



'''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_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_=0		)->		List[str]:





       '''simple docstring'''


       if name is None:
              _UpperCAmelCase  :						List[str]			     = None
       else:
              _UpperCAmelCase  :						Union[str, Any]			     = """.""" * max(0       ,   spaces - 2		) + """# {:""" + str(50 - spaces		) + """s}"""
              _UpperCAmelCase  :						Union[str, Any]			     = fmt.format(lowerCAmelCase_		)

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




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		str:





       '''simple docstring'''


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




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		List[str]:





       '''simple docstring'''


       _UpperCAmelCase  :						str			     = {}

       # old versions did not store training args
       _UpperCAmelCase  :						str			     = input_state_dict.get("""args"""       ,   lowerCAmelCase_		)
       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))

              _UpperCAmelCase  :						Any			     = ds_args.padded_vocab_size
              _UpperCAmelCase  :						List[Any]			     = ds_args.max_position_embeddings
              _UpperCAmelCase  :						List[Any]			     = ds_args.hidden_size
              _UpperCAmelCase  :						Union[str, Any]			     = ds_args.num_layers
              _UpperCAmelCase  :						Dict			     = ds_args.num_attention_heads
              _UpperCAmelCase  :						Any			     = ds_args.ffn_hidden_size
              # pprint(config)

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

       # The model.
       _UpperCAmelCase  :						List[Any]			     = input_state_dict["""model"""]
       # The language model.
       _UpperCAmelCase  :						Tuple			     = model["""language_model"""]
       # The embeddings.
       _UpperCAmelCase  :						List[Any]			     = lm["""embedding"""]

       # The word embeddings.
       _UpperCAmelCase  :						Optional[int]			     = embeddings["""word_embeddings"""]["""weight"""]
       # Truncate the embedding table to vocab_size rows.
       _UpperCAmelCase  :						Union[str, Any]			     = word_embeddings[: config.vocab_size, :]
       _UpperCAmelCase  :						str			     = word_embeddings

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

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

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

       # The simple map of names for "automated" rules.
       _UpperCAmelCase  :						Dict			     = {
           """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.
              _UpperCAmelCase  :						int			     = layer_re.match(lowerCAmelCase_		)

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

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

              # The name of the layer.
              _UpperCAmelCase  :						List[Any]			     = F'''transformer.h.{layer_idx}'''

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

                     # Insert a "dummy" tensor for masked_bias.
                     _UpperCAmelCase  :						Any			     = torch.tensor(-1e4       ,   dtype=torch.floataa		)
                     _UpperCAmelCase  :						str			     = masked_bias

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

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

              # Copy the bias.
              elif weight_or_bias == "bias":
                     _UpperCAmelCase  :						Any			     = megatron_to_transformers[op_name]
                     _UpperCAmelCase  :						List[Any]			     = val

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

       # The final layernorm.
       _UpperCAmelCase  :						List[str]			     = transformer["""final_layernorm.weight"""]
       _UpperCAmelCase  :						Union[str, Any]			     = transformer["""final_layernorm.bias"""]

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

       # It should be done!
       return output_state_dict




def     snake_case_					(     )->		int:





       '''simple docstring'''


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

       # Extract the basename.
       _UpperCAmelCase  :						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:
                            _UpperCAmelCase  :						List[Any]			     = torch.load(lowerCAmelCase_       ,   map_location="""cpu"""		)
       else:
              _UpperCAmelCase  :						str			     = torch.load(args.path_to_checkpoint       ,   map_location="""cpu"""		)

       _UpperCAmelCase  :						List[str]			     = input_state_dict.get("""args"""       ,   lowerCAmelCase_		)

       # 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:
                            _UpperCAmelCase  :						int			     = """gelu_fast"""
                     elif ds_args.openai_gelu:
                            _UpperCAmelCase  :						Optional[int]			     = """gelu_new"""
                     else:
                            _UpperCAmelCase  :						Dict			     = """gelu"""
              else:
                     # in the very early days this used to be "gelu_new"
                     _UpperCAmelCase  :						str			     = """gelu_new"""

              # Spell out all parameters in case the defaults change.
              _UpperCAmelCase  :						Any			     = GPTaConfig(
                  vocab_size=50257       ,   n_positions=1024       ,   n_embd=1024       ,   n_layer=24       ,   n_head=16       ,   n_inner=4096       ,   activation_function=lowerCAmelCase_       ,   resid_pdrop=0.1       ,   embd_pdrop=0.1       ,   attn_pdrop=0.1       ,   layer_norm_epsilon=1e-5       ,   initializer_range=0.0_2       ,   summary_type="""cls_index"""       ,   summary_use_proj=lowerCAmelCase_       ,   summary_activation=lowerCAmelCase_       ,   summary_proj_to_labels=lowerCAmelCase_       ,   summary_first_dropout=0.1       ,   scale_attn_weights=lowerCAmelCase_       ,   use_cache=lowerCAmelCase_       ,   bos_token_id=50256       ,   eos_token_id=50256       ,   )
       else:
              _UpperCAmelCase  :						List[str]			     = GPTaConfig.from_json_file(args.config_file		)

       _UpperCAmelCase  :						Optional[int]			     = ["""GPT2LMHeadModel"""]

       # Convert.
       print("""Converting"""		)
       _UpperCAmelCase  :						Union[str, Any]			     = convert_megatron_checkpoint(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)

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

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

       _UpperCAmelCase  :						Optional[Any]			     = AutoTokenizer.from_pretrained(lowerCAmelCase_		)
       _UpperCAmelCase  :						Dict			     = type(lowerCAmelCase_		).__name__
       _UpperCAmelCase  :						List[str]			     = tokenizer_class

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

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

       # Store the state_dict to file.
       _UpperCAmelCase  :						List[str]			     = os.path.join(lowerCAmelCase_       ,   """pytorch_model.bin"""		)
       print(F'''Saving checkpoint to "{output_checkpoint_file}"'''		)
       torch.save(lowerCAmelCase_       ,   lowerCAmelCase_		)


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

if __name__ == "__main__":
   main()

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








'''simple docstring'''






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
   from ...onnx.config import PatchingSpec
   from ...tokenization_utils_base import PreTrainedTokenizerBase


A_							: Any						 =	logging.get_logger(__name__)

A_							: Optional[int]						 =	{
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  """longformer"""







 def __init__(							self  ,a_ = 512  ,a_ = 2  ,a_ = 1  ,a_ = 0  ,a_ = 2  ,a_ = 30_522  ,a_ = 768  ,a_ = 12  ,a_ = 12  ,a_ = 3_072  ,a_ = "gelu"  ,a_ = 0.1  ,a_ = 0.1  ,a_ = 512  ,a_ = 2  ,a_ = 0.02  ,a_ = 1E-1_2  ,a_ = False  ,**a_  ,)		->   List[Any]:
        super().__init__(pad_token_id=a_  ,**a_							)

        _UpperCAmelCase  :						List[Any]			     = attention_window
        _UpperCAmelCase  :						Any			     = sep_token_id
        _UpperCAmelCase  :						Dict			     = bos_token_id
        _UpperCAmelCase  :						Tuple			     = eos_token_id
        _UpperCAmelCase  :						Tuple			     = vocab_size
        _UpperCAmelCase  :						Optional[Any]			     = hidden_size
        _UpperCAmelCase  :						Optional[int]			     = num_hidden_layers
        _UpperCAmelCase  :						Union[str, Any]			     = num_attention_heads
        _UpperCAmelCase  :						Optional[int]			     = hidden_act
        _UpperCAmelCase  :						str			     = intermediate_size
        _UpperCAmelCase  :						List[Any]			     = hidden_dropout_prob
        _UpperCAmelCase  :						Union[str, Any]			     = attention_probs_dropout_prob
        _UpperCAmelCase  :						List[str]			     = max_position_embeddings
        _UpperCAmelCase  :						Optional[int]			     = type_vocab_size
        _UpperCAmelCase  :						Any			     = initializer_range
        _UpperCAmelCase  :						Optional[int]			     = layer_norm_eps
        _UpperCAmelCase  :						Union[str, Any]			     = onnx_export







class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_ = "default"  ,a_ = None							)		->   int:
        super().__init__(a_  ,a_  ,a_							)
        _UpperCAmelCase  :						Tuple			     = True







 @property
 def _snake_case (							self							)		->   Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
               _UpperCAmelCase  :						Optional[int]			     = {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
               _UpperCAmelCase  :						Tuple			     = {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""global_attention_mask""", dynamic_axis),
            ]							)







 @property
 def _snake_case (							self							)		->   Mapping[str, Mapping[int, str]]:
        _UpperCAmelCase  :						str			     = super().outputs
        if self.task == "default":
               _UpperCAmelCase  :						int			     = {0: """batch"""}
        return outputs







 @property
 def _snake_case (							self							)		->   float:
        return 1E-4







 @property
 def _snake_case (							self							)		->   int:
        # needs to be >= 14 to support tril operator
        return max(super().default_onnx_opset  ,14							)







 def _snake_case (							self  ,a_  ,a_ = -1  ,a_ = -1  ,a_ = False  ,a_ = None  ,)		->   Mapping[str, Any]:
        _UpperCAmelCase  :						List[str]			     = super().generate_dummy_inputs(
            preprocessor=a_  ,batch_size=a_  ,seq_length=a_  ,is_pair=a_  ,framework=a_							)
        import torch

        # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
        # makes the export fail randomly
        _UpperCAmelCase  :						int			     = torch.zeros_like(inputs["""input_ids"""]							)
        # make every second token global
        _UpperCAmelCase  :						List[str]			     = 1

        return inputs



