


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 lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=2		,				_UpperCAmelCase=3		,				_UpperCAmelCase=4		,				_UpperCAmelCase=2		,				_UpperCAmelCase=7		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=99		,				_UpperCAmelCase=36		,				_UpperCAmelCase=3		,				_UpperCAmelCase=4		,				_UpperCAmelCase=37		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=5_12		,				_UpperCAmelCase=16		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=6		,				_UpperCAmelCase=6		,				_UpperCAmelCase=3		,				_UpperCAmelCase=4		,				_UpperCAmelCase=None		,				_UpperCAmelCase=10_00		,				):
       snake_case_	=				parent
       snake_case_	=				batch_size
       snake_case_	=				num_channels
       snake_case_	=				image_size
       snake_case_	=				patch_size
       snake_case_	=				text_seq_length
       snake_case_	=				is_training
       snake_case_	=				use_input_mask
       snake_case_	=				use_token_type_ids
       snake_case_	=				use_labels
       snake_case_	=				vocab_size
       snake_case_	=				hidden_size
       snake_case_	=				num_hidden_layers
       snake_case_	=				num_attention_heads
       snake_case_	=				intermediate_size
       snake_case_	=				hidden_act
       snake_case_	=				hidden_dropout_prob
       snake_case_	=				attention_probs_dropout_prob
       snake_case_	=				max_position_embeddings
       snake_case_	=				type_vocab_size
       snake_case_	=				type_sequence_label_size
       snake_case_	=				initializer_range
       snake_case_	=				coordinate_size
       snake_case_	=				shape_size
       snake_case_	=				num_labels
       snake_case_	=				num_choices
       snake_case_	=				scope
       snake_case_	=				range_bbox

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



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				ids_tensor([self.batch_size, self.text_seq_length]		,				self.vocab_size )

       snake_case_	=				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]:
                         snake_case_	=				bbox[i, j, 3]
                         snake_case_	=				bbox[i, j, 1]
                         snake_case_	=				t
                   if bbox[i, j, 2] < bbox[i, j, 0]:
                         snake_case_	=				bbox[i, j, 2]
                         snake_case_	=				bbox[i, j, 0]
                         snake_case_	=				t

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

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

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

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

       snake_case_	=				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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				LayoutLMvaModel(config=_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()

       # text + image
       snake_case_	=				model(_UpperCAmelCase		,				pixel_values=_UpperCAmelCase )
       snake_case_	=				model(
           _UpperCAmelCase		,				bbox=_UpperCAmelCase		,				pixel_values=_UpperCAmelCase		,				attention_mask=_UpperCAmelCase		,				token_type_ids=_UpperCAmelCase )
       snake_case_	=				model(_UpperCAmelCase		,				bbox=_UpperCAmelCase		,				pixel_values=_UpperCAmelCase		,				token_type_ids=_UpperCAmelCase )
       snake_case_	=				model(_UpperCAmelCase		,				bbox=_UpperCAmelCase		,				pixel_values=_UpperCAmelCase )

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

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

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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				self.num_labels
       snake_case_	=				LayoutLMvaForSequenceClassification(_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(
           _UpperCAmelCase		,				bbox=_UpperCAmelCase		,				pixel_values=_UpperCAmelCase		,				attention_mask=_UpperCAmelCase		,				token_type_ids=_UpperCAmelCase		,				labels=_UpperCAmelCase		,				)
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels) )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				self.num_labels
       snake_case_	=				LayoutLMvaForTokenClassification(config=_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(
           _UpperCAmelCase		,				bbox=_UpperCAmelCase		,				pixel_values=_UpperCAmelCase		,				attention_mask=_UpperCAmelCase		,				token_type_ids=_UpperCAmelCase		,				labels=_UpperCAmelCase		,				)
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.text_seq_length, self.num_labels) )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				LayoutLMvaForQuestionAnswering(config=_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(
           _UpperCAmelCase		,				bbox=_UpperCAmelCase		,				pixel_values=_UpperCAmelCase		,				attention_mask=_UpperCAmelCase		,				token_type_ids=_UpperCAmelCase		,				start_positions=_UpperCAmelCase		,				end_positions=_UpperCAmelCase		,				)
       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 						UpperCamelCase__					(     self ):
       snake_case_	=				self.prepare_config_and_inputs()
       (
           (
           snake_case_
       )	,   (
           snake_case_
       )	,   (
           snake_case_
       )	,   (
           snake_case_
       )	,   (
           snake_case_
       )	,   (
           snake_case_
       )	,   (
           snake_case_
       )	,   (
           snake_case_
       )	,   
       )	=				config_and_inputs
       snake_case_	=				{
           '''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 lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  False
 __snake_case												=  False
 __snake_case												=  False

 __snake_case												=  (
     (
         LayoutLMvaModel,
         LayoutLMvaForSequenceClassification,
         LayoutLMvaForTokenClassification,
         LayoutLMvaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 __snake_case												=  (
     {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
     if is_torch_available()
     else {}
 )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       # `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 						UpperCamelCase__					(     self ):
       snake_case_	=				LayoutLMvaModelTester(self )
       snake_case_	=				ConfigTester(self		,				config_class=_UpperCAmelCase		,				hidden_size=37 )



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

       return inputs_dict



 def 						UpperCamelCase__					(     self ):
       self.config_tester.run_common_tests()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
             snake_case_	=				type
             self.model_tester.create_and_check_model(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase )





 @slow
 def 						UpperCamelCase__					(     self ):
       for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case_	=				LayoutLMvaModel.from_pretrained(_UpperCAmelCase )
             self.assertIsNotNone(_UpperCAmelCase )






def 	__lowerCAmelCase   ()->  Dict:




      """simple docstring"""

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







@require_torch
class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 @cached_property
 def 						UpperCamelCase__					(     self ):
       return LayoutLMvaImageProcessor(apply_ocr=_UpperCAmelCase ) if is_vision_available() else None





 @slow
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base''' ).to(_UpperCAmelCase )

       snake_case_	=				self.default_image_processor
       snake_case_	=				prepare_img()
       snake_case_	=				image_processor(images=_UpperCAmelCase		,				return_tensors='''pt''' ).pixel_values.to(_UpperCAmelCase )

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

       # forward pass
       snake_case_	=				model(
           input_ids=input_ids.to(_UpperCAmelCase )		,				bbox=bbox.to(_UpperCAmelCase )		,				pixel_values=pixel_values.to(_UpperCAmelCase )		,				)

       # verify the logits
       snake_case_	=				torch.Size((1, 1_99, 7_68) )
       self.assertEqual(outputs.last_hidden_state.shape		,				_UpperCAmelCase )

       snake_case_	=				torch.tensor(
           [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]] ).to(_UpperCAmelCase )

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



# Function to print upper half of diamond (pyramid)






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      for i in range(0 , SCREAMING_SNAKE_CASE ):
            for _ in range(0 , n - i - 1 ):  # printing spaces
                  print(''' ''' , end='''''' )
            for _ in range(0 , i + 1 ):  # printing stars
                  print('''* ''' , end='''''' )
            print()






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      for i in range(SCREAMING_SNAKE_CASE , 0 , -1 ):
            for _ in range(SCREAMING_SNAKE_CASE , 0 , -1 ):  # printing stars
                  print('''* ''' , end='''''' )
            print()
            for _ in range(n - i + 1 , 0 , -1 ):  # printing spaces
                  print(''' ''' , end='''''' )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      if n <= 0:
            print('''       ...       ....        nothing printing :(''' )
            return
      floyd(SCREAMING_SNAKE_CASE )  # upper half
      reverse_floyd(SCREAMING_SNAKE_CASE )  # lower half


if __name__ == "__main__":
   print(r"""| /\ | |- |  |-  |--| |\  /| |-""")
   print(r"""|/  \| |- |_ |_  |__| | \/ | |_""")
   UpperCAmelCase      =   1
   while K:
      UpperCAmelCase      =   int(input("""enter the number and , and see the magic : """))
      print()
      pretty_print(user_number)
      UpperCAmelCase      =   int(input("""press 0 to exit... and 1 to continue..."""))

   print("""Good Bye...""")