




'''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


UpperCamelCase			     =					getLogger(__name__)


UpperCamelCase			     =					'''cuda''' if torch.cuda.is_available() else '''cpu'''
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase = 8		,				__lowercase = DEFAULT_DEVICE		,				__lowercase=False		,				__lowercase="summarization"		,				__lowercase=None		,				**__lowercase		,				)			-> Dict:
     A:		Tuple          =		Path(__lowercase	).open('''w'''		,				encoding='''utf-8'''	)
     A:		List[Any]          =		str(__lowercase	)
     A:		Optional[int]          =		AutoModelForSeqaSeqLM.from_pretrained(__lowercase	).to(__lowercase	)
     if fpaa:
          A:		Tuple          =		model.half()

     A:		Any          =		AutoTokenizer.from_pretrained(__lowercase	)
     logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}"""	)  # if this is wrong, check config.model_type.

     A:		Dict          =		time.time()
     # update config with task specific params
     use_task_specific_params(__lowercase		,				__lowercase	)
     if prefix is None:
          A:		Optional[int]          =		prefix or getattr(model.config		,				'''prefix'''		,				''''''	) or ''''''
     for examples_chunk in tqdm(list(chunks(__lowercase		,				__lowercase	)	)	):
          A:		str          =		[prefix + text for text in examples_chunk]
          A:		Optional[Any]          =		tokenizer(__lowercase		,				return_tensors='''pt'''		,				truncation=__lowercase		,				padding='''longest'''	).to(__lowercase	)
          A:		Tuple          =		model.generate(
              input_ids=batch.input_ids		,				attention_mask=batch.attention_mask		,				**__lowercase		,				)
          A:		int          =		tokenizer.batch_decode(__lowercase		,				skip_special_tokens=__lowercase		,				clean_up_tokenization_spaces=__lowercase	)
          for hypothesis in dec:
               fout.write(hypothesis + '''\n'''	)
               fout.flush()
     fout.close()
     A:		Optional[int]          =		int(time.time() - start_time	)  # seconds
     A:		Optional[int]          =		len(__lowercase	)
     return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs		,				4	)}
def 					SCREAMING_SNAKE_CASE(   )			-> int:
     return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''	)
def 					SCREAMING_SNAKE_CASE(   __lowercase=True	)			-> Optional[Any]:
     A:		Optional[Any]          =		argparse.ArgumentParser()
     parser.add_argument('''model_name'''		,				type=__lowercase		,				help='''like facebook/bart-large-cnn,t5-base, etc.'''	)
     parser.add_argument('''input_path'''		,				type=__lowercase		,				help='''like cnn_dm/test.source'''	)
     parser.add_argument('''save_path'''		,				type=__lowercase		,				help='''where to save summaries'''	)
     parser.add_argument('''--reference_path'''		,				type=__lowercase		,				required=__lowercase		,				help='''like cnn_dm/test.target'''	)
     parser.add_argument('''--score_path'''		,				type=__lowercase		,				required=__lowercase		,				default='''metrics.json'''		,				help='''where to save metrics'''	)
     parser.add_argument('''--device'''		,				type=__lowercase		,				required=__lowercase		,				default=__lowercase		,				help='''cuda, cuda:1, cpu etc.'''	)
     parser.add_argument(
         '''--prefix'''		,				type=__lowercase		,				required=__lowercase		,				default=__lowercase		,				help='''will be added to the begininng of src examples'''	)
     parser.add_argument('''--task'''		,				type=__lowercase		,				default='''summarization'''		,				help='''used for task_specific_params + metrics'''	)
     parser.add_argument('''--bs'''		,				type=__lowercase		,				default=8		,				required=__lowercase		,				help='''batch size'''	)
     parser.add_argument(
         '''--n_obs'''		,				type=__lowercase		,				default=-1		,				required=__lowercase		,				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=__lowercase		,				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
     A     ,    A:		List[str]          =		parser.parse_known_args()
     A:		Union[str, Any]          =		parse_numeric_n_bool_cl_kwargs(__lowercase	)
     if parsed_args and verbose:
          print(F"""parsed the following generate kwargs: {parsed_args}"""	)
     with open(args.input_path	) as f:
          A:		Optional[Any]          =		[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
     if args.n_obs > 0:
          A:		str          =		examples[: args.n_obs]
     Path(args.save_path	).parent.mkdir(exist_ok=__lowercase	)
     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."""	)
     A:		str          =		generate_summaries_or_translations(
         __lowercase		,				args.save_path		,				args.model_name		,				batch_size=args.bs		,				device=args.device		,				fpaa=args.fpaa		,				task=args.task		,				prefix=args.prefix		,				**__lowercase		,				)

     if args.reference_path is None:
          return {}

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

     if args.dump_args:
          scores.update(__lowercase	)
     if args.info:
          A:		Any          =		args.info

     if verbose:
          print(__lowercase	)

     if args.score_path is not None:
          json.dump(__lowercase		,				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 YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         YosoForMaskedLM,
         YosoForMultipleChoice,
         YosoForQuestionAnswering,
         YosoForSequenceClassification,
         YosoForTokenClassification,
         YosoModel,
     )
     from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST






class 						lowerCAmelCase_	:







  '''simple docstring'''

  def __init__( self				:							List[Any]					,  SCREAMING_SNAKE_CASE_				:							Dict					,  SCREAMING_SNAKE_CASE_				:							int=13					,  SCREAMING_SNAKE_CASE_				:							List[str]=7					,  SCREAMING_SNAKE_CASE_				:							Union[str, Any]=True					,  SCREAMING_SNAKE_CASE_				:							Tuple=True					,  SCREAMING_SNAKE_CASE_				:							Optional[int]=True					,  SCREAMING_SNAKE_CASE_				:							Optional[int]=True					,  SCREAMING_SNAKE_CASE_				:							Dict=99					,  SCREAMING_SNAKE_CASE_				:							Tuple=32					,  SCREAMING_SNAKE_CASE_				:							List[Any]=5					,  SCREAMING_SNAKE_CASE_				:							List[Any]=4					,  SCREAMING_SNAKE_CASE_				:							Dict=37					,  SCREAMING_SNAKE_CASE_				:							str="gelu"					,  SCREAMING_SNAKE_CASE_				:							str=0.1					,  SCREAMING_SNAKE_CASE_				:							Optional[Any]=0.1					,  SCREAMING_SNAKE_CASE_				:							Dict=5_12					,  SCREAMING_SNAKE_CASE_				:							Union[str, Any]=16					,  SCREAMING_SNAKE_CASE_				:							Union[str, Any]=2					,  SCREAMING_SNAKE_CASE_				:							Any=0.02					,  SCREAMING_SNAKE_CASE_				:							Dict=3					,  SCREAMING_SNAKE_CASE_				:							Any=4					,  SCREAMING_SNAKE_CASE_				:							List[str]=None					,  )			->		Optional[int]:







       '''simple docstring'''



       A:		int          =		parent
       A:		Any          =		batch_size
       A:		List[Any]          =		seq_length
       A:		Union[str, Any]          =		is_training
       A:		List[str]          =		use_input_mask
       A:		List[str]          =		use_token_type_ids
       A:		Union[str, Any]          =		use_labels
       A:		str          =		vocab_size
       A:		Union[str, Any]          =		hidden_size
       A:		Tuple          =		num_hidden_layers
       A:		Dict          =		num_attention_heads
       A:		Any          =		intermediate_size
       A:		Tuple          =		hidden_act
       A:		Dict          =		hidden_dropout_prob
       A:		List[Any]          =		attention_probs_dropout_prob
       A:		int          =		max_position_embeddings
       A:		Any          =		type_vocab_size
       A:		int          =		type_sequence_label_size
       A:		str          =		initializer_range
       A:		Dict          =		num_labels
       A:		int          =		num_choices
       A:		str          =		scope



  def 					_snake_case     ( self				:							Optional[int]	)			->		List[Any]:







       '''simple docstring'''



       A:		Optional[int]          =		ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size	)

       A:		List[str]          =		None
       if self.use_input_mask:
            A:		Union[str, Any]          =		random_attention_mask([self.batch_size, self.seq_length]	)

       A:		Optional[int]          =		None
       if self.use_token_type_ids:
            A:		str          =		ids_tensor([self.batch_size, self.seq_length]					,  self.type_vocab_size	)

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

       A:		List[str]          =		self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



  def 					_snake_case     ( self				:							Union[str, Any]	)			->		Dict:







       '''simple docstring'''



       return YosoConfig(
           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					,  is_decoder=SCREAMING_SNAKE_CASE_					,  initializer_range=self.initializer_range					,  )



  def 					_snake_case     ( self				:							Optional[Any]	)			->		Tuple:







       '''simple docstring'''



       A:		str          =		self.get_config()
       A:		Tuple          =		3_00
       return config



  def 					_snake_case     ( self				:							Union[str, Any]	)			->		Optional[Any]:







       '''simple docstring'''



       (
           (
           A
       )     ,    (
           A
       )     ,    (
           A
       )     ,    (
           A
       )     ,    (
           A
       )     ,    (
           A
       )     ,    (
           A
       )     ,    
       ):		Optional[int]          =		self.prepare_config_and_inputs()

       A:		Any          =		True
       A:		Tuple          =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
       A:		int          =		ids_tensor([self.batch_size, self.seq_length]					,  vocab_size=2	)

       return (
           config,
           input_ids,
           token_type_ids,
           input_mask,
           sequence_labels,
           token_labels,
           choice_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )



  def 					_snake_case     ( self				:							int					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							Union[str, Any]					,  SCREAMING_SNAKE_CASE_				:							Union[str, Any]					,  SCREAMING_SNAKE_CASE_				:							Union[str, Any]					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							Union[str, Any]	)			->		Optional[Any]:







       '''simple docstring'''



       A:		int          =		YosoModel(config=SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       A:		str          =		model(SCREAMING_SNAKE_CASE_					,  attention_mask=SCREAMING_SNAKE_CASE_					,  token_type_ids=SCREAMING_SNAKE_CASE_	)
       A:		str          =		model(SCREAMING_SNAKE_CASE_					,  token_type_ids=SCREAMING_SNAKE_CASE_	)
       A:		Any          =		model(SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size)	)



  def 					_snake_case     ( self				:							Optional[int]					,  SCREAMING_SNAKE_CASE_				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							Any					,  SCREAMING_SNAKE_CASE_				:							Dict					,  SCREAMING_SNAKE_CASE_				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							Optional[int]					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							Optional[Any]					,  SCREAMING_SNAKE_CASE_				:							List[Any]					,  )			->		List[Any]:







       '''simple docstring'''



       A:		int          =		True
       A:		int          =		YosoModel(SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       A:		Optional[int]          =		model(
           SCREAMING_SNAKE_CASE_					,  attention_mask=SCREAMING_SNAKE_CASE_					,  token_type_ids=SCREAMING_SNAKE_CASE_					,  encoder_hidden_states=SCREAMING_SNAKE_CASE_					,  encoder_attention_mask=SCREAMING_SNAKE_CASE_					,  )
       A:		Any          =		model(
           SCREAMING_SNAKE_CASE_					,  attention_mask=SCREAMING_SNAKE_CASE_					,  token_type_ids=SCREAMING_SNAKE_CASE_					,  encoder_hidden_states=SCREAMING_SNAKE_CASE_					,  )
       A:		List[str]          =		model(SCREAMING_SNAKE_CASE_					,  attention_mask=SCREAMING_SNAKE_CASE_					,  token_type_ids=SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size)	)



  def 					_snake_case     ( self				:							Optional[int]					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							Tuple					,  SCREAMING_SNAKE_CASE_				:							Dict					,  SCREAMING_SNAKE_CASE_				:							Dict					,  SCREAMING_SNAKE_CASE_				:							Optional[Any]					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							Tuple	)			->		Union[str, Any]:







       '''simple docstring'''



       A:		List[Any]          =		YosoForMaskedLM(config=SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       A:		Any          =		model(SCREAMING_SNAKE_CASE_					,  attention_mask=SCREAMING_SNAKE_CASE_					,  token_type_ids=SCREAMING_SNAKE_CASE_					,  labels=SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.vocab_size)	)



  def 					_snake_case     ( self				:							Dict					,  SCREAMING_SNAKE_CASE_				:							Optional[Any]					,  SCREAMING_SNAKE_CASE_				:							Any					,  SCREAMING_SNAKE_CASE_				:							Optional[int]					,  SCREAMING_SNAKE_CASE_				:							Tuple					,  SCREAMING_SNAKE_CASE_				:							Dict					,  SCREAMING_SNAKE_CASE_				:							List[Any]					,  SCREAMING_SNAKE_CASE_				:							Union[str, Any]	)			->		int:







       '''simple docstring'''



       A:		Optional[int]          =		YosoForQuestionAnswering(config=SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       A:		Dict          =		model(
           SCREAMING_SNAKE_CASE_					,  attention_mask=SCREAMING_SNAKE_CASE_					,  token_type_ids=SCREAMING_SNAKE_CASE_					,  start_positions=SCREAMING_SNAKE_CASE_					,  end_positions=SCREAMING_SNAKE_CASE_					,  )
       self.parent.assertEqual(result.start_logits.shape					,  (self.batch_size, self.seq_length)	)
       self.parent.assertEqual(result.end_logits.shape					,  (self.batch_size, self.seq_length)	)



  def 					_snake_case     ( self				:							Optional[int]					,  SCREAMING_SNAKE_CASE_				:							List[Any]					,  SCREAMING_SNAKE_CASE_				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							Tuple					,  SCREAMING_SNAKE_CASE_				:							Dict					,  SCREAMING_SNAKE_CASE_				:							Dict					,  SCREAMING_SNAKE_CASE_				:							Tuple					,  SCREAMING_SNAKE_CASE_				:							Dict	)			->		List[Any]:







       '''simple docstring'''



       A:		str          =		self.num_labels
       A:		int          =		YosoForSequenceClassification(SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       A:		Any          =		model(SCREAMING_SNAKE_CASE_					,  attention_mask=SCREAMING_SNAKE_CASE_					,  token_type_ids=SCREAMING_SNAKE_CASE_					,  labels=SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.num_labels)	)



  def 					_snake_case     ( self				:							Dict					,  SCREAMING_SNAKE_CASE_				:							Any					,  SCREAMING_SNAKE_CASE_				:							Dict					,  SCREAMING_SNAKE_CASE_				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							Any					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							Dict					,  SCREAMING_SNAKE_CASE_				:							Dict	)			->		Optional[Any]:







       '''simple docstring'''



       A:		Tuple          =		self.num_labels
       A:		Optional[Any]          =		YosoForTokenClassification(config=SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       A:		Dict          =		model(SCREAMING_SNAKE_CASE_					,  attention_mask=SCREAMING_SNAKE_CASE_					,  token_type_ids=SCREAMING_SNAKE_CASE_					,  labels=SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.seq_length, self.num_labels)	)



  def 					_snake_case     ( self				:							Union[str, Any]					,  SCREAMING_SNAKE_CASE_				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							Optional[int]					,  SCREAMING_SNAKE_CASE_				:							Optional[Any]					,  SCREAMING_SNAKE_CASE_				:							Any					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							Optional[Any]	)			->		Any:







       '''simple docstring'''



       A:		Any          =		self.num_choices
       A:		Tuple          =		YosoForMultipleChoice(config=SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       A:		List[str]          =		input_ids.unsqueeze(1	).expand(-1					,  self.num_choices					,  -1	).contiguous()
       A:		Any          =		token_type_ids.unsqueeze(1	).expand(-1					,  self.num_choices					,  -1	).contiguous()
       A:		str          =		input_mask.unsqueeze(1	).expand(-1					,  self.num_choices					,  -1	).contiguous()
       A:		Union[str, Any]          =		model(
           SCREAMING_SNAKE_CASE_					,  attention_mask=SCREAMING_SNAKE_CASE_					,  token_type_ids=SCREAMING_SNAKE_CASE_					,  labels=SCREAMING_SNAKE_CASE_					,  )
       self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.num_choices)	)




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







       '''simple docstring'''



       A:		List[Any]          =		self.prepare_config_and_inputs()
       (
           (
           A
       )     ,    (
           A
       )     ,    (
           A
       )     ,    (
           A
       )     ,    (
           A
       )     ,    (
           A
       )     ,    (
           A
       )     ,    
       ):		Optional[int]          =		config_and_inputs
       A:		Any          =		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict






@require_torch
class 						lowerCAmelCase_	(       UpperCAmelCase_       ,					UpperCAmelCase_       ,					unittest.TestCase							):







  '''simple docstring'''

  UpperCamelCase_			:      List[str]										=				(
      (
          YosoModel,
          YosoForMaskedLM,
          YosoForMultipleChoice,
          YosoForQuestionAnswering,
          YosoForSequenceClassification,
          YosoForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  UpperCamelCase_			:      Tuple										=				False
  UpperCamelCase_			:      Tuple										=				False
  UpperCamelCase_			:      List[str]										=				False

  UpperCamelCase_			:      List[str]										=				()
  UpperCamelCase_			:      Optional[int]										=				(
      {
          """feature-extraction""": YosoModel,
          """fill-mask""": YosoForMaskedLM,
          """question-answering""": YosoForQuestionAnswering,
          """text-classification""": YosoForSequenceClassification,
          """token-classification""": YosoForTokenClassification,
          """zero-shot""": YosoForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )



  def 					_snake_case     ( self				:							Dict	)			->		Any:







       '''simple docstring'''



       A:		Tuple          =		YosoModelTester(self	)
       A:		str          =		ConfigTester(self					,  config_class=SCREAMING_SNAKE_CASE_					,  hidden_size=37	)



  def 					_snake_case     ( self				:							Union[str, Any]	)			->		Union[str, Any]:







       '''simple docstring'''



       self.config_tester.run_common_tests()



  def 					_snake_case     ( self				:							str	)			->		Optional[Any]:







       '''simple docstring'''



       A:		int          =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							Union[str, Any]	)			->		int:







       '''simple docstring'''



       A:		Any          =		self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
            A:		Tuple          =		type
            self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							Tuple	)			->		Optional[int]:







       '''simple docstring'''



       A:		List[str]          =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							List[Any]	)			->		str:







       '''simple docstring'''



       A:		str          =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							List[str]	)			->		str:







       '''simple docstring'''



       A:		Tuple          =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							Tuple	)			->		Dict:







       '''simple docstring'''



       A:		int          =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							Dict	)			->		Dict:







       '''simple docstring'''



       A:		Dict          =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_	)



  @slow
  def 					_snake_case     ( self				:							int	)			->		int:







       '''simple docstring'''



       for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A:		Dict          =		YosoModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)




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







       '''simple docstring'''



       return






@require_torch
class 						lowerCAmelCase_	(       unittest.TestCase							):







  '''simple docstring'''

  @slow
  def 					_snake_case     ( self				:							Union[str, Any]	)			->		Union[str, Any]:







       '''simple docstring'''



       A:		List[Any]          =		YosoModel.from_pretrained('''uw-madison/yoso-4096'''	)
       A:		Dict          =		torch.tensor([[0, 1, 2, 3, 4, 5]]	)

       with torch.no_grad():
            A:		Union[str, Any]          =		model(SCREAMING_SNAKE_CASE_	)[0]

       A:		List[str]          =		torch.Size((1, 6, 7_68)	)
       self.assertEqual(output.shape					,  SCREAMING_SNAKE_CASE_	)

       A:		int          =		torch.tensor(
           [[[-0.0611, 0.1242, 0.0840], [0.0280, -0.0048, 0.1125], [0.0106, 0.0226, 0.0751]]]	)

       self.assertTrue(torch.allclose(output[:, :3, :3]					,  SCREAMING_SNAKE_CASE_					,  atol=1E-4	)	)



  @slow
  def 					_snake_case     ( self				:							int	)			->		Tuple:







       '''simple docstring'''



       A:		List[str]          =		YosoForMaskedLM.from_pretrained('''uw-madison/yoso-4096'''	)
       A:		Optional[int]          =		torch.tensor([[0, 1, 2, 3, 4, 5]]	)

       with torch.no_grad():
            A:		str          =		model(SCREAMING_SNAKE_CASE_	)[0]

       A:		Optional[int]          =		5_02_65

       A:		List[str]          =		torch.Size((1, 6, vocab_size)	)
       self.assertEqual(output.shape					,  SCREAMING_SNAKE_CASE_	)

       A:		Dict          =		torch.tensor(
           [[[-2.1313, -3.7285, -2.2407], [-2.7047, -3.3314, -2.6408], [0.0629, -2.5166, -0.3356]]]	)

       self.assertTrue(torch.allclose(output[:, :3, :3]					,  SCREAMING_SNAKE_CASE_					,  atol=1E-4	)	)




  @slow
  def 					_snake_case     ( self				:							Dict	)			->		Tuple:







       '''simple docstring'''



       A:		List[str]          =		YosoForMaskedLM.from_pretrained('''uw-madison/yoso-4096'''	)
       A:		Optional[int]          =		torch.arange(40_96	).unsqueeze(0	)

       with torch.no_grad():
            A:		int          =		model(SCREAMING_SNAKE_CASE_	)[0]

       A:		Optional[int]          =		5_02_65

       A:		Dict          =		torch.Size((1, 40_96, vocab_size)	)
       self.assertEqual(output.shape					,  SCREAMING_SNAKE_CASE_	)

       A:		Optional[int]          =		torch.tensor(
           [[[-2.3914, -4.3742, -5.0956], [-4.0988, -4.2384, -7.0406], [-3.1427, -3.7192, -6.6800]]]	)

       self.assertTrue(torch.allclose(output[:, :3, :3]					,  SCREAMING_SNAKE_CASE_					,  atol=1E-4	)	)






