





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case=0       )		->						int:
		# Format the message.
		if name is None:
				__lowercase					=    None
		else:
				__lowercase					=    '.' * max(0	,      spaces - 2       ) + '# {:' + str(50 - spaces       ) + 's}'
				__lowercase					=    fmt.format(__snake_case       )

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



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case	,      snake_case       )		->						Union[str, Any]:
		# 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 GPT2.
		__lowercase					=    param.size()
		# other versions store [num_heads * num_splits * hidden_size, :]
		__lowercase					=    (num_heads, num_splits, hidden_size) + input_shape[1:]
		__lowercase					=    param.view(*__snake_case       )
		__lowercase					=    param.transpose(0	,      1       ).contiguous()
		__lowercase					=    param.view(*__snake_case       )
		return param



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Dict:
		__lowercase					=    config.n_positions
		__lowercase					=    config.n_layer
		__lowercase					=    config.vocab_size
		__lowercase					=    config.n_head
		__lowercase					=    config.n_embd // config.n_head

		__lowercase					=    sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
		__lowercase					=    {
		    'transformer.wte.weight': word_embeddings,
		    'transformer.wpe.weight': sd_megatron['model.language_model.embedding.position_embeddings.weight'],
		    'transformer.ln_f.weight': sd_megatron['model.language_model.encoder.final_layernorm.weight'],
		    'transformer.ln_f.bias': sd_megatron['model.language_model.encoder.final_layernorm.bias'],
		}

		__lowercase					=    'model.language_model.encoder.layers.'
		for i in range(__snake_case       ):
				__lowercase					=    torch.tril(torch.ones((n_positions, n_positions)	,      dtype=torch.bool       )       )
				__lowercase					=    causal_mask.view(1	,      1	,      __snake_case	,      __snake_case       )
				__lowercase					=    causal_mask
				__lowercase					=    torch.tensor(-1E4	,      dtype=torch.bfloataa       )

				__lowercase					=    sd_megatron[F"{pf}{i}.input_layernorm.weight"]
				__lowercase					=    sd_megatron[F"{pf}{i}.input_layernorm.bias"]

				__lowercase					=    sd_megatron[F"{pf}{i}.self_attention.query_key_value.weight"]
				__lowercase					=    fix_query_key_value_ordering(__snake_case	,      3	,      __snake_case	,      __snake_case       )
				__lowercase					=    vala.transpose(0	,      1       ).contiguous()

				__lowercase					=    sd_megatron[F"{pf}{i}.self_attention.query_key_value.bias"]
				__lowercase					=    fix_query_key_value_ordering(__snake_case	,      3	,      __snake_case	,      __snake_case       )
				__lowercase					=    vala

				__lowercase					=    sd_megatron[F"{pf}{i}.self_attention.dense.weight"].transpose(
				    0	,      1       )
				__lowercase					=    sd_megatron[F"{pf}{i}.self_attention.dense.bias"]
				__lowercase					=    sd_megatron[F"{pf}{i}.post_attention_layernorm.weight"]
				__lowercase					=    sd_megatron[F"{pf}{i}.post_attention_layernorm.bias"]
				__lowercase					=    sd_megatron[F"{pf}{i}.mlp.dense_h_to_4h.weight"].transpose(0	,      1       )
				__lowercase					=    sd_megatron[F"{pf}{i}.mlp.dense_h_to_4h.bias"]
				__lowercase					=    sd_megatron[F"{pf}{i}.mlp.dense_4h_to_h.weight"].transpose(
				    0	,      1       )
				__lowercase					=    sd_megatron[F"{pf}{i}.mlp.dense_4h_to_h.bias"]

		# For LM head, transformers' wants the matrix to weight embeddings.
		__lowercase					=    word_embeddings

		return sd_hf



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case       )		->						Tuple:
		__lowercase					=    64_000
		__lowercase					=    config_megatron['encoder_seq_length']
		__lowercase					=    config_megatron['hidden_size']
		__lowercase					=    config_megatron['num_layers']
		__lowercase					=    config_megatron['num_attention_heads']
		__lowercase					=    config_megatron['ffn_hidden_size']
		__lowercase					=    'gelu'
		__lowercase					=    0.1
		__lowercase					=    0.1
		__lowercase					=    0.1
		__lowercase					=    config_megatron['layernorm_epsilon']  # 1e-5
		__lowercase					=    config_megatron['init_method_std']  # 0.02
		__lowercase					=    config_megatron['apply_query_key_layer_scaling']  # True
		__lowercase					=    True
		__lowercase					=    True

		# This identifies the 6.7B (7B) model which uses a different tokenizer
		if config_megatron["hidden_size"] == 4_096:
				__lowercase					=    1  # <|endoftext|>
				__lowercase					=    1  # <|endoftext|>
				__lowercase					=    0  # <unk>
		else:
				__lowercase					=    2  # <s>
				__lowercase					=    3  # <|endoftext|>
				__lowercase					=    0  # <pad>

		return config_hf



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						Union[str, Any]:
		print(__snake_case       )

		__lowercase					=    args.checkpoint_path
		__lowercase					=    args.save_path
		if isfile(__snake_case       ):
				raise FileNotFoundError(F"ERROR! could not find file {checkpoint_path}"       )

		# Load the model.
		__lowercase					=    torch.load(__snake_case	,      map_location='cpu'       )

		# Load the config.
		__lowercase					=    checkpoint['hyper_parameters']['cfg']
		__lowercase					=    GPTaConfig()
		__lowercase					=    copy_config(config_hf=__snake_case	,      config_megatron=__snake_case       )
		__lowercase					=    ['GPT2LMHeadModel']

		__lowercase					=    checkpoint['state_dict']

		# Convert.
		print('Converting'       )
		__lowercase					=    convert_megatron_checkpoint(__snake_case	,      __snake_case       )

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

		__lowercase					=    'GPTSw3Tokenizer'

		# Store the config to file.
		print('Saving config'       )
		config_hf.save_pretrained(__snake_case       )

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


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_      :			Optional[Any]		  =      argparse.ArgumentParser()
		parser.add_argument(
		    '''--checkpoint_path''',
		    type=str,
		    required=True,
		    help='''e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000''',
		)
		parser.add_argument('''--save_path''', type=str, required=True, help='''e.g. /home/user/gpt-sw3/hf''')
		parser.add_argument('''--print-checkpoint-structure''', action='''store_true''')
		SCREAMING_SNAKE_CASE_      :			Any		  =      parser.parse_args()
		main(_args)











import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


SCREAMING_SNAKE_CASE_      :			Any		  =      '''\
@inproceedings{ramshaw-marcus-1995-text,
    title = "Text Chunking using Transformation-Based Learning",
    author = "Ramshaw, Lance  and
      Marcus, Mitch",
    booktitle = "Third Workshop on Very Large Corpora",
    year = "1995",
    url = "https://www.aclweb.org/anthology/W95-0107",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
'''

SCREAMING_SNAKE_CASE_      :			Tuple		  =      '''\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
'''

SCREAMING_SNAKE_CASE_      :			Dict		  =      '''
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode="strict". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        "warn". "warn" acts as 0, but the warning is raised.

Returns:
    \'scores\': dict. Summary of the scores for overall and per type
        Overall:
            \'accuracy\': accuracy,
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure,
        Per type:
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> seqeval = datasets.load_metric("seqeval")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']
    >>> print(results["overall_f1"])
    0.5
    >>> print(results["PER"]["f1"])
    1.0
'''

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







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    Any      )					->   str:

			'''simple docstring'''




			return datasets.MetricInfo(
			    description=_DESCRIPTION				,			citation=_CITATION				,			homepage='https://github.com/chakki-works/seqeval'				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
			        {
			            'predictions': datasets.Sequence(datasets.Value('string'				,			id='label'      )				,			id='sequence'      ),
			            'references': datasets.Sequence(datasets.Value('string'				,			id='label'      )				,			id='sequence'      ),
			        }      )				,			codebase_urls=['https://github.com/chakki-works/seqeval']				,			reference_urls=['https://github.com/chakki-works/seqeval']				,			)






	def       UpperCAmelCase (	self       :    Optional[Any]				,			__lowerCamelCase       :    int				,			__lowerCamelCase       :    str				,			__lowerCamelCase       :    bool = False				,			__lowerCamelCase       :    Optional[str] = None				,			__lowerCamelCase       :    Optional[str] = None				,			__lowerCamelCase       :    Optional[List[int]] = None				,			__lowerCamelCase       :    Union[str, int] = "warn"				,			)					->   Tuple:

			'''simple docstring'''




			if scheme is not None:
					try:
							__lowercase					=    importlib.import_module('seqeval.scheme'      )
							__lowercase					=    getattr(__lowerCamelCase				,			__lowerCamelCase      )
					except AttributeError:
							raise ValueError(F"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"      )
			__lowercase					=    classification_report(
			    y_true=__lowerCamelCase				,			y_pred=__lowerCamelCase				,			suffix=__lowerCamelCase				,			output_dict=__lowerCamelCase				,			scheme=__lowerCamelCase				,			mode=__lowerCamelCase				,			sample_weight=__lowerCamelCase				,			zero_division=__lowerCamelCase				,			)
			report.pop('macro avg'      )
			report.pop('weighted avg'      )
			__lowercase					=    report.pop('micro avg'      )

			__lowercase					=    {
			    type_name: {
			        'precision': score['precision'],
			        'recall': score['recall'],
			        'f1': score['f1-score'],
			        'number': score['support'],
			    }
			    for type_name, score in report.items()
			}
			__lowercase					=    overall_score['precision']
			__lowercase					=    overall_score['recall']
			__lowercase					=    overall_score['f1-score']
			__lowercase					=    accuracy_score(y_true=__lowerCamelCase				,			y_pred=__lowerCamelCase      )

			return scores




