


from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
      TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      LayoutLMvaConfig,
      TFLayoutLMvaForQuestionAnswering,
      TFLayoutLMvaForSequenceClassification,
      TFLayoutLMvaForTokenClassification,
      TFLayoutLMvaModel,
  )

if is_vision_available():
  from PIL import Image

  from transformers import LayoutLMvaImageProcessor







class       lowerCamelCase__   :





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=2		,     UpperCAmelCase=3		,     UpperCAmelCase=4		,     UpperCAmelCase=2		,     UpperCAmelCase=7		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=9_9		,     UpperCAmelCase=3_6		,     UpperCAmelCase=2		,     UpperCAmelCase=4		,     UpperCAmelCase=3_7		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=5_1_2		,     UpperCAmelCase=1_6		,     UpperCAmelCase=2		,     UpperCAmelCase=0.02		,     UpperCAmelCase=6		,     UpperCAmelCase=6		,     UpperCAmelCase=3		,     UpperCAmelCase=4		,     UpperCAmelCase=None		,     UpperCAmelCase=1_0_0_0		,     )     ->   Dict:
      _lowercase							      =parent
      _lowercase							      =batch_size
      _lowercase							      =num_channels
      _lowercase							      =image_size
      _lowercase							      =patch_size
      _lowercase							      =is_training
      _lowercase							      =use_input_mask
      _lowercase							      =use_token_type_ids
      _lowercase							      =use_labels
      _lowercase							      =vocab_size
      _lowercase							      =hidden_size
      _lowercase							      =num_hidden_layers
      _lowercase							      =num_attention_heads
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_act
      _lowercase							      =hidden_dropout_prob
      _lowercase							      =attention_probs_dropout_prob
      _lowercase							      =max_position_embeddings
      _lowercase							      =type_vocab_size
      _lowercase							      =type_sequence_label_size
      _lowercase							      =initializer_range
      _lowercase							      =coordinate_size
      _lowercase							      =shape_size
      _lowercase							      =num_labels
      _lowercase							      =num_choices
      _lowercase							      =scope
      _lowercase							      =range_bbox

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





     def      __A					(self       )     ->   Optional[Any]:
      _lowercase							      =ids_tensor([self.batch_size, self.text_seq_length]		,     self.vocab_size       )

      _lowercase							      =ids_tensor([self.batch_size, self.text_seq_length, 4]		,     self.range_bbox       )
      _lowercase							      =bbox.numpy()
      # 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]:
         _lowercase							      =bbox[i, j, 3]
         _lowercase							      =bbox[i, j, 1]
         _lowercase							      =tmp_coordinate
        if bbox[i, j, 2] < bbox[i, j, 0]:
         _lowercase							      =bbox[i, j, 2]
         _lowercase							      =bbox[i, j, 0]
         _lowercase							      =tmp_coordinate
      _lowercase							      =tf.constant(UpperCAmelCase       )

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

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

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

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

      _lowercase							      =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		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[int]:
      _lowercase							      =TFLayoutLMvaModel(config=UpperCAmelCase       )

      # text + image
      _lowercase							      =model(UpperCAmelCase		,     pixel_values=UpperCAmelCase		,     training=UpperCAmelCase       )
      _lowercase							      =model(
          UpperCAmelCase		,     bbox=UpperCAmelCase		,     pixel_values=UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     token_type_ids=UpperCAmelCase		,     training=UpperCAmelCase		,     )
      _lowercase							      =model(UpperCAmelCase		,     bbox=UpperCAmelCase		,     pixel_values=UpperCAmelCase		,     training=UpperCAmelCase       )

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

      # text only
      _lowercase							      =model(UpperCAmelCase		,     training=UpperCAmelCase       )
      self.parent.assertEqual(
          result.last_hidden_state.shape		,     (self.batch_size, self.text_seq_length, self.hidden_size)       )

      # image only
      _lowercase							      =model({'''pixel_values''': pixel_values}		,     training=UpperCAmelCase       )
      self.parent.assertEqual(
          result.last_hidden_state.shape		,     (self.batch_size, self.image_seq_length, self.hidden_size)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Union[str, Any]:
      _lowercase							      =self.num_labels
      _lowercase							      =TFLayoutLMvaForSequenceClassification(config=UpperCAmelCase       )
      _lowercase							      =model(
          UpperCAmelCase		,     bbox=UpperCAmelCase		,     pixel_values=UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     token_type_ids=UpperCAmelCase		,     labels=UpperCAmelCase		,     training=UpperCAmelCase		,     )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[int]:
      _lowercase							      =self.num_labels
      _lowercase							      =TFLayoutLMvaForTokenClassification(config=UpperCAmelCase       )
      _lowercase							      =model(
          UpperCAmelCase		,     bbox=UpperCAmelCase		,     pixel_values=UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     token_type_ids=UpperCAmelCase		,     labels=UpperCAmelCase		,     training=UpperCAmelCase		,     )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.text_seq_length, self.num_labels)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Dict:
      _lowercase							      =2
      _lowercase							      =TFLayoutLMvaForQuestionAnswering(config=UpperCAmelCase       )
      _lowercase							      =model(
          UpperCAmelCase		,     bbox=UpperCAmelCase		,     pixel_values=UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     token_type_ids=UpperCAmelCase		,     start_positions=UpperCAmelCase		,     end_positions=UpperCAmelCase		,     training=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      __A					(self       )     ->   Union[str, Any]:
      _lowercase							      =self.prepare_config_and_inputs()
      ((_lowercase)      ,    (_lowercase)      ,    (_lowercase)      ,    (_lowercase)      ,    (_lowercase)      ,    (_lowercase)      ,    (_lowercase)      ,    (_lowercase))							      =config_and_inputs
      _lowercase							      ={
          '''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_tf
class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			(
         (
             TFLayoutLMvaModel,
             TFLayoutLMvaForQuestionAnswering,
             TFLayoutLMvaForSequenceClassification,
             TFLayoutLMvaForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE__      			=			(
         {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
         if is_tf_available()
         else {}
     )

     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   str:
      return True





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=False       )     ->   dict:
      _lowercase							      =copy.deepcopy(UpperCAmelCase       )

      if model_class in get_values(UpperCAmelCase       ):
       _lowercase							      ={
           k: tf.tile(tf.expand_dims(UpperCAmelCase		,     1       )		,     (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)       )
           if isinstance(UpperCAmelCase		,     tf.Tensor       ) and v.ndim > 0
           else v
           for k, v in inputs_dict.items()
       }

      if return_labels:
       if model_class in get_values(UpperCAmelCase       ):
        _lowercase							      =tf.ones(self.model_tester.batch_size		,     dtype=tf.intaa       )
       elif model_class in get_values(UpperCAmelCase       ):
        _lowercase							      =tf.zeros(self.model_tester.batch_size		,     dtype=tf.intaa       )
        _lowercase							      =tf.zeros(self.model_tester.batch_size		,     dtype=tf.intaa       )
       elif model_class in get_values(UpperCAmelCase       ):
        _lowercase							      =tf.zeros(self.model_tester.batch_size		,     dtype=tf.intaa       )
       elif model_class in get_values(UpperCAmelCase       ):
        _lowercase							      =tf.zeros(
            (self.model_tester.batch_size, self.model_tester.text_seq_length)		,     dtype=tf.intaa       )

      return inputs_dict





     def      __A					(self       )     ->   str:
      _lowercase							      =TFLayoutLMvaModelTester(self       )
      _lowercase							      =ConfigTester(self		,     config_class=UpperCAmelCase		,     hidden_size=3_7       )





     def      __A					(self       )     ->   Optional[Any]:
      self.config_tester.run_common_tests()





     def      __A					(self       )     ->   Optional[int]:
      _lowercase      ,    _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
       _lowercase							      =model_class(UpperCAmelCase       )
       if getattr(UpperCAmelCase		,     '''hf_compute_loss'''		,     UpperCAmelCase       ):
        # The number of elements in the loss should be the same as the number of elements in the label
        _lowercase							      =self._prepare_for_class(inputs_dict.copy()		,     UpperCAmelCase		,     return_labels=UpperCAmelCase       )
        _lowercase							      =prepared_for_class[
            sorted(prepared_for_class.keys() - inputs_dict.keys()		,     reverse=UpperCAmelCase       )[0]
        ]
        _lowercase							      =added_label.shape.as_list()[:1]

        # Test that model correctly compute the loss with kwargs
        _lowercase							      =self._prepare_for_class(inputs_dict.copy()		,     UpperCAmelCase		,     return_labels=UpperCAmelCase       )
        _lowercase							      =prepared_for_class.pop('''input_ids'''       )

        _lowercase							      =model(UpperCAmelCase		,     **UpperCAmelCase       )[0]
        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )

        # Test that model correctly compute the loss when we mask some positions
        _lowercase							      =self._prepare_for_class(inputs_dict.copy()		,     UpperCAmelCase		,     return_labels=UpperCAmelCase       )
        _lowercase							      =prepared_for_class.pop('''input_ids'''       )
        if "labels" in prepared_for_class:
         _lowercase							      =prepared_for_class['''labels'''].numpy()
         if len(labels.shape       ) > 1 and labels.shape[1] != 1:
          _lowercase							      =-1_0_0
          _lowercase							      =tf.convert_to_tensor(UpperCAmelCase       )
          _lowercase							      =model(UpperCAmelCase		,     **UpperCAmelCase       )[0]
          self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )
          self.assertTrue(not np.any(np.isnan(loss.numpy()       )       )       )

                # Test that model correctly compute the loss with a dict
        _lowercase							      =self._prepare_for_class(inputs_dict.copy()		,     UpperCAmelCase		,     return_labels=UpperCAmelCase       )
        _lowercase							      =model(UpperCAmelCase       )[0]
        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )

        # Test that model correctly compute the loss with a tuple
        _lowercase							      =self._prepare_for_class(inputs_dict.copy()		,     UpperCAmelCase		,     return_labels=UpperCAmelCase       )

        # Get keys that were added with the _prepare_for_class function
        _lowercase							      =prepared_for_class.keys() - inputs_dict.keys()
        _lowercase							      =inspect.signature(model.call       ).parameters
        _lowercase							      =list(signature.keys()       )

        # Create a dictionary holding the location of the tensors in the tuple
        _lowercase							      ={0: '''input_ids'''}
        for label_key in label_keys:
         _lowercase							      =signature_names.index(UpperCAmelCase       )
         _lowercase							      =label_key
        _lowercase							      =sorted(tuple_index_mapping.items()       )
        # Initialize a list with their default values, update the values and convert to a tuple
        _lowercase							      =[]

        for name in signature_names:
         if name != "kwargs":
          list_input.append(signature[name].default       )

        for index, value in sorted_tuple_index_mapping:
         _lowercase							      =prepared_for_class[value]

        _lowercase							      =tuple(UpperCAmelCase       )

        # Send to model
        _lowercase							      =model(tuple_input[:-1]       )[0]

        self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )





     def      __A					(self       )     ->   List[Any]:
      (
          (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    
      )							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )





     def      __A					(self       )     ->   int:
      (
          (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    
      )							      =self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
       _lowercase							      =type
       self.model_tester.create_and_check_model(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )





     def      __A					(self       )     ->   Dict:
      (
          (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    
      )							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(
          UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )





     def      __A					(self       )     ->   Tuple:
      (
          (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    
      )							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(
          UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )





     def      __A					(self       )     ->   Any:
      (
          (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    
      )							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(
          UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )





     @slow
     def      __A					(self       )     ->   Optional[Any]:
      for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _lowercase							      =TFLayoutLMvaModel.from_pretrained(UpperCAmelCase       )
       self.assertIsNotNone(UpperCAmelCase       )



def 				UpperCAmelCase_					(							)     ->			List[Any]:







 """simple docstring"""


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







@require_tf
class       lowerCamelCase__   (       unittest.TestCase):





     @cached_property
     def      __A					(self       )     ->   Any:
      return LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase       ) if is_vision_available() else None





     @slow
     def      __A					(self       )     ->   Any:
      _lowercase							      =TFLayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''       )

      _lowercase							      =self.default_image_processor
      _lowercase							      =prepare_img()
      _lowercase							      =image_processor(images=UpperCAmelCase		,     return_tensors='''tf'''       ).pixel_values

      _lowercase							      =tf.constant([[1, 2]]       )
      _lowercase							      =tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]       )		,     axis=0       )

      # forward pass
      _lowercase							      =model(input_ids=UpperCAmelCase		,     bbox=UpperCAmelCase		,     pixel_values=UpperCAmelCase		,     training=UpperCAmelCase       )

      # verify the logits
      _lowercase							      =(1, 1_9_9, 7_6_8)
      self.assertEqual(outputs.last_hidden_state.shape		,     UpperCAmelCase       )

      _lowercase							      =tf.constant(
          [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]       )

      self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]		,     UpperCAmelCase		,     atol=1e-4       )       )





import requests
from bsa import BeautifulSoup
def 				UpperCAmelCase_					(							__snake_case = "https://www.worldometers.info/coronavirus"						)     ->			dict:







 """simple docstring"""


 _lowercase							      =BeautifulSoup(requests.get(__snake_case						).text     ,						'''html.parser'''						)
 _lowercase							      =soup.findAll('''h1'''						)
 _lowercase							      =soup.findAll('''div'''     ,						{'''class''': '''maincounter-number'''}						)
 keys += soup.findAll('''span'''     ,						{'''class''': '''panel-title'''}						)
 values += soup.findAll('''div'''     ,						{'''class''': '''number-table-main'''}						)
 return {key.text.strip(): value.text.strip() for key, value in zip(__snake_case     ,						__snake_case						)}


if __name__ == "__main__":
  print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
  for key, value in world_covidaa_stats().items():
    print(f'''{key}\n{value}\n''')

