
"""simple docstring"""




from scipy.stats import spearmanr

import datasets


a		:       Tuple     						=       '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'

a		:       Tuple     						=       '\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {\'spearmanr\': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results[\'spearmanr\'])\n        -0.7\n        >>> print(round(results[\'spearmanr_pvalue\'], 2))\n        0.19\n'

a		:       List[Any]     						=       r'\\n@book{kokoska2000crc,\n  title={CRC standard probability and statistics tables and formulae},\n  author={Kokoska, Stephen and Zwillinger, Daniel},\n  year={2000},\n  publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n            Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n            Kern, Robert and Larson, Eric and Carey, C J and\n            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n            Harris, Charles R. and Archibald, Anne M. and\n            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n            Computing in Python}},\n  journal = {Nature Methods},\n  year    = {2020},\n  volume  = {17},\n  pages   = {261--272},\n  adsurl  = {https://rdcu.be/b08Wh},\n  doi     = {10.1038/s41592-019-0686-2},\n}\n'







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



     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         return datasets.MetricInfo(
             description=_DESCRIPTION     ,				citation=_CITATION     ,				inputs_description=_KWARGS_DESCRIPTION     ,				features=datasets.Features(
                 {
                     'predictions': datasets.Value('float'   ),
                     'references': datasets.Value('float'   ),
                 }   )     ,				reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html']     ,				)




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=False   )		->     Optional[int]:







         """simple docstring"""



         a__		=			spearmanr(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         if return_pvalue:
             return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
         else:
             return {"spearmanr": results[0]}




"""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,
    )


