




from sklearn.metrics import mean_squared_error

import datasets


__A								=       "\\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"

__A								=       "\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


__A								=       "\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					):

			"""simple docstring"""







			def  lowercase_       ( self				)       -> Union[str, Any]:



							'''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  lowercase_       ( self				)       -> Tuple:



							'''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  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__=None     ,		lowerCamelCase__="uniform_average"     ,		lowerCamelCase__=True				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       mean_squared_error(
							    lowerCamelCase__     ,		lowerCamelCase__     ,		sample_weight=lowerCamelCase__     ,		multioutput=lowerCamelCase__     ,		squared=lowerCamelCase__				)

							return {"mse": mse}








from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block







@dataclass
class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			42







class        __lowerCAmelCase					(			__magic_name__     ,							__magic_name__					):

			"""simple docstring"""







			@register_to_config
			def __init__( self     ,		lowerCamelCase__ = 65_536     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = 2     ,		lowerCamelCase__ = 2     ,		lowerCamelCase__ = 0     ,		lowerCamelCase__ = "fourier"     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = False     ,		lowerCamelCase__ = 0.0     ,		lowerCamelCase__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")     ,		lowerCamelCase__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")     ,		lowerCamelCase__ = "UNetMidBlock1D"     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = (32, 32, 64)     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = 8     ,		lowerCamelCase__ = 1     ,		lowerCamelCase__ = False     ,		)       -> Dict:



							'''simple docstring'''
							super().__init__()
							__lowerCamelCase									=       sample_size

							# time
							if time_embedding_type == "fourier":
											__lowerCamelCase									=       GaussianFourierProjection(
											    embedding_size=8     ,		set_W_to_weight=lowerCamelCase__     ,		log=lowerCamelCase__     ,		flip_sin_to_cos=lowerCamelCase__				)
											__lowerCamelCase									=       2 * block_out_channels[0]
							elif time_embedding_type == "positional":
											__lowerCamelCase									=       Timesteps(
											    block_out_channels[0]     ,		flip_sin_to_cos=lowerCamelCase__     ,		downscale_freq_shift=lowerCamelCase__				)
											__lowerCamelCase									=       block_out_channels[0]

							if use_timestep_embedding:
											__lowerCamelCase									=       block_out_channels[0] * 4
											__lowerCamelCase									=       TimestepEmbedding(
											    in_channels=lowerCamelCase__     ,		time_embed_dim=lowerCamelCase__     ,		act_fn=lowerCamelCase__     ,		out_dim=block_out_channels[0]     ,		)

							__lowerCamelCase									=       nn.ModuleList([]				)
							__lowerCamelCase									=       None
							__lowerCamelCase									=       nn.ModuleList([]				)
							__lowerCamelCase									=       None

							# down
							__lowerCamelCase									=       in_channels
							for i, down_block_type in enumerate(lowerCamelCase__				):
											__lowerCamelCase									=       output_channel
											__lowerCamelCase									=       block_out_channels[i]

											if i == 0:
															input_channel += extra_in_channels

											__lowerCamelCase									=       i == len(lowerCamelCase__				) - 1

											__lowerCamelCase									=       get_down_block(
											    lowerCamelCase__     ,		num_layers=lowerCamelCase__     ,		in_channels=lowerCamelCase__     ,		out_channels=lowerCamelCase__     ,		temb_channels=block_out_channels[0]     ,		add_downsample=not is_final_block or downsample_each_block     ,		)
											self.down_blocks.append(lowerCamelCase__				)

							# mid
							__lowerCamelCase									=       get_mid_block(
							    lowerCamelCase__     ,		in_channels=block_out_channels[-1]     ,		mid_channels=block_out_channels[-1]     ,		out_channels=block_out_channels[-1]     ,		embed_dim=block_out_channels[0]     ,		num_layers=lowerCamelCase__     ,		add_downsample=lowerCamelCase__     ,		)

							# up
							__lowerCamelCase									=       list(reversed(lowerCamelCase__				)				)
							__lowerCamelCase									=       reversed_block_out_channels[0]
							if out_block_type is None:
											__lowerCamelCase									=       out_channels
							else:
											__lowerCamelCase									=       block_out_channels[0]

							for i, up_block_type in enumerate(lowerCamelCase__				):
											__lowerCamelCase									=       output_channel
											__lowerCamelCase									=       (
											    reversed_block_out_channels[i + 1] if i < len(lowerCamelCase__				) - 1 else final_upsample_channels
											)

											__lowerCamelCase									=       i == len(lowerCamelCase__				) - 1

											__lowerCamelCase									=       get_up_block(
											    lowerCamelCase__     ,		num_layers=lowerCamelCase__     ,		in_channels=lowerCamelCase__     ,		out_channels=lowerCamelCase__     ,		temb_channels=block_out_channels[0]     ,		add_upsample=not is_final_block     ,		)
											self.up_blocks.append(lowerCamelCase__				)
											__lowerCamelCase									=       output_channel

							# out
							__lowerCamelCase									=       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4     ,		32				)
							__lowerCamelCase									=       get_out_block(
							    out_block_type=lowerCamelCase__     ,		num_groups_out=lowerCamelCase__     ,		embed_dim=block_out_channels[0]     ,		out_channels=lowerCamelCase__     ,		act_fn=lowerCamelCase__     ,		fc_dim=block_out_channels[-1] // 4     ,		)






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = True     ,		)       -> Union[UNetaDOutput, Tuple]:



							'''simple docstring'''
							__lowerCamelCase									=       timestep
							if not torch.is_tensor(lowerCamelCase__				):
											__lowerCamelCase									=       torch.tensor([timesteps]     ,		dtype=torch.long     ,		device=sample.device				)
							elif torch.is_tensor(lowerCamelCase__				) and len(timesteps.shape				) == 0:
											__lowerCamelCase									=       timesteps[None].to(sample.device				)

							__lowerCamelCase									=       self.time_proj(lowerCamelCase__				)
							if self.config.use_timestep_embedding:
											__lowerCamelCase									=       self.time_mlp(lowerCamelCase__				)
							else:
											__lowerCamelCase									=       timestep_embed[..., None]
											__lowerCamelCase									=       timestep_embed.repeat([1, 1, sample.shape[2]]				).to(sample.dtype				)
											__lowerCamelCase									=       timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])				)

							# 2. down
							__lowerCamelCase									=       ()
							for downsample_block in self.down_blocks:
											__lowerCamelCase			,		__lowerCamelCase									=       downsample_block(hidden_states=lowerCamelCase__     ,		temb=lowerCamelCase__				)
											down_block_res_samples += res_samples

							# 3. mid
							if self.mid_block:
											__lowerCamelCase									=       self.mid_block(lowerCamelCase__     ,		lowerCamelCase__				)

							# 4. up
							for i, upsample_block in enumerate(self.up_blocks				):
											__lowerCamelCase									=       down_block_res_samples[-1:]
											__lowerCamelCase									=       down_block_res_samples[:-1]
											__lowerCamelCase									=       upsample_block(lowerCamelCase__     ,		res_hidden_states_tuple=lowerCamelCase__     ,		temb=lowerCamelCase__				)

							# 5. post-process
							if self.out_block:
											__lowerCamelCase									=       self.out_block(lowerCamelCase__     ,		lowerCamelCase__				)

							if not return_dict:
											return (sample,)

							return UNetaDOutput(sample=lowerCamelCase__				)


