






"""simple docstring"""



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

import numpy as np
import torch
from scipy import integrate

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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 	a					(       a_     ):
    UpperCAmelCase_			:					torch.FloatTensor
    UpperCAmelCase_			:					Optional[torch.FloatTensor]     					=None


def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  Any		, __snake_case      :  Any=0.999		, __snake_case      :  Tuple="cosine"		, ):





  '''simple docstring'''





  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

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



class 	a					(       a_,    a_     ):
    UpperCAmelCase_			:					str     					=[e.name for e in KarrasDiffusionSchedulers]
    UpperCAmelCase_			:					Any     					=1







    @register_to_config
    def __init__(      self							,		_lowerCamelCase = 1_0_0_0							,		_lowerCamelCase = 0.0_0_0_1							,		_lowerCamelCase = 0.0_2							,		_lowerCamelCase = "linear"							,		_lowerCamelCase = None							,		_lowerCamelCase = False							,		_lowerCamelCase = "epsilon"							,		_lowerCamelCase = "linspace"							,		_lowerCamelCase = 0							,		):
      if trained_betas is not None:
        lowercase							   =	torch.tensor(_lowerCamelCase							,		dtype=torch.floataa	)
      elif beta_schedule == "linear":
        lowercase							   =	torch.linspace(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		dtype=torch.floataa	)
      elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        lowercase							   =	(
            torch.linspace(beta_start**0.5							,		beta_end**0.5							,		_lowerCamelCase							,		dtype=torch.floataa	) ** 2
        )
      elif beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        lowercase							   =	betas_for_alpha_bar(_lowerCamelCase	)
      else:
        raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'	)

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

      lowercase							   =	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
      lowercase							   =	np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
      lowercase							   =	torch.from_numpy(_lowerCamelCase	)

      # setable values
      lowercase							   =	None
      lowercase							   =	use_karras_sigmas
      self.set_timesteps(_lowerCamelCase							,		_lowerCamelCase	)
      lowercase							   =	[]
      lowercase							   =	False







    @property
    def       UpperCamelCase_				(      self	):
      # 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       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase	):
      if isinstance(_lowerCamelCase							,		torch.Tensor	):
        lowercase							   =	timestep.to(self.timesteps.device	)
      lowercase							   =	(self.timesteps == timestep).nonzero().item()
      lowercase							   =	self.sigmas[step_index]
      lowercase							   =	sample / ((sigma**2 + 1) ** 0.5)
      lowercase							   =	True
      return sample







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):

      def lms_derivative(_lowerCamelCase	):
        lowercase							   =	1.0
        for k in range(_lowerCamelCase	):
          if current_order == k:
            continue
          prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
        return prod

      lowercase							   =	integrate.quad(_lowerCamelCase							,		self.sigmas[t]							,		self.sigmas[t + 1]							,		epsrel=1e-4	)[0]

      return integrated_coeff







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None	):
      lowercase							   =	num_inference_steps

      # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
      if self.config.timestep_spacing == "linspace":
        lowercase							   =	np.linspace(0							,		self.config.num_train_timesteps - 1							,		_lowerCamelCase							,		dtype=_lowerCamelCase	)[
            ::-1
        ].copy()
      elif self.config.timestep_spacing == "leading":
        lowercase							   =	self.config.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
        lowercase							   =	(np.arange(0							,		_lowerCamelCase	) * step_ratio).round()[::-1].copy().astype(_lowerCamelCase	)
        timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
        lowercase							   =	self.config.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
        lowercase							   =	(np.arange(self.config.num_train_timesteps							,		0							,		-step_ratio	)).round().copy().astype(_lowerCamelCase	)
        timesteps -= 1
      else:
        raise ValueError(
            F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'	)

      lowercase							   =	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
      lowercase							   =	np.log(_lowerCamelCase	)
      lowercase							   =	np.interp(_lowerCamelCase							,		np.arange(0							,		len(_lowerCamelCase	)	)							,		_lowerCamelCase	)

      if self.use_karras_sigmas:
        lowercase							   =	self._convert_to_karras(in_sigmas=_lowerCamelCase	)
        lowercase							   =	np.array([self._sigma_to_t(_lowerCamelCase							,		_lowerCamelCase	) for sigma in sigmas]	)

      lowercase							   =	np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)

      lowercase							   =	torch.from_numpy(_lowerCamelCase	).to(device=_lowerCamelCase	)
      if str(_lowerCamelCase	).startswith('mps'	):
        # mps does not support float64
        lowercase							   =	torch.from_numpy(_lowerCamelCase	).to(_lowerCamelCase							,		dtype=torch.floataa	)
      else:
        lowercase							   =	torch.from_numpy(_lowerCamelCase	).to(device=_lowerCamelCase	)

      lowercase							   =	[]







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase	):
      # get log sigma
      lowercase							   =	np.log(_lowerCamelCase	)

      # get distribution
      lowercase							   =	log_sigma - log_sigmas[:, np.newaxis]

      # get sigmas range
      lowercase							   =	np.cumsum((dists >= 0)							,		axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
      lowercase							   =	low_idx + 1

      lowercase							   =	log_sigmas[low_idx]
      lowercase							   =	log_sigmas[high_idx]

      # interpolate sigmas
      lowercase							   =	(low - log_sigma) / (low - high)
      lowercase							   =	np.clip(_lowerCamelCase							,		0							,		1	)

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







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	in_sigmas[-1].item()
      lowercase							   =	in_sigmas[0].item()

      lowercase							   =	7.0  # 7.0 is the value used in the paper
      lowercase							   =	np.linspace(0							,		1							,		self.num_inference_steps	)
      lowercase							   =	sigma_min ** (1 / rho)
      lowercase							   =	sigma_max ** (1 / rho)
      lowercase							   =	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return sigmas







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = 4							,		_lowerCamelCase = True							,		):
      if not self.is_scale_input_called:
        warnings.warn(
            'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
            'See `StableDiffusionPipeline` for a usage example.'	)

      if isinstance(_lowerCamelCase							,		torch.Tensor	):
        lowercase							   =	timestep.to(self.timesteps.device	)
      lowercase							   =	(self.timesteps == timestep).nonzero().item()
      lowercase							   =	self.sigmas[step_index]

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
        lowercase							   =	sample - sigma * model_output
      elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        lowercase							   =	model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
      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 `v_prediction`'	)

      # 2. Convert to an ODE derivative
      lowercase							   =	(sample - pred_original_sample) / sigma
      self.derivatives.append(_lowerCamelCase	)
      if len(self.derivatives	) > order:
        self.derivatives.pop(0	)

      # 3. Compute linear multistep coefficients
      lowercase							   =	min(step_index + 1							,		_lowerCamelCase	)
      lowercase							   =	[self.get_lms_coefficient(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	) for curr_order in range(_lowerCamelCase	)]

      # 4. Compute previous sample based on the derivatives path
      lowercase							   =	sample + sum(
          coeff * derivative for coeff, derivative in zip(_lowerCamelCase							,		reversed(self.derivatives	)	)	)

      if not return_dict:
        return (prev_sample,)

      return LMSDiscreteSchedulerOutput(prev_sample=_lowerCamelCase							,		pred_original_sample=_lowerCamelCase	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		):
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      lowercase							   =	self.sigmas.to(device=original_samples.device							,		dtype=original_samples.dtype	)
      if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCamelCase	):
        # mps does not support float64
        lowercase							   =	self.timesteps.to(original_samples.device							,		dtype=torch.floataa	)
        lowercase							   =	timesteps.to(original_samples.device							,		dtype=torch.floataa	)
      else:
        lowercase							   =	self.timesteps.to(original_samples.device	)
        lowercase							   =	timesteps.to(original_samples.device	)

      lowercase							   =	[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

      lowercase							   =	original_samples + noise * sigma
      return noisy_samples







    def __len__(      self	):
      return self.config.num_train_timesteps










"""simple docstring"""



import unittest

from transformers import 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        RemBertConfig,
        RemBertForCausalLM,
        RemBertForMaskedLM,
        RemBertForMultipleChoice,
        RemBertForQuestionAnswering,
        RemBertForSequenceClassification,
        RemBertForTokenClassification,
        RemBertModel,
    )
    from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class 	a					:







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase=1_3							,		_lowerCamelCase=7							,		_lowerCamelCase=True							,		_lowerCamelCase=True							,		_lowerCamelCase=True							,		_lowerCamelCase=True							,		_lowerCamelCase=9_9							,		_lowerCamelCase=3_2							,		_lowerCamelCase=1_8							,		_lowerCamelCase=4_3							,		_lowerCamelCase=5							,		_lowerCamelCase=4							,		_lowerCamelCase=3_7							,		_lowerCamelCase="gelu"							,		_lowerCamelCase=0.1							,		_lowerCamelCase=0.1							,		_lowerCamelCase=5_1_2							,		_lowerCamelCase=1_6							,		_lowerCamelCase=2							,		_lowerCamelCase=0.0_2							,		_lowerCamelCase=3							,		_lowerCamelCase=4							,		_lowerCamelCase=None							,		):
      lowercase							   =	parent
      lowercase							   =	batch_size
      lowercase							   =	seq_length
      lowercase							   =	is_training
      lowercase							   =	use_input_mask
      lowercase							   =	use_token_type_ids
      lowercase							   =	use_labels
      lowercase							   =	vocab_size
      lowercase							   =	hidden_size
      lowercase							   =	input_embedding_size
      lowercase							   =	output_embedding_size
      lowercase							   =	num_hidden_layers
      lowercase							   =	num_attention_heads
      lowercase							   =	intermediate_size
      lowercase							   =	hidden_act
      lowercase							   =	hidden_dropout_prob
      lowercase							   =	attention_probs_dropout_prob
      lowercase							   =	max_position_embeddings
      lowercase							   =	type_vocab_size
      lowercase							   =	type_sequence_label_size
      lowercase							   =	initializer_range
      lowercase							   =	num_labels
      lowercase							   =	num_choices
      lowercase							   =	scope







    def       UpperCamelCase_				(      self	):
      lowercase							   =	ids_tensor([self.batch_size, self.seq_length]							,		self.vocab_size	)

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

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

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

      lowercase							   =	RemBertConfig(
          vocab_size=self.vocab_size							,		hidden_size=self.hidden_size							,		input_embedding_size=self.input_embedding_size							,		output_embedding_size=self.output_embedding_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=_lowerCamelCase							,		initializer_range=self.initializer_range							,		)

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







    def       UpperCamelCase_				(      self	):
      (
          (
          lowercase
      )   ,					(
          lowercase
      )   ,					(
          lowercase
      )   ,					(
          lowercase
      )   ,					(
          lowercase
      )   ,					(
          lowercase
      )   ,					(
          lowercase
      )   ,					
      )							   =	self.prepare_config_and_inputs()

      lowercase							   =	True
      lowercase							   =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
      lowercase							   =	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       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase							   =	RemBertModel(config=_lowerCamelCase	)
      model.to(_lowerCamelCase	)
      model.eval()
      lowercase							   =	model(_lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase	)
      lowercase							   =	model(_lowerCamelCase							,		token_type_ids=_lowerCamelCase	)
      lowercase							   =	model(_lowerCamelCase	)
      self.parent.assertEqual(result.last_hidden_state.shape							,		(self.batch_size, self.seq_length, self.hidden_size)	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		):
      lowercase							   =	True
      lowercase							   =	RemBertModel(_lowerCamelCase	)
      model.to(_lowerCamelCase	)
      model.eval()
      lowercase							   =	model(
          _lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase							,		encoder_hidden_states=_lowerCamelCase							,		encoder_attention_mask=_lowerCamelCase							,		)
      lowercase							   =	model(
          _lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase							,		encoder_hidden_states=_lowerCamelCase							,		)
      lowercase							   =	model(_lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase	)
      self.parent.assertEqual(result.last_hidden_state.shape							,		(self.batch_size, self.seq_length, self.hidden_size)	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		):
      lowercase							   =	RemBertForCausalLM(config=_lowerCamelCase	)
      model.to(_lowerCamelCase	)
      model.eval()
      lowercase							   =	model(_lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase							,		labels=_lowerCamelCase	)
      self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.seq_length, self.vocab_size)	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase							   =	RemBertForMaskedLM(config=_lowerCamelCase	)
      model.to(_lowerCamelCase	)
      model.eval()
      lowercase							   =	model(_lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase							,		labels=_lowerCamelCase	)
      self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.seq_length, self.vocab_size)	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		):
      lowercase							   =	True
      lowercase							   =	True
      lowercase							   =	RemBertForCausalLM(config=_lowerCamelCase	)
      model.to(_lowerCamelCase	)
      model.eval()

      # first forward pass
      lowercase							   =	model(
          _lowerCamelCase							,		attention_mask=_lowerCamelCase							,		encoder_hidden_states=_lowerCamelCase							,		encoder_attention_mask=_lowerCamelCase							,		use_cache=_lowerCamelCase							,		)
      lowercase							   =	outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      lowercase							   =	ids_tensor((self.batch_size, 3)							,		config.vocab_size	)
      lowercase							   =	ids_tensor((self.batch_size, 3)							,		vocab_size=2	)

      # append to next input_ids and
      lowercase							   =	torch.cat([input_ids, next_tokens]							,		dim=-1	)
      lowercase							   =	torch.cat([input_mask, next_mask]							,		dim=-1	)

      lowercase							   =	model(
          _lowerCamelCase							,		attention_mask=_lowerCamelCase							,		encoder_hidden_states=_lowerCamelCase							,		encoder_attention_mask=_lowerCamelCase							,		output_hidden_states=_lowerCamelCase							,		)['hidden_states'][0]
      lowercase							   =	model(
          _lowerCamelCase							,		attention_mask=_lowerCamelCase							,		encoder_hidden_states=_lowerCamelCase							,		encoder_attention_mask=_lowerCamelCase							,		past_key_values=_lowerCamelCase							,		output_hidden_states=_lowerCamelCase							,		)['hidden_states'][0]

      # select random slice
      lowercase							   =	ids_tensor((1,)							,		output_from_past.shape[-1]	).item()
      lowercase							   =	output_from_no_past[:, -3:, random_slice_idx].detach()
      lowercase							   =	output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]	)

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(_lowerCamelCase							,		_lowerCamelCase							,		atol=1e-3	)	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase							   =	RemBertForQuestionAnswering(config=_lowerCamelCase	)
      model.to(_lowerCamelCase	)
      model.eval()
      lowercase							   =	model(
          _lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase							,		start_positions=_lowerCamelCase							,		end_positions=_lowerCamelCase							,		)
      self.parent.assertEqual(result.start_logits.shape							,		(self.batch_size, self.seq_length)	)
      self.parent.assertEqual(result.end_logits.shape							,		(self.batch_size, self.seq_length)	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase							   =	self.num_labels
      lowercase							   =	RemBertForSequenceClassification(_lowerCamelCase	)
      model.to(_lowerCamelCase	)
      model.eval()
      lowercase							   =	model(_lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase							,		labels=_lowerCamelCase	)
      self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.num_labels)	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase							   =	self.num_labels
      lowercase							   =	RemBertForTokenClassification(config=_lowerCamelCase	)
      model.to(_lowerCamelCase	)
      model.eval()
      lowercase							   =	model(_lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase							,		labels=_lowerCamelCase	)
      self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.seq_length, self.num_labels)	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase							   =	self.num_choices
      lowercase							   =	RemBertForMultipleChoice(config=_lowerCamelCase	)
      model.to(_lowerCamelCase	)
      model.eval()
      lowercase							   =	input_ids.unsqueeze(1	).expand(-1							,		self.num_choices							,		-1	).contiguous()
      lowercase							   =	token_type_ids.unsqueeze(1	).expand(-1							,		self.num_choices							,		-1	).contiguous()
      lowercase							   =	input_mask.unsqueeze(1	).expand(-1							,		self.num_choices							,		-1	).contiguous()
      lowercase							   =	model(
          _lowerCamelCase							,		attention_mask=_lowerCamelCase							,		token_type_ids=_lowerCamelCase							,		labels=_lowerCamelCase							,		)
      self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.num_choices)	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.prepare_config_and_inputs()
      (
          (
          lowercase
      )   ,					(
          lowercase
      )   ,					(
          lowercase
      )   ,					(
          lowercase
      )   ,					(
          lowercase
      )   ,					(
          lowercase
      )   ,					(
          lowercase
      )   ,					
      )							   =	config_and_inputs
      lowercase							   =	{'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     ):
    UpperCAmelCase_			:					int     					=(
        (
            RemBertModel,
            RemBertForMaskedLM,
            RemBertForCausalLM,
            RemBertForMultipleChoice,
            RemBertForQuestionAnswering,
            RemBertForSequenceClassification,
            RemBertForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase_			:					Optional[int]     					=(RemBertForCausalLM,) if is_torch_available() else ()
    UpperCAmelCase_			:					Dict     					=(
        {
            "feature-extraction": RemBertModel,
            "fill-mask": RemBertForMaskedLM,
            "question-answering": RemBertForQuestionAnswering,
            "text-classification": RemBertForSequenceClassification,
            "text-generation": RemBertForCausalLM,
            "token-classification": RemBertForTokenClassification,
            "zero-shot": RemBertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )







    def       UpperCamelCase_				(      self	):
      lowercase							   =	RemBertModelTester(self	)
      lowercase							   =	ConfigTester(self							,		config_class=_lowerCamelCase							,		hidden_size=3_7	)







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







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_lowerCamelCase	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
        lowercase							   =	type
        self.model_tester.create_and_check_model(*_lowerCamelCase	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*_lowerCamelCase	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(*_lowerCamelCase	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*_lowerCamelCase	)







    def       UpperCamelCase_				(      self	):
      # This regression test was failing with PyTorch < 1.3
      (
          (
          lowercase
      )   ,					(
          lowercase
      )   ,					(
          lowercase
      )   ,					(
          lowercase
      )   ,					(
          lowercase
      )   ,					(
          lowercase
      )   ,					(
          lowercase
      )   ,					(
          lowercase
      )   ,					(
          lowercase
      )   ,					
      )							   =	self.model_tester.prepare_config_and_inputs_for_decoder()

      lowercase							   =	None

      self.model_tester.create_and_check_model_as_decoder(
          _lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		)







    @slow
    def       UpperCamelCase_				(      self	):
      for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase							   =	RemBertModel.from_pretrained(_lowerCamelCase	)
        self.assertIsNotNone(_lowerCamelCase	)


@require_torch
class 	a					(       unittest.TestCase     ):







    @slow
    def       UpperCamelCase_				(      self	):
      # Test exact values at the last hidden layer
      lowercase							   =	RemBertModel.from_pretrained('google/rembert'	)
      lowercase							   =	torch.tensor([[3_1_2, 5_6_4_9_8, 3_1_3, 2_1_2_5, 3_1_3]]	)
      lowercase							   =	torch.tensor([[0, 0, 0, 1, 1]]	)
      with torch.no_grad():
        lowercase							   =	model(_lowerCamelCase							,		token_type_ids=_lowerCamelCase							,		output_hidden_states=_lowerCamelCase	)

      lowercase							   =	1_1_5_2

      lowercase							   =	torch.Size((1, 5, hidden_size)	)
      self.assertEqual(output['last_hidden_state'].shape							,		_lowerCamelCase	)

      lowercase							   =	torch.tensor(
          [
              [
                  [0.0_7_5_4, -0.2_0_2_2, 0.1_9_0_4],
                  [-0.3_3_5_4, -0.3_6_9_2, -0.4_7_9_1],
                  [-0.2_3_1_4, -0.6_7_2_9, -0.0_7_4_9],
                  [-0.0_3_9_6, -0.3_1_0_5, -0.4_2_3_4],
                  [-0.1_5_7_1, -0.0_5_2_5, 0.5_3_5_3],
              ]
          ]	)

      # Running on the original tf implementation gives slightly different results here.
      # Not clear why this variations is present
      # TODO: Find reason for discrepancy
      # expected_original_implementation = [[
      #     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
      #     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
      #     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
      #     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
      #     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
      # ]]

      self.assertTrue(torch.allclose(output['last_hidden_state'][:, :, :3]							,		_lowerCamelCase							,		atol=1e-4	)	)


