





'''simple docstring'''

import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def lowerCamelCase						(     __lowerCamelCase :    Tuple					,       __lowerCamelCase :    Union[str, Any]=0.999					,       __lowerCamelCase :    Dict="cosine"					,       )						->Tuple:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__lowerCamelCase :    int    ):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__lowerCamelCase :    List[str]    ):
            return math.exp(t * -12.0    )

    else:
        raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'    )

    _SCREAMING_SNAKE_CASE									=			[]
    for i in range(__lowerCamelCase    ):
        _SCREAMING_SNAKE_CASE									=			i / num_diffusion_timesteps
        _SCREAMING_SNAKE_CASE									=			(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(__lowerCamelCase    ) / alpha_bar_fn(__lowerCamelCase    )					,       __lowerCamelCase    )    )
    return torch.tensor(__lowerCamelCase					,       dtype=torch.floataa    )






class 					a_     (				snake_case_      ,    snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = [e.name for e in KarrasDiffusionSchedulers]
    UpperCamelCase     = 2


    @register_to_config
    def __init__(		self ,		A = 1000 ,		A = 0.0_0085 ,		A = 0.012 ,		A = "linear" ,		A = None ,		A = "epsilon" ,		A = "linspace" ,		A = 0 ,		)		->   Dict:
        if trained_betas is not None:
            _SCREAMING_SNAKE_CASE									=			torch.tensor(A ,		dtype=torch.floataa       )
        elif beta_schedule == "linear":
            _SCREAMING_SNAKE_CASE									=			torch.linspace(A ,		A ,		A ,		dtype=torch.floataa       )
        elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            _SCREAMING_SNAKE_CASE									=			(
                torch.linspace(beta_start**0.5 ,		beta_end**0.5 ,		A ,		dtype=torch.floataa       ) ** 2
            )
        elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            _SCREAMING_SNAKE_CASE									=			betas_for_alpha_bar(A       )
        else:
            raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'       )

        _SCREAMING_SNAKE_CASE									=			1.0 - self.betas
        _SCREAMING_SNAKE_CASE									=			torch.cumprod(self.alphas ,		dim=0       )

        #  set all values
        self.set_timesteps(A ,		A ,		A       )


    def 			snake_case_(		self ,		A ,		A=None       )		->   Union[str, Any]:
        if schedule_timesteps is None:
            _SCREAMING_SNAKE_CASE									=			self.timesteps

        _SCREAMING_SNAKE_CASE									=			(schedule_timesteps == timestep).nonzero()

        # The sigma index that is taken for the **very** first `step`
        # is always the second index (or the last index if there is only 1)
        # This way we can ensure we don't accidentally skip a sigma in
        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
        if len(self._index_counter       ) == 0:
            _SCREAMING_SNAKE_CASE									=			1 if len(A       ) > 1 else 0
        else:
            _SCREAMING_SNAKE_CASE									=			timestep.cpu().item() if torch.is_tensor(A       ) else timestep
            _SCREAMING_SNAKE_CASE									=			self._index_counter[timestep_int]

        return indices[pos].item()


    @property
    def 			snake_case_(		self       )		->   Tuple:
        # standard deviation of the initial noise distribution
        if self.config.timestep_spacing in ["linspace", "trailing"]:
            return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5


    def 			snake_case_(		self ,		A ,		A ,		)		->   torch.FloatTensor:
        _SCREAMING_SNAKE_CASE									=			self.index_for_timestep(A       )

        if self.state_in_first_order:
            _SCREAMING_SNAKE_CASE									=			self.sigmas[step_index]
        else:
            _SCREAMING_SNAKE_CASE									=			self.sigmas_interpol[step_index]

        _SCREAMING_SNAKE_CASE									=			sample / ((sigma**2 + 1) ** 0.5)
        return sample


    def 			snake_case_(		self ,		A ,		A = None ,		A = None ,		)		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			num_inference_steps

        _SCREAMING_SNAKE_CASE									=			num_train_timesteps or self.config.num_train_timesteps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
            _SCREAMING_SNAKE_CASE									=			np.linspace(0 ,		num_train_timesteps - 1 ,		A ,		dtype=A       )[::-1].copy()
        elif self.config.timestep_spacing == "leading":
            _SCREAMING_SNAKE_CASE									=			num_train_timesteps // self.num_inference_steps
            # creates integer timesteps by multiplying by ratio
            # casting to int to avoid issues when num_inference_step is power of 3
            _SCREAMING_SNAKE_CASE									=			(np.arange(0 ,		A       ) * step_ratio).round()[::-1].copy().astype(A       )
            timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
            _SCREAMING_SNAKE_CASE									=			num_train_timesteps / self.num_inference_steps
            # creates integer timesteps by multiplying by ratio
            # casting to int to avoid issues when num_inference_step is power of 3
            _SCREAMING_SNAKE_CASE									=			(np.arange(A ,		0 ,		-step_ratio       )).round().copy().astype(A       )
            timesteps -= 1
        else:
            raise ValueError(
                f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'       )

        _SCREAMING_SNAKE_CASE									=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
        _SCREAMING_SNAKE_CASE									=			torch.from_numpy(np.log(A       )       ).to(A       )

        _SCREAMING_SNAKE_CASE									=			np.interp(A ,		np.arange(0 ,		len(A       )       ) ,		A       )
        _SCREAMING_SNAKE_CASE									=			np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
        _SCREAMING_SNAKE_CASE									=			torch.from_numpy(A       ).to(device=A       )

        # interpolate sigmas
        _SCREAMING_SNAKE_CASE									=			sigmas.log().lerp(sigmas.roll(1       ).log() ,		0.5       ).exp()

        _SCREAMING_SNAKE_CASE									=			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2       ), sigmas[-1:]]       )
        _SCREAMING_SNAKE_CASE									=			torch.cat(
            [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2       ), sigmas_interpol[-1:]]       )

        if str(A       ).startswith("""mps"""       ):
            # mps does not support float64
            _SCREAMING_SNAKE_CASE									=			torch.from_numpy(A       ).to(A ,		dtype=torch.floataa       )
        else:
            _SCREAMING_SNAKE_CASE									=			torch.from_numpy(A       ).to(A       )

        # interpolate timesteps
        _SCREAMING_SNAKE_CASE									=			self.sigma_to_t(A       ).to(A ,		dtype=timesteps.dtype       )
        _SCREAMING_SNAKE_CASE									=			torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,		dim=-1       ).flatten()

        _SCREAMING_SNAKE_CASE									=			torch.cat([timesteps[:1], interleaved_timesteps]       )

        _SCREAMING_SNAKE_CASE									=			None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        _SCREAMING_SNAKE_CASE									=			defaultdict(A       )


    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        # get log sigma
        _SCREAMING_SNAKE_CASE									=			sigma.log()

        # get distribution
        _SCREAMING_SNAKE_CASE									=			log_sigma - self.log_sigmas[:, None]

        # get sigmas range
        _SCREAMING_SNAKE_CASE									=			dists.ge(0       ).cumsum(dim=0       ).argmax(dim=0       ).clamp(max=self.log_sigmas.shape[0] - 2       )
        _SCREAMING_SNAKE_CASE									=			low_idx + 1

        _SCREAMING_SNAKE_CASE									=			self.log_sigmas[low_idx]
        _SCREAMING_SNAKE_CASE									=			self.log_sigmas[high_idx]

        # interpolate sigmas
        _SCREAMING_SNAKE_CASE									=			(low - log_sigma) / (low - high)
        _SCREAMING_SNAKE_CASE									=			w.clamp(0 ,		1       )

        # transform interpolation to time range
        _SCREAMING_SNAKE_CASE									=			(1 - w) * low_idx + w * high_idx
        _SCREAMING_SNAKE_CASE									=			t.view(sigma.shape       )
        return t


    @property
    def 			snake_case_(		self       )		->   int:
        return self.sample is None


    def 			snake_case_(		self ,		A ,		A ,		A ,		A = True ,		)		->   Union[SchedulerOutput, Tuple]:
        _SCREAMING_SNAKE_CASE									=			self.index_for_timestep(A       )

        # advance index counter by 1
        _SCREAMING_SNAKE_CASE									=			timestep.cpu().item() if torch.is_tensor(A       ) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
            _SCREAMING_SNAKE_CASE									=			self.sigmas[step_index]
            _SCREAMING_SNAKE_CASE									=			self.sigmas_interpol[step_index + 1]
            _SCREAMING_SNAKE_CASE									=			self.sigmas[step_index + 1]
        else:
            # 2nd order / KDPM2's method
            _SCREAMING_SNAKE_CASE									=			self.sigmas[step_index - 1]
            _SCREAMING_SNAKE_CASE									=			self.sigmas_interpol[step_index]
            _SCREAMING_SNAKE_CASE									=			self.sigmas[step_index]

        # currently only gamma=0 is supported. This usually works best anyways.
        # We can support gamma in the future but then need to scale the timestep before
        # passing it to the model which requires a change in API
        _SCREAMING_SNAKE_CASE									=			0
        _SCREAMING_SNAKE_CASE									=			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
            _SCREAMING_SNAKE_CASE									=			sigma_hat if self.state_in_first_order else sigma_interpol
            _SCREAMING_SNAKE_CASE									=			sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
            _SCREAMING_SNAKE_CASE									=			sigma_hat if self.state_in_first_order else sigma_interpol
            _SCREAMING_SNAKE_CASE									=			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                sample / (sigma_input**2 + 1)
            )
        elif self.config.prediction_type == "sample":
            raise NotImplementedError("""prediction_type not implemented yet: sample"""       )
        else:
            raise ValueError(
                f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'       )

        if self.state_in_first_order:
            # 2. Convert to an ODE derivative for 1st order
            _SCREAMING_SNAKE_CASE									=			(sample - pred_original_sample) / sigma_hat
            # 3. delta timestep
            _SCREAMING_SNAKE_CASE									=			sigma_interpol - sigma_hat

            # store for 2nd order step
            _SCREAMING_SNAKE_CASE									=			sample
        else:
            # DPM-Solver-2
            # 2. Convert to an ODE derivative for 2nd order
            _SCREAMING_SNAKE_CASE									=			(sample - pred_original_sample) / sigma_interpol

            # 3. delta timestep
            _SCREAMING_SNAKE_CASE									=			sigma_next - sigma_hat

            _SCREAMING_SNAKE_CASE									=			self.sample
            _SCREAMING_SNAKE_CASE									=			None

        _SCREAMING_SNAKE_CASE									=			sample + derivative * dt

        if not return_dict:
            return (prev_sample,)

        return SchedulerOutput(prev_sample=A       )


    def 			snake_case_(		self ,		A ,		A ,		A ,		)		->   torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        _SCREAMING_SNAKE_CASE									=			self.sigmas.to(device=original_samples.device ,		dtype=original_samples.dtype       )
        if original_samples.device.type == "mps" and torch.is_floating_point(A       ):
            # mps does not support float64
            _SCREAMING_SNAKE_CASE									=			self.timesteps.to(original_samples.device ,		dtype=torch.floataa       )
            _SCREAMING_SNAKE_CASE									=			timesteps.to(original_samples.device ,		dtype=torch.floataa       )
        else:
            _SCREAMING_SNAKE_CASE									=			self.timesteps.to(original_samples.device       )
            _SCREAMING_SNAKE_CASE									=			timesteps.to(original_samples.device       )

        _SCREAMING_SNAKE_CASE									=			[self.index_for_timestep(A ,		A       ) for t in timesteps]

        _SCREAMING_SNAKE_CASE									=			sigmas[step_indices].flatten()
        while len(sigma.shape       ) < len(original_samples.shape       ):
            _SCREAMING_SNAKE_CASE									=			sigma.unsqueeze(-1       )

        _SCREAMING_SNAKE_CASE									=			original_samples + noise * sigma
        return noisy_samples





    def __len__(		self       )		->   str:
        return self.config.num_train_timesteps




"""simple docstring"""




import unittest

from transformers import DebertaVaConfig, 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 (
         DebertaVaForMaskedLM,
         DebertaVaForMultipleChoice,
         DebertaVaForQuestionAnswering,
         DebertaVaForSequenceClassification,
         DebertaVaForTokenClassification,
         DebertaVaModel,
     )
     from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class 						__A							( SCREAMING_SNAKE_CASE_		):






     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__=0.0_2				,     a__=False				,     a__=True				,     a__="None"				,     a__=3				,     a__=4				,     a__=None				,     ):
       _lowerCAmelCase						:       Dict  			=     parent
       _lowerCAmelCase						:       str  			=     batch_size
       _lowerCAmelCase						:       List[Any]  			=     seq_length
       _lowerCAmelCase						:       Dict  			=     is_training
       _lowerCAmelCase						:       Dict  			=     use_input_mask
       _lowerCAmelCase						:       int  			=     use_token_type_ids
       _lowerCAmelCase						:       int  			=     use_labels
       _lowerCAmelCase						:       Optional[int]  			=     vocab_size
       _lowerCAmelCase						:       Tuple  			=     hidden_size
       _lowerCAmelCase						:       Tuple  			=     num_hidden_layers
       _lowerCAmelCase						:       Dict  			=     num_attention_heads
       _lowerCAmelCase						:       Union[str, Any]  			=     intermediate_size
       _lowerCAmelCase						:       str  			=     hidden_act
       _lowerCAmelCase						:       Any  			=     hidden_dropout_prob
       _lowerCAmelCase						:       List[str]  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       List[str]  			=     max_position_embeddings
       _lowerCAmelCase						:       List[str]  			=     type_vocab_size
       _lowerCAmelCase						:       Tuple  			=     type_sequence_label_size
       _lowerCAmelCase						:       List[Any]  			=     initializer_range
       _lowerCAmelCase						:       Union[str, Any]  			=     num_labels
       _lowerCAmelCase						:       Optional[Any]  			=     num_choices
       _lowerCAmelCase						:       Tuple  			=     relative_attention
       _lowerCAmelCase						:       Tuple  			=     position_biased_input
       _lowerCAmelCase						:       Dict  			=     pos_att_type
       _lowerCAmelCase						:       Any  			=     scope






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size  )

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

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

       _lowerCAmelCase						:       Union[str, Any]  			=     None
       _lowerCAmelCase						:       Union[str, Any]  			=     None
       _lowerCAmelCase						:       Any  			=     None
       if self.use_labels:
         _lowerCAmelCase						:       str  			=     ids_tensor([self.batch_size]				,     self.type_sequence_label_size  )
         _lowerCAmelCase						:       int  			=     ids_tensor([self.batch_size, self.seq_length]				,     self.num_labels  )
         _lowerCAmelCase						:       str  			=     ids_tensor([self.batch_size]				,     self.num_choices  )

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

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






     def     __A  (		self  ):
       return DebertaVaConfig(
           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				,     a__  ):
       self.parent.assertListEqual(list(result.loss.size()  )				,     []  )






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     DebertaVaModel(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       List[Any]  			=     model(a__				,     attention_mask=a__				,     token_type_ids=a__  )[0]
       _lowerCAmelCase						:       List[Any]  			=     model(a__				,     token_type_ids=a__  )[0]
       _lowerCAmelCase						:       Any  			=     model(a__  )[0]

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






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       List[str]  			=     DebertaVaForMaskedLM(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[Any]  			=     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				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       int  			=     self.num_labels
       _lowerCAmelCase						:       int  			=     DebertaVaForSequenceClassification(a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[Any]  			=     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				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       List[Any]  			=     self.num_labels
       _lowerCAmelCase						:       str  			=     DebertaVaForTokenClassification(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[Any]  			=     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				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Any  			=     DebertaVaForQuestionAnswering(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Dict  			=     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				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     DebertaVaForMultipleChoice(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[int]  			=     input_ids.unsqueeze(1  ).expand(-1				,     self.num_choices				,     -1  ).contiguous()
       _lowerCAmelCase						:       Optional[Any]  			=     token_type_ids.unsqueeze(1  ).expand(-1				,     self.num_choices				,     -1  ).contiguous()
       _lowerCAmelCase						:       str  			=     input_mask.unsqueeze(1  ).expand(-1				,     self.num_choices				,     -1  ).contiguous()
       _lowerCAmelCase						:       List[str]  			=     model(
           a__				,     attention_mask=a__				,     token_type_ids=a__				,     labels=a__				,     )
       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_choices)  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     self.prepare_config_and_inputs()
       (
           (
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	
       )						:       Union[str, Any]  			=     config_and_inputs
       _lowerCAmelCase						:       Union[str, Any]  			=     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
       return config, inputs_dict







@require_torch
class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			Union[str, Any]							=		(
         (
             DebertaVaModel,
             DebertaVaForMaskedLM,
             DebertaVaForSequenceClassification,
             DebertaVaForTokenClassification,
             DebertaVaForQuestionAnswering,
             DebertaVaForMultipleChoice,
         )
         if is_torch_available()
         else ()
     )
     _UpperCamelCase							:			str							=		(
         {
             "feature-extraction": DebertaVaModel,
             "fill-mask": DebertaVaForMaskedLM,
             "question-answering": DebertaVaForQuestionAnswering,
             "text-classification": DebertaVaForSequenceClassification,
             "token-classification": DebertaVaForTokenClassification,
             "zero-shot": DebertaVaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )

     _UpperCamelCase							:			Optional[Any]							=		True
     _UpperCamelCase							:			List[Any]							=		False
     _UpperCamelCase							:			List[Any]							=		False
     _UpperCamelCase							:			Dict							=		False
     _UpperCamelCase							:			Tuple							=		False






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     DebertaVaModelTester(self  )
       _lowerCAmelCase						:       Any  			=     ConfigTester(self				,     config_class=a__				,     hidden_size=37  )






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






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_model(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_sequence_classification(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_masked_lm(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_question_answering(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_token_classification(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_multiple_choice(*a__  )






     @slow
     def     __A  (		self  ):
       for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						:       Tuple  			=     DebertaVaModel.from_pretrained(a__  )
         self.assertIsNotNone(a__  )







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






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






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     DebertaVaModel.from_pretrained("""microsoft/deberta-v2-xlarge"""  )

       _lowerCAmelCase						:       Dict  			=     torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  )
       _lowerCAmelCase						:       Any  			=     torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
       with torch.no_grad():
         _lowerCAmelCase						:       Optional[Any]  			=     model(a__				,     attention_mask=a__  )[0]
       # compare the actual values for a slice.
       _lowerCAmelCase						:       str  			=     torch.tensor(
           [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]  )
       self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,     a__				,     atol=1e-4  )				,     F"{output[:, 1:4, 1:4]}"  )




