





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCAmelCase_   =   logging.get_logger(__name__)

lowerCAmelCase_   =   {
    """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json""",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}

class 				A    (__lowerCamelCase				):
      _SCREAMING_SNAKE_CASE					=							"""m2m_100"""
      _SCREAMING_SNAKE_CASE					=							["""past_key_values"""]
      _SCREAMING_SNAKE_CASE					=							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



      def __init__(					self     ,  lowercase_=12_8112     ,  lowercase_=1024     ,  lowercase_=12     ,  lowercase_=4096     ,  lowercase_=16     ,  lowercase_=12     ,  lowercase_=4096     ,  lowercase_=16     ,  lowercase_=0.05     ,  lowercase_=0.05     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_="relu"     ,  lowercase_=1024     ,  lowercase_=0.1     ,  lowercase_=0.1     ,  lowercase_=0.0     ,  lowercase_=0.02     ,  lowercase_=2     ,  lowercase_=True     ,  lowercase_=1     ,  lowercase_=0     ,  lowercase_=2     ,  **lowercase_     ,  )      ->   List[str]:



             '''simple docstring'''

             _snake_case      :							str     				=						vocab_size
             _snake_case      :							Tuple     				=						max_position_embeddings
             _snake_case      :							Tuple     				=						d_model
             _snake_case      :							str     				=						encoder_ffn_dim
             _snake_case      :							Optional[int]     				=						encoder_layers
             _snake_case      :							int     				=						encoder_attention_heads
             _snake_case      :							Optional[Any]     				=						decoder_ffn_dim
             _snake_case      :							Optional[Any]     				=						decoder_layers
             _snake_case      :							List[str]     				=						decoder_attention_heads
             _snake_case      :							int     				=						dropout
             _snake_case      :							Any     				=						attention_dropout
             _snake_case      :							Union[str, Any]     				=						activation_dropout
             _snake_case      :							List[Any]     				=						activation_function
             _snake_case      :							Tuple     				=						init_std
             _snake_case      :							Dict     				=						encoder_layerdrop
             _snake_case      :							List[str]     				=						decoder_layerdrop
             _snake_case      :							int     				=						use_cache
             _snake_case      :							List[str]     				=						encoder_layers
             _snake_case      :							int     				=						scale_embedding  # scale factor will be sqrt(d_model) if True

             super().__init__(
                 pad_token_id=_lowerCAmelCase     ,  bos_token_id=_lowerCAmelCase     ,  eos_token_id=_lowerCAmelCase     ,  is_encoder_decoder=_lowerCAmelCase     ,  decoder_start_token_id=_lowerCAmelCase     ,  **_lowerCAmelCase     ,  )


class 				A    (__lowerCamelCase				):



      @property
      def    __a (					self					)      ->   Optional[int]:



             '''simple docstring'''

             _snake_case      :							str     				=						OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]					)

             if self.use_past:
                    _snake_case      :							Optional[Any]     				=						{0: """batch"""}
                    _snake_case      :							int     				=						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
             else:
                    _snake_case      :							Optional[Any]     				=						{0: """batch""", 1: """decoder_sequence"""}
                    _snake_case      :							List[Any]     				=						{0: """batch""", 1: """decoder_sequence"""}

             if self.use_past:
                    self.fill_with_past_key_values_(_lowerCAmelCase     ,  direction='''inputs'''					)
             return common_inputs



      def    __a (					self     ,  lowercase_     ,  lowercase_ = -1     ,  lowercase_ = -1     ,  lowercase_ = False     ,  lowercase_ = None     ,  )      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						compute_effective_axis_dimension(
                 _lowerCAmelCase     ,  fixed_dimension=OnnxConfig.default_fixed_batch     ,  num_token_to_add=0					)

             # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
             _snake_case      :							List[str]     				=						tokenizer.num_special_tokens_to_add(_lowerCAmelCase					)
             _snake_case      :							int     				=						compute_effective_axis_dimension(
                 _lowerCAmelCase     ,  fixed_dimension=OnnxConfig.default_fixed_sequence     ,  num_token_to_add=_lowerCAmelCase					)

             # Generate dummy inputs according to compute batch and sequence
             _snake_case      :							Any     				=						[""" """.join([tokenizer.unk_token]					) * seq_length] * batch_size
             _snake_case      :							Tuple     				=						dict(tokenizer(_lowerCAmelCase     ,  return_tensors=_lowerCAmelCase					)					)
             return common_inputs



      def    __a (					self     ,  lowercase_     ,  lowercase_ = -1     ,  lowercase_ = -1     ,  lowercase_ = False     ,  lowercase_ = None     ,  )      ->   Any:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 _lowerCAmelCase     ,  _lowerCAmelCase     ,  _lowerCAmelCase     ,  _lowerCAmelCase     ,  _lowerCAmelCase					)

             # Generate decoder inputs
             _snake_case      :							str     				=						seq_length if not self.use_past else 1
             _snake_case      :							Optional[Any]     				=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 _lowerCAmelCase     ,  _lowerCAmelCase     ,  _lowerCAmelCase     ,  _lowerCAmelCase     ,  _lowerCAmelCase					)
             _snake_case      :							Dict     				=						{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
             _snake_case      :							List[str]     				=						dict(**_lowerCAmelCase     ,  **_lowerCAmelCase					)

             if self.use_past:
                    if not is_torch_available():
                           raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
                    else:
                           import torch
                    _snake_case      :							Union[str, Any]     				=						common_inputs["""input_ids"""].shape
                    _snake_case      :							Union[str, Any]     				=						common_inputs["""decoder_input_ids"""].shape[1]
                    _snake_case      :							Any     				=						self.num_attention_heads
                    _snake_case      :							List[str]     				=						(
                        batch,
                        num_encoder_attention_heads,
                        encoder_seq_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )
                    _snake_case      :							Tuple     				=						decoder_seq_length + 3
                    _snake_case      :							List[Any]     				=						(
                        batch,
                        num_decoder_attention_heads,
                        decoder_past_length,
                        self._config.hidden_size // num_decoder_attention_heads,
                    )

                    _snake_case      :							Tuple     				=						torch.cat(
                        [common_inputs['''decoder_attention_mask'''], torch.ones(_lowerCAmelCase     ,  _lowerCAmelCase					)]     ,  dim=1					)

                    _snake_case      :							str     				=						[]
                    # If the number of encoder and decoder layers are present in the model configuration, both are considered
                    _snake_case      :							Any     				=						self.num_layers
                    _snake_case      :							Tuple     				=						min(_lowerCAmelCase     ,  _lowerCAmelCase					)
                    _snake_case      :							str     				=						max(_lowerCAmelCase     ,  _lowerCAmelCase					) - min_num_layers
                    _snake_case      :							List[Any]     				=						"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

                    for _ in range(_lowerCAmelCase					):
                           common_inputs["past_key_values"].append(
                               (
                                   torch.zeros(_lowerCAmelCase					),
                                   torch.zeros(_lowerCAmelCase					),
                                   torch.zeros(_lowerCAmelCase					),
                                   torch.zeros(_lowerCAmelCase					),
                               )					)
                    # TODO: test this.
                    _snake_case      :							Union[str, Any]     				=						encoder_shape if remaining_side_name == """encoder""" else decoder_shape
                    for _ in range(_lowerCAmelCase     ,  _lowerCAmelCase					):
                           common_inputs["past_key_values"].append((torch.zeros(_lowerCAmelCase					), torch.zeros(_lowerCAmelCase					))					)
             return common_inputs
      _SCREAMING_SNAKE_CASE					=							_generate_dummy_inputs_for_default_and_seqaseq_lm













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     ,  lowercase_     ,  lowercase_=13     ,  lowercase_=7     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_=99     ,  lowercase_=32     ,  lowercase_=5     ,  lowercase_=4     ,  lowercase_=37     ,  lowercase_="gelu"     ,  lowercase_=0.1     ,  lowercase_=0.1     ,  lowercase_=512     ,  lowercase_=16     ,  lowercase_=2     ,  lowercase_=0.02     ,  lowercase_=3     ,  lowercase_=None     ,  lowercase_=20     ,  lowercase_=30     ,  lowercase_=2     ,  lowercase_=2     ,  lowercase_=10     ,  )      ->   Optional[int]:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						parent
             _snake_case      :							List[str]     				=						batch_size
             _snake_case      :							str     				=						seq_length
             _snake_case      :							str     				=						is_training
             _snake_case      :							Optional[Any]     				=						use_input_mask
             _snake_case      :							Any     				=						use_token_type_ids
             _snake_case      :							str     				=						use_labels
             _snake_case      :							int     				=						vocab_size
             _snake_case      :							Tuple     				=						hidden_size
             _snake_case      :							str     				=						num_hidden_layers
             _snake_case      :							str     				=						num_attention_heads
             _snake_case      :							Optional[int]     				=						intermediate_size
             _snake_case      :							Tuple     				=						hidden_act
             _snake_case      :							Any     				=						hidden_dropout_prob
             _snake_case      :							Tuple     				=						attention_probs_dropout_prob
             _snake_case      :							str     				=						max_position_embeddings
             _snake_case      :							List[Any]     				=						type_vocab_size
             _snake_case      :							str     				=						type_sequence_label_size
             _snake_case      :							Any     				=						initializer_range
             _snake_case      :							Optional[Any]     				=						num_labels
             _snake_case      :							Union[str, Any]     				=						scope
             _snake_case      :							int     				=						max_xpath_tag_unit_embeddings
             _snake_case      :							Any     				=						max_xpath_subs_unit_embeddings
             _snake_case      :							Union[str, Any]     				=						tag_pad_id
             _snake_case      :							Union[str, Any]     				=						subs_pad_id
             _snake_case      :							int     				=						max_depth



      def    __a (					self					)      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)

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

             _snake_case      :							Tuple     				=						ids_tensor(
                 [self.batch_size, self.seq_length, self.max_depth]     ,  self.max_xpath_subs_unit_embeddings					)

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

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

             _snake_case      :							List[Any]     				=						None
             _snake_case      :							int     				=						None
             if self.use_labels:
                    _snake_case      :							Optional[int]     				=						ids_tensor([self.batch_size]     ,  self.type_sequence_label_size					)
                    _snake_case      :							Optional[int]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.num_labels					)

             _snake_case      :							Tuple     				=						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					)      ->   Tuple:



             '''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    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						MarkupLMModel(config=lowercase_					)
             model.to(lowercase_					)
             model.eval()
             print('''Configs:'''     ,  model.config.tag_pad_id     ,  model.config.subs_pad_id					)
             _snake_case      :							int     				=						model(lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_					)
             _snake_case      :							Dict     				=						model(lowercase_     ,  token_type_ids=lowercase_					)
             _snake_case      :							str     				=						model(lowercase_					)
             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     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Any:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						self.num_labels
             _snake_case      :							List[str]     				=						MarkupLMForSequenceClassification(lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _snake_case      :							Optional[int]     				=						model(
                 lowercase_     ,  xpath_tags_seq=lowercase_     ,  xpath_subs_seq=lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_     ,  labels=lowercase_     ,  )
             self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels)					)



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Optional[Any]:



             '''simple docstring'''

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



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						MarkupLMForQuestionAnswering(config=lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _snake_case      :							Optional[int]     				=						model(
                 lowercase_     ,  xpath_tags_seq=lowercase_     ,  xpath_subs_seq=lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_     ,  start_positions=lowercase_     ,  end_positions=lowercase_     ,  )
             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					)      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.prepare_config_and_inputs()
             (
                 (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    (
                 _snake_case
             )				,    
             )      :							Tuple     				=						config_and_inputs
             _snake_case      :							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    (__UpperCAmelCase       ,__UpperCAmelCase       ,unittest.TestCase				):
      _SCREAMING_SNAKE_CASE					=							(
          (
              MarkupLMModel,
              MarkupLMForSequenceClassification,
              MarkupLMForTokenClassification,
              MarkupLMForQuestionAnswering,
          )
          if is_torch_available()
          else None
      )
      _SCREAMING_SNAKE_CASE					=							(
          {
              """feature-extraction""": MarkupLMModel,
              """question-answering""": MarkupLMForQuestionAnswering,
              """text-classification""": MarkupLMForSequenceClassification,
              """token-classification""": MarkupLMForTokenClassification,
              """zero-shot""": MarkupLMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   List[str]:



             '''simple docstring'''

             return True



      def    __a (					self					)      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							Any     				=						MarkupLMModelTester(self					)
             _snake_case      :							Dict     				=						ConfigTester(self     ,  config_class=lowercase_     ,  hidden_size=37					)



      def    __a (					self					)      ->   Optional[int]:



             '''simple docstring'''

             self.config_tester.run_common_tests()



      def    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*lowercase_					)



      def    __a (					self					)      ->   Optional[Any]:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*lowercase_					)



      def    __a (					self					)      ->   Any:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*lowercase_					)



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*lowercase_					)




def 						A_			(  )			->						Any:
       _snake_case      :							Tuple     				=						'''
    <!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 				A    (unittest.TestCase				):



      @cached_property
      def    __a (					self					)      ->   int:



             '''simple docstring'''

             _snake_case      :							List[str]     				=						MarkupLMFeatureExtractor()
             _snake_case      :							Any     				=						MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''					)

             return MarkupLMProcessor(lowercase_     ,  lowercase_					)



      @slow
      def    __a (					self					)      ->   Optional[int]:



             '''simple docstring'''

             _snake_case      :							Any     				=						MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''					).to(lowercase_					)

             _snake_case      :							Tuple     				=						self.default_processor

             _snake_case      :							Union[str, Any]     				=						processor(prepare_html_string()     ,  return_tensors='''pt'''					)
             _snake_case      :							List[Any]     				=						inputs.to(lowercase_					)

             # forward pass
             with torch.no_grad():
                    _snake_case      :							List[str]     				=						model(**lowercase_					)

             # verify the last hidden states
             _snake_case      :							Union[str, Any]     				=						torch.Size([1, 14, 768]					)
             self.assertEqual(outputs.last_hidden_state.shape     ,  lowercase_					)

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

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






