			lowerCAmelCase_							( self       :       Dict		):
       _A					   =   LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'		).to(_a		)

       _A					   =   self.default_image_processor
       _A					   =   prepare_img()
       _A					   =   image_processor(images=_a    ,							return_tensors='pt'		).pixel_values.to(_a		)

       _A					   =   torch.tensor([[1, 2]]		)
       _A					   =   torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]		).unsqueeze(0		)

       # forward pass
       _A					   =   model(
           input_ids=input_ids.to(_a		)    ,							bbox=bbox.to(_a		)    ,							pixel_values=pixel_values.to(_a		)    ,							)

       # verify the logits
       _A					   =   torch.Size((1, 199, 768)		)
       self.assertEqual(outputs.last_hidden_state.shape    ,							_a		)

       _A					   =   torch.tensor(
           [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]		).to(_a		)

       self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]    ,							_a    ,							atol=1E-4		)		)




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a__				=		logging.get_logger(__name__)

a__				=		{
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}

class 				UpperCAmelCase_		(			__lowercase				,       __lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "bit"
							UpperCAmelCase__       :						Optional[int]	    =    ["preactivation", "bottleneck"]
							UpperCAmelCase__       :						Optional[Any]	    =    ["SAME", "VALID"]


							def __init__(			self       ,							_a=3       ,							_a=6_4       ,							_a=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]       ,							_a=[3, 4, 6, 3]       ,							_a="preactivation"       ,							_a="relu"       ,							_a=None       ,							_a=3_2       ,							_a=0.0       ,							_a=False       ,							_a=3_2       ,							_a=1       ,							_a=None       ,							_a=None       ,							**_a       ,							)    ->    Union[str, Any]:
												super().__init__(**_a       )
												if layer_type not in self.layer_types:
																	raise ValueError(F"""layer_type={layer_type} is not one of {','.join(self.layer_types       )}"""       )
												if global_padding is not None:
																	if global_padding.upper() in self.supported_padding:
																						_a	:    Any         =		global_padding.upper()
																	else:
																						raise ValueError(F"""Padding strategy {global_padding} not supported"""       )
												_a	:    Optional[int]         =		num_channels
												_a	:    List[Any]         =		embedding_size
												_a	:    Any         =		hidden_sizes
												_a	:    int         =		depths
												_a	:    Dict         =		layer_type
												_a	:    int         =		hidden_act
												_a	:    Optional[Any]         =		global_padding
												_a	:    Optional[Any]         =		num_groups
												_a	:    Union[str, Any]         =		drop_path_rate
												_a	:    Tuple         =		embedding_dynamic_padding
												_a	:    Union[str, Any]         =		output_stride
												_a	:    Any         =		width_factor

												_a	:    Any         =		['''stem'''] + [F"""stage{idx}""" for idx in range(1       ,							len(_a       ) + 1       )]
												_a      ,					_a	:    List[str]         =		get_aligned_output_features_output_indices(
												    out_features=_a       ,							out_indices=_a       ,							stage_names=self.stage_names       )



