
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 				lowercase		(					A__		):
							lowercase_       :       Tuple       					=42
							lowercase_       :       Optional[int]       					=None



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__=0.9_99 ,			lowerCAmelCase__="cosine" ,			):
					'''simple docstring'''
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(lowerCAmelCase__		):
															return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2		) ** 2

					elif alpha_transform_type == "exp":

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

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

					lowercase							=						[]
					for i in range(_lowerCAmelCase		):
										lowercase							=						i / num_diffusion_timesteps
										lowercase							=						(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 				lowercase		(					A__       ,  A__		):




							@register_to_config
							def __init__(				self   ,A__ = 1_0_0_0   ,A__ = "fixed_small_log"   ,A__ = True   ,A__ = 1.0   ,A__ = "epsilon"   ,A__ = "squaredcos_cap_v2"   ,):
												if beta_schedule != "squaredcos_cap_v2":
																	raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'''')

												lowercase							=						betas_for_alpha_bar(__snake_case)

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

												# standard deviation of the initial noise distribution
												lowercase							=						1.0

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

												lowercase							=						variance_type




							def  A__      (				self   ,A__   ,A__ = None):
												return sample




							def  A__      (				self   ,A__   ,A__ = None):
												lowercase							=						num_inference_steps
												lowercase							=						(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
												lowercase							=						(np.arange(0   ,__snake_case) * step_ratio).round()[::-1].copy().astype(np.intaa)
												lowercase							=						torch.from_numpy(__snake_case).to(__snake_case)




							def  A__      (				self   ,A__   ,A__=None   ,A__=None   ,A__=None):
												if prev_timestep is None:
																	lowercase							=						t - 1

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

												if prev_timestep == t - 1:
																	lowercase							=						self.betas[t]
												else:
																	lowercase							=						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
												lowercase							=						beta_prod_t_prev / beta_prod_t * beta

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

												# hacks - were probably added for training stability
												if variance_type == "fixed_small_log":
																	lowercase							=						torch.log(torch.clamp(__snake_case   ,min=1E-20))
																	lowercase							=						torch.exp(0.5 * variance)
												elif variance_type == "learned_range":
																	# NOTE difference with DDPM scheduler
																	lowercase							=						variance.log()
																	lowercase							=						beta.log()

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

												return variance




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__ = None   ,A__=None   ,A__ = True   ,):
												lowercase							=						timestep

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

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

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

												if prev_timestep == t - 1:
																	lowercase							=						self.betas[t]
																	lowercase							=						self.alphas[t]
												else:
																	lowercase							=						1 - alpha_prod_t / alpha_prod_t_prev
																	lowercase							=						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":
																	lowercase							=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
												elif self.config.prediction_type == "sample":
																	lowercase							=						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:
																	lowercase							=						torch.clamp(
																	    __snake_case   ,-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
												lowercase							=						(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
												lowercase							=						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
												lowercase							=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

																	lowercase							=						self._get_variance(
																	    __snake_case   ,predicted_variance=__snake_case   ,prev_timestep=__snake_case   ,)

																	if self.variance_type == "fixed_small_log":
																						lowercase							=						variance
																	elif self.variance_type == "learned_range":
																						lowercase							=						(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.''')

																	lowercase							=						variance * variance_noise

												lowercase							=						pred_prev_sample + variance

												if not return_dict:
																	return (pred_prev_sample,)

												return UnCLIPSchedulerOutput(prev_sample=__snake_case   ,pred_original_sample=__snake_case)





							def  A__      (				self   ,A__   ,A__   ,A__   ,):
												# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
												lowercase							=						self.alphas_cumprod.to(device=original_samples.device   ,dtype=original_samples.dtype)
												lowercase							=						timesteps.to(original_samples.device)

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

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

												lowercase							=						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			:     Optional[int]		,			__snake_case			:     str		,			__snake_case			:     Union[str, Any]=2		,			__snake_case			:     Optional[int]=8		,			__snake_case			:     Any=True		,			__snake_case			:     Union[str, Any]=True		,			__snake_case			:     Dict=True		,			__snake_case			:     int=True		,			__snake_case			:     List[Any]=99		,			__snake_case			:     str=16		,			__snake_case			:     Tuple=5		,			__snake_case			:     Tuple=2		,			__snake_case			:     str=36		,			__snake_case			:     Dict="gelu"		,			__snake_case			:     str=0.0		,			__snake_case			:     Optional[int]=0.0		,			__snake_case			:     Optional[int]=512		,			__snake_case			:     Optional[Any]=16		,			__snake_case			:     int=2		,			__snake_case			:     int=0.02		,			__snake_case			:     str=3		,			__snake_case			:     Dict=4		,			__snake_case			:     str=None		,			)			->		Optional[int]:
								UpperCAmelCase :       Optional[int] 							=		parent
								UpperCAmelCase :       Tuple 							=		batch_size
								UpperCAmelCase :       List[str] 							=		seq_length
								UpperCAmelCase :       List[Any] 							=		is_training
								UpperCAmelCase :       int 							=		use_input_mask
								UpperCAmelCase :       Any 							=		use_token_type_ids
								UpperCAmelCase :       str 							=		use_labels
								UpperCAmelCase :       Union[str, Any] 							=		vocab_size
								UpperCAmelCase :       List[str] 							=		hidden_size
								UpperCAmelCase :       Optional[Any] 							=		num_hidden_layers
								UpperCAmelCase :       Union[str, Any] 							=		num_attention_heads
								UpperCAmelCase :       Optional[Any] 							=		intermediate_size
								UpperCAmelCase :       Union[str, Any] 							=		hidden_act
								UpperCAmelCase :       int 							=		hidden_dropout_prob
								UpperCAmelCase :       Optional[int] 							=		attention_probs_dropout_prob
								UpperCAmelCase :       Union[str, Any] 							=		max_position_embeddings
								UpperCAmelCase :       str 							=		type_vocab_size
								UpperCAmelCase :       List[str] 							=		type_sequence_label_size
								UpperCAmelCase :       Tuple 							=		initializer_range
								UpperCAmelCase :       Optional[Any] 							=		num_labels
								UpperCAmelCase :       Optional[int] 							=		num_choices
								UpperCAmelCase :       Any 							=		scope







	def      A	(						self			:     Tuple		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[int] 							=		ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size		)

								UpperCAmelCase :       Optional[int] 							=		None
								if self.use_input_mask:
															UpperCAmelCase :       Dict 							=		random_attention_mask([self.batch_size, self.seq_length]		)

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

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

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

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







	def      A	(						self			:     int		)			->		Tuple:
								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=__snake_case		,			initializer_range=self.initializer_range		,			)







	def      A	(						self			:     Optional[Any]		)			->		Any:
								UpperCAmelCase :       Optional[Any] 							=		self.get_config()
								UpperCAmelCase :       int 							=		300
								return config







	def      A	(						self			:     Optional[Any]		)			->		Any:
								(
								    (
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		
								) :       Tuple 							=		self.prepare_config_and_inputs()

								UpperCAmelCase :       Dict 							=		True
								UpperCAmelCase :       Tuple 							=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
								UpperCAmelCase :       str 							=		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			:     Union[str, Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     int		,			__snake_case			:     Optional[int]		,			__snake_case			:     int		,			__snake_case			:     Dict		,			__snake_case			:     Tuple		,			__snake_case			:     Optional[Any]		)			->		List[str]:
								UpperCAmelCase :       int 							=		MraModel(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       Tuple 							=		model(__snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		model(__snake_case		,			token_type_ids=__snake_case		)
								UpperCAmelCase :       Dict 							=		model(__snake_case		)
								self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)		)







	def      A	(						self			:     Tuple		,			__snake_case			:     Optional[Any]		,			__snake_case			:     List[str]		,			__snake_case			:     List[str]		,			__snake_case			:     int		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Any		,			__snake_case			:     List[Any]		,			__snake_case			:     Optional[Any]		,			)			->		Tuple:
								UpperCAmelCase :       str 							=		True
								UpperCAmelCase :       Tuple 							=		MraModel(__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       Optional[int] 							=		model(
								    __snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		,			encoder_hidden_states=__snake_case		,			encoder_attention_mask=__snake_case		,			)
								UpperCAmelCase :       Optional[Any] 							=		model(
								    __snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		,			encoder_hidden_states=__snake_case		,			)
								UpperCAmelCase :       str 							=		model(__snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		)
								self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)		)







	def      A	(						self			:     Tuple		,			__snake_case			:     str		,			__snake_case			:     Optional[Any]		,			__snake_case			:     List[str]		,			__snake_case			:     Tuple		,			__snake_case			:     Optional[Any]		,			__snake_case			:     List[str]		,			__snake_case			:     int		)			->		Any:
								UpperCAmelCase :       Dict 							=		MraForMaskedLM(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       Optional[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      A	(						self			:     Tuple		,			__snake_case			:     Tuple		,			__snake_case			:     Dict		,			__snake_case			:     Dict		,			__snake_case			:     Any		,			__snake_case			:     int		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Tuple		)			->		Optional[int]:
								UpperCAmelCase :       List[str] 							=		MraForQuestionAnswering(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       List[Any] 							=		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      A	(						self			:     str		,			__snake_case			:     Optional[int]		,			__snake_case			:     List[str]		,			__snake_case			:     str		,			__snake_case			:     int		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     List[Any]		)			->		int:
								UpperCAmelCase :       int 							=		self.num_labels
								UpperCAmelCase :       Union[str, Any] 							=		MraForSequenceClassification(__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       List[str] 							=		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.num_labels)		)







	def      A	(						self			:     str		,			__snake_case			:     Dict		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Dict		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Dict		)			->		int:
								UpperCAmelCase :       Tuple 							=		self.num_labels
								UpperCAmelCase :       List[str] 							=		MraForTokenClassification(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       str 							=		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      A	(						self			:     str		,			__snake_case			:     int		,			__snake_case			:     Any		,			__snake_case			:     Tuple		,			__snake_case			:     Optional[int]		,			__snake_case			:     List[str]		,			__snake_case			:     str		,			__snake_case			:     Union[str, Any]		)			->		Optional[Any]:
								UpperCAmelCase :       Tuple 							=		self.num_choices
								UpperCAmelCase :       int 							=		MraForMultipleChoice(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       str 							=		input_ids.unsqueeze(1		).expand(-1		,			self.num_choices		,			-1		).contiguous()
								UpperCAmelCase :       List[Any] 							=		token_type_ids.unsqueeze(1		).expand(-1		,			self.num_choices		,			-1		).contiguous()
								UpperCAmelCase :       List[str] 							=		input_mask.unsqueeze(1		).expand(-1		,			self.num_choices		,			-1		).contiguous()
								UpperCAmelCase :       List[str] 							=		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.num_choices)		)







	def      A	(						self			:     str		)			->		Dict:
								UpperCAmelCase :       Any 							=		self.prepare_config_and_inputs()
								(
								    (
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		
								) :       List[str] 							=		config_and_inputs
								UpperCAmelCase :       Any 							=		{'''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"""






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

	lowerCamelCase__			 =       ()







	def      A	(						self			:     int		)			->		Union[str, Any]:
								UpperCAmelCase :       List[str] 							=		MraModelTester(self		)
								UpperCAmelCase :       Optional[int] 							=		ConfigTester(self		,			config_class=__snake_case		,			hidden_size=37		)







	def      A	(						self			:     Optional[Any]		)			->		str:
								self.config_tester.run_common_tests()







	def      A	(						self			:     Tuple		)			->		Optional[Any]:
								UpperCAmelCase :       Optional[int] 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__snake_case		)







	def      A	(						self			:     List[Any]		)			->		Optional[Any]:
								UpperCAmelCase :       Tuple 							=		self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
															UpperCAmelCase :       List[Any] 							=		type
															self.model_tester.create_and_check_model(*__snake_case		)







	def      A	(						self			:     Tuple		)			->		Dict:
								UpperCAmelCase :       List[Any] 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*__snake_case		)







	def      A	(						self			:     Tuple		)			->		List[str]:
								UpperCAmelCase :       Tuple 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*__snake_case		)







	def      A	(						self			:     int		)			->		Dict:
								UpperCAmelCase :       Optional[int] 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*__snake_case		)







	def      A	(						self			:     Dict		)			->		Optional[int]:
								UpperCAmelCase :       Any 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*__snake_case		)







	def      A	(						self			:     Any		)			->		Optional[int]:
								UpperCAmelCase :       List[str] 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*__snake_case		)







	@slow
	def      A	(						self			:     Dict		)			->		Any:
								for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase :       str 							=		MraModel.from_pretrained(__snake_case		)
															self.assertIsNotNone(__snake_case		)







	@unittest.skip(reason='''MRA does not output attentions'''		)
	def      A	(						self			:     str		)			->		Optional[Any]:
								return

@require_torch
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	@slow
	def      A	(						self			:     Tuple		)			->		List[Any]:
								UpperCAmelCase :       int 							=		MraModel.from_pretrained('''uw-madison/mra-base-512-4'''		)
								UpperCAmelCase :       Optional[Any] 							=		torch.arange(256		).unsqueeze(0		)

								with torch.no_grad():
															UpperCAmelCase :       List[Any] 							=		model(__snake_case		)[0]

								UpperCAmelCase :       Optional[Any] 							=		torch.Size((1, 256, 768)		)
								self.assertEqual(output.shape		,			__snake_case		)

								UpperCAmelCase :       Any 							=		torch.tensor(
								    [[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]]		)

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







	@slow
	def      A	(						self			:     Optional[Any]		)			->		Any:
								UpperCAmelCase :       Optional[int] 							=		MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''		)
								UpperCAmelCase :       Dict 							=		torch.arange(256		).unsqueeze(0		)

								with torch.no_grad():
															UpperCAmelCase :       List[Any] 							=		model(__snake_case		)[0]

								UpperCAmelCase :       int 							=		50265

								UpperCAmelCase :       int 							=		torch.Size((1, 256, vocab_size)		)
								self.assertEqual(output.shape		,			__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		torch.tensor(
								    [[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]]		)

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







	@slow
	def      A	(						self			:     str		)			->		List[Any]:
								UpperCAmelCase :       List[Any] 							=		MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''		)
								UpperCAmelCase :       List[Any] 							=		torch.arange(4096		).unsqueeze(0		)

								with torch.no_grad():
															UpperCAmelCase :       Tuple 							=		model(__snake_case		)[0]

								UpperCAmelCase :       Optional[int] 							=		50265

								UpperCAmelCase :       Tuple 							=		torch.Size((1, 4096, vocab_size)		)
								self.assertEqual(output.shape		,			__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		torch.tensor(
								    [[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]]		)

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





