import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

__lowerCamelCase     :					int				  =						logging.getLogger()
__lowerCamelCase     :					Tuple				  =						torch.cuda.is_available()
__lowerCamelCase     :					Optional[int]				  =						{
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 42,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1e-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Path					,	__UpperCamelCase       :			list      )				->   str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= """\n""".join(_lowerCAmelCase      )
  Path(_lowerCAmelCase      ).open("""w"""      ).writelines(_lowerCAmelCase      )


__lowerCamelCase     :					Dict				  =						[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__lowerCamelCase     :					Optional[Any]				  =						['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__lowerCamelCase     :					List[str]				  =						'''patrickvonplaten/t5-tiny-random'''
__lowerCamelCase     :					Optional[Any]				  =						'''sshleifer/t5-tinier-random'''
__lowerCamelCase     :					Optional[Any]				  =						'''sshleifer/bart-tiny-random'''
__lowerCamelCase     :					Optional[Any]				  =						'''sshleifer/tiny-mbart'''
__lowerCamelCase     :					List[str]				  =						'''sshleifer/tiny-marian-en-de'''
__lowerCamelCase     :					List[Any]				  =						'''stas/tiny-wmt19-en-de'''


__lowerCamelCase     :					Union[str, Any]				  =						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			List[Any]      )				->   Tuple:







  """simple docstring"""





  for split in ["train", "val", "test"]:
    _dump_articles(os.path.join(_lowerCAmelCase					,	f"""{split}.source"""      )					,	_lowerCAmelCase      )
    _dump_articles(os.path.join(_lowerCAmelCase					,	f"""{split}.target"""      )					,	_lowerCAmelCase      )
  return tmp_dir







class __snake_case							(		A_						):




   @classmethod
   def       __a		(       cls     :							Optional[Any] ):



     """simple docstring"""






     logging.disable(logging.CRITICAL )  # remove noisy download output from tracebacks
     return cls




   @slow
   @require_torch_gpu
   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= list_models()
     SCREAMING_SNAKE_CASE__     				= """sshleifer"""
     SCREAMING_SNAKE_CASE__     				= [x.modelId for x in model_list if x.modelId.startswith(lowerCamelCase_ )]
     SCREAMING_SNAKE_CASE__     				= ["""sshleifer/blenderbot-3B""", """sshleifer/blenderbot-90M"""]
     SCREAMING_SNAKE_CASE__     				= []
     for m in model_ids:
       if m in allowed_to_be_broken:
         continue
       try:
         AutoConfig.from_pretrained(lowerCamelCase_ )
       except Exception:
         failures.append(lowerCamelCase_ )
     assert not failures, f"""The following models could not be loaded through AutoConfig: {failures}"""




   def       __a		(       self     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= {"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
     self._test_distiller_cli(lowerCamelCase_ )




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= {
         """student_encoder_layers""": 2,
         """student_decoder_layers""": 1,
         """max_epochs""": 4,
         """val_check_interval""": 0.25,
         """alpha_hid""": 2.0,
         """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
     }
     SCREAMING_SNAKE_CASE__     				= self._test_distiller_cli(lowerCamelCase_			,      check_contents=lowerCamelCase_ )

     SCREAMING_SNAKE_CASE__     				= list(Path(model.output_dir ).glob("""*.ckpt""" ) )
     self.assertEqual(1			,      len(lowerCamelCase_ ) )
     SCREAMING_SNAKE_CASE__     				= list(Path(model.output_dir ).glob("""**/*.bin""" ) )
     self.assertEqual(len(lowerCamelCase_ )			,      2 )
     SCREAMING_SNAKE_CASE__     				= lmap(str.strip			,      Path(model.hparams.data_dir ).joinpath("""test.source""" ).open().readlines() )
     SCREAMING_SNAKE_CASE__     				= tempfile.mktemp()  # XXX: not being cleaned up
     generate_summaries_or_translations(lowerCamelCase_			,      lowerCamelCase_			,      str(model.output_dir / """best_tfmr""" ) )
     self.assertTrue(Path(lowerCamelCase_ ).exists() )

     SCREAMING_SNAKE_CASE__     				= self.get_auto_remove_tmp_dir()
     convert_pl_to_hf(ckpts[0]			,      transformer_ckpts[0].parent			,      lowerCamelCase_ )
     assert os.path.exists(os.path.join(lowerCamelCase_			,      """pytorch_model.bin""" ) )




   def       __a		(       self     :							Optional[int] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_ )
     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= model.dummy_inputs["""input_ids"""], model.dummy_inputs["""attention_mask"""]
     SCREAMING_SNAKE_CASE__     				= torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]			,      dtype=torch.long			,      device=model.device )
     SCREAMING_SNAKE_CASE__     				= target_ids[:, :-1].contiguous()  # Why this line?
     SCREAMING_SNAKE_CASE__     				= target_ids[:, 1:].clone()  # why clone?
     SCREAMING_SNAKE_CASE__     				= model(
         lowerCamelCase_			,      attention_mask=lowerCamelCase_			,      decoder_input_ids=lowerCamelCase_			,      labels=lowerCamelCase_			,      use_cache=lowerCamelCase_ ).loss

     SCREAMING_SNAKE_CASE__     				= model(lowerCamelCase_			,      attention_mask=lowerCamelCase_			,      decoder_input_ids=lowerCamelCase_			,      use_cache=lowerCamelCase_ ).logits

     SCREAMING_SNAKE_CASE__     				= nn.functional.log_softmax(lowerCamelCase_			,      dim=-1 )
     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= label_smoothed_nll_loss(
         lowerCamelCase_			,      lowerCamelCase_			,      0.1			,      ignore_index=model.config.pad_token_id )
     with self.assertRaises(lowerCamelCase_ ):
       # TODO: understand why this breaks
       self.assertEqual(lowerCamelCase_			,      lowerCamelCase_ )




   def       __a		(       self     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= {
         """student_encoder_layers""": 2,
         """student_decoder_layers""": 1,
         """num_train_epochs""": 4,
         """val_check_interval""": 0.25,
         """alpha_hid""": 2.0,
         """task""": """translation""",
         """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
         """tokenizer_name""": MBART_TINY,
         """teacher""": MBART_TINY,
         """src_lang""": """en_XX""",
         """tgt_lang""": """ro_RO""",
     }
     SCREAMING_SNAKE_CASE__     				= self._test_distiller_cli(lowerCamelCase_			,      check_contents=lowerCamelCase_ )
     assert model.model.config.model_type == "mbart"

     SCREAMING_SNAKE_CASE__     				= list(Path(model.output_dir ).glob("""*.ckpt""" ) )
     self.assertEqual(1			,      len(lowerCamelCase_ ) )
     SCREAMING_SNAKE_CASE__     				= list(Path(model.output_dir ).glob("""**/*.bin""" ) )
     SCREAMING_SNAKE_CASE__     				= list(Path(model.output_dir ).glob("""best_tfmr/*""" ) )
     assert len(lowerCamelCase_ ) > 2
     self.assertEqual(len(lowerCamelCase_ )			,      2 )




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= {
         """student_encoder_layers""": 1,
         """student_decoder_layers""": 1,
         """alpha_hid""": 2.0,
         """teacher""": T5_TINY,
         """model_name_or_path""": T5_TINY,
         """tokenizer_name""": T5_TINY,
     }
     self._test_distiller_cli(lowerCamelCase_ )




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= {
         """teacher""": T5_TINY,
         """student""": T5_TINIER,
         """model_name_or_path""": T5_TINIER,
         """tokenizer_name""": T5_TINIER,
     }
     self._test_distiller_cli(lowerCamelCase_ )




   def       __a		(       self     :							int			,      _lowercase     :							List[Any]			,      _lowercase     :							Union[str, Any]=True ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= {
         """label_smoothing""": 0.0,
         """early_stopping_patience""": -1,
         """train_batch_size""": 1,
         """eval_batch_size""": 2,
         """max_epochs""": 2,
         """alpha_mlm""": 0.2,
         """alpha_ce""": 0.8,
         """do_predict""": True,
         """model_name_or_path""": """sshleifer/tinier_bart""",
         """teacher""": CHEAP_ARGS["""model_name_or_path"""],
         """val_check_interval""": 0.5,
     }
     default_updates.update(lowerCamelCase_ )
     SCREAMING_SNAKE_CASE__     				= CHEAP_ARGS.copy()
     SCREAMING_SNAKE_CASE__     				= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
     SCREAMING_SNAKE_CASE__     				= self.get_auto_remove_tmp_dir()

     args_d.update(data_dir=lowerCamelCase_			,      output_dir=lowerCamelCase_			,      **lowerCamelCase_ )
     SCREAMING_SNAKE_CASE__     				= distill_main(argparse.Namespace(**lowerCamelCase_ ) )
     if not check_contents:
       return model
     SCREAMING_SNAKE_CASE__     				= os.listdir(lowerCamelCase_ )
     SCREAMING_SNAKE_CASE__     				= {os.path.basename(lowerCamelCase_ ) for p in contents}
     SCREAMING_SNAKE_CASE__     				= [p for p in contents if p.endswith("""ckpt""" )]
     assert len(lowerCamelCase_ ) > 0

     self.assertIn("""test_generations.txt"""			,      lowerCamelCase_ )
     self.assertIn("""test_results.txt"""			,      lowerCamelCase_ )

     SCREAMING_SNAKE_CASE__     				= load_json(model.metrics_save_path )
     SCREAMING_SNAKE_CASE__     				= metrics["""val"""][-1]
     self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]			,      0.01 )
     self.assertGreaterEqual(1.0			,      last_step_stats["""val_avg_gen_time"""] )
     self.assertIsInstance(last_step_stats[f"""val_avg_{model.val_metric}"""]			,      lowerCamelCase_ )
     SCREAMING_SNAKE_CASE__     				= int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1 )
     self.assertEqual(len(metrics["""val"""] )			,      lowerCamelCase_ )
     self.assertEqual(len(metrics["""test"""] )			,      1 )
     return model







class __snake_case							(		A_						):




   @parameterized.expand(
       [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]			,      )
   def       __a		(       self     :							List[Any]			,      _lowercase     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= CHEAP_ARGS.copy()
     SCREAMING_SNAKE_CASE__     				= """translation""" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else """summarization"""
     SCREAMING_SNAKE_CASE__     				= 0.1 if task == """translation""" else 0

     SCREAMING_SNAKE_CASE__     				= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
     SCREAMING_SNAKE_CASE__     				= self.get_auto_remove_tmp_dir()
     args_d.update(
         data_dir=lowerCamelCase_			,      model_name_or_path=lowerCamelCase_			,      tokenizer_name=lowerCamelCase_			,      train_batch_size=2			,      eval_batch_size=2			,      output_dir=lowerCamelCase_			,      do_predict=lowerCamelCase_			,      task=lowerCamelCase_			,      src_lang="""en_XX"""			,      tgt_lang="""ro_RO"""			,      freeze_encoder=lowerCamelCase_			,      freeze_embeds=lowerCamelCase_			,      )
     assert "n_train" in args_d
     SCREAMING_SNAKE_CASE__     				= argparse.Namespace(**lowerCamelCase_ )
     SCREAMING_SNAKE_CASE__     				= main(lowerCamelCase_ )

     SCREAMING_SNAKE_CASE__     				= module.model.get_input_embeddings()
     assert not input_embeds.weight.requires_grad
     if model == T5_TINY:
       SCREAMING_SNAKE_CASE__     				= module.model.lm_head
       assert not lm_head.weight.requires_grad
       assert (lm_head.weight == input_embeds.weight).all().item()
     elif model == FSMT_TINY:
       SCREAMING_SNAKE_CASE__     				= module.model.model
       SCREAMING_SNAKE_CASE__     				= fsmt.decoder.embed_positions
       assert not embed_pos.weight.requires_grad
       assert not fsmt.decoder.embed_tokens.weight.requires_grad
       # check that embeds are not the same
       assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
     else:
       SCREAMING_SNAKE_CASE__     				= module.model.model
       SCREAMING_SNAKE_CASE__     				= bart.decoder.embed_positions
       assert not embed_pos.weight.requires_grad
       assert not bart.shared.weight.requires_grad
       # check that embeds are the same
       assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
       assert bart.decoder.embed_tokens == bart.shared

     SCREAMING_SNAKE_CASE__     				= load_json(module.output_dir / """text_batch.json""" )
     assert isinstance(lowerCamelCase_			,      lowerCamelCase_ )
     assert len(lowerCamelCase_ ) >= 4




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= CHEAP_ARGS.copy()

     SCREAMING_SNAKE_CASE__     				= """summarization"""
     SCREAMING_SNAKE_CASE__     				= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )

     args_d.update(
         data_dir=lowerCamelCase_			,      tokenizer_name=lowerCamelCase_			,      train_batch_size=2			,      eval_batch_size=2			,      do_predict=lowerCamelCase_			,      task=lowerCamelCase_			,      src_lang="""en_XX"""			,      tgt_lang="""ro_RO"""			,      freeze_encoder=lowerCamelCase_			,      freeze_embeds=lowerCamelCase_			,      )

     # test models whose config includes the extra_model_args
     SCREAMING_SNAKE_CASE__     				= BART_TINY
     SCREAMING_SNAKE_CASE__     				= self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__     				= args_d.copy()
     args_da.update(
         model_name_or_path=lowerCamelCase_			,      output_dir=lowerCamelCase_			,      )
     SCREAMING_SNAKE_CASE__     				= ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
     for p in extra_model_params:
       SCREAMING_SNAKE_CASE__     				= 0.5
     SCREAMING_SNAKE_CASE__     				= argparse.Namespace(**lowerCamelCase_ )
     SCREAMING_SNAKE_CASE__     				= main(lowerCamelCase_ )
     for p in extra_model_params:
       assert getattr(model.config			,      lowerCamelCase_ ) == 0.5, f"""failed to override the model config for param {p}"""

     # test models whose config doesn't include the extra_model_args
     SCREAMING_SNAKE_CASE__     				= T5_TINY
     SCREAMING_SNAKE_CASE__     				= self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__     				= args_d.copy()
     args_da.update(
         model_name_or_path=lowerCamelCase_			,      output_dir=lowerCamelCase_			,      )
     SCREAMING_SNAKE_CASE__     				= """encoder_layerdrop"""
     SCREAMING_SNAKE_CASE__     				= 0.5
     SCREAMING_SNAKE_CASE__     				= argparse.Namespace(**lowerCamelCase_ )
     with pytest.raises(lowerCamelCase_ ) as excinfo:
       SCREAMING_SNAKE_CASE__     				= main(lowerCamelCase_ )
     assert str(excinfo.value ) == f"""model config doesn't have a `{unsupported_param}` attribute"""




   def       __a		(       self     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= CHEAP_ARGS.copy()

     SCREAMING_SNAKE_CASE__     				= """summarization"""
     SCREAMING_SNAKE_CASE__     				= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )

     SCREAMING_SNAKE_CASE__     				= BART_TINY
     SCREAMING_SNAKE_CASE__     				= self.get_auto_remove_tmp_dir()

     args_d.update(
         data_dir=lowerCamelCase_			,      model_name_or_path=lowerCamelCase_			,      output_dir=lowerCamelCase_			,      tokenizer_name=lowerCamelCase_			,      train_batch_size=2			,      eval_batch_size=2			,      do_predict=lowerCamelCase_			,      task=lowerCamelCase_			,      src_lang="""en_XX"""			,      tgt_lang="""ro_RO"""			,      freeze_encoder=lowerCamelCase_			,      freeze_embeds=lowerCamelCase_			,      )

     # emulate finetune.py
     SCREAMING_SNAKE_CASE__     				= argparse.ArgumentParser()
     SCREAMING_SNAKE_CASE__     				= pl.Trainer.add_argparse_args(lowerCamelCase_ )
     SCREAMING_SNAKE_CASE__     				= SummarizationModule.add_model_specific_args(lowerCamelCase_			,      os.getcwd() )
     SCREAMING_SNAKE_CASE__     				= {"""--help""": True}

     # --help test
     with pytest.raises(lowerCamelCase_ ) as excinfo:
       with CaptureStdout() as cs:
         SCREAMING_SNAKE_CASE__     				= parser.parse_args(lowerCamelCase_ )
       assert False, "--help is expected to sys.exit"
     assert excinfo.type == SystemExit
     SCREAMING_SNAKE_CASE__     				= lightning_base.arg_to_scheduler_metavar
     assert expected in cs.out, "--help is expected to list the supported schedulers"

     # --lr_scheduler=non_existing_scheduler test
     SCREAMING_SNAKE_CASE__     				= """non_existing_scheduler"""
     SCREAMING_SNAKE_CASE__     				= {f"""--lr_scheduler={unsupported_param}"""}
     with pytest.raises(lowerCamelCase_ ) as excinfo:
       with CaptureStderr() as cs:
         SCREAMING_SNAKE_CASE__     				= parser.parse_args(lowerCamelCase_ )
       assert False, "invalid argument is expected to sys.exit"
     assert excinfo.type == SystemExit
     SCREAMING_SNAKE_CASE__     				= f"""invalid choice: '{unsupported_param}'"""
     assert expected in cs.err, f"""should have bailed on invalid choice of scheduler {unsupported_param}"""

     # --lr_scheduler=existing_scheduler test
     SCREAMING_SNAKE_CASE__     				= """cosine"""
     SCREAMING_SNAKE_CASE__     				= args_d.copy()
     SCREAMING_SNAKE_CASE__     				= supported_param
     SCREAMING_SNAKE_CASE__     				= argparse.Namespace(**lowerCamelCase_ )
     SCREAMING_SNAKE_CASE__     				= main(lowerCamelCase_ )
     assert (
         getattr(model.hparams			,      """lr_scheduler""" ) == supported_param
     ), f"""lr_scheduler={supported_param} shouldn't fail"""

"""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   :    str						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    Tuple=13						,  lowerCamelCase_   :    List[Any]=7						,  lowerCamelCase_   :    List[str]=True						,  lowerCamelCase_   :    str=True						,  lowerCamelCase_   :    Tuple=True						,  lowerCamelCase_   :    Optional[Any]=True						,  lowerCamelCase_   :    str=99						,  lowerCamelCase_   :    str=32						,  lowerCamelCase_   :    List[Any]=5						,  lowerCamelCase_   :    Dict=4						,  lowerCamelCase_   :    str=37						,  lowerCamelCase_   :    List[Any]="gelu"						,  lowerCamelCase_   :    List[str]=0.1						,  lowerCamelCase_   :    Optional[int]=0.1						,  lowerCamelCase_   :    List[str]=5_12						,  lowerCamelCase_   :    Union[str, Any]=16						,  lowerCamelCase_   :    Any=2						,  lowerCamelCase_   :    List[str]=0.02						,  lowerCamelCase_   :    List[Any]=3						,  lowerCamelCase_   :    Any=None						,  lowerCamelCase_   :    List[Any]=20						,  lowerCamelCase_   :    Tuple=30						,  lowerCamelCase_   :    Optional[Any]=2						,  lowerCamelCase_   :    List[Any]=2						,  lowerCamelCase_   :    Union[str, Any]=10						,  )      -> Any:
      __a			      =						parent
      __a			      =						batch_size
      __a			      =						seq_length
      __a			      =						is_training
      __a			      =						use_input_mask
      __a			      =						use_token_type_ids
      __a			      =						use_labels
      __a			      =						vocab_size
      __a			      =						hidden_size
      __a			      =						num_hidden_layers
      __a			      =						num_attention_heads
      __a			      =						intermediate_size
      __a			      =						hidden_act
      __a			      =						hidden_dropout_prob
      __a			      =						attention_probs_dropout_prob
      __a			      =						max_position_embeddings
      __a			      =						type_vocab_size
      __a			      =						type_sequence_label_size
      __a			      =						initializer_range
      __a			      =						num_labels
      __a			      =						scope
      __a			      =						max_xpath_tag_unit_embeddings
      __a			      =						max_xpath_subs_unit_embeddings
      __a			      =						tag_pad_id
      __a			      =						subs_pad_id
      __a			      =						max_depth



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Dict:
      __a			      =						ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size     )

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

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

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

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

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

      __a			      =						self.get_config()

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



  def        lowerCAmelCase_				(		self   :    List[Any]     )      -> Optional[Any]:
      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        lowerCAmelCase_				(		self   :    Tuple						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    List[Any]						,  )      -> int:
      __a			      =						MarkupLMModel(config=lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      print("""Configs:"""						,  model.config.tag_pad_id						,  model.config.subs_pad_id     )
      __a			      =						model(lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_     )
      __a			      =						model(lowerCamelCase_						,  token_type_ids=lowerCamelCase_     )
      __a			      =						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        lowerCAmelCase_				(		self   :    Optional[int]						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Tuple						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    int						,  )      -> Optional[Any]:
      __a			      =						self.num_labels
      __a			      =						MarkupLMForSequenceClassification(lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						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        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    Union[str, Any]						,  )      -> int:
      __a			      =						self.num_labels
      __a			      =						MarkupLMForTokenClassification(config=lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						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        lowerCAmelCase_				(		self   :    List[Any]						,  lowerCamelCase_   :    Tuple						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    List[Any]						,  )      -> Tuple:
      __a			      =						MarkupLMForQuestionAnswering(config=lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						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        lowerCAmelCase_				(		self   :    str     )      -> str:
      __a			      =						self.prepare_config_and_inputs()
      (
          (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  
      )			      =						config_and_inputs
      __a			      =						{
          """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					(				A_   ,		A_   ,		unittest.TestCase							):
  A_ :				int 			=							(
      (
          MarkupLMModel,
          MarkupLMForSequenceClassification,
          MarkupLMForTokenClassification,
          MarkupLMForQuestionAnswering,
      )
      if is_torch_available()
      else None
  )
  A_ :				Any 			=							(
      {
          '''feature-extraction''': MarkupLMModel,
          '''question-answering''': MarkupLMForQuestionAnswering,
          '''text-classification''': MarkupLMForSequenceClassification,
          '''token-classification''': MarkupLMForTokenClassification,
          '''zero-shot''': MarkupLMForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )



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



  def        lowerCAmelCase_				(		self   :    Tuple     )      -> str:
      __a			      =						MarkupLMModelTester(self     )
      __a			      =						ConfigTester(self						,  config_class=lowerCamelCase_						,  hidden_size=37     )



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



  def        lowerCAmelCase_				(		self   :    str     )      -> Dict:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Optional[int]:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Tuple     )      -> Tuple:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]     )      -> int:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_     )

def 						UpperCamelCase				(	):
    __a			      =						"""
    <!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        lowerCAmelCase_				(		self   :    int     )      -> Any:
      # TODO use from_pretrained here
      __a			      =						MarkupLMFeatureExtractor()
      __a			      =						MarkupLMTokenizer.from_pretrained("""microsoft/markuplm-base"""     )

      return MarkupLMProcessor(lowerCamelCase_						,  lowerCamelCase_     )



  @slow
  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Dict:
      __a			      =						MarkupLMModel.from_pretrained("""microsoft/markuplm-base"""     ).to(lowerCamelCase_     )

      __a			      =						self.default_processor

      __a			      =						processor(prepare_html_string()						,  return_tensors="""pt"""     )
      __a			      =						inputs.to(lowerCamelCase_     )

      # forward pass
      with torch.no_grad():
          __a			      =						model(**lowerCamelCase_     )

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

      __a			      =						torch.tensor(
          [[0.06_75, -0.00_52, 0.50_01], [-0.22_81, 0.08_02, 0.21_92], [-0.05_83, -0.33_11, 0.11_85]]     ).to(lowerCamelCase_     )

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