



'''simple docstring'''





import os

import numpy
import onnx




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     _a           =    a.name
     _a           =    b.name

     _a           =    ''''''
     _a           =    ''''''

     _a           =    a == b

     _a           =    name_a
     _a           =    name_b

     return res




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     for i, input_name in enumerate(node_proto.input):
          if input_name == name:
               node_proto.input.insert(__A    ,      __A)
               node_proto.input.pop(i + 1)

     if node_proto.op_type == "If":
          _graph_replace_input_with(node_proto.attribute[0].g    ,      __A    ,      __A)
          _graph_replace_input_with(node_proto.attribute[1].g    ,      __A    ,      __A)
     if node_proto.op_type == "Loop":
          _graph_replace_input_with(node_proto.attribute[0].g    ,      __A    ,      __A)




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     for n in graph_proto.node:
          _node_replace_input_with(__A    ,      __A    ,      __A)




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    list(model.graph.initializer)
     _a           =    list(model_without_ext.graph.initializer)
     for i, ref_i in ind_to_replace:
          assert inits_with_data[i].name == inits[i].name
          assert inits_with_data[ref_i].name == inits[ref_i].name
          assert i > ref_i

          _a           =    inits[i].name
          _a           =    inits[ref_i].name

          model_without_ext.graph.initializer.remove(inits[i])

          # for n in model.graph.node:
          _graph_replace_input_with(model_without_ext.graph    ,      __A    ,      __A)




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    os.path.dirname(__A)
     _a           =    os.path.basename(__A)

     _a           =    onnx.load(os.path.join(__A    ,      __A))

     _a           =    list(model.graph.initializer)

     _a           =    set()
     _a           =    {}
     _a           =    []

     _a           =    0

     for i in range(len(__A)):
          if i in dup_set:
               continue

          for j in range(i + 1    ,      len(__A)):
               if j in dup_set:
                    continue
               if _is_equal_tensor_proto(inits[i]    ,      inits[j]):
                    dup_set.add(__A)
                    dup_set.add(__A)

                    _a           =    inits[j].data_type
                    _a           =    numpy.prod(inits[j].dims)
                    if dtype == 1:
                         mem_size *= 4
                    elif dtype == 6:
                         mem_size *= 4
                    elif dtype == 7 or dtype == 11:
                         mem_size *= 8
                    else:
                         print('''unexpected data type: '''    ,      __A)
                    total_reduced_size += mem_size

                    _a           =    inits[i].name
                    _a           =    inits[j].name

                    if name_i in dup_map:
                         dup_map[name_i].append(__A)
                    else:
                         _a           =    [name_j]
                    ind_to_replace.append((j, i))

     print('''total reduced size: '''    ,      total_reduced_size / 1_024 / 1_024 / 1_024    ,      '''GB''')

     _a           =    sorted(__A)
     _remove_dup_initializers_from_model(__A    ,      __A    ,      __A)

     _a           =    '''optimized_''' + model_file_name
     _a           =    os.path.join(__A    ,      __A)
     onnx.save(__A    ,      __A)

     return new_model











from typing import List, Optional, Tuple, Union

import torch

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




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       Optional[Any]:
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								snake_case_           =				DDIMScheduler.from_config(scheduler.config   )

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






	@torch.no_grad()
	def __call__(			self			,     a = 1			,     a = None			,     a = 0.0			,     a = 50			,     a = None			,     a = "pil"			,     a = True			,     )			->       Union[ImagePipelineOutput, Tuple]:

								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size			,     a   ):
															snake_case_           =				(
															    batch_size,
															    self.unet.config.in_channels,
															    self.unet.config.sample_size,
															    self.unet.config.sample_size,
															)
								else:
															snake_case_           =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								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.'''   )

								snake_case_           =				randn_tensor(a			,     generator=a			,     device=self.device			,     dtype=self.unet.dtype   )

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

								for t in self.progress_bar(self.scheduler.timesteps   ):
															# 1. predict noise model_output
															snake_case_           =				self.unet(a			,     a   ).sample

															# 2. predict previous mean of image x_t-1 and add variance depending on eta
															# eta corresponds to η in paper and should be between [0, 1]
															# do x_t -> x_t-1
															snake_case_           =				self.scheduler.step(
															    a			,     a			,     a			,     eta=a			,     use_clipped_model_output=a			,     generator=a   ).prev_sample

								snake_case_           =				(image / 2 + 0.5).clamp(0			,     1   )
								snake_case_           =				image.cpu().permute(0			,     2			,     3			,     1   ).numpy()
								if output_type == "pil":
															snake_case_           =				self.numpy_to_pil(a   )

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a   )





