


'''simple docstring'''






from argparse import ArgumentParser

from . import BaseTransformersCLICommand





def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		return DownloadCommand(args.model				,      args.cache_dir				,      args.force				,      args.trust_remote_code )






class 						__UpperCamelCase	(						lowercase__							):





							@staticmethod
							def 		a__   (  _UpperCamelCase		:ArgumentParser			):
									snake_case_		:							int			       =						parser.add_parser("""download"""			)
									download_parser.add_argument(
									    """--cache-dir"""  ,type=_UpperCamelCase  ,default=_UpperCamelCase  ,help="""Path to location to store the models"""			)
									download_parser.add_argument(
									    """--force"""  ,action="""store_true"""  ,help="""Force the model to be download even if already in cache-dir"""			)
									download_parser.add_argument(
									    """--trust-remote-code"""  ,action="""store_true"""  ,help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""  ,)
									download_parser.add_argument("""model"""  ,type=_UpperCamelCase  ,help="""Name of the model to download"""			)
									download_parser.set_defaults(func=_UpperCamelCase			)





							def __init__(  self		:List[Any]  ,_UpperCamelCase		:str  ,_UpperCamelCase		:str  ,_UpperCamelCase		:bool  ,_UpperCamelCase		:bool			):
									snake_case_		:							Tuple			       =						model
									snake_case_		:							List[str]			       =						cache
									snake_case_		:							List[str]			       =						force
									snake_case_		:							List[str]			       =						trust_remote_code





							def 		a__   (  self		:Dict			):
									from ..models.auto import AutoModel, AutoTokenizer

									AutoModel.from_pretrained(
									    self._model  ,cache_dir=self._cache  ,force_download=self._force  ,trust_remote_code=self._trust_remote_code			)
									AutoTokenizer.from_pretrained(
									    self._model  ,cache_dir=self._cache  ,force_download=self._force  ,trust_remote_code=self._trust_remote_code			)



'''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      UpperCAmelCase      (		lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Union[str, Any]				,      lowerCamelCase_  :List[Any]=0 ):







		'''simple docstring'''
		# Format the message.
		if name is None:
				snake_case_		:							Tuple			       =						None
		else:
				snake_case_		:							Optional[Any]			       =						""".""" * max(0				,      spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
				snake_case_		:							Optional[Any]			       =						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      UpperCAmelCase      (		lowerCamelCase_  :Optional[int]				,      lowerCamelCase_  :List[str]				,      lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :List[str]				,      lowerCamelCase_  :int ):







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





def      UpperCAmelCase      (		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :Tuple ):







		'''simple docstring'''
		# The converted output model.
		snake_case_		:							Tuple			       =						{}

		# old versions did not store training args
		snake_case_		:							Optional[Any]			       =						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))

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

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

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

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

		# The position embeddings.
		snake_case_		:							Tuple			       =						embeddings["""position_embeddings"""]["""weight"""]
		# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
		snake_case_		:							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.
		snake_case_		:							List[str]			       =						pos_embeddings

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

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

		# The simple map of names for "automated" rules.
		snake_case_		:							Tuple			       =						{
		    """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.
				snake_case_		:							List[Any]			       =						layer_re.match(lowerCamelCase_ )

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

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

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

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

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

						snake_case_		:							Optional[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.
						snake_case_		:							Any			       =						out_val.transpose(0				,      1 ).contiguous()
						# Store.
						snake_case_		:							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":
						snake_case_		:							Dict			       =						fix_query_key_value_ordering(lowerCamelCase_				,      lowerCamelCase_				,      3				,      lowerCamelCase_				,      lowerCamelCase_ )
						# Store. No change of shape.
						snake_case_		:							Tuple			       =						out_val

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

				# Copy the bias.
				elif weight_or_bias == "bias":
						snake_case_		:							str			       =						megatron_to_transformers[op_name]
						snake_case_		:							List[str]			       =						val

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

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

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

		# It should be done!
		return output_state_dict





def      UpperCAmelCase      (		):







		'''simple docstring'''
		# Create the argument parser.
		snake_case_		:							str			       =						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."""				,      )
		snake_case_		:							Tuple			       =						parser.parse_args()

		# Extract the basename.
		snake_case_		:							List[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:
								snake_case_		:							Dict			       =						torch.load(lowerCamelCase_				,      map_location="""cpu""" )
		else:
				snake_case_		:							Optional[Any]			       =						torch.load(args.path_to_checkpoint				,      map_location="""cpu""" )

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

				# Spell out all parameters in case the defaults change.
				snake_case_		:							int			       =						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=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=5_02_56				,      eos_token_id=5_02_56				,      )
		else:
				snake_case_		:							int			       =						GPTaConfig.from_json_file(args.config_file )

		snake_case_		:							Optional[int]			       =						["""GPT2LMHeadModel"""]

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

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


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

if __name__ == "__main__":
							main()

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