
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							:Union[str, Any]											=       logging.get_logger(__name__)  # pylint: disable=invalid-name


SCREAMING_SNAKE_CASE							:int											=       '\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 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	def __init__( self      :				Optional[Any]							,A      :				List[str]							,A      :				Optional[int]				):
					super().__init__()

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

					__A         =				None






	def        UpperCamelCase_						( self      :				Any							,A      :				Optional[Any]=0				):
					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"				)

					__A         =				torch.device(f'''cuda:{gpu_id}'''				)

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

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






	def        UpperCamelCase_						( self      :				Tuple							,A      :				Union[str, Any]=0				):
					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."				)

					__A         =				torch.device(f'''cuda:{gpu_id}'''				)

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

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

					if self.safety_checker is not None:
									__A         =				cpu_offload_with_hook(self.safety_checker							,__lowerCamelCase							,prev_module_hook=__lowerCamelCase				)

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






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

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






	def        UpperCamelCase_						( self      :				Any							,A      :				int							,A      :				str = "pil"				):
					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
					__A         =				(sample / 2 + 0.5).clamp(0							,1				)
					if output_type == "pt":
									return sample

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

					# Output_type must be 'pil'
					__A         =				self.numpy_to_pil(__lowerCamelCase				)
					return sample






	def        UpperCamelCase_						( self      :				str							,A      :				str							,A      :				Optional[Any]							,A      :				Tuple=None				):
					if self.unet.config.num_class_embeds is not None:
									if isinstance(__lowerCamelCase							,__lowerCamelCase				):
													__A         =				torch.tensor(__lowerCamelCase							,dtype=torch.int				)
									elif isinstance(__lowerCamelCase							,__lowerCamelCase				):
													assert batch_size == 1, "Batch size must be 1 if classes is an int"
													__A         =				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
													__A         =				torch.randint(0							,self.unet.config.num_class_embeds							,size=(batch_size,)				)
									__A         =				class_labels.to(__lowerCamelCase				)
					else:
									__A         =				None
					return class_labels






	def        UpperCamelCase_						( self      :				str							,A      :				Optional[Any]							,A      :				Optional[int]							,A      :				Dict							,A      :				Any							,A      :				List[str]							,A      :				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:
									__A         =				(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(__lowerCamelCase							,__lowerCamelCase				) or callback_steps <= 0)
					):
									raise ValueError(
									    f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
									    f''' {type(__lowerCamelCase				)}.'''				)







	@torch.no_grad()
	@replace_example_docstring(__lowerCamelCase				)
	def __call__( self      :				str							,A      :				Optional[int] = 1							,A      :				str = None							,A      :				int = 1							,A      :				Optional[int] = None							,A      :				List[Any] = None							,A      :				Optional[Any] = None							,A      :				List[Any] = "pil"							,A      :				Tuple = True							,A      :				Dict = None							,A      :				Optional[int] = 1							,):
					__A         =				self.unet.config.sample_size
					__A         =				self._execution_device

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

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

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

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

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

													__A         =				self.scheduler.step(__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,generator=__lowerCamelCase				)[0]

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

        # 6. Post-process image sample
					__A         =				self.postprocess_image(__lowerCamelCase							,output_type=__lowerCamelCase				)

					# 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=__lowerCamelCase				)


from typing import Any

import numpy as np





class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				Dict							,A      :				np.ndarray							,A      :				int				):
					# Check if RHS is negative
					if np.any(tableau[:, -1]							,where=tableau[:, -1] < 0				):
									raise ValueError("RHS must be > 0"				)

					__A         =				tableau
					__A    ,							__A         =				tableau.shape

					# Number of decision variables x1, x2, x3...
					__A         =				n_vars

					# Number of artificial variables to be minimised
					__A         =				len(np.where(tableau[self.n_vars : -1] == -1				)[0]				)

					# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
					__A         =				(self.n_art_vars > 0) + 1

					# Number of slack variables added to make inequalities into equalities
					__A         =				self.n_rows - self.n_stages

					# Objectives for each stage
					__A         =				["max"]

					# In two stage simplex, first minimise then maximise
					if self.n_art_vars:
									self.objectives.append("min"				)

					__A         =				[""]

					# Index of current pivot row and column
					__A         =				None
					__A         =				None

					# Does objective row only contain (non)-negative values?
					__A         =				False






	@staticmethod
	def        UpperCamelCase_						( *A      :				int				):
					if len(A				) != 3:
									raise ValueError("Must provide n_vars, n_slack, and n_art_vars"				)

					if not all(x >= 0 and isinstance(A							,A				) for x in args				):
									raise ValueError("All arguments must be non-negative integers"				)

					# decision | slack | artificial
					__A         =				["x", "s", "a"]
					__A         =				[]
					for i in range(3				):
									for j in range(args[i]				):
													titles.append(string_starts[i] + str(j + 1				)				)
					titles.append("RHS"				)
					return titles






	def        UpperCamelCase_						( self      :				List[Any]							,A      :				np.ndarray				):
					__A         =				self.objectives[-1]

					# Find entries of highest magnitude in objective rows
					__A         =				(objective == "min") - (objective == "max")
					__A         =				np.argmax(sign * tableau[0, : self.n_vars]				)

					# Choice is only valid if below 0 for maximise, and above for minimise
					if sign * self.tableau[0, col_idx] <= 0:
									__A         =				True
									return 0, 0

					# Pivot row is chosen as having the lowest quotient when elements of
					# the pivot column divide the right-hand side

					# Slice excluding the objective rows
					__A         =				slice(self.n_stages							,self.n_rows				)

					# RHS
					__A         =				tableau[s, -1]

					# Elements of pivot column within slice
					__A         =				tableau[s, col_idx]

					# Array filled with nans
					__A         =				np.full(self.n_rows - self.n_stages							,np.nan				)

					# If element in pivot column is greater than zeron_stages, return
					# quotient or nan otherwise
					__A         =				np.divide(A							,A							,out=A							,where=divisor > 0				)

					# Arg of minimum quotient excluding the nan values. n_stages is added
					# to compensate for earlier exclusion of objective columns
					__A         =				np.nanargmin(A				) + self.n_stages
					return row_idx, col_idx






	def        UpperCamelCase_						( self      :				List[Any]							,A      :				np.ndarray							,A      :				int							,A      :				int				):
					__A         =				tableau[row_idx].copy()

					__A         =				piv_row[col_idx]

					# Entry becomes 1
					piv_row *= 1 / piv_val

					# Variable in pivot column becomes basic, ie the only non-zero entry
					for idx, coeff in enumerate(tableau[:, col_idx]				):
									tableau[idx] += -coeff * piv_row
					__A         =				piv_row
					return tableau






	def        UpperCamelCase_						( self      :				Optional[Any]							,A      :				np.ndarray				):
					self.objectives.pop()

					if not self.objectives:
									return tableau

					# Slice containing ids for artificial columns
					__A         =				slice(-self.n_art_vars - 1							,-1				)

					# Delete the artificial variable columns
					__A         =				np.delete(A							,A							,axis=1				)

					# Delete the objective row of the first stage
					__A         =				np.delete(A							,0							,axis=0				)

					__A         =				1
					self.n_rows -= 1
					__A         =				0
					__A         =				False
					return tableau






	def        UpperCamelCase_						( self      :				Dict				):
					# Stop simplex algorithm from cycling.
					for _ in range(1_00				):
									# Completion of each stage removes an objective. If both stages
									# are complete, then no objectives are left
									if not self.objectives:
													__A         =				self.generate_col_titles(
													    self.n_vars							,self.n_slack							,self.n_art_vars				)

													# Find the values of each variable at optimal solution
													return self.interpret_tableau(self.tableau							,self.col_titles				)

									__A    ,							__A         =				self.find_pivot(self.tableau				)

									# If there are no more negative values in objective row
									if self.stop_iter:
													# Delete artificial variable columns and rows. Update attributes
													__A         =				self.change_stage(self.tableau				)
									else:
													__A         =				self.pivot(self.tableau							,A							,A				)
					return {}







	def        UpperCamelCase_						( self      :				Dict							,A      :				np.ndarray							,A      :				list[str]				):
					__A         =				{"P": abs(tableau[0, -1]				)}

					for i in range(self.n_vars				):
									# Gives ids of nonzero entries in the ith column
									__A         =				np.nonzero(tableau[:, i]				)
									__A         =				len(nonzero[0]				)

									# First entry in the nonzero ids
									__A         =				nonzero[0][0]
									__A         =				tableau[nonzero_rowidx, i]

									# If there is only one nonzero value in column, which is one
									if n_nonzero == nonzero_val == 1:
													__A         =				tableau[nonzero_rowidx, -1]
													__A         =				rhs_val

        # Check for basic variables
					for title in col_titles:
									# Don't add RHS or slack variables to output dict
									if title[0] not in "R-s-a":
													output_dict.setdefault(A							,0				)
					return output_dict


if __name__ == "__main__":
					import doctest

					doctest.testmod()
