


'''simple docstring'''



from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


__UpperCAmelCase      :Optional[Any]			    =       8




def      _a     (						_lowercase :     Tuple      , _lowercase :     Optional[Any]=BITS  ):





     '''simple docstring'''






     __UpperCAmelCase     :			List[Any]  							=      x.device

     __UpperCAmelCase     :			Any  							=      (x * 255).int().clamp(0      , 255  )

     __UpperCAmelCase     :			Optional[int]  							=      2 ** torch.arange(bits - 1      , -1      , -1      , device=_lowercase  )
     __UpperCAmelCase     :			Dict  							=      rearrange(_lowercase      , '''d -> d 1 1'''  )
     __UpperCAmelCase     :			List[str]  							=      rearrange(_lowercase      , '''b c h w -> b c 1 h w'''  )

     __UpperCAmelCase     :			Optional[int]  							=      ((x & mask) != 0).float()
     __UpperCAmelCase     :			List[str]  							=      rearrange(_lowercase      , '''b c d h w -> b (c d) h w'''  )
     __UpperCAmelCase     :			Optional[Any]  							=      bits * 2 - 1
     return bits




def      _a     (						_lowercase :     Any      , _lowercase :     Optional[Any]=BITS  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Dict  							=      x.device

     __UpperCAmelCase     :			Optional[Any]  							=      (x > 0).int()
     __UpperCAmelCase     :			Tuple  							=      2 ** torch.arange(bits - 1      , -1      , -1      , device=_lowercase      , dtype=torch.intaa  )

     __UpperCAmelCase     :			List[str]  							=      rearrange(_lowercase      , '''d -> d 1 1'''  )
     __UpperCAmelCase     :			Optional[int]  							=      rearrange(_lowercase      , '''b (c d) h w -> b c d h w'''      , d=8  )
     __UpperCAmelCase     :			Optional[int]  							=      reduce(x * mask      , '''b c d h w -> b c h w'''      , '''sum'''  )
     return (dec / 255).clamp(0.0      , 1.0  )




def      _a     (						self :     Tuple      , _lowercase :     torch.FloatTensor      , _lowercase :     int      , _lowercase :     torch.FloatTensor      , _lowercase :     float = 0.0      , _lowercase :     bool = True      , _lowercase :     int=None      , _lowercase :     bool = True      , ):





     '''simple docstring'''






     if self.num_inference_steps is None:
          raise ValueError(
              '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''  )

     # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
     # Ideally, read DDIM paper in-detail understanding

     # Notation (<variable name> -> <name in paper>
     # - pred_noise_t -> e_theta(x_t, t)
     # - pred_original_sample -> f_theta(x_t, t) or x_0
     # - std_dev_t -> sigma_t
     # - eta -> η
     # - pred_sample_direction -> "direction pointing to x_t"
     # - pred_prev_sample -> "x_t-1"

     # 1. get previous step value (=t-1)
     __UpperCAmelCase     :			Dict  							=      timestep - self.config.num_train_timesteps // self.num_inference_steps

     # 2. compute alphas, betas
     __UpperCAmelCase     :			List[str]  							=      self.alphas_cumprod[timestep]
     __UpperCAmelCase     :			Optional[Any]  							=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

     __UpperCAmelCase     :			Union[str, Any]  							=      1 - alpha_prod_t

     # 3. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     __UpperCAmelCase     :			List[str]  							=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

     # 4. Clip "predicted x_0"
     __UpperCAmelCase     :			int  							=      self.bit_scale
     if self.config.clip_sample:
          __UpperCAmelCase     :			Optional[Any]  							=      torch.clamp(_lowercase      , -scale      , _lowercase  )

     # 5. compute variance: "sigma_t(η)" -> see formula (16)
     # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
     __UpperCAmelCase     :			Optional[Any]  							=      self._get_variance(_lowercase      , _lowercase  )
     __UpperCAmelCase     :			str  							=      eta * variance ** 0.5

     if use_clipped_model_output:
          # the model_output is always re-derived from the clipped x_0 in Glide
          __UpperCAmelCase     :			Optional[int]  							=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

     # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     __UpperCAmelCase     :			str  							=      (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

     # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     __UpperCAmelCase     :			Any  							=      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

     if eta > 0:
          # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
          __UpperCAmelCase     :			Optional[Any]  							=      model_output.device if torch.is_tensor(_lowercase  ) else '''cpu'''
          __UpperCAmelCase     :			List[str]  							=      torch.randn(model_output.shape      , dtype=model_output.dtype      , generator=_lowercase  ).to(_lowercase  )
          __UpperCAmelCase     :			int  							=      self._get_variance(_lowercase      , _lowercase  ) ** 0.5 * eta * noise

          __UpperCAmelCase     :			Optional[Any]  							=      prev_sample + variance

     if not return_dict:
          return (prev_sample,)

     return DDIMSchedulerOutput(prev_sample=_lowercase      , pred_original_sample=_lowercase  )




def      _a     (						self :     Optional[int]      , _lowercase :     torch.FloatTensor      , _lowercase :     int      , _lowercase :     torch.FloatTensor      , _lowercase :     List[Any]="epsilon"      , _lowercase :     Any=None      , _lowercase :     bool = True      , ):





     '''simple docstring'''






     __UpperCAmelCase     :			List[str]  							=      timestep

     if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
          __UpperCAmelCase							,       __UpperCAmelCase     :			List[str]  							=      torch.split(_lowercase      , sample.shape[1]      , dim=1  )
     else:
          __UpperCAmelCase     :			Dict  							=      None

     # 1. compute alphas, betas
     __UpperCAmelCase     :			Union[str, Any]  							=      self.alphas_cumprod[t]
     __UpperCAmelCase     :			Optional[Any]  							=      self.alphas_cumprod[t - 1] if t > 0 else self.one
     __UpperCAmelCase     :			int  							=      1 - alpha_prod_t
     __UpperCAmelCase     :			Tuple  							=      1 - alpha_prod_t_prev

     # 2. compute predicted original sample from predicted noise also called
     # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
     if prediction_type == "epsilon":
          __UpperCAmelCase     :			Tuple  							=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif prediction_type == "sample":
          __UpperCAmelCase     :			Dict  							=      model_output
     else:
          raise ValueError(F'Unsupported prediction_type {prediction_type}.'  )

     # 3. Clip "predicted x_0"
     __UpperCAmelCase     :			Optional[int]  							=      self.bit_scale
     if self.config.clip_sample:
          __UpperCAmelCase     :			List[Any]  							=      torch.clamp(_lowercase      , -scale      , _lowercase  )

     # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     __UpperCAmelCase     :			str  							=      (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
     __UpperCAmelCase     :			Optional[int]  							=      self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

     # 5. Compute predicted previous sample µ_t
     # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
     __UpperCAmelCase     :			List[Any]  							=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     __UpperCAmelCase     :			Dict  							=      0
     if t > 0:
          __UpperCAmelCase     :			List[Any]  							=      torch.randn(
              model_output.size()      , dtype=model_output.dtype      , layout=model_output.layout      , generator=_lowercase  ).to(model_output.device  )
          __UpperCAmelCase     :			Dict  							=      (self._get_variance(_lowercase      , predicted_variance=_lowercase  ) ** 0.5) * noise

     __UpperCAmelCase     :			Union[str, Any]  							=      pred_prev_sample + variance

     if not return_dict:
          return (pred_prev_sample,)

     return DDPMSchedulerOutput(prev_sample=_lowercase      , pred_original_sample=_lowercase  )



class      a    (		_a	):


     """simple docstring"""






     def __init__(    self					:		int							,			snake_case					:		UNetaDConditionModel							,			snake_case					:		Union[DDIMScheduler, DDPMScheduler]							,			snake_case					:		Optional[float] = 1.0							,			)						->							List[Any]:
          super().__init__()
          __UpperCAmelCase     :			List[Any]  							=      bit_scale
          __UpperCAmelCase     :			Dict  							=      (
              ddim_bit_scheduler_step if isinstance(snake_case							,			snake_case      ) else ddpm_bit_scheduler_step
          )

          self.register_modules(unet=snake_case							,			scheduler=snake_case      )






     @torch.no_grad()
     def __call__(    self					:		Dict							,			snake_case					:		Optional[int] = 256							,			snake_case					:		Optional[int] = 256							,			snake_case					:		Optional[int] = 50							,			snake_case					:		Optional[torch.Generator] = None							,			snake_case					:		Optional[int] = 1							,			snake_case					:		Optional[str] = "pil"							,			snake_case					:		bool = True							,			**snake_case					:		Tuple							,			)						->							Union[Tuple, ImagePipelineOutput]:
          __UpperCAmelCase     :			List[Any]  							=      torch.randn(
              (batch_size, self.unet.config.in_channels, height, width)							,			generator=snake_case							,			)
          __UpperCAmelCase     :			Tuple  							=      decimal_to_bits(snake_case      ) * self.bit_scale
          __UpperCAmelCase     :			List[Any]  							=      latents.to(self.device      )

          self.scheduler.set_timesteps(snake_case      )

          for t in self.progress_bar(self.scheduler.timesteps      ):
               # predict the noise residual
               __UpperCAmelCase     :			Optional[int]  							=      self.unet(snake_case							,			snake_case      ).sample

               # compute the previous noisy sample x_t -> x_t-1
               __UpperCAmelCase     :			Optional[int]  							=      self.scheduler.step(snake_case							,			snake_case							,			snake_case      ).prev_sample

          __UpperCAmelCase     :			str  							=      bits_to_decimal(snake_case      )

          if output_type == "pil":
               __UpperCAmelCase     :			int  							=      self.numpy_to_pil(snake_case      )

          if not return_dict:
               return (image,)

          return ImagePipelineOutput(images=snake_case      )



'''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      a    (		_a	):


     """simple docstring"""






     def __init__(    self					:		Optional[Any]							,			snake_case					:		Dict							,			snake_case					:		Dict=13							,			snake_case					:		str=7							,			snake_case					:		Dict=True							,			snake_case					:		Any=True							,			snake_case					:		Optional[Any]=True							,			snake_case					:		Optional[Any]=True							,			snake_case					:		List[str]=99							,			snake_case					:		str=32							,			snake_case					:		Any=5							,			snake_case					:		List[str]=4							,			snake_case					:		List[str]=37							,			snake_case					:		int="gelu"							,			snake_case					:		int=0.1							,			snake_case					:		int=0.1							,			snake_case					:		Union[str, Any]=512							,			snake_case					:		int=16							,			snake_case					:		Optional[Any]=2							,			snake_case					:		List[Any]=0.02							,			snake_case					:		Any=False							,			snake_case					:		int=True							,			snake_case					:		Union[str, Any]="None"							,			snake_case					:		str=3							,			snake_case					:		Union[str, Any]=4							,			snake_case					:		Any=None							,			)						->							List[Any]:
          __UpperCAmelCase     :			List[str]  							=      parent
          __UpperCAmelCase     :			Dict  							=      batch_size
          __UpperCAmelCase     :			Any  							=      seq_length
          __UpperCAmelCase     :			List[Any]  							=      is_training
          __UpperCAmelCase     :			List[str]  							=      use_input_mask
          __UpperCAmelCase     :			Union[str, Any]  							=      use_token_type_ids
          __UpperCAmelCase     :			Any  							=      use_labels
          __UpperCAmelCase     :			Any  							=      vocab_size
          __UpperCAmelCase     :			Dict  							=      hidden_size
          __UpperCAmelCase     :			List[str]  							=      num_hidden_layers
          __UpperCAmelCase     :			Union[str, Any]  							=      num_attention_heads
          __UpperCAmelCase     :			List[str]  							=      intermediate_size
          __UpperCAmelCase     :			str  							=      hidden_act
          __UpperCAmelCase     :			Union[str, Any]  							=      hidden_dropout_prob
          __UpperCAmelCase     :			int  							=      attention_probs_dropout_prob
          __UpperCAmelCase     :			Tuple  							=      max_position_embeddings
          __UpperCAmelCase     :			Any  							=      type_vocab_size
          __UpperCAmelCase     :			Tuple  							=      type_sequence_label_size
          __UpperCAmelCase     :			Union[str, Any]  							=      initializer_range
          __UpperCAmelCase     :			Dict  							=      num_labels
          __UpperCAmelCase     :			Any  							=      num_choices
          __UpperCAmelCase     :			Any  							=      relative_attention
          __UpperCAmelCase     :			Dict  							=      position_biased_input
          __UpperCAmelCase     :			Optional[int]  							=      pos_att_type
          __UpperCAmelCase     :			Dict  							=      scope

     def 			lowerCamelCase__			(    self					:		Dict      )						->							Optional[int]:
          __UpperCAmelCase     :			int  							=      ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size      )

          __UpperCAmelCase     :			Optional[Any]  							=      None
          if self.use_input_mask:
               __UpperCAmelCase     :			str  							=      ids_tensor([self.batch_size, self.seq_length]							,			vocab_size=2      )

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

          __UpperCAmelCase     :			List[str]  							=      None
          __UpperCAmelCase     :			int  							=      None
          __UpperCAmelCase     :			str  							=      None
          if self.use_labels:
               __UpperCAmelCase     :			List[str]  							=      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     :			Optional[Any]  							=      ids_tensor([self.batch_size]							,			self.num_choices      )

          __UpperCAmelCase     :			str  							=      self.get_config()

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

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							List[str]:
          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 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							str:
          __UpperCAmelCase     :			Optional[int]  							=      self.get_config()
          __UpperCAmelCase     :			Dict  							=      300
          return config

     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		int      )						->							Any:
          self.parent.assertListEqual(list(result.loss.size()      )							,			[]      )

     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		Optional[int]							,			snake_case					:		Any							,			snake_case					:		List[Any]							,			snake_case					:		Any							,			snake_case					:		Union[str, Any]							,			snake_case					:		Optional[Any]							,			snake_case					:		Union[str, Any]      )						->							Optional[Any]:
          __UpperCAmelCase     :			List[str]  							=      DebertaModel(config=snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			str  							=      model(snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case      )[0]
          __UpperCAmelCase     :			Tuple  							=      model(snake_case							,			token_type_ids=snake_case      )[0]
          __UpperCAmelCase     :			Optional[int]  							=      model(snake_case      )[0]

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

     def 			lowerCamelCase__			(    self					:		Optional[int]							,			snake_case					:		int							,			snake_case					:		Tuple							,			snake_case					:		Any							,			snake_case					:		Tuple							,			snake_case					:		Union[str, Any]							,			snake_case					:		Optional[int]							,			snake_case					:		List[str]      )						->							Optional[int]:
          __UpperCAmelCase     :			Union[str, Any]  							=      DebertaForMaskedLM(config=snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			int  							=      model(snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			labels=snake_case      )

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

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		Union[str, Any]							,			snake_case					:		Tuple							,			snake_case					:		Optional[Any]							,			snake_case					:		Tuple							,			snake_case					:		List[Any]							,			snake_case					:		str							,			snake_case					:		Tuple      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Tuple  							=      self.num_labels
          __UpperCAmelCase     :			List[Any]  							=      DebertaForSequenceClassification(snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			Any  							=      model(snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			labels=snake_case      )
          self.parent.assertListEqual(list(result.logits.size()      )							,			[self.batch_size, self.num_labels]      )
          self.check_loss_output(snake_case      )

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		List[str]							,			snake_case					:		Union[str, Any]							,			snake_case					:		List[str]							,			snake_case					:		Dict							,			snake_case					:		Optional[Any]							,			snake_case					:		Dict							,			snake_case					:		Optional[int]      )						->							int:
          __UpperCAmelCase     :			List[Any]  							=      self.num_labels
          __UpperCAmelCase     :			Union[str, Any]  							=      DebertaForTokenClassification(config=snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			Any  							=      model(snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			labels=snake_case      )
          self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.num_labels)      )

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		List[Any]							,			snake_case					:		Optional[Any]							,			snake_case					:		str							,			snake_case					:		Optional[int]							,			snake_case					:		int							,			snake_case					:		int							,			snake_case					:		str      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Union[str, Any]  							=      DebertaForQuestionAnswering(config=snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			Optional[int]  							=      model(
              snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			start_positions=snake_case							,			end_positions=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 			lowerCamelCase__			(    self					:		str      )						->							int:
          __UpperCAmelCase     :			int  							=      self.prepare_config_and_inputs()
          (
              (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       
          )     :			Any  							=      config_and_inputs
          __UpperCAmelCase     :			str  							=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
          return config, inputs_dict



@require_torch
class      a    (		_a     ,  _a     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       (
         (
             DebertaModel,
             DebertaForMaskedLM,
             DebertaForSequenceClassification,
             DebertaForTokenClassification,
             DebertaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE	:			Dict							       =       (
         {
             "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	:			Union[str, Any]							       =       True
     SCREAMING_SNAKE_CASE	:			Dict							       =       False
     SCREAMING_SNAKE_CASE	:			List[Any]							       =       False
     SCREAMING_SNAKE_CASE	:			List[str]							       =       False
     SCREAMING_SNAKE_CASE	:			Any							       =       False

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Dict  							=      DebertaModelTester(self      )
          __UpperCAmelCase     :			List[Any]  							=      ConfigTester(self							,			config_class=snake_case							,			hidden_size=37      )

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Dict:
          self.config_tester.run_common_tests()

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Optional[Any]:
          __UpperCAmelCase     :			Any  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_model(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Dict      )						->							List[str]:
          __UpperCAmelCase     :			Tuple  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_sequence_classification(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Dict      )						->							str:
          __UpperCAmelCase     :			Any  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_masked_lm(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Any:
          __UpperCAmelCase     :			Optional[int]  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_question_answering(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Dict      )						->							Optional[Any]:
          __UpperCAmelCase     :			Any  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_token_classification(*snake_case      )






     @slow
     def 			lowerCamelCase__			(    self					:		Dict      )						->							Tuple:
          for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCAmelCase     :			str  							=      DebertaModel.from_pretrained(snake_case      )
               self.assertIsNotNone(snake_case      )



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


     """simple docstring"""






     @unittest.skip(reason='''Model not available yet'''      )
     def 			lowerCamelCase__			(    self					:		Dict      )						->							Tuple:
          pass






     @slow
     def 			lowerCamelCase__			(    self					:		int      )						->							Optional[Any]:
          __UpperCAmelCase     :			Any  							=      DebertaModel.from_pretrained('''microsoft/deberta-base'''      )

          __UpperCAmelCase     :			Any  							=      torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]      )
          __UpperCAmelCase     :			Dict  							=      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
          with torch.no_grad():
               __UpperCAmelCase     :			int  							=      model(snake_case							,			attention_mask=snake_case      )[0]
          # compare the actual values for a slice.
          __UpperCAmelCase     :			Optional[Any]  							=      torch.tensor(
              [[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]]      )
          self.assertTrue(torch.allclose(output[:, 1:4, 1:4]							,			snake_case							,			atol=1E-4      )							,			f'{output[:, 1:4, 1:4]}'      )