



'''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 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase=0			) -> List[Any]:






  if name is None:
    __lowerCamelCase			:			Dict       			=					None
  else:
    __lowerCamelCase			:			str       			=					'.' * max(0			,spaces - 2			) + '# {:' + str(50 - spaces			) + 's}'
    __lowerCamelCase			:			str       			=					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 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> Optional[int]:
  __lowerCamelCase			:			Any       			=					param.size()
  if checkpoint_version == 1.0:
    # version 1.0 stores [num_heads * hidden_size * num_splits, :]
    __lowerCamelCase			:			Tuple       			=					(num_heads, hidden_size, num_splits) + input_shape[1:]
    __lowerCamelCase			:			int       			=					param.view(*_lowerCAmelCase			)
    __lowerCamelCase			:			str       			=					param.transpose(0			,2			)
    __lowerCamelCase			:			str       			=					param.transpose(1			,2			).contiguous()
  elif checkpoint_version >= 2.0:
    # other versions store [num_heads * num_splits * hidden_size, :]
    __lowerCamelCase			:			Dict       			=					(num_heads, num_splits, hidden_size) + input_shape[1:]
    __lowerCamelCase			:			Union[str, Any]       			=					param.view(*_lowerCAmelCase			)
    __lowerCamelCase			:			Dict       			=					param.transpose(0			,1			).contiguous()
  __lowerCamelCase			:			Optional[Any]       			=					param.view(*_lowerCAmelCase			)
  return param

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> Union[str, Any]:
  __lowerCamelCase			:			int       			=					{}

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

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

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

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

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

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

  # The transformer.
  __lowerCamelCase			:			Optional[Any]       			=					lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

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

  # The simple map of names for "automated" rules.
  __lowerCamelCase			:			List[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.
    __lowerCamelCase			:			Union[str, Any]       			=					layer_re.match(_lowerCAmelCase			)

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

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

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

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith('layernorm'			):
      __lowerCamelCase			:			int       			=					'ln_1' if op_name.startswith('input'			) else 'ln_2'
      __lowerCamelCase			:			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":
      # Insert a tensor of 1x1xDxD bias.
      __lowerCamelCase			:			Optional[int]       			=					torch.tril(torch.ones((n_positions, n_positions)			,dtype=torch.floataa			)			).view(
          1			,1			,_lowerCAmelCase			,_lowerCAmelCase			)
      __lowerCamelCase			:			Tuple       			=					causal_mask

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

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

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

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

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

  # The final layernorm.
  __lowerCamelCase			:			Tuple       			=					transformer['final_layernorm.weight']
  __lowerCamelCase			:			int       			=					transformer['final_layernorm.bias']

  # For LM head, transformers' wants the matrix to weight embeddings.
  __lowerCamelCase			:			Union[str, Any]       			=					word_embeddings

  # It should be done!
  return output_state_dict

def 			a_	(				) -> Any:
  __lowerCamelCase			:			Optional[int]       			=					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.'			,)
  __lowerCamelCase			:			Optional[Any]       			=					parser.parse_args()

  # Extract the basename.
  __lowerCamelCase			:			int       			=					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:
        __lowerCamelCase			:			int       			=					torch.load(_lowerCAmelCase			,map_location='cpu'			)
  else:
    __lowerCamelCase			:			Tuple       			=					torch.load(args.path_to_checkpoint			,map_location='cpu'			)

  __lowerCamelCase			:			Tuple       			=					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:
        __lowerCamelCase			:			str       			=					'gelu_fast'
      elif ds_args.openai_gelu:
        __lowerCamelCase			:			Optional[Any]       			=					'gelu_new'
      else:
        __lowerCamelCase			:			Tuple       			=					'gelu'
    else:
      # in the very early days this used to be "gelu_new"
      __lowerCamelCase			:			str       			=					'gelu_new'

    # Spell out all parameters in case the defaults change.
    __lowerCamelCase			:			str       			=					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=0.02			,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:
    __lowerCamelCase			:			Optional[Any]       			=					GPTaConfig.from_json_file(args.config_file			)

  __lowerCamelCase			:			List[Any]       			=					['GPT2LMHeadModel']

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

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


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

if __name__ == "__main__":
   main()

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










import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
A				:       int      =  logging.get_logger()






@dataclass
class      UpperCamelCase:
					snake_case_		:					nn.Module
					snake_case_		:					List[nn.Module]    					=						field(default_factory=_a )
					snake_case_		:					list    					=						field(default_factory=_a )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Tensor  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		len(list(m.modules()    )    ) == 1 or isinstance(SCREAMING_SNAKE_CASE  ,     nn.Convad    ) or isinstance(SCREAMING_SNAKE_CASE  ,     nn.BatchNormad    )
											if has_not_submodules:
																	self.traced.append(SCREAMING_SNAKE_CASE    )


					def __call__(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						List[str]:




											'''simple docstring'''

											for m in self.module.modules():
																	self.handles.append(m.register_forward_hook(self._forward_hook    )    )
											self.module(SCREAMING_SNAKE_CASE    )
											[x.remove() for x in self.handles]
											return self






					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Dict:




											'''simple docstring'''

											return list(filter(lambda SCREAMING_SNAKE_CASE    : len(list(x.state_dict().keys()    )    ) > 0  ,     self.traced    )    )






@dataclass
class      UpperCamelCase:
					snake_case_		:					nn.Module
					snake_case_		:					nn.Module
					snake_case_		:					int    					=						0
					snake_case_		:					List    					=						field(default_factory=_a )
					snake_case_		:					List    					=						field(default_factory=_a )


					def __call__(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		Tracker(self.dest    )(SCREAMING_SNAKE_CASE    ).parametrized
											__snake_case				=		Tracker(self.src    )(SCREAMING_SNAKE_CASE    ).parametrized

											__snake_case				=		list(filter(lambda SCREAMING_SNAKE_CASE    : type(SCREAMING_SNAKE_CASE    ) not in self.src_skip  ,     SCREAMING_SNAKE_CASE    )    )
											__snake_case				=		list(filter(lambda SCREAMING_SNAKE_CASE    : type(SCREAMING_SNAKE_CASE    ) not in self.dest_skip  ,     SCREAMING_SNAKE_CASE    )    )

											if len(SCREAMING_SNAKE_CASE    ) != len(SCREAMING_SNAKE_CASE    ):
																	raise Exception(
																	    f'''Numbers of operations are different. Source module has {len(SCREAMING_SNAKE_CASE    )} operations while'''
																	    f''' destination module has {len(SCREAMING_SNAKE_CASE    )}.'''    )

											for dest_m, src_m in zip(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    ):
																	dest_m.load_state_dict(src_m.state_dict()    )
																	if self.verbose == 1:
																							print(f'''Transfered from={src_m} to={dest_m}'''    )





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = True			)    ->						Dict:


						'''simple docstring'''






						print(F'''Converting {name}...'''			)
						with torch.no_grad():
												__snake_case				=		timm.create_model(_lowerCAmelCase   ,						pretrained=_lowerCAmelCase			).eval()
												__snake_case				=		ResNetForImageClassification(_lowerCAmelCase			).eval()
												__snake_case				=		ModuleTransfer(src=_lowerCAmelCase   ,						dest=_lowerCAmelCase			)
												__snake_case				=		torch.randn((1, 3, 224, 224)			)
												module_transfer(_lowerCAmelCase			)

						assert torch.allclose(from_model(_lowerCAmelCase			)   ,						our_model(_lowerCAmelCase			).logits			), "The model logits don't match the original one."

						__snake_case				=		F'''resnet{'-'.join(name.split('resnet'			)			)}'''
						print(_lowerCAmelCase			)

						if push_to_hub:
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name   ,						commit_message="Add model"   ,						use_temp_dir=_lowerCAmelCase   ,						)

												# we can use the convnext one
												__snake_case				=		AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"			)
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name   ,						commit_message="Add image processor"   ,						use_temp_dir=_lowerCAmelCase   ,						)

												print(F'''Pushed {checkpoint_name}'''			)





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase = None   ,						_lowerCAmelCase = True			)    ->						Dict:


						'''simple docstring'''






						__snake_case				=		"imagenet-1k-id2label.json"
						__snake_case				=		1000
						__snake_case				=		(1, num_labels)

						__snake_case				=		"huggingface/label-files"
						__snake_case				=		num_labels
						__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				=		partial(_lowerCAmelCase   ,						num_labels=_lowerCAmelCase   ,						idalabel=_lowerCAmelCase   ,						labelaid=_lowerCAmelCase			)

						__snake_case				=		{
						    "resnet18": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]   ,						hidden_sizes=[64, 128, 256, 512]   ,						layer_type="basic"			),
						    "resnet26": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]   ,						hidden_sizes=[256, 512, 1024, 2048]   ,						layer_type="bottleneck"			),
						    "resnet34": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]   ,						hidden_sizes=[64, 128, 256, 512]   ,						layer_type="basic"			),
						    "resnet50": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]   ,						hidden_sizes=[256, 512, 1024, 2048]   ,						layer_type="bottleneck"			),
						    "resnet101": ImageNetPreTrainedConfig(
						        depths=[3, 4, 23, 3]   ,						hidden_sizes=[256, 512, 1024, 2048]   ,						layer_type="bottleneck"			),
						    "resnet152": ImageNetPreTrainedConfig(
						        depths=[3, 8, 36, 3]   ,						hidden_sizes=[256, 512, 1024, 2048]   ,						layer_type="bottleneck"			),
						}

						if model_name:
												convert_weight_and_push(_lowerCAmelCase   ,						names_to_config[model_name]   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)
						return config, expected_shape


if __name__ == "__main__":
	A				:       Optional[Any]      =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default=None,
	    type=str,
	    help=(
	        'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
	        ' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default=None,
	    type=Path,
	    required=True,
	    help='Path to the output PyTorch model directory.',
	)
	parser.add_argument(
	    '--push_to_hub',
	    default=True,
	    type=bool,
	    required=False,
	    help='If True, push model and image processor to the hub.',
	)

	A				:       Union[str, Any]      =  parser.parse_args()
	A				:       Path      =  args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





