'''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 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_=13				,			snake_case_=7				,			snake_case_=True				,			snake_case_=True				,			snake_case_=True				,			snake_case_=True				,			snake_case_=99				,			snake_case_=32				,			snake_case_=18				,			snake_case_=43				,			snake_case_=5				,			snake_case_=4				,			snake_case_=37				,			snake_case_="gelu"				,			snake_case_=0.1				,			snake_case_=0.1				,			snake_case_=512				,			snake_case_=16				,			snake_case_=2				,			snake_case_=0.02				,			snake_case_=3				,			snake_case_=4				,			snake_case_=None				,			):
									snake_case :       Optional[Any]				=    parent
									snake_case :       Dict				=    batch_size
									snake_case :       Dict				=    seq_length
									snake_case :       Tuple				=    is_training
									snake_case :       Union[str, Any]				=    use_input_mask
									snake_case :       List[str]				=    use_token_type_ids
									snake_case :       List[str]				=    use_labels
									snake_case :       int				=    vocab_size
									snake_case :       Dict				=    hidden_size
									snake_case :       int				=    input_embedding_size
									snake_case :       Optional[int]				=    output_embedding_size
									snake_case :       Optional[Any]				=    num_hidden_layers
									snake_case :       Optional[Any]				=    num_attention_heads
									snake_case :       int				=    intermediate_size
									snake_case :       List[str]				=    hidden_act
									snake_case :       str				=    hidden_dropout_prob
									snake_case :       List[Any]				=    attention_probs_dropout_prob
									snake_case :       Any				=    max_position_embeddings
									snake_case :       Union[str, Any]				=    type_vocab_size
									snake_case :       Any				=    type_sequence_label_size
									snake_case :       Any				=    initializer_range
									snake_case :       Dict				=    num_labels
									snake_case :       Optional[Any]				=    num_choices
									snake_case :       Dict				=    scope



			def   lowerCamelCase (							self					):
									snake_case :       Optional[Any]				=    ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size					)

									snake_case :       Union[str, Any]				=    None
									if self.use_input_mask:
															snake_case :       int				=    random_attention_mask([self.batch_size, self.seq_length]					)

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

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

									snake_case :       str				=    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=snake_case_				,			initializer_range=self.initializer_range				,			)

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



			def   lowerCamelCase (							self					):
									(
									    (
									    snake_case
									)				,					(
									    snake_case
									)				,					(
									    snake_case
									)				,					(
									    snake_case
									)				,					(
									    snake_case
									)				,					(
									    snake_case
									)				,					(
									    snake_case
									)				,					
									) :       int				=    self.prepare_config_and_inputs()

									snake_case :       List[Any]				=    True
									snake_case :       List[Any]				=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
									snake_case :       Dict				=    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   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       Any				=    RemBertModel(config=snake_case_					)
									model.to(snake_case_					)
									model.eval()
									snake_case :       int				=    model(snake_case_				,			attention_mask=snake_case_				,			token_type_ids=snake_case_					)
									snake_case :       Dict				=    model(snake_case_				,			token_type_ids=snake_case_					)
									snake_case :       Union[str, Any]				=    model(snake_case_					)
									self.parent.assertEqual(result.last_hidden_state.shape				,			(self.batch_size, self.seq_length, self.hidden_size)					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			):
									snake_case :       int				=    True
									snake_case :       Union[str, Any]				=    RemBertModel(snake_case_					)
									model.to(snake_case_					)
									model.eval()
									snake_case :       int				=    model(
									    snake_case_				,			attention_mask=snake_case_				,			token_type_ids=snake_case_				,			encoder_hidden_states=snake_case_				,			encoder_attention_mask=snake_case_				,			)
									snake_case :       Optional[int]				=    model(
									    snake_case_				,			attention_mask=snake_case_				,			token_type_ids=snake_case_				,			encoder_hidden_states=snake_case_				,			)
									snake_case :       Dict				=    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   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			):
									snake_case :       Dict				=    RemBertForCausalLM(config=snake_case_					)
									model.to(snake_case_					)
									model.eval()
									snake_case :       Union[str, Any]				=    model(snake_case_				,			attention_mask=snake_case_				,			token_type_ids=snake_case_				,			labels=snake_case_					)
									self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.vocab_size)					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       str				=    RemBertForMaskedLM(config=snake_case_					)
									model.to(snake_case_					)
									model.eval()
									snake_case :       int				=    model(snake_case_				,			attention_mask=snake_case_				,			token_type_ids=snake_case_				,			labels=snake_case_					)
									self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.vocab_size)					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			):
									snake_case :       Union[str, Any]				=    True
									snake_case :       Tuple				=    True
									snake_case :       List[Any]				=    RemBertForCausalLM(config=snake_case_					)
									model.to(snake_case_					)
									model.eval()

									# first forward pass
									snake_case :       Optional[int]				=    model(
									    snake_case_				,			attention_mask=snake_case_				,			encoder_hidden_states=snake_case_				,			encoder_attention_mask=snake_case_				,			use_cache=snake_case_				,			)
									snake_case :       Optional[int]				=    outputs.past_key_values

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

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

									snake_case :       List[Any]				=    model(
									    snake_case_				,			attention_mask=snake_case_				,			encoder_hidden_states=snake_case_				,			encoder_attention_mask=snake_case_				,			output_hidden_states=snake_case_				,			)["hidden_states"][0]
									snake_case :       Optional[int]				=    model(
									    snake_case_				,			attention_mask=snake_case_				,			encoder_hidden_states=snake_case_				,			encoder_attention_mask=snake_case_				,			past_key_values=snake_case_				,			output_hidden_states=snake_case_				,			)["hidden_states"][0]

									# select random slice
									snake_case :       Tuple				=    ids_tensor((1,)				,			output_from_past.shape[-1]					).item()
									snake_case :       Any				=    output_from_no_past[:, -3:, random_slice_idx].detach()
									snake_case :       Tuple				=    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(snake_case_				,			snake_case_				,			atol=1e-3					)					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       Any				=    RemBertForQuestionAnswering(config=snake_case_					)
									model.to(snake_case_					)
									model.eval()
									snake_case :       Tuple				=    model(
									    snake_case_				,			attention_mask=snake_case_				,			token_type_ids=snake_case_				,			start_positions=snake_case_				,			end_positions=snake_case_				,			)
									self.parent.assertEqual(result.start_logits.shape				,			(self.batch_size, self.seq_length)					)
									self.parent.assertEqual(result.end_logits.shape				,			(self.batch_size, self.seq_length)					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       str				=    self.num_labels
									snake_case :       Tuple				=    RemBertForSequenceClassification(snake_case_					)
									model.to(snake_case_					)
									model.eval()
									snake_case :       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   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       Optional[Any]				=    self.num_labels
									snake_case :       str				=    RemBertForTokenClassification(config=snake_case_					)
									model.to(snake_case_					)
									model.eval()
									snake_case :       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   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       Optional[int]				=    self.num_choices
									snake_case :       Dict				=    RemBertForMultipleChoice(config=snake_case_					)
									model.to(snake_case_					)
									model.eval()
									snake_case :       List[Any]				=    input_ids.unsqueeze(1					).expand(-1				,			self.num_choices				,			-1					).contiguous()
									snake_case :       Dict				=    token_type_ids.unsqueeze(1					).expand(-1				,			self.num_choices				,			-1					).contiguous()
									snake_case :       List[Any]				=    input_mask.unsqueeze(1					).expand(-1				,			self.num_choices				,			-1					).contiguous()
									snake_case :       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.num_choices)					)






			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    self.prepare_config_and_inputs()
									(
									    (
									    snake_case
									)				,					(
									    snake_case
									)				,					(
									    snake_case
									)				,					(
									    snake_case
									)				,					(
									    snake_case
									)				,					(
									    snake_case
									)				,					(
									    snake_case
									)				,					
									) :       Union[str, Any]				=    config_and_inputs
									snake_case :       Union[str, 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_					(_UpperCAmelCase							,				_UpperCAmelCase							,				unittest.TestCase      ):



			"""simple docstring"""






			__magic_name__ :				Union[str, Any]   							=       (
			    (
			        RemBertModel,
			        RemBertForMaskedLM,
			        RemBertForCausalLM,
			        RemBertForMultipleChoice,
			        RemBertForQuestionAnswering,
			        RemBertForSequenceClassification,
			        RemBertForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			__magic_name__ :				str   							=       (RemBertForCausalLM,) if is_torch_available() else ()
			__magic_name__ :				int   							=       (
			    {
			        """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   lowerCamelCase (							self					):
									snake_case :       Dict				=    RemBertModelTester(self					)
									snake_case :       str				=    ConfigTester(self				,			config_class=snake_case_				,			hidden_size=37					)



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



			def   lowerCamelCase (							self					):
									snake_case :       Optional[Any]				=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case_					)



			def   lowerCamelCase (							self					):
									snake_case :       str				=    self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
															snake_case :       int				=    type
															self.model_tester.create_and_check_model(*snake_case_					)



			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*snake_case_					)



			def   lowerCamelCase (							self					):
									snake_case :       str				=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_multiple_choice(*snake_case_					)



			def   lowerCamelCase (							self					):
									snake_case :       str				=    self.model_tester.prepare_config_and_inputs_for_decoder()
									self.model_tester.create_and_check_decoder_model_past_large_inputs(*snake_case_					)



			def   lowerCamelCase (							self					):
									snake_case :       Optional[int]				=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*snake_case_					)



			def   lowerCamelCase (							self					):
									snake_case :       Dict				=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*snake_case_					)



			def   lowerCamelCase (							self					):
									snake_case :       Optional[int]				=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*snake_case_					)



			def   lowerCamelCase (							self					):
									snake_case :       str				=    self.model_tester.prepare_config_and_inputs_for_decoder()
									self.model_tester.create_and_check_model_as_decoder(*snake_case_					)



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

									snake_case :       Optional[int]				=    None

									self.model_tester.create_and_check_model_as_decoder(
									    snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			)






			@slow
			def   lowerCamelCase (							self					):
									for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case :       Union[str, Any]				=    RemBertModel.from_pretrained(snake_case_					)
															self.assertIsNotNone(snake_case_					)







@require_torch
class 		SCREAMING_SNAKE_CASE_					(unittest.TestCase      ):



			"""simple docstring"""






			@slow
			def   lowerCamelCase (							self					):
									# Test exact values at the last hidden layer
									snake_case :       Dict				=    RemBertModel.from_pretrained("google/rembert"					)
									snake_case :       Dict				=    torch.tensor([[312, 56_498, 313, 2_125, 313]]					)
									snake_case :       Optional[int]				=    torch.tensor([[0, 0, 0, 1, 1]]					)
									with torch.no_grad():
															snake_case :       Optional[Any]				=    model(snake_case_				,			token_type_ids=snake_case_				,			output_hidden_states=snake_case_					)

									snake_case :       List[Any]				=    1_152

									snake_case :       Tuple				=    torch.Size((1, 5, hidden_size)					)
									self.assertEqual(output["last_hidden_state"].shape				,			snake_case_					)

									snake_case :       Optional[Any]				=    torch.tensor(
									    [
									        [
									            [0.0_754, -0.2_022, 0.1_904],
									            [-0.3_354, -0.3_692, -0.4_791],
									            [-0.2_314, -0.6_729, -0.0_749],
									            [-0.0_396, -0.3_105, -0.4_234],
									            [-0.1_571, -0.0_525, 0.5_353],
									        ]
									    ]					)

									# 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]				,			snake_case_				,			atol=1e-4					)					)






'''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
class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				torch.FloatTensor
			__magic_name__ :				torch.FloatTensor







def        UpperCAmelCase__   (				_lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			List[Any]=0.999,      _lowerCAmelCase    :			Any="cosine",      )    ->		List[Any]:



						"""simple docstring"""

						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_lowerCAmelCase    :			Union[str, Any]):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

						elif alpha_transform_type == "exp":

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

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

						snake_case :       List[Any]				=    []
						for i in range(_lowerCAmelCase):
												snake_case :       int				=    i / num_diffusion_timesteps
												snake_case :       Optional[Any]				=    (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 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase							,				_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Optional[Any]   							=       1



			@register_to_config
			def __init__(							self				,			snake_case_ = 1_000				,			snake_case_ = 0.0_001				,			snake_case_ = 0.02				,			snake_case_ = "linear"				,			snake_case_ = 0.0				,			snake_case_ = None				,			snake_case_ = True				,			):
									if trained_betas is not None:
															snake_case :       Optional[Any]				=    torch.from_numpy(snake_case_					)
									elif beta_schedule == "linear":
															snake_case :       Union[str, Any]				=    torch.linspace(snake_case_				,			snake_case_				,			snake_case_				,			dtype=torch.floataa					)
									elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															snake_case :       Optional[int]				=    (
															    torch.linspace(beta_start**0.5				,			beta_end**0.5				,			snake_case_				,			dtype=torch.floataa					) ** 2
															)
									elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															snake_case :       str				=    betas_for_alpha_bar(snake_case_					)
									elif beta_schedule == "sigmoid":
															# GeoDiff sigmoid schedule
															snake_case :       int				=    torch.linspace(-6				,			6				,			snake_case_					)
															snake_case :       Union[str, Any]				=    torch.sigmoid(snake_case_					) * (beta_end - beta_start) + beta_start
									else:
															raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""					)

									snake_case :       List[Any]				=    1.0 - self.betas
									snake_case :       Any				=    torch.cumprod(self.alphas				,			dim=0					)
									snake_case :       Tuple				=    torch.tensor(1.0					)

									snake_case :       Tuple				=    torch.tensor(1.0					)

									# standard deviation of the initial noise distribution
									snake_case :       str				=    1.0

									# setable values
									snake_case :       Optional[int]				=    None
									snake_case :       str				=    torch.from_numpy(np.arange(0				,			snake_case_					)[::-1].copy()					)

									snake_case :       Tuple				=    eta



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_ = None					):
									return sample



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_ = 10				,			snake_case_ = 10				,			snake_case_ = None				,			):
									snake_case :       Optional[Any]				=    min(self.config.num_train_timesteps				,			snake_case_					)
									snake_case :       Tuple				=    num_inference_steps

									snake_case :       Optional[int]				=    []

									snake_case :       Tuple				=    {}
									for j in range(0				,			num_inference_steps - jump_length				,			snake_case_					):
															snake_case :       Union[str, Any]				=    jump_n_sample - 1

									snake_case :       Optional[int]				=    num_inference_steps
									while t >= 1:
															snake_case :       Optional[Any]				=    t - 1
															timesteps.append(snake_case_					)

															if jumps.get(snake_case_				,			0					) > 0:
																					snake_case :       List[Any]				=    jumps[t] - 1
																					for _ in range(snake_case_					):
																											snake_case :       int				=    t + 1
																											timesteps.append(snake_case_					)

									snake_case :       Optional[Any]				=    np.array(snake_case_					) * (self.config.num_train_timesteps // self.num_inference_steps)
									snake_case :       int				=    torch.from_numpy(snake_case_					).to(snake_case_					)



			def   lowerCamelCase (							self				,			snake_case_					):
									snake_case :       Union[str, Any]				=    t - self.config.num_train_timesteps // self.num_inference_steps

									snake_case :       Optional[int]				=    self.alphas_cumprod[t]
									snake_case :       Any				=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
									snake_case :       List[Any]				=    1 - alpha_prod_t
									snake_case :       int				=    1 - 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
									# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
									# without eta.
									# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
									snake_case :       List[str]				=    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

									return variance



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_ = None				,			snake_case_ = True				,			):
									snake_case :       Optional[int]				=    timestep
									snake_case :       List[Any]				=    timestep - self.config.num_train_timesteps // self.num_inference_steps

									# 1. compute alphas, betas
									snake_case :       Any				=    self.alphas_cumprod[t]
									snake_case :       Optional[Any]				=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
									snake_case :       str				=    1 - alpha_prod_t

									# 2. compute predicted original sample from predicted noise also called
									# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
									snake_case :       str				=    (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

									# 3. Clip "predicted x_0"
									if self.config.clip_sample:
															snake_case :       List[Any]				=    torch.clamp(snake_case_				,			-1				,			1					)

									# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
									# substitute formula (7) in the algorithm coming from DDPM paper
									# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
									# DDIM schedule gives the same results as DDPM with eta = 1.0
									# Noise is being reused in 7. and 8., but no impact on quality has
									# been observed.

									# 5. Add noise
									snake_case :       Dict				=    model_output.device
									snake_case :       Optional[Any]				=    randn_tensor(model_output.shape				,			generator=snake_case_				,			device=snake_case_				,			dtype=model_output.dtype					)
									snake_case :       Tuple				=    self.eta * self._get_variance(snake_case_					) ** 0.5

									snake_case :       Optional[int]				=    0
									if t > 0 and self.eta > 0:
															snake_case :       str				=    std_dev_t * noise

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

									# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
									snake_case :       Union[str, Any]				=    alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

									# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
									snake_case :       Optional[int]				=    (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

									# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
									snake_case :       Optional[Any]				=    mask * prev_known_part + (1.0 - mask) * prev_unknown_part

									if not return_dict:
															return (
															    pred_prev_sample,
															    pred_original_sample,
															)

									return RePaintSchedulerOutput(prev_sample=snake_case_				,			pred_original_sample=snake_case_					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_=None					):
									snake_case :       Union[str, Any]				=    self.config.num_train_timesteps // self.num_inference_steps

									for i in range(snake_case_					):
															snake_case :       List[Any]				=    self.betas[timestep + i]
															if sample.device.type == "mps":
																					# randn does not work reproducibly on mps
																					snake_case :       Optional[int]				=    randn_tensor(sample.shape				,			dtype=sample.dtype				,			generator=snake_case_					)
																					snake_case :       Any				=    noise.to(sample.device					)
															else:
																					snake_case :       List[Any]				=    randn_tensor(sample.shape				,			generator=snake_case_				,			device=sample.device				,			dtype=sample.dtype					)

															# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
															snake_case :       Union[str, Any]				=    (1 - beta) ** 0.5 * sample + beta**0.5 * noise

									return sample



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			):
									raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."					)






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





