
"""simple docstring"""







import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowerCamelCase__     =						getLogger(__name__)


lowerCamelCase__     =						"""cuda""" if torch.cuda.is_available() else """cpu"""
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase = 8			,    _UpperCamelCase = DEFAULT_DEVICE			,    _UpperCamelCase=False			,    _UpperCamelCase="summarization"			,    _UpperCamelCase=None			,    **_UpperCamelCase			,    ):
	__lowerCAmelCase     :		str		     = Path(_lowerCAmelCase						).open('w'			,    encoding='utf-8'						)
	__lowerCAmelCase     :		List[Any]		     = str(_lowerCAmelCase						)
	__lowerCAmelCase     :		Union[str, Any]		     = AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase						).to(_lowerCAmelCase						)
	if fpaa:
		__lowerCAmelCase     :		int		     = model.half()

	__lowerCAmelCase     :		List[str]		     = AutoTokenizer.from_pretrained(_lowerCAmelCase						)
	logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"						)  # if this is wrong, check config.model_type.

	__lowerCAmelCase     :		Any		     = time.time()
	# update config with task specific params
	use_task_specific_params(_lowerCAmelCase			,    _lowerCAmelCase						)
	if prefix is None:
		__lowerCAmelCase     :		List[str]		     = prefix or getattr(model.config			,    'prefix'			,    ''						) or ''
	for examples_chunk in tqdm(list(chunks(_lowerCAmelCase			,    _lowerCAmelCase						)						)						):
		__lowerCAmelCase     :		Union[str, Any]		     = [prefix + text for text in examples_chunk]
		__lowerCAmelCase     :		int		     = tokenizer(_lowerCAmelCase			,    return_tensors='pt'			,    truncation=_lowerCAmelCase			,    padding='longest'						).to(_lowerCAmelCase						)
		__lowerCAmelCase     :		Union[str, Any]		     = model.generate(
		    input_ids=batch.input_ids			,    attention_mask=batch.attention_mask			,    **_lowerCAmelCase			,    )
		__lowerCAmelCase     :		Optional[int]		     = tokenizer.batch_decode(_lowerCAmelCase			,    skip_special_tokens=_lowerCAmelCase			,    clean_up_tokenization_spaces=_lowerCAmelCase						)
		for hypothesis in dec:
			fout.write(hypothesis + '\n'						)
			fout.flush()
	fout.close()
	__lowerCAmelCase     :		List[Any]		     = int(time.time() - start_time						)  # seconds
	__lowerCAmelCase     :		Union[str, Any]		     = len(_lowerCAmelCase						)
	return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs			,    4						)}
def        __lowerCAmelCase							():
	return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'						)
def        __lowerCAmelCase							(_UpperCamelCase=True						):
	__lowerCAmelCase     :		Optional[int]		     = argparse.ArgumentParser()
	parser.add_argument('model_name'			,    type=_lowerCAmelCase			,    help='like facebook/bart-large-cnn,t5-base, etc.'						)
	parser.add_argument('input_path'			,    type=_lowerCAmelCase			,    help='like cnn_dm/test.source'						)
	parser.add_argument('save_path'			,    type=_lowerCAmelCase			,    help='where to save summaries'						)
	parser.add_argument('--reference_path'			,    type=_lowerCAmelCase			,    required=_lowerCAmelCase			,    help='like cnn_dm/test.target'						)
	parser.add_argument('--score_path'			,    type=_lowerCAmelCase			,    required=_lowerCAmelCase			,    default='metrics.json'			,    help='where to save metrics'						)
	parser.add_argument('--device'			,    type=_lowerCAmelCase			,    required=_lowerCAmelCase			,    default=_lowerCAmelCase			,    help='cuda, cuda:1, cpu etc.'						)
	parser.add_argument(
	    '--prefix'			,    type=_lowerCAmelCase			,    required=_lowerCAmelCase			,    default=_lowerCAmelCase			,    help='will be added to the begininng of src examples'						)
	parser.add_argument('--task'			,    type=_lowerCAmelCase			,    default='summarization'			,    help='used for task_specific_params + metrics'						)
	parser.add_argument('--bs'			,    type=_lowerCAmelCase			,    default=8			,    required=_lowerCAmelCase			,    help='batch size'						)
	parser.add_argument(
	    '--n_obs'			,    type=_lowerCAmelCase			,    default=-1			,    required=_lowerCAmelCase			,    help='How many observations. Defaults to all.'						)
	parser.add_argument('--fp16'			,    action='store_true'						)
	parser.add_argument('--dump-args'			,    action='store_true'			,    help='print the custom hparams with the results'						)
	parser.add_argument(
	    '--info'			,    nargs='?'			,    type=_lowerCAmelCase			,    const=datetime_now()			,    help=(
	        'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
	        ' lang=en-ru. If no value is passed, the current datetime string will be used.'
	    )			,    )
	# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
	__lowerCAmelCase     ,  __lowerCAmelCase     :		List[Any]		     = parser.parse_known_args()
	__lowerCAmelCase     :		Any		     = parse_numeric_n_bool_cl_kwargs(_lowerCAmelCase						)
	if parsed_args and verbose:
		print(F"parsed the following generate kwargs: {parsed_args}"						)
	with open(args.input_path						) as f:
		__lowerCAmelCase     :		str		     = [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
	if args.n_obs > 0:
		__lowerCAmelCase     :		Optional[Any]		     = examples[: args.n_obs]
	Path(args.save_path						).parent.mkdir(exist_ok=_lowerCAmelCase						)
	if args.reference_path is None and Path(args.score_path						).exists():
		warnings.warn(F"score_path {args.score_path} will be overwritten unless you type ctrl-c."						)
	__lowerCAmelCase     :		int		     = generate_summaries_or_translations(
	    _lowerCAmelCase			,    args.save_path			,    args.model_name			,    batch_size=args.bs			,    device=args.device			,    fpaa=args.fpaa			,    task=args.task			,    prefix=args.prefix			,    **_lowerCAmelCase			,    )

	if args.reference_path is None:
		return {}

	# Compute scores
	__lowerCAmelCase     :		int		     = calculate_bleu if 'translation' in args.task else calculate_rouge
	__lowerCAmelCase     :		Union[str, Any]		     = [x.rstrip() for x in open(args.save_path						).readlines()]
	__lowerCAmelCase     :		Optional[Any]		     = [x.rstrip() for x in open(args.reference_path						).readlines()][: len(_lowerCAmelCase						)]
	__lowerCAmelCase     :		int		     = score_fn(_lowerCAmelCase			,    _lowerCAmelCase						)
	scores.update(_lowerCAmelCase						)

	if args.dump_args:
		scores.update(_lowerCAmelCase						)
	if args.info:
		__lowerCAmelCase     :		Union[str, Any]		     = args.info

	if verbose:
		print(_lowerCAmelCase						)

	if args.score_path is not None:
		json.dump(_lowerCAmelCase			,    open(args.score_path			,    'w'						)						)

	return scores


if __name__ == "__main__":
							# Usage for MT:
							# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
							run_generate(verbose=True)
"""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     )     )
