



"""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 	snake_case_  (				A_ :		int,    A_ :		Dict,    A_ :		List[str]=0						):


       '''simple docstring'''





       if name is None:
              _lowerCamelCase						:    List[Any]         =			None
       else:
              _lowerCamelCase						:    Dict         =			'''.''' * max(0,    spaces - 2						) + '''# {:''' + str(50 - spaces						) + '''s}'''
              _lowerCamelCase						:    Optional[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 	snake_case_  (				A_ :		Any,    A_ :		Optional[Any],    A_ :		List[str],    A_ :		Any,    A_ :		int						):


       '''simple docstring'''





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






def 	snake_case_  (				A_ :		Optional[Any],    A_ :		str,    A_ :		Optional[int]						):


       '''simple docstring'''





       _lowerCamelCase						:    Any         =			{}

       # old versions did not store training args
       _lowerCamelCase						:    Any         =			input_state_dict.get('''args''',    A_						)
       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))

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

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

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

       # The word embeddings.
       _lowerCamelCase						:    List[Any]         =			embeddings['''word_embeddings''']['''weight''']
       # Truncate the embedding table to vocab_size rows.
       _lowerCamelCase						:    Optional[Any]         =			word_embeddings[: config.vocab_size, :]
       _lowerCamelCase						:    List[Any]         =			word_embeddings

       # The position embeddings.
       _lowerCamelCase						:    Any         =			embeddings['''position_embeddings''']['''weight''']
       # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
       _lowerCamelCase						:    List[str]         =			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.
       _lowerCamelCase						:    int         =			pos_embeddings

       # The transformer.
       _lowerCamelCase						:    int         =			lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

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

       # The simple map of names for "automated" rules.
       _lowerCamelCase						:    List[Any]         =			{
           '''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.
              _lowerCamelCase						:    Optional[int]         =			layer_re.match(A_						)

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

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

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

              # For layernorm(s), simply store the layer norm.
              if op_name.endswith('''layernorm'''						):
                     _lowerCamelCase						:    Optional[int]         =			'''ln_1''' if op_name.startswith('''input'''						) else '''ln_2'''
                     _lowerCamelCase						:    str         =			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.
                     _lowerCamelCase						:    Dict         =			torch.tril(torch.ones((n_positions, n_positions),    dtype=torch.floataa						)						).view(
                         1,    1,    A_,    A_						)
                     _lowerCamelCase						:    List[str]         =			causal_mask

                     # Insert a "dummy" tensor for masked_bias.
                     _lowerCamelCase						:    Dict         =			torch.tensor(-1E4,    dtype=torch.floataa						)
                     _lowerCamelCase						:    str         =			masked_bias

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

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

              # Copy the bias.
              elif weight_or_bias == "bias":
                     _lowerCamelCase						:    Optional[int]         =			megatron_to_transformers[op_name]
                     _lowerCamelCase						:    Any         =			val

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

       # The final layernorm.
       _lowerCamelCase						:    Optional[int]         =			transformer['''final_layernorm.weight''']
       _lowerCamelCase						:    List[str]         =			transformer['''final_layernorm.bias''']

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

       # It should be done!
       return output_state_dict






def 	snake_case_  (				):


       '''simple docstring'''





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

       # Extract the basename.
       _lowerCamelCase						:    Dict         =			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:
                            _lowerCamelCase						:    Optional[int]         =			torch.load(A_,    map_location='''cpu'''						)
       else:
              _lowerCamelCase						:    Dict         =			torch.load(args.path_to_checkpoint,    map_location='''cpu'''						)

       _lowerCamelCase						:    Union[str, Any]         =			input_state_dict.get('''args''',    A_						)

       # 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:
                            _lowerCamelCase						:    List[Any]         =			'''gelu_fast'''
                     elif ds_args.openai_gelu:
                            _lowerCamelCase						:    Union[str, Any]         =			'''gelu_new'''
                     else:
                            _lowerCamelCase						:    List[Any]         =			'''gelu'''
              else:
                     # in the very early days this used to be "gelu_new"
                     _lowerCamelCase						:    Union[str, Any]         =			'''gelu_new'''

              # Spell out all parameters in case the defaults change.
              _lowerCamelCase						:    Optional[int]         =			GPTaConfig(
                  vocab_size=5_02_57,    n_positions=10_24,    n_embd=10_24,    n_layer=24,    n_head=16,    n_inner=40_96,    activation_function=A_,    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=A_,    summary_activation=A_,    summary_proj_to_labels=A_,    summary_first_dropout=0.1,    scale_attn_weights=A_,    use_cache=A_,    bos_token_id=5_02_56,    eos_token_id=5_02_56,    )
       else:
              _lowerCamelCase						:    Any         =			GPTaConfig.from_json_file(args.config_file						)

       _lowerCamelCase						:    List[str]         =			['''GPT2LMHeadModel''']

       # Convert.
       print('''Converting'''						)
       _lowerCamelCase						:    Optional[Any]         =			convert_megatron_checkpoint(A_,    A_,    A_						)

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

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

       _lowerCamelCase						:    Optional[int]         =			AutoTokenizer.from_pretrained(A_						)
       _lowerCamelCase						:    int         =			type(A_						).__name__
       _lowerCamelCase						:    List[str]         =			tokenizer_class

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

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

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


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

if __name__ == "__main__":
   main()

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







"""simple docstring"""






import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def 	snake_case_  (				A_ :		Optional[int]						):


       '''simple docstring'''





       def wrapper(*A_ :		int,    **A_ :		List[str]						):
              _lowerCamelCase						:    Tuple         =			timeit.default_timer()
              _lowerCamelCase						:    Optional[Any]         =			func(*A_,    **A_						)
              _lowerCamelCase						:    Tuple         =			timeit.default_timer() - starttime
              return delta

       _lowerCamelCase						:    Optional[Any]         =			func.__name__

       return wrapper






def 	snake_case_  (				A_ :		dict,    A_ :		List[str]=1_00,    A_ :		Optional[Any]=None						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			[]
       _lowerCamelCase						:    Optional[int]         =			seq_shapes or {}
       for i in range(A_						):
              _lowerCamelCase						:    Any         =			{}
              for col_id, (k, v) in enumerate(features.items()						):
                     if isinstance(A_,    _ArrayXD						):
                            _lowerCamelCase						:    str         =			np.random.rand(*v.shape						).astype(v.dtype						)
                     elif isinstance(A_,    datasets.Value						):
                            if v.dtype == "string":
                                   _lowerCamelCase						:    List[str]         =			'''The small grey turtle was surprisingly fast when challenged.'''
                            else:
                                   _lowerCamelCase						:    int         =			np.random.randint(10,    size=1						).astype(v.dtype						).item()
                     elif isinstance(A_,    datasets.Sequence						):
                            while isinstance(A_,    datasets.Sequence						):
                                   _lowerCamelCase						:    Union[str, Any]         =			v.feature
                            _lowerCamelCase						:    Dict         =			seq_shapes[k]
                            _lowerCamelCase						:    int         =			np.random.rand(*A_						).astype(v.dtype						)
                     _lowerCamelCase						:    Optional[Any]         =			data

              dummy_data.append((i, example)						)

       return dummy_data







def 	snake_case_  (				A_ :		List[str],    A_ :		Optional[int],    A_ :		Optional[int]=1_00,    A_ :		Optional[Any]=None						):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[int]         =			generate_examples(A_,    num_examples=A_,    seq_shapes=A_						)

       with ArrowWriter(features=A_,    path=A_						) as writer:
              for key, record in dummy_data:
                     _lowerCamelCase						:    Optional[Any]         =			features.encode_example(A_						)
                     writer.write(A_						)

              _lowerCamelCase      ,   _lowerCamelCase						:    Dict         =			writer.finalize()

       if not num_final_examples == num_examples:
              raise ValueError(
                  F'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'''						)

       _lowerCamelCase						:    List[Any]         =			datasets.Dataset.from_file(filename=A_,    info=datasets.DatasetInfo(features=A_						)						)

       return dataset


