




"""simple docstring"""

import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VivitConfig
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


if is_torch_available():
							import torch
							from torch import nn

							from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
							from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from transformers import VivitImageProcessor
class      lowerCamelCase__      :


			def __init__(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=2			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=10			,	SCREAMING_SNAKE_CASE=10			,	SCREAMING_SNAKE_CASE=8			,	SCREAMING_SNAKE_CASE=[2, 4, 4]			,	SCREAMING_SNAKE_CASE=3			,	SCREAMING_SNAKE_CASE=768			,	SCREAMING_SNAKE_CASE=5			,	SCREAMING_SNAKE_CASE=4			,	SCREAMING_SNAKE_CASE=37			,	SCREAMING_SNAKE_CASE="gelu_fast"			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.02			,	SCREAMING_SNAKE_CASE=1E-06			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=None			,	):



					"""simple docstring"""







					snake_case						:  List[str]        =			parent
					snake_case						:  Union[str, Any]        =			batch_size
					snake_case						:  Tuple        =			is_training
					snake_case						:  Optional[Any]        =			use_labels
					snake_case						:  int        =			num_labels
					snake_case						:  Union[str, Any]        =			image_size
					snake_case						:  List[str]        =			num_frames
					snake_case						:  Any        =			tubelet_size
					snake_case						:  str        =			num_channels
					snake_case						:  List[Any]        =			hidden_size
					snake_case						:  List[str]        =			num_hidden_layers
					snake_case						:  Union[str, Any]        =			num_attention_heads
					snake_case						:  Optional[Any]        =			intermediate_size
					snake_case						:  Tuple        =			hidden_act
					snake_case						:  str        =			hidden_dropout_prob
					snake_case						:  Union[str, Any]        =			attention_probs_dropout_prob
					snake_case						:  int        =			initializer_range
					snake_case						:  Dict        =			layer_norm_eps
					snake_case						:  Optional[Any]        =			qkv_bias
					snake_case						:  int        =			scope

					snake_case						:  str        =			(
					    (self.image_size // self.tubelet_size[2])
					    * (self.image_size // self.tubelet_size[1])
					    * (self.num_frames // self.tubelet_size[0])
					) + 1  # CLS token


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Tuple        =			floats_tensor(
					    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]							)

					snake_case						:  str        =			None
					if self.use_labels:
							snake_case						:  Union[str, Any]        =			ids_tensor([self.batch_size]			,	self.num_labels							)

					snake_case						:  str        =			self.get_config()

					return config, pixel_values, labels


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[Any]        =			VivitConfig(
					    num_frames=self.num_frames			,	image_size=self.image_size			,	tubelet_size=self.tubelet_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			,	initializer_range=self.initializer_range			,	layer_norm_eps=self.layer_norm_eps			,	qkv_bias=self.qkv_bias			,	)
					snake_case						:  Optional[Any]        =			self.num_labels
					return config


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Any        =			VivitModel(config=SCREAMING_SNAKE_CASE							)
					model.to(SCREAMING_SNAKE_CASE							)
					model.eval()
					snake_case						:  List[Any]        =			model(SCREAMING_SNAKE_CASE							)
					self.parent.assertEqual(result.last_hidden_state.shape			,	(self.batch_size, self.seq_length, self.hidden_size)							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  List[str]        =			VivitForVideoClassification(SCREAMING_SNAKE_CASE							)
					model.to(SCREAMING_SNAKE_CASE							)
					model.eval()

					snake_case						:  Optional[int]        =			model(SCREAMING_SNAKE_CASE							)

					# verify the logits shape
					snake_case						:  List[Any]        =			torch.Size((self.batch_size, self.num_labels)							)
					self.parent.assertEqual(result.logits.shape			,	SCREAMING_SNAKE_CASE							)



			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Dict        =			self.prepare_config_and_inputs()
					snake_case     ,			snake_case     ,			snake_case						:  int        =			config_and_inputs
					snake_case						:  Tuple        =			{"pixel_values": pixel_values}
					return config, inputs_dict







@require_torch
class      lowerCamelCase__      (					lowerCamelCase_			,					unittest.TestCase     ):
			a__	: List[str]        =       (VivitModel, VivitForVideoClassification) if is_torch_available() else ()

			a__	: Any        =       False
			a__	: Any        =       False
			a__	: Optional[int]        =       False
			a__	: Optional[int]        =       False


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Union[str, Any]        =			VivitModelTester(self							)
					snake_case						:  Tuple        =			ConfigTester(self			,	config_class=SCREAMING_SNAKE_CASE			,	has_text_modality=SCREAMING_SNAKE_CASE			,	hidden_size=37							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=False							):



					"""simple docstring"""







					snake_case						:  int        =			copy.deepcopy(SCREAMING_SNAKE_CASE							)

					if return_labels:
							if model_class in get_values(SCREAMING_SNAKE_CASE							):
									snake_case						:  str        =			torch.zeros(
									    self.model_tester.batch_size			,	dtype=torch.long			,	device=SCREAMING_SNAKE_CASE							)

					return inputs_dict


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					self.config_tester.run_common_tests()


			@unittest.skip(reason="Vivit does not use inputs_embeds"							)
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					pass


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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

					for model_class in self.all_model_classes:
							snake_case						:  Optional[int]        =			model_class(SCREAMING_SNAKE_CASE							)
							self.assertIsInstance(model.get_input_embeddings()			,	(nn.Module)							)
							snake_case						:  Optional[int]        =			model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE			,	nn.Linear							)							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case     ,			snake_case						:  Any        =			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							snake_case						:  Optional[int]        =			model_class(SCREAMING_SNAKE_CASE							)
							snake_case						:  str        =			inspect.signature(model.forward							)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							snake_case						:  Optional[Any]        =			[*signature.parameters.keys()]

							snake_case						:  Optional[int]        =			["pixel_values", "head_mask"]
							self.assertListEqual(arg_names[:2]			,	SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_video_classification(*SCREAMING_SNAKE_CASE							)


			@slow
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case						:  List[Any]        =			VivitModel.from_pretrained(SCREAMING_SNAKE_CASE							)
							self.assertIsNotNone(SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case     ,			snake_case						:  Tuple        =			self.model_tester.prepare_config_and_inputs_for_common()
					snake_case						:  str        =			True

					for model_class in self.all_model_classes:
							snake_case						:  Tuple        =			self.model_tester.seq_length

							snake_case						:  Tuple        =			True
							snake_case						:  Tuple        =			False
							snake_case						:  str        =			True
							snake_case						:  str        =			model_class(SCREAMING_SNAKE_CASE							)
							model.to(SCREAMING_SNAKE_CASE							)
							model.eval()
							with torch.no_grad():
									snake_case						:  Tuple        =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)							)
							snake_case						:  List[Any]        =			outputs.attentions
							self.assertEqual(len(SCREAMING_SNAKE_CASE							)			,	self.model_tester.num_hidden_layers							)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							snake_case						:  str        =			True
							snake_case						:  List[str]        =			model_class(SCREAMING_SNAKE_CASE							)
							model.to(SCREAMING_SNAKE_CASE							)
							model.eval()
							with torch.no_grad():
									snake_case						:  Optional[Any]        =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)							)
							snake_case						:  Optional[Any]        =			outputs.attentions
							self.assertEqual(len(SCREAMING_SNAKE_CASE							)			,	self.model_tester.num_hidden_layers							)

							self.assertListEqual(
							    list(attentions[0].shape[-3:]							)			,	[self.model_tester.num_attention_heads, seq_len, seq_len]			,	)
							snake_case						:  Any        =			len(SCREAMING_SNAKE_CASE							)

							# Check attention is always last and order is fine
							snake_case						:  Any        =			True
							snake_case						:  Tuple        =			True
							snake_case						:  Optional[int]        =			model_class(SCREAMING_SNAKE_CASE							)
							model.to(SCREAMING_SNAKE_CASE							)
							model.eval()
							with torch.no_grad():
									snake_case						:  Union[str, Any]        =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)							)

							self.assertEqual(out_len + 1			,	len(SCREAMING_SNAKE_CASE							)							)

							snake_case						:  List[Any]        =			outputs.attentions

							self.assertEqual(len(SCREAMING_SNAKE_CASE							)			,	self.model_tester.num_hidden_layers							)

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


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					def check_hidden_states_output(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):
							snake_case						:  Dict        =			model_class(SCREAMING_SNAKE_CASE							)
							model.to(SCREAMING_SNAKE_CASE							)
							model.eval()

							with torch.no_grad():
									snake_case						:  List[Any]        =			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)							)

							snake_case						:  List[Any]        =			outputs.hidden_states
							snake_case						:  Any        =			self.model_tester.num_hidden_layers + 1
							self.assertEqual(len(SCREAMING_SNAKE_CASE							)			,	SCREAMING_SNAKE_CASE							)

							snake_case						:  str        =			self.model_tester.seq_length

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

					snake_case     ,			snake_case						:  Any        =			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							snake_case						:  Optional[Any]        =			True
							check_hidden_states_output(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							snake_case						:  Union[str, Any]        =			True

							check_hidden_states_output(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)



			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."							)
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					pass







def 					UpperCamelCase__       (						):
		snake_case						:  Any        =			hf_hub_download(
		    repo_id="hf-internal-testing/spaghetti-video"    ,       filename="eating_spaghetti_32_frames.npy"    ,       repo_type="dataset"				)
		snake_case						:  List[Any]        =			np.load(lowercase__				)
		return list(lowercase__				)
@require_torch
@require_vision
class      lowerCamelCase__      (					unittest.TestCase     ):


			@cached_property
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					return VivitImageProcessor() if is_vision_available() else None



			@slow
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			VivitForVideoClassification.from_pretrained("google/vivit-b-16x2-kinetics400"							).to(SCREAMING_SNAKE_CASE							)

					snake_case						:  Optional[int]        =			self.default_image_processor
					snake_case						:  List[str]        =			prepare_video()
					snake_case						:  List[Any]        =			image_processor(SCREAMING_SNAKE_CASE			,	return_tensors="pt"							).to(SCREAMING_SNAKE_CASE							)

					# forward pass
					with torch.no_grad():
							snake_case						:  str        =			model(**SCREAMING_SNAKE_CASE							)

					# verify the logits
					snake_case						:  str        =			torch.Size((1, 400)							)
					self.assertEqual(outputs.logits.shape			,	SCREAMING_SNAKE_CASE							)

					# taken from original model
					snake_case						:  Optional[Any]        =			torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58]							).to(SCREAMING_SNAKE_CASE							)

					self.assertTrue(torch.allclose(outputs.logits[0, :5]			,	SCREAMING_SNAKE_CASE			,	atol=1E-4							)							)







"""simple docstring"""

from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__A							=     logging.get_logger(__name__)  # pylint: disable=invalid-name


__A							=     "\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = \"cuda\"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")\n        ```\n"
class      lowerCamelCase__      (					lowerCamelCase_     ):


			def __init__(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					super().__init__()

					self.register_modules(
					    unet=SCREAMING_SNAKE_CASE			,	scheduler=SCREAMING_SNAKE_CASE			,	)

					snake_case						:  List[str]        =			None


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE=0							):



					"""simple docstring"""







					if is_accelerate_available() and is_accelerate_version(">="			,	"0.14.0"							):
							from accelerate import cpu_offload
					else:
							raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"							)

					snake_case						:  Tuple        =			torch.device(F'''cuda:{gpu_id}'''							)

					if self.device.type != "cpu":
							self.to("cpu"			,	silence_dtype_warnings=SCREAMING_SNAKE_CASE							)
							torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

					for cpu_offloaded_model in [self.unet]:
							cpu_offload(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

					if self.safety_checker is not None:
							cpu_offload(self.safety_checker			,	execution_device=SCREAMING_SNAKE_CASE			,	offload_buffers=SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE=0							):



					"""simple docstring"""







					if is_accelerate_available() and is_accelerate_version(">="			,	"0.17.0.dev0"							):
							from accelerate import cpu_offload_with_hook
					else:
							raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."							)

					snake_case						:  Union[str, Any]        =			torch.device(F'''cuda:{gpu_id}'''							)

					if self.device.type != "cpu":
							self.to("cpu"			,	silence_dtype_warnings=SCREAMING_SNAKE_CASE							)
							torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

					snake_case						:  List[str]        =			None
					for cpu_offloaded_model in [self.unet]:
							snake_case     ,			snake_case						:  Optional[int]        =			cpu_offload_with_hook(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	prev_module_hook=SCREAMING_SNAKE_CASE							)

					if self.safety_checker is not None:
							snake_case     ,			snake_case						:  List[str]        =			cpu_offload_with_hook(self.safety_checker			,	SCREAMING_SNAKE_CASE			,	prev_module_hook=SCREAMING_SNAKE_CASE							)

					# We'll offload the last model manually.
					snake_case						:  Optional[Any]        =			hook


			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					if not hasattr(self.unet			,	"_hf_hook"							):
							return self.device
					for module in self.unet.modules():
							if (
							    hasattr(SCREAMING_SNAKE_CASE			,	"_hf_hook"							)
							    and hasattr(module._hf_hook			,	"execution_device"							)
							    and module._hf_hook.execution_device is not None
							):
									return torch.device(module._hf_hook.execution_device							)
					return self.device


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=None							):



					"""simple docstring"""







					snake_case						:  Any        =			(batch_size, num_channels, height, width)
					if isinstance(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							) and len(SCREAMING_SNAKE_CASE							) != batch_size:
							raise ValueError(
							    F'''You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE							)}, but requested an effective batch'''
							    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''							)

					if latents is None:
							snake_case						:  Any        =			randn_tensor(SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE			,	device=SCREAMING_SNAKE_CASE			,	dtype=SCREAMING_SNAKE_CASE							)
					else:
							snake_case						:  Tuple        =			latents.to(device=SCREAMING_SNAKE_CASE			,	dtype=SCREAMING_SNAKE_CASE							)

					# scale the initial noise by the standard deviation required by the scheduler
					snake_case						:  Optional[int]        =			latents * self.scheduler.init_noise_sigma
					return latents


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = "pil"							):



					"""simple docstring"""







					if output_type not in ["pt", "np", "pil"]:
							raise ValueError(
							    F'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''							)

					# Equivalent to diffusers.VaeImageProcessor.denormalize
					snake_case						:  List[str]        =			(sample / 2 + 0.5).clamp(0			,	1							)
					if output_type == "pt":
							return sample

					# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
					snake_case						:  Optional[int]        =			sample.cpu().permute(0			,	2			,	3			,	1							).numpy()
					if output_type == "np":
							return sample

					# Output_type must be 'pil'
					snake_case						:  List[str]        =			self.numpy_to_pil(SCREAMING_SNAKE_CASE							)
					return sample


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=None							):



					"""simple docstring"""







					if self.unet.config.num_class_embeds is not None:
							if isinstance(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):
									snake_case						:  Optional[Any]        =			torch.tensor(SCREAMING_SNAKE_CASE			,	dtype=torch.int							)
							elif isinstance(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):
									assert batch_size == 1, "Batch size must be 1 if classes is an int"
									snake_case						:  str        =			torch.tensor([class_labels]			,	dtype=torch.int							)
							elif class_labels is None:
									# Randomly generate batch_size class labels
									# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
									snake_case						:  Tuple        =			torch.randint(0			,	self.unet.config.num_class_embeds			,	size=(batch_size,)							)
							snake_case						:  int        =			class_labels.to(SCREAMING_SNAKE_CASE							)
					else:
							snake_case						:  str        =			None
					return class_labels


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if num_inference_steps is None and timesteps is None:
							raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."							)

					if num_inference_steps is not None and timesteps is not None:
							logger.warning(
							    F'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
							    " `timesteps` will be used over `num_inference_steps`."							)

					if latents is not None:
							snake_case						:  str        =			(batch_size, 3, img_size, img_size)
							if latents.shape != expected_shape:
									raise ValueError(F'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''							)

					if (callback_steps is None) or (
					    callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							) or callback_steps <= 0)
					):
							raise ValueError(
							    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
							    F''' {type(SCREAMING_SNAKE_CASE							)}.'''							)



			@torch.no_grad()
			@replace_example_docstring(SCREAMING_SNAKE_CASE							)
			def __call__(       self			,	SCREAMING_SNAKE_CASE = 1			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = 1			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = "pil"			,	SCREAMING_SNAKE_CASE = True			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = 1			,	):



					"""simple docstring"""







					snake_case						:  List[str]        =			self.unet.config.sample_size
					snake_case						:  Dict        =			self._execution_device

					# 1. Check inputs
					self.check_inputs(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

					# 2. Prepare image latents
					# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
					snake_case						:  Any        =			self.prepare_latents(
					    batch_size=SCREAMING_SNAKE_CASE			,	num_channels=self.unet.config.in_channels			,	height=SCREAMING_SNAKE_CASE			,	width=SCREAMING_SNAKE_CASE			,	dtype=self.unet.dtype			,	device=SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE			,	latents=SCREAMING_SNAKE_CASE			,	)

					# 3. Handle class_labels for class-conditional models
					snake_case						:  Union[str, Any]        =			self.prepare_class_labels(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	class_labels=SCREAMING_SNAKE_CASE							)

					# 4. Prepare timesteps
					if timesteps is not None:
							self.scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE			,	device=SCREAMING_SNAKE_CASE							)
							snake_case						:  Dict        =			self.scheduler.timesteps
							snake_case						:  str        =			len(SCREAMING_SNAKE_CASE							)
					else:
							self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE							)
							snake_case						:  str        =			self.scheduler.timesteps

					# 5. Denoising loop
					# Multistep sampling: implements Algorithm 1 in the paper
					with self.progress_bar(total=SCREAMING_SNAKE_CASE							) as progress_bar:
							for i, t in enumerate(SCREAMING_SNAKE_CASE							):
									snake_case						:  Any        =			self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)
									snake_case						:  Dict        =			self.unet(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	class_labels=SCREAMING_SNAKE_CASE			,	return_dict=SCREAMING_SNAKE_CASE							)[0]

									snake_case						:  List[Any]        =			self.scheduler.step(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE							)[0]

									# call the callback, if provided
									progress_bar.update()
									if callback is not None and i % callback_steps == 0:
											callback(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

        # 6. Post-process image sample
					snake_case						:  Dict        =			self.postprocess_image(SCREAMING_SNAKE_CASE			,	output_type=SCREAMING_SNAKE_CASE							)

					# Offload last model to CPU
					if hasattr(self			,	"final_offload_hook"							) and self.final_offload_hook is not None:
							self.final_offload_hook.offload()

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE							)

