



'''simple docstring'''


import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  None







def 				__lowerCamelCase				(   A__    ,						A__=0.999    ,						A__="cosine"    ,						)       ->	Tuple:






   """simple docstring"""
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(A__    ):
         return math.exp(t * -12.0    )

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

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

class  SCREAMING_SNAKE_CASE (  _a   ,						_a		):






    """simple docstring"""





    @register_to_config
    def __init__(  self      :					List[str]  ,						UpperCamelCase__      :					int = 1_0_0_0  ,						UpperCamelCase__      :					str = "fixed_small_log"  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Optional[float] = 1.0  ,						UpperCamelCase__      :					str = "epsilon"  ,						UpperCamelCase__      :					str = "squaredcos_cap_v2"  ,						):


       """simple docstring"""







       if beta_schedule != "squaredcos_cap_v2":
          raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'' )

       UpperCamelCase					=  betas_for_alpha_bar(UpperCamelCase__ )

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

       # standard deviation of the initial noise distribution
       UpperCamelCase					=  1.0

       # setable values
       UpperCamelCase					=  None
       UpperCamelCase					=  torch.from_numpy(np.arange(0  ,						UpperCamelCase__ )[::-1].copy() )

       UpperCamelCase					=  variance_type






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None ):


       """simple docstring"""







       return sample






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Union[str, torch.device] = None ):


       """simple docstring"""







       UpperCamelCase					=  num_inference_steps
       UpperCamelCase					=  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
       UpperCamelCase					=  (np.arange(0  ,						UpperCamelCase__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
       UpperCamelCase					=  torch.from_numpy(UpperCamelCase__ ).to(UpperCamelCase__ )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						UpperCamelCase__      :					Optional[int]=None  ,						UpperCamelCase__      :					Tuple=None ):


       """simple docstring"""







       if prev_timestep is None:
          UpperCamelCase					=  t - 1

       UpperCamelCase					=  self.alphas_cumprod[t]
       UpperCamelCase					=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       UpperCamelCase					=  1 - alpha_prod_t
       UpperCamelCase					=  1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
          UpperCamelCase					=  self.betas[t]
       else:
          UpperCamelCase					=  1 - alpha_prod_t / alpha_prod_t_prev

       # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
       # and sample from it to get previous sample
       # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
       UpperCamelCase					=  beta_prod_t_prev / beta_prod_t * beta

       if variance_type is None:
          UpperCamelCase					=  self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small_log":
          UpperCamelCase					=  torch.log(torch.clamp(UpperCamelCase__  ,						min=1E-2_0 ) )
          UpperCamelCase					=  torch.exp(0.5 * variance )
       elif variance_type == "learned_range":
          # NOTE difference with DDPM scheduler
          UpperCamelCase					=  variance.log()
          UpperCamelCase					=  beta.log()

          UpperCamelCase					=  (predicted_variance + 1) / 2
          UpperCamelCase					=  frac * max_log + (1 - frac) * min_log

       return variance






    def  A       (  self      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					bool = True  ,						):


       """simple docstring"""







       UpperCamelCase					=  timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
          UpperCamelCase				,      UpperCamelCase					=  torch.split(UpperCamelCase__  ,						sample.shape[1]  ,						dim=1 )
       else:
          UpperCamelCase					=  None

       # 1. compute alphas, betas
       if prev_timestep is None:
          UpperCamelCase					=  t - 1

       UpperCamelCase					=  self.alphas_cumprod[t]
       UpperCamelCase					=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       UpperCamelCase					=  1 - alpha_prod_t
       UpperCamelCase					=  1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
          UpperCamelCase					=  self.betas[t]
          UpperCamelCase					=  self.alphas[t]
       else:
          UpperCamelCase					=  1 - alpha_prod_t / alpha_prod_t_prev
          UpperCamelCase					=  1 - beta

       # 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 self.config.prediction_type == "epsilon":
          UpperCamelCase					=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
          UpperCamelCase					=  model_output
       else:
          raise ValueError(
              f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
              ' for the UnCLIPScheduler.' )

       # 3. Clip "predicted x_0"
       if self.config.clip_sample:
          UpperCamelCase					=  torch.clamp(
              UpperCamelCase__  ,						-self.config.clip_sample_range  ,						self.config.clip_sample_range )

       # 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					=  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
       UpperCamelCase					=  alpha ** 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					=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       UpperCamelCase					=  0
       if t > 0:
          UpperCamelCase					=  randn_tensor(
              model_output.shape  ,						dtype=model_output.dtype  ,						generator=UpperCamelCase__  ,						device=model_output.device )

          UpperCamelCase					=  self._get_variance(
              UpperCamelCase__  ,						predicted_variance=UpperCamelCase__  ,						prev_timestep=UpperCamelCase__  ,						)

          if self.variance_type == "fixed_small_log":
             UpperCamelCase					=  variance
          elif self.variance_type == "learned_range":
             UpperCamelCase					=  (0.5 * variance).exp()
          else:
             raise ValueError(
                 f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
                 ' for the UnCLIPScheduler.' )

          UpperCamelCase					=  variance * variance_noise

       UpperCamelCase					=  pred_prev_sample + variance

       if not return_dict:
          return (pred_prev_sample,)

       return UnCLIPSchedulerOutput(prev_sample=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.IntTensor  ,						):


       """simple docstring"""







       UpperCamelCase					=  self.alphas_cumprod.to(device=original_samples.device  ,						dtype=original_samples.dtype )
       UpperCamelCase					=  timesteps.to(original_samples.device )

       UpperCamelCase					=  alphas_cumprod[timesteps] ** 0.5
       UpperCamelCase					=  sqrt_alpha_prod.flatten()
       while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
          UpperCamelCase					=  sqrt_alpha_prod.unsqueeze(-1 )

       UpperCamelCase					=  (1 - alphas_cumprod[timesteps]) ** 0.5
       UpperCamelCase					=  sqrt_one_minus_alpha_prod.flatten()
       while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
          UpperCamelCase					=  sqrt_one_minus_alpha_prod.unsqueeze(-1 )

       UpperCamelCase					=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples






'''simple docstring'''


import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
   import torch

   from transformers import (
       MraForMaskedLM,
       MraForMultipleChoice,
       MraForQuestionAnswering,
       MraForSequenceClassification,
       MraForTokenClassification,
       MraModel,
   )
   from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					Any  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					Any=2  ,						UpperCamelCase__      :					Union[str, Any]=8  ,						UpperCamelCase__      :					List[Any]=True  ,						UpperCamelCase__      :					Any=True  ,						UpperCamelCase__      :					str=True  ,						UpperCamelCase__      :					Dict=True  ,						UpperCamelCase__      :					List[Any]=9_9  ,						UpperCamelCase__      :					List[Any]=1_6  ,						UpperCamelCase__      :					List[str]=5  ,						UpperCamelCase__      :					Dict=2  ,						UpperCamelCase__      :					Optional[int]=3_6  ,						UpperCamelCase__      :					str="gelu"  ,						UpperCamelCase__      :					Dict=0.0  ,						UpperCamelCase__      :					Dict=0.0  ,						UpperCamelCase__      :					Optional[int]=5_1_2  ,						UpperCamelCase__      :					Dict=1_6  ,						UpperCamelCase__      :					List[str]=2  ,						UpperCamelCase__      :					Any=0.0_2  ,						UpperCamelCase__      :					str=3  ,						UpperCamelCase__      :					Tuple=4  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						):


       """simple docstring"""







       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					=  scope






    def  A       (  self      :					int ):


       """simple docstring"""







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

       UpperCamelCase					=  None
       if self.use_input_mask:
          UpperCamelCase					=  random_attention_mask([self.batch_size, self.seq_length] )

       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  A       (  self      :					Optional[int] ):


       """simple docstring"""







       return MraConfig(
           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  ,						is_decoder=UpperCamelCase__  ,						initializer_range=self.initializer_range  ,						)






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.get_config()
       UpperCamelCase					=  3_0_0
       return config






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       (
           (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      (
           UpperCamelCase
       )				,      
       )					=  self.prepare_config_and_inputs()

       UpperCamelCase					=  True
       UpperCamelCase					=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
       UpperCamelCase					=  ids_tensor([self.batch_size, self.seq_length]  ,						vocab_size=2 )

       return (
           config,
           input_ids,
           token_type_ids,
           input_mask,
           sequence_labels,
           token_labels,
           choice_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  MraModel(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__ )
       UpperCamelCase					=  model(UpperCamelCase__  ,						token_type_ids=UpperCamelCase__ )
       UpperCamelCase					=  model(UpperCamelCase__ )
       self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size) )






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					Optional[Any]  ,						):


       """simple docstring"""







       UpperCamelCase					=  True
       UpperCamelCase					=  MraModel(UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						encoder_hidden_states=UpperCamelCase__  ,						encoder_attention_mask=UpperCamelCase__  ,						)
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						encoder_hidden_states=UpperCamelCase__  ,						)
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__ )
       self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size) )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  MraForMaskedLM(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.vocab_size) )






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  MraForQuestionAnswering(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						start_positions=UpperCamelCase__  ,						end_positions=UpperCamelCase__  ,						)
       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]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  self.num_labels
       UpperCamelCase					=  MraForSequenceClassification(UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels) )






    def  A       (  self      :					Any  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.num_labels
       UpperCamelCase					=  MraForTokenClassification(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.num_labels) )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  self.num_choices
       UpperCamelCase					=  MraForMultipleChoice(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  input_ids.unsqueeze(1 ).expand(-1  ,						self.num_choices  ,						-1 ).contiguous()
       UpperCamelCase					=  token_type_ids.unsqueeze(1 ).expand(-1  ,						self.num_choices  ,						-1 ).contiguous()
       UpperCamelCase					=  input_mask.unsqueeze(1 ).expand(-1  ,						self.num_choices  ,						-1 ).contiguous()
       UpperCamelCase					=  model(
           UpperCamelCase__  ,						attention_mask=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						labels=UpperCamelCase__  ,						)
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_choices) )






    def  A       (  self      :					int ):


       """simple docstring"""







       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  SCREAMING_SNAKE_CASE (  _a   ,						unittest.TestCase		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (
        (
            MraModel,
            MraForMaskedLM,
            MraForMultipleChoice,
            MraForQuestionAnswering,
            MraForSequenceClassification,
            MraForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False

    _SCREAMING_SNAKE_CASE	  =  ()






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  MraModelTester(self )
       UpperCamelCase					=  ConfigTester(self  ,						config_class=UpperCamelCase__  ,						hidden_size=3_7 )






    def  A       (  self      :					str ):


       """simple docstring"""







       self.config_tester.run_common_tests()






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__ )






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
          UpperCamelCase					=  type
          self.model_tester.create_and_check_model(*UpperCamelCase__ )






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__ )






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase__ )






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__ )






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__ )






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__ )






    @slow
    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          UpperCamelCase					=  MraModel.from_pretrained(UpperCamelCase__ )
          self.assertIsNotNone(UpperCamelCase__ )






    @unittest.skip(reason='MRA does not output attentions' )
    def  A       (  self      :					List[str] ):


       """simple docstring"""







       return

@require_torch
class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    @slow
    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  MraModel.from_pretrained('uw-madison/mra-base-512-4' )
       UpperCamelCase					=  torch.arange(2_5_6 ).unsqueeze(0 )

       with torch.no_grad():
          UpperCamelCase					=  model(UpperCamelCase__ )[0]

       UpperCamelCase					=  torch.Size((1, 2_5_6, 7_6_8) )
       self.assertEqual(output.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor(
           [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]  ,						UpperCamelCase__  ,						atol=1E-4 ) )






    @slow
    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4' )
       UpperCamelCase					=  torch.arange(2_5_6 ).unsqueeze(0 )

       with torch.no_grad():
          UpperCamelCase					=  model(UpperCamelCase__ )[0]

       UpperCamelCase					=  5_0_2_6_5

       UpperCamelCase					=  torch.Size((1, 2_5_6, vocab_size) )
       self.assertEqual(output.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor(
           [[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]  ,						UpperCamelCase__  ,						atol=1E-4 ) )






    @slow
    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3' )
       UpperCamelCase					=  torch.arange(4_0_9_6 ).unsqueeze(0 )

       with torch.no_grad():
          UpperCamelCase					=  model(UpperCamelCase__ )[0]

       UpperCamelCase					=  5_0_2_6_5

       UpperCamelCase					=  torch.Size((1, 4_0_9_6, vocab_size) )
       self.assertEqual(output.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor(
           [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]] )

       self.assertTrue(torch.allclose(output[:, :3, :3]  ,						UpperCamelCase__  ,						atol=1E-4 ) )

