



import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__A       =						{
    '''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''',
}

__A       =						{
    '''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''',
}

__A       =						{
    '''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''',
}

__A       =						{
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 80.0,
}

__A       =						{
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 80.0,
}

__A       =						{
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 80.0,
}



def 	__a					(   lowerCAmelCase_       : Tuple    )				->			Any:






						'''simple docstring'''



						if isinstance(lowerCAmelCase_      ,lowerCAmelCase_    ):
												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 	__a					(   lowerCAmelCase_       : int      ,lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : Any=False    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.out_layers.3.bias"""]

						if has_skip:
												UpperCAmelCase_=       checkpoint[F"""{old_prefix}.skip_connection.weight"""]
												UpperCAmelCase_=       checkpoint[F"""{old_prefix}.skip_connection.bias"""]

						return new_checkpoint



def 	__a					(   lowerCAmelCase_       : Any      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : Any=None    )				->			Any:






						'''simple docstring'''



						UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3      ,dim=0    )
						UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3      ,dim=0    )

						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.norm.weight"""]
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.norm.bias"""]

						UpperCAmelCase_=       weight_q.squeeze(-1    ).squeeze(-1    )
						UpperCAmelCase_=       bias_q.squeeze(-1    ).squeeze(-1    )
						UpperCAmelCase_=       weight_k.squeeze(-1    ).squeeze(-1    )
						UpperCAmelCase_=       bias_k.squeeze(-1    ).squeeze(-1    )
						UpperCAmelCase_=       weight_v.squeeze(-1    ).squeeze(-1    )
						UpperCAmelCase_=       bias_v.squeeze(-1    ).squeeze(-1    )

						UpperCAmelCase_=       (
						    checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1    ).squeeze(-1    )
						)
						UpperCAmelCase_=       checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1    ).squeeze(-1    )

						return new_checkpoint



def 	__a					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : int    )				->			List[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       torch.load(lowerCAmelCase_      ,map_location="""cpu"""    )
						UpperCAmelCase_=       {}

						UpperCAmelCase_=       checkpoint["""time_embed.0.weight"""]
						UpperCAmelCase_=       checkpoint["""time_embed.0.bias"""]
						UpperCAmelCase_=       checkpoint["""time_embed.2.weight"""]
						UpperCAmelCase_=       checkpoint["""time_embed.2.bias"""]

						if unet_config["num_class_embeds"] is not None:
												UpperCAmelCase_=       checkpoint["""label_emb.weight"""]

						UpperCAmelCase_=       checkpoint["""input_blocks.0.0.weight"""]
						UpperCAmelCase_=       checkpoint["""input_blocks.0.0.bias"""]

						UpperCAmelCase_=       unet_config["""down_block_types"""]
						UpperCAmelCase_=       unet_config["""layers_per_block"""]
						UpperCAmelCase_=       unet_config["""attention_head_dim"""]
						UpperCAmelCase_=       unet_config["""block_out_channels"""]
						UpperCAmelCase_=       1
						UpperCAmelCase_=       channels_list[0]

						for i, layer_type in enumerate(lowerCAmelCase_    ):
												UpperCAmelCase_=       channels_list[i]
												UpperCAmelCase_=       current_channels != prev_channels
												if layer_type == "ResnetDownsampleBlock2D":
																		for j in range(lowerCAmelCase_    ):
																								UpperCAmelCase_=       F"""down_blocks.{i}.resnets.{j}"""
																								UpperCAmelCase_=       F"""input_blocks.{current_layer}.0"""
																								UpperCAmelCase_=       True if j == 0 and downsample_block_has_skip else False
																								UpperCAmelCase_=       convert_resnet(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,has_skip=lowerCAmelCase_    )
																								current_layer += 1

												elif layer_type == "AttnDownBlock2D":
																		for j in range(lowerCAmelCase_    ):
																								UpperCAmelCase_=       F"""down_blocks.{i}.resnets.{j}"""
																								UpperCAmelCase_=       F"""input_blocks.{current_layer}.0"""
																								UpperCAmelCase_=       True if j == 0 and downsample_block_has_skip else False
																								UpperCAmelCase_=       convert_resnet(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,has_skip=lowerCAmelCase_    )
																								UpperCAmelCase_=       F"""down_blocks.{i}.attentions.{j}"""
																								UpperCAmelCase_=       F"""input_blocks.{current_layer}.1"""
																								UpperCAmelCase_=       convert_attention(
																								    lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
																								current_layer += 1

												if i != len(lowerCAmelCase_    ) - 1:
																		UpperCAmelCase_=       F"""down_blocks.{i}.downsamplers.0"""
																		UpperCAmelCase_=       F"""input_blocks.{current_layer}.0"""
																		UpperCAmelCase_=       convert_resnet(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
																		current_layer += 1

												UpperCAmelCase_=       current_channels

						# hardcoded the mid-block for now
						UpperCAmelCase_=       """mid_block.resnets.0"""
						UpperCAmelCase_=       """middle_block.0"""
						UpperCAmelCase_=       convert_resnet(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
						UpperCAmelCase_=       """mid_block.attentions.0"""
						UpperCAmelCase_=       """middle_block.1"""
						UpperCAmelCase_=       convert_attention(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
						UpperCAmelCase_=       """mid_block.resnets.1"""
						UpperCAmelCase_=       """middle_block.2"""
						UpperCAmelCase_=       convert_resnet(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

						UpperCAmelCase_=       0
						UpperCAmelCase_=       unet_config["""up_block_types"""]

						for i, layer_type in enumerate(lowerCAmelCase_    ):
												if layer_type == "ResnetUpsampleBlock2D":
																		for j in range(layers_per_block + 1    ):
																								UpperCAmelCase_=       F"""up_blocks.{i}.resnets.{j}"""
																								UpperCAmelCase_=       F"""output_blocks.{current_layer}.0"""
																								UpperCAmelCase_=       convert_resnet(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,has_skip=lowerCAmelCase_    )
																								current_layer += 1

																		if i != len(lowerCAmelCase_    ) - 1:
																								UpperCAmelCase_=       F"""up_blocks.{i}.upsamplers.0"""
																								UpperCAmelCase_=       F"""output_blocks.{current_layer-1}.1"""
																								UpperCAmelCase_=       convert_resnet(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
												elif layer_type == "AttnUpBlock2D":
																		for j in range(layers_per_block + 1    ):
																								UpperCAmelCase_=       F"""up_blocks.{i}.resnets.{j}"""
																								UpperCAmelCase_=       F"""output_blocks.{current_layer}.0"""
																								UpperCAmelCase_=       convert_resnet(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,has_skip=lowerCAmelCase_    )
																								UpperCAmelCase_=       F"""up_blocks.{i}.attentions.{j}"""
																								UpperCAmelCase_=       F"""output_blocks.{current_layer}.1"""
																								UpperCAmelCase_=       convert_attention(
																								    lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
																								current_layer += 1

																		if i != len(lowerCAmelCase_    ) - 1:
																								UpperCAmelCase_=       F"""up_blocks.{i}.upsamplers.0"""
																								UpperCAmelCase_=       F"""output_blocks.{current_layer-1}.2"""
																								UpperCAmelCase_=       convert_resnet(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

						UpperCAmelCase_=       checkpoint["""out.0.weight"""]
						UpperCAmelCase_=       checkpoint["""out.0.bias"""]
						UpperCAmelCase_=       checkpoint["""out.2.weight"""]
						UpperCAmelCase_=       checkpoint["""out.2.bias"""]

						return new_checkpoint


if __name__ == "__main__":
		__A       =						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.''')

		__A       =						parser.parse_args()
		__A       =						strabool(args.class_cond)

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

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

		if not args.class_cond:
				__A       =						None

		__A       =						con_pt_to_diffuser(args.unet_path, unet_config)

		__A       =						UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				__A       =						CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				__A       =						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)):
				__A       =						CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(f'Checkpoint type {ckpt_name} is not currently supported.')

		__A       =						CMStochasticIterativeScheduler(**scheduler_config)

		__A       =						ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)









import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm
	import torch_xla.debug.metrics as met






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						Any						,  *__UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						List[str]=None						,  __UpperCAmelCase     :						Optional[int]=None						,  **__UpperCAmelCase     :						Dict					)      ->			Optional[int]:
							super().__init__(*__UpperCAmelCase						,  **__UpperCAmelCase					)
							UpperCAmelCase_=       eval_examples
							UpperCAmelCase_=       post_process_function




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						Optional[Dataset] = None						,  __UpperCAmelCase     :						Optional[Any]=None						,  __UpperCAmelCase     :						Optional[List[str]] = None						,  __UpperCAmelCase     :						str = "eval"						,  **__UpperCAmelCase     :						Any						,  )      ->			Dict[str, float]:
							UpperCAmelCase_=       gen_kwargs.copy()
							UpperCAmelCase_=       (
							    gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""					) is not None else self.args.generation_max_length
							)
							UpperCAmelCase_=       (
							    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""					) is not None else self.args.generation_num_beams
							)
							UpperCAmelCase_=       gen_kwargs

							UpperCAmelCase_=       self.eval_dataset if eval_dataset is None else eval_dataset
							UpperCAmelCase_=       self.get_eval_dataloader(__UpperCAmelCase					)
							UpperCAmelCase_=       self.eval_examples if eval_examples is None else eval_examples

							# Temporarily disable metric computation, we will do it in the loop here.
							UpperCAmelCase_=       self.compute_metrics
							UpperCAmelCase_=       None
							UpperCAmelCase_=       time.time()
							UpperCAmelCase_=       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							try:
													UpperCAmelCase_=       eval_loop(
													    __UpperCAmelCase						,  description="""Evaluation"""						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=__UpperCAmelCase						,  metric_key_prefix=__UpperCAmelCase						,  )
							finally:
													UpperCAmelCase_=       compute_metrics
							UpperCAmelCase_=       self.args.eval_batch_size * self.args.world_size
							if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
													start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
							output.metrics.update(
							    speed_metrics(
							        __UpperCAmelCase						,  __UpperCAmelCase						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size					)						,  )					)

							if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
													# Only the main node write the results by default
													UpperCAmelCase_=       self.post_process_function(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)
													UpperCAmelCase_=       self.compute_metrics(__UpperCAmelCase					)

													# Prefix all keys with metric_key_prefix + '_'
													for key in list(metrics.keys()					):
																			if not key.startswith(F"""{metric_key_prefix}_"""					):
																									UpperCAmelCase_=       metrics.pop(__UpperCAmelCase					)

													metrics.update(output.metrics					)
							else:
													UpperCAmelCase_=       output.metrics

							if self.args.should_log:
													# Only the main node log the results by default
													self.log(__UpperCAmelCase					)

							if self.args.tpu_metrics_debug or self.args.debug:
													# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
													xm.master_print(met.metrics_report()					)

							UpperCAmelCase_=       self.callback_handler.on_evaluate(self.args						,  self.state						,  self.control						,  __UpperCAmelCase					)
							return metrics




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						Dict						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						Optional[int]=None						,  __UpperCAmelCase     :						str = "test"						,  **__UpperCAmelCase     :						List[str]					)      ->			Tuple:
							UpperCAmelCase_=       gen_kwargs.copy()

							UpperCAmelCase_=       self.get_test_dataloader(__UpperCAmelCase					)

							# Temporarily disable metric computation, we will do it in the loop here.
							UpperCAmelCase_=       self.compute_metrics
							UpperCAmelCase_=       None
							UpperCAmelCase_=       time.time()
							UpperCAmelCase_=       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							try:
													UpperCAmelCase_=       eval_loop(
													    __UpperCAmelCase						,  description="""Prediction"""						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=__UpperCAmelCase						,  metric_key_prefix=__UpperCAmelCase						,  )
							finally:
													UpperCAmelCase_=       compute_metrics

							UpperCAmelCase_=       self.args.eval_batch_size * self.args.world_size
							if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
													start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
							output.metrics.update(
							    speed_metrics(
							        __UpperCAmelCase						,  __UpperCAmelCase						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size					)						,  )					)
							if self.post_process_function is None or self.compute_metrics is None:
													return output

							UpperCAmelCase_=       self.post_process_function(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  """predict"""					)
							UpperCAmelCase_=       self.compute_metrics(__UpperCAmelCase					)

							# Prefix all keys with metric_key_prefix + '_'
							for key in list(metrics.keys()					):
													if not key.startswith(F"""{metric_key_prefix}_"""					):
																			UpperCAmelCase_=       metrics.pop(__UpperCAmelCase					)
							metrics.update(output.metrics					)
							return PredictionOutput(predictions=predictions.predictions						,  label_ids=predictions.label_ids						,  metrics=__UpperCAmelCase					)




