



'''simple docstring'''






import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)
def 				lowerCamelCase__	(						_A    ):
  a						: Optional[Any]					       =					VideoMAEConfig()

  set_architecture_configs(_A			,      _A    )

  if "finetuned" not in model_name:
    a						: Union[str, Any]					       =					False

  if "finetuned" in model_name:
    a						: Any					       =					'huggingface/label-files'
    if "kinetics" in model_name:
      a						: Dict					       =					400
      a						: Dict					       =					'kinetics400-id2label.json'
    elif "ssv2" in model_name:
      a						: Any					       =					174
      a						: Union[str, Any]					       =					'something-something-v2-id2label.json'
    else:
      raise ValueError('Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'    )
    a						: Union[str, Any]					       =					json.load(open(hf_hub_download(_A			,      _A			,      repo_type='dataset'    )			,      'r'    )    )
    a						: str					       =					{int(_A    ): v for k, v in idalabel.items()}
    a						: str					       =					idalabel
    a						: Optional[Any]					       =					{v: k for k, v in idalabel.items()}

  return config
def 				lowerCamelCase__	(						_A			,      _A    ):
  if "small" in model_name:
    a						: Any					       =					384
    a						: str					       =					1536
    a						: Optional[Any]					       =					12
    a						: Union[str, Any]					       =					16
    a						: Optional[Any]					       =					12
    a						: Tuple					       =					3
    a						: str					       =					192
    a						: Dict					       =					768
  elif "large" in model_name:
    a						: Any					       =					1024
    a						: Dict					       =					4096
    a						: str					       =					24
    a						: str					       =					16
    a						: Union[str, Any]					       =					12
    a						: int					       =					8
    a						: List[str]					       =					512
    a						: Union[str, Any]					       =					2048
  elif "huge" in model_name:
    a						: Union[str, Any]					       =					1280
    a						: Dict					       =					5120
    a						: Optional[int]					       =					32
    a						: Dict					       =					16
    a						: Dict					       =					12
    a						: List[str]					       =					8
    a						: List[str]					       =					640
    a						: Union[str, Any]					       =					2560
  elif "base" not in model_name:
    raise ValueError('Model name should include either "small", "base", "large", or "huge"'    )
def 				lowerCamelCase__	(						_A    ):
  if "encoder." in name:
    a						: List[str]					       =					name.replace('encoder.'			,      ''    )
  if "cls_token" in name:
    a						: int					       =					name.replace('cls_token'			,      'videomae.embeddings.cls_token'    )
  if "decoder_pos_embed" in name:
    a						: List[Any]					       =					name.replace('decoder_pos_embed'			,      'decoder.decoder_pos_embed'    )
  if "pos_embed" in name and "decoder" not in name:
    a						: int					       =					name.replace('pos_embed'			,      'videomae.embeddings.position_embeddings'    )
  if "patch_embed.proj" in name:
    a						: Tuple					       =					name.replace('patch_embed.proj'			,      'videomae.embeddings.patch_embeddings.projection'    )
  if "patch_embed.norm" in name:
    a						: Tuple					       =					name.replace('patch_embed.norm'			,      'videomae.embeddings.norm'    )
  if "decoder.blocks" in name:
    a						: Optional[int]					       =					name.replace('decoder.blocks'			,      'decoder.decoder_layers'    )
  if "blocks" in name:
    a						: Tuple					       =					name.replace('blocks'			,      'videomae.encoder.layer'    )
  if "attn.proj" in name:
    a						: Optional[int]					       =					name.replace('attn.proj'			,      'attention.output.dense'    )
  if "attn" in name and "bias" not in name:
    a						: Optional[int]					       =					name.replace('attn'			,      'attention.self'    )
  if "attn" in name:
    a						: int					       =					name.replace('attn'			,      'attention.attention'    )
  if "norm1" in name:
    a						: Union[str, Any]					       =					name.replace('norm1'			,      'layernorm_before'    )
  if "norm2" in name:
    a						: Tuple					       =					name.replace('norm2'			,      'layernorm_after'    )
  if "mlp.fc1" in name:
    a						: Tuple					       =					name.replace('mlp.fc1'			,      'intermediate.dense'    )
  if "mlp.fc2" in name:
    a						: Any					       =					name.replace('mlp.fc2'			,      'output.dense'    )
  if "decoder_embed" in name:
    a						: Tuple					       =					name.replace('decoder_embed'			,      'decoder.decoder_embed'    )
  if "decoder_norm" in name:
    a						: Optional[Any]					       =					name.replace('decoder_norm'			,      'decoder.decoder_norm'    )
  if "decoder_pred" in name:
    a						: List[Any]					       =					name.replace('decoder_pred'			,      'decoder.decoder_pred'    )
  if "norm.weight" in name and "decoder" not in name and "fc" not in name:
    a						: Tuple					       =					name.replace('norm.weight'			,      'videomae.layernorm.weight'    )
  if "norm.bias" in name and "decoder" not in name and "fc" not in name:
    a						: Optional[int]					       =					name.replace('norm.bias'			,      'videomae.layernorm.bias'    )
  if "head" in name and "decoder" not in name:
    a						: Tuple					       =					name.replace('head'			,      'classifier'    )

  return name
def 				lowerCamelCase__	(						_A			,      _A    ):
  for key in orig_state_dict.copy().keys():
    a						: Any					       =					orig_state_dict.pop(_A    )

    if key.startswith('encoder.'    ):
      a						: Union[str, Any]					       =					key.replace('encoder.'			,      ''    )

    if "qkv" in key:
      a						: Dict					       =					key.split('.'    )
      if key.startswith('decoder.blocks'    ):
        a						: Union[str, Any]					       =					config.decoder_hidden_size
        a						: Any					       =					int(key_split[2]    )
        a						: Optional[Any]					       =					'decoder.decoder_layers.'
        if "weight" in key:
          a						: Dict					       =					val[:dim, :]
          a						: Dict					       =					val[dim : dim * 2, :]
          a						: Union[str, Any]					       =					val[-dim:, :]
      else:
        a						: Any					       =					config.hidden_size
        a						: int					       =					int(key_split[1]    )
        a						: Any					       =					'videomae.encoder.layer.'
        if "weight" in key:
          a						: Tuple					       =					val[:dim, :]
          a						: List[Any]					       =					val[dim : dim * 2, :]
          a						: Any					       =					val[-dim:, :]
    else:
      a						: Dict					       =					val

  return orig_state_dict
def 				lowerCamelCase__	(						):
  a						: List[str]					       =					hf_hub_download(
      repo_id='hf-internal-testing/spaghetti-video'			,      filename='eating_spaghetti.npy'			,      repo_type='dataset'    )
  a						: Optional[Any]					       =					np.load(_A    )
  return list(_A    )
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A    ):
  a						: Tuple					       =					get_videomae_config(_A    )

  if "finetuned" in model_name:
    a						: Union[str, Any]					       =					VideoMAEForVideoClassification(_A    )
  else:
    a						: List[Any]					       =					VideoMAEForPreTraining(_A    )

  # download original checkpoint, hosted on Google Drive
  a						: List[Any]					       =					'pytorch_model.bin'
  gdown.cached_download(_A			,      _A			,      quiet=_A    )
  a						: Dict					       =					torch.load(_A			,      map_location='cpu'    )
  if "model" in files:
    a						: Dict					       =					files['model']
  else:
    a						: List[Any]					       =					files['module']
  a						: str					       =					convert_state_dict(_A			,      _A    )

  model.load_state_dict(_A    )
  model.eval()

  # verify model on basic input
  a						: Dict					       =					VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]			,      image_std=[0.5, 0.5, 0.5]    )
  a						: Optional[int]					       =					prepare_video()
  a						: Union[str, Any]					       =					image_processor(_A			,      return_tensors='pt'    )

  if "finetuned" not in model_name:
    a						: Any					       =					hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'			,      filename='bool_masked_pos.pt'    )
    a						: int					       =					torch.load(_A    )

  a						: str					       =					model(**_A    )
  a						: Optional[int]					       =					outputs.logits

  a						: List[Any]					       =					[
      'videomae-small-finetuned-kinetics',
      'videomae-small-finetuned-ssv2',
      # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
      'videomae-base-short',
      'videomae-base-short-finetuned-kinetics',
      'videomae-base',
      'videomae-base-finetuned-kinetics',
      'videomae-large',
      'videomae-large-finetuned-kinetics',
      'videomae-huge-finetuned-kinetics',
      # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
      'videomae-base-short-ssv2',
      'videomae-base-short-finetuned-ssv2',
      'videomae-base-ssv2',
      'videomae-base-finetuned-ssv2',
  ]

  # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
  if model_name == "videomae-small-finetuned-kinetics":
    a						: int					       =					torch.Size([1, 400]    )
    a						: str					       =					torch.tensor([-0.9291, -0.4061, -0.9307]    )
  elif model_name == "videomae-small-finetuned-ssv2":
    a						: Union[str, Any]					       =					torch.Size([1, 174]    )
    a						: List[Any]					       =					torch.tensor([0.2671, -0.4689, -0.8235]    )
  elif model_name == "videomae-base":
    a						: Optional[int]					       =					torch.Size([1, 1408, 1536]    )
    a						: Any					       =					torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]]    )
  elif model_name == "videomae-base-short":
    a						: str					       =					torch.Size([1, 1408, 1536]    )
    a						: Union[str, Any]					       =					torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]    )
    # we verified the loss both for normalized and unnormalized targets for this one
    a						: int					       =					torch.tensor([0.5142]    ) if config.norm_pix_loss else torch.tensor([0.6469]    )
  elif model_name == "videomae-large":
    a						: Optional[Any]					       =					torch.Size([1, 1408, 1536]    )
    a						: Optional[Any]					       =					torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]]    )
  elif model_name == "videomae-large-finetuned-kinetics":
    a						: List[Any]					       =					torch.Size([1, 400]    )
    a						: Union[str, Any]					       =					torch.tensor([0.0771, 0.0011, -0.3625]    )
  elif model_name == "videomae-huge-finetuned-kinetics":
    a						: Optional[Any]					       =					torch.Size([1, 400]    )
    a						: List[Any]					       =					torch.tensor([0.2433, 0.1632, -0.4894]    )
  elif model_name == "videomae-base-short-finetuned-kinetics":
    a						: List[Any]					       =					torch.Size([1, 400]    )
    a						: Tuple					       =					torch.tensor([0.6588, 0.0990, -0.2493]    )
  elif model_name == "videomae-base-finetuned-kinetics":
    a						: Dict					       =					torch.Size([1, 400]    )
    a						: Optional[Any]					       =					torch.tensor([0.3669, -0.0688, -0.2421]    )
  elif model_name == "videomae-base-short-ssv2":
    a						: str					       =					torch.Size([1, 1408, 1536]    )
    a						: Any					       =					torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]]    )
  elif model_name == "videomae-base-short-finetuned-ssv2":
    a						: List[Any]					       =					torch.Size([1, 174]    )
    a						: int					       =					torch.tensor([-0.0537, -0.1539, -0.3266]    )
  elif model_name == "videomae-base-ssv2":
    a						: Optional[Any]					       =					torch.Size([1, 1408, 1536]    )
    a						: Tuple					       =					torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]]    )
  elif model_name == "videomae-base-finetuned-ssv2":
    a						: Tuple					       =					torch.Size([1, 174]    )
    a						: List[str]					       =					torch.tensor([0.1961, -0.8337, -0.6389]    )
  else:
    raise ValueError(f"""Model name not supported. Should be one of {model_names}"""    )

  # verify logits
  assert logits.shape == expected_shape
  if "finetuned" in model_name:
    assert torch.allclose(logits[0, :3]			,      _A			,      atol=1E-4    )
  else:
    print('Logits:'			,      logits[0, :3, :3]    )
    assert torch.allclose(logits[0, :3, :3]			,      _A			,      atol=1E-4    )
  print('Logits ok!'    )

  # verify loss, if applicable
  if model_name == "videomae-base-short":
    a						: Dict					       =					outputs.loss
    assert torch.allclose(_A			,      _A			,      atol=1E-4    )
    print('Loss ok!'    )

  if pytorch_dump_folder_path is not None:
    print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""    )
    image_processor.save_pretrained(_A    )
    model.save_pretrained(_A    )

  if push_to_hub:
    print('Pushing to the hub...'    )
    model.push_to_hub(_A			,      organization='nielsr'    )


if __name__ == "__main__":
     lowerCAmelCase: str							=						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--checkpoint_url',
         default='https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4',
         type=str,
         help=(
             'URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'
             ' download link.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_folder_path',
         default='/Users/nielsrogge/Documents/VideoMAE/Test',
         type=str,
         help='Path to the output PyTorch model directory.',
     )
     parser.add_argument('--model_name', default='videomae-base', type=str, help='Name of the model.')
     parser.add_argument(
         '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
     )

     lowerCAmelCase: List[str]							=						parser.parse_args()
     convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)




'''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__	(						_A			,      _A			,      _A=0    ):
  # Format the message.
  if name is None:
    a						: Tuple					       =					None
  else:
    a						: Dict					       =					'.' * max(0			,      spaces - 2    ) + '# {:' + str(50 - spaces    ) + 's}'
    a						: Tuple					       =					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 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A			,      _A    ):
  # 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.
  a						: str					       =					param.size()
  if checkpoint_version == 1.0:
    # version 1.0 stores [num_heads * hidden_size * num_splits, :]
    a						: List[Any]					       =					(num_heads, hidden_size, num_splits) + input_shape[1:]
    a						: int					       =					param.view(*_A    )
    a						: List[str]					       =					param.transpose(0			,      2    )
    a						: Union[str, Any]					       =					param.transpose(1			,      2    ).contiguous()
  elif checkpoint_version >= 2.0:
    # other versions store [num_heads * num_splits * hidden_size, :]
    a						: Union[str, Any]					       =					(num_heads, num_splits, hidden_size) + input_shape[1:]
    a						: List[str]					       =					param.view(*_A    )
    a						: Union[str, Any]					       =					param.transpose(0			,      1    ).contiguous()
  a						: List[Any]					       =					param.view(*_A    )
  return param
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  # The converted output model.
  a						: Optional[Any]					       =					{}

  # old versions did not store training args
  a						: Dict					       =					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))

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

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

  # The model.
  a						: Optional[int]					       =					input_state_dict['model']
  # The language model.
  a						: Optional[Any]					       =					model['language_model']
  # The embeddings.
  a						: List[str]					       =					lm['embedding']

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

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

  # The transformer.
  a						: Union[str, Any]					       =					lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

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

  # The simple map of names for "automated" rules.
  a						: Optional[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.
    a						: Tuple					       =					layer_re.match(_A    )

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

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

    # The name of the layer.
    a						: Any					       =					f"""transformer.h.{layer_idx}"""

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

      # Insert a "dummy" tensor for masked_bias.
      a						: List[Any]					       =					torch.tensor(-1E4			,      dtype=torch.floataa    )
      a						: List[str]					       =					masked_bias

      a						: Union[str, 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.
      a						: int					       =					out_val.transpose(0			,      1    ).contiguous()
      # Store.
      a						: 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":
      a						: str					       =					fix_query_key_value_ordering(_A			,      _A			,      3			,      _A			,      _A    )
      # Store. No change of shape.
      a						: List[str]					       =					out_val

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

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

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

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

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

  # It should be done!
  return output_state_dict
def 				lowerCamelCase__	(						):
  # Create the argument parser.
  a						: Dict					       =					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.'			,      )
  a						: Union[str, Any]					       =					parser.parse_args()

  # Extract the basename.
  a						: Optional[Any]					       =					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:
        a						: Union[str, Any]					       =					torch.load(_A			,      map_location='cpu'    )
  else:
    a						: Any					       =					torch.load(args.path_to_checkpoint			,      map_location='cpu'    )

  a						: List[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:
        a						: int					       =					'gelu_fast'
      elif ds_args.openai_gelu:
        a						: Dict					       =					'gelu_new'
      else:
        a						: Any					       =					'gelu'
    else:
      # in the very early days this used to be "gelu_new"
      a						: Any					       =					'gelu_new'

    # Spell out all parameters in case the defaults change.
    a						: Tuple					       =					GPTaConfig(
        vocab_size=5_0257			,      n_positions=1024			,      n_embd=1024			,      n_layer=24			,      n_head=16			,      n_inner=4096			,      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_0256			,      eos_token_id=5_0256			,      )
  else:
    a						: str					       =					GPTaConfig.from_json_file(args.config_file    )

  a						: Any					       =					['GPT2LMHeadModel']

  # Convert.
  print('Converting'    )
  a						: Union[str, 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:
    a						: Union[str, Any]					       =					ds_args.tokenizer_type
    if tokenizer_type == "GPT2BPETokenizer":
      a						: Tuple					       =					'gpt2'
    elif tokenizer_type == "PretrainedFromHF":
      a						: List[str]					       =					ds_args.tokenizer_name_or_path
    else:
      raise ValueError(f"""Unrecognized tokenizer_type {tokenizer_type}"""    )
  else:
    a						: Optional[Any]					       =					'gpt2'

  a						: Tuple					       =					AutoTokenizer.from_pretrained(_A    )
  a						: str					       =					type(_A    ).__name__
  a						: 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.
  a						: Optional[int]					       =					os.path.join(_A			,      'pytorch_model.bin'    )
  print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""    )
  torch.save(_A			,      _A    )


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

if __name__ == "__main__":
     main()

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