





'''simple docstring'''

import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def lowerCamelCase						(					__lowerCamelCase			:   Union[str, Any] ,	__lowerCamelCase			:   Any ,	__lowerCamelCase			:   Any=0   )						->Tuple:







    if name is None:
        _SCREAMING_SNAKE_CASE     =      None
    else:
        _SCREAMING_SNAKE_CASE     =      """.""" * max(0 ,	spaces - 2   ) + """# {:""" + str(50 - spaces   ) + """s}"""
        _SCREAMING_SNAKE_CASE     =      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						(					__lowerCamelCase			:   List[str] ,	__lowerCamelCase			:   Tuple ,	__lowerCamelCase			:   str ,	__lowerCamelCase			:   List[str]   )						->Union[str, Any]:
    _SCREAMING_SNAKE_CASE     =      param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    _SCREAMING_SNAKE_CASE     =      (num_heads, num_splits, hidden_size) + input_shape[1:]
    _SCREAMING_SNAKE_CASE     =      param.view(*__UpperCamelCase   )
    _SCREAMING_SNAKE_CASE     =      param.transpose(0 ,	1   ).contiguous()
    _SCREAMING_SNAKE_CASE     =      param.view(*__UpperCamelCase   )
    return param





def lowerCamelCase						(					__lowerCamelCase			:   int ,	__lowerCamelCase			:   int   )						->Dict:
    _SCREAMING_SNAKE_CASE     =      config.n_positions
    _SCREAMING_SNAKE_CASE     =      config.n_layer
    _SCREAMING_SNAKE_CASE     =      config.vocab_size
    _SCREAMING_SNAKE_CASE     =      config.n_head
    _SCREAMING_SNAKE_CASE     =      config.n_embd // config.n_head

    _SCREAMING_SNAKE_CASE     =      sd_megatron["""model.language_model.embedding.word_embeddings.weight"""][:vocab_size, :]
    _SCREAMING_SNAKE_CASE     =      {
        """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"""],
    }

    _SCREAMING_SNAKE_CASE     =      """model.language_model.encoder.layers."""
    for i in range(__UpperCamelCase   ):
        _SCREAMING_SNAKE_CASE     =      torch.tril(torch.ones((n_positions, n_positions) ,	dtype=torch.bool   )   )
        _SCREAMING_SNAKE_CASE     =      causal_mask.view(1 ,	1 ,	__UpperCamelCase ,	__UpperCamelCase   )
        _SCREAMING_SNAKE_CASE     =      causal_mask
        _SCREAMING_SNAKE_CASE     =      torch.tensor(-1e4 ,	dtype=torch.bfloataa   )

        _SCREAMING_SNAKE_CASE     =      sd_megatron[F'{pf}{i}.input_layernorm.weight']
        _SCREAMING_SNAKE_CASE     =      sd_megatron[F'{pf}{i}.input_layernorm.bias']

        _SCREAMING_SNAKE_CASE     =      sd_megatron[F'{pf}{i}.self_attention.query_key_value.weight']
        _SCREAMING_SNAKE_CASE     =      fix_query_key_value_ordering(__UpperCamelCase ,	3 ,	__UpperCamelCase ,	__UpperCamelCase   )
        _SCREAMING_SNAKE_CASE     =      vala.transpose(0 ,	1   ).contiguous()

        _SCREAMING_SNAKE_CASE     =      sd_megatron[F'{pf}{i}.self_attention.query_key_value.bias']
        _SCREAMING_SNAKE_CASE     =      fix_query_key_value_ordering(__UpperCamelCase ,	3 ,	__UpperCamelCase ,	__UpperCamelCase   )
        _SCREAMING_SNAKE_CASE     =      vala

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

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

    return sd_hf





def lowerCamelCase						(					__lowerCamelCase			:   List[Any] ,	__lowerCamelCase			:   int   )						->List[Any]:
    _SCREAMING_SNAKE_CASE     =      6_4000
    _SCREAMING_SNAKE_CASE     =      config_megatron["""encoder_seq_length"""]
    _SCREAMING_SNAKE_CASE     =      config_megatron["""hidden_size"""]
    _SCREAMING_SNAKE_CASE     =      config_megatron["""num_layers"""]
    _SCREAMING_SNAKE_CASE     =      config_megatron["""num_attention_heads"""]
    _SCREAMING_SNAKE_CASE     =      config_megatron["""ffn_hidden_size"""]
    _SCREAMING_SNAKE_CASE     =      """gelu"""
    _SCREAMING_SNAKE_CASE     =      0.1
    _SCREAMING_SNAKE_CASE     =      0.1
    _SCREAMING_SNAKE_CASE     =      0.1
    _SCREAMING_SNAKE_CASE     =      config_megatron["""layernorm_epsilon"""]  # 1e-5
    _SCREAMING_SNAKE_CASE     =      config_megatron["""init_method_std"""]  # 0.02
    _SCREAMING_SNAKE_CASE     =      config_megatron["""apply_query_key_layer_scaling"""]  # True
    _SCREAMING_SNAKE_CASE     =      True
    _SCREAMING_SNAKE_CASE     =      True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 4096:
        _SCREAMING_SNAKE_CASE     =      1  # <|endoftext|>
        _SCREAMING_SNAKE_CASE     =      1  # <|endoftext|>
        _SCREAMING_SNAKE_CASE     =      0  # <unk>
    else:
        _SCREAMING_SNAKE_CASE     =      2  # <s>
        _SCREAMING_SNAKE_CASE     =      3  # <|endoftext|>
        _SCREAMING_SNAKE_CASE     =      0  # <pad>

    return config_hf





def lowerCamelCase						(					__lowerCamelCase			:   Tuple   )						->Optional[Any]:
    print(__UpperCamelCase   )

    _SCREAMING_SNAKE_CASE     =      args.checkpoint_path
    _SCREAMING_SNAKE_CASE     =      args.save_path
    if isfile(__UpperCamelCase   ):
        raise FileNotFoundError(F'ERROR! could not find file {checkpoint_path}'   )

    # Load the model.
    _SCREAMING_SNAKE_CASE     =      torch.load(__UpperCamelCase ,	map_location="""cpu"""   )

    # Load the config.
    _SCREAMING_SNAKE_CASE     =      checkpoint["""hyper_parameters"""]["""cfg"""]
    _SCREAMING_SNAKE_CASE     =      GPTaConfig()
    _SCREAMING_SNAKE_CASE     =      copy_config(config_hf=__UpperCamelCase ,	config_megatron=__UpperCamelCase   )
    _SCREAMING_SNAKE_CASE     =      ["""GPT2LMHeadModel"""]

    _SCREAMING_SNAKE_CASE     =      checkpoint["""state_dict"""]

    # Convert.
    print("""Converting"""   )
    _SCREAMING_SNAKE_CASE     =      convert_megatron_checkpoint(__UpperCamelCase ,	__UpperCamelCase   )

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

    _SCREAMING_SNAKE_CASE     =      """GPTSw3Tokenizer"""

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

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


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










'''simple docstring'''

import sacrebleu as scb
from packaging import version

import datasets


lowercase_         =			"""\
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

lowercase_         =			"""\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
"""

lowercase_         =			"""
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).
    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""


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

    '''simple docstring'''


    def       snake_case_(		self	)		->       Union[str, Any]:
        if version.parse(scb.__version__	) < version.parse("""1.4.12"""	):
            raise ImportWarning(
                """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
                """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""	)
        return datasets.MetricInfo(
            description=_DESCRIPTION			,		citation=_CITATION			,		homepage="""https://github.com/mjpost/sacreBLEU"""			,		inputs_description=_KWARGS_DESCRIPTION			,		features=datasets.Features(
                {
                    """predictions""": datasets.Value("""string"""			,		id="""sequence"""	),
                    """references""": datasets.Sequence(datasets.Value("""string"""			,		id="""sequence"""	)			,		id="""references"""	),
                }	)			,		codebase_urls=["""https://github.com/mjpost/sacreBLEU"""]			,		reference_urls=[
                """https://github.com/mjpost/sacreBLEU""",
                """https://en.wikipedia.org/wiki/BLEU""",
                """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
            ]			,		)





    def       snake_case_(		self			,		A			,		A			,		A="exp"			,		A=None			,		A=False			,		A=False			,		A=None			,		A=False			,		)		->       Optional[Any]:
        _SCREAMING_SNAKE_CASE     =      len(references[0]	)
        if any(len(A	) != references_per_prediction for refs in references	):
            raise ValueError("""Sacrebleu requires the same number of references for each prediction"""	)
        _SCREAMING_SNAKE_CASE     =      [[refs[i] for refs in references] for i in range(A	)]
        _SCREAMING_SNAKE_CASE     =      scb.corpus_bleu(
            A			,		A			,		smooth_method=A			,		smooth_value=A			,		force=A			,		lowercase=A			,		use_effective_order=A			,		**({"""tokenize""": tokenize} if tokenize else {})			,		)
        _SCREAMING_SNAKE_CASE     =      {
            """score""": output.score,
            """counts""": output.counts,
            """totals""": output.totals,
            """precisions""": output.precisions,
            """bp""": output.bp,
            """sys_len""": output.sys_len,
            """ref_len""": output.ref_len,
        }
        return output_dict



