






"""simple docstring"""



import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None			)    ->str:
 UpperCAmelCase__ 						=				None
 # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
 if str(weight.dtype			) == "torch.bfloat16":
  # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
  UpperCAmelCase__ 						=				weight.view(torch.intaa			)
  UpperCAmelCase__ 						=				"bfloat16"
 UpperCAmelCase__ 						=				weight.cpu().numpy()
 UpperCAmelCase__ 						=				os.path.join(_lowerCamelCase       ,	F'''{weight_name}.dat'''			)
 if index is not None:
  if dtype is None:
   UpperCAmelCase__ 						=				str(array.dtype			)
  UpperCAmelCase__ 						=				{"dtype": dtype, "shape": list(array.shape			)}
 if array.ndim == 0:
  UpperCAmelCase__ 						=				array[None]
 UpperCAmelCase__ 						=				np.memmap(_lowerCamelCase       ,	dtype=array.dtype       ,	mode="""w+"""       ,	shape=array.shape			)
 UpperCAmelCase__ 						=				array[:]
 file_array.flush()
 return index




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Tuple:
 UpperCAmelCase__ 						=				tuple(weight_info["""shape"""]			)
 if shape == ():
  # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
  UpperCAmelCase__ 						=				(1,)

 UpperCAmelCase__ 						=				weight_info["dtype"]
 if dtype == "bfloat16":
  # NumPy does not support bfloat16 so this was saved as a int16
  UpperCAmelCase__ 						=				"int16"

 UpperCAmelCase__ 						=				np.memmap(_lowerCamelCase       ,	dtype=_lowerCamelCase       ,	shape=_lowerCamelCase       ,	mode="""r"""			)

 if len(weight_info["""shape"""]			) == 0:
  UpperCAmelCase__ 						=				weight[0]
 UpperCAmelCase__ 						=				torch.tensor(_lowerCamelCase			)
 if weight_info["dtype"] == "bfloat16":
  UpperCAmelCase__ 						=				weight.view(torch.bfloataa			)

 return weight




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Union[str, Any]:


 if index is None or len(_lowerCamelCase			) == 0:
  # Nothing to save
  return

 UpperCAmelCase__ 						=				os.path.join(_lowerCamelCase       ,	"""index.json"""			)
 if os.path.isfile(_lowerCamelCase			):
  with open(_lowerCamelCase       ,	"""r"""       ,	encoding="""utf-8"""			) as f:
   UpperCAmelCase__ 						=				json.load(_lowerCamelCase			)
 else:
  UpperCAmelCase__ 						=				{}
 current_index.update(_lowerCamelCase			)

 with open(_lowerCamelCase       ,	"""w"""       ,	encoding="""utf-8"""			) as f:
  json.dump(_lowerCamelCase       ,	_lowerCamelCase       ,	indent=2			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->List[Any]:
 os.makedirs(_lowerCamelCase       ,	exist_ok=_lowerCamelCase			)
 UpperCAmelCase__ 						=				{}
 for name, parameter in state_dict.items():
  UpperCAmelCase__ 						=				offload_weight(_lowerCamelCase       ,	_lowerCamelCase       ,	_lowerCamelCase       ,	index=_lowerCamelCase			)

 # Update index
 save_offload_index(_lowerCamelCase       ,	_lowerCamelCase			)


class 	_UpperCamelCase ( lowercase__							):



    '''simple docstring'''


    def __init__(      self   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				dataset
     UpperCAmelCase__ 						=				prefix


    def __getitem__(      self   ,			__lowercase			):
     return self.dataset[F'''{self.prefix}{key}''']


    def __iter__(      self			):
     return iter([key for key in self.dataset if key.startswith(self.prefix			)]			)






    def __len__(      self			):
     return len(self.dataset			)



class 	_UpperCamelCase ( lowercase__							):



    '''simple docstring'''


    def __init__(      self   ,			__lowercase = None   ,			__lowercase = None   ,			__lowercase = None   ,			__lowercase=None   ,			):
     if state_dict is None and save_folder is None:
      raise ValueError("""Need either a `state_dict` or a `save_folder` containing offloaded weights."""			)

     UpperCAmelCase__ 						=				{} if state_dict is None else state_dict
     UpperCAmelCase__ 						=				save_folder
     if index is None and save_folder is not None:
      with open(os.path.join(__lowercase   ,			"""index.json"""			)			) as f:
       UpperCAmelCase__ 						=				json.load(__lowercase			)
     UpperCAmelCase__ 						=				{} if index is None else index
     UpperCAmelCase__ 						=				list(self.state_dict.keys()			)
     self.all_keys.extend([key for key in self.index if key not in self.all_keys]			)
     UpperCAmelCase__ 						=				device


    def __getitem__(      self   ,			__lowercase			):
     # State dict gets priority
     if key in self.state_dict:
      return self.state_dict[key]
     UpperCAmelCase__ 						=				self.index[key]
     if weight_info.get("""safetensors_file"""			) is not None:
      if not is_safetensors_available():
       raise ImportError("""These offloaded weights require the use of safetensors: `pip install safetensors`."""			)

      from safetensors import safe_open

      UpperCAmelCase__ 						=				"cpu" if self.device is None else self.device
      with safe_open(weight_info["""safetensors_file"""]   ,			framework="""pt"""   ,			device=__lowercase			) as f:
       UpperCAmelCase__ 						=				f.get_tensor(weight_info.get("""weight_name"""   ,			__lowercase			)			)

      if "dtype" in weight_info:
       return tensor.to(getattr(__lowercase   ,			weight_info["""dtype"""]			)			)
      else:
       return tensor

     UpperCAmelCase__ 						=				os.path.join(self.save_folder   ,			F'''{key}.dat'''			)
     return load_offloaded_weight(__lowercase   ,			__lowercase			)


    def __iter__(      self			):
     return iter(self.all_keys			)






    def __len__(      self			):
     return len(self.all_keys			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Dict:
 UpperCAmelCase__ 						=				{}
 for module_name in submodule_names:
  # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
  # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
  result.update(
      {
          key: param
          for key, param in state_dict.items()
          if key == module_name or key.startswith(module_name + """."""			)
      }			)
 return result










from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  Dict				,						a					:  str				,						a					:  Optional[Any]     )						->Any:
													super().__init__()
													self.register_modules(unet=a				,						scheduler=a     )






							@torch.no_grad()
							def __call__(   self					:  int				,						a					:  int = 1				,						a					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None				,						a					:  int = 10_00				,						a					:  Optional[str] = "pil"				,						a					:  bool = True				,						)						->Union[ImagePipelineOutput, Tuple]:
													# Sample gaussian noise to begin loop
													if isinstance(self.unet.config.sample_size				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					int =       (
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			SCREAMING_SNAKE_CASE__  :					Dict =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													if self.device.type == "mps":
																			# randn does not work reproducibly on mps
																			SCREAMING_SNAKE_CASE__  :					List[str] =       randn_tensor(a				,						generator=a     )
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       image.to(self.device     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       randn_tensor(a				,						generator=a				,						device=self.device     )

													# set step values
													self.scheduler.set_timesteps(a     )

													for t in self.progress_bar(self.scheduler.timesteps     ):
																			# 1. predict noise model_output
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.unet(a				,						a     ).sample

																			# 2. compute previous image: x_t -> x_t-1
																			SCREAMING_SNAKE_CASE__  :					Tuple =       self.scheduler.step(a				,						a				,						a				,						generator=a     ).prev_sample

													SCREAMING_SNAKE_CASE__  :					List[str] =       (image / 2 + 0.5).clamp(0				,						1     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       image.cpu().permute(0				,						2				,						3				,						1     ).numpy()
													if output_type == "pil":
																			SCREAMING_SNAKE_CASE__  :					Dict =       self.numpy_to_pil(a     )

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=a     )