

"""simple docstring"""


from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


a					:str    = "Create a default config file for Accelerate with only a few flags set."




def  _lowercase					(    __lowerCAmelCase="no"  ,					__lowerCAmelCase = default_json_config_file  ,					__lowerCAmelCase = False							)       ->				List[Any]:
      SCREAMING_SNAKE_CASE__   :      int      		=	Path(__lowerCAmelCase							)
      path.parent.mkdir(parents=__lowerCAmelCase  ,					exist_ok=__lowerCAmelCase							)
      if path.exists():
            print(
                F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''							)
            return False
      SCREAMING_SNAKE_CASE__   :      Dict      		=	mixed_precision.lower()
      if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
            raise ValueError(
                F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	{
          """compute_environment""": """LOCAL_MACHINE""",
          """mixed_precision""": mixed_precision,
      }
      if torch.cuda.is_available():
            SCREAMING_SNAKE_CASE__   :      str      		=	torch.cuda.device_count()
            SCREAMING_SNAKE_CASE__   :      str      		=	num_gpus
            SCREAMING_SNAKE_CASE__   :      str      		=	False
            if num_gpus > 1:
                  SCREAMING_SNAKE_CASE__   :      str      		=	"""MULTI_GPU"""
            else:
                  SCREAMING_SNAKE_CASE__   :      int      		=	"""NO"""
      elif is_xpu_available() and use_xpu:
            SCREAMING_SNAKE_CASE__   :      Dict      		=	torch.xpu.device_count()
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	num_xpus
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	False
            if num_xpus > 1:
                  SCREAMING_SNAKE_CASE__   :      List[Any]      		=	"""MULTI_XPU"""
            else:
                  SCREAMING_SNAKE_CASE__   :      Dict      		=	"""NO"""
      elif is_npu_available():
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	torch.npu.device_count()
            SCREAMING_SNAKE_CASE__   :      Any      		=	num_npus
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	False
            if num_npus > 1:
                  SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""MULTI_NPU"""
            else:
                  SCREAMING_SNAKE_CASE__   :      int      		=	"""NO"""
      else:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	0
            SCREAMING_SNAKE_CASE__   :      int      		=	True
            SCREAMING_SNAKE_CASE__   :      Any      		=	1
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	"""NO"""
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	ClusterConfig(**__lowerCAmelCase							)
      config.to_json_file(__lowerCAmelCase							)
      return path




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Optional[Any]:
      SCREAMING_SNAKE_CASE__   :      Any      		=	parser.add_parser("""default"""  ,					parents=__lowerCAmelCase  ,					help=__lowerCAmelCase  ,					formatter_class=__lowerCAmelCase							)
      parser.add_argument(
          """--config_file"""  ,					default=__lowerCAmelCase  ,					help=(
              """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
              """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
              """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
              """with 'huggingface'."""
          )  ,					dest="""save_location"""  ,					)

      parser.add_argument(
          """--mixed_precision"""  ,					choices=["""no""", """fp16""", """bf16"""]  ,					type=__lowerCAmelCase  ,					help="""Whether or not to use mixed precision training. """
          """Choose between FP16 and BF16 (bfloat16) training. """
          """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."""  ,					default="""no"""  ,					)
      parser.set_defaults(func=__lowerCAmelCase							)
      return parser




def  _lowercase					(    __lowerCAmelCase							)       ->				str:
      SCREAMING_SNAKE_CASE__   :      str      		=	write_basic_config(args.mixed_precision  ,					args.save_location							)
      if config_file:
            print(F'''accelerate configuration saved at {config_file}'''							)







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




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Tuple:
      # The converted output model.
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	{}

      # old versions did not store training args
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	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))

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

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

      # The model.
      SCREAMING_SNAKE_CASE__   :      Any      		=	input_state_dict["""model"""]
      # The language model.
      SCREAMING_SNAKE_CASE__   :      Any      		=	model["""language_model"""]
      # The embeddings.
      SCREAMING_SNAKE_CASE__   :      str      		=	lm["""embedding"""]

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

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

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

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

      # The simple map of names for "automated" rules.
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{
          """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.
            SCREAMING_SNAKE_CASE__   :      str      		=	layer_re.match(__lowerCAmelCase							)

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

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

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

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

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

                  SCREAMING_SNAKE_CASE__   :      List[str]      		=	fix_query_key_value_ordering(__lowerCAmelCase  ,					__lowerCAmelCase  ,					3  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)
                  # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
                  SCREAMING_SNAKE_CASE__   :      str      		=	out_val.transpose(0  ,					1							).contiguous()
                  # Store.
                  SCREAMING_SNAKE_CASE__   :      Dict      		=	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":
                  SCREAMING_SNAKE_CASE__   :      Any      		=	fix_query_key_value_ordering(__lowerCAmelCase  ,					__lowerCAmelCase  ,					3  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)
                  # Store. No change of shape.
                  SCREAMING_SNAKE_CASE__   :      str      		=	out_val

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

            # Copy the bias.
            elif weight_or_bias == "bias":
                  SCREAMING_SNAKE_CASE__   :      int      		=	megatron_to_transformers[op_name]
                  SCREAMING_SNAKE_CASE__   :      Dict      		=	val

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

      # The final layernorm.
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	transformer["""final_layernorm.weight"""]
      SCREAMING_SNAKE_CASE__   :      str      		=	transformer["""final_layernorm.bias"""]

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

      # It should be done!
      return output_state_dict




def  _lowercase					(    )       ->				List[Any]:
      # Create the argument parser.
      SCREAMING_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."""  ,					)
      SCREAMING_SNAKE_CASE__   :      Dict      		=	parser.parse_args()

      # Extract the basename.
      SCREAMING_SNAKE_CASE__   :      Optional[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:
                        SCREAMING_SNAKE_CASE__   :      List[Any]      		=	torch.load(__lowerCAmelCase  ,					map_location="""cpu"""							)
      else:
            SCREAMING_SNAKE_CASE__   :      str      		=	torch.load(args.path_to_checkpoint  ,					map_location="""cpu"""							)

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

            # Spell out all parameters in case the defaults change.
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	GPTaConfig(
                vocab_size=5_0257  ,					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=5_0256  ,					eos_token_id=5_0256  ,					)
      else:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	GPTaConfig.from_json_file(args.config_file							)

      SCREAMING_SNAKE_CASE__   :      Tuple      		=	["""GPT2LMHeadModel"""]

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

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


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

if __name__ == "__main__":
  main()

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




