




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 						A(		UpperCamelCase     ):



 '''simple docstring'''


 def __init__(       self      :						int		,							A_      :						Any		,							A_      :						Any=13		,							A_      :						Union[str, Any]=7		,							A_      :						Dict=True		,							A_      :						List[Any]=True		,							A_      :						Dict=True		,							A_      :						Optional[int]=True		,							A_      :						Any=99		,							A_      :						Tuple=32		,							A_      :						List[str]=5		,							A_      :						List[str]=4		,							A_      :						Dict=37		,							A_      :						List[str]="gelu"		,							A_      :						Any=0.1		,							A_      :						str=0.1		,							A_      :						List[str]=512		,							A_      :						Optional[int]=16		,							A_      :						Dict=2		,							A_      :						Tuple=0.02		,							A_      :						Tuple=False		,							A_      :						Optional[int]=True		,							A_      :						Union[str, Any]="None"		,							A_      :						int=3		,							A_      :						int=4		,							A_      :						Any=None		,							)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					parent
       lowerCamelCase_	  =					batch_size
       lowerCamelCase_	  =					seq_length
       lowerCamelCase_	  =					is_training
       lowerCamelCase_	  =					use_input_mask
       lowerCamelCase_	  =					use_token_type_ids
       lowerCamelCase_	  =					use_labels
       lowerCamelCase_	  =					vocab_size
       lowerCamelCase_	  =					hidden_size
       lowerCamelCase_	  =					num_hidden_layers
       lowerCamelCase_	  =					num_attention_heads
       lowerCamelCase_	  =					intermediate_size
       lowerCamelCase_	  =					hidden_act
       lowerCamelCase_	  =					hidden_dropout_prob
       lowerCamelCase_	  =					attention_probs_dropout_prob
       lowerCamelCase_	  =					max_position_embeddings
       lowerCamelCase_	  =					type_vocab_size
       lowerCamelCase_	  =					type_sequence_label_size
       lowerCamelCase_	  =					initializer_range
       lowerCamelCase_	  =					num_labels
       lowerCamelCase_	  =					num_choices
       lowerCamelCase_	  =					relative_attention
       lowerCamelCase_	  =					position_biased_input
       lowerCamelCase_	  =					pos_att_type
       lowerCamelCase_	  =					scope

 def        a__				(       self      :						List[Any]						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					ids_tensor([self.batch_size, self.seq_length]		,							self.vocab_size						)

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

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

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

       lowerCamelCase_	  =					self.get_config()

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

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

       """simple docstring"""






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

       """simple docstring"""






       lowerCamelCase_	  =					self.get_config()
       lowerCamelCase_	  =					300
       return config

 def        a__				(       self      :						Any		,							A_      :						Optional[int]						)     ->			List[Any]:

       """simple docstring"""






       self.parent.assertListEqual(list(result.loss.size()						)		,							[]						)

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

       """simple docstring"""






       lowerCamelCase_	  =					DebertaModel(config=A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					model(A_		,							attention_mask=A_		,							token_type_ids=A_						)[0]
       lowerCamelCase_	  =					model(A_		,							token_type_ids=A_						)[0]
       lowerCamelCase_	  =					model(A_						)[0]

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

 def        a__				(       self      :						Dict		,							A_      :						List[Any]		,							A_      :						Optional[int]		,							A_      :						int		,							A_      :						Any		,							A_      :						int		,							A_      :						Any		,							A_      :						int						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					DebertaForMaskedLM(config=A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					model(A_		,							attention_mask=A_		,							token_type_ids=A_		,							labels=A_						)

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

 def        a__				(       self      :						Optional[int]		,							A_      :						List[str]		,							A_      :						Tuple		,							A_      :						str		,							A_      :						List[Any]		,							A_      :						List[Any]		,							A_      :						List[Any]		,							A_      :						Union[str, Any]						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					self.num_labels
       lowerCamelCase_	  =					DebertaForSequenceClassification(A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					model(A_		,							attention_mask=A_		,							token_type_ids=A_		,							labels=A_						)
       self.parent.assertListEqual(list(result.logits.size()						)		,							[self.batch_size, self.num_labels]						)
       self.check_loss_output(A_						)

 def        a__				(       self      :						Tuple		,							A_      :						Optional[Any]		,							A_      :						Optional[int]		,							A_      :						Union[str, Any]		,							A_      :						Optional[Any]		,							A_      :						Optional[Any]		,							A_      :						str		,							A_      :						Union[str, Any]						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					self.num_labels
       lowerCamelCase_	  =					DebertaForTokenClassification(config=A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					model(A_		,							attention_mask=A_		,							token_type_ids=A_		,							labels=A_						)
       self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.seq_length, self.num_labels)						)

 def        a__				(       self      :						str		,							A_      :						str		,							A_      :						Tuple		,							A_      :						Union[str, Any]		,							A_      :						Optional[int]		,							A_      :						Union[str, Any]		,							A_      :						int		,							A_      :						List[Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					DebertaForQuestionAnswering(config=A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					model(
           A_		,							attention_mask=A_		,							token_type_ids=A_		,							start_positions=A_		,							end_positions=A_		,							)
       self.parent.assertEqual(result.start_logits.shape		,							(self.batch_size, self.seq_length)						)
       self.parent.assertEqual(result.end_logits.shape		,							(self.batch_size, self.seq_length)						)





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

       """simple docstring"""






       lowerCamelCase_	  =					self.prepare_config_and_inputs()
       (
           (
           lowerCamelCase_       
       )  ,       (
           lowerCamelCase_       
       )  ,       (
           lowerCamelCase_       
       )  ,       (
           lowerCamelCase_       
       )  ,       (
           lowerCamelCase_       
       )  ,       (
           lowerCamelCase_       
       )  ,       (
           lowerCamelCase_       
       )  ,              
       )	  =					config_and_inputs
       lowerCamelCase_	  =					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
       return config, inputs_dict







@require_torch
class 						A(		UpperCamelCase							,   UpperCamelCase							,   unittest.TestCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		(
     (
         DebertaModel,
         DebertaForMaskedLM,
         DebertaForSequenceClassification,
         DebertaForTokenClassification,
         DebertaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 UpperCamelCase		     =		(
     {
         '''feature-extraction''': DebertaModel,
         '''fill-mask''': DebertaForMaskedLM,
         '''question-answering''': DebertaForQuestionAnswering,
         '''text-classification''': DebertaForSequenceClassification,
         '''token-classification''': DebertaForTokenClassification,
         '''zero-shot''': DebertaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )

 UpperCamelCase		     =		True
 UpperCamelCase		     =		False
 UpperCamelCase		     =		False
 UpperCamelCase		     =		False
 UpperCamelCase		     =		False

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

       """simple docstring"""






       lowerCamelCase_	  =					DebertaModelTester(self						)
       lowerCamelCase_	  =					ConfigTester(self		,							config_class=A_		,							hidden_size=37						)

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

       """simple docstring"""






       self.config_tester.run_common_tests()

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

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_model(*A_						)

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

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_sequence_classification(*A_						)

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

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_masked_lm(*A_						)

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

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_question_answering(*A_						)

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

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_token_classification(*A_						)





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

       """simple docstring"""






       for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase_	  =					DebertaModel.from_pretrained(A_						)
             self.assertIsNotNone(A_						)







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



 '''simple docstring'''


 @unittest.skip(reason='Model not available yet'						)
 def        a__				(       self      :						Union[str, Any]						)     ->			int:

       """simple docstring"""






       pass





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

       """simple docstring"""






       lowerCamelCase_	  =					DebertaModel.from_pretrained('microsoft/deberta-base'						)

       lowerCamelCase_	  =					torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]						)
       lowerCamelCase_	  =					torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
       with torch.no_grad():
             lowerCamelCase_	  =					model(A_		,							attention_mask=A_						)[0]
       # compare the actual values for a slice.
       lowerCamelCase_	  =					torch.tensor(
           [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]						)
       self.assertTrue(torch.allclose(output[:, 1:4, 1:4]		,							A_		,							atol=1E-4						)		,							f"""{output[:, 1:4, 1:4]}"""						)









import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCamelCase       :	List[Any]									=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
lowerCamelCase       :	Tuple									=     "    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"







class 						A(		unittest.TestCase     ):



 '''simple docstring'''


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

       """simple docstring"""






       lowerCamelCase_	  =					tempfile.mkdtemp()
       os.makedirs(os.path.join(self.diffusers_dir		,							'schedulers/'						)						)
       lowerCamelCase_	  =					self.diffusers_dir
       shutil.copy(
           os.path.join(A_		,							'src/diffusers/schedulers/scheduling_ddpm.py'						)		,							os.path.join(self.diffusers_dir		,							'schedulers/scheduling_ddpm.py'						)		,							)

 def        a__				(       self      :						Tuple						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					'src/diffusers'
       shutil.rmtree(self.diffusers_dir						)

 def        a__				(       self      :						str		,							A_      :						Optional[Any]		,							A_      :						Optional[int]		,							A_      :						str		,							A_      :						Optional[Any]=None						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
       if overwrite_result is not None:
             lowerCamelCase_	  =					comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
       lowerCamelCase_	  =					black.Mode(target_versions={black.TargetVersion.PYaa}		,							line_length=119						)
       lowerCamelCase_	  =					black.format_str(A_		,							mode=A_						)
       lowerCamelCase_	  =					os.path.join(self.diffusers_dir		,							'new_code.py'						)
       with open(A_		,							'w'		,							newline='\n'						) as f:
             f.write(A_						)
       if overwrite_result is None:
             self.assertTrue(len(check_copies.is_copy_consistent(A_						)						) == 0						)
       else:
             check_copies.is_copy_consistent(f.name		,							overwrite=A_						)
             with open(A_		,							'r'						) as f:
                   self.assertTrue(f.read()		,							A_						)

 def        a__				(       self      :						Optional[int]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'						)
       self.assertEqual(A_		,							A_						)





 def        a__				(       self      :						Any						)     ->			Dict:

       """simple docstring"""






       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'		,							'DDPMSchedulerOutput'		,							REFERENCE_CODE + '\n'		,							)

       # With no empty line at the end
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'		,							'DDPMSchedulerOutput'		,							A_		,							)

       # Copy consistency with rename
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'		,							'TestSchedulerOutput'		,							re.sub('DDPM'		,							'Test'		,							A_						)		,							)

       # Copy consistency with a really long name
       lowerCamelCase_	  =					'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
       self.check_copy_consistency(
           f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""		,							f"""{long_class_name}SchedulerOutput"""		,							re.sub('Bert'		,							A_		,							A_						)		,							)

       # Copy consistency with overwrite
       self.check_copy_consistency(
           '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'		,							'TestSchedulerOutput'		,							A_		,							overwrite_result=re.sub('DDPM'		,							'Test'		,							A_						)		,							)



