




from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


__lowerCamelCase   :						Union[str, Any]				=			logging.get_logger(__name__)







def 		lowercase__   (		__A:	str							,__A:	Union[str, Any]				):





					'''simple docstring'''






					try:
										with open(__A							,'''rb'''				) as flax_state_f:
															__magic_name__							:	str    							=							from_bytes(__A							,flax_state_f.read()				)
					except UnpicklingError as e:
										try:
															with open(__A				) as f:
																				if f.read().startswith('''version'''				):
																									raise OSError(
																									    '''You seem to have cloned a repository without having git-lfs installed. Please'''
																									    ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
																									    ''' folder you cloned.'''				)
																				else:
																									raise ValueError from e
										except (UnicodeDecodeError, ValueError):
															raise EnvironmentError(F'''Unable to convert {model_file} to Flax deserializable object. '''				)

					return load_flax_weights_in_pytorch_model(__A							,__A				)







def 		lowercase__   (		__A:	Optional[int]							,__A:	Any				):





					'''simple docstring'''






					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
										    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
										    ''' instructions.'''				)
										raise

					# check if we have bf16 weights
					__magic_name__							:	str    							=							flatten_dict(jax.tree_util.tree_map(lambda __A				: x.dtype == jnp.bfloataa							,__A				)				).values()
					if any(__A				):
										# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

										# and bf16 is not fully supported in PT yet.
										logger.warning(
										    '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
										    '''before loading those in PyTorch model.'''				)
										__magic_name__							:	List[str]    							=							jax.tree_util.tree_map(
										    lambda __A				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params							,__A				)

					__magic_name__							:	Optional[Any]    							=							''''''

					__magic_name__							:	Tuple    							=							flatten_dict(__A							,sep='''.'''				)
					__magic_name__							:	Optional[int]    							=							pt_model.state_dict()

					# keep track of unexpected & missing keys
					__magic_name__							:	Optional[Any]    							=							[]
					__magic_name__							:	List[Any]    							=							set(pt_model_dict.keys()				)

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										__magic_name__							:	int    							=							flax_key_tuple.split('''.'''				)

										if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
															__magic_name__							:	Any    							=							flax_key_tuple_array[:-1] + ['''weight''']
															__magic_name__							:	Any    							=							jnp.transpose(__A							,(3, 2, 0, 1)				)
										elif flax_key_tuple_array[-1] == "kernel":
															__magic_name__							:	Union[str, Any]    							=							flax_key_tuple_array[:-1] + ['''weight''']
															__magic_name__							:	Dict    							=							flax_tensor.T
										elif flax_key_tuple_array[-1] == "scale":
															__magic_name__							:	Dict    							=							flax_key_tuple_array[:-1] + ['''weight''']

										if "time_embedding" not in flax_key_tuple_array:
															for i, flax_key_tuple_string in enumerate(__A				):
																				__magic_name__							:	Any    							=							(
																				    flax_key_tuple_string.replace('''_0'''							,'''.0'''				)
																				    .replace('''_1'''							,'''.1'''				)
																				    .replace('''_2'''							,'''.2'''				)
																				    .replace('''_3'''							,'''.3'''				)
																				    .replace('''_4'''							,'''.4'''				)
																				    .replace('''_5'''							,'''.5'''				)
																				    .replace('''_6'''							,'''.6'''				)
																				    .replace('''_7'''							,'''.7'''				)
																				    .replace('''_8'''							,'''.8'''				)
																				    .replace('''_9'''							,'''.9'''				)
																				)

										__magic_name__							:	Any    							=							'''.'''.join(__A				)

										if flax_key in pt_model_dict:
															if flax_tensor.shape != pt_model_dict[flax_key].shape:
																				raise ValueError(
																				    F'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
																				    F'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''				)
															else:
																				# add weight to pytorch dict
																				__magic_name__							:	Optional[int]    							=							np.asarray(__A				) if not isinstance(__A							,np.ndarray				) else flax_tensor
																				__magic_name__							:	Any    							=							torch.from_numpy(__A				)
																				# remove from missing keys
																				missing_keys.remove(__A				)
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(__A				)

					pt_model.load_state_dict(__A				)

					# re-transform missing_keys to list
					__magic_name__							:	Optional[Any]    							=							list(__A				)

					if len(__A				) > 0:
										logger.warning(
										    '''Some weights of the Flax model were not used when initializing the PyTorch model'''
										    F''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
										    F''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
										    ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
										    F''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
										    ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
										    ''' FlaxBertForSequenceClassification model).'''				)
					if len(__A				) > 0:
										logger.warning(
										    F'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
										    F''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
										    ''' use it for predictions and inference.'''				)

					return pt_model









from sklearn.metrics import mean_squared_error

import datasets


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

__lowerCamelCase   :						Optional[int]				=			'''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


__lowerCamelCase   :						str				=			'''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        "raw_values" : Returns a full set of errors in case of multioutput input.

        "uniform_average" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric("mse")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {\'mse\': 0.6123724356957945}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric("mse", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mse\': array([0.41666667, 1. ])}
'''


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,_KWARGS_DESCRIPTION						)
class    lowerCamelCase    (		datasets.Metric						):






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   Dict  )						->   Tuple:
								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			:   List[Any]  )						->   Any:
								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			:   Union[str, Any]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   int=None       ,						lowerCamelCase_			:   List[Any]="uniform_average"       ,						lowerCamelCase_			:   List[str]=True  )						->   List[str]:
								__magic_name__							:	Tuple    							=							mean_squared_error(
								    lowerCamelCase_       ,						lowerCamelCase_       ,						sample_weight=lowerCamelCase_       ,						multioutput=lowerCamelCase_       ,						squared=lowerCamelCase_  )

								return {"mse": mse}



