






"""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_bert/convert_megatron_bert_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 MegatronBertConfig


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str       , snake_case    :      Optional[Any]       , snake_case    :      Optional[Any]=0							)->						Union[str, Any]:






     '''simple docstring'''



     if name is None:
          UpperCAmelCase__		:  Optional[Any]				=				None
     else:
          UpperCAmelCase__		:  Union[str, Any]				=				"." * max(0       , spaces - 2							) + "# {:" + str(50 - spaces							) + "s}"
          UpperCAmelCase__		:  List[Any]				=				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      SCREAMING_SNAKE_CASE__    (   snake_case    :      Optional[int]       , snake_case    :      Optional[int]       , snake_case    :      Union[str, Any]       , snake_case    :      List[str]       , snake_case    :      int							)->						Optional[int]:






     '''simple docstring'''



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


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Optional[int]       , snake_case    :      Any       , snake_case    :      Union[str, Any]							)->						Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__		:  List[str]				=				{}

     # old versions did not store training args
     UpperCAmelCase__		:  Tuple				=				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))

          UpperCAmelCase__		:  Union[str, Any]				=				ds_args.tokenizer_type
          UpperCAmelCase__		:  str				=				ds_args.padded_vocab_size
          UpperCAmelCase__		:  Any				=				ds_args.max_position_embeddings
          UpperCAmelCase__		:  str				=				ds_args.hidden_size
          UpperCAmelCase__		:  List[str]				=				ds_args.num_layers
          UpperCAmelCase__		:  Optional[Any]				=				ds_args.num_attention_heads
          UpperCAmelCase__		:  Optional[Any]				=				ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
          # pprint(config)

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

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

     # The word embeddings.
     UpperCAmelCase__		:  List[str]				=				embeddings["word_embeddings"]["weight"]
     # Truncate the embedding table to vocab_size rows.
     UpperCAmelCase__		:  Union[str, Any]				=				word_embeddings[: config.vocab_size, :]
     # Store the word embeddings.
     UpperCAmelCase__		:  Dict				=				word_embeddings

     # The position embeddings.
     UpperCAmelCase__		:  Optional[int]				=				embeddings["position_embeddings"]["weight"]
     assert pos_embeddings.size(0							) == config.max_position_embeddings and pos_embeddings.size(1							) == config.hidden_size
     # Store the position embeddings.
     UpperCAmelCase__		:  int				=				pos_embeddings

     # The token-type embeddings.
     UpperCAmelCase__		:  Dict				=				embeddings["tokentype_embeddings"]["weight"]
     # Store the position embeddings.
     UpperCAmelCase__		:  Optional[Any]				=				tokentype_embeddings

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

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

     # The simple map of names for "automated" rules.
     UpperCAmelCase__		:  str				=				{
         "attention.dense": ".attention.output.dense.",
         "self_attention.dense": ".attention.output.dense.",
         "mlp.dense_h_to_4h": ".intermediate.dense.",
         "mlp.dense_4h_to_h": ".output.dense.",
     }

     # Keep track of the attention/query/value tensor.
     UpperCAmelCase__		:  Optional[Any]				=				None

     # Extract the layers.
     for key, val in transformer.items():
          # Match the name.
          UpperCAmelCase__		:  int				=				layer_re.match(snake_case							)

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

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

          # The name of the layer.
          UpperCAmelCase__		:  Union[str, Any]				=				f'bert.encoder.layer.{layer_idx}'

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith("layernorm"							):
               UpperCAmelCase__		:  int				=				"attention.ln" if op_name.startswith("input"							) else "ln"
               UpperCAmelCase__		:  List[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":
               # Make sure the QKV pointer is nil.
               assert attention_qkv_weight is None, ""

               UpperCAmelCase__		:  Union[str, Any]				=				fix_query_key_value_ordering(snake_case       , snake_case       , 3       , snake_case       , snake_case							)
               # Store the tensor as we need the bias as well to interleave QKV and biases.
               UpperCAmelCase__		:  Tuple				=				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":
               # Make sure we read the weight tensor.
               assert attention_qkv_weight is not None, ""

               # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
               UpperCAmelCase__		:  Union[str, Any]				=				attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
               UpperCAmelCase__		:  List[Any]				=				attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
               UpperCAmelCase__		:  Optional[Any]				=				attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

               UpperCAmelCase__		:  Dict				=				fix_query_key_value_ordering(snake_case       , snake_case       , 3       , snake_case       , snake_case							)
               # Split the bias.
               UpperCAmelCase__		:  Optional[Any]				=				out_val[0 * config.hidden_size : 1 * config.hidden_size]
               UpperCAmelCase__		:  Union[str, Any]				=				out_val[1 * config.hidden_size : 2 * config.hidden_size]
               UpperCAmelCase__		:  Optional[int]				=				out_val[2 * config.hidden_size : 3 * config.hidden_size]

               # Store.
               UpperCAmelCase__		:  Dict				=				q
               UpperCAmelCase__		:  Optional[Any]				=				q_bias
               UpperCAmelCase__		:  List[str]				=				k
               UpperCAmelCase__		:  Optional[Any]				=				k_bias
               UpperCAmelCase__		:  Union[str, Any]				=				v
               UpperCAmelCase__		:  Optional[Any]				=				v_bias

               # Clear the stored tensor.
               UpperCAmelCase__		:  str				=				None

          # Copy weights and biases as is.
          elif weight_or_bias in ["weight", "bias"]:
               UpperCAmelCase__		:  Union[str, Any]				=				megatron_to_transformers[op_name]
               UpperCAmelCase__		:  Union[str, Any]				=				val

    # The final layernorm.
     UpperCAmelCase__		:  Optional[int]				=				transformer["final_layernorm.weight"]
     UpperCAmelCase__		:  str				=				transformer["final_layernorm.bias"]

     # The pooler.
     UpperCAmelCase__		:  str				=				lm["pooler"]

     # Store the matrix and the bias.
     UpperCAmelCase__		:  Dict				=				pooler["dense.weight"]
     UpperCAmelCase__		:  Tuple				=				pooler["dense.bias"]

     # The LM head from Megatron (for RACE).
     UpperCAmelCase__		:  List[Any]				=				model["lm_head"]

     # The transform matrix.
     UpperCAmelCase__		:  List[str]				=				lm_head["dense.weight"]
     UpperCAmelCase__		:  str				=				lm_head["dense.bias"]

     # The transform LN.
     UpperCAmelCase__		:  Dict				=				lm_head["layernorm.weight"]
     UpperCAmelCase__		:  List[str]				=				lm_head["layernorm.bias"]

     # For the decoder, we replicate the weights.
     UpperCAmelCase__		:  Dict				=				word_embeddings
     UpperCAmelCase__		:  Union[str, Any]				=				lm_head["bias"]

     # The classifier from Megatron (for MLNI).
     UpperCAmelCase__		:  Dict				=				model["binary_head"]

     # Store the classifier.
     UpperCAmelCase__		:  Tuple				=				binary_head["weight"]
     UpperCAmelCase__		:  Tuple				=				binary_head["bias"]

     # It should be done!
     return output_state_dict


def      SCREAMING_SNAKE_CASE__    (   )->						Union[str, Any]:






     '''simple docstring'''



     UpperCAmelCase__		:  Dict				=				argparse.ArgumentParser()
     parser.add_argument("--print-checkpoint-structure"       , action="store_true"							)
     parser.add_argument("path_to_checkpoint"       , type=snake_case       , help="Path to the ZIP file containing the checkpoint"							)
     parser.add_argument(
         "--config_file"       , default=""       , type=snake_case       , help="An optional config json file describing the pre-trained model."       , )
     UpperCAmelCase__		:  List[Any]				=				parser.parse_args()

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

     if args.config_file == "":
          # Default config of megatron-bert 345m
          UpperCAmelCase__		:  Any				=				MegatronBertConfig()

          # different megatron-bert-*-345m models have different vocab sizes, so override the default
          # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
          UpperCAmelCase__		:  str				=				input_state_dict["model"]["lm_head"]["bias"].numel()
     else:
          UpperCAmelCase__		:  str				=				MegatronBertConfig.from_json_file(args.config_file							)

     # Convert.
     print("Converting"							)
     UpperCAmelCase__		:  Tuple				=				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							)

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

     # Store the state_dict to file.
     UpperCAmelCase__		:  Optional[Any]				=				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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase				:    List[Any]     =						{
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    Any     =						['OpenAIGPTTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    int     =						[
       'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'OpenAIGPTDoubleHeadsModel',
       'OpenAIGPTForSequenceClassification',
       'OpenAIGPTLMHeadModel',
       'OpenAIGPTModel',
       'OpenAIGPTPreTrainedModel',
       'load_tf_weights_in_openai_gpt',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    List[str]     =						[
       'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFOpenAIGPTDoubleHeadsModel',
       'TFOpenAIGPTForSequenceClassification',
       'TFOpenAIGPTLMHeadModel',
       'TFOpenAIGPTMainLayer',
       'TFOpenAIGPTModel',
       'TFOpenAIGPTPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
   from .tokenization_openai import OpenAIGPTTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_openai_fast import OpenAIGPTTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_openai import (
          OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OpenAIGPTDoubleHeadsModel,
          OpenAIGPTForSequenceClassification,
          OpenAIGPTLMHeadModel,
          OpenAIGPTModel,
          OpenAIGPTPreTrainedModel,
          load_tf_weights_in_openai_gpt,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_openai import (
          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFOpenAIGPTDoubleHeadsModel,
          TFOpenAIGPTForSequenceClassification,
          TFOpenAIGPTLMHeadModel,
          TFOpenAIGPTMainLayer,
          TFOpenAIGPTModel,
          TFOpenAIGPTPreTrainedModel,
      )

else:
   import sys

   _UpperCamelCase				:    Union[str, Any]     =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




