





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 		SCREAMING_SNAKE_CASE__				:


      def __init__(self       :      Union[str, Any]				,			a__       :      List[str]				,			a__       :      Any=2				,			a__       :      Dict=3				,			a__       :      Dict=4				,			a__       :      Tuple=2				,			a__       :      str=7				,			a__       :      List[Any]=True				,			a__       :      str=True				,			a__       :      Optional[Any]=True				,			a__       :      Optional[Any]=True				,			a__       :      Optional[Any]=99				,			a__       :      List[Any]=36				,			a__       :      Optional[Any]=3				,			a__       :      List[str]=4				,			a__       :      Tuple=37				,			a__       :      Any="gelu"				,			a__       :      str=0.1				,			a__       :      Any=0.1				,			a__       :      List[str]=512				,			a__       :      Any=16				,			a__       :      Tuple=2				,			a__       :      List[str]=0.0_2				,			a__       :      int=6				,			a__       :      Union[str, Any]=6				,			a__       :      str=3				,			a__       :      Union[str, Any]=4				,			a__       :      Tuple=None				,			a__       :      List[Any]=1000				,			):





         """simple docstring"""



         __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    a (self       :      Dict      ):





         """simple docstring"""



         __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    a (self       :      int				,			a__       :      int				,			a__       :      List[Any]				,			a__       :      Any				,			a__       :      Union[str, Any]				,			a__       :      Dict				,			a__       :      Any				,			a__       :      Dict				,			a__       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   LayoutLMvaModel(config=a__      )
         model.to(a__      )
         model.eval()

         # text + image
         __snake_case							       =   model(a__				,			pixel_values=a__      )
         __snake_case							       =   model(
             a__				,			bbox=a__				,			pixel_values=a__				,			attention_mask=a__				,			token_type_ids=a__      )
         __snake_case							       =   model(a__				,			bbox=a__				,			pixel_values=a__				,			token_type_ids=a__      )
         __snake_case							       =   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
         __snake_case							       =   model(a__      )
         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=a__      )
         self.parent.assertEqual(
             result.last_hidden_state.shape				,			(self.batch_size, self.image_seq_length, self.hidden_size)      )


      def    a (self       :      Dict				,			a__       :      Dict				,			a__       :      Tuple				,			a__       :      Tuple				,			a__       :      List[Any]				,			a__       :      Union[str, Any]				,			a__       :      Tuple				,			a__       :      Union[str, Any]				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.num_labels
         __snake_case							       =   LayoutLMvaForSequenceClassification(a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   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    a (self       :      Optional[Any]				,			a__       :      Optional[Any]				,			a__       :      List[Any]				,			a__       :      Optional[int]				,			a__       :      Tuple				,			a__       :      Optional[Any]				,			a__       :      int				,			a__       :      Union[str, Any]				,			a__       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   self.num_labels
         __snake_case							       =   LayoutLMvaForTokenClassification(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   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    a (self       :      List[str]				,			a__       :      Dict				,			a__       :      Tuple				,			a__       :      Dict				,			a__       :      int				,			a__       :      str				,			a__       :      List[str]				,			a__       :      List[Any]				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   LayoutLMvaForQuestionAnswering(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   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    a (self       :      Any      ):





         """simple docstring"""



         __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 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				List[Any]							=    False
      A_						:				Any							=    False
      A_						:				Optional[int]							=    False

      A_						:				List[str]							=    (
          (
              LayoutLMvaModel,
              LayoutLMvaForSequenceClassification,
              LayoutLMvaForTokenClassification,
              LayoutLMvaForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      A_						:				Union[str, Any]							=    (
          {'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
          if is_torch_available()
          else {}
      )


      def    a (self       :      Any				,			a__       :      Optional[Any]				,			a__       :      Dict				,			a__       :      Optional[Any]				,			a__       :      Tuple				,			a__       :      List[str]      ):





         """simple docstring"""



         return True


      def    a (self       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   LayoutLMvaModelTester(self      )
         __snake_case							       =   ConfigTester(self				,			config_class=a__				,			hidden_size=37      )


      def    a (self       :      Optional[int]				,			a__       :      Union[str, Any]				,			a__       :      Optional[Any]				,			a__       :      Tuple=False      ):





         """simple docstring"""



         __snake_case							       =   copy.deepcopy(a__      )
         if model_class in get_values(a__      ):
            __snake_case							       =   {
                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__      ):
               __snake_case							       =   torch.ones(self.model_tester.batch_size				,			dtype=torch.long				,			device=a__      )
            elif model_class in get_values(a__      ):
               __snake_case							       =   torch.zeros(
                   self.model_tester.batch_size				,			dtype=torch.long				,			device=a__      )
               __snake_case							       =   torch.zeros(
                   self.model_tester.batch_size				,			dtype=torch.long				,			device=a__      )
            elif model_class in [
                *get_values(a__      ),
            ]:
               __snake_case							       =   torch.zeros(
                   self.model_tester.batch_size				,			dtype=torch.long				,			device=a__      )
            elif model_class in [
                *get_values(a__      ),
            ]:
               __snake_case							       =   torch.zeros(
                   (self.model_tester.batch_size, self.model_tester.text_seq_length)				,			dtype=torch.long				,			device=a__				,			)

         return inputs_dict


      def    a (self       :      str      ):





         """simple docstring"""



         self.config_tester.run_common_tests()


      def    a (self       :      Any      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         __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(*a__      )


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*a__      )


      def    a (self       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*a__      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*a__      )


      @slow
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __snake_case							       =   LayoutLMvaModel.from_pretrained(a__      )
            self.assertIsNotNone(a__      )






def lowerCamelCase__    (							)      ->				Optional[int]:
   __snake_case							       =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
   return image







@require_torch
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      @cached_property
      def    a (self       :      int      ):





         """simple docstring"""



         return LayoutLMvaImageProcessor(apply_ocr=a__      ) if is_vision_available() else None


      @slow
      def    a (self       :      Any      ):





         """simple docstring"""



         __snake_case							       =   LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''      ).to(a__      )

         __snake_case							       =   self.default_image_processor
         __snake_case							       =   prepare_img()
         __snake_case							       =   image_processor(images=a__				,			return_tensors='''pt'''      ).pixel_values.to(a__      )

         __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(a__      )				,			bbox=bbox.to(a__      )				,			pixel_values=pixel_values.to(a__      )				,			)

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

         __snake_case							       =   torch.tensor(
             [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]      ).to(a__      )

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








import socket






def lowerCamelCase__    (							)      ->				Any:
   __snake_case							       =   socket.socket(socket.AF_INET       ,      socket.SOCK_STREAM       )
   __snake_case							       =   socket.gethostname()
   __snake_case							       =   1_2312

   sock.connect((host, port)       )
   sock.send(B'''Hello server!'''       )

   with open('''Received_file'''       ,      '''wb'''       ) as out_file:
      print('''File opened'''       )
      print('''Receiving data...'''       )
      while True:
         __snake_case							       =   sock.recv(1024       )
         if not data:
            break
         out_file.write(snake_case_       )

   print('''Successfully received the file'''       )
   sock.close()
   print('''Connection closed'''       )


if __name__ == "__main__":
    main()

