



'''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	(					_snake_case	:    List[Any]  ,_snake_case	:    int  ,_snake_case	:    str=0							):





    '''simple docstring'''


    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 							lowerCamelCase	(					_snake_case	:    Union[str, Any]  ,_snake_case	:    Any  ,_snake_case	:    int  ,_snake_case	:    List[Any]  ,_snake_case	:    List[str]							):





    '''simple docstring'''


    lowercase__									=    param.size()
    if checkpoint_version == 1.0:
        # version 1.0 stores [num_heads * hidden_size * num_splits, :]
        lowercase__									=    (num_heads, hidden_size, num_splits) + input_shape[1:]
        lowercase__									=    param.view(*_snake_case							)
        lowercase__									=    param.transpose(0  ,2							)
        lowercase__									=    param.transpose(1  ,2							).contiguous()
    elif checkpoint_version >= 2.0:
        # 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 							lowerCamelCase	(					_snake_case	:    List[Any]  ,_snake_case	:    Union[str, Any]  ,_snake_case	:    str							):





    '''simple docstring'''


    lowercase__									=    {}

    # old versions did not store training args
    lowercase__									=    input_state_dict.get("args"  ,_snake_case							)
    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))

        lowercase__									=    ds_args.padded_vocab_size
        lowercase__									=    ds_args.max_position_embeddings
        lowercase__									=    ds_args.hidden_size
        lowercase__									=    ds_args.num_layers
        lowercase__									=    ds_args.num_attention_heads
        lowercase__									=    ds_args.ffn_hidden_size
        # pprint(config)

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

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

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

    # The position embeddings.
    lowercase__									=    embeddings["position_embeddings"]["weight"]
    # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
    lowercase__									=    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.
    lowercase__									=    pos_embeddings

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

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

    # The simple map of names for "automated" rules.
    lowercase__									=    {
        "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.
        lowercase__									=    layer_re.match(_snake_case							)

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

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

        # The name of the layer.
        lowercase__									=    f'''transformer.h.{layer_idx}'''

        # For layernorm(s), simply store the layer norm.
        if op_name.endswith("layernorm"							):
            lowercase__									=    "ln_1" if op_name.startswith("input"							) else "ln_2"
            lowercase__									=    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.
            lowercase__									=    torch.tril(torch.ones((n_positions, n_positions)  ,dtype=torch.floataa							)							).view(
                1  ,1  ,_snake_case  ,_snake_case							)
            lowercase__									=    causal_mask

            # Insert a "dummy" tensor for masked_bias.
            lowercase__									=    torch.tensor(-1e4  ,dtype=torch.floataa							)
            lowercase__									=    masked_bias

            lowercase__									=    fix_query_key_value_ordering(_snake_case  ,_snake_case  ,3  ,_snake_case  ,_snake_case							)
            # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
            lowercase__									=    out_val.transpose(0  ,1							).contiguous()
            # Store.
            lowercase__									=    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":
            lowercase__									=    fix_query_key_value_ordering(_snake_case  ,_snake_case  ,3  ,_snake_case  ,_snake_case							)
            # Store. No change of shape.
            lowercase__									=    out_val

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

        # Copy the bias.
        elif weight_or_bias == "bias":
            lowercase__									=    megatron_to_transformers[op_name]
            lowercase__									=    val

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

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

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

    # It should be done!
    return output_state_dict




def 							lowerCamelCase	(					):





    '''simple docstring'''


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

    # Extract the basename.
    lowercase__									=    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:
                lowercase__									=    torch.load(_snake_case  ,map_location="cpu"							)
    else:
        lowercase__									=    torch.load(args.path_to_checkpoint  ,map_location="cpu"							)

    lowercase__									=    input_state_dict.get("args"  ,_snake_case							)

    # 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:
                lowercase__									=    "gelu_fast"
            elif ds_args.openai_gelu:
                lowercase__									=    "gelu_new"
            else:
                lowercase__									=    "gelu"
        else:
            # in the very early days this used to be "gelu_new"
            lowercase__									=    "gelu_new"

        # Spell out all parameters in case the defaults change.
        lowercase__									=    GPTaConfig(
            vocab_size=50_257  ,n_positions=1_024  ,n_embd=1_024  ,n_layer=24  ,n_head=16  ,n_inner=4_096  ,activation_function=_snake_case  ,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=_snake_case  ,summary_activation=_snake_case  ,summary_proj_to_labels=_snake_case  ,summary_first_dropout=0.1  ,scale_attn_weights=_snake_case  ,use_cache=_snake_case  ,bos_token_id=50_256  ,eos_token_id=50_256  ,)
    else:
        lowercase__									=    GPTaConfig.from_json_file(args.config_file							)

    lowercase__									=    ["GPT2LMHeadModel"]

    # Convert.
    print("Converting"							)
    lowercase__									=    convert_megatron_checkpoint(_snake_case  ,_snake_case  ,_snake_case							)

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

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

    lowercase__									=    AutoTokenizer.from_pretrained(_snake_case							)
    lowercase__									=    type(_snake_case							).__name__
    lowercase__									=    tokenizer_class

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

    # Save tokenizer based on args
    print(f'''Adding {tokenizer_class} tokenizer files'''							)
    tokenizer.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__":
 main()

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









'''simple docstring'''





from sklearn.metrics import mean_squared_error

import datasets


SCREAMING_SNAKE_CASE__          =  "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"

SCREAMING_SNAKE_CASE__          =  "\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


SCREAMING_SNAKE_CASE__          =  "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"


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



       def 				_a   (					self   )	->				Optional[int]:
           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_squared_error.html"
               ]  ,)



       def 				_a   (					self   )	->				Tuple:
           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 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_=None  ,UpperCAmelCase_="uniform_average"  ,UpperCAmelCase_=True   )	->				Tuple:
           lowercase__									=    mean_squared_error(
               UpperCAmelCase_  ,UpperCAmelCase_  ,sample_weight=UpperCAmelCase_  ,multioutput=UpperCAmelCase_  ,squared=UpperCAmelCase_   )

           return {"mse": mse}




