



"""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  lowerCamelCase__ (	_lowerCamelCase			):






    def __init__(			self     ,A     ,A=13     ,A=7     ,A=True     ,A=True     ,A=True     ,A=True     ,A=99     ,A=32     ,A=5     ,A=4     ,A=37     ,A="gelu"     ,A=0.1     ,A=0.1     ,A=512     ,A=16     ,A=2     ,A=0.02     ,A=False     ,A=True     ,A="None"     ,A=3     ,A=4     ,A=None     ,):
          UpperCAmelCase          =	parent
          UpperCAmelCase          =	batch_size
          UpperCAmelCase          =	seq_length
          UpperCAmelCase          =	is_training
          UpperCAmelCase          =	use_input_mask
          UpperCAmelCase          =	use_token_type_ids
          UpperCAmelCase          =	use_labels
          UpperCAmelCase          =	vocab_size
          UpperCAmelCase          =	hidden_size
          UpperCAmelCase          =	num_hidden_layers
          UpperCAmelCase          =	num_attention_heads
          UpperCAmelCase          =	intermediate_size
          UpperCAmelCase          =	hidden_act
          UpperCAmelCase          =	hidden_dropout_prob
          UpperCAmelCase          =	attention_probs_dropout_prob
          UpperCAmelCase          =	max_position_embeddings
          UpperCAmelCase          =	type_vocab_size
          UpperCAmelCase          =	type_sequence_label_size
          UpperCAmelCase          =	initializer_range
          UpperCAmelCase          =	num_labels
          UpperCAmelCase          =	num_choices
          UpperCAmelCase          =	relative_attention
          UpperCAmelCase          =	position_biased_input
          UpperCAmelCase          =	pos_att_type
          UpperCAmelCase          =	scope






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size )

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

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

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

          UpperCAmelCase          =	self.get_config()

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






    def     _UpperCamelCase							(			self ):
          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     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.get_config()
          UpperCAmelCase          =	300
          return config






    def     _UpperCamelCase							(			self     ,A ):
          self.parent.assertListEqual(list(result.loss.size() )     ,[] )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A     ,A     ,A ):
          UpperCAmelCase          =	DebertaModel(config=A )
          model.to(A )
          model.eval()
          UpperCAmelCase          =	model(A     ,attention_mask=A     ,token_type_ids=A )[0]
          UpperCAmelCase          =	model(A     ,token_type_ids=A )[0]
          UpperCAmelCase          =	model(A )[0]

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






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A     ,A     ,A ):
          UpperCAmelCase          =	DebertaForMaskedLM(config=A )
          model.to(A )
          model.eval()
          UpperCAmelCase          =	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     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A     ,A     ,A ):
          UpperCAmelCase          =	self.num_labels
          UpperCAmelCase          =	DebertaForSequenceClassification(A )
          model.to(A )
          model.eval()
          UpperCAmelCase          =	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     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A     ,A     ,A ):
          UpperCAmelCase          =	self.num_labels
          UpperCAmelCase          =	DebertaForTokenClassification(config=A )
          model.to(A )
          model.eval()
          UpperCAmelCase          =	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     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A     ,A     ,A ):
          UpperCAmelCase          =	DebertaForQuestionAnswering(config=A )
          model.to(A )
          model.eval()
          UpperCAmelCase          =	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     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase			
          )      , (
              UpperCAmelCase			
          )      , (
              UpperCAmelCase			
          )      , (
              UpperCAmelCase			
          )      , (
              UpperCAmelCase			
          )      , (
              UpperCAmelCase			
          )      , (
              UpperCAmelCase			
          )      , 			
          )          =	config_and_inputs
          UpperCAmelCase          =	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
          return config, inputs_dict







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

    SCREAMING_SNAKE_CASE		=      True
    SCREAMING_SNAKE_CASE		=      False
    SCREAMING_SNAKE_CASE		=      False
    SCREAMING_SNAKE_CASE		=      False
    SCREAMING_SNAKE_CASE		=      False






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	DebertaModelTester(self )
          UpperCAmelCase          =	ConfigTester(self     ,config_class=A     ,hidden_size=37 )






    def     _UpperCamelCase							(			self ):
          self.config_tester.run_common_tests()






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_model(*A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_sequence_classification(*A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_masked_lm(*A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_question_answering(*A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_token_classification(*A )






    @slow
    def     _UpperCamelCase							(			self ):


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







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






    @unittest.skip(reason="""Model not available yet""" )
    def     _UpperCamelCase							(			self ):
          pass






    @slow
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	DebertaModel.from_pretrained("""microsoft/deberta-base""" )

          UpperCAmelCase          =	torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
          UpperCAmelCase          =	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
          with torch.no_grad():
                UpperCAmelCase          =	model(A     ,attention_mask=A )[0]
          # compare the actual values for a slice.
          UpperCAmelCase          =	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]}''' )











"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      42
    SCREAMING_SNAKE_CASE		=      42






    def __init__(			self     ,A     ,A ):
          super().__init__()
          self.register_modules(unet=A     ,scheduler=A )






    @torch.no_grad()
    def __call__(			self     ,A = 1     ,A = 2_000     ,A = None     ,A = "pil"     ,A = True     ,**A     ,):
          UpperCAmelCase          =	self.unet.config.sample_size
          UpperCAmelCase          =	(batch_size, 3, img_size, img_size)

          UpperCAmelCase          =	self.unet

          UpperCAmelCase          =	randn_tensor(A     ,generator=A ) * self.scheduler.init_noise_sigma
          UpperCAmelCase          =	sample.to(self.device )

          self.scheduler.set_timesteps(A )
          self.scheduler.set_sigmas(A )

          for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
                UpperCAmelCase          =	self.scheduler.sigmas[i] * torch.ones(shape[0]     ,device=self.device )

                # correction step
                for _ in range(self.scheduler.config.correct_steps ):
                      UpperCAmelCase          =	self.unet(A     ,A ).sample
                      UpperCAmelCase          =	self.scheduler.step_correct(A     ,A     ,generator=A ).prev_sample

                # prediction step
                UpperCAmelCase          =	model(A     ,A ).sample
                UpperCAmelCase          =	self.scheduler.step_pred(A     ,A     ,A     ,generator=A )

                UpperCAmelCase      , UpperCAmelCase          =	output.prev_sample, output.prev_sample_mean

          UpperCAmelCase          =	sample_mean.clamp(0     ,1 )
          UpperCAmelCase          =	sample.cpu().permute(0     ,2     ,3     ,1 ).numpy()
          if output_type == "pil":
                UpperCAmelCase          =	self.numpy_to_pil(A )

          if not return_dict:
                return (sample,)

          return ImagePipelineOutput(images=A )



