






import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

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

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

def        _lowerCamelCase		(  __A      :							Tuple						,						__A      :							Optional[Any]						,						__A      :							int						,						__A      :							Optional[int]	) ->   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.
    _UpperCAmelCase			: Tuple     					=			param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    _UpperCAmelCase			: Any     					=			(num_heads, num_splits, hidden_size) + input_shape[1:]
    _UpperCAmelCase			: List[str]     					=			param.view(*__A	)
    _UpperCAmelCase			: Tuple     					=			param.transpose(0						,						1	).contiguous()
    _UpperCAmelCase			: Tuple     					=			param.view(*__A	)
    return param

def        _lowerCamelCase		(  __A      :							Optional[int]						,						__A      :							int	) ->   str:
    _UpperCAmelCase			: Union[str, Any]     					=			config.n_positions
    _UpperCAmelCase			: Dict     					=			config.n_layer
    _UpperCAmelCase			: List[str]     					=			config.vocab_size
    _UpperCAmelCase			: str     					=			config.n_head
    _UpperCAmelCase			: Dict     					=			config.n_embd // config.n_head

    _UpperCAmelCase			: int     					=			sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
    _UpperCAmelCase			: List[Any]     					=			{
        '''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'''],
    }

    _UpperCAmelCase			: str     					=			'''model.language_model.encoder.layers.'''
    for i in range(__A	):
        _UpperCAmelCase			: Union[str, Any]     					=			torch.tril(torch.ones((n_positions, n_positions)						,						dtype=torch.bool	)	)
        _UpperCAmelCase			: Any     					=			causal_mask.view(1						,						1						,						__A						,						__A	)
        _UpperCAmelCase			: Tuple     					=			causal_mask
        _UpperCAmelCase			: Union[str, Any]     					=			torch.tensor(-1e4						,						dtype=torch.bfloataa	)

        _UpperCAmelCase			: Union[str, Any]     					=			sd_megatron[f'''{pf}{i}.input_layernorm.weight''']
        _UpperCAmelCase			: List[Any]     					=			sd_megatron[f'''{pf}{i}.input_layernorm.bias''']

        _UpperCAmelCase			: Tuple     					=			sd_megatron[f'''{pf}{i}.self_attention.query_key_value.weight''']
        _UpperCAmelCase			: Optional[Any]     					=			fix_query_key_value_ordering(__A						,						3						,						__A						,						__A	)
        _UpperCAmelCase			: Tuple     					=			vala.transpose(0						,						1	).contiguous()

        _UpperCAmelCase			: Optional[Any]     					=			sd_megatron[f'''{pf}{i}.self_attention.query_key_value.bias''']
        _UpperCAmelCase			: Union[str, Any]     					=			fix_query_key_value_ordering(__A						,						3						,						__A						,						__A	)
        _UpperCAmelCase			: List[str]     					=			vala

        _UpperCAmelCase			: Dict     					=			sd_megatron[f'''{pf}{i}.self_attention.dense.weight'''].transpose(
            0						,						1	)
        _UpperCAmelCase			: Any     					=			sd_megatron[f'''{pf}{i}.self_attention.dense.bias''']
        _UpperCAmelCase			: Dict     					=			sd_megatron[f'''{pf}{i}.post_attention_layernorm.weight''']
        _UpperCAmelCase			: Dict     					=			sd_megatron[f'''{pf}{i}.post_attention_layernorm.bias''']
        _UpperCAmelCase			: Optional[Any]     					=			sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0						,						1	)
        _UpperCAmelCase			: Tuple     					=			sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.bias''']
        _UpperCAmelCase			: List[str]     					=			sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
            0						,						1	)
        _UpperCAmelCase			: Optional[Any]     					=			sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.bias''']

    # For LM head, transformers' wants the matrix to weight embeddings.
    _UpperCAmelCase			: Union[str, Any]     					=			word_embeddings

    return sd_hf

def        _lowerCamelCase		(  __A      :							Dict						,						__A      :							Optional[Any]	) ->   Dict:
    _UpperCAmelCase			: int     					=			64_000
    _UpperCAmelCase			: str     					=			config_megatron['''encoder_seq_length''']
    _UpperCAmelCase			: List[Any]     					=			config_megatron['''hidden_size''']
    _UpperCAmelCase			: List[str]     					=			config_megatron['''num_layers''']
    _UpperCAmelCase			: Tuple     					=			config_megatron['''num_attention_heads''']
    _UpperCAmelCase			: Tuple     					=			config_megatron['''ffn_hidden_size''']
    _UpperCAmelCase			: Optional[int]     					=			'''gelu'''
    _UpperCAmelCase			: Optional[Any]     					=			0.1
    _UpperCAmelCase			: List[Any]     					=			0.1
    _UpperCAmelCase			: Optional[Any]     					=			0.1
    _UpperCAmelCase			: Tuple     					=			config_megatron['''layernorm_epsilon''']  # 1e-5
    _UpperCAmelCase			: Dict     					=			config_megatron['''init_method_std''']  # 0.02
    _UpperCAmelCase			: Tuple     					=			config_megatron['''apply_query_key_layer_scaling''']  # True
    _UpperCAmelCase			: Dict     					=			True
    _UpperCAmelCase			: Union[str, Any]     					=			True

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

    return config_hf

def        _lowerCamelCase		(  __A      :							Union[str, Any]	) ->   Dict:
    print(__A	)

    _UpperCAmelCase			: str     					=			args.checkpoint_path
    _UpperCAmelCase			: Union[str, Any]     					=			args.save_path
    if isfile(__A	):
        raise FileNotFoundError(f'''ERROR! could not find file {checkpoint_path}'''	)

    # Load the model.
    _UpperCAmelCase			: str     					=			torch.load(__A						,						map_location='''cpu'''	)

    # Load the config.
    _UpperCAmelCase			: List[str]     					=			checkpoint['''hyper_parameters''']['''cfg''']
    _UpperCAmelCase			: Optional[int]     					=			GPTaConfig()
    _UpperCAmelCase			: Optional[Any]     					=			copy_config(config_hf=__A						,						config_megatron=__A	)
    _UpperCAmelCase			: Optional[int]     					=			['''GPT2LMHeadModel''']

    _UpperCAmelCase			: List[Any]     					=			checkpoint['''state_dict''']

    # Convert.
    print('''Converting'''	)
    _UpperCAmelCase			: Any     					=			convert_megatron_checkpoint(__A						,						__A	)

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

    _UpperCAmelCase			: Union[str, Any]     					=			'''GPTSw3Tokenizer'''

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

    # Store the state_dict to file.
    _UpperCAmelCase			: Tuple     					=			os.path.join(__A						,						'''pytorch_model.bin'''	)
    print(f'''Saving checkpoint to "{output_checkpoint_file}"'''	)
    torch.save(__A						,						__A	)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE								=  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								=  parser.parse_args()
  main(_args)












import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


SCREAMING_SNAKE_CASE								=  '\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

SCREAMING_SNAKE_CASE								=  '\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n'

SCREAMING_SNAKE_CASE								=  '\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer \'id\' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\n    references: List of question-answers dictionaries with the following key-values:\n            - \'id\': id of the question-answer pair (see above),\n            - \'answers\': a list of Dict {\'text\': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    \'exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'total\': Number of score considered\n    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'HasAns_total\': Number of score considered\n    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'NoAns_total\': Number of score considered\n    \'best_exact\': Best exact match (with varying threshold)\n    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match\n    \'best_f1\': Best F1 (with varying threshold)\n    \'best_f1_thresh\': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_v2_metric = datasets.load_metric("squad_v2")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,				_KWARGS_DESCRIPTION     )
class 							A_      (  datasets.Metric     ):






 '''simple docstring'''




 def 	snake_case__			(		self)	->						List[str]:

     """simple docstring"""



     return datasets.MetricInfo(
         description=_DESCRIPTION							,					citation=_CITATION							,					inputs_description=_KWARGS_DESCRIPTION							,					features=datasets.Features(
             {
                 '''predictions''': {
                     '''id''': datasets.Value('''string'''),
                     '''prediction_text''': datasets.Value('''string'''),
                     '''no_answer_probability''': datasets.Value('''float32'''),
                 },
                 '''references''': {
                     '''id''': datasets.Value('''string'''),
                     '''answers''': datasets.features.Sequence(
                         {'''text''': datasets.Value('''string'''), '''answer_start''': datasets.Value('''int32''')}),
                 },
             })							,					codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']							,					reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']							,					)


 def 	snake_case__			(		self							,					_A							,					_A							,					_A=1.0)	->						List[Any]:

     """simple docstring"""



     _UpperCAmelCase			: str     					=			{p['''id''']: p['''no_answer_probability'''] for p in predictions}
     _UpperCAmelCase			: List[str]     					=			[{'''paragraphs''': [{'''qas''': references}]}]
     _UpperCAmelCase			: List[Any]     					=			{p['''id''']: p['''prediction_text'''] for p in predictions}

     _UpperCAmelCase			: List[str]     					=			make_qid_to_has_ans(_A)  # maps qid to True/False
     _UpperCAmelCase			: str     					=			[k for k, v in qid_to_has_ans.items() if v]
     _UpperCAmelCase			: List[Any]     					=			[k for k, v in qid_to_has_ans.items() if not v]

     _UpperCAmelCase					,	_UpperCAmelCase			: Union[str, Any]     					=			get_raw_scores(_A							,					_A)
     _UpperCAmelCase			: Optional[Any]     					=			apply_no_ans_threshold(_A							,					_A							,					_A							,					_A)
     _UpperCAmelCase			: List[Any]     					=			apply_no_ans_threshold(_A							,					_A							,					_A							,					_A)
     _UpperCAmelCase			: Optional[Any]     					=			make_eval_dict(_A							,					_A)

     if has_ans_qids:
         _UpperCAmelCase			: Tuple     					=			make_eval_dict(_A							,					_A							,					qid_list=_A)
         merge_eval(_A							,					_A							,					'''HasAns''')
     if no_ans_qids:
         _UpperCAmelCase			: Any     					=			make_eval_dict(_A							,					_A							,					qid_list=_A)
         merge_eval(_A							,					_A							,					'''NoAns''')
     find_all_best_thresh(_A							,					_A							,					_A							,					_A							,					_A							,					_A)
     return dict(_A)




