


from sklearn.metrics import mean_squared_error

import datasets


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


_SCREAMING_SNAKE_CASE     :					int      =       '\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      ):







    """simple docstring"""

    def 						a_     (			self       ):
         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       ):



         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     ,   __snake_case     ,   __snake_case     ,   __snake_case=None     ,   __snake_case="uniform_average"     ,   __snake_case=True       ):
         snake_case											=							mean_squared_error(
             __SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE     ,   sample_weight=__SCREAMING_SNAKE_CASE     ,   multioutput=__SCREAMING_SNAKE_CASE     ,   squared=__SCREAMING_SNAKE_CASE       )

         return {"mse": mse}






"""simple docstring"""




import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
a		:       int     						=       {
    '/attention/': '/0/SelfAttention/',
    '/self_attention/': '/0/SelfAttention/',
    '/encoder_decoder_attention/': '/1/EncDecAttention/',
    'value': 'v',
    'query': 'q',
    'key': 'k',
    'out': 'o',
    'pre_self_attention_layer_norm': '0/layer_norm',
    'pre_cross_attention_layer_norm': '1/layer_norm',
    'pre_attention_layer_norm': '0/layer_norm',  # previously 1, but seems wrong
    'token_embedder': 'shared',
    'encoder_norm': 'final_layer_norm',
    'decoder_norm': 'final_layer_norm',
    'relpos_bias/rel_embedding': 'block/0/layer/0/SelfAttention/relative_attention_bias/weight',
    'router/router_weights/w/': 'router/classifier/',
    'roer/roer_weights/w/': 'router/classifier/',
    'logits_dense': 'lm_head',
}



def     __magic_name__		(					UpperCamelCase     :		List[Any]     )     ->						Union[str, Any]:
    # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
    # the original model
    a__		=			list(s_dict.keys()     )
    for key in keys:
        a__		=			r'.*/layers_(\d+)'
        a__		=			key
        if re.match(UpperCamelCase							,		UpperCamelCase     ):
            a__		=			re.sub(r'layers_(\d+)'							,		r'block/\1/layer'							,		UpperCamelCase     )

        a__		=			r'(encoder|decoder)\/'

        if re.match(UpperCamelCase							,		UpperCamelCase     ):
            a__		=			re.match(UpperCamelCase							,		UpperCamelCase     ).groups()
            if groups[0] == "encoder":
                a__		=			re.sub(r'/mlp/'							,		r'/1/mlp/'							,		UpperCamelCase     )
                a__		=			re.sub(r'/pre_mlp_layer_norm/'							,		r'/1/layer_norm/'							,		UpperCamelCase     )

            elif groups[0] == "decoder":
                a__		=			re.sub(r'/mlp/'							,		r'/2/mlp/'							,		UpperCamelCase     )
                a__		=			re.sub(r'/pre_mlp_layer_norm/'							,		r'/2/layer_norm/'							,		UpperCamelCase     )

        # 2. Convert other classic mappings
        for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
            if old_key in new_key:
                a__		=			new_key.replace(UpperCamelCase							,		UpperCamelCase     )

        print(f'{key} -> {new_key}'     )
        a__		=			s_dict.pop(UpperCamelCase     )

    if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        a__		=			s_dict[
            'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
        ].T
    if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        a__		=			s_dict[
            'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
        ].T

    # 3. Take extra care of the EXPERTS layer
    for key in list(s_dict.keys()     ):
        if "expert" in key:
            a__		=			s_dict[key].shape[0]
            a__		=			s_dict[key]
            for idx in range(UpperCamelCase     ):
                a__		=			expert_weihts[idx]
                print(f'{key} -> {key.replace("expert/"							,		"nested fstring"     )}'     )

            s_dict.pop(UpperCamelCase     )

    return s_dict


a		:       List[str]     						=       {
    'NUM_ENCODER_LAYERS': 'num_layers',
    'NUM_DECODER_LAYERS': 'num_decoder_layers',
    'NUM_HEADS': 'num_heads',
    'HEAD_DIM': 'd_kv',
    'EMBED_DIM': 'd_model',
    'MLP_DIM': 'd_ff',
    'NUM_SELECTED_EXPERTS': 'num_selected_experts',
    'NUM_ENCODER_SPARSE_LAYERS': 'num_sparse_encoder_layers',
    'NUM_DECODER_SPARSE_LAYERS': 'num_sparse_decoder_layers',
    'dense.MlpBlock.activations': 'feed_forward_proj',
}



def     __magic_name__		(					UpperCamelCase     :		Optional[int]							,		UpperCamelCase     :		Optional[int]     )     ->						Optional[int]:
    # Convert a google style config to the hugging face fromat
    import regex as re

    with open(UpperCamelCase							,		'r'     ) as f:
        a__		=			f.read()

    a__		=			re.findall(r'(.*) = ([0-9.]*)'							,		UpperCamelCase     )
    a__		=			{}
    for param, value in regex_match:
        if param in GIN_TO_CONFIG_MAPPING and value != "":
            a__		=			float(UpperCamelCase     ) if '.' in value else int(UpperCamelCase     )

    a__		=			re.findall(r'(.*activations) = \(\'(.*)\',\)'							,		UpperCamelCase     )[0]
    a__		=			str(activation[1]     )

    a__		=			num_experts
    a__		=			SwitchTransformersConfig(**UpperCamelCase     )
    return config



def     __magic_name__		(					UpperCamelCase     :		str							,		UpperCamelCase     :		Tuple							,		UpperCamelCase     :		Union[str, Any]=None							,		UpperCamelCase     :		List[str]="./"							,		UpperCamelCase     :		Any=8     )     ->						Union[str, Any]:
    # Initialise PyTorch model

    print(f'Loading flax weights from : {flax_checkpoint_path}'     )
    a__		=			checkpoints.load_tax_checkpoint(UpperCamelCase     )

    if gin_file is not None:
        a__		=			convert_gin_to_config(UpperCamelCase							,		UpperCamelCase     )
    else:
        a__		=			SwitchTransformersConfig.from_pretrained(UpperCamelCase     )

    a__		=			SwitchTransformersForConditionalGeneration(UpperCamelCase     )

    a__		=			flax_params['target']
    a__		=			flatten_dict(UpperCamelCase							,		sep='/'     )
    a__		=			rename_keys(UpperCamelCase     )
    a__		=			unflatten_dict(UpperCamelCase							,		sep='/'     )

    # Load the flax params in the PT model
    load_flax_weights_in_pytorch_model(UpperCamelCase							,		UpperCamelCase     )

    print(f'Save PyTorch model to {pytorch_dump_path}'     )
    pt_model.save_pretrained(UpperCamelCase     )


if __name__ == "__main__":
    a		:       Dict     						=       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--switch_t5x_checkpoint_path',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'
            ' model architecture. If not provided, a `gin_file` has to be provided.'
        ),
    )
    parser.add_argument(
        '--gin_file',
        default=None,
        type=str,
        required=False,
        help='Path to the gin config file. If not provided, a `config_file` has to be passed   ',
    )
    parser.add_argument(
        '--config_name', default=None, type=str, required=False, help='Config name of SwitchTransformers model.'
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output pytorch model.'
    )
    parser.add_argument('--num_experts', default=8, type=int, required=False, help='Number of experts')
    a		:       List[str]     						=       parser.parse_args()
    convert_flax_checkpoint_to_pytorch(
        args.switch_tax_checkpoint_path,
        args.config_name,
        args.gin_file,
        args.pytorch_dump_folder_path,
        args.num_experts,
    )


