






from scipy.stats import spearmanr

import datasets


__snake_case    :    int         =     """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""

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

    Example 2:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results['spearmanr'])
        -0.7
        >>> print(round(results['spearmanr_pvalue'], 2))
        0.19
"""

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


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






						def       UpperCamelCase__      (	self   ):






												"""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       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase=False   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						spearmanr(_UpperCamelCase     ,						_UpperCamelCase   )
												if return_pvalue:
																		return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
												else:
																		return {"spearmanr": results[0]}













import argparse
import json
import os
import re

import torch

from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging


logging.set_verbosity_info()

__snake_case    :    str         =     [
    """word_embeddings_layernorm.weight""",
    """word_embeddings_layernorm.bias""",
    """input_layernorm.weight""",
    """input_layernorm.bias""",
    """post_attention_layernorm.weight""",
    """post_attention_layernorm.bias""",
    """self_attention.dense.bias""",
    """mlp.dense_4h_to_h.bias""",
    """ln_f.weight""",
    """ln_f.bias""",
]

__snake_case    :    int         =     [
    """mlp.dense_4h_to_h.weight""",
    """self_attention.dense.weight""",
]







def 	_UpperCamelCase   (    UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					Dict		)  ->   Dict:

						"""simple docstring"""



						lowerCAmelCase__    						=						{
						    'word_embeddings.weight': 'word_embeddings.weight',
						    'word_embeddings.norm.weight': 'word_embeddings_layernorm.weight',
						    'word_embeddings.norm.bias': 'word_embeddings_layernorm.bias',
						    'weight': 'ln_f.weight',
						    'bias': 'ln_f.bias',
						}

						if key in layer_rename_map:
												return layer_rename_map[key]

						# Handle transformer blocks
						lowerCAmelCase__    						=						int(re.match(r'.*layer_(\d*).*'    ,       UpperCamelCase_		)[1]		)
						layer_number -= 3
						return F"h.{layer_number}." + key







def 	_UpperCamelCase   (    UpperCamelCase_       :					Any		)  ->   Optional[Any]:

						"""simple docstring"""



						if dtype == torch.bool:
												return 1 / 8
						lowerCAmelCase__    						=						re.search(r'[^\d](\d+)$'    ,       str(UpperCamelCase_		)		)
						if bit_search is None:
												raise ValueError(F"`dtype` is not a valid dtype: {dtype}."		)
						lowerCAmelCase__    						=						int(bit_search.groups()[0]		)
						return bit_size // 8







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Any    ,       UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					Tuple		)  ->   Union[str, Any]:

						"""simple docstring"""



						if bloom_config_file == "":
												lowerCAmelCase__    						=						BloomConfig()
						else:
												lowerCAmelCase__    						=						BloomConfig.from_json_file(UpperCamelCase_		)

						if shard_model:
												lowerCAmelCase__    						=						os.listdir(UpperCamelCase_		)
												lowerCAmelCase__    						=						sorted(filter(lambda UpperCamelCase_		: s.startswith('layer'		) and "model_00" in s    ,       UpperCamelCase_		)		)

												lowerCAmelCase__    						=						{'weight_map': {}, 'metadata': {}}
												lowerCAmelCase__    						=						0

												lowerCAmelCase__    						=						None

												lowerCAmelCase__    						=						BloomConfig()

												for j, file in enumerate(UpperCamelCase_		):
																		print('Processing file: {}'.format(UpperCamelCase_		)		)
																		lowerCAmelCase__    						=						None

																		for i in range(UpperCamelCase_		):
																								# load all TP files
																								lowerCAmelCase__    						=						file.replace('model_00'    ,       F"model_0{i}"		)
																								lowerCAmelCase__    						=						torch.load(os.path.join(UpperCamelCase_    ,       UpperCamelCase_		)    ,       map_location='cpu'		)

																								# Rename keys in the transformers names
																								lowerCAmelCase__    						=						list(temp.keys()		)
																								for key in keys:
																														lowerCAmelCase__    						=						temp.pop(UpperCamelCase_		)

																								if tensors is None:
																														lowerCAmelCase__    						=						temp
																								else:
																														for key in tensors.keys():
																																				if any(key.endswith(UpperCamelCase_		) for end in WEIGHTS_TO_AVERAGE_ENDSWITH		):
																																										# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
																																										tensors[key] += temp[key]
																																				else:
																																										# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
																																										lowerCAmelCase__    						=						1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN		) else 0
																																										# We concatenate these weights accross TP ranks
																																										lowerCAmelCase__    						=						torch.cat([tensors[key], temp[key]]    ,       dim=UpperCamelCase_		)

            # Divide by the number of TP the weights we want to average
																		for key in tensors.keys():
																								if any(key.endswith(UpperCamelCase_		) for end in WEIGHTS_TO_AVERAGE_ENDSWITH		):
																														lowerCAmelCase__    						=						tensors[key] / pretraining_tp
																		torch.save(
																		    UpperCamelCase_    ,       os.path.join(
																		        UpperCamelCase_    ,       'pytorch_model_{}-of-{}.bin'.format(str(j + 1		).zfill(5		)    ,       str(len(UpperCamelCase_		)		).zfill(5		)		)    ,       )    ,       )

																		for key in tensors.keys():
																								lowerCAmelCase__    						=						tensors[key]
																								total_size += value.numel() * get_dtype_size(value.dtype		)
																								if key not in index_dict["weight_map"]:
																														lowerCAmelCase__    						=						'pytorch_model_{}-of-{}.bin'.format(
																														    str(j + 1		).zfill(5		)    ,       str(len(UpperCamelCase_		)		).zfill(5		)		)

												lowerCAmelCase__    						=						BloomConfig()
												lowerCAmelCase__    						=						pytorch_dump_folder_path + '/' + CONFIG_NAME
												lowerCAmelCase__    						=						total_size
												with open(UpperCamelCase_    ,       'w'    ,       encoding='utf-8'		) as f:
																		f.write(config.to_json_string()		)
												with open(os.path.join(UpperCamelCase_    ,       WEIGHTS_NAME + '.index.json'		)    ,       'w'    ,       encoding='utf-8'		) as f:
																		lowerCAmelCase__    						=						json.dumps(UpperCamelCase_    ,       indent=2    ,       sort_keys=UpperCamelCase_		) + '\n'
																		f.write(UpperCamelCase_		)
						else:
												lowerCAmelCase__    						=						BloomModel(UpperCamelCase_		)

												lowerCAmelCase__    						=						os.listdir(UpperCamelCase_		)
												lowerCAmelCase__    						=						sorted(filter(lambda UpperCamelCase_		: s.startswith('layer'		) and "model_00" in s    ,       UpperCamelCase_		)		)

												lowerCAmelCase__    						=						None
												for i, file in enumerate(UpperCamelCase_		):
																		lowerCAmelCase__    						=						None
																		for i in range(UpperCamelCase_		):
																								# load all TP files
																								lowerCAmelCase__    						=						file.replace('model_00'    ,       F"model_0{i}"		)
																								lowerCAmelCase__    						=						torch.load(os.path.join(UpperCamelCase_    ,       UpperCamelCase_		)    ,       map_location='cpu'		)

																								# Rename keys in the transformers names
																								lowerCAmelCase__    						=						list(temp.keys()		)
																								for key in keys:
																														lowerCAmelCase__    						=						temp.pop(UpperCamelCase_		)

																								if tensors is None:
																														lowerCAmelCase__    						=						temp
																								else:
																														for key in tensors.keys():
																																				# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
																																				if any(key.endswith(UpperCamelCase_		) for end in WEIGHTS_TO_AVERAGE_ENDSWITH		):
																																										tensors[key] += temp[key]
																																				else:
																																										# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
																																										lowerCAmelCase__    						=						1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN		) else 0
																																										# We concatenate these weights accross TP ranks
																																										lowerCAmelCase__    						=						torch.cat([tensors[key], temp[key]]    ,       dim=UpperCamelCase_		)

            # Divide by the number of TP the weights we want to average
																		for key in tensors.keys():
																								if any(key.endswith(UpperCamelCase_		) for end in WEIGHTS_TO_AVERAGE_ENDSWITH		):
																														lowerCAmelCase__    						=						tensors[key] / pretraining_tp

																		lowerCAmelCase__    						=						model.load_state_dict(UpperCamelCase_    ,       strict=UpperCamelCase_		)
																		assert not other_keys.unexpected_keys, F"The keys {other_keys.unexpected_keys} are unexpected"
																		if missing_keys is None:
																								lowerCAmelCase__    						=						set(other_keys.missing_keys		)
																		else:
																								lowerCAmelCase__    						=						missing_keys.intersection(set(other_keys.missing_keys		)		)

												assert not missing_keys, F"The keys {missing_keys} are missing"

												# Save pytorch-model
												os.makedirs(UpperCamelCase_    ,       exist_ok=UpperCamelCase_		)
												lowerCAmelCase__    						=						pytorch_dump_folder_path + '/' + WEIGHTS_NAME
												lowerCAmelCase__    						=						pytorch_dump_folder_path + '/' + CONFIG_NAME
												print(F"Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}"		)
												if config.torch_dtype is not None:
																		lowerCAmelCase__    						=						model.to(config.torch_dtype		)
												torch.save(model.state_dict()    ,       UpperCamelCase_		)
												print(F"Save configuration file to {pytorch_config_dump_path}"		)
												with open(UpperCamelCase_    ,       'w'    ,       encoding='utf-8'		) as f:
																		f.write(config.to_json_string()		)


if __name__ == "__main__":
					__snake_case    :    Union[str, Any]         =     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--bloom_checkpoint_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to the Megatron-LM checkpoint path.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--bloom_config_file""",
					    default="""""",
					    type=str,
					    help=(
					        """An optional config json file corresponding to the pre-trained model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--shard_model""",
					    action="""store_true""",
					    help="""An optional setting to shard the output model \nThis enables sharding the converted checkpoint""",
					)
					parser.add_argument(
					    """--pretraining_tp""",
					    default=4,
					    type=int,
					    help="""Pretraining TP rank that has been used when training the model in Megatron-LM \n""",
					)
					__snake_case    :    str         =     parser.parse_args()
					convert_bloom_checkpoint_to_pytorch(
					    args.bloom_checkpoint_path,
					    args.bloom_config_file,
					    args.pytorch_dump_folder_path,
					    args.shard_model,
					    args.pretraining_tp,
					)





