






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_		:



      """simple docstring"""
      def __init__(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_=13					,       SCREAMING_SNAKE_CASE_=7					,       SCREAMING_SNAKE_CASE_=True					,       SCREAMING_SNAKE_CASE_=True					,       SCREAMING_SNAKE_CASE_=True					,       SCREAMING_SNAKE_CASE_=True					,       SCREAMING_SNAKE_CASE_=99					,       SCREAMING_SNAKE_CASE_=32					,       SCREAMING_SNAKE_CASE_=5					,       SCREAMING_SNAKE_CASE_=4					,       SCREAMING_SNAKE_CASE_=37					,       SCREAMING_SNAKE_CASE_="gelu"					,       SCREAMING_SNAKE_CASE_=0.1					,       SCREAMING_SNAKE_CASE_=0.1					,       SCREAMING_SNAKE_CASE_=512					,       SCREAMING_SNAKE_CASE_=16					,       SCREAMING_SNAKE_CASE_=2					,       SCREAMING_SNAKE_CASE_=0.02					,       SCREAMING_SNAKE_CASE_=3					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=20					,       SCREAMING_SNAKE_CASE_=30					,       SCREAMING_SNAKE_CASE_=2					,       SCREAMING_SNAKE_CASE_=2					,       SCREAMING_SNAKE_CASE_=10					,       )       ->   Optional[Any]:
          UpperCamelCase :Union[str, Any]										=     parent
          UpperCamelCase :Dict										=     batch_size
          UpperCamelCase :Optional[int]										=     seq_length
          UpperCamelCase :int										=     is_training
          UpperCamelCase :Dict										=     use_input_mask
          UpperCamelCase :Dict										=     use_token_type_ids
          UpperCamelCase :List[str]										=     use_labels
          UpperCamelCase :Any										=     vocab_size
          UpperCamelCase :str										=     hidden_size
          UpperCamelCase :int										=     num_hidden_layers
          UpperCamelCase :str										=     num_attention_heads
          UpperCamelCase :List[Any]										=     intermediate_size
          UpperCamelCase :Union[str, Any]										=     hidden_act
          UpperCamelCase :Dict										=     hidden_dropout_prob
          UpperCamelCase :Optional[int]										=     attention_probs_dropout_prob
          UpperCamelCase :Tuple										=     max_position_embeddings
          UpperCamelCase :List[Any]										=     type_vocab_size
          UpperCamelCase :Tuple										=     type_sequence_label_size
          UpperCamelCase :Any										=     initializer_range
          UpperCamelCase :List[Any]										=     num_labels
          UpperCamelCase :List[str]										=     scope
          UpperCamelCase :Optional[int]										=     max_xpath_tag_unit_embeddings
          UpperCamelCase :List[Any]										=     max_xpath_subs_unit_embeddings
          UpperCamelCase :List[Any]										=     tag_pad_id
          UpperCamelCase :str										=     subs_pad_id
          UpperCamelCase :Dict										=     max_depth
      def 					UpperCAmelCase		(       self				)       ->   Dict:
          UpperCamelCase :Tuple										=     ids_tensor([self.batch_size, self.seq_length]					,       self.vocab_size				)

          UpperCamelCase :Dict										=     ids_tensor(
              [self.batch_size, self.seq_length, self.max_depth]					,       self.max_xpath_tag_unit_embeddings				)

          UpperCamelCase :List[str]										=     ids_tensor(
              [self.batch_size, self.seq_length, self.max_depth]					,       self.max_xpath_subs_unit_embeddings				)

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

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

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

          UpperCamelCase :Tuple										=     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				)       ->   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 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       )       ->   str:
          UpperCamelCase :Optional[Any]										=     MarkupLMModel(config=SCREAMING_SNAKE_CASE_				)
          model.to(SCREAMING_SNAKE_CASE_				)
          model.eval()
          print('''Configs:'''					,       model.config.tag_pad_id					,       model.config.subs_pad_id				)
          UpperCamelCase :List[str]										=     model(SCREAMING_SNAKE_CASE_					,       attention_mask=SCREAMING_SNAKE_CASE_					,       token_type_ids=SCREAMING_SNAKE_CASE_				)
          UpperCamelCase :int										=     model(SCREAMING_SNAKE_CASE_					,       token_type_ids=SCREAMING_SNAKE_CASE_				)
          UpperCamelCase :Optional[int]										=     model(SCREAMING_SNAKE_CASE_				)
          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					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       )       ->   Tuple:
          UpperCamelCase :Dict										=     self.num_labels
          UpperCamelCase :List[str]										=     MarkupLMForSequenceClassification(SCREAMING_SNAKE_CASE_				)
          model.to(SCREAMING_SNAKE_CASE_				)
          model.eval()
          UpperCamelCase :Optional[int]										=     model(
              SCREAMING_SNAKE_CASE_					,       xpath_tags_seq=SCREAMING_SNAKE_CASE_					,       xpath_subs_seq=SCREAMING_SNAKE_CASE_					,       attention_mask=SCREAMING_SNAKE_CASE_					,       token_type_ids=SCREAMING_SNAKE_CASE_					,       labels=SCREAMING_SNAKE_CASE_					,       )
          self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.num_labels)				)
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       )       ->   List[Any]:
          UpperCamelCase :str										=     self.num_labels
          UpperCamelCase :Any										=     MarkupLMForTokenClassification(config=SCREAMING_SNAKE_CASE_				)
          model.to(SCREAMING_SNAKE_CASE_				)
          model.eval()
          UpperCamelCase :Optional[int]										=     model(
              SCREAMING_SNAKE_CASE_					,       xpath_tags_seq=SCREAMING_SNAKE_CASE_					,       xpath_subs_seq=SCREAMING_SNAKE_CASE_					,       attention_mask=SCREAMING_SNAKE_CASE_					,       token_type_ids=SCREAMING_SNAKE_CASE_					,       labels=SCREAMING_SNAKE_CASE_					,       )
          self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.seq_length, self.num_labels)				)
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       )       ->   str:
          UpperCamelCase :Dict										=     MarkupLMForQuestionAnswering(config=SCREAMING_SNAKE_CASE_				)
          model.to(SCREAMING_SNAKE_CASE_				)
          model.eval()
          UpperCamelCase :str										=     model(
              SCREAMING_SNAKE_CASE_					,       xpath_tags_seq=SCREAMING_SNAKE_CASE_					,       xpath_subs_seq=SCREAMING_SNAKE_CASE_					,       attention_mask=SCREAMING_SNAKE_CASE_					,       token_type_ids=SCREAMING_SNAKE_CASE_					,       start_positions=SCREAMING_SNAKE_CASE_					,       end_positions=SCREAMING_SNAKE_CASE_					,       )
          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				)       ->   int:
          UpperCamelCase :List[Any]										=     self.prepare_config_and_inputs()
          (
              (
              UpperCamelCase
          )							,     (
              UpperCamelCase
          )							,     (
              UpperCamelCase
          )							,     (
              UpperCamelCase
          )							,     (
              UpperCamelCase
          )							,     (
              UpperCamelCase
          )							,     (
              UpperCamelCase
          )							,     (
              UpperCamelCase
          )							,     
          ) :Union[str, Any]										=     config_and_inputs
          UpperCamelCase :List[Any]										=     {
              '''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_		(      lowercase,			lowercase,			unittest.TestCase				):



      """simple docstring"""
      UpperCamelCase_       :      Union[str, Any]     =(
          (
              MarkupLMModel,
              MarkupLMForSequenceClassification,
              MarkupLMForTokenClassification,
              MarkupLMForQuestionAnswering,
          )
          if is_torch_available()
          else None
      )
      UpperCamelCase_       :      str     =(
          {
              'feature-extraction': MarkupLMModel,
              'question-answering': MarkupLMForQuestionAnswering,
              'text-classification': MarkupLMForSequenceClassification,
              'token-classification': MarkupLMForTokenClassification,
              'zero-shot': MarkupLMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   List[str]:
          # ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
          # (batch of pretokenized examples).
          return True
      def 					UpperCAmelCase		(       self				)       ->   int:
          UpperCamelCase :Dict										=     MarkupLMModelTester(self				)
          UpperCamelCase :Any										=     ConfigTester(self					,       config_class=SCREAMING_SNAKE_CASE_					,       hidden_size=37				)
      def 					UpperCAmelCase		(       self				)       ->   str:
          self.config_tester.run_common_tests()
      def 					UpperCAmelCase		(       self				)       ->   List[str]:
          UpperCamelCase :List[Any]										=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self				)       ->   Tuple:
          UpperCamelCase :Any										=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self				)       ->   Dict:
          UpperCamelCase :List[Any]										=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_				)




      def 					UpperCAmelCase		(       self				)       ->   Any:
          UpperCamelCase :Any										=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_				)







def 						_A     (       ):
    UpperCamelCase :Dict										=     '''
    <!DOCTYPE html>
    <html>
    <head>
    <title>Page Title</title>
    </head>
    <body>

    <h1>This is a Heading</h1>
    <p>This is a paragraph.</p>

    </body>
    </html>
    '''

    return html_string







@require_torch
class     UpperCAmelCase_		(      unittest.TestCase				):



      """simple docstring"""
      @cached_property
      def 					UpperCAmelCase		(       self				)       ->   List[Any]:
          # TODO use from_pretrained here
          UpperCamelCase :int										=     MarkupLMFeatureExtractor()
          UpperCamelCase :Any										=     MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''				)

          return MarkupLMProcessor(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)




      @slow
      def 					UpperCAmelCase		(       self				)       ->   Union[str, Any]:
          UpperCamelCase :int										=     MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''				).to(SCREAMING_SNAKE_CASE_				)

          UpperCamelCase :Any										=     self.default_processor

          UpperCamelCase :Union[str, Any]										=     processor(prepare_html_string()					,       return_tensors='''pt'''				)
          UpperCamelCase :Any										=     inputs.to(SCREAMING_SNAKE_CASE_				)

          # forward pass
          with torch.no_grad():
              UpperCamelCase :int										=     model(**SCREAMING_SNAKE_CASE_				)

          # verify the last hidden states
          UpperCamelCase :int										=     torch.Size([1, 14, 768]				)
          self.assertEqual(outputs.last_hidden_state.shape					,       SCREAMING_SNAKE_CASE_				)

          UpperCamelCase :List[str]										=     torch.tensor(
              [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]				).to(SCREAMING_SNAKE_CASE_				)

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













# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json







class     UpperCAmelCase_		(      lowercase				):



      """simple docstring"""
      @classmethod
      def 					UpperCAmelCase		(       cls				)       ->   List[Any]:
          return cls




      @slow
      @require_torch_gpu
      def 					UpperCAmelCase		(       self				)       ->   int:
          UpperCamelCase :int										=     self.get_auto_remove_tmp_dir()
          UpperCamelCase :List[str]										=     F'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

          # we want this test to run even if there is only one GPU, but if there are more we use them all
          UpperCamelCase :List[str]										=     get_gpu_count()
          UpperCamelCase :Optional[Any]										=     F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
          UpperCamelCase :List[Any]										=     [sys.executable] + distributed_args + args
          execute_subprocess_async(SCREAMING_SNAKE_CASE_					,       env=self.get_env()				)

          UpperCamelCase :Tuple										=     os.path.join(SCREAMING_SNAKE_CASE_					,       '''test_bleu.json'''				)
          UpperCamelCase :str										=     load_json(SCREAMING_SNAKE_CASE_				)
          # print(metrics)
          self.assertGreaterEqual(metrics['''bleu''']					,       25				)





