





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 				__snake_case  :


    '''simple docstring'''







    def __init__(					self      ,							A_      ,							A_=13      ,							A_=7      ,							A_=True      ,							A_=True      ,							A_=True      ,							A_=True      ,							A_=99      ,							A_=32      ,							A_=5      ,							A_=4      ,							A_=37      ,							A_="gelu"      ,							A_=0.1      ,							A_=0.1      ,							A_=5_12      ,							A_=16      ,							A_=2      ,							A_=0.02      ,							A_=3      ,							A_=None      ,							A_=20      ,							A_=30      ,							A_=2      ,							A_=2      ,							A_=10      ,							):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= parent
        SCREAMING_SNAKE_CASE__       							= batch_size
        SCREAMING_SNAKE_CASE__       							= seq_length
        SCREAMING_SNAKE_CASE__       							= is_training
        SCREAMING_SNAKE_CASE__       							= use_input_mask
        SCREAMING_SNAKE_CASE__       							= use_token_type_ids
        SCREAMING_SNAKE_CASE__       							= use_labels
        SCREAMING_SNAKE_CASE__       							= vocab_size
        SCREAMING_SNAKE_CASE__       							= hidden_size
        SCREAMING_SNAKE_CASE__       							= num_hidden_layers
        SCREAMING_SNAKE_CASE__       							= num_attention_heads
        SCREAMING_SNAKE_CASE__       							= intermediate_size
        SCREAMING_SNAKE_CASE__       							= hidden_act
        SCREAMING_SNAKE_CASE__       							= hidden_dropout_prob
        SCREAMING_SNAKE_CASE__       							= attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE__       							= max_position_embeddings
        SCREAMING_SNAKE_CASE__       							= type_vocab_size
        SCREAMING_SNAKE_CASE__       							= type_sequence_label_size
        SCREAMING_SNAKE_CASE__       							= initializer_range
        SCREAMING_SNAKE_CASE__       							= num_labels
        SCREAMING_SNAKE_CASE__       							= scope
        SCREAMING_SNAKE_CASE__       							= max_xpath_tag_unit_embeddings
        SCREAMING_SNAKE_CASE__       							= max_xpath_subs_unit_embeddings
        SCREAMING_SNAKE_CASE__       							= tag_pad_id
        SCREAMING_SNAKE_CASE__       							= subs_pad_id
        SCREAMING_SNAKE_CASE__       							= max_depth



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= ids_tensor([self.batch_size, self.seq_length]      ,							self.vocab_size    )

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

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

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

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

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

        SCREAMING_SNAKE_CASE__       							= self.get_config()

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



    def    lowercase_				(					self    ):





        '''simple docstring'''

        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    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= MarkupLMModel(config=A_    )
        model.to(A_    )
        model.eval()
        print('''Configs:'''      ,							model.config.tag_pad_id      ,							model.config.subs_pad_id    )
        SCREAMING_SNAKE_CASE__       							= model(A_      ,							attention_mask=A_      ,							token_type_ids=A_    )
        SCREAMING_SNAKE_CASE__       							= model(A_      ,							token_type_ids=A_    )
        SCREAMING_SNAKE_CASE__       							= 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    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.num_labels
        SCREAMING_SNAKE_CASE__       							= MarkupLMForSequenceClassification(A_    )
        model.to(A_    )
        model.eval()
        SCREAMING_SNAKE_CASE__       							= 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    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.num_labels
        SCREAMING_SNAKE_CASE__       							= MarkupLMForTokenClassification(config=A_    )
        model.to(A_    )
        model.eval()
        SCREAMING_SNAKE_CASE__       							= 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    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= MarkupLMForQuestionAnswering(config=A_    )
        model.to(A_    )
        model.eval()
        SCREAMING_SNAKE_CASE__       							= 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    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.prepare_config_and_inputs()
        (
            SCREAMING_SNAKE_CASE__	
        )       							= config_and_inputs
        SCREAMING_SNAKE_CASE__       							= {
            '''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 				__snake_case  (     A_ ,    A_ ,    unittest.TestCase						):


    '''simple docstring'''







    lowerCamelCase__						:		str							=  (
        (
            MarkupLMModel,
            MarkupLMForSequenceClassification,
            MarkupLMForTokenClassification,
            MarkupLMForQuestionAnswering,
        )
        if is_torch_available()
        else None
    )
    lowerCamelCase__						:		Tuple							=  (
        {
            """feature-extraction""": MarkupLMModel,
            """question-answering""": MarkupLMForQuestionAnswering,
            """text-classification""": MarkupLMForSequenceClassification,
            """token-classification""": MarkupLMForTokenClassification,
            """zero-shot""": MarkupLMForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )



    def    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_    ):





        '''simple docstring'''

        return True



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= MarkupLMModelTester(self    )
        SCREAMING_SNAKE_CASE__       							= ConfigTester(self      ,							config_class=A_      ,							hidden_size=37    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        self.config_tester.run_common_tests()



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A_    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*A_    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*A_    )




    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*A_    )





def        __snake_case       (						)							->	Union[str, Any]:
    SCREAMING_SNAKE_CASE__       							= '''
    <!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 				__snake_case  (     unittest.TestCase						):


    '''simple docstring'''







    @cached_property
    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= MarkupLMFeatureExtractor()
        SCREAMING_SNAKE_CASE__       							= MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''    )

        return MarkupLMProcessor(A_      ,							A_    )




    @slow
    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''    ).to(A_    )

        SCREAMING_SNAKE_CASE__       							= self.default_processor

        SCREAMING_SNAKE_CASE__       							= processor(prepare_html_string()      ,							return_tensors='''pt'''    )
        SCREAMING_SNAKE_CASE__       							= inputs.to(A_    )

        # forward pass
        with torch.no_grad():
            SCREAMING_SNAKE_CASE__       							= model(**A_    )

        # verify the last hidden states
        SCREAMING_SNAKE_CASE__       							= torch.Size([1, 14, 7_68]    )
        self.assertEqual(outputs.last_hidden_state.shape      ,							A_    )

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

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







import io
import json
import subprocess


_A				:							str							= [
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

_A				:							int							= 8


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Optional[Any]:
    SCREAMING_SNAKE_CASE__       							= {}
    for src, tgt in pairs:
        SCREAMING_SNAKE_CASE__       							= f'''{src}-{tgt}'''
        SCREAMING_SNAKE_CASE__       							= f'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
        SCREAMING_SNAKE_CASE__       							= subprocess.run(lowerCAmelCase_   ,       stdout=subprocess.PIPE  ).stdout.decode('''utf-8'''  ).splitlines()
        SCREAMING_SNAKE_CASE__       							= f'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
        SCREAMING_SNAKE_CASE__       							= subprocess.run(lowerCAmelCase_   ,       stdout=subprocess.PIPE  ).stdout.decode('''utf-8'''  ).splitlines()
        SCREAMING_SNAKE_CASE__       							= {'''src''': src_lines[:n_objs], '''tgt''': tgt_lines[:n_objs]}
    return text


_A				:							Optional[Any]							= get_all_data(pairs, n_objs)
_A				:							Optional[Any]							= """./fsmt_val_data.json"""
with io.open(filename, """w""", encoding="""utf-8""") as f:
  _A				:							int							= json.dump(text, f, indent=2, ensure_ascii=False)
