


from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
				import tensorflow as tf

				from transformers.models.swin.modeling_tf_swin import (
				    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSwinForImageClassification,
				    TFSwinForMaskedImageModeling,
				    TFSwinModel,
				)


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor







class      __lowercase  :





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__=1_3       ,		lowerCAmelCase__=3_2       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=3       ,		lowerCAmelCase__=1_6       ,		lowerCAmelCase__=[1, 2, 1]       ,		lowerCAmelCase__=[2, 2, 4]       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=2.0       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=0.0       ,		lowerCAmelCase__=0.0       ,		lowerCAmelCase__=0.1       ,		lowerCAmelCase__="gelu"       ,		lowerCAmelCase__=False       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=0.02       ,		lowerCAmelCase__=1E-5       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=1_0       ,		lowerCAmelCase__=8       ,		):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= parent
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= batch_size
										SCREAMING_SNAKE_CASE_						:			Tuple       						= image_size
										SCREAMING_SNAKE_CASE_						:			Tuple       						= patch_size
										SCREAMING_SNAKE_CASE_						:			List[str]       						= num_channels
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= embed_dim
										SCREAMING_SNAKE_CASE_						:			Any       						= depths
										SCREAMING_SNAKE_CASE_						:			Dict       						= num_heads
										SCREAMING_SNAKE_CASE_						:			int       						= window_size
										SCREAMING_SNAKE_CASE_						:			Dict       						= mlp_ratio
										SCREAMING_SNAKE_CASE_						:			str       						= qkv_bias
										SCREAMING_SNAKE_CASE_						:			List[str]       						= hidden_dropout_prob
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_						:			str       						= drop_path_rate
										SCREAMING_SNAKE_CASE_						:			str       						= hidden_act
										SCREAMING_SNAKE_CASE_						:			Dict       						= use_absolute_embeddings
										SCREAMING_SNAKE_CASE_						:			Dict       						= patch_norm
										SCREAMING_SNAKE_CASE_						:			Any       						= layer_norm_eps
										SCREAMING_SNAKE_CASE_						:			Any       						= initializer_range
										SCREAMING_SNAKE_CASE_						:			Dict       						= is_training
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= scope
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= use_labels
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= type_sequence_label_size
										SCREAMING_SNAKE_CASE_						:			int       						= encoder_stride






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= None
										if self.use_labels:
													SCREAMING_SNAKE_CASE_						:			List[str]       						= ids_tensor([self.batch_size]       ,		self.type_sequence_label_size    )

										SCREAMING_SNAKE_CASE_						:			int       						= self.get_config()

										return config, pixel_values, labels






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return SwinConfig(
										    image_size=self.image_size       ,		patch_size=self.patch_size       ,		num_channels=self.num_channels       ,		embed_dim=self.embed_dim       ,		depths=self.depths       ,		num_heads=self.num_heads       ,		window_size=self.window_size       ,		mlp_ratio=self.mlp_ratio       ,		qkv_bias=self.qkv_bias       ,		hidden_dropout_prob=self.hidden_dropout_prob       ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,		drop_path_rate=self.drop_path_rate       ,		hidden_act=self.hidden_act       ,		use_absolute_embeddings=self.use_absolute_embeddings       ,		path_norm=self.patch_norm       ,		layer_norm_eps=self.layer_norm_eps       ,		initializer_range=self.initializer_range       ,		encoder_stride=self.encoder_stride       ,		)






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= TFSwinModel(config=lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			str       						= model(lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

										self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, expected_seq_len, expected_dim)    )






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= TFSwinForMaskedImageModeling(config=lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Any       						= model(lowerCAmelCase__    )
										self.parent.assertEqual(
										    result.logits.shape       ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)    )

										# test greyscale images
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 1
										SCREAMING_SNAKE_CASE_						:			str       						= TFSwinForMaskedImageModeling(lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			List[str]       						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
										SCREAMING_SNAKE_CASE_						:			int       						= model(lowerCAmelCase__    )
										self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, 1, self.image_size, self.image_size)    )






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= self.type_sequence_label_size
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= TFSwinForImageClassification(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model(lowerCAmelCase__       ,		labels=lowerCAmelCase__    )
										self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.type_sequence_label_size)    )

										# test greyscale images
										SCREAMING_SNAKE_CASE_						:			int       						= 1
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= TFSwinForImageClassification(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= model(lowerCAmelCase__    )
										self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.type_sequence_label_size)    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			str       						= config_and_inputs
										SCREAMING_SNAKE_CASE_						:			Any       						= {'pixel_values': pixel_values}
										return config, inputs_dict







@require_tf
class      __lowercase  (__SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    unittest.TestCase   ):





							"""simple docstring"""







							_UpperCAmelCase     =		(
							    (
							        TFSwinModel,
							        TFSwinForImageClassification,
							        TFSwinForMaskedImageModeling,
							    )
							    if is_tf_available()
							    else ()
							)
							_UpperCAmelCase     =		(
							    {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
							    if is_tf_available()
							    else {}
							)

							_UpperCAmelCase     =		False
							_UpperCAmelCase     =		False
							_UpperCAmelCase     =		False
							_UpperCAmelCase     =		False






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= TFSwinModelTester(self    )
										SCREAMING_SNAKE_CASE_						:			Dict       						= ConfigTester(self       ,		config_class=lowerCAmelCase__       ,		embed_dim=3_7    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__    )






							@unittest.skip(reason='Swin does not use inputs_embeds'    )
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										pass






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			List[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= model_class(lowerCAmelCase__    )
													self.assertIsInstance(model.get_input_embeddings()       ,		tf.keras.layers.Layer    )
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(lowerCAmelCase__       ,		tf.keras.layers.Dense    )    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			List[str]       						= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			str       						= model_class(lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			str       						= inspect.signature(model.call    )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													SCREAMING_SNAKE_CASE_						:			int       						= [*signature.parameters.keys()]

													SCREAMING_SNAKE_CASE_						:			Dict       						= ['pixel_values']
													self.assertListEqual(arg_names[:1]       ,		lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			List[str]       						= self.model_tester.prepare_config_and_inputs_for_common()
										SCREAMING_SNAKE_CASE_						:			Dict       						= True

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= True
													SCREAMING_SNAKE_CASE_						:			str       						= False
													SCREAMING_SNAKE_CASE_						:			Dict       						= True
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model_class(lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= model(**self._prepare_for_class(lowerCAmelCase__       ,		lowerCAmelCase__    )    )
													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= outputs.attentions
													SCREAMING_SNAKE_CASE_						:			int       						= len(self.model_tester.depths    )
													self.assertEqual(len(lowerCAmelCase__    )       ,		lowerCAmelCase__    )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= True
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= config.window_size**2
													SCREAMING_SNAKE_CASE_						:			List[str]       						= model_class(lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			str       						= model(**self._prepare_for_class(lowerCAmelCase__       ,		lowerCAmelCase__    )    )
													SCREAMING_SNAKE_CASE_						:			Dict       						= outputs.attentions
													self.assertEqual(len(lowerCAmelCase__    )       ,		lowerCAmelCase__    )

													self.assertListEqual(
													    list(attentions[0].shape[-3:]    )       ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,		)
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= len(lowerCAmelCase__    )

													# Check attention is always last and order is fine
													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= True
													SCREAMING_SNAKE_CASE_						:			Dict       						= True
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= model_class(lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= model(**self._prepare_for_class(lowerCAmelCase__       ,		lowerCAmelCase__    )    )

													if hasattr(self.model_tester       ,		'num_hidden_states_types'    ):
																SCREAMING_SNAKE_CASE_						:			int       						= self.model_tester.num_hidden_states_types
													else:
																# also another +1 for reshaped_hidden_states
																SCREAMING_SNAKE_CASE_						:			int       						= 2
													self.assertEqual(out_len + added_hidden_states       ,		len(lowerCAmelCase__    )    )

													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= outputs.attentions

													self.assertEqual(len(lowerCAmelCase__    )       ,		lowerCAmelCase__    )

													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]    )       ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,		)






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Tuple       						= model_class(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= model(**self._prepare_for_class(lowerCAmelCase__       ,		lowerCAmelCase__    )    )
										SCREAMING_SNAKE_CASE_						:			str       						= outputs.hidden_states

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= getattr(
										    self.model_tester       ,		'expected_num_hidden_layers'       ,		len(self.model_tester.depths    ) + 1    )
										self.assertEqual(len(lowerCAmelCase__    )       ,		lowerCAmelCase__    )

										# Swin has a different seq_length
										SCREAMING_SNAKE_CASE_						:			str       						= to_atuple(config.patch_size    )

										SCREAMING_SNAKE_CASE_						:			Tuple       						= (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]    )       ,		[num_patches, self.model_tester.embed_dim]       ,		)

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= outputs.reshaped_hidden_states
										self.assertEqual(len(lowerCAmelCase__    )       ,		lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			str       						= reshaped_hidden_states[0].shape

										SCREAMING_SNAKE_CASE_						:			int       						= tf.reshape(reshaped_hidden_states[0]       ,		(batch_size, num_channels, height * width)    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= tf.transpose(lowerCAmelCase__       ,		(0, 2, 1)    )

										self.assertListEqual(
										    list(reshaped_hidden_states.shape[-2:]    )       ,		[num_patches, self.model_tester.embed_dim]       ,		)






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Tuple       						= self.model_tester.prepare_config_and_inputs_for_common()

										SCREAMING_SNAKE_CASE_						:			List[str]       						= to_atuple(self.model_tester.image_size    )

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= True
													self.check_hidden_states_output(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													SCREAMING_SNAKE_CASE_						:			Dict       						= True

													self.check_hidden_states_output(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			List[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()
										SCREAMING_SNAKE_CASE_						:			int       						= 3

										SCREAMING_SNAKE_CASE_						:			Any       						= to_atuple(self.model_tester.image_size    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= to_atuple(config.patch_size    )

										SCREAMING_SNAKE_CASE_						:			str       						= image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= True
													self.check_hidden_states_output(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		(padded_height, padded_width)    )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													SCREAMING_SNAKE_CASE_						:			List[str]       						= True
													self.check_hidden_states_output(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		(padded_height, padded_width)    )






							@slow
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE_						:			Any       						= TFSwinModel.from_pretrained(lowerCAmelCase__    )
													self.assertIsNotNone(lowerCAmelCase__    )







@require_vision
@require_tf
class      __lowercase  (unittest.TestCase   ):





							"""simple docstring"""







							@cached_property
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return (
										    AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'    )
										    if is_vision_available()
										    else None
										)






							@slow
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			int       						= TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.default_image_processor

										SCREAMING_SNAKE_CASE_						:			int       						= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
										SCREAMING_SNAKE_CASE_						:			str       						= image_processor(images=lowerCAmelCase__       ,		return_tensors='tf'    )

										# forward pass
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= model(lowerCAmelCase__    )

										# verify the logits
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= tf.TensorShape((1, 1_0_0_0)    )
										self.assertEqual(outputs.logits.shape       ,		lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= tf.constant([-0.0_948, -0.6_454, -0.0_921]    )
										self.assertTrue(np.allclose(outputs.logits[0, :3]       ,		lowerCAmelCase__       ,		atol=1E-4    )    )







'''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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_=0.999 ,       lowerCAmelCase_="cosine" ,       ):
     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

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

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

     _UpperCAmelCase					:  str         =    []
     for i in range(lowerCAmelCase_       ):
          _UpperCAmelCase					:  List[str]         =    i / num_diffusion_timesteps
          _UpperCAmelCase					:  str         =    (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 	__lowerCAmelCase    (  __a       ,					__a				):
     snake_case   :    Any							      =      [e.name for e in KarrasDiffusionSchedulers]
     snake_case   :    List[Any]							      =      2



     @register_to_config
     def __init__(self						,	lowerCAmelCase__ = 1_0_0_0						,	lowerCAmelCase__ = 0.0_0_0_8_5						,	lowerCAmelCase__ = 0.0_1_2						,	lowerCAmelCase__ = "linear"						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = "epsilon"						,	lowerCAmelCase__ = "linspace"						,	lowerCAmelCase__ = 0						,	):
          if trained_betas is not None:
               _UpperCAmelCase					:  Optional[int]         =    torch.tensor(lowerCAmelCase__						,	dtype=torch.floataa	)
          elif beta_schedule == "linear":
               _UpperCAmelCase					:  Optional[int]         =    torch.linspace(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	dtype=torch.floataa	)
          elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _UpperCAmelCase					:  Tuple         =    (
                   torch.linspace(beta_start**0.5						,	beta_end**0.5						,	lowerCAmelCase__						,	dtype=torch.floataa	) ** 2
               )
          elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _UpperCAmelCase					:  Union[str, Any]         =    betas_for_alpha_bar(lowerCAmelCase__	)
          else:
               raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"	)

          _UpperCAmelCase					:  Union[str, Any]         =    1.0 - self.betas
          _UpperCAmelCase					:  List[Any]         =    torch.cumprod(self.alphas						,	dim=0	)

          #  set all values
          self.set_timesteps(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__=None	):
          if schedule_timesteps is None:
               _UpperCAmelCase					:  Optional[Any]         =    self.timesteps

          _UpperCAmelCase					:  Optional[Any]         =    (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:
               _UpperCAmelCase					:  Dict         =    1 if len(lowerCAmelCase__	) > 1 else 0
          else:
               _UpperCAmelCase					:  str         =    timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__	) else timestep
               _UpperCAmelCase					:  Any         =    self._index_counter[timestep_int]

          return indices[pos].item()



     @property
     def     snake_case_ (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     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          _UpperCAmelCase					:  Dict         =    self.index_for_timestep(lowerCAmelCase__	)

          if self.state_in_first_order:
               _UpperCAmelCase					:  Any         =    self.sigmas[step_index]
          else:
               _UpperCAmelCase					:  Tuple         =    self.sigmas_interpol[step_index - 1]

          _UpperCAmelCase					:  Dict         =    sample / ((sigma**2 + 1) ** 0.5)
          return sample



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = None						,	):
          _UpperCAmelCase					:  Dict         =    num_inference_steps

          _UpperCAmelCase					:  int         =    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":
               _UpperCAmelCase					:  Dict         =    np.linspace(0						,	num_train_timesteps - 1						,	lowerCAmelCase__						,	dtype=lowerCAmelCase__	)[::-1].copy()
          elif self.config.timestep_spacing == "leading":
               _UpperCAmelCase					:  List[str]         =    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
               _UpperCAmelCase					:  Optional[Any]         =    (np.arange(0						,	lowerCAmelCase__	) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__	)
               timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
               _UpperCAmelCase					:  Any         =    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
               _UpperCAmelCase					:  List[Any]         =    (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'."	)

          _UpperCAmelCase					:  List[str]         =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
          _UpperCAmelCase					:  Dict         =    torch.from_numpy(np.log(lowerCAmelCase__	)	).to(lowerCAmelCase__	)

          _UpperCAmelCase					:  Optional[Any]         =    np.interp(lowerCAmelCase__						,	np.arange(0						,	len(lowerCAmelCase__	)	)						,	lowerCAmelCase__	)
          _UpperCAmelCase					:  Dict         =    np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
          _UpperCAmelCase					:  Any         =    torch.from_numpy(lowerCAmelCase__	).to(device=lowerCAmelCase__	)

          # compute up and down sigmas
          _UpperCAmelCase					:  str         =    sigmas.roll(-1	)
          _UpperCAmelCase					:  List[Any]         =    0.0
          _UpperCAmelCase					:  Optional[int]         =    (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
          _UpperCAmelCase					:  Any         =    (sigmas_next**2 - sigmas_up**2) ** 0.5
          _UpperCAmelCase					:  Tuple         =    0.0

          # compute interpolated sigmas
          _UpperCAmelCase					:  Union[str, Any]         =    sigmas.log().lerp(sigmas_down.log()						,	0.5	).exp()
          _UpperCAmelCase					:  int         =    0.0

          # set sigmas
          _UpperCAmelCase					:  List[Any]         =    torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2	), sigmas[-1:]]	)
          _UpperCAmelCase					:  str         =    torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2	), sigmas_interpol[-1:]]	)
          _UpperCAmelCase					:  Optional[int]         =    torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2	), sigmas_up[-1:]]	)
          _UpperCAmelCase					:  List[Any]         =    torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2	), sigmas_down[-1:]]	)

          if str(lowerCAmelCase__	).startswith("""mps"""	):
               # mps does not support float64
               _UpperCAmelCase					:  List[str]         =    torch.from_numpy(lowerCAmelCase__	).to(lowerCAmelCase__						,	dtype=torch.floataa	)
          else:
               _UpperCAmelCase					:  int         =    torch.from_numpy(lowerCAmelCase__	).to(lowerCAmelCase__	)

          _UpperCAmelCase					:  int         =    self.sigma_to_t(lowerCAmelCase__	).to(lowerCAmelCase__						,	dtype=timesteps.dtype	)
          _UpperCAmelCase					:  Optional[int]         =    torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])						,	dim=-1	).flatten()

          _UpperCAmelCase					:  Any         =    torch.cat([timesteps[:1], interleaved_timesteps]	)

          _UpperCAmelCase					:  Dict         =    None

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



     def     snake_case_ (self						,	lowerCAmelCase__	):
          # get log sigma
          _UpperCAmelCase					:  Union[str, Any]         =    sigma.log()

          # get distribution
          _UpperCAmelCase					:  str         =    log_sigma - self.log_sigmas[:, None]

          # get sigmas range
          _UpperCAmelCase					:  str         =    dists.ge(0	).cumsum(dim=0	).argmax(dim=0	).clamp(max=self.log_sigmas.shape[0] - 2	)
          _UpperCAmelCase					:  List[Any]         =    low_idx + 1

          _UpperCAmelCase					:  Optional[int]         =    self.log_sigmas[low_idx]
          _UpperCAmelCase					:  Dict         =    self.log_sigmas[high_idx]

          # interpolate sigmas
          _UpperCAmelCase					:  Union[str, Any]         =    (low - log_sigma) / (low - high)
          _UpperCAmelCase					:  Union[str, Any]         =    w.clamp(0						,	1	)

          # transform interpolation to time range
          _UpperCAmelCase					:  Union[str, Any]         =    (1 - w) * low_idx + w * high_idx
          _UpperCAmelCase					:  str         =    t.view(sigma.shape	)
          return t



     @property
     def     snake_case_ (self	):
          return self.sample is None



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = True						,	):
          _UpperCAmelCase					:  Union[str, Any]         =    self.index_for_timestep(lowerCAmelCase__	)

          # advance index counter by 1
          _UpperCAmelCase					:  Optional[Any]         =    timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__	) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
               _UpperCAmelCase					:  Tuple         =    self.sigmas[step_index]
               _UpperCAmelCase					:  List[str]         =    self.sigmas_interpol[step_index]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_up[step_index]
               _UpperCAmelCase					:  Optional[int]         =    self.sigmas_down[step_index - 1]
          else:
               # 2nd order / KPDM2's method
               _UpperCAmelCase					:  List[Any]         =    self.sigmas[step_index - 1]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_interpol[step_index - 1]
               _UpperCAmelCase					:  List[Any]         =    self.sigmas_up[step_index - 1]
               _UpperCAmelCase					:  Tuple         =    self.sigmas_down[step_index - 1]

          # 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
          _UpperCAmelCase					:  int         =    0
          _UpperCAmelCase					:  str         =    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          _UpperCAmelCase					:  Optional[int]         =    model_output.device
          _UpperCAmelCase					:  Tuple         =    randn_tensor(model_output.shape						,	dtype=model_output.dtype						,	device=lowerCAmelCase__						,	generator=lowerCAmelCase__	)

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
               _UpperCAmelCase					:  int         =    sigma_hat if self.state_in_first_order else sigma_interpol
               _UpperCAmelCase					:  List[Any]         =    sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
               _UpperCAmelCase					:  Tuple         =    sigma_hat if self.state_in_first_order else sigma_interpol
               _UpperCAmelCase					:  Dict         =    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                   sample / (sigma_input**2 + 1)
               )
          elif self.config.prediction_type == "sample":
               raise NotImplementedError("""prediction_type not implemented yet: sample"""	)
          else:
               raise ValueError(
                   F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"	)

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

               # store for 2nd order step
               _UpperCAmelCase					:  List[str]         =    sample
               _UpperCAmelCase					:  Optional[Any]         =    dt
               _UpperCAmelCase					:  Optional[int]         =    sample + derivative * dt
          else:
               # DPM-Solver-2
               # 2. Convert to an ODE derivative for 2nd order
               _UpperCAmelCase					:  Optional[int]         =    (sample - pred_original_sample) / sigma_interpol
               # 3. delta timestep
               _UpperCAmelCase					:  List[Any]         =    sigma_down - sigma_hat

               _UpperCAmelCase					:  int         =    self.sample
               _UpperCAmelCase					:  Union[str, Any]         =    None

               _UpperCAmelCase					:  str         =    sample + derivative * dt
               _UpperCAmelCase					:  List[str]         =    prev_sample + noise * sigma_up

          if not return_dict:
               return (prev_sample,)

          return SchedulerOutput(prev_sample=lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	):
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          _UpperCAmelCase					:  List[Any]         =    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
               _UpperCAmelCase					:  Any         =    self.timesteps.to(original_samples.device						,	dtype=torch.floataa	)
               _UpperCAmelCase					:  Dict         =    timesteps.to(original_samples.device						,	dtype=torch.floataa	)
          else:
               _UpperCAmelCase					:  Dict         =    self.timesteps.to(original_samples.device	)
               _UpperCAmelCase					:  Tuple         =    timesteps.to(original_samples.device	)

          _UpperCAmelCase					:  Any         =    [self.index_for_timestep(lowerCAmelCase__						,	lowerCAmelCase__	) for t in timesteps]

          _UpperCAmelCase					:  List[Any]         =    sigmas[step_indices].flatten()
          while len(sigma.shape	) < len(original_samples.shape	):
               _UpperCAmelCase					:  Dict         =    sigma.unsqueeze(-1	)

          _UpperCAmelCase					:  str         =    original_samples + noise * sigma
          return noisy_samples



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


