



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


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


UpperCAmelCase__     			=							'\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	,  UpperCAmelCase	,  UpperCAmelCase )  ->     Tuple:
      super().__init__()

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

      _lowercase							   =None





     def   __A      (self	,  UpperCAmelCase=0 )  ->     Optional[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''' )

      _lowercase							   =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   __A      (self	,  UpperCAmelCase=0 )  ->     Optional[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.''' )

      _lowercase							   =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)

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

      if self.safety_checker is not None:
       _lowercase							   =cpu_offload_with_hook(self.safety_checker	,  __a	,  prev_module_hook=__a )

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





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





      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   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase=None )  ->     List[str]:
      _lowercase							   =(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:
       _lowercase							   =randn_tensor(__a	,  generator=__a	,  device=__a	,  dtype=__a )
      else:
       _lowercase							   =latents.to(device=__a	,  dtype=__a )

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





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase = "pil" )  ->     Union[str, Any]:





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

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

      # Output_type must be 'pil'
      _lowercase							   =self.numpy_to_pil(__a )
      return sample





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase=None )  ->     int:





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





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )  ->     Union[str, 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:
       _lowercase							   =(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	,  UpperCAmelCase = 1	,  UpperCAmelCase = None	,  UpperCAmelCase = 1	,  UpperCAmelCase = None	,  UpperCAmelCase = None	,  UpperCAmelCase = None	,  UpperCAmelCase = "pil"	,  UpperCAmelCase = True	,  UpperCAmelCase = None	,  UpperCAmelCase = 1	,  )  ->     Optional[int]:
      _lowercase							   =self.unet.config.sample_size
      _lowercase							   =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)
      _lowercase							   =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
      _lowercase							   =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 )
       _lowercase							   =self.scheduler.timesteps
       _lowercase							   =len(__a )
      else:
       self.scheduler.set_timesteps(__a )
       _lowercase							   =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 ):
        _lowercase							   =self.scheduler.scale_model_input(__a	,  __a )
        _lowercase							   =self.unet(__a	,  __a	,  class_labels=__a	,  return_dict=__a )[0]

        _lowercase							   =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
      _lowercase							   =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

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf

  from transformers.activations_tf import get_tf_activation







@require_tf
class    lowerCamelCase__     ( unittest.TestCase):





     def   __A      (self )  ->     Optional[Any]:
      _lowercase							   =tf.constant([-1_0_0, -1.0, -0.1, 0, 0.1, 1.0, 100.0] )
      _lowercase							   =get_tf_activation('''gelu''' )
      _lowercase							   =get_tf_activation('''gelu_10''' )

      _lowercase							   =gelu(UpperCAmelCase )
      _lowercase							   =geluaa(UpperCAmelCase )

      _lowercase							   =tf.where(y_gelu_aa < 10.0	,  1.0	,  0.0 )

      self.assertEqual(tf.math.reduce_max(UpperCAmelCase ).numpy().item()	,  10.0 )
      self.assertTrue(np.allclose(y_gelu * clipped_mask	,  y_gelu_aa * clipped_mask ) )





     def   __A      (self )  ->     Optional[Any]:
      get_tf_activation('''gelu''' )
      get_tf_activation('''gelu_10''' )
      get_tf_activation('''gelu_fast''' )
      get_tf_activation('''gelu_new''' )
      get_tf_activation('''glu''' )
      get_tf_activation('''mish''' )
      get_tf_activation('''quick_gelu''' )
      get_tf_activation('''relu''' )
      get_tf_activation('''sigmoid''' )
      get_tf_activation('''silu''' )
      get_tf_activation('''swish''' )
      get_tf_activation('''tanh''' )
      with self.assertRaises(UpperCAmelCase ):
       get_tf_activation('''bogus''' )
      with self.assertRaises(UpperCAmelCase ):
       get_tf_activation(UpperCAmelCase )

