






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_							=      {
    '''configuration_switch_transformers''': [
        '''SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwitchTransformersConfig''',
        '''SwitchTransformersOnnxConfig''',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=      [
	    '''SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SwitchTransformersEncoderModel''',
	    '''SwitchTransformersForConditionalGeneration''',
	    '''SwitchTransformersModel''',
	    '''SwitchTransformersPreTrainedModel''',
	    '''SwitchTransformersTop1Router''',
	    '''SwitchTransformersSparseMLP''',
	]


if TYPE_CHECKING:
	from .configuration_switch_transformers import (
	    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwitchTransformersConfig,
	    SwitchTransformersOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_switch_transformers import (
		    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwitchTransformersEncoderModel,
		    SwitchTransformersForConditionalGeneration,
		    SwitchTransformersModel,
		    SwitchTransformersPreTrainedModel,
		    SwitchTransformersSparseMLP,
		    SwitchTransformersTopaRouter,
		)


else:
	import sys

	a_							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)














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_							=      '''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''


class        lowercase__       (     _UpperCAmelCase		):

	def __init__(							self						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   None:







							'''simple docstring'''





							super().__init__()

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

							lowerCAmelCase__	 =  None

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase=0    )->   Optional[Any]:







							'''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"    )

							lowerCAmelCase__	 =  torch.device(F"cuda:{gpu_id}"    )

							if self.device.type != "cpu":
													self.to("cpu"						,      silence_dtype_warnings=__UpperCAmelCase    )
													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(__UpperCAmelCase						,      __UpperCAmelCase    )

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

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase=0    )->   Any:







							'''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."    )

							lowerCAmelCase__	 =  torch.device(F"cuda:{gpu_id}"    )

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

							lowerCAmelCase__	 =  None
							for cpu_offloaded_model in [self.unet]:
													lowerCAmelCase__ ,							lowerCAmelCase__	 =  cpu_offload_with_hook(__UpperCAmelCase						,      __UpperCAmelCase						,      prev_module_hook=__UpperCAmelCase    )

							if self.safety_checker is not None:
													lowerCAmelCase__ ,							lowerCAmelCase__	 =  cpu_offload_with_hook(self.safety_checker						,      __UpperCAmelCase						,      prev_module_hook=__UpperCAmelCase    )

							# We'll offload the last model manually.
							lowerCAmelCase__	 =  hook

	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							if not hasattr(self.unet						,      "_hf_hook"    ):
													return self.device
							for module in self.unet.modules():
													if (
													    hasattr(__UpperCAmelCase						,      "_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 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase=None    )->   Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  (batch_size, num_channels, height, width)
							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ) and len(__UpperCAmelCase    ) != batch_size:
													raise ValueError(
													    F"You have passed a list of generators of length {len(__UpperCAmelCase    )}, 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:
													lowerCAmelCase__	 =  randn_tensor(__UpperCAmelCase						,      generator=__UpperCAmelCase						,      device=__UpperCAmelCase						,      dtype=__UpperCAmelCase    )
							else:
													lowerCAmelCase__	 =  latents.to(device=__UpperCAmelCase						,      dtype=__UpperCAmelCase    )

							# scale the initial noise by the standard deviation required by the scheduler
							lowerCAmelCase__	 =  latents * self.scheduler.init_noise_sigma
							return latents

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase = "pil"    )->   Tuple:







							'''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
							lowerCAmelCase__	 =  (sample / 2 + 0.5).clamp(0						,      1    )
							if output_type == "pt":
													return sample

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

							# Output_type must be 'pil'
							lowerCAmelCase__	 =  self.numpy_to_pil(__UpperCAmelCase    )
							return sample

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase=None    )->   List[Any]:







							'''simple docstring'''





							if self.unet.config.num_class_embeds is not None:
													if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
																			lowerCAmelCase__	 =  torch.tensor(__UpperCAmelCase						,      dtype=torch.int    )
													elif isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
																			assert batch_size == 1, "Batch size must be 1 if classes is an int"
																			lowerCAmelCase__	 =  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
																			lowerCAmelCase__	 =  torch.randint(0						,      self.unet.config.num_class_embeds						,      size=(batch_size,)    )
													lowerCAmelCase__	 =  class_labels.to(__UpperCAmelCase    )
							else:
													lowerCAmelCase__	 =  None
							return class_labels

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   List[Any]:







							'''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:
													lowerCAmelCase__	 =  (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(__UpperCAmelCase						,      __UpperCAmelCase    ) or callback_steps <= 0)
							):
													raise ValueError(
													    F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
													    F" {type(__UpperCAmelCase    )}."    )


	@torch.no_grad()
	@replace_example_docstring(__UpperCAmelCase    )
	def __call__(							self						,      __UpperCAmelCase = 1						,      __UpperCAmelCase = None						,      __UpperCAmelCase = 1						,      __UpperCAmelCase = None						,      __UpperCAmelCase = None						,      __UpperCAmelCase = None						,      __UpperCAmelCase = "pil"						,      __UpperCAmelCase = True						,      __UpperCAmelCase = None						,      __UpperCAmelCase = 1						,      )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.unet.config.sample_size
							lowerCAmelCase__	 =  self._execution_device

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

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

							# 3. Handle class_labels for class-conditional models
							lowerCAmelCase__	 =  self.prepare_class_labels(__UpperCAmelCase						,      __UpperCAmelCase						,      class_labels=__UpperCAmelCase    )

							# 4. Prepare timesteps
							if timesteps is not None:
													self.scheduler.set_timesteps(timesteps=__UpperCAmelCase						,      device=__UpperCAmelCase    )
													lowerCAmelCase__	 =  self.scheduler.timesteps
													lowerCAmelCase__	 =  len(__UpperCAmelCase    )
							else:
													self.scheduler.set_timesteps(__UpperCAmelCase    )
													lowerCAmelCase__	 =  self.scheduler.timesteps

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

																			lowerCAmelCase__	 =  self.scheduler.step(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      generator=__UpperCAmelCase    )[0]

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

        # 6. Post-process image sample
							lowerCAmelCase__	 =  self.postprocess_image(__UpperCAmelCase						,      output_type=__UpperCAmelCase    )

							# 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=__UpperCAmelCase    )






