




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


__SCREAMING_SNAKE_CASE			:      List[str]      			=logging.get_logger(__name__)  # pylint: disable=invalid-name


__SCREAMING_SNAKE_CASE			:      Tuple      			="""
    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 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def __init__(			self     :      Any		,     A__     :      UNetaDModel		,     A__     :      CMStochasticIterativeScheduler	) ->				None:
           super().__init__()

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

           lowerCamelCase__					     = None




     def        __snake_case      (			self     :      List[str]		,     A__     :      str=0	) ->				Any:
           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=A__	)
                 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(A__		,     A__	)

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




     def        __snake_case      (			self     :      Tuple		,     A__     :      Optional[Any]=0	) ->				List[Any]:
           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=A__	)
                 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(A__		,     A__		,     prev_module_hook=A__	)

           if self.safety_checker is not None:
                 lowerCamelCase__     ,						lowerCamelCase__					     = cpu_offload_with_hook(self.safety_checker		,     A__		,     prev_module_hook=A__	)

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




     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def        __snake_case      (			self     :      Dict	) ->				int:
           if not hasattr(self.unet		,     '_hf_hook'	):
                 return self.device
           for module in self.unet.modules():
                 if (
                     hasattr(A__		,     '_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        __snake_case      (			self     :      Tuple		,     A__     :      Optional[int]		,     A__     :      int		,     A__     :      List[Any]		,     A__     :      List[Any]		,     A__     :      List[str]		,     A__     :      int		,     A__     :      List[str]		,     A__     :      Union[str, Any]=None	) ->				Tuple:
           lowerCamelCase__					     = (batch_size, num_channels, height, width)
           if isinstance(A__		,     A__	) and len(A__	) != batch_size:
                 raise ValueError(
                     F"""You have passed a list of generators of length {len(A__	)}, 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(A__		,     generator=A__		,     device=A__		,     dtype=A__	)
           else:
                 lowerCamelCase__					     = latents.to(device=A__		,     dtype=A__	)

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




     def        __snake_case      (			self     :      str		,     A__     :      torch.FloatTensor		,     A__     :      str = "pil"	) ->				Tuple:
           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(A__	)
           return sample




     def        __snake_case      (			self     :      Optional[Any]		,     A__     :      str		,     A__     :      str		,     A__     :      Dict=None	) ->				int:
           if self.unet.config.num_class_embeds is not None:
                 if isinstance(A__		,     A__	):
                       lowerCamelCase__					     = torch.tensor(A__		,     dtype=torch.int	)
                 elif isinstance(A__		,     A__	):
                       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(A__	)
           else:
                 lowerCamelCase__					     = None
           return class_labels




     def        __snake_case      (			self     :      str		,     A__     :      Any		,     A__     :      Optional[int]		,     A__     :      List[Any]		,     A__     :      Dict		,     A__     :      Any		,     A__     :      List[Any]	) ->				List[Any]:
           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(A__		,     A__	) or callback_steps <= 0)
           ):
                 raise ValueError(
                     F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                     F""" {type(A__	)}."""	)




     @torch.no_grad()
     @replace_example_docstring(A__	)
     def __call__(			self     :      Optional[int]		,     A__     :      int = 1		,     A__     :      Optional[Union[torch.Tensor, List[int], int]] = None		,     A__     :      int = 1		,     A__     :      List[int] = None		,     A__     :      Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     A__     :      Optional[torch.FloatTensor] = None		,     A__     :      Optional[str] = "pil"		,     A__     :      bool = True		,     A__     :      Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,     A__     :      int = 1		,     ) ->				Optional[Any]:
           lowerCamelCase__					     = self.unet.config.sample_size
           lowerCamelCase__					     = self._execution_device

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

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

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

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

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

                       lowerCamelCase__					     = self.scheduler.step(A__		,     A__		,     A__		,     generator=A__	)[0]

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

        # 6. Post-process image sample
           lowerCamelCase__					     = self.postprocess_image(A__		,     output_type=A__	)

           # 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=A__	)





import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 	A	(						unittest.TestCase						):
     """simple docstring"""

     @cached_property
     def        __snake_case      (			self     :      Union[str, Any]	) ->				str:
           return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'	)




     @cached_property
     def        __snake_case      (			self     :      Tuple	) ->				Dict:
           return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'	)




     def        __snake_case      (			self     :      List[str]	) ->				int:
           lowerCamelCase__					     = self.tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      List[str]	) ->				str:
           lowerCamelCase__					     = self.rust_tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      Any	) ->				str:
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]




     def        __snake_case      (			self     :      str	) ->				Union[str, Any]:
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]