






"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TFRoFormerForCausalLM,
		    TFRoFormerForMaskedLM,
		    TFRoFormerForMultipleChoice,
		    TFRoFormerForQuestionAnswering,
		    TFRoFormerForSequenceClassification,
		    TFRoFormerForTokenClassification,
		    TFRoFormerModel,
		)
		from transformers.models.roformer.modeling_tf_roformer import (
		    TFRoFormerSelfAttention,
		    TFRoFormerSinusoidalPositionalEmbedding,
		)






class       SCREAMING_SNAKE_CASE__					:


						def __init__(							self				:			Any						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			Dict=13						,						lowerCAmelCase				:			int=7						,						lowerCAmelCase				:			Dict=True						,						lowerCAmelCase				:			str=True						,						lowerCAmelCase				:			Any=True						,						lowerCAmelCase				:			str=True						,						lowerCAmelCase				:			Union[str, Any]=99						,						lowerCAmelCase				:			str=32						,						lowerCAmelCase				:			Optional[int]=2						,						lowerCAmelCase				:			Union[str, Any]=4						,						lowerCAmelCase				:			List[str]=37						,						lowerCAmelCase				:			List[str]="gelu"						,						lowerCAmelCase				:			List[str]=0.1						,						lowerCAmelCase				:			List[Any]=0.1						,						lowerCAmelCase				:			Any=512						,						lowerCAmelCase				:			Union[str, Any]=16						,						lowerCAmelCase				:			Optional[int]=2						,						lowerCAmelCase				:			Any=0.02						,						lowerCAmelCase				:			List[str]=3						,						lowerCAmelCase				:			List[Any]=4						,						lowerCAmelCase				:			List[str]=None						,						):
										lowerCAmelCase  =				parent
										lowerCAmelCase  =				13
										lowerCAmelCase  =				7
										lowerCAmelCase  =				True
										lowerCAmelCase  =				True
										lowerCAmelCase  =				True
										lowerCAmelCase  =				True
										lowerCAmelCase  =				99
										lowerCAmelCase  =				32
										lowerCAmelCase  =				2
										lowerCAmelCase  =				4
										lowerCAmelCase  =				37
										lowerCAmelCase  =				"""gelu"""
										lowerCAmelCase  =				0.1
										lowerCAmelCase  =				0.1
										lowerCAmelCase  =				512
										lowerCAmelCase  =				16
										lowerCAmelCase  =				2
										lowerCAmelCase  =				0.02
										lowerCAmelCase  =				3
										lowerCAmelCase  =				4
										lowerCAmelCase  =				None


						def   __lowercase (							self				:			List[Any]					):
										lowerCAmelCase  =				ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size					)

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

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

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

										lowerCAmelCase  =				RoFormerConfig(
										    vocab_size=self.vocab_size						,						hidden_size=self.hidden_size						,						num_hidden_layers=self.num_hidden_layers						,						num_attention_heads=self.num_attention_heads						,						intermediate_size=self.intermediate_size						,						hidden_act=self.hidden_act						,						hidden_dropout_prob=self.hidden_dropout_prob						,						attention_probs_dropout_prob=self.attention_probs_dropout_prob						,						max_position_embeddings=self.max_position_embeddings						,						type_vocab_size=self.type_vocab_size						,						initializer_range=self.initializer_range						,						return_dict=lowerCAmelCase						,						)

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


						def   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			str					):
										lowerCAmelCase  =				TFRoFormerModel(config=lowerCAmelCase					)
										lowerCAmelCase  =				{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

										lowerCAmelCase  =				[input_ids, input_mask]
										lowerCAmelCase  =				model(lowerCAmelCase					)

										lowerCAmelCase  =				model(lowerCAmelCase					)

										self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, self.hidden_size)					)


						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			Union[str, Any]					):
										lowerCAmelCase  =				True
										lowerCAmelCase  =				TFRoFormerForCausalLM(config=lowerCAmelCase					)
										lowerCAmelCase  =				{
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										lowerCAmelCase  =				model(lowerCAmelCase					)["""logits"""]
										self.parent.assertListEqual(
										    list(prediction_scores.numpy().shape					)						,						[self.batch_size, self.seq_length, self.vocab_size]					)


						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Optional[int]					):
										lowerCAmelCase  =				TFRoFormerForMaskedLM(config=lowerCAmelCase					)
										lowerCAmelCase  =				{
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										lowerCAmelCase  =				model(lowerCAmelCase					)
										self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.vocab_size)					)


						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			Dict					):
										lowerCAmelCase  =				self.num_labels
										lowerCAmelCase  =				TFRoFormerForSequenceClassification(config=lowerCAmelCase					)
										lowerCAmelCase  =				{
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}

										lowerCAmelCase  =				model(lowerCAmelCase					)
										self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_labels)					)


						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			Optional[Any]					):
										lowerCAmelCase  =				self.num_choices
										lowerCAmelCase  =				TFRoFormerForMultipleChoice(config=lowerCAmelCase					)
										lowerCAmelCase  =				tf.tile(tf.expand_dims(lowerCAmelCase						,						1					)						,						(1, self.num_choices, 1)					)
										lowerCAmelCase  =				tf.tile(tf.expand_dims(lowerCAmelCase						,						1					)						,						(1, self.num_choices, 1)					)
										lowerCAmelCase  =				tf.tile(tf.expand_dims(lowerCAmelCase						,						1					)						,						(1, self.num_choices, 1)					)
										lowerCAmelCase  =				{
										    """input_ids""": multiple_choice_inputs_ids,
										    """attention_mask""": multiple_choice_input_mask,
										    """token_type_ids""": multiple_choice_token_type_ids,
										}
										lowerCAmelCase  =				model(lowerCAmelCase					)
										self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_choices)					)


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			Tuple					):
										lowerCAmelCase  =				self.num_labels
										lowerCAmelCase  =				TFRoFormerForTokenClassification(config=lowerCAmelCase					)
										lowerCAmelCase  =				{
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										lowerCAmelCase  =				model(lowerCAmelCase					)
										self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.num_labels)					)


						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			List[str]					):
										lowerCAmelCase  =				TFRoFormerForQuestionAnswering(config=lowerCAmelCase					)
										lowerCAmelCase  =				{
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}

										lowerCAmelCase  =				model(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   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				self.prepare_config_and_inputs()
										(
										    (
										    lowerCAmelCase					
										) ,						(
										    lowerCAmelCase					
										) ,						(
										    lowerCAmelCase					
										) ,						(
										    lowerCAmelCase					
										) ,						(
										    lowerCAmelCase					
										) ,						(
										    lowerCAmelCase					
										) ,						(
										    lowerCAmelCase					
										) ,											
										)  =				config_and_inputs
										lowerCAmelCase  =				{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
										return config, inputs_dict






@require_tf
class       SCREAMING_SNAKE_CASE__					(					lowercase_						,				lowercase_						,				unittest.TestCase					):
						_a    						=						(
						    (
						        TFRoFormerModel,
						        TFRoFormerForCausalLM,
						        TFRoFormerForMaskedLM,
						        TFRoFormerForQuestionAnswering,
						        TFRoFormerForSequenceClassification,
						        TFRoFormerForTokenClassification,
						        TFRoFormerForMultipleChoice,
						    )
						    if is_tf_available()
						    else ()
						)
						_a    						=						(
						    {
						        '''feature-extraction''': TFRoFormerModel,
						        '''fill-mask''': TFRoFormerForMaskedLM,
						        '''question-answering''': TFRoFormerForQuestionAnswering,
						        '''text-classification''': TFRoFormerForSequenceClassification,
						        '''text-generation''': TFRoFormerForCausalLM,
						        '''token-classification''': TFRoFormerForTokenClassification,
						        '''zero-shot''': TFRoFormerForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)

						_a    						=						False
						_a    						=						False


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			List[Any]					):
										if pipeline_test_casse_name == "TextGenerationPipelineTests":
														return True

										return False


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				TFRoFormerModelTester(self					)
										lowerCAmelCase  =				ConfigTester(self						,						config_class=lowerCAmelCase						,						hidden_size=37					)


						def   __lowercase (							self				:			Tuple					):
										self.config_tester.run_common_tests()


						def   __lowercase (							self				:			Dict					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase					)


						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase					)


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head(*lowerCAmelCase					)


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase					)


						def   __lowercase (							self				:			Optional[Any]					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase					)


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase					)


						def   __lowercase (							self				:			Any					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase					)




						@slow
						def   __lowercase (							self				:			Optional[Any]					):
										lowerCAmelCase  =				TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""					)
										self.assertIsNotNone(lowerCAmelCase					)






@require_tf
class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						@slow
						def   __lowercase (							self				:			Dict					):
										lowerCAmelCase  =				TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""					)
										lowerCAmelCase  =				tf.constant([[0, 1, 2, 3, 4, 5]]					)
										lowerCAmelCase  =				model(lowerCAmelCase					)[0]

										# TODO Replace vocab size
										lowerCAmelCase  =				5_0000

										lowerCAmelCase  =				[1, 6, vocab_size]
										self.assertEqual(output.shape						,						lowerCAmelCase					)

										print(output[:, :3, :3]					)

										# TODO Replace values below with what was printed above.
										lowerCAmelCase  =				tf.constant(
										    [
										        [
										            [-0.1205_3341, -1.026_4901, 0.2922_1946],
										            [-1.513_3783, 0.19_7433, 0.1519_0607],
										            [-5.013_5403, -3.90_0256, -0.8403_8764],
										        ]
										    ]					)
										tf.debugging.assert_near(output[:, :3, :3]						,						lowerCAmelCase						,						atol=1e-4					)






@require_tf
class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):
						_a    						=						1E-4


						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				tf.constant([[4, 10]]					)
										lowerCAmelCase  =				TFRoFormerSinusoidalPositionalEmbedding(num_positions=6						,						embedding_dim=6					)

										lowerCAmelCase  =				emba(input_ids.shape					)
										lowerCAmelCase  =				tf.constant(
										    [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]					)

										tf.debugging.assert_near(lowerCAmelCase						,						lowerCAmelCase						,						atol=self.tolerance					)




						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				tf.constant(
										    [
										        [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
										        [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
										        [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
										    ]					)
										lowerCAmelCase  =				TFRoFormerSinusoidalPositionalEmbedding(num_positions=512						,						embedding_dim=512					)
										emba([2, 16, 512]					)
										lowerCAmelCase  =				emba.weight[:3, :5]

										tf.debugging.assert_near(lowerCAmelCase						,						lowerCAmelCase						,						atol=self.tolerance					)






@require_tf
class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):
						_a    						=						1E-4


						def   __lowercase (							self				:			Tuple					):
										# 2,12,16,64
										lowerCAmelCase  =				tf.reshape(tf.range(2 * 12 * 16 * 64						,						dtype=tf.floataa					)						,						shape=(2, 12, 16, 64)					) / 100

										lowerCAmelCase  =				-tf.reshape(tf.range(2 * 12 * 16 * 64						,						dtype=tf.floataa					)						,						shape=(2, 12, 16, 64)					) / 100

										lowerCAmelCase  =				TFRoFormerSinusoidalPositionalEmbedding(num_positions=32						,						embedding_dim=64					)
										lowerCAmelCase  =				embed_positions([2, 16, 768]					)[None, None, :, :]

										lowerCAmelCase ,						lowerCAmelCase  =				TFRoFormerSelfAttention.apply_rotary_position_embeddings(
										    lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)

										lowerCAmelCase  =				tf.constant(
										    [
										        [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
										        [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
										        [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
										        [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
										        [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
										        [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
										    ]					)
										lowerCAmelCase  =				tf.constant(
										    [
										        [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
										        [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
										        [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
										        [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
										        [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
										        [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
										    ]					)

										tf.debugging.assert_near(query_layer[0, 0, :6, :8]						,						lowerCAmelCase						,						atol=self.tolerance					)
										tf.debugging.assert_near(key_layer[0, 0, :6, :8]						,						lowerCAmelCase						,						atol=self.tolerance					)











"""simple docstring"""







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

import numpy as np
import torch

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





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : int=0.999      ,       snake_case__   : Tuple="cosine"      ,       )     ->  Any:


				'''simple docstring'''






				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

				lowerCAmelCase  =				[]
				for i in range(snake_case__ ):
								lowerCAmelCase  =				i / num_diffusion_timesteps
								lowerCAmelCase  =				(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       SCREAMING_SNAKE_CASE__					(					_a						,				_a					):
						_a    						=						[e.name for e in KarrasDiffusionSchedulers]
						_a    						=						2


						@register_to_config
						def __init__(							self				:			List[Any]						,						lowerCAmelCase				:			int = 1000						,						lowerCAmelCase				:			float = 0.0_0085						,						lowerCAmelCase				:			float = 0.012						,						lowerCAmelCase				:			str = "linear"						,						lowerCAmelCase				:			Optional[Union[np.ndarray, List[float]]] = None						,						lowerCAmelCase				:			str = "epsilon"						,						lowerCAmelCase				:			Optional[bool] = False						,						lowerCAmelCase				:			Optional[bool] = False						,						lowerCAmelCase				:			float = 1.0						,						lowerCAmelCase				:			str = "linspace"						,						lowerCAmelCase				:			int = 0						,						):
										if trained_betas is not None:
														lowerCAmelCase  =				torch.tensor(lowerCAmelCase						,						dtype=torch.floataa					)
										elif beta_schedule == "linear":
														lowerCAmelCase  =				torch.linspace(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase						,						dtype=torch.floataa					)
										elif beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														lowerCAmelCase  =				(
														    torch.linspace(beta_start**0.5						,						beta_end**0.5						,						lowerCAmelCase						,						dtype=torch.floataa					) ** 2
														)
										elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														lowerCAmelCase  =				betas_for_alpha_bar(lowerCAmelCase						,						alpha_transform_type="""cosine"""					)
										elif beta_schedule == "exp":
														lowerCAmelCase  =				betas_for_alpha_bar(lowerCAmelCase						,						alpha_transform_type="""exp"""					)
										else:
														raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''					)

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

										#  set all values
										self.set_timesteps(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
										lowerCAmelCase  =				use_karras_sigmas


						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Optional[int]=None					):
										if schedule_timesteps is None:
														lowerCAmelCase  =				self.timesteps

										lowerCAmelCase  =				(schedule_timesteps == timestep).nonzero()

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

										return indices[pos].item()


						@property
						def   __lowercase (							self				:			Optional[Any]					):
										# 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   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			torch.FloatTensor						,						lowerCAmelCase				:			Union[float, torch.FloatTensor]						,						):
										lowerCAmelCase  =				self.index_for_timestep(lowerCAmelCase					)

										lowerCAmelCase  =				self.sigmas[step_index]
										lowerCAmelCase  =				sample / ((sigma**2 + 1) ** 0.5)
										return sample


						def   __lowercase (							self				:			List[Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			Union[str, torch.device] = None						,						lowerCAmelCase				:			Optional[int] = None						,						):
										lowerCAmelCase  =				num_inference_steps

										lowerCAmelCase  =				num_train_timesteps or self.config.num_train_timesteps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
														lowerCAmelCase  =				np.linspace(0						,						num_train_timesteps - 1						,						lowerCAmelCase						,						dtype=lowerCAmelCase					)[::-1].copy()
										elif self.config.timestep_spacing == "leading":
														lowerCAmelCase  =				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
														lowerCAmelCase  =				(np.arange(0						,						lowerCAmelCase					) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase					)
														timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
														lowerCAmelCase  =				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
														lowerCAmelCase  =				(np.arange(lowerCAmelCase						,						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\'.'''					)

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

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

										lowerCAmelCase  =				np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
										lowerCAmelCase  =				torch.from_numpy(lowerCAmelCase					).to(device=lowerCAmelCase					)
										lowerCAmelCase  =				torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2					), sigmas[-1:]]					)

										lowerCAmelCase  =				torch.from_numpy(lowerCAmelCase					)
										lowerCAmelCase  =				torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2					)]					)

										if str(lowerCAmelCase					).startswith("""mps"""					):
														# mps does not support float64
														lowerCAmelCase  =				timesteps.to(lowerCAmelCase						,						dtype=torch.floataa					)
										else:
														lowerCAmelCase  =				timesteps.to(device=lowerCAmelCase					)

										# empty dt and derivative
										lowerCAmelCase  =				None
										lowerCAmelCase  =				None

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


						def   __lowercase (							self				:			List[Any]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			Any					):
										# get log sigma
										lowerCAmelCase  =				np.log(lowerCAmelCase					)

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

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

										lowerCAmelCase  =				log_sigmas[low_idx]
										lowerCAmelCase  =				log_sigmas[high_idx]

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

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


						def   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			torch.FloatTensor						,						lowerCAmelCase				:			Union[str, Any]					):
										lowerCAmelCase  =				in_sigmas[-1].item()
										lowerCAmelCase  =				in_sigmas[0].item()

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


						@property
						def   __lowercase (							self				:			Dict					):
										return self.dt is None


						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			Union[torch.FloatTensor, np.ndarray]						,						lowerCAmelCase				:			Union[float, torch.FloatTensor]						,						lowerCAmelCase				:			Union[torch.FloatTensor, np.ndarray]						,						lowerCAmelCase				:			bool = True						,						):
										lowerCAmelCase  =				self.index_for_timestep(lowerCAmelCase					)

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

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

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

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
														lowerCAmelCase  =				sigma_hat if self.state_in_first_order else sigma_next
														lowerCAmelCase  =				sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
														lowerCAmelCase  =				sigma_hat if self.state_in_first_order else sigma_next
														lowerCAmelCase  =				model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
														    sample / (sigma_input**2 + 1)
														)
										elif self.config.prediction_type == "sample":
														lowerCAmelCase  =				model_output
										else:
														raise ValueError(
														    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''					)

										if self.config.clip_sample:
														lowerCAmelCase  =				pred_original_sample.clamp(
														    -self.config.clip_sample_range						,						self.config.clip_sample_range					)

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

														# store for 2nd order step
														lowerCAmelCase  =				derivative
														lowerCAmelCase  =				dt
														lowerCAmelCase  =				sample
										else:
														# 2. 2nd order / Heun's method
														lowerCAmelCase  =				(sample - pred_original_sample) / sigma_next
														lowerCAmelCase  =				(self.prev_derivative + derivative) / 2

														# 3. take prev timestep & sample
														lowerCAmelCase  =				self.dt
														lowerCAmelCase  =				self.sample

														# free dt and derivative
														# Note, this puts the scheduler in "first order mode"
														lowerCAmelCase  =				None
														lowerCAmelCase  =				None
														lowerCAmelCase  =				None

										lowerCAmelCase  =				sample + derivative * dt

										if not return_dict:
														return (prev_sample,)

										return SchedulerOutput(prev_sample=lowerCAmelCase					)


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			torch.FloatTensor						,						lowerCAmelCase				:			torch.FloatTensor						,						lowerCAmelCase				:			torch.FloatTensor						,						):
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										lowerCAmelCase  =				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
														lowerCAmelCase  =				self.timesteps.to(original_samples.device						,						dtype=torch.floataa					)
														lowerCAmelCase  =				timesteps.to(original_samples.device						,						dtype=torch.floataa					)
										else:
														lowerCAmelCase  =				self.timesteps.to(original_samples.device					)
														lowerCAmelCase  =				timesteps.to(original_samples.device					)

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

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

										lowerCAmelCase  =				original_samples + noise * sigma
										return noisy_samples




						def __len__(							self				:			Union[str, Any]					):
										return self.config.num_train_timesteps





