






'''simple docstring'''




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCamelCase   :					str   							=					{
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1000,
    '''block_out_channels''': [32, 64],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

__lowerCamelCase   :					int   							=					{
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1000,
    '''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

__lowerCamelCase   :					Tuple   							=					{
    '''sample_size''': 256,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

__lowerCamelCase   :					Dict   							=					{
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

__lowerCamelCase   :					Optional[int]   							=					{
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

__lowerCamelCase   :					Optional[int]   							=					{
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

def 	__UpperCAmelCase       ( __magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			if isinstance(__magic_name__     ,__magic_name__						):
						return v
			if v.lower() in ("yes", "true", "t", "y", "1"):
						return True
			elif v.lower() in ("no", "false", "f", "n", "0"):
						return False
			else:
						raise argparse.ArgumentTypeError("boolean value expected"						)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__=False						)->    str:



			"""simple docstring"""



			snake_case_				:					Optional[int]   =       checkpoint[F'''{old_prefix}.in_layers.0.weight''']
			snake_case_				:					Dict   =       checkpoint[F'''{old_prefix}.in_layers.0.bias''']
			snake_case_				:					List[Any]   =       checkpoint[F'''{old_prefix}.in_layers.2.weight''']
			snake_case_				:					Tuple   =       checkpoint[F'''{old_prefix}.in_layers.2.bias''']
			snake_case_				:					Optional[Any]   =       checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
			snake_case_				:					Dict   =       checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
			snake_case_				:					List[str]   =       checkpoint[F'''{old_prefix}.out_layers.0.weight''']
			snake_case_				:					int   =       checkpoint[F'''{old_prefix}.out_layers.0.bias''']
			snake_case_				:					Any   =       checkpoint[F'''{old_prefix}.out_layers.3.weight''']
			snake_case_				:					int   =       checkpoint[F'''{old_prefix}.out_layers.3.bias''']

			if has_skip:
						snake_case_				:					List[str]   =       checkpoint[F'''{old_prefix}.skip_connection.weight''']
						snake_case_				:					str   =       checkpoint[F'''{old_prefix}.skip_connection.bias''']

			return new_checkpoint

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__=None						)->    Union[str, Any]:



			"""simple docstring"""



			snake_case_,	snake_case_,	snake_case_				:					Any   =       checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3     ,dim=0						)
			snake_case_,	snake_case_,	snake_case_				:					Optional[int]   =       checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3     ,dim=0						)

			snake_case_				:					Optional[int]   =       checkpoint[F'''{old_prefix}.norm.weight''']
			snake_case_				:					str   =       checkpoint[F'''{old_prefix}.norm.bias''']

			snake_case_				:					int   =       weight_q.squeeze(-1						).squeeze(-1						)
			snake_case_				:					Dict   =       bias_q.squeeze(-1						).squeeze(-1						)
			snake_case_				:					Optional[int]   =       weight_k.squeeze(-1						).squeeze(-1						)
			snake_case_				:					Any   =       bias_k.squeeze(-1						).squeeze(-1						)
			snake_case_				:					Tuple   =       weight_v.squeeze(-1						).squeeze(-1						)
			snake_case_				:					Union[str, Any]   =       bias_v.squeeze(-1						).squeeze(-1						)

			snake_case_				:					Dict   =       (
			    checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1						).squeeze(-1						)
			)
			snake_case_				:					Optional[Any]   =       checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1						).squeeze(-1						)

			return new_checkpoint

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    int:



			"""simple docstring"""



			snake_case_				:					Optional[int]   =       torch.load(__magic_name__     ,map_location="cpu"						)
			snake_case_				:					Tuple   =       {}

			snake_case_				:					Any   =       checkpoint["time_embed.0.weight"]
			snake_case_				:					Union[str, Any]   =       checkpoint["time_embed.0.bias"]
			snake_case_				:					Tuple   =       checkpoint["time_embed.2.weight"]
			snake_case_				:					Optional[Any]   =       checkpoint["time_embed.2.bias"]

			if unet_config["num_class_embeds"] is not None:
						snake_case_				:					Union[str, Any]   =       checkpoint["label_emb.weight"]

			snake_case_				:					Optional[int]   =       checkpoint["input_blocks.0.0.weight"]
			snake_case_				:					Dict   =       checkpoint["input_blocks.0.0.bias"]

			snake_case_				:					Any   =       unet_config["down_block_types"]
			snake_case_				:					Union[str, Any]   =       unet_config["layers_per_block"]
			snake_case_				:					Optional[int]   =       unet_config["attention_head_dim"]
			snake_case_				:					List[str]   =       unet_config["block_out_channels"]
			snake_case_				:					Any   =       1
			snake_case_				:					int   =       channels_list[0]

			for i, layer_type in enumerate(__magic_name__						):
						snake_case_				:					int   =       channels_list[i]
						snake_case_				:					Any   =       current_channels != prev_channels
						if layer_type == "ResnetDownsampleBlock2D":
									for j in range(__magic_name__						):
												snake_case_				:					str   =       F'''down_blocks.{i}.resnets.{j}'''
												snake_case_				:					Optional[int]   =       F'''input_blocks.{current_layer}.0'''
												snake_case_				:					List[str]   =       True if j == 0 and downsample_block_has_skip else False
												snake_case_				:					Optional[Any]   =       convert_resnet(__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,has_skip=__magic_name__						)
												current_layer += 1

						elif layer_type == "AttnDownBlock2D":
									for j in range(__magic_name__						):
												snake_case_				:					Dict   =       F'''down_blocks.{i}.resnets.{j}'''
												snake_case_				:					List[Any]   =       F'''input_blocks.{current_layer}.0'''
												snake_case_				:					int   =       True if j == 0 and downsample_block_has_skip else False
												snake_case_				:					Dict   =       convert_resnet(__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,has_skip=__magic_name__						)
												snake_case_				:					List[Any]   =       F'''down_blocks.{i}.attentions.{j}'''
												snake_case_				:					List[str]   =       F'''input_blocks.{current_layer}.1'''
												snake_case_				:					Dict   =       convert_attention(
												    __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)
												current_layer += 1

						if i != len(__magic_name__						) - 1:
									snake_case_				:					Optional[Any]   =       F'''down_blocks.{i}.downsamplers.0'''
									snake_case_				:					List[str]   =       F'''input_blocks.{current_layer}.0'''
									snake_case_				:					int   =       convert_resnet(__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)
									current_layer += 1

						snake_case_				:					Any   =       current_channels

			# hardcoded the mid-block for now
			snake_case_				:					Optional[Any]   =       "mid_block.resnets.0"
			snake_case_				:					int   =       "middle_block.0"
			snake_case_				:					Optional[Any]   =       convert_resnet(__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)
			snake_case_				:					int   =       "mid_block.attentions.0"
			snake_case_				:					Optional[int]   =       "middle_block.1"
			snake_case_				:					Dict   =       convert_attention(__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)
			snake_case_				:					Dict   =       "mid_block.resnets.1"
			snake_case_				:					Tuple   =       "middle_block.2"
			snake_case_				:					Dict   =       convert_resnet(__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)

			snake_case_				:					Tuple   =       0
			snake_case_				:					Union[str, Any]   =       unet_config["up_block_types"]

			for i, layer_type in enumerate(__magic_name__						):
						if layer_type == "ResnetUpsampleBlock2D":
									for j in range(layers_per_block + 1						):
												snake_case_				:					Tuple   =       F'''up_blocks.{i}.resnets.{j}'''
												snake_case_				:					Any   =       F'''output_blocks.{current_layer}.0'''
												snake_case_				:					str   =       convert_resnet(__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,has_skip=__magic_name__						)
												current_layer += 1

									if i != len(__magic_name__						) - 1:
												snake_case_				:					Optional[int]   =       F'''up_blocks.{i}.upsamplers.0'''
												snake_case_				:					Any   =       F'''output_blocks.{current_layer-1}.1'''
												snake_case_				:					Tuple   =       convert_resnet(__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)
						elif layer_type == "AttnUpBlock2D":
									for j in range(layers_per_block + 1						):
												snake_case_				:					Tuple   =       F'''up_blocks.{i}.resnets.{j}'''
												snake_case_				:					int   =       F'''output_blocks.{current_layer}.0'''
												snake_case_				:					Optional[int]   =       convert_resnet(__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,has_skip=__magic_name__						)
												snake_case_				:					Dict   =       F'''up_blocks.{i}.attentions.{j}'''
												snake_case_				:					Optional[int]   =       F'''output_blocks.{current_layer}.1'''
												snake_case_				:					Any   =       convert_attention(
												    __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)
												current_layer += 1

									if i != len(__magic_name__						) - 1:
												snake_case_				:					Optional[Any]   =       F'''up_blocks.{i}.upsamplers.0'''
												snake_case_				:					Optional[int]   =       F'''output_blocks.{current_layer-1}.2'''
												snake_case_				:					int   =       convert_resnet(__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)

			snake_case_				:					List[str]   =       checkpoint["out.0.weight"]
			snake_case_				:					Optional[int]   =       checkpoint["out.0.bias"]
			snake_case_				:					Dict   =       checkpoint["out.2.weight"]
			snake_case_				:					List[str]   =       checkpoint["out.2.bias"]

			return new_checkpoint


if __name__ == "__main__":
		__lowerCamelCase   :					int   							=					argparse.ArgumentParser()

		parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
		parser.add_argument(
		    '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
		)
		parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

		__lowerCamelCase   :					str   							=					parser.parse_args()
		__lowerCamelCase   :					Dict   							=					strabool(args.class_cond)

		__lowerCamelCase   :					Dict   							=					os.path.basename(args.unet_path)
		print(f'''Checkpoint: {ckpt_name}''')

		# Get U-Net config
		if "imagenet64" in ckpt_name:
				__lowerCamelCase   :					List[Any]   							=					IMAGENET_64_UNET_CONFIG
		elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				__lowerCamelCase   :					Union[str, Any]   							=					LSUN_256_UNET_CONFIG
		elif "test" in ckpt_name:
				__lowerCamelCase   :					List[Any]   							=					TEST_UNET_CONFIG
		else:
				raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

		if not args.class_cond:
				__lowerCamelCase   :					List[str]   							=					None

		__lowerCamelCase   :					Any   							=					con_pt_to_diffuser(args.unet_path, unet_config)

		__lowerCamelCase   :					List[Any]   							=					UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				__lowerCamelCase   :					List[str]   							=					CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				__lowerCamelCase   :					Optional[Any]   							=					CT_IMAGENET_64_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				__lowerCamelCase   :					Dict   							=					CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

		__lowerCamelCase   :					Optional[int]   							=					CMStochasticIterativeScheduler(**scheduler_config)

		__lowerCamelCase   :					Dict   							=					ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)












'''simple docstring'''




import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__lowerCamelCase   :					str   							=					'''.'''

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
__lowerCamelCase   :					Tuple   							=					[
    '''Assert''',
    '''AssignVariableOp''',
    '''EmptyTensorList''',
    '''MergeV2Checkpoints''',
    '''ReadVariableOp''',
    '''ResourceGather''',
    '''RestoreV2''',
    '''SaveV2''',
    '''ShardedFilename''',
    '''StatefulPartitionedCall''',
    '''StaticRegexFullMatch''',
    '''VarHandleOp''',
]

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    List[str]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       SavedModel()
			snake_case_				:					Dict   =       []

			with open(os.path.join(__magic_name__     ,"utils"     ,"tf_ops"     ,"onnx.json"						)						) as f:
						snake_case_				:					Dict   =       json.load(__magic_name__						)["opsets"]

			for i in range(1     ,opset + 1						):
						onnx_ops.extend(onnx_opsets[str(__magic_name__						)]						)

			with open(__magic_name__     ,"rb"						) as f:
						saved_model.ParseFromString(f.read()						)

			snake_case_				:					Tuple   =       set()

			# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
			for meta_graph in saved_model.meta_graphs:
						# Add operations in the graph definition
						model_op_names.update(node.op for node in meta_graph.graph_def.node						)

						# Go through the functions in the graph definition
						for func in meta_graph.graph_def.library.function:
									# Add operations in each function
									model_op_names.update(node.op for node in func.node_def						)

    # Convert to list, sorted if you want
			snake_case_				:					str   =       sorted(__magic_name__						)
			snake_case_				:					Optional[int]   =       []

			for op in model_op_names:
						if op not in onnx_ops and op not in INTERNAL_OPS:
									incompatible_ops.append(__magic_name__						)

			if strict and len(__magic_name__						) > 0:
						raise Exception(F'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops						)
			elif len(__magic_name__						) > 0:
						print(F'''Found the following incompatible ops for the opset {opset}:'''						)
						print(*__magic_name__     ,sep="\n"						)
			else:
						print(F'''The saved model {saved_model_path} can properly be converted with ONNX.'''						)


if __name__ == "__main__":
		__lowerCamelCase   :					Any   							=					argparse.ArgumentParser()
		parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
		parser.add_argument(
		    '''--opset''', default=12, type=int, help='''The ONNX opset against which the model has to be tested.'''
		)
		parser.add_argument(
		    '''--framework''', choices=['''onnx'''], default='''onnx''', help='''Frameworks against which to test the saved model.'''
		)
		parser.add_argument(
		    '''--strict''', action='''store_true''', help='''Whether make the checking strict (raise errors) or not (raise warnings)'''
		)
		__lowerCamelCase   :					Dict   							=					parser.parse_args()

		if args.framework == "onnx":
				onnx_compliancy(args.saved_model_path, args.strict, args.opset)




