



"""simple docstring"""
import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
			    MODEL_MAPPING,
			    EfficientFormerForImageClassification,
			    EfficientFormerForImageClassificationWithTeacher,
			    EfficientFormerModel,
			)
			from transformers.models.efficientformer.modeling_efficientformer import (
			    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
			)


if is_vision_available():
			from PIL import Image

			from transformers import EfficientFormerImageProcessor


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__ = 1_3 ,				lowerCamelCase__ = 6_4 ,				lowerCamelCase__ = 2 ,				lowerCamelCase__ = 3 ,				lowerCamelCase__ = 3 ,				lowerCamelCase__ = True ,				lowerCamelCase__ = True ,				lowerCamelCase__ = 1_2_8 ,				lowerCamelCase__=[1_6, 3_2, 6_4, 1_2_8] ,				lowerCamelCase__ = 7 ,				lowerCamelCase__ = 4 ,				lowerCamelCase__ = 3_7 ,				lowerCamelCase__ = "gelu" ,				lowerCamelCase__ = 0.1 ,				lowerCamelCase__ = 0.1 ,				lowerCamelCase__ = 1_0 ,				lowerCamelCase__ = 0.0_2 ,				lowerCamelCase__ = 2 ,				lowerCamelCase__ = 1 ,				lowerCamelCase__ = 1_2_8 ,				lowerCamelCase__ = [2, 2, 2, 2] ,				lowerCamelCase__ = 2 ,				lowerCamelCase__ = 2 ,				):
									_lowerCamelCase					     = parent
									_lowerCamelCase					     = batch_size
									_lowerCamelCase					     = image_size
									_lowerCamelCase					     = patch_size
									_lowerCamelCase					     = num_channels
									_lowerCamelCase					     = is_training
									_lowerCamelCase					     = use_labels
									_lowerCamelCase					     = hidden_size
									_lowerCamelCase					     = num_hidden_layers
									_lowerCamelCase					     = num_attention_heads
									_lowerCamelCase					     = intermediate_size
									_lowerCamelCase					     = hidden_act
									_lowerCamelCase					     = hidden_dropout_prob
									_lowerCamelCase					     = attention_probs_dropout_prob
									_lowerCamelCase					     = type_sequence_label_size
									_lowerCamelCase					     = initializer_range
									_lowerCamelCase					     = encoder_stride
									_lowerCamelCase					     = num_attention_outputs
									_lowerCamelCase					     = embed_dim
									_lowerCamelCase					     = embed_dim + 1
									_lowerCamelCase					     = resolution
									_lowerCamelCase					     = depths
									_lowerCamelCase					     = hidden_sizes
									_lowerCamelCase					     = dim
									_lowerCamelCase					     = mlp_expansion_ratio

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

									_lowerCamelCase					     = None
									if self.use_labels:
													_lowerCamelCase					     = ids_tensor([self.batch_size] ,				self.type_sequence_label_size      )

									_lowerCamelCase					     = self.get_config()
									return config, pixel_values, labels

					def 	snake_case__					(			self      ):
									return EfficientFormerConfig(
									    image_size=self.image_size ,				patch_size=self.patch_size ,				num_channels=self.num_channels ,				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 ,				is_decoder=lowercase__ ,				initializer_range=self.initializer_range ,				encoder_stride=self.encoder_stride ,				resolution=self.resolution ,				depths=self.depths ,				hidden_sizes=self.hidden_sizes ,				dim=self.dim ,				mlp_expansion_ratio=self.mlp_expansion_ratio ,				)

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = EfficientFormerModel(config=lowercase__      )
									model.to(lowercase__      )
									model.eval()
									_lowerCamelCase					     = model(lowercase__      )
									self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = self.type_sequence_label_size
									_lowerCamelCase					     = EfficientFormerForImageClassification(lowercase__      )
									model.to(lowercase__      )
									model.eval()
									_lowerCamelCase					     = model(lowercase__ ,				labels=lowercase__      )
									self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)      )

									# test greyscale images
									_lowerCamelCase					     = 1
									_lowerCamelCase					     = EfficientFormerForImageClassification(lowercase__      )
									model.to(lowercase__      )
									model.eval()

									_lowerCamelCase					     = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
									_lowerCamelCase					     = model(lowercase__      )
									self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)      )





					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.prepare_config_and_inputs()
									(
									    _lowerCamelCase						
									)					     = config_and_inputs
									_lowerCamelCase					     = {"pixel_values": pixel_values}
									return config, inputs_dict


@require_torch
class 							lowerCamelCase_(      _UpperCAmelCase, _UpperCAmelCase, unittest.TestCase			):





					'''simple docstring'''



					lowercase__      :				Optional[Any]        =       (
					    (
					        EfficientFormerModel,
					        EfficientFormerForImageClassificationWithTeacher,
					        EfficientFormerForImageClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					lowercase__      :				Dict        =       (
					    {
					        'feature-extraction': EfficientFormerModel,
					        'image-classification': (
					            EfficientFormerForImageClassification,
					            EfficientFormerForImageClassificationWithTeacher,
					        ),
					    }
					    if is_torch_available()
					    else {}
					)
					lowercase__      :				int        =       False

					lowercase__      :				Tuple        =       False
					lowercase__      :				Dict        =       False
					lowercase__      :				Optional[Any]        =       False

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = EfficientFormerModelTester(self      )
									_lowerCamelCase					     = ConfigTester(
									    self ,				config_class=lowercase__ ,				has_text_modality=lowercase__ ,				hidden_size=3_7      )

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

					@unittest.skip(reason='''EfficientFormer does not use inputs_embeds'''      )
					def 	snake_case__					(			self      ):
									pass

					@unittest.skip(reason='''EfficientFormer does not support input and output embeddings'''      )
					def 	snake_case__					(			self      ):
									pass

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													_lowerCamelCase					     = model_class(lowercase__      )
													_lowerCamelCase					     = inspect.signature(model.forward      )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													_lowerCamelCase					     = [*signature.parameters.keys()]

													_lowerCamelCase					     = ["pixel_values"]
													self.assertListEqual(arg_names[:1] ,				lowercase__      )

					def 	snake_case__					(			self      ):






									def check_hidden_states_output(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
													_lowerCamelCase					     = model_class(lowercase__      )
													model.to(lowercase__      )
													model.eval()

													with torch.no_grad():
																	_lowerCamelCase					     = model(**self._prepare_for_class(lowercase__ ,				lowercase__      )      )

													_lowerCamelCase					     = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													_lowerCamelCase					     = getattr(
													    self.model_tester ,				'''expected_num_hidden_layers''' ,				self.model_tester.num_hidden_layers + 1      )
													self.assertEqual(len(lowercase__      ) ,				lowercase__      )

													if hasattr(self.model_tester ,				'''encoder_seq_length'''      ):
																	_lowerCamelCase					     = self.model_tester.encoder_seq_length
																	if hasattr(self.model_tester ,				'''chunk_length'''      ) and self.model_tester.chunk_length > 1:
																					_lowerCamelCase					     = seq_length * self.model_tester.chunk_length
													else:
																	_lowerCamelCase					     = self.model_tester.seq_length

													self.assertListEqual(
													    list(hidden_states[-1].shape[-2:]      ) ,				[seq_length, self.model_tester.hidden_size] ,				)

													if config.is_encoder_decoder:
																	_lowerCamelCase					     = outputs.decoder_hidden_states

																	self.assertIsInstance(lowercase__ ,				(list, tuple)      )
																	self.assertEqual(len(lowercase__      ) ,				lowercase__      )
																	_lowerCamelCase					     = getattr(self.model_tester ,				'''seq_length''' ,				lowercase__      )
																	_lowerCamelCase					     = getattr(self.model_tester ,				'''decoder_seq_length''' ,				lowercase__      )

																	self.assertListEqual(
																	    list(hidden_states[-1].shape[-2:]      ) ,				[decoder_seq_length, self.model_tester.hidden_size] ,				)

									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													_lowerCamelCase					     = True
													check_hidden_states_output(lowercase__ ,				lowercase__ ,				lowercase__      )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													_lowerCamelCase					     = True

													check_hidden_states_output(lowercase__ ,				lowercase__ ,				lowercase__      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__=False      ):
									_lowerCamelCase					     = super()._prepare_for_class(lowercase__ ,				lowercase__ ,				return_labels=lowercase__      )

									if return_labels:
													if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
																	del inputs_dict["labels"]

									return inputs_dict

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase__      )

					@unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet'''      )
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*lowercase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowercase__      )

					def 	snake_case__					(			self      ):






									if not self.model_tester.is_training:
													return

									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs_for_common()
									_lowerCamelCase					     = True

									for model_class in self.all_model_classes:
													# EfficientFormerForImageClassificationWithTeacher supports inference-only
													if (
													    model_class in get_values(lowercase__      )
													    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
													):
																	continue
													_lowerCamelCase					     = model_class(lowercase__      )
													model.to(lowercase__      )
													model.train()
													_lowerCamelCase					     = self._prepare_for_class(lowercase__ ,				lowercase__ ,				return_labels=lowercase__      )
													_lowerCamelCase					     = model(**lowercase__      ).loss
													loss.backward()

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs_for_common()

									_lowerCamelCase					     = [
									    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
									    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
									    {"title": "regression", "num_labels": 1, "dtype": torch.float},
									]

									for model_class in self.all_model_classes:
													if (
													    model_class
													    not in [
													        *get_values(lowercase__      ),
													    ]
													    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
													):
																	continue

													for problem_type in problem_types:
																	with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}"""      ):
																					_lowerCamelCase					     = problem_type["title"]
																					_lowerCamelCase					     = problem_type["num_labels"]

																					_lowerCamelCase					     = model_class(lowercase__      )
																					model.to(lowercase__      )
																					model.train()

																					_lowerCamelCase					     = self._prepare_for_class(lowercase__ ,				lowercase__ ,				return_labels=lowercase__      )

																					if problem_type["num_labels"] > 1:
																									_lowerCamelCase					     = inputs["labels"].unsqueeze(1      ).repeat(1 ,				problem_type['''num_labels''']      )

																					_lowerCamelCase					     = inputs["labels"].to(problem_type['''dtype''']      )

																					# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																					# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																					# they have the same size." which is a symptom something in wrong for the regression problem.
																					# See https://github.com/huggingface/transformers/issues/11780
																					with warnings.catch_warnings(record=lowercase__      ) as warning_list:
																									_lowerCamelCase					     = model(**lowercase__      ).loss
																					for w in warning_list:
																									if "Using a target size that is different to the input size" in str(w.message      ):
																													raise ValueError(
																													    F"""Something is going wrong in the regression problem: intercepted {w.message}"""      )

																					loss.backward()

					@slow
					def 	snake_case__					(			self      ):






									for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowerCamelCase					     = EfficientFormerModel.from_pretrained(lowercase__      )
													self.assertIsNotNone(lowercase__      )





					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs_for_common()
									_lowerCamelCase					     = True

									_lowerCamelCase					     = getattr(self.model_tester ,				'''seq_length''' ,				lowercase__      )
									_lowerCamelCase					     = getattr(self.model_tester ,				'''encoder_seq_length''' ,				lowercase__      )
									_lowerCamelCase					     = getattr(self.model_tester ,				'''key_length''' ,				lowercase__      )
									_lowerCamelCase					     = getattr(self.model_tester ,				'''chunk_length''' ,				lowercase__      )

									if chunk_length is not None and hasattr(self.model_tester ,				'''num_hashes'''      ):
													_lowerCamelCase					     = encoder_seq_length * self.model_tester.num_hashes

									for model_class in self.all_model_classes:
													_lowerCamelCase					     = True
													_lowerCamelCase					     = False
													_lowerCamelCase					     = True
													_lowerCamelCase					     = model_class(lowercase__      )
													model.to(lowercase__      )
													model.eval()
													with torch.no_grad():
																	_lowerCamelCase					     = model(**self._prepare_for_class(lowercase__ ,				lowercase__      )      )
													_lowerCamelCase					     = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
													self.assertEqual(len(lowercase__      ) ,				self.model_tester.num_attention_outputs      )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													_lowerCamelCase					     = True
													_lowerCamelCase					     = model_class(lowercase__      )
													model.to(lowercase__      )
													model.eval()
													with torch.no_grad():
																	_lowerCamelCase					     = model(**self._prepare_for_class(lowercase__ ,				lowercase__      )      )
													_lowerCamelCase					     = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
													self.assertEqual(len(lowercase__      ) ,				self.model_tester.num_attention_outputs      )

													if chunk_length is not None:
																	self.assertListEqual(
																	    list(attentions[0].shape[-4:]      ) ,				[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] ,				)
													else:
																	self.assertListEqual(
																	    list(attentions[0].shape[-3:]      ) ,				[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] ,				)



def 				lowerCAmelCase_(      )		->				Any:
				_lowerCamelCase					     = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
				return image




@require_torch
@require_vision
class 							lowerCamelCase_(      unittest.TestCase			):





					'''simple docstring'''



					@cached_property
					def 	snake_case__					(			self      ):
									return (
									    EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300'''      )
									    if is_vision_available()
									    else None
									)

					@slow
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = EfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300'''      ).to(
									    lowercase__      )

									_lowerCamelCase					     = self.default_image_processor
									_lowerCamelCase					     = prepare_img()
									_lowerCamelCase					     = image_processor(images=lowercase__ ,				return_tensors='''pt'''      ).to(lowercase__      )

									# forward pass
									with torch.no_grad():
													_lowerCamelCase					     = model(**lowercase__      )

									# verify the logits
									_lowerCamelCase					     = (1, 1_0_0_0)
									self.assertEqual(outputs.logits.shape ,				lowercase__      )

									_lowerCamelCase					     = torch.tensor([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]      ).to(lowercase__      )
									self.assertTrue(torch.allclose(outputs.logits[0][:3] ,				lowercase__ ,				atol=1e-4      )      )





					@slow
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = EfficientFormerForImageClassificationWithTeacher.from_pretrained(
									    '''snap-research/efficientformer-l1-300'''      ).to(lowercase__      )

									_lowerCamelCase					     = self.default_image_processor
									_lowerCamelCase					     = prepare_img()
									_lowerCamelCase					     = image_processor(images=lowercase__ ,				return_tensors='''pt'''      ).to(lowercase__      )

									# forward pass
									with torch.no_grad():
													_lowerCamelCase					     = model(**lowercase__      )

									# verify the logits
									_lowerCamelCase					     = (1, 1_0_0_0)
									self.assertEqual(outputs.logits.shape ,				lowercase__      )

									_lowerCamelCase					     = torch.tensor([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]      ).to(lowercase__      )
									self.assertTrue(torch.allclose(outputs.logits[0][:3] ,				lowercase__ ,				atol=1e-4      )      )



'''simple docstring'''






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

import numpy as np
import torch

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


snake_case_       =       logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							42
    _A			    =							None






def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Any  ,  SCREAMING_SNAKE_CASE_     :	int=0.999  ,  SCREAMING_SNAKE_CASE_     :	Optional[Any]="cosine"  ,  )							->   Optional[int]:
      """simple docstring"""

      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

      SCREAMING_SNAKE_CASE_      :			List[str]     =       []
      for i in range(SCREAMING_SNAKE_CASE_    ):
            SCREAMING_SNAKE_CASE_      :			List[Any]     =       i / num_diffusion_timesteps
            SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE_    ) / alpha_bar_fn(SCREAMING_SNAKE_CASE_    )  ,  SCREAMING_SNAKE_CASE_    )    )
      return torch.tensor(SCREAMING_SNAKE_CASE_  ,  dtype=torch.floataa    )





class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase,_UpperCAmelCase      ):
    _A			    =							[e.name for e in KarrasDiffusionSchedulers]
    _A			    =							1




    @register_to_config
    def __init__(					self				,     lowercase__ = 1000				,     lowercase__ = 0.0001				,     lowercase__ = 0.02				,     lowercase__ = "linear"				,     lowercase__ = None				,     lowercase__ = "epsilon"				,     lowercase__ = "linspace"				,     lowercase__ = 0				,     ):






          """simple docstring"""






          if trained_betas is not None:
                SCREAMING_SNAKE_CASE_      :			Any     =       torch.tensor(lowercase__				,     dtype=torch.floataa			)
          elif beta_schedule == "linear":
                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       torch.linspace(lowercase__				,     lowercase__				,     lowercase__				,     dtype=torch.floataa			)
          elif beta_schedule == "scaled_linear":
                # this schedule is very specific to the latent diffusion model.
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       (
                    torch.linspace(beta_start**0.5				,     beta_end**0.5				,     lowercase__				,     dtype=torch.floataa			) ** 2
                )
          elif beta_schedule == "squaredcos_cap_v2":
                # Glide cosine schedule
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       betas_for_alpha_bar(lowercase__			)
          else:
                raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"			)

          SCREAMING_SNAKE_CASE_      :			Any     =       1.0 - self.betas
          SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.cumprod(self.alphas				,     dim=0			)

          SCREAMING_SNAKE_CASE_      :			Dict     =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       torch.from_numpy(lowercase__			)

          # setable values
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       None
          SCREAMING_SNAKE_CASE_      :			str     =       np.linspace(0				,     num_train_timesteps - 1				,     lowercase__				,     dtype=lowercase__			)[::-1].copy()
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       torch.from_numpy(lowercase__			)
          SCREAMING_SNAKE_CASE_      :			int     =       False




    @property
    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          if self.config.timestep_spacing in ["linspace", "trailing"]:
                return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__			):






          """simple docstring"""






          if isinstance(lowercase__				,     torch.Tensor			):
                SCREAMING_SNAKE_CASE_      :			Any     =       timestep.to(self.timesteps.device			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       (self.timesteps == timestep).nonzero().item()
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.sigmas[step_index]
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       sample / ((sigma**2 + 1) ** 0.5)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       True
          return sample




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__ = None			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			str     =       num_inference_steps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
                SCREAMING_SNAKE_CASE_      :			Dict     =       np.linspace(0				,     self.config.num_train_timesteps - 1				,     lowercase__				,     dtype=lowercase__			)[
                    ::-1
                ].copy()
          elif self.config.timestep_spacing == "leading":
                SCREAMING_SNAKE_CASE_      :			Dict     =       self.config.num_train_timesteps // self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       (np.arange(0				,     lowercase__			) * step_ratio).round()[::-1].copy().astype(lowercase__			)
                timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.config.num_train_timesteps / self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                SCREAMING_SNAKE_CASE_      :			Any     =       (np.arange(self.config.num_train_timesteps				,     0				,     -step_ratio			)).round().copy().astype(lowercase__			)
                timesteps -= 1
          else:
                raise ValueError(
                    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."			)

          SCREAMING_SNAKE_CASE_      :			int     =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
          SCREAMING_SNAKE_CASE_      :			Any     =       np.interp(lowercase__				,     np.arange(0				,     len(lowercase__			)			)				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
          SCREAMING_SNAKE_CASE_      :			Any     =       torch.from_numpy(lowercase__			).to(device=lowercase__			)
          if str(lowercase__			).startswith("mps"			):
                # mps does not support float64
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       torch.from_numpy(lowercase__			).to(lowercase__				,     dtype=torch.floataa			)
          else:
                SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.from_numpy(lowercase__			).to(device=lowercase__			)




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     lowercase__ = True				,     ):






          """simple docstring"""






          if (
              isinstance(lowercase__				,     lowercase__			)
              or isinstance(lowercase__				,     torch.IntTensor			)
              or isinstance(lowercase__				,     torch.LongTensor			)
          ):
                raise ValueError(
                    (
                        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                        " one of the `scheduler.timesteps` as a timestep."
                    )				,     )

          if not self.is_scale_input_called:
                logger.warning(
                    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
                    "See `StableDiffusionPipeline` for a usage example."			)

          if isinstance(lowercase__				,     torch.Tensor			):
                SCREAMING_SNAKE_CASE_      :			Tuple     =       timestep.to(self.timesteps.device			)

          SCREAMING_SNAKE_CASE_      :			Tuple     =       (self.timesteps == timestep).nonzero().item()
          SCREAMING_SNAKE_CASE_      :			str     =       self.sigmas[step_index]

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

          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.sigmas[step_index]
          SCREAMING_SNAKE_CASE_      :			Any     =       self.sigmas[step_index + 1]
          SCREAMING_SNAKE_CASE_      :			Any     =       (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       (sigma_to**2 - sigma_up**2) ** 0.5

          # 2. Convert to an ODE derivative
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       (sample - pred_original_sample) / sigma

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       sigma_down - sigma

          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       sample + derivative * dt

          SCREAMING_SNAKE_CASE_      :			Dict     =       model_output.device
          SCREAMING_SNAKE_CASE_      :			int     =       randn_tensor(model_output.shape				,     dtype=model_output.dtype				,     device=lowercase__				,     generator=lowercase__			)

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       prev_sample + noise * sigma_up

          if not return_dict:
                return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=lowercase__				,     pred_original_sample=lowercase__			)




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__				,     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			str     =       self.sigmas.to(device=original_samples.device				,     dtype=original_samples.dtype			)
          if original_samples.device.type == "mps" and torch.is_floating_point(lowercase__			):
                # mps does not support float64
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.timesteps.to(original_samples.device				,     dtype=torch.floataa			)
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       timesteps.to(original_samples.device				,     dtype=torch.floataa			)
          else:
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.timesteps.to(original_samples.device			)
                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       timesteps.to(original_samples.device			)

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

          SCREAMING_SNAKE_CASE_      :			Dict     =       original_samples + noise * sigma
          return noisy_samples







    def __len__(					self			):






          """simple docstring"""






          return self.config.num_train_timesteps

