





import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def A_	(		A__		,      A__		,      A__=1024		,      A__=1024		,      A__=False		,      **A__				)  ->		List[str]:
       a__						:	int =				AutoTokenizer.from_pretrained(A__				)
       a__						:	Union[str, Any] =				SeqaSeqDataset(A__		,      A__		,      A__		,      A__		,      type_path='train'		,      **A__				)
       a__						:	Optional[int] =				tok.pad_token_id

       def get_lens(A__				):
              a__						:	Optional[Any] =				tqdm(
                  DataLoader(A__		,      batch_size=512		,      num_workers=8		,      shuffle=A__		,      collate_fn=ds.collate_fn				)		,      desc=str(ds.len_file				)		,      )
              a__						:	int =				[]
              for batch in dl:
                     a__						:	int =				batch['input_ids'].ne(A__				).sum(1				).tolist()
                     a__						:	Dict =				batch['labels'].ne(A__				).sum(1				).tolist()
                     if consider_target:
                            for src, tgt in zip(A__		,      A__				):
                                   max_lens.append(max(A__		,      A__				)				)
                     else:
                            max_lens.extend(A__				)
              return max_lens

       a__						:	Any =				get_lens(A__				)
       a__						:	List[str] =				SeqaSeqDataset(A__		,      A__		,      A__		,      A__		,      type_path='val'		,      **A__				)
       a__						:	Union[str, Any] =				get_lens(A__				)
       pickle_save(A__		,      train_ds.len_file				)
       pickle_save(A__		,      val_ds.len_file				)


if __name__ == "__main__":
    fire.Fire(save_len_file)










"""simple docstring"""
import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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 (
			    DebertaForMaskedLM,
			    DebertaForQuestionAnswering,
			    DebertaForSequenceClassification,
			    DebertaForTokenClassification,
			    DebertaModel,
			)
			from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST







class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: str	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: List[Any]=13	,	lowerCAmelCase_					: Tuple=7	,	lowerCAmelCase_					: Dict=True	,	lowerCAmelCase_					: str=True	,	lowerCAmelCase_					: List[str]=True	,	lowerCAmelCase_					: List[str]=True	,	lowerCAmelCase_					: List[str]=99	,	lowerCAmelCase_					: int=32	,	lowerCAmelCase_					: List[str]=5	,	lowerCAmelCase_					: Optional[int]=4	,	lowerCAmelCase_					: str=37	,	lowerCAmelCase_					: List[Any]="gelu"	,	lowerCAmelCase_					: Dict=0.1	,	lowerCAmelCase_					: List[str]=0.1	,	lowerCAmelCase_					: List[Any]=512	,	lowerCAmelCase_					: Optional[int]=16	,	lowerCAmelCase_					: Union[str, Any]=2	,	lowerCAmelCase_					: List[str]=0.0_2	,	lowerCAmelCase_					: List[Any]=False	,	lowerCAmelCase_					: Dict=True	,	lowerCAmelCase_					: Union[str, Any]="None"	,	lowerCAmelCase_					: List[Any]=3	,	lowerCAmelCase_					: Optional[int]=4	,	lowerCAmelCase_					: int=None	,	)						->			Dict:
				UpperCAmelCase_     : Dict			     =					parent
				UpperCAmelCase_     : Union[str, Any]			     =					batch_size
				UpperCAmelCase_     : Optional[Any]			     =					seq_length
				UpperCAmelCase_     : List[Any]			     =					is_training
				UpperCAmelCase_     : Optional[int]			     =					use_input_mask
				UpperCAmelCase_     : int			     =					use_token_type_ids
				UpperCAmelCase_     : Any			     =					use_labels
				UpperCAmelCase_     : Optional[int]			     =					vocab_size
				UpperCAmelCase_     : Any			     =					hidden_size
				UpperCAmelCase_     : Dict			     =					num_hidden_layers
				UpperCAmelCase_     : List[Any]			     =					num_attention_heads
				UpperCAmelCase_     : List[Any]			     =					intermediate_size
				UpperCAmelCase_     : int			     =					hidden_act
				UpperCAmelCase_     : Optional[Any]			     =					hidden_dropout_prob
				UpperCAmelCase_     : Union[str, Any]			     =					attention_probs_dropout_prob
				UpperCAmelCase_     : Any			     =					max_position_embeddings
				UpperCAmelCase_     : Union[str, Any]			     =					type_vocab_size
				UpperCAmelCase_     : Union[str, Any]			     =					type_sequence_label_size
				UpperCAmelCase_     : Tuple			     =					initializer_range
				UpperCAmelCase_     : int			     =					num_labels
				UpperCAmelCase_     : Optional[Any]			     =					num_choices
				UpperCAmelCase_     : List[str]			     =					relative_attention
				UpperCAmelCase_     : List[Any]			     =					position_biased_input
				UpperCAmelCase_     : Dict			     =					pos_att_type
				UpperCAmelCase_     : Optional[Any]			     =					scope





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			Dict:
				UpperCAmelCase_     : Any			     =					ids_tensor([self.batch_size, self.seq_length]	,	self.vocab_size						)

				UpperCAmelCase_     : Tuple			     =					None
				if self.use_input_mask:
							UpperCAmelCase_     : List[str]			     =					ids_tensor([self.batch_size, self.seq_length]	,	vocab_size=2						)

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

				UpperCAmelCase_     : Optional[Any]			     =					None
				UpperCAmelCase_     : List[str]			     =					None
				UpperCAmelCase_     : Union[str, Any]			     =					None
				if self.use_labels:
							UpperCAmelCase_     : Dict			     =					ids_tensor([self.batch_size]	,	self.type_sequence_label_size						)
							UpperCAmelCase_     : Union[str, Any]			     =					ids_tensor([self.batch_size, self.seq_length]	,	self.num_labels						)
							UpperCAmelCase_     : Tuple			     =					ids_tensor([self.batch_size]	,	self.num_choices						)

				UpperCAmelCase_     : int			     =					self.get_config()

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





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Dict:
				return DebertaConfig(
				    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	,	relative_attention=self.relative_attention	,	position_biased_input=self.position_biased_input	,	pos_att_type=self.pos_att_type	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Union[str, Any]:
				UpperCAmelCase_     : List[str]			     =					self.get_config()
				UpperCAmelCase_     : int			     =					300
				return config





	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	lowerCAmelCase_					: int						)						->			List[Any]:
				self.parent.assertListEqual(list(result.loss.size()						)	,	[]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: Dict	,	lowerCAmelCase_					: Any	,	lowerCAmelCase_					: Any	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Dict	,	lowerCAmelCase_					: List[Any]						)						->			List[Any]:
				UpperCAmelCase_     : Optional[Any]			     =					DebertaModel(config=lowerCAmelCase_						)
				model.to(lowerCAmelCase_						)
				model.eval()
				UpperCAmelCase_     : Optional[Any]			     =					model(lowerCAmelCase_	,	attention_mask=lowerCAmelCase_	,	token_type_ids=lowerCAmelCase_						)[0]
				UpperCAmelCase_     : Optional[int]			     =					model(lowerCAmelCase_	,	token_type_ids=lowerCAmelCase_						)[0]
				UpperCAmelCase_     : Optional[Any]			     =					model(lowerCAmelCase_						)[0]

				self.parent.assertListEqual(list(sequence_output.size()						)	,	[self.batch_size, self.seq_length, self.hidden_size]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Optional[int]	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: Dict	,	lowerCAmelCase_					: Optional[int]						)						->			List[Any]:
				UpperCAmelCase_     : Union[str, Any]			     =					DebertaForMaskedLM(config=lowerCAmelCase_						)
				model.to(lowerCAmelCase_						)
				model.eval()
				UpperCAmelCase_     : List[Any]			     =					model(lowerCAmelCase_	,	attention_mask=lowerCAmelCase_	,	token_type_ids=lowerCAmelCase_	,	labels=lowerCAmelCase_						)

				self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.seq_length, self.vocab_size)						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Dict	,	lowerCAmelCase_					: Optional[Any]	,	lowerCAmelCase_					: Any	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Optional[Any]						)						->			Optional[Any]:
				UpperCAmelCase_     : Any			     =					self.num_labels
				UpperCAmelCase_     : List[Any]			     =					DebertaForSequenceClassification(lowerCAmelCase_						)
				model.to(lowerCAmelCase_						)
				model.eval()
				UpperCAmelCase_     : Optional[Any]			     =					model(lowerCAmelCase_	,	attention_mask=lowerCAmelCase_	,	token_type_ids=lowerCAmelCase_	,	labels=lowerCAmelCase_						)
				self.parent.assertListEqual(list(result.logits.size()						)	,	[self.batch_size, self.num_labels]						)
				self.check_loss_output(lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Optional[int]	,	lowerCAmelCase_					: Optional[int]	,	lowerCAmelCase_					: Optional[Any]	,	lowerCAmelCase_					: Union[str, Any]	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Tuple						)						->			str:
				UpperCAmelCase_     : Optional[int]			     =					self.num_labels
				UpperCAmelCase_     : Optional[int]			     =					DebertaForTokenClassification(config=lowerCAmelCase_						)
				model.to(lowerCAmelCase_						)
				model.eval()
				UpperCAmelCase_     : Optional[Any]			     =					model(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      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: Optional[int]	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: Union[str, Any]	,	lowerCAmelCase_					: Optional[int]	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: str						)						->			List[Any]:
				UpperCAmelCase_     : Dict			     =					DebertaForQuestionAnswering(config=lowerCAmelCase_						)
				model.to(lowerCAmelCase_						)
				model.eval()
				UpperCAmelCase_     : Any			     =					model(
				    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      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Union[str, Any]:
				UpperCAmelCase_     : Union[str, Any]			     =					self.prepare_config_and_inputs()
				(
				    (
				    UpperCAmelCase_
				)     ,	(
				    UpperCAmelCase_
				)     ,	(
				    UpperCAmelCase_
				)     ,	(
				    UpperCAmelCase_
				)     ,	(
				    UpperCAmelCase_
				)     ,	(
				    UpperCAmelCase_
				)     ,	(
				    UpperCAmelCase_
				)     ,	
				)     : Tuple			     =					config_and_inputs
				UpperCAmelCase_     : List[Any]			     =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
				return config, inputs_dict







@require_torch
class 			UpperCamelCase_       (__A				,						__A				,						unittest.TestCase     ):
	__magic_name__	      =				(
	    (
	        DebertaModel,
	        DebertaForMaskedLM,
	        DebertaForSequenceClassification,
	        DebertaForTokenClassification,
	        DebertaForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	__magic_name__	      =				(
	    {
	        '''feature-extraction''': DebertaModel,
	        '''fill-mask''': DebertaForMaskedLM,
	        '''question-answering''': DebertaForQuestionAnswering,
	        '''text-classification''': DebertaForSequenceClassification,
	        '''token-classification''': DebertaForTokenClassification,
	        '''zero-shot''': DebertaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)

	__magic_name__	      =				True
	__magic_name__	      =				False
	__magic_name__	      =				False
	__magic_name__	      =				False
	__magic_name__	      =				False





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			int:
				UpperCAmelCase_     : int			     =					DebertaModelTester(self						)
				UpperCAmelCase_     : Any			     =					ConfigTester(self	,	config_class=lowerCAmelCase_	,	hidden_size=37						)





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





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Optional[Any]:
				UpperCAmelCase_     : Union[str, Any]			     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_model(*lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Union[str, Any]:
				UpperCAmelCase_     : int			     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			Optional[int]:
				UpperCAmelCase_     : Any			     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			Union[str, Any]:
				UpperCAmelCase_     : Tuple			     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_question_answering(*lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			Union[str, Any]:
				UpperCAmelCase_     : Any			     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_token_classification(*lowerCAmelCase_						)





	@slow
	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Tuple:
				for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_     : Optional[int]			     =					DebertaModel.from_pretrained(lowerCAmelCase_						)
							self.assertIsNotNone(lowerCAmelCase_						)







@require_torch
@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase_       (unittest.TestCase     ):





	@unittest.skip(reason="Model not available yet"						)
	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Optional[Any]:
				pass





	@slow
	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			List[Any]:
				UpperCAmelCase_     : Optional[int]			     =					DebertaModel.from_pretrained("microsoft/deberta-base"						)

				UpperCAmelCase_     : List[Any]			     =					torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						)
				UpperCAmelCase_     : Tuple			     =					torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
				with torch.no_grad():
							UpperCAmelCase_     : Optional[Any]			     =					model(lowerCAmelCase_	,	attention_mask=lowerCAmelCase_						)[0]
				# compare the actual values for a slice.
				UpperCAmelCase_     : Tuple			     =					torch.tensor(
				    [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]						)
				self.assertTrue(torch.allclose(output[:, 1:4, 1:4]	,	lowerCAmelCase_	,	atol=1e-4						)	,	f"""{output[:, 1:4, 1:4]}"""						)



