

import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


__UpperCAmelCase     					=  [nn.LayerNorm]

__UpperCAmelCase     					=  logging.get_logger(__name__)

__UpperCAmelCase     					=  version.parse(version.parse(torch.__version__).base_version)

__UpperCAmelCase     					=  parsed_torch_version_base >= version.parse('2.0')
__UpperCAmelCase     					=  parsed_torch_version_base >= version.parse('1.12')
__UpperCAmelCase     					=  parsed_torch_version_base >= version.parse('1.11')
__UpperCAmelCase     					=  parsed_torch_version_base < version.parse('1.11')







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				self					)							->   Optional[int]:

     """simple docstring"""







     from torch import _softmax_backward_data

     if is_torch_less_than_1_11:
          return _softmax_backward_data(_lowerCamelCase		,				_lowerCamelCase		,				parent.dim		,				self					)
     else:
          return _softmax_backward_data(_lowerCamelCase		,				_lowerCamelCase		,				parent.dim		,				self.dtype					)







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 0					)							->   str:

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     index.to(layer.weight.device					)
     _lowerCAmelCase :       List[str]	   =     layer.weight.index_select(_lowerCamelCase		,				_lowerCamelCase					).clone().detach()
     if layer.bias is not None:
          if dim == 1:
               _lowerCAmelCase :       Union[str, Any]	   =     layer.bias.clone().detach()
          else:
               _lowerCAmelCase :       Dict	   =     layer.bias[index].clone().detach()
     _lowerCAmelCase :       str	   =     list(layer.weight.size()					)
     _lowerCAmelCase :       Tuple	   =     len(_lowerCamelCase					)
     _lowerCAmelCase :       Dict	   =     nn.Linear(new_size[1]		,				new_size[0]		,				bias=layer.bias is not None					).to(layer.weight.device					)
     _lowerCAmelCase :       List[Any]	   =     False
     new_layer.weight.copy_(W.contiguous()					)
     _lowerCAmelCase :       Optional[int]	   =     True
     if layer.bias is not None:
          _lowerCAmelCase :       List[str]	   =     False
          new_layer.bias.copy_(b.contiguous()					)
          _lowerCAmelCase :       int	   =     True
     return new_layer





class A__     (			nn.Module			):



 """simple docstring"""







 def __init__(		self			:						Dict  ,    A_			:						List[Any]  ,    A_			:						Tuple							):






      '''simple docstring'''




      super().__init__()
      _lowerCAmelCase :       List[Any]	   =     nf
      _lowerCAmelCase :       List[str]	   =     nn.Parameter(torch.empty(A_  ,    A_							)							)
      _lowerCAmelCase :       Dict	   =     nn.Parameter(torch.zeros(A_							)							)
      nn.init.normal_(self.weight  ,    std=0.02							)


 def  __magic_name__							(		self			:						int  ,    A_			:						Optional[Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       Union[str, Any]	   =     x.size()[:-1] + (self.nf,)
      _lowerCAmelCase :       List[Any]	   =     torch.addmm(self.bias  ,    x.view(-1  ,    x.size(-1							)							)  ,    self.weight							)
      _lowerCAmelCase :       Dict	   =     x.view(A_							)
      return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 1					)							->   Optional[int]:

     """simple docstring"""







     _lowerCAmelCase :       int	   =     index.to(layer.weight.device					)
     _lowerCAmelCase :       List[Any]	   =     layer.weight.index_select(_lowerCamelCase		,				_lowerCamelCase					).clone().detach()
     if dim == 0:
          _lowerCAmelCase :       int	   =     layer.bias.clone().detach()
     else:
          _lowerCAmelCase :       List[Any]	   =     layer.bias[index].clone().detach()
     _lowerCAmelCase :       Optional[int]	   =     list(layer.weight.size()					)
     _lowerCAmelCase :       Tuple	   =     len(_lowerCamelCase					)
     _lowerCAmelCase :       Optional[int]	   =     ConvaD(new_size[1]		,				new_size[0]					).to(layer.weight.device					)
     _lowerCAmelCase :       Tuple	   =     False
     new_layer.weight.copy_(W.contiguous()					)
     _lowerCAmelCase :       Tuple	   =     True
     _lowerCAmelCase :       Dict	   =     False
     new_layer.bias.copy_(b.contiguous()					)
     _lowerCAmelCase :       Optional[int]	   =     True
     return new_layer







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None					)							->   Dict:

     """simple docstring"""







     if isinstance(_lowerCamelCase		,				nn.Linear					):
          return prune_linear_layer(_lowerCamelCase		,				_lowerCamelCase		,				dim=0 if dim is None else dim					)
     elif isinstance(_lowerCamelCase		,				_lowerCamelCase					):
          return prune_convad_layer(_lowerCamelCase		,				_lowerCamelCase		,				dim=1 if dim is None else dim					)
     else:
          raise ValueError(f'''Can\'t prune layer of class {layer.__class__}'''					)







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE					)							->   Tuple:

     """simple docstring"""







     assert len(_lowerCamelCase					) > 0, f'''{input_tensors} has to be a tuple/list of tensors'''

     # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
     _lowerCAmelCase :       Optional[int]	   =     len(inspect.signature(_lowerCamelCase					).parameters					)
     if num_args_in_forward_chunk_fn != len(_lowerCamelCase					):
          raise ValueError(
              f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_lowerCamelCase					)} input '''
              "tensors are given"					)

     if chunk_size > 0:
          _lowerCAmelCase :       int	   =     input_tensors[0].shape[chunk_dim]
          for input_tensor in input_tensors:
               if input_tensor.shape[chunk_dim] != tensor_shape:
                    raise ValueError(
                        f'''All input tenors have to be of the same shape: {tensor_shape}, '''
                        f'''found shape {input_tensor.shape[chunk_dim]}'''					)

          if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
               raise ValueError(
                   f'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
                   f'''size {chunk_size}'''					)

          _lowerCAmelCase :       Optional[int]	   =     input_tensors[0].shape[chunk_dim] // chunk_size

          # chunk input tensor into tuples
          _lowerCAmelCase :       Any	   =     tuple(input_tensor.chunk(_lowerCamelCase		,				dim=_lowerCamelCase					) for input_tensor in input_tensors					)
          # apply forward fn to every tuple
          _lowerCAmelCase :       List[str]	   =     tuple(forward_fn(*_lowerCamelCase					) for input_tensors_chunk in zip(*_lowerCamelCase					)					)
          # concatenate output at same dimension
          return torch.cat(_lowerCamelCase		,				dim=_lowerCamelCase					)

     return forward_fn(*_lowerCamelCase					)







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase :       Union[str, Any]	   =     torch.ones(_lowerCamelCase		,				_lowerCamelCase					)
     _lowerCAmelCase :       Dict	   =     set(_lowerCamelCase					) - already_pruned_heads  # Convert to set and remove already pruned heads
     for head in heads:
          # Compute how many pruned heads are before the head and move the index accordingly
          _lowerCAmelCase :       Union[str, Any]	   =     head - sum(1 if h < head else 0 for h in already_pruned_heads					)
          _lowerCAmelCase :       Union[str, Any]	   =     0
     _lowerCAmelCase :       Optional[int]	   =     mask.view(-1					).contiguous().eq(1					)
     _lowerCAmelCase :       torch.LongTensor	   =     torch.arange(len(_lowerCamelCase					)					)[mask].long()
     return heads, index







def 						_snake_case		(     *SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None					)							->   List[str]:

     """simple docstring"""







     return torch.meshgrid(*_lowerCamelCase		,				indexing=_lowerCamelCase					)







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   str:

     """simple docstring"""







     return tensor.device, storage_ptr(_lowerCamelCase					), storage_size(_lowerCamelCase					)













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     )