

"""simple docstring"""





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 				SCREAMING_SNAKE_CASE					(		A_       ):






					"""simple docstring"""



					lowercase__				 =       42
					lowercase__				 =       42







					def __init__(       self			:				List[Any]   ,lowercase_			:				List[Any]   ,lowercase_			:				str		):
						super().__init__()
						self.register_modules(unet=lowercase_   ,scheduler=lowercase_		)







					@torch.no_grad()
					def __call__(       self			:				Dict   ,lowercase_			:				Union[str, Any] = 1   ,lowercase_			:				Optional[Any] = 5_0   ,lowercase_			:				Optional[Any] = None   ,lowercase_			:				Optional[Any] = "pil"   ,lowercase_			:				Dict = True   ,**lowercase_			:				List[Any]   ,):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.unet.config.sample_size
						lowerCAmelCase__	:						Dict  				=	(batch_size, 3, img_size, img_size)

						lowerCAmelCase__	:						str  				=	self.unet

						# sample x_0 ~ N(0, sigma_0^2 * I)
						lowerCAmelCase__	:						List[Any]  				=	randn_tensor(lowercase_   ,generator=lowercase_   ,device=self.device		) * self.scheduler.init_noise_sigma

						self.scheduler.set_timesteps(lowercase_		)

						for t in self.progress_bar(self.scheduler.timesteps		):
							# here sigma_t == t_i from the paper
							lowerCAmelCase__	:						Optional[Any]  				=	self.scheduler.schedule[t]
							lowerCAmelCase__	:						Dict  				=	self.scheduler.schedule[t - 1] if t > 0 else 0

							# 1. Select temporarily increased noise level sigma_hat
							# 2. Add new noise to move from sample_i to sample_hat
							lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	self.scheduler.add_noise_to_input(lowercase_   ,lowercase_   ,generator=lowercase_		)

							# 3. Predict the noise residual given the noise magnitude `sigma_hat`
							# The model inputs and output are adjusted by following eq. (213) in [1].
							lowerCAmelCase__	:						Tuple  				=	(sigma_hat / 2) * model((sample_hat + 1) / 2   ,sigma_hat / 2		).sample

							# 4. Evaluate dx/dt at sigma_hat
							# 5. Take Euler step from sigma to sigma_prev
							lowerCAmelCase__	:						Optional[int]  				=	self.scheduler.step(lowercase_   ,lowercase_   ,lowercase_   ,lowercase_		)

							if sigma_prev != 0:
								# 6. Apply 2nd order correction
								# The model inputs and output are adjusted by following eq. (213) in [1].
								lowerCAmelCase__	:						str  				=	(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2   ,sigma_prev / 2		).sample
								lowerCAmelCase__	:						Any  				=	self.scheduler.step_correct(
								    lowercase_   ,lowercase_   ,lowercase_   ,lowercase_   ,step_output.prev_sample   ,step_output['''derivative''']   ,)
							lowerCAmelCase__	:						Tuple  				=	step_output.prev_sample

						lowerCAmelCase__	:						Optional[Any]  				=	(sample / 2 + 0.5).clamp(0   ,1		)
						lowerCAmelCase__	:						List[Any]  				=	sample.cpu().permute(0   ,2   ,3   ,1		).numpy()
						if output_type == "pil":
							lowerCAmelCase__	:						Union[str, Any]  				=	self.numpy_to_pil(lowercase_		)

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=lowercase_		)








import os

import numpy
import onnx







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		__lowercase=			a.name
		__lowercase=			b.name

		__lowercase=			''
		__lowercase=			''

		__lowercase=			a == b

		__lowercase=			name_a
		__lowercase=			name_b

		return res







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







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

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







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	str:

		'''simple docstring'''







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







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	Any:

		'''simple docstring'''







		__lowercase=			list(model.graph.initializer	)
		__lowercase=			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

				__lowercase=			inits[i].name
				__lowercase=			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   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__	)						->	Dict:

		'''simple docstring'''







		__lowercase=			os.path.dirname(lowercase__	)
		__lowercase=			os.path.basename(lowercase__	)

		__lowercase=			onnx.load(os.path.join(lowercase__   , lowercase__	)	)

		__lowercase=			list(model.graph.initializer	)

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

		__lowercase=			0

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

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

								__lowercase=			inits[j].data_type
								__lowercase=			numpy.prod(inits[j].dims	)
								if dtype == 1:
										mem_size *= 4
								elif dtype == 6:
										mem_size *= 4
								elif dtype == 7 or dtype == 1_1:
										mem_size *= 8
								else:
										print('unexpected data type: '   , lowercase__	)
								total_reduced_size += mem_size

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

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

		print('total reduced size: '   , total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4   , 'GB'	)

		__lowercase=			sorted(lowercase__	)
		_remove_dup_initializers_from_model(lowercase__   , lowercase__   , lowercase__	)

		__lowercase=			'optimized_' + model_file_name
		__lowercase=			os.path.join(lowercase__   , lowercase__	)
		onnx.save(lowercase__   , lowercase__	)

		return new_model






