



"""simple docstring"""





from sklearn.metrics import matthews_corrcoef

import datasets


__lowerCAmelCase       :					Optional[Any]      					=					'''
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
'''

__lowerCAmelCase       :					Dict      					=					'''
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results[\'matthews_correlation\'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results[\'matthews_correlation\'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results[\'matthews_correlation\'], 2))
        -0.25
'''

__lowerCAmelCase       :					int      					=					'''\
@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}
}
'''



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





		"""simple docstring"""






		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								return datasets.MetricInfo(
								    description=_DESCRIPTION							,    citation=_CITATION							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
								        {
								            """predictions""": datasets.Value("""int32"""   ),
								            """references""": datasets.Value("""int32"""   ),
								        }   )							,    reference_urls=[
								        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
								    ]							,    )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase=None   ) ->						str:







								'''simple docstring'''

								return {
								    "matthews_correlation": float(matthews_corrcoef(_lowercase							,    _lowercase							,    sample_weight=_lowercase   )   ),
								}






"""simple docstring"""





# 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_gpt2/convert_megatron_gpt2_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 AutoTokenizer, GPTaConfig
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Tuple					, __UpperCamelCase      :			Tuple					, __UpperCamelCase      :			Any=0    ):


						'''simple docstring'''






						if name is None:
												snake_case_   : Dict						=			None
						else:
												snake_case_   : Dict						=			""".""" * max(0					, spaces - 2    ) + """# {:""" + str(5_0 - spaces    ) + """s}"""
												snake_case_   : Any						=			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 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			Dict					, __UpperCamelCase      :			str					, __UpperCamelCase      :			Any    ):


						'''simple docstring'''






						snake_case_   : Any						=			param.size()
						if checkpoint_version == 1.0:
												# version 1.0 stores [num_heads * hidden_size * num_splits, :]
												snake_case_   : List[str]						=			(num_heads, hidden_size, num_splits) + input_shape[1:]
												snake_case_   : Tuple						=			param.view(*__UpperCamelCase    )
												snake_case_   : Tuple						=			param.transpose(0					, 2    )
												snake_case_   : Any						=			param.transpose(1					, 2    ).contiguous()
						elif checkpoint_version >= 2.0:
												# other versions store [num_heads * num_splits * hidden_size, :]
												snake_case_   : Optional[Any]						=			(num_heads, num_splits, hidden_size) + input_shape[1:]
												snake_case_   : str						=			param.view(*__UpperCamelCase    )
												snake_case_   : Dict						=			param.transpose(0					, 1    ).contiguous()
						snake_case_   : int						=			param.view(*__UpperCamelCase    )
						return param
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						snake_case_   : Dict						=			{}

						# old versions did not store training args
						snake_case_   : List[str]						=			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))

												snake_case_   : Tuple						=			ds_args.padded_vocab_size
												snake_case_   : Optional[int]						=			ds_args.max_position_embeddings
												snake_case_   : Union[str, Any]						=			ds_args.hidden_size
												snake_case_   : Union[str, Any]						=			ds_args.num_layers
												snake_case_   : str						=			ds_args.num_attention_heads
												snake_case_   : str						=			ds_args.ffn_hidden_size
												# pprint(config)

						# The number of heads.
						snake_case_   : Union[str, Any]						=			config.n_head
						# The hidden_size per head.
						snake_case_   : Optional[Any]						=			config.n_embd // config.n_head
						# Megatron-LM checkpoint version
						if "checkpoint_version" in input_state_dict.keys():
												snake_case_   : Optional[Any]						=			input_state_dict["""checkpoint_version"""]
						else:
												snake_case_   : int						=			0.0

						# The model.
						snake_case_   : List[str]						=			input_state_dict["""model"""]
						# The language model.
						snake_case_   : str						=			model["""language_model"""]
						# The embeddings.
						snake_case_   : Tuple						=			lm["""embedding"""]

						# The word embeddings.
						snake_case_   : List[str]						=			embeddings["""word_embeddings"""]["""weight"""]
						# Truncate the embedding table to vocab_size rows.
						snake_case_   : Optional[int]						=			word_embeddings[: config.vocab_size, :]
						snake_case_   : Optional[int]						=			word_embeddings

						# The position embeddings.
						snake_case_   : List[Any]						=			embeddings["""position_embeddings"""]["""weight"""]
						# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
						snake_case_   : Tuple						=			pos_embeddings.size(0    )
						if n_positions != config.n_positions:
												raise ValueError(
												    F'pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'    )
						# Store the position embeddings.
						snake_case_   : Union[str, Any]						=			pos_embeddings

						# The transformer.
						snake_case_   : Optional[Any]						=			lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]

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

						# The simple map of names for "automated" rules.
						snake_case_   : List[str]						=			{
						    """attention.dense""": """.attn.c_proj.""",
						    """self_attention.dense""": """.attn.c_proj.""",
						    """mlp.dense_h_to_4h""": """.mlp.c_fc.""",
						    """mlp.dense_4h_to_h""": """.mlp.c_proj.""",
						}

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

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

												# The index of the layer.
												snake_case_   : Tuple						=			int(m.group(1    )    )
												# The name of the operation.
												snake_case_   : Any						=			m.group(2    )
												# Is it a weight or a bias?
												snake_case_   : Union[str, Any]						=			m.group(3    )

												# The name of the layer.
												snake_case_   : str						=			F'transformer.h.{layer_idx}'

												# For layernorm(s), simply store the layer norm.
												if op_name.endswith("""layernorm"""    ):
																		snake_case_   : Dict						=			"""ln_1""" if op_name.startswith("""input"""    ) else """ln_2"""
																		snake_case_   : Optional[int]						=			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":
																		# Insert a tensor of 1x1xDxD bias.
																		snake_case_   : Optional[Any]						=			torch.tril(torch.ones((n_positions, n_positions)					, dtype=torch.floataa    )    ).view(
																		    1					, 1					, __UpperCamelCase					, __UpperCamelCase    )
																		snake_case_   : List[Any]						=			causal_mask

																		# Insert a "dummy" tensor for masked_bias.
																		snake_case_   : str						=			torch.tensor(-1E4					, dtype=torch.floataa    )
																		snake_case_   : List[Any]						=			masked_bias

																		snake_case_   : Optional[int]						=			fix_query_key_value_ordering(__UpperCamelCase					, __UpperCamelCase					, 3					, __UpperCamelCase					, __UpperCamelCase    )
																		# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
																		snake_case_   : str						=			out_val.transpose(0					, 1    ).contiguous()
																		# Store.
																		snake_case_   : Tuple						=			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":
																		snake_case_   : Optional[Any]						=			fix_query_key_value_ordering(__UpperCamelCase					, __UpperCamelCase					, 3					, __UpperCamelCase					, __UpperCamelCase    )
																		# Store. No change of shape.
																		snake_case_   : List[Any]						=			out_val

												# Transpose the weights.
												elif weight_or_bias == "weight":
																		snake_case_   : Any						=			megatron_to_transformers[op_name]
																		snake_case_   : str						=			val.transpose(0					, 1    )

												# Copy the bias.
												elif weight_or_bias == "bias":
																		snake_case_   : List[str]						=			megatron_to_transformers[op_name]
																		snake_case_   : Tuple						=			val

    # DEBUG.
						assert config.n_layer == layer_idx + 1

						# The final layernorm.
						snake_case_   : Dict						=			transformer["""final_layernorm.weight"""]
						snake_case_   : Dict						=			transformer["""final_layernorm.bias"""]

						# For LM head, transformers' wants the matrix to weight embeddings.
						snake_case_   : Optional[int]						=			word_embeddings

						# It should be done!
						return output_state_dict
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : List[str]						=			argparse.ArgumentParser()
						parser.add_argument("""--print-checkpoint-structure"""					, action="""store_true"""    )
						parser.add_argument(
						    """path_to_checkpoint"""					, type=__UpperCamelCase					, help="""Path to the checkpoint file (.zip archive or direct .pt file)"""					, )
						parser.add_argument(
						    """--config_file"""					, default=""""""					, type=__UpperCamelCase					, help="""An optional config json file describing the pre-trained model."""					, )
						snake_case_   : str						=			parser.parse_args()

						# Extract the basename.
						snake_case_   : Optional[Any]						=			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:
																								snake_case_   : Optional[int]						=			torch.load(__UpperCamelCase					, map_location="""cpu"""    )
						else:
												snake_case_   : List[Any]						=			torch.load(args.path_to_checkpoint					, map_location="""cpu"""    )

						snake_case_   : Any						=			input_state_dict.get("""args"""					, __UpperCamelCase    )

						# Read the config, or default to the model released by NVIDIA.
						if args.config_file == "":
												if ds_args is not None:
																		if ds_args.bias_gelu_fusion:
																								snake_case_   : Any						=			"""gelu_fast"""
																		elif ds_args.openai_gelu:
																								snake_case_   : Tuple						=			"""gelu_new"""
																		else:
																								snake_case_   : List[str]						=			"""gelu"""
												else:
																		# in the very early days this used to be "gelu_new"
																		snake_case_   : Dict						=			"""gelu_new"""

												# Spell out all parameters in case the defaults change.
												snake_case_   : List[str]						=			GPTaConfig(
												    vocab_size=5_0_2_5_7					, n_positions=1_0_2_4					, n_embd=1_0_2_4					, n_layer=2_4					, n_head=1_6					, n_inner=4_0_9_6					, activation_function=__UpperCamelCase					, resid_pdrop=0.1					, embd_pdrop=0.1					, attn_pdrop=0.1					, layer_norm_epsilon=1E-5					, initializer_range=0.02					, summary_type="""cls_index"""					, summary_use_proj=__UpperCamelCase					, summary_activation=__UpperCamelCase					, summary_proj_to_labels=__UpperCamelCase					, summary_first_dropout=0.1					, scale_attn_weights=__UpperCamelCase					, use_cache=__UpperCamelCase					, bos_token_id=5_0_2_5_6					, eos_token_id=5_0_2_5_6					, )
						else:
												snake_case_   : List[Any]						=			GPTaConfig.from_json_file(args.config_file    )

						snake_case_   : int						=			["""GPT2LMHeadModel"""]

						# Convert.
						print("""Converting"""    )
						snake_case_   : Tuple						=			convert_megatron_checkpoint(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )

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

						# Add tokenizer class info to config
						# see https://github.com/huggingface/transformers/issues/13906)
						if ds_args is not None:
												snake_case_   : str						=			ds_args.tokenizer_type
												if tokenizer_type == "GPT2BPETokenizer":
																		snake_case_   : Optional[Any]						=			"""gpt2"""
												elif tokenizer_type == "PretrainedFromHF":
																		snake_case_   : str						=			ds_args.tokenizer_name_or_path
												else:
																		raise ValueError(F'Unrecognized tokenizer_type {tokenizer_type}'    )
						else:
												snake_case_   : List[str]						=			"""gpt2"""

						snake_case_   : List[Any]						=			AutoTokenizer.from_pretrained(__UpperCamelCase    )
						snake_case_   : List[str]						=			type(__UpperCamelCase    ).__name__
						snake_case_   : Optional[int]						=			tokenizer_class

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

						# Save tokenizer based on args
						print(F'Adding {tokenizer_class} tokenizer files'    )
						tokenizer.save_pretrained(__UpperCamelCase    )

						# Store the state_dict to file.
						snake_case_   : List[Any]						=			os.path.join(__UpperCamelCase					, """pytorch_model.bin"""    )
						print(F'Saving checkpoint to "{output_checkpoint_file}"'    )
						torch.save(__UpperCamelCase					, __UpperCamelCase    )


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

if __name__ == "__main__":
					main()

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

