




'''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						:




				'''simple docstring'''





				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase=1_3					,    _UpperCAmelCase=7					,    _UpperCAmelCase=True					,    _UpperCAmelCase=True					,    _UpperCAmelCase=True					,    _UpperCAmelCase=True					,    _UpperCAmelCase=9_9					,    _UpperCAmelCase=3_2					,    _UpperCAmelCase=5					,    _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=3					,    _UpperCAmelCase=None					,    _UpperCAmelCase=2_0					,    _UpperCAmelCase=3_0					,    _UpperCAmelCase=2					,    _UpperCAmelCase=2					,    _UpperCAmelCase=1_0					,    )      ->			Optional[Any]:
							__UpperCamelCase :     Optional[int]										=  parent
							__UpperCamelCase :     Any										=  batch_size
							__UpperCamelCase :     Union[str, Any]										=  seq_length
							__UpperCamelCase :     List[str]										=  is_training
							__UpperCamelCase :     List[str]										=  use_input_mask
							__UpperCamelCase :     Tuple										=  use_token_type_ids
							__UpperCamelCase :     Dict										=  use_labels
							__UpperCamelCase :     str										=  vocab_size
							__UpperCamelCase :     Optional[Any]										=  hidden_size
							__UpperCamelCase :     Tuple										=  num_hidden_layers
							__UpperCamelCase :     Union[str, Any]										=  num_attention_heads
							__UpperCamelCase :     List[str]										=  intermediate_size
							__UpperCamelCase :     Dict										=  hidden_act
							__UpperCamelCase :     List[Any]										=  hidden_dropout_prob
							__UpperCamelCase :     Tuple										=  attention_probs_dropout_prob
							__UpperCamelCase :     Optional[int]										=  max_position_embeddings
							__UpperCamelCase :     int										=  type_vocab_size
							__UpperCamelCase :     List[str]										=  type_sequence_label_size
							__UpperCamelCase :     Any										=  initializer_range
							__UpperCamelCase :     List[Any]										=  num_labels
							__UpperCamelCase :     int										=  scope
							__UpperCamelCase :     Optional[int]										=  max_xpath_tag_unit_embeddings
							__UpperCamelCase :     Union[str, Any]										=  max_xpath_subs_unit_embeddings
							__UpperCamelCase :     str										=  tag_pad_id
							__UpperCamelCase :     List[str]										=  subs_pad_id
							__UpperCamelCase :     Any										=  max_depth
				def 			a_			(self     )      ->			List[str]:
							__UpperCamelCase :     int										=  ids_tensor([self.batch_size, self.seq_length]					,    self.vocab_size     )

							__UpperCamelCase :     Any										=  ids_tensor(
							    [self.batch_size, self.seq_length, self.max_depth]					,    self.max_xpath_tag_unit_embeddings     )

							__UpperCamelCase :     int										=  ids_tensor(
							    [self.batch_size, self.seq_length, self.max_depth]					,    self.max_xpath_subs_unit_embeddings     )

							__UpperCamelCase :     List[Any]										=  None
							if self.use_input_mask:
										__UpperCamelCase :     Optional[int]										=  ids_tensor([self.batch_size, self.seq_length]					,    vocab_size=2     )

							__UpperCamelCase :     Any										=  None
							if self.use_token_type_ids:
										__UpperCamelCase :     Optional[int]										=  ids_tensor([self.batch_size, self.seq_length]					,    self.type_vocab_size     )

							__UpperCamelCase :     Dict										=  None
							__UpperCamelCase :     Tuple										=  None
							if self.use_labels:
										__UpperCamelCase :     Union[str, Any]										=  ids_tensor([self.batch_size]					,    self.type_sequence_label_size     )
										__UpperCamelCase :     Optional[int]										=  ids_tensor([self.batch_size, self.seq_length]					,    self.num_labels     )

							__UpperCamelCase :     Any										=  self.get_config()

							return (
							    config,
							    input_ids,
							    xpath_tags_seq,
							    xpath_subs_seq,
							    token_type_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							)
				def 			a_			(self     )      ->			Optional[int]:
							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 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    )      ->			Union[str, Any]:
							__UpperCamelCase :     Optional[Any]										=  MarkupLMModel(config=_UpperCAmelCase     )
							model.to(_UpperCAmelCase     )
							model.eval()
							print("Configs:"					,    model.config.tag_pad_id					,    model.config.subs_pad_id     )
							__UpperCamelCase :     Optional[int]										=  model(_UpperCAmelCase					,    attention_mask=_UpperCAmelCase					,    token_type_ids=_UpperCAmelCase     )
							__UpperCamelCase :     List[Any]										=  model(_UpperCAmelCase					,    token_type_ids=_UpperCAmelCase     )
							__UpperCamelCase :     Union[str, Any]										=  model(_UpperCAmelCase     )
							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 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    )      ->			Optional[Any]:
							__UpperCamelCase :     Optional[Any]										=  self.num_labels
							__UpperCamelCase :     Dict										=  MarkupLMForSequenceClassification(_UpperCAmelCase     )
							model.to(_UpperCAmelCase     )
							model.eval()
							__UpperCamelCase :     Any										=  model(
							    _UpperCAmelCase					,    xpath_tags_seq=_UpperCAmelCase					,    xpath_subs_seq=_UpperCAmelCase					,    attention_mask=_UpperCAmelCase					,    token_type_ids=_UpperCAmelCase					,    labels=_UpperCAmelCase					,    )
							self.parent.assertEqual(result.logits.shape					,    (self.batch_size, self.num_labels)     )
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    )      ->			Union[str, Any]:
							__UpperCamelCase :     Optional[Any]										=  self.num_labels
							__UpperCamelCase :     Any										=  MarkupLMForTokenClassification(config=_UpperCAmelCase     )
							model.to(_UpperCAmelCase     )
							model.eval()
							__UpperCamelCase :     List[str]										=  model(
							    _UpperCAmelCase					,    xpath_tags_seq=_UpperCAmelCase					,    xpath_subs_seq=_UpperCAmelCase					,    attention_mask=_UpperCAmelCase					,    token_type_ids=_UpperCAmelCase					,    labels=_UpperCAmelCase					,    )
							self.parent.assertEqual(result.logits.shape					,    (self.batch_size, self.seq_length, self.num_labels)     )
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    )      ->			Union[str, Any]:
							__UpperCamelCase :     List[Any]										=  MarkupLMForQuestionAnswering(config=_UpperCAmelCase     )
							model.to(_UpperCAmelCase     )
							model.eval()
							__UpperCamelCase :     Union[str, Any]										=  model(
							    _UpperCAmelCase					,    xpath_tags_seq=_UpperCAmelCase					,    xpath_subs_seq=_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 			a_			(self     )      ->			Union[str, Any]:
							__UpperCamelCase :     Optional[Any]										=  self.prepare_config_and_inputs()
							(
							    (
							    __UpperCamelCase
							)						,   (
							    __UpperCamelCase
							)						,   (
							    __UpperCamelCase
							)						,   (
							    __UpperCamelCase
							)						,   (
							    __UpperCamelCase
							)						,   (
							    __UpperCamelCase
							)						,   (
							    __UpperCamelCase
							)						,   (
							    __UpperCamelCase
							)						,   
							) :     int										=  config_and_inputs
							__UpperCamelCase :     List[str]										=  {
							    "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						(					SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				unittest.TestCase						):




				'''simple docstring'''





				A       =							(
				    (
				        MarkupLMModel,
				        MarkupLMForSequenceClassification,
				        MarkupLMForTokenClassification,
				        MarkupLMForQuestionAnswering,
				    )
				    if is_torch_available()
				    else None
				)
				A       =							(
				    {
				        "feature-extraction": MarkupLMModel,
				        "question-answering": MarkupLMForQuestionAnswering,
				        "text-classification": MarkupLMForSequenceClassification,
				        "token-classification": MarkupLMForTokenClassification,
				        "zero-shot": MarkupLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			Union[str, Any]:
							# ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
							# (batch of pretokenized examples).
							return True
				def 			a_			(self     )      ->			Optional[int]:
							__UpperCamelCase :     Tuple										=  MarkupLMModelTester(self     )
							__UpperCamelCase :     Any										=  ConfigTester(self					,    config_class=_UpperCAmelCase					,    hidden_size=3_7     )
				def 			a_			(self     )      ->			Any:
							self.config_tester.run_common_tests()
				def 			a_			(self     )      ->			Tuple:
							__UpperCamelCase :     str										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCAmelCase     )
				def 			a_			(self     )      ->			Tuple:
							__UpperCamelCase :     str										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase     )
				def 			a_			(self     )      ->			str:
							__UpperCamelCase :     Optional[int]										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase     )





				def 			a_			(self     )      ->			Optional[int]:
							__UpperCamelCase :     str										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase     )







def      __lowerCAmelCase   (       ):
			__UpperCamelCase :     Any										=  "\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						):




				'''simple docstring'''





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

							return MarkupLMProcessor(_UpperCAmelCase					,    _UpperCAmelCase     )





				@slow
				def 			a_			(self     )      ->			Any:
							__UpperCamelCase :     int										=  MarkupLMModel.from_pretrained("microsoft/markuplm-base"     ).to(_UpperCAmelCase     )

							__UpperCamelCase :     str										=  self.default_processor

							__UpperCamelCase :     Tuple										=  processor(prepare_html_string()					,    return_tensors="pt"     )
							__UpperCamelCase :     int										=  inputs.to(_UpperCAmelCase     )

							# forward pass
							with torch.no_grad():
										__UpperCamelCase :     Dict										=  model(**_UpperCAmelCase     )

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

							__UpperCamelCase :     Union[str, Any]										=  torch.tensor(
							    [[0.0_675, -0.0_052, 0.5_001], [-0.2_281, 0.0_802, 0.2_192], [-0.0_583, -0.3_311, 0.1_185]]     ).to(_UpperCAmelCase     )

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









'''simple docstring'''






import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class    A						(					SCREAMING_SNAKE_CASE__     ,				unittest.TestCase						):




				'''simple docstring'''





				A       =							BertweetTokenizer
				A       =							False
				def 			a_			(self     )      ->			Any:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							__UpperCamelCase :     str										=  ["I", "m", "V@@", "R@@", "r", "e@@"]
							__UpperCamelCase :     int										=  dict(zip(_UpperCAmelCase					,    range(len(_UpperCAmelCase     )     )     )     )
							__UpperCamelCase :     List[Any]										=  ["#version: 0.2", "a m</w>"]
							__UpperCamelCase :     str										=  {"unk_token": "<unk>"}

							__UpperCamelCase :     Any										=  os.path.join(self.tmpdirname					,    VOCAB_FILES_NAMES["vocab_file"]     )
							__UpperCamelCase :     Tuple										=  os.path.join(self.tmpdirname					,    VOCAB_FILES_NAMES["merges_file"]     )
							with open(self.vocab_file					,    "w"					,    encoding="utf-8"     ) as fp:
										for token in vocab_tokens:
													fp.write(f"{token} {vocab_tokens[token]}\n"     )
							with open(self.merges_file					,    "w"					,    encoding="utf-8"     ) as fp:
										fp.write("\n".join(_UpperCAmelCase     )     )
				def 			a_			(self					,    **_UpperCAmelCase     )      ->			str:
							kwargs.update(self.special_tokens_map     )
							return BertweetTokenizer.from_pretrained(self.tmpdirname					,    **_UpperCAmelCase     )
				def 			a_			(self					,    _UpperCAmelCase     )      ->			Dict:
							__UpperCamelCase :     Dict										=  "I am VinAI Research"
							__UpperCamelCase :     Any										=  "I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
							return input_text, output_text





				def 			a_			(self     )      ->			str:
							__UpperCamelCase :     Tuple										=  BertweetTokenizer(self.vocab_file					,    self.merges_file					,    **self.special_tokens_map     )
							__UpperCamelCase :     Union[str, Any]										=  "I am VinAI Research"
							__UpperCamelCase :     List[str]										=  "I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
							__UpperCamelCase :     Optional[int]										=  tokenizer.tokenize(_UpperCAmelCase     )
							self.assertListEqual(_UpperCAmelCase					,    _UpperCAmelCase     )

							__UpperCamelCase :     Optional[Any]										=  tokens + [tokenizer.unk_token]

							__UpperCamelCase :     Any										=  [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )					,    _UpperCAmelCase     )



