






"""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     A_				:







				def __init__(				self				:    Any    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    List[Any]=1_3    ,     __lowerCamelCase				:    Tuple=7    ,     __lowerCamelCase				:    int=True    ,     __lowerCamelCase				:    Dict=True    ,     __lowerCamelCase				:    Union[str, Any]=True    ,     __lowerCamelCase				:    List[str]=True    ,     __lowerCamelCase				:    Union[str, Any]=9_9    ,     __lowerCamelCase				:    Optional[Any]=3_2    ,     __lowerCamelCase				:    int=5    ,     __lowerCamelCase				:    Union[str, Any]=4    ,     __lowerCamelCase				:    str=3_7    ,     __lowerCamelCase				:    Dict="gelu"    ,     __lowerCamelCase				:    Dict=0.1    ,     __lowerCamelCase				:    Optional[int]=0.1    ,     __lowerCamelCase				:    int=5_1_2    ,     __lowerCamelCase				:    Optional[int]=1_6    ,     __lowerCamelCase				:    List[Any]=2    ,     __lowerCamelCase				:    List[Any]=0.02    ,     __lowerCamelCase				:    List[Any]=3    ,     __lowerCamelCase				:    Tuple=None    ,     __lowerCamelCase				:    Tuple=2_0    ,     __lowerCamelCase				:    List[Any]=3_0    ,     __lowerCamelCase				:    Dict=2    ,     __lowerCamelCase				:    List[str]=2    ,     __lowerCamelCase				:    Union[str, Any]=1_0    ,     )					->       int:
										__magic_name__      =  parent
										__magic_name__      =  batch_size
										__magic_name__      =  seq_length
										__magic_name__      =  is_training
										__magic_name__      =  use_input_mask
										__magic_name__      =  use_token_type_ids
										__magic_name__      =  use_labels
										__magic_name__      =  vocab_size
										__magic_name__      =  hidden_size
										__magic_name__      =  num_hidden_layers
										__magic_name__      =  num_attention_heads
										__magic_name__      =  intermediate_size
										__magic_name__      =  hidden_act
										__magic_name__      =  hidden_dropout_prob
										__magic_name__      =  attention_probs_dropout_prob
										__magic_name__      =  max_position_embeddings
										__magic_name__      =  type_vocab_size
										__magic_name__      =  type_sequence_label_size
										__magic_name__      =  initializer_range
										__magic_name__      =  num_labels
										__magic_name__      =  scope
										__magic_name__      =  max_xpath_tag_unit_embeddings
										__magic_name__      =  max_xpath_subs_unit_embeddings
										__magic_name__      =  tag_pad_id
										__magic_name__      =  subs_pad_id
										__magic_name__      =  max_depth







				def      _snake_case       (				self				:    Optional[Any]						)					->       Optional[Any]:
										__magic_name__      =  ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size						)

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

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

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

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

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

										__magic_name__      =  self.get_config()

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







				def      _snake_case       (				self				:    List[Any]						)					->       List[str]:
										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      _snake_case       (				self				:    Dict    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Optional[Any]    ,     __lowerCamelCase				:    Optional[int]    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    Optional[Any]    ,     )					->       int:
										__magic_name__      =  MarkupLMModel(config=__lowerCamelCase						)
										model.to(__lowerCamelCase						)
										model.eval()
										print("Configs:"    ,     model.config.tag_pad_id    ,     model.config.subs_pad_id						)
										__magic_name__      =  model(__lowerCamelCase    ,     attention_mask=__lowerCamelCase    ,     token_type_ids=__lowerCamelCase						)
										__magic_name__      =  model(__lowerCamelCase    ,     token_type_ids=__lowerCamelCase						)
										__magic_name__      =  model(__lowerCamelCase						)
										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      _snake_case       (				self				:    Dict    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Optional[int]    ,     __lowerCamelCase				:    Optional[Any]    ,     __lowerCamelCase				:    Dict    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    str    ,     )					->       Union[str, Any]:
										__magic_name__      =  self.num_labels
										__magic_name__      =  MarkupLMForSequenceClassification(__lowerCamelCase						)
										model.to(__lowerCamelCase						)
										model.eval()
										__magic_name__      =  model(
										    __lowerCamelCase    ,     xpath_tags_seq=__lowerCamelCase    ,     xpath_subs_seq=__lowerCamelCase    ,     attention_mask=__lowerCamelCase    ,     token_type_ids=__lowerCamelCase    ,     labels=__lowerCamelCase    ,     )
										self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_labels)						)







				def      _snake_case       (				self				:    List[Any]    ,     __lowerCamelCase				:    Dict    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    List[str]    ,     __lowerCamelCase				:    List[Any]    ,     __lowerCamelCase				:    Tuple    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    Union[str, Any]    ,     )					->       Tuple:
										__magic_name__      =  self.num_labels
										__magic_name__      =  MarkupLMForTokenClassification(config=__lowerCamelCase						)
										model.to(__lowerCamelCase						)
										model.eval()
										__magic_name__      =  model(
										    __lowerCamelCase    ,     xpath_tags_seq=__lowerCamelCase    ,     xpath_subs_seq=__lowerCamelCase    ,     attention_mask=__lowerCamelCase    ,     token_type_ids=__lowerCamelCase    ,     labels=__lowerCamelCase    ,     )
										self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.num_labels)						)







				def      _snake_case       (				self				:    Optional[int]    ,     __lowerCamelCase				:    Dict    ,     __lowerCamelCase				:    List[str]    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    Dict    ,     __lowerCamelCase				:    int    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Tuple    ,     )					->       Any:
										__magic_name__      =  MarkupLMForQuestionAnswering(config=__lowerCamelCase						)
										model.to(__lowerCamelCase						)
										model.eval()
										__magic_name__      =  model(
										    __lowerCamelCase    ,     xpath_tags_seq=__lowerCamelCase    ,     xpath_subs_seq=__lowerCamelCase    ,     attention_mask=__lowerCamelCase    ,     token_type_ids=__lowerCamelCase    ,     start_positions=__lowerCamelCase    ,     end_positions=__lowerCamelCase    ,     )
										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      _snake_case       (				self				:    int						)					->       Any:
										__magic_name__      =  self.prepare_config_and_inputs()
										(
										    (
										    __magic_name__
										)   ,     (
										    __magic_name__
										)   ,     (
										    __magic_name__
										)   ,     (
										    __magic_name__
										)   ,     (
										    __magic_name__
										)   ,     (
										    __magic_name__
										)   ,     (
										    __magic_name__
										)   ,     (
										    __magic_name__
										)   ,     
										)      =  config_and_inputs
										__magic_name__      =  {
										    "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     A_				(   snake_case_       ,				snake_case_       ,				unittest.TestCase	):
				UpperCAmelCase__										=					(
				    (
				        MarkupLMModel,
				        MarkupLMForSequenceClassification,
				        MarkupLMForTokenClassification,
				        MarkupLMForQuestionAnswering,
				    )
				    if is_torch_available()
				    else None
				)
				UpperCAmelCase__										=					(
				    {
				        '''feature-extraction''': MarkupLMModel,
				        '''question-answering''': MarkupLMForQuestionAnswering,
				        '''text-classification''': MarkupLMForSequenceClassification,
				        '''token-classification''': MarkupLMForTokenClassification,
				        '''zero-shot''': MarkupLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)







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







				def      _snake_case       (				self				:    str						)					->       Any:
										__magic_name__      =  MarkupLMModelTester(self						)
										__magic_name__      =  ConfigTester(self    ,     config_class=__lowerCamelCase    ,     hidden_size=3_7						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Dict:
										self.config_tester.run_common_tests()







				def      _snake_case       (				self				:    str						)					->       List[Any]:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__lowerCamelCase						)







				def      _snake_case       (				self				:    str						)					->       str:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase						)







				def      _snake_case       (				self				:    Dict						)					->       List[Any]:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase						)







				def      _snake_case       (				self				:    Tuple						)					->       Optional[Any]:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase						)






def _lowerCAmelCase						(	):

						'''simple docstring'''






						__magic_name__      =  "\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     A_				(   unittest.TestCase	):







				@cached_property
				def      _snake_case       (				self				:    str						)					->       Union[str, Any]:
										# TODO use from_pretrained here
										__magic_name__      =  MarkupLMFeatureExtractor()
										__magic_name__      =  MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base"						)

										return MarkupLMProcessor(__lowerCamelCase    ,     __lowerCamelCase						)







				@slow
				def      _snake_case       (				self				:    Union[str, Any]						)					->       List[Any]:
										__magic_name__      =  MarkupLMModel.from_pretrained("microsoft/markuplm-base"						).to(__lowerCamelCase						)

										__magic_name__      =  self.default_processor

										__magic_name__      =  processor(prepare_html_string()    ,     return_tensors="pt"						)
										__magic_name__      =  inputs.to(__lowerCamelCase						)

										# forward pass
										with torch.no_grad():
																__magic_name__      =  model(**__lowerCamelCase						)

										# verify the last hidden states
										__magic_name__      =  torch.Size([1, 1_4, 7_6_8]						)
										self.assertEqual(outputs.last_hidden_state.shape    ,     __lowerCamelCase						)

										__magic_name__      =  torch.tensor(
										    [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]						).to(__lowerCamelCase						)

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











"""simple docstring"""

import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


lowercase           =							logging.get_logger(__name__)

class     A_				(   snake_case_	):







				def __init__(				self				:    str    ,     *__lowerCamelCase				:    List[Any]    ,     **__lowerCamelCase				:    List[str]						)					->       None:
										warnings.warn(
										    "The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use ConditionalDetrImageProcessor instead."    ,     __lowerCamelCase    ,     )
										super().__init__(*__lowerCamelCase    ,     **__lowerCamelCase						)



