



'''simple docstring'''



import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
	import torch

	from transformers import (
	    MarkupLMForQuestionAnswering,
	    MarkupLMForSequenceClassification,
	    MarkupLMForTokenClassification,
	    MarkupLMModel,
	)

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer






class        UpperCAmelCase__						:



					def __init__(     self      ,					UpperCamelCase      ,					UpperCamelCase=13      ,					UpperCamelCase=7      ,					UpperCamelCase=True      ,					UpperCamelCase=True      ,					UpperCamelCase=True      ,					UpperCamelCase=True      ,					UpperCamelCase=99      ,					UpperCamelCase=32      ,					UpperCamelCase=5      ,					UpperCamelCase=4      ,					UpperCamelCase=37      ,					UpperCamelCase="gelu"      ,					UpperCamelCase=0.1      ,					UpperCamelCase=0.1      ,					UpperCamelCase=512      ,					UpperCamelCase=16      ,					UpperCamelCase=2      ,					UpperCamelCase=0.02      ,					UpperCamelCase=3      ,					UpperCamelCase=None      ,					UpperCamelCase=20      ,					UpperCamelCase=30      ,					UpperCamelCase=2      ,					UpperCamelCase=2      ,					UpperCamelCase=10      ,					)  ->      List[str]:
						__lowerCAmelCase       				=	parent
						__lowerCAmelCase       				=	batch_size
						__lowerCAmelCase       				=	seq_length
						__lowerCAmelCase       				=	is_training
						__lowerCAmelCase       				=	use_input_mask
						__lowerCAmelCase       				=	use_token_type_ids
						__lowerCAmelCase       				=	use_labels
						__lowerCAmelCase       				=	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       				=	type_sequence_label_size
						__lowerCAmelCase       				=	initializer_range
						__lowerCAmelCase       				=	num_labels
						__lowerCAmelCase       				=	scope
						__lowerCAmelCase       				=	max_xpath_tag_unit_embeddings
						__lowerCAmelCase       				=	max_xpath_subs_unit_embeddings
						__lowerCAmelCase       				=	tag_pad_id
						__lowerCAmelCase       				=	subs_pad_id
						__lowerCAmelCase       				=	max_depth



					def 							UpperCAmelCase_			(     self  )  ->      Union[str, Any]:
						__lowerCAmelCase       				=	ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size  )

						__lowerCAmelCase       				=	ids_tensor(
						    [self.batch_size, self.seq_length, self.max_depth]      ,					self.max_xpath_tag_unit_embeddings  )

						__lowerCAmelCase       				=	ids_tensor(
						    [self.batch_size, self.seq_length, self.max_depth]      ,					self.max_xpath_subs_unit_embeddings  )

						__lowerCAmelCase       				=	None
						if self.use_input_mask:
							__lowerCAmelCase       				=	ids_tensor([self.batch_size, self.seq_length]      ,					vocab_size=2  )

						__lowerCAmelCase       				=	None
						if self.use_token_type_ids:
							__lowerCAmelCase       				=	ids_tensor([self.batch_size, self.seq_length]      ,					self.type_vocab_size  )

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

						__lowerCAmelCase       				=	self.get_config()

						return (
						    config,
						    input_ids,
						    xpath_tags_seq,
						    xpath_subs_seq,
						    token_type_ids,
						    input_mask,
						    sequence_labels,
						    token_labels,
						)



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						return MarkupLMConfig(
						    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      ,					max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings      ,					max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings      ,					tag_pad_id=self.tag_pad_id      ,					subs_pad_id=self.subs_pad_id      ,					max_depth=self.max_depth      ,					)



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					)  ->      Optional[int]:
						__lowerCAmelCase       				=	MarkupLMModel(config=a_  )
						model.to(a_  )
						model.eval()
						print("Configs:"      ,					model.config.tag_pad_id      ,					model.config.subs_pad_id  )
						__lowerCAmelCase       				=	model(a_      ,					attention_mask=a_      ,					token_type_ids=a_  )
						__lowerCAmelCase       				=	model(a_      ,					token_type_ids=a_  )
						__lowerCAmelCase       				=	model(a_  )
						self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size)  )
						self.parent.assertEqual(result.pooler_output.shape      ,					(self.batch_size, self.hidden_size)  )



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					)  ->      int:
						__lowerCAmelCase       				=	self.num_labels
						__lowerCAmelCase       				=	MarkupLMForSequenceClassification(a_  )
						model.to(a_  )
						model.eval()
						__lowerCAmelCase       				=	model(
						    a_      ,					xpath_tags_seq=a_      ,					xpath_subs_seq=a_      ,					attention_mask=a_      ,					token_type_ids=a_      ,					labels=a_      ,					)
						self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_labels)  )



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					)  ->      int:
						__lowerCAmelCase       				=	self.num_labels
						__lowerCAmelCase       				=	MarkupLMForTokenClassification(config=a_  )
						model.to(a_  )
						model.eval()
						__lowerCAmelCase       				=	model(
						    a_      ,					xpath_tags_seq=a_      ,					xpath_subs_seq=a_      ,					attention_mask=a_      ,					token_type_ids=a_      ,					labels=a_      ,					)
						self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.seq_length, self.num_labels)  )



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					)  ->      Dict:
						__lowerCAmelCase       				=	MarkupLMForQuestionAnswering(config=a_  )
						model.to(a_  )
						model.eval()
						__lowerCAmelCase       				=	model(
						    a_      ,					xpath_tags_seq=a_      ,					xpath_subs_seq=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 							UpperCAmelCase_			(     self  )  ->      List[str]:
						__lowerCAmelCase       				=	self.prepare_config_and_inputs()
						(
						    (
						    __lowerCAmelCase      
						)				,							(
						    __lowerCAmelCase      
						)				,							(
						    __lowerCAmelCase      
						)				,							(
						    __lowerCAmelCase      
						)				,							(
						    __lowerCAmelCase      
						)				,							(
						    __lowerCAmelCase      
						)				,							(
						    __lowerCAmelCase      
						)				,							(
						    __lowerCAmelCase      
						)				,							      
						)       				=	config_and_inputs
						__lowerCAmelCase       				=	{
						    "input_ids": input_ids,
						    "xpath_tags_seq": xpath_tags_seq,
						    "xpath_subs_seq": xpath_subs_seq,
						    "token_type_ids": token_type_ids,
						    "attention_mask": input_mask,
						}
						return config, inputs_dict






@require_torch
class        UpperCAmelCase__						(		UpperCamelCase__				,							UpperCamelCase__				,							unittest.TestCase			):
					a   :  str   =				(
					    (
					        MarkupLMModel,
					        MarkupLMForSequenceClassification,
					        MarkupLMForTokenClassification,
					        MarkupLMForQuestionAnswering,
					    )
					    if is_torch_available()
					    else None
					)
					a   :  List[Any]   =				(
					    {
					        """feature-extraction""": MarkupLMModel,
					        """question-answering""": MarkupLMForQuestionAnswering,
					        """text-classification""": MarkupLMForSequenceClassification,
					        """token-classification""": MarkupLMForTokenClassification,
					        """zero-shot""": MarkupLMForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      int:
						# ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
						# (batch of pretokenized examples).
						return True



					def 							UpperCAmelCase_			(     self  )  ->      List[str]:
						__lowerCAmelCase       				=	MarkupLMModelTester(self  )
						__lowerCAmelCase       				=	ConfigTester(self      ,					config_class=a_      ,					hidden_size=37  )



					def 							UpperCAmelCase_			(     self  )  ->      str:
						self.config_tester.run_common_tests()



					def 							UpperCAmelCase_			(     self  )  ->      List[Any]:
						__lowerCAmelCase       				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*a_  )



					def 							UpperCAmelCase_			(     self  )  ->      List[str]:
						__lowerCAmelCase       				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*a_  )



					def 							UpperCAmelCase_			(     self  )  ->      Union[str, Any]:
						__lowerCAmelCase       				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*a_  )



					def 							UpperCAmelCase_			(     self  )  ->      Union[str, Any]:
						__lowerCAmelCase       				=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*a_  )



def 	__lowerCAmelCase       (				):






	'''simple docstring'''




	__lowerCAmelCase       				=	"\n    <!DOCTYPE html>\n    <html>\n    <head>\n    <title>Page Title</title>\n    </head>\n    <body>\n\n    <h1>This is a Heading</h1>\n    <p>This is a paragraph.</p>\n\n    </body>\n    </html>\n    "

	return html_string






@require_torch
class        UpperCAmelCase__						(		unittest.TestCase			):



					@cached_property
					def 							UpperCAmelCase_			(     self  )  ->      int:
						# TODO use from_pretrained here
						__lowerCAmelCase       				=	MarkupLMFeatureExtractor()
						__lowerCAmelCase       				=	MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base"  )

						return MarkupLMProcessor(a_      ,					a_  )



					@slow
					def 							UpperCAmelCase_			(     self  )  ->      List[Any]:
						__lowerCAmelCase       				=	MarkupLMModel.from_pretrained("microsoft/markuplm-base"  ).to(a_  )

						__lowerCAmelCase       				=	self.default_processor

						__lowerCAmelCase       				=	processor(prepare_html_string()      ,					return_tensors="pt"  )
						__lowerCAmelCase       				=	inputs.to(a_  )

						# forward pass
						with torch.no_grad():
							__lowerCAmelCase       				=	model(**a_  )

						# verify the last hidden states
						__lowerCAmelCase       				=	torch.Size([1, 14, 768]  )
						self.assertEqual(outputs.last_hidden_state.shape      ,					a_  )

						__lowerCAmelCase       				=	torch.tensor(
						    [[0.06_75, -0.00_52, 0.50_01], [-0.22_81, 0.08_02, 0.21_92], [-0.05_83, -0.33_11, 0.11_85]]  ).to(a_  )

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




from collections import defaultdict

def       A_   (						__a  :					str		):

				"""simple docstring"""
				a__					   =		defaultdict(__a		)
				# Creating a dictionary containing count of each word
				for word in sentence.split():
								occurrence[word] += 1
				return occurrence


if __name__ == "__main__":
	for word, count in word_occurrence("""INPUT STRING""").items():
		print(f"""{word}: {count}""")

