

# 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						( __UpperCamelCase	: Tuple				,__UpperCamelCase	: Dict				,__UpperCamelCase	: Tuple=0    ):



      """simple docstring"""

      if name is None:
            A_						  =				None
      else:
            A_						  =				"." * max(0				,spaces - 2    ) + "# {:" + str(50 - spaces    ) + "s}"
            A_						  =				fmt.format(__UpperCamelCase    )

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

def       __snake_case						( __UpperCamelCase	: List[str]				,__UpperCamelCase	: Tuple				,__UpperCamelCase	: Union[str, Any]				,__UpperCamelCase	: Any				,__UpperCamelCase	: Any    ):



      """simple docstring"""

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

def       __snake_case						( __UpperCamelCase	: str				,__UpperCamelCase	: Any				,__UpperCamelCase	: Any    ):



      """simple docstring"""

      A_						  =				{}

      # old versions did not store training args
      A_						  =				input_state_dict.get("args"				,__UpperCamelCase    )
      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_						  =				ds_args.padded_vocab_size
            A_						  =				ds_args.max_position_embeddings
            A_						  =				ds_args.hidden_size
            A_						  =				ds_args.num_layers
            A_						  =				ds_args.num_attention_heads
            A_						  =				ds_args.ffn_hidden_size
            # pprint(config)

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

      # The model.
      A_						  =				input_state_dict["model"]
      # The language model.
      A_						  =				model["language_model"]
      # The embeddings.
      A_						  =				lm["embedding"]

      # The word embeddings.
      A_						  =				embeddings["word_embeddings"]["weight"]
      # Truncate the embedding table to vocab_size rows.
      A_						  =				word_embeddings[: config.vocab_size, :]
      A_						  =				word_embeddings

      # The position embeddings.
      A_						  =				embeddings["position_embeddings"]["weight"]
      # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
      A_						  =				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_						  =				pos_embeddings

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

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

      # The simple map of names for "automated" rules.
      A_						  =				{
          "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_						  =				layer_re.match(__UpperCamelCase    )

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

            # The index of the layer.
            A_						  =				int(m.group(1    )    )
            # The name of the operation.
            A_						  =				m.group(2    )
            # Is it a weight or a bias?
            A_						  =				m.group(3    )

            # The name of the layer.
            A_						  =				f'''transformer.h.{layer_idx}'''

            # For layernorm(s), simply store the layer norm.
            if op_name.endswith("layernorm"    ):
                  A_						  =				"ln_1" if op_name.startswith("input"    ) else "ln_2"
                  A_						  =				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_						  =				torch.tril(torch.ones((n_positions, n_positions)				,dtype=torch.floataa    )    ).view(
                      1				,1				,__UpperCamelCase				,__UpperCamelCase    )
                  A_						  =				causal_mask

                  # Insert a "dummy" tensor for masked_bias.
                  A_						  =				torch.tensor(-1E4				,dtype=torch.floataa    )
                  A_						  =				masked_bias

                  A_						  =				fix_query_key_value_ordering(__UpperCamelCase				,__UpperCamelCase				,3				,__UpperCamelCase				,__UpperCamelCase    )
                  # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
                  A_						  =				out_val.transpose(0				,1    ).contiguous()
                  # Store.
                  A_						  =				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_						  =				fix_query_key_value_ordering(__UpperCamelCase				,__UpperCamelCase				,3				,__UpperCamelCase				,__UpperCamelCase    )
                  # Store. No change of shape.
                  A_						  =				out_val

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

            # Copy the bias.
            elif weight_or_bias == "bias":
                  A_						  =				megatron_to_transformers[op_name]
                  A_						  =				val

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

      # The final layernorm.
      A_						  =				transformer["final_layernorm.weight"]
      A_						  =				transformer["final_layernorm.bias"]

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

      # It should be done!
      return output_state_dict

def       __snake_case						( ):



      """simple docstring"""

      A_						  =				argparse.ArgumentParser()
      parser.add_argument("--print-checkpoint-structure"				,action="store_true"    )
      parser.add_argument(
          "path_to_checkpoint"				,type=__UpperCamelCase				,help="Path to the checkpoint file (.zip archive or direct .pt file)"				,)
      parser.add_argument(
          "--config_file"				,default=""				,type=__UpperCamelCase				,help="An optional config json file describing the pre-trained model."				,)
      A_						  =				parser.parse_args()

      # Extract the basename.
      A_						  =				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_						  =				torch.load(__UpperCamelCase				,map_location="cpu"    )
      else:
            A_						  =				torch.load(args.path_to_checkpoint				,map_location="cpu"    )

      A_						  =				input_state_dict.get("args"				,__UpperCamelCase    )

      # 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_						  =				"gelu_fast"
                  elif ds_args.openai_gelu:
                        A_						  =				"gelu_new"
                  else:
                        A_						  =				"gelu"
            else:
                  # in the very early days this used to be "gelu_new"
                  A_						  =				"gelu_new"

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

      A_						  =				["GPT2LMHeadModel"]

      # Convert.
      print("Converting"    )
      A_						  =				convert_megatron_checkpoint(__UpperCamelCase				,__UpperCamelCase				,__UpperCamelCase    )

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

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

      A_						  =				AutoTokenizer.from_pretrained(__UpperCamelCase    )
      A_						  =				type(__UpperCamelCase    ).__name__
      A_						  =				tokenizer_class

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

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

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


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

if __name__ == "__main__":
      main()

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






'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Optional[int]      ,    __lowerCAmelCase       :							List[Any]=False		)		->  List[str]:
   snake_case 						=   []
   for i in range(config.num_hidden_layers		):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')		)
      rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')		)
      rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')		)
      rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')		)
      rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')		)
      rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')		)
      rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')		)
      rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')		)
      rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')		)
      rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')		)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           ("""cls_token""", """vit.embeddings.cls_token"""),
           ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
           ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
           ("""pos_embed""", """vit.embeddings.position_embeddings"""),
       ]		)

   if base_model:
      # layernorm + pooler
      rename_keys.extend(
          [
              ("""norm.weight""", """layernorm.weight"""),
              ("""norm.bias""", """layernorm.bias"""),
              ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
              ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
          ]		)

      # if just the base model, we should remove "vit" from all keys that start with "vit"
      snake_case 						=   [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""		) else pair for pair in rename_keys]
   else:
      # layernorm + classification head
      rename_keys.extend(
          [
              ("""norm.weight""", """vit.layernorm.weight"""),
              ("""norm.bias""", """vit.layernorm.bias"""),
              ("""head.weight""", """classifier.weight"""),
              ("""head.bias""", """classifier.bias"""),
          ]		)

   return rename_keys





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							Optional[Any]      ,    __lowerCAmelCase       :							int=False		)		->  Union[str, Any]:
   for i in range(config.num_hidden_layers		):
      if base_model:
         snake_case 						=   """"""
      else:
         snake_case 						=   """vit."""
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      snake_case 						=   state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''		)
      snake_case 						=   state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''		)
      # next, add query, keys and values (in that order) to the state dict
      snake_case 						=   in_proj_weight[
          : config.hidden_size, :
      ]
      snake_case 						=   in_proj_bias[: config.hidden_size]
      snake_case 						=   in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      snake_case 						=   in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      snake_case 						=   in_proj_weight[
          -config.hidden_size :, :
      ]
      snake_case 						=   in_proj_bias[-config.hidden_size :]





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]		)		->  List[str]:
   snake_case 						=   ["""head.weight""", """head.bias"""]
   for k in ignore_keys:
      state_dict.pop(__lowerCAmelCase      ,    __lowerCAmelCase		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Optional[Any]      ,    __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							Tuple		)		->  int:
   snake_case 						=   dct.pop(__lowerCAmelCase		)
   snake_case 						=   val





def 	__lowerCamelCase			(      )		->  Optional[Any]:
   snake_case 						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case 						=   Image.open(requests.get(__lowerCAmelCase      ,    stream=__lowerCAmelCase		).raw		)
   return im





@torch.no_grad()
def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							Any		)		->  Dict:
   snake_case 						=   ViTConfig()
   snake_case 						=   False
   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   if vit_name[-5:] == "in21k":
      snake_case 						=   True
      snake_case 						=   int(vit_name[-12:-10]		)
      snake_case 						=   int(vit_name[-9:-6]		)
   else:
      snake_case 						=   10_00
      snake_case 						=   """huggingface/label-files"""
      snake_case 						=   """imagenet-1k-id2label.json"""
      snake_case 						=   json.load(open(hf_hub_download(__lowerCAmelCase      ,    __lowerCAmelCase      ,    repo_type="""dataset"""		)      ,    """r"""		)		)
      snake_case 						=   {int(__lowerCAmelCase		): v for k, v in idalabel.items()}
      snake_case 						=   idalabel
      snake_case 						=   {v: k for k, v in idalabel.items()}
      snake_case 						=   int(vit_name[-6:-4]		)
      snake_case 						=   int(vit_name[-3:]		)
   # size of the architecture
   if "deit" in vit_name:
      if vit_name[9:].startswith("""tiny"""		):
         snake_case 						=   1_92
         snake_case 						=   7_68
         snake_case 						=   12
         snake_case 						=   3
      elif vit_name[9:].startswith("""small"""		):
         snake_case 						=   3_84
         snake_case 						=   15_36
         snake_case 						=   12
         snake_case 						=   6
      else:
         pass
   else:
      if vit_name[4:].startswith("""small"""		):
         snake_case 						=   7_68
         snake_case 						=   23_04
         snake_case 						=   8
         snake_case 						=   8
      elif vit_name[4:].startswith("""base"""		):
         pass
      elif vit_name[4:].startswith("""large"""		):
         snake_case 						=   10_24
         snake_case 						=   40_96
         snake_case 						=   24
         snake_case 						=   16
      elif vit_name[4:].startswith("""huge"""		):
         snake_case 						=   12_80
         snake_case 						=   51_20
         snake_case 						=   32
         snake_case 						=   16

    # load original model from timm
   snake_case 						=   timm.create_model(__lowerCAmelCase      ,    pretrained=__lowerCAmelCase		)
   timm_model.eval()

   # load state_dict of original model, remove and rename some keys
   snake_case 						=   timm_model.state_dict()
   if base_model:
      remove_classification_head_(__lowerCAmelCase		)
   snake_case 						=   create_rename_keys(__lowerCAmelCase      ,    __lowerCAmelCase		)
   for src, dest in rename_keys:
      rename_key(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   read_in_q_k_v(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)

   # load HuggingFace model
   if vit_name[-5:] == "in21k":
      snake_case 						=   ViTModel(__lowerCAmelCase		).eval()
   else:
      snake_case 						=   ViTForImageClassification(__lowerCAmelCase		).eval()
   model.load_state_dict(__lowerCAmelCase		)

   # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
   if "deit" in vit_name:
      snake_case 						=   DeiTImageProcessor(size=config.image_size		)
   else:
      snake_case 						=   ViTImageProcessor(size=config.image_size		)
   snake_case 						=   image_processor(images=prepare_img()      ,    return_tensors="""pt"""		)
   snake_case 						=   encoding["""pixel_values"""]
   snake_case 						=   model(__lowerCAmelCase		)

   if base_model:
      snake_case 						=   timm_model.forward_features(__lowerCAmelCase		)
      assert timm_pooled_output.shape == outputs.pooler_output.shape
      assert torch.allclose(__lowerCAmelCase      ,    outputs.pooler_output      ,    atol=1e-3		)
   else:
      snake_case 						=   timm_model(__lowerCAmelCase		)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(__lowerCAmelCase      ,    outputs.logits      ,    atol=1e-3		)

   Path(__lowerCAmelCase		).mkdir(exist_ok=__lowerCAmelCase		)
   print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''		)
   model.save_pretrained(__lowerCAmelCase		)
   print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
   image_processor.save_pretrained(__lowerCAmelCase		)


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--vit_name",
     default="vit_base_patch16_224",
     type=str,
     help="Name of the ViT timm model you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )

 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)

