


from sklearn.metrics import mean_squared_error

import datasets


SCREAMING_SNAKE_CASE       :      Tuple												= "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"

SCREAMING_SNAKE_CASE       :      List[str]												= "\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


SCREAMING_SNAKE_CASE       :      Optional[Any]												= "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION   )
class _lowerCamelCase(						datasets.Metric   ):
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           return datasets.MetricInfo(
               description=_DESCRIPTION,      citation=_CITATION,      inputs_description=_KWARGS_DESCRIPTION,      features=datasets.Features(self._get_feature_types()),      reference_urls=[
                   'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'
               ],      )
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           if self.config_name == "multilist":
               return {
                   "predictions": datasets.Sequence(datasets.Value('float')),
                   "references": datasets.Sequence(datasets.Value('float')),
               }
           else:
               return {
                   "predictions": datasets.Value('float'),
                   "references": datasets.Value('float'),
               }




       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=None,      lowerCamelCase="uniform_average",      lowerCamelCase=True)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		mean_squared_error(
               lowerCamelCase,      lowerCamelCase,      sample_weight=lowerCamelCase,      multioutput=lowerCamelCase,      squared=lowerCamelCase)

           return {"mse": mse}





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": 1_000,
    "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					:			List[Any]  				=			{
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1_000,
    "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					:			str  				=			{
    "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					:			List[str]  				=			{
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_lowerCAmelCase					:			Optional[Any]  				=			{
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_lowerCAmelCase					:			List[str]  				=			{
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






			if isinstance(_snake_case       ,     _snake_case	):
						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_(							_snake_case   :  Tuple       ,     _snake_case   :  List[str]       ,     _snake_case   :  Any       ,     _snake_case   :  str       ,     _snake_case   :  Union[str, Any]=False	):




			"""simple docstring"""






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

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

			return new_checkpoint






def        UpperCamelCase_(							_snake_case   :  Dict       ,     _snake_case   :  Union[str, Any]       ,     _snake_case   :  Union[str, Any]       ,     _snake_case   :  Optional[Any]       ,     _snake_case   :  Optional[int]=None	):




			"""simple docstring"""






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

			__a        =checkpoint[F'{old_prefix}.norm.weight']
			__a        =checkpoint[F'{old_prefix}.norm.bias']

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

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

			return new_checkpoint






def        UpperCamelCase_(							_snake_case   :  str       ,     _snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     map_location='cpu'	)
			__a        ={}

			__a        =checkpoint['time_embed.0.weight']
			__a        =checkpoint['time_embed.0.bias']
			__a        =checkpoint['time_embed.2.weight']
			__a        =checkpoint['time_embed.2.bias']

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

			__a        =checkpoint['input_blocks.0.0.weight']
			__a        =checkpoint['input_blocks.0.0.bias']

			__a        =unet_config['down_block_types']
			__a        =unet_config['layers_per_block']
			__a        =unet_config['attention_head_dim']
			__a        =unet_config['block_out_channels']
			__a        =1
			__a        =channels_list[0]

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

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

						if i != len(_snake_case	) - 1:
									__a        =F'down_blocks.{i}.downsamplers.0'
									__a        =F'input_blocks.{current_layer}.0'
									__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
									current_layer += 1

						__a        =current_channels

			# hardcoded the mid-block for now
			__a        ='mid_block.resnets.0'
			__a        ='middle_block.0'
			__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
			__a        ='mid_block.attentions.0'
			__a        ='middle_block.1'
			__a        =convert_attention(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
			__a        ='mid_block.resnets.1'
			__a        ='middle_block.2'
			__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)

			__a        =0
			__a        =unet_config['up_block_types']

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

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

									if i != len(_snake_case	) - 1:
												__a        =F'up_blocks.{i}.upsamplers.0'
												__a        =F'output_blocks.{current_layer-1}.2'
												__a        =convert_resnet(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)

			__a        =checkpoint['out.0.weight']
			__a        =checkpoint['out.0.bias']
			__a        =checkpoint['out.2.weight']
			__a        =checkpoint['out.2.bias']

			return new_checkpoint


if __name__ == "__main__":
						_lowerCAmelCase					:			Optional[Any]  				=			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					:			Optional[Any]  				=			parser.parse_args()
						_lowerCAmelCase					:			Optional[Any]  				=			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					:			Tuple  				=			IMAGENET_64_UNET_CONFIG
						elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
												_lowerCAmelCase					:			Optional[int]  				=			LSUN_256_UNET_CONFIG
						elif "test" in ckpt_name:
												_lowerCAmelCase					:			int  				=			TEST_UNET_CONFIG
						else:
												raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

						if not args.class_cond:
												_lowerCAmelCase					:			List[Any]  				=			None

						_lowerCAmelCase					:			Tuple  				=			con_pt_to_diffuser(args.unet_path, unet_config)

						_lowerCAmelCase					:			Optional[int]  				=			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					:			int  				=			CD_SCHEDULER_CONFIG
						elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
												_lowerCAmelCase					:			Optional[int]  				=			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					:			List[str]  				=			CT_LSUN_256_SCHEDULER_CONFIG
						else:
												raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

						_lowerCAmelCase					:			Any  				=			CMStochasticIterativeScheduler(**scheduler_config)

						_lowerCAmelCase					:			str  				=			ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
						consistency_model.save_pretrained(args.dump_path)






