



"""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      __magic_name__				(							lowercase							,			lowercase							,			lowercase=0 ):
   # Format the message.
   if name is None:
      SCREAMING_SNAKE_CASE_: Any     			=None
   else:
      SCREAMING_SNAKE_CASE_: str     			=""".""" * max(0							,			spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
      SCREAMING_SNAKE_CASE_: Optional[int]     			=fmt.format(lowercase )

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





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





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   # The converted output model.
   SCREAMING_SNAKE_CASE_: int     			={}

   # old versions did not store training args
   SCREAMING_SNAKE_CASE_: Tuple     			=input_state_dict.get("""args"""							,			lowercase )
   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))

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

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

   # The model.
   SCREAMING_SNAKE_CASE_: Tuple     			=input_state_dict["""model"""]
   # The language model.
   SCREAMING_SNAKE_CASE_: List[str]     			=model["""language_model"""]
   # The embeddings.
   SCREAMING_SNAKE_CASE_: Optional[int]     			=lm["""embedding"""]

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

   # The position embeddings.
   SCREAMING_SNAKE_CASE_: Dict     			=embeddings["""position_embeddings"""]["""weight"""]
   # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=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.
   SCREAMING_SNAKE_CASE_: Dict     			=pos_embeddings

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

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

   # The simple map of names for "automated" rules.
   SCREAMING_SNAKE_CASE_: List[str]     			={
       """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.
      SCREAMING_SNAKE_CASE_: List[Any]     			=layer_re.match(lowercase )

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

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

      # The name of the layer.
      SCREAMING_SNAKE_CASE_: str     			=f'''transformer.h.{layer_idx}'''

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

         # Insert a "dummy" tensor for masked_bias.
         SCREAMING_SNAKE_CASE_: int     			=torch.tensor(-1e4							,			dtype=torch.floataa )
         SCREAMING_SNAKE_CASE_: Tuple     			=masked_bias

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

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

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

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

   # The final layernorm.
   SCREAMING_SNAKE_CASE_: str     			=transformer["""final_layernorm.weight"""]
   SCREAMING_SNAKE_CASE_: List[str]     			=transformer["""final_layernorm.bias"""]

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

   # It should be done!
   return output_state_dict





def      __magic_name__				(							):
   # Create the argument parser.
   SCREAMING_SNAKE_CASE_: Dict     			=argparse.ArgumentParser()
   parser.add_argument("""--print-checkpoint-structure"""							,			action="""store_true""" )
   parser.add_argument(
       """path_to_checkpoint"""							,			type=lowercase							,			help="""Path to the checkpoint file (.zip archive or direct .pt file)"""							,			)
   parser.add_argument(
       """--config_file"""							,			default=""""""							,			type=lowercase							,			help="""An optional config json file describing the pre-trained model."""							,			)
   SCREAMING_SNAKE_CASE_: Optional[int]     			=parser.parse_args()

   # Extract the basename.
   SCREAMING_SNAKE_CASE_: str     			=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:
            SCREAMING_SNAKE_CASE_: Union[str, Any]     			=torch.load(lowercase							,			map_location="""cpu""" )
   else:
      SCREAMING_SNAKE_CASE_: int     			=torch.load(args.path_to_checkpoint							,			map_location="""cpu""" )

   SCREAMING_SNAKE_CASE_: int     			=input_state_dict.get("""args"""							,			lowercase )

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

      # Spell out all parameters in case the defaults change.
      SCREAMING_SNAKE_CASE_: Any     			=GPTaConfig(
          vocab_size=5_0257							,			n_positions=1024							,			n_embd=1024							,			n_layer=24							,			n_head=16							,			n_inner=4096							,			activation_function=lowercase							,			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=lowercase							,			summary_activation=lowercase							,			summary_proj_to_labels=lowercase							,			summary_first_dropout=0.1							,			scale_attn_weights=lowercase							,			use_cache=lowercase							,			bos_token_id=5_0256							,			eos_token_id=5_0256							,			)
   else:
      SCREAMING_SNAKE_CASE_: List[str]     			=GPTaConfig.from_json_file(args.config_file )

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=["""GPT2LMHeadModel"""]

   # Convert.
   print("""Converting""" )
   SCREAMING_SNAKE_CASE_: Optional[int]     			=convert_megatron_checkpoint(lowercase							,			lowercase							,			lowercase )

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

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

   SCREAMING_SNAKE_CASE_: Tuple     			=AutoTokenizer.from_pretrained(lowercase )
   SCREAMING_SNAKE_CASE_: str     			=type(lowercase ).__name__
   SCREAMING_SNAKE_CASE_: List[str]     			=tokenizer_class

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

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

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


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

if __name__ == "__main__":
   main()

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








"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_UpperCAmelCase     =			{
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """XLMForMultipleChoice""",
       """XLMForQuestionAnswering""",
       """XLMForQuestionAnsweringSimple""",
       """XLMForSequenceClassification""",
       """XLMForTokenClassification""",
       """XLMModel""",
       """XLMPreTrainedModel""",
       """XLMWithLMHeadModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFXLMForMultipleChoice""",
       """TFXLMForQuestionAnsweringSimple""",
       """TFXLMForSequenceClassification""",
       """TFXLMForTokenClassification""",
       """TFXLMMainLayer""",
       """TFXLMModel""",
       """TFXLMPreTrainedModel""",
       """TFXLMWithLMHeadModel""",
   ]


if TYPE_CHECKING:
   from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
   from .tokenization_xlm import XLMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm import (
          XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMPreTrainedModel,
          XLMWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlm import (
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMForMultipleChoice,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForSequenceClassification,
          TFXLMForTokenClassification,
          TFXLMMainLayer,
          TFXLMModel,
          TFXLMPreTrainedModel,
          TFXLMWithLMHeadModel,
      )

else:
   import sys

   _UpperCAmelCase     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



