


# 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		(		_lowercase					,  _lowercase					,  _lowercase=0 )					-> Any:


     """simple docstring"""


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

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






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


     """simple docstring"""


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






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> List[Any]:


     """simple docstring"""


     __UpperCamelCase =			{}

     # old versions did not store training args
     __UpperCamelCase =			input_state_dict.get('args'					,  _lowercase )
     if ds_args is not None:
          # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
          # from pprint import pprint
          # pprint(vars(ds_args))

          __UpperCamelCase =			ds_args.padded_vocab_size
          __UpperCamelCase =			ds_args.max_position_embeddings
          __UpperCamelCase =			ds_args.hidden_size
          __UpperCamelCase =			ds_args.num_layers
          __UpperCamelCase =			ds_args.num_attention_heads
          __UpperCamelCase =			ds_args.ffn_hidden_size
          # pprint(config)

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

     # The model.
     __UpperCamelCase =			input_state_dict['model']
     # The language model.
     __UpperCamelCase =			model['language_model']
     # The embeddings.
     __UpperCamelCase =			lm['embedding']

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

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

     # The transformer.
     __UpperCamelCase =			lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

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

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

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

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

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

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

               # Insert a "dummy" tensor for masked_bias.
               __UpperCamelCase =			torch.tensor(-1e4					,  dtype=torch.floataa )
               __UpperCamelCase =			masked_bias

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

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

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

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

     # The final layernorm.
     __UpperCamelCase =			transformer['final_layernorm.weight']
     __UpperCamelCase =			transformer['final_layernorm.bias']

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

     # It should be done!
     return output_state_dict






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


     """simple docstring"""


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

     # Extract the basename.
     __UpperCamelCase =			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 =			torch.load(_lowercase					,  map_location='cpu' )
     else:
          __UpperCamelCase =			torch.load(args.path_to_checkpoint					,  map_location='cpu' )

     __UpperCamelCase =			input_state_dict.get('args'					,  _lowercase )

     # Read the config, or default to the model released by NVIDIA.
     if args.config_file == "":
          if ds_args is not None:
               if ds_args.bias_gelu_fusion:
                    __UpperCamelCase =			'gelu_fast'
               elif ds_args.openai_gelu:
                    __UpperCamelCase =			'gelu_new'
               else:
                    __UpperCamelCase =			'gelu'
          else:
               # in the very early days this used to be "gelu_new"
               __UpperCamelCase =			'gelu_new'

          # Spell out all parameters in case the defaults change.
          __UpperCamelCase =			GPTaConfig(
              vocab_size=5_02_57					,  n_positions=10_24					,  n_embd=10_24					,  n_layer=24					,  n_head=16					,  n_inner=40_96					,  activation_function=_lowercase					,  resid_pdrop=0.1					,  embd_pdrop=0.1					,  attn_pdrop=0.1					,  layer_norm_epsilon=1e-5					,  initializer_range=0.02					,  summary_type='cls_index'					,  summary_use_proj=_lowercase					,  summary_activation=_lowercase					,  summary_proj_to_labels=_lowercase					,  summary_first_dropout=0.1					,  scale_attn_weights=_lowercase					,  use_cache=_lowercase					,  bos_token_id=5_02_56					,  eos_token_id=5_02_56					,  )
     else:
          __UpperCamelCase =			GPTaConfig.from_json_file(args.config_file )

     __UpperCamelCase =			['GPT2LMHeadModel']

     # Convert.
     print('Converting' )
     __UpperCamelCase =			convert_megatron_checkpoint(_lowercase					,  _lowercase					,  _lowercase )

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

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

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

     # Store the config to file.
     print('Saving config' )
     config.save_pretrained(_lowercase )

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

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


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

if __name__ == "__main__":
    main()

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





"""simple docstring"""





def     __SCREAMING_SNAKE_CASE    ( A_					):
	for i in range(len(A_					) - 1		,	0		,	-1					):
		lowerCAmelCase__      :		Optional[Any]    							=      False

		for j in range(A_		,	0		,	-1					):
			if unsorted[j] < unsorted[j - 1]:
				lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      unsorted[j - 1], unsorted[j]
				lowerCAmelCase__      :		Dict    							=      True

		for j in range(A_					):
			if unsorted[j] > unsorted[j + 1]:
				lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      unsorted[j + 1], unsorted[j]
				lowerCAmelCase__      :		Any    							=      True

		if not swapped:
			break
	return unsorted


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							__UpperCamelCase    : int				 =						input('''Enter numbers separated by a comma:\n''').strip()
							__UpperCamelCase    : Optional[Any]				 =						[int(item) for item in user_input.split(''',''')]
							print(F'''{cocktail_shaker_sort(unsorted) = }''')




