




import os
from pickle import UnpicklingError
from typing import Dict, Tuple

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

import transformers

from .utils import logging


lowerCAmelCase_						=			logging.get_logger(__name__)






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__=False   )			->							Dict:





     '''simple docstring'''


     try:
          import torch  # noqa: F401
     except ImportError:
          logger.error(
              '''Loading a PyTorch model in Flax, 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

     if not is_sharded:
          lowercase :       Any       =			os.path.abspath(__magic_name__   )
          logger.info(F"""Loading PyTorch weights from {pt_path}"""   )

          lowercase :       List[str]       =			torch.load(__magic_name__			,		map_location='''cpu'''   )
          logger.info(F"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()   ):,} parameters."""   )

          lowercase :       int       =			convert_pytorch_state_dict_to_flax(__magic_name__			,		__magic_name__   )
     else:
          # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
          lowercase :       Optional[Any]       =			convert_pytorch_sharded_state_dict_to_flax(__magic_name__			,		__magic_name__   )
     return flax_state_dict






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		)			->							(Tuple[str], np.ndarray):





     '''simple docstring'''


     def is_key_or_prefix_key_in_dict(__magic_name__   ) -> bool:
          return len(set(__magic_name__   ) & {key, (model_prefix,) + key}   ) > 0

     # layer norm
     lowercase :       str       =			pt_tuple_key[:-1] + ('''scale''',)
     if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__magic_name__   ):
          return renamed_pt_tuple_key, pt_tensor

     # batch norm layer mean
     lowercase :       Dict       =			pt_tuple_key[:-1] + ('''mean''',)
     if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__magic_name__   ):
          return renamed_pt_tuple_key, pt_tensor

     # batch norm layer var
     lowercase :       List[Any]       =			pt_tuple_key[:-1] + ('''var''',)
     if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__magic_name__   ):
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     lowercase :       Optional[int]       =			pt_tuple_key[:-1] + ('''embedding''',)
     if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__magic_name__   ):
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     lowercase :       Optional[int]       =			pt_tuple_key[:-1] + ('''kernel''',)
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__magic_name__   ):
          lowercase :       Tuple       =			pt_tensor.transpose(2			,		3			,		1			,		0   )
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     lowercase :       Optional[Any]       =			pt_tuple_key[:-1] + ('''kernel''',)
     if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__magic_name__   ):
          lowercase :       List[Any]       =			pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     lowercase :       str       =			pt_tuple_key[:-1] + ('''weight''',)
     if pt_tuple_key[-1] == "gamma":
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm bias
     lowercase :       Optional[Any]       =			pt_tuple_key[:-1] + ('''bias''',)
     if pt_tuple_key[-1] == "beta":
          return renamed_pt_tuple_key, pt_tensor

     # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
     lowercase :       List[str]       =			None
     if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
          lowercase :       str       =			pt_tuple_key[-2] + '''_g'''
     elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
          lowercase :       List[Any]       =			pt_tuple_key[-2] + '''_v'''
     if name is not None:
          lowercase :       int       =			pt_tuple_key[:-3] + (name,)
          return renamed_pt_tuple_key, pt_tensor

     return pt_tuple_key, pt_tensor






def  snake_case(    __magic_name__			,		__magic_name__   )			->							Tuple:





     '''simple docstring'''


     lowercase :       List[Any]       =			{k: v.numpy() for k, v in pt_state_dict.items()}

     lowercase :       Optional[int]       =			flax_model.base_model_prefix

     # use params dict if the model contains batch norm layers
     if "params" in flax_model.params:
          lowercase :       Any       =			flax_model.params['''params''']
     else:
          lowercase :       List[Any]       =			flax_model.params
     lowercase :       str       =			flatten_dict(__magic_name__   )

     # add batch_stats keys,values to dict
     if "batch_stats" in flax_model.params:
          lowercase :       Tuple       =			flatten_dict(flax_model.params['''batch_stats''']   )
          random_flax_state_dict.update(__magic_name__   )

     lowercase :       Optional[Any]       =			{}

     lowercase :       List[Any]       =			(model_prefix not in flax_model_params) and (
         model_prefix in {k.split('''.'''   )[0] for k in pt_state_dict.keys()}
     )
     lowercase :       Optional[Any]       =			(model_prefix in flax_model_params) and (
         model_prefix not in {k.split('''.'''   )[0] for k in pt_state_dict.keys()}
     )

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          lowercase :       int       =			tuple(pt_key.split('''.'''   )   )

          # remove base model prefix if necessary
          lowercase :       List[Any]       =			pt_tuple_key[0] == model_prefix
          if load_model_with_head_into_base_model and has_base_model_prefix:
               lowercase :       Optional[Any]       =			pt_tuple_key[1:]

          # Correctly rename weight parameters
          lowercase      ,	lowercase :       List[str]       =			rename_key_and_reshape_tensor(
              __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )

          # add model prefix if necessary
          lowercase :       Dict       =			(model_prefix,) + flax_key in random_flax_state_dict
          if load_base_model_into_model_with_head and require_base_model_prefix:
               lowercase :       List[Any]       =			(model_prefix,) + flax_key

          if flax_key in random_flax_state_dict:
               if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                        F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""   )

        # add batch stats if the model contains batchnorm layers
          if "batch_stats" in flax_model.params:
               if "mean" in flax_key[-1] or "var" in flax_key[-1]:
                    lowercase :       str       =			jnp.asarray(__magic_name__   )
                    continue
               # remove num_batches_tracked key
               if "num_batches_tracked" in flax_key[-1]:
                    flax_state_dict.pop(__magic_name__			,		__magic_name__   )
                    continue

               # also add unexpected weight so that warning is thrown
               lowercase :       Any       =			jnp.asarray(__magic_name__   )

          else:
               # also add unexpected weight so that warning is thrown
               lowercase :       Tuple       =			jnp.asarray(__magic_name__   )

     return unflatten_dict(__magic_name__   )






def  snake_case(    __magic_name__			,		__magic_name__   )			->							int:





     '''simple docstring'''


     import torch

     # Load the index
     lowercase :       Optional[int]       =			{}
     for shard_file in shard_filenames:
          # load using msgpack utils
          lowercase :       Any       =			torch.load(__magic_name__   )
          lowercase :       Dict       =			{k: v.numpy() for k, v in pt_state_dict.items()}

          lowercase :       Optional[int]       =			flax_model.base_model_prefix

          # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
          if "batch_stats" in flax_model.params:
               lowercase :       Tuple       =			flax_model.params['''params''']

               lowercase :       List[str]       =			flatten_dict(__magic_name__   )
               random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']   )   )
          else:
               lowercase :       Tuple       =			flax_model.params
               lowercase :       Optional[int]       =			flatten_dict(__magic_name__   )

          lowercase :       int       =			(model_prefix not in flax_model_params) and (
              model_prefix in {k.split('''.'''   )[0] for k in pt_state_dict.keys()}
          )
          lowercase :       List[Any]       =			(model_prefix in flax_model_params) and (
              model_prefix not in {k.split('''.'''   )[0] for k in pt_state_dict.keys()}
          )
          # Need to change some parameters name to match Flax names
          for pt_key, pt_tensor in pt_state_dict.items():
               lowercase :       Dict       =			tuple(pt_key.split('''.'''   )   )

               # remove base model prefix if necessary
               lowercase :       str       =			pt_tuple_key[0] == model_prefix
               if load_model_with_head_into_base_model and has_base_model_prefix:
                    lowercase :       Union[str, Any]       =			pt_tuple_key[1:]

               # Correctly rename weight parameters
               lowercase      ,	lowercase :       str       =			rename_key_and_reshape_tensor(
                   __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )
               # add model prefix if necessary
               lowercase :       Optional[int]       =			(model_prefix,) + flax_key in random_flax_state_dict
               if load_base_model_into_model_with_head and require_base_model_prefix:
                    lowercase :       List[str]       =			(model_prefix,) + flax_key

               if flax_key in random_flax_state_dict:
                    if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                         raise ValueError(
                             F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                             F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""   )

            # add batch stats if the model contains batchnorm layers
               if "batch_stats" in flax_model.params:
                    if "mean" in flax_key[-1]:
                         lowercase :       List[Any]       =			jnp.asarray(__magic_name__   )
                         continue
                    if "var" in flax_key[-1]:
                         lowercase :       Union[str, Any]       =			jnp.asarray(__magic_name__   )
                         continue
                    # remove num_batches_tracked key
                    if "num_batches_tracked" in flax_key[-1]:
                         flax_state_dict.pop(__magic_name__			,		__magic_name__   )
                         continue

                    # also add unexpected weight so that warning is thrown
                    lowercase :       str       =			jnp.asarray(__magic_name__   )

               else:
                    # also add unexpected weight so that warning is thrown
                    lowercase :       Optional[int]       =			jnp.asarray(__magic_name__   )
     return unflatten_dict(__magic_name__   )






def  snake_case(    __magic_name__			,		__magic_name__   )			->							Optional[Any]:





     '''simple docstring'''


     lowercase :       str       =			os.path.abspath(__magic_name__   )
     logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""   )

     # import correct flax class
     lowercase :       Union[str, Any]       =			getattr(__magic_name__			,		'''Flax''' + model.__class__.__name__   )

     # load flax weight dict
     with open(__magic_name__			,		'''rb'''   ) as state_f:
          try:
               lowercase :       List[Any]       =			from_bytes(__magic_name__			,		state_f.read()   )
          except UnpicklingError:
               raise EnvironmentError(F"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """   )

     return load_flax_weights_in_pytorch_model(__magic_name__			,		__magic_name__   )






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     try:
          import torch  # noqa: F401
     except ImportError:
          logger.error(
              '''Loading a 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
     lowercase :       str       =			flatten_dict(jax.tree_util.tree_map(lambda __magic_name__   : x.dtype == jnp.bfloataa			,		__magic_name__   )   ).values()
     if any(__magic_name__   ):
          # convert all weights to fp32 if the 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.'''   )
          lowercase :       Dict       =			jax.tree_util.tree_map(
              lambda __magic_name__   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params			,		__magic_name__   )

     lowercase :       Tuple       =			flatten_dict(__magic_name__   )
     lowercase :       List[Any]       =			pt_model.state_dict()

     lowercase :       Optional[int]       =			(pt_model.base_model_prefix in flax_state) and (
         pt_model.base_model_prefix not in {k.split('''.'''   )[0] for k in pt_model_dict.keys()}
     )
     lowercase :       Dict       =			(pt_model.base_model_prefix not in flax_state) and (
         pt_model.base_model_prefix in {k.split('''.'''   )[0] for k in pt_model_dict.keys()}
     )

     # keep track of unexpected & missing keys
     lowercase :       Optional[int]       =			[]
     lowercase :       List[str]       =			set(pt_model_dict.keys()   )

     for flax_key_tuple, flax_tensor in flax_state_dict.items():
          lowercase :       Dict       =			flax_key_tuple[0] == pt_model.base_model_prefix
          lowercase :       Any       =			'''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple   ) in pt_model_dict

          # adapt flax_key to prepare for loading from/to base model only
          if load_model_with_head_into_base_model and has_base_model_prefix:
               lowercase :       Tuple       =			flax_key_tuple[1:]
          elif load_base_model_into_model_with_head and require_base_model_prefix:
               lowercase :       Optional[int]       =			(pt_model.base_model_prefix,) + flax_key_tuple

          # rename flax weights to PyTorch format
          if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__magic_name__   ) not in pt_model_dict:
               # conv layer
               lowercase :       Dict       =			flax_key_tuple[:-1] + ('''weight''',)
               lowercase :       Dict       =			jnp.transpose(__magic_name__			,		(3, 2, 0, 1)   )
          elif flax_key_tuple[-1] == "kernel" and ".".join(__magic_name__   ) not in pt_model_dict:
               # linear layer
               lowercase :       str       =			flax_key_tuple[:-1] + ('''weight''',)
               lowercase :       List[str]       =			flax_tensor.T
          elif flax_key_tuple[-1] in ["scale", "embedding"]:
               lowercase :       List[Any]       =			flax_key_tuple[:-1] + ('''weight''',)

          # adding batch stats from flax batch norm to pt
          elif "mean" in flax_key_tuple[-1]:
               lowercase :       List[str]       =			flax_key_tuple[:-1] + ('''running_mean''',)
          elif "var" in flax_key_tuple[-1]:
               lowercase :       List[str]       =			flax_key_tuple[:-1] + ('''running_var''',)

          if "batch_stats" in flax_state:
               lowercase :       List[Any]       =			'''.'''.join(flax_key_tuple[1:]   )  # Remove the params/batch_stats header
          else:
               lowercase :       int       =			'''.'''.join(__magic_name__   )

          # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
          lowercase :       str       =			{}
          # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
          for key in pt_model_dict:
               lowercase :       Optional[Any]       =			key.split('''.'''   )
               lowercase :       Union[str, Any]       =			None
               if key_components[-3::2] == ["parametrizations", "original0"]:
                    lowercase :       Tuple       =			key_components[-2] + '''_g'''
               elif key_components[-3::2] == ["parametrizations", "original1"]:
                    lowercase :       List[str]       =			key_components[-2] + '''_v'''
               if name is not None:
                    lowercase :       Dict       =			key_components[:-3] + [name]
                    lowercase :       int       =			'''.'''.join(__magic_name__   )
                    lowercase :       int       =			key

          if flax_key in special_pt_names:
               lowercase :       str       =			special_pt_names[flax_key]

          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
                    lowercase :       str       =			np.asarray(__magic_name__   ) if not isinstance(__magic_name__			,		np.ndarray   ) else flax_tensor
                    lowercase :       Tuple       =			torch.from_numpy(__magic_name__   )
                    # remove from missing keys
                    missing_keys.remove(__magic_name__   )
          else:
               # weight is not expected by PyTorch model
               unexpected_keys.append(__magic_name__   )

     pt_model.load_state_dict(__magic_name__   )

     # re-transform missing_keys to list
     lowercase :       int       =			list(__magic_name__   )

     if len(__magic_name__   ) > 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).'''   )
     else:
          logger.warning(F"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""   )
     if len(__magic_name__   ) > 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.'''   )
     else:
          logger.warning(
              F"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
              '''If your task is similar to the task the model of the checkpoint was trained on, '''
              F"""you can already use {pt_model.__class__.__name__} for predictions without further training."""   )

     return pt_model





from sklearn.metrics import mean_squared_error

import datasets


lowerCAmelCase_						=			'\\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'

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


lowerCAmelCase_						=			'\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 						_A (  datasets.Metric				):






  def  __a      (  self		:     List[Any]				)		->   Optional[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  __a      (  self		:     List[Any]				)		->   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  __a      (  self		:     Any				,						_A		:     Dict				,						_A		:     Any				,						_A		:     Any=None				,						_A		:     Any="uniform_average"				,						_A		:     Optional[Any]=True				)		->   Dict:






       """simple docstring"""







       lowercase :       Any       =			mean_squared_error(
           _A				,						_A				,						sample_weight=_A				,						multioutput=_A				,						squared=_A				)

       return {"mse": mse}