





from sklearn.metrics import mean_absolute_error

import datasets


UpperCAmelCase__  :	Tuple     ='''\
@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}
}
'''

UpperCAmelCase__  :	Optional[Any]     ='''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


UpperCAmelCase__  :	Any     ='''
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.

Returns:
    mae : mean absolute error.
        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric("mae")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.5}

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

    >>> mae_metric = datasets.load_metric("mae", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results)
    {\'mae\': array([0.5, 1. ])}
'''

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


				def _snake_case				(						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_absolute_error.html"""
								    ]      ,       )


				def _snake_case				(						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 _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_=None      ,       UpperCAmelCase_="uniform_average"							):
								lowerCamelCase		       =mean_absolute_error(UpperCAmelCase_      ,       UpperCAmelCase_      ,       sample_weight=UpperCAmelCase_      ,       multioutput=UpperCAmelCase_							)

								return {"mae": mae_score}












# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase=0 )  ->     Optional[Any]:
				# Format the message.
				if name is None:
								lowerCamelCase		       =None
				else:
								lowerCamelCase		       =""".""" * max(0	,					spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
								lowerCamelCase		       =fmt.format(_UpperCAmelCase )

				# Print and recurse (if needed).
				if isinstance(_UpperCAmelCase	,					_UpperCAmelCase ):
								if msg is not None:
												print(_UpperCAmelCase )
								for k in val.keys():
												recursive_print(_UpperCAmelCase	,					val[k]	,					spaces + 2 )
				elif isinstance(_UpperCAmelCase	,					torch.Tensor ):
								print(_UpperCAmelCase	,					""":"""	,					val.size() )
				else:
								print(_UpperCAmelCase	,					""":"""	,					_UpperCAmelCase )





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     Tuple:
				# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
				# for compatibility with later versions of NVIDIA Megatron-LM.
				# The inverse operation is performed inside Megatron-LM to read checkpoints:
				# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
				# If param is the weight tensor of the self-attention block, the returned tensor
				# will have to be transposed one more time to be read by HuggingFace BERT.
				lowerCamelCase		       =param.size()
				if checkpoint_version == 1.0:
								# version 1.0 stores [num_heads * hidden_size * num_splits, :]
								lowerCamelCase		       =(num_heads, hidden_size, num_splits) + input_shape[1:]
								lowerCamelCase		       =param.view(*_UpperCAmelCase )
								lowerCamelCase		       =param.transpose(0	,					2 )
								lowerCamelCase		       =param.transpose(1	,					2 ).contiguous()
				elif checkpoint_version >= 2.0:
								# other versions store [num_heads * num_splits * hidden_size, :]
								lowerCamelCase		       =(num_heads, num_splits, hidden_size) + input_shape[1:]
								lowerCamelCase		       =param.view(*_UpperCAmelCase )
								lowerCamelCase		       =param.transpose(0	,					1 ).contiguous()
				lowerCamelCase		       =param.view(*_UpperCAmelCase )
				return param





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     List[Any]:
				# The converted output model.
				lowerCamelCase		       ={}

				# old versions did not store training args
				lowerCamelCase		       =input_state_dict.get("""args"""	,					_UpperCAmelCase )
				if ds_args is not None:
								# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
								# from pprint import pprint
								# pprint(vars(ds_args))

								lowerCamelCase		       =ds_args.tokenizer_type
								lowerCamelCase		       =ds_args.padded_vocab_size
								lowerCamelCase		       =ds_args.max_position_embeddings
								lowerCamelCase		       =ds_args.hidden_size
								lowerCamelCase		       =ds_args.num_layers
								lowerCamelCase		       =ds_args.num_attention_heads
								lowerCamelCase		       =ds_args.ffn_hidden_size if """ffn_hidden_size""" in ds_args else 4 * ds_args.hidden_size
								# pprint(config)

				# The number of heads.
				lowerCamelCase		       =config.num_attention_heads
				# The hidden_size per head.
				lowerCamelCase		       =config.hidden_size // heads
				# Megatron-LM checkpoint version
				if "checkpoint_version" in input_state_dict.keys():
								lowerCamelCase		       =input_state_dict["""checkpoint_version"""]
				else:
								lowerCamelCase		       =0.0

				# The model.
				lowerCamelCase		       =input_state_dict["""model"""]
				# The language model.
				lowerCamelCase		       =model["""language_model"""]
				# The embeddings.
				lowerCamelCase		       =lm["""embedding"""]

				# The word embeddings.
				lowerCamelCase		       =embeddings["""word_embeddings"""]["""weight"""]
				# Truncate the embedding table to vocab_size rows.
				lowerCamelCase		       =word_embeddings[: config.vocab_size, :]
				# Store the word embeddings.
				lowerCamelCase		       =word_embeddings

				# The position embeddings.
				lowerCamelCase		       =embeddings["""position_embeddings"""]["""weight"""]
				assert pos_embeddings.size(0 ) == config.max_position_embeddings and pos_embeddings.size(1 ) == config.hidden_size
				# Store the position embeddings.
				lowerCamelCase		       =pos_embeddings

				# The token-type embeddings.
				lowerCamelCase		       =embeddings["""tokentype_embeddings"""]["""weight"""]
				# Store the position embeddings.
				lowerCamelCase		       =tokentype_embeddings

				# The transformer.
				lowerCamelCase		       =lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]

				# The regex to extract layer names.
				lowerCamelCase		       =re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )

				# The simple map of names for "automated" rules.
				lowerCamelCase		       ={
				    """attention.dense""": """.attention.output.dense.""",
				    """self_attention.dense""": """.attention.output.dense.""",
				    """mlp.dense_h_to_4h""": """.intermediate.dense.""",
				    """mlp.dense_4h_to_h""": """.output.dense.""",
				}

				# Keep track of the attention/query/value tensor.
				lowerCamelCase		       =None

				# Extract the layers.
				for key, val in transformer.items():
								# Match the name.
								lowerCamelCase		       =layer_re.match(_UpperCAmelCase )

								# Stop if that's not a layer
								if m is None:
												break

								# The index of the layer.
								lowerCamelCase		       =int(m.group(1 ) )
								# The name of the operation.
								lowerCamelCase		       =m.group(2 )
								# Is it a weight or a bias?
								lowerCamelCase		       =m.group(3 )

								# The name of the layer.
								lowerCamelCase		       =F"""bert.encoder.layer.{layer_idx}"""

								# For layernorm(s), simply store the layer norm.
								if op_name.endswith("""layernorm""" ):
												lowerCamelCase		       ="""attention.ln""" if op_name.startswith("""input""" ) else """ln"""
												lowerCamelCase		       =val

								# Transpose the QKV matrix.
								elif (
								    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
								) and weight_or_bias == "weight":
												# Make sure the QKV pointer is nil.
												assert attention_qkv_weight is None, ""

												lowerCamelCase		       =fix_query_key_value_ordering(_UpperCAmelCase	,					_UpperCAmelCase	,					3	,					_UpperCAmelCase	,					_UpperCAmelCase )
												# Store the tensor as we need the bias as well to interleave QKV and biases.
												lowerCamelCase		       =out_val

								# Transpose the bias.
								elif (
								    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
								) and weight_or_bias == "bias":
												# Make sure we read the weight tensor.
												assert attention_qkv_weight is not None, ""

												# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
												lowerCamelCase		       =attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
												lowerCamelCase		       =attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
												lowerCamelCase		       =attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

												lowerCamelCase		       =fix_query_key_value_ordering(_UpperCAmelCase	,					_UpperCAmelCase	,					3	,					_UpperCAmelCase	,					_UpperCAmelCase )
												# Split the bias.
												lowerCamelCase		       =out_val[0 * config.hidden_size : 1 * config.hidden_size]
												lowerCamelCase		       =out_val[1 * config.hidden_size : 2 * config.hidden_size]
												lowerCamelCase		       =out_val[2 * config.hidden_size : 3 * config.hidden_size]

												# Store.
												lowerCamelCase		       =q
												lowerCamelCase		       =q_bias
												lowerCamelCase		       =k
												lowerCamelCase		       =k_bias
												lowerCamelCase		       =v
												lowerCamelCase		       =v_bias

												# Clear the stored tensor.
												lowerCamelCase		       =None

								# Copy weights and biases as is.
								elif weight_or_bias in ["weight", "bias"]:
												lowerCamelCase		       =megatron_to_transformers[op_name]
												lowerCamelCase		       =val

    # The final layernorm.
				lowerCamelCase		       =transformer["""final_layernorm.weight"""]
				lowerCamelCase		       =transformer["""final_layernorm.bias"""]

				# The pooler.
				lowerCamelCase		       =lm["""pooler"""]

				# Store the matrix and the bias.
				lowerCamelCase		       =pooler["""dense.weight"""]
				lowerCamelCase		       =pooler["""dense.bias"""]

				# The LM head from Megatron (for RACE).
				lowerCamelCase		       =model["""lm_head"""]

				# The transform matrix.
				lowerCamelCase		       =lm_head["""dense.weight"""]
				lowerCamelCase		       =lm_head["""dense.bias"""]

				# The transform LN.
				lowerCamelCase		       =lm_head["""layernorm.weight"""]
				lowerCamelCase		       =lm_head["""layernorm.bias"""]

				# For the decoder, we replicate the weights.
				lowerCamelCase		       =word_embeddings
				lowerCamelCase		       =lm_head["""bias"""]

				# The classifier from Megatron (for MLNI).
				lowerCamelCase		       =model["""binary_head"""]

				# Store the classifier.
				lowerCamelCase		       =binary_head["""weight"""]
				lowerCamelCase		       =binary_head["""bias"""]

				# It should be done!
				return output_state_dict





def    _lowercase    (  )  ->     str:
				# Create the argument parser.
				lowerCamelCase		       =argparse.ArgumentParser()
				parser.add_argument("""--print-checkpoint-structure"""	,					action="""store_true""" )
				parser.add_argument("""path_to_checkpoint"""	,					type=_UpperCAmelCase	,					help="""Path to the ZIP file containing the checkpoint""" )
				parser.add_argument(
				    """--config_file"""	,					default=""""""	,					type=_UpperCAmelCase	,					help="""An optional config json file describing the pre-trained model."""	,					)
				lowerCamelCase		       =parser.parse_args()

				# Extract the basename.
				lowerCamelCase		       =os.path.dirname(args.path_to_checkpoint )

				# Load the model.
				# the .zip is very optional, let's keep it for backward compatibility
				print(F"""Extracting PyTorch state dictionary from \"{args.path_to_checkpoint}\"""" )
				if args.path_to_checkpoint.endswith(""".zip""" ):
								with zipfile.ZipFile(args.path_to_checkpoint	,					"""r""" ) as checkpoint:
												with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict:
																lowerCamelCase		       =torch.load(_UpperCAmelCase	,					map_location="""cpu""" )
				else:
								lowerCamelCase		       =torch.load(args.path_to_checkpoint	,					map_location="""cpu""" )

				if args.config_file == "":
								# Default config of megatron-bert 345m
								lowerCamelCase		       =MegatronBertConfig()

								# different megatron-bert-*-345m models have different vocab sizes, so override the default
								# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
								lowerCamelCase		       =input_state_dict["""model"""]["""lm_head"""]["""bias"""].numel()
				else:
								lowerCamelCase		       =MegatronBertConfig.from_json_file(args.config_file )

				# Convert.
				print("""Converting""" )
				lowerCamelCase		       =convert_megatron_checkpoint(_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )

				# Print the structure of converted state dict.
				if args.print_checkpoint_structure:
								recursive_print(_UpperCAmelCase	,					_UpperCAmelCase )

				# Store the config to file.
				print("""Saving config""" )
				config.save_pretrained(_UpperCAmelCase )

				# Store the state_dict to file.
				lowerCamelCase		       =os.path.join(_UpperCAmelCase	,					"""pytorch_model.bin""" )
				print(F"""Saving checkpoint to \"{output_checkpoint_file}\"""" )
				torch.save(_UpperCAmelCase	,					_UpperCAmelCase )


####################################################################################################

if __name__ == "__main__":
							main()

####################################################################################################





