

# 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     (      A__			:  Optional[Any]			,  A__			:  List[str]			,  A__			:  Optional[int]=0       ):
    # Format the message.
    if name is None:
        SCREAMING_SNAKE_CASE										=   None
    else:
        SCREAMING_SNAKE_CASE										=   "." * max(0			,  spaces - 2       ) + "# {:" + str(50 - spaces       ) + "s}"
        SCREAMING_SNAKE_CASE										=   fmt.format(A__       )

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





def 	__a     (      A__			:  Tuple			,  A__			:  List[Any]			,  A__			:  Tuple			,  A__			:  Optional[Any]			,  A__			:  Dict       ):
    # 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										=   param.size()
    if checkpoint_version == 1.0:
        # version 1.0 stores [num_heads * hidden_size * num_splits, :]
        SCREAMING_SNAKE_CASE										=   (num_heads, hidden_size, num_splits) + input_shape[1:]
        SCREAMING_SNAKE_CASE										=   param.view(*A__       )
        SCREAMING_SNAKE_CASE										=   param.transpose(0			,  2       )
        SCREAMING_SNAKE_CASE										=   param.transpose(1			,  2       ).contiguous()
    elif checkpoint_version >= 2.0:
        # other versions store [num_heads * num_splits * hidden_size, :]
        SCREAMING_SNAKE_CASE										=   (num_heads, num_splits, hidden_size) + input_shape[1:]
        SCREAMING_SNAKE_CASE										=   param.view(*A__       )
        SCREAMING_SNAKE_CASE										=   param.transpose(0			,  1       ).contiguous()
    SCREAMING_SNAKE_CASE										=   param.view(*A__       )
    return param





def 	__a     (      A__			:  Optional[Any]			,  A__			:  Tuple			,  A__			:  Any       ):
    # The converted output model.
    SCREAMING_SNAKE_CASE										=   {}

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

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

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

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

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

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

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

    # The simple map of names for "automated" rules.
    SCREAMING_SNAKE_CASE										=   {
        "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										=   layer_re.match(A__       )

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

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

        # The name of the layer.
        SCREAMING_SNAKE_CASE										=   F"transformer.h.{layer_idx}"

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

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

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

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

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

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

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

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

    # It should be done!
    return output_state_dict





def 	__a     (      ):
    # Create the argument parser.
    SCREAMING_SNAKE_CASE										=   argparse.ArgumentParser()
    parser.add_argument("--print-checkpoint-structure"			,  action="store_true"       )
    parser.add_argument(
        "path_to_checkpoint"			,  type=A__			,  help="Path to the checkpoint file (.zip archive or direct .pt file)"			,  )
    parser.add_argument(
        "--config_file"			,  default=""			,  type=A__			,  help="An optional config json file describing the pre-trained model."			,  )
    SCREAMING_SNAKE_CASE										=   parser.parse_args()

    # Extract the basename.
    SCREAMING_SNAKE_CASE										=   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										=   torch.load(A__			,  map_location="cpu"       )
    else:
        SCREAMING_SNAKE_CASE										=   torch.load(args.path_to_checkpoint			,  map_location="cpu"       )

    SCREAMING_SNAKE_CASE										=   input_state_dict.get("args"			,  A__       )

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

        # Spell out all parameters in case the defaults change.
        SCREAMING_SNAKE_CASE										=   GPTaConfig(
            vocab_size=50257			,  n_positions=1024			,  n_embd=1024			,  n_layer=24			,  n_head=16			,  n_inner=4096			,  activation_function=A__			,  resid_pdrop=0.1			,  embd_pdrop=0.1			,  attn_pdrop=0.1			,  layer_norm_epsilon=1E-5			,  initializer_range=0.0_2			,  summary_type="cls_index"			,  summary_use_proj=A__			,  summary_activation=A__			,  summary_proj_to_labels=A__			,  summary_first_dropout=0.1			,  scale_attn_weights=A__			,  use_cache=A__			,  bos_token_id=50256			,  eos_token_id=50256			,  )
    else:
        SCREAMING_SNAKE_CASE										=   GPTaConfig.from_json_file(args.config_file       )

    SCREAMING_SNAKE_CASE										=   ["GPT2LMHeadModel"]

    # Convert.
    print("Converting"       )
    SCREAMING_SNAKE_CASE										=   convert_megatron_checkpoint(A__			,  A__			,  A__       )

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

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

    SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(A__       )
    SCREAMING_SNAKE_CASE										=   type(A__       ).__name__
    SCREAMING_SNAKE_CASE										=   tokenizer_class

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

    # Save tokenizer based on args
    print(F"Adding {tokenizer_class} tokenizer files"       )
    tokenizer.save_pretrained(A__       )

    # Store the state_dict to file.
    SCREAMING_SNAKE_CASE										=   os.path.join(A__			,  "pytorch_model.bin"       )
    print(F"Saving checkpoint to \"{output_checkpoint_file}\""       )
    torch.save(A__			,  A__       )


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

if __name__ == "__main__":
    main()

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


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
    import tensorflow as tf


__A			:					Dict						=			logging.get_logger(__name__)





@dataclass
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      [
       "no_inference",
       "no_cuda",
       "no_tpu",
       "no_speed",
       "no_memory",
       "no_env_print",
       "no_multi_process",
   ]





   def __init__(       self					:			List[Any]     , **__lowerCamelCase					:			Any			):
       for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
               SCREAMING_SNAKE_CASE										=   deprecated_arg[3:]
               SCREAMING_SNAKE_CASE										=   not kwargs.pop(__lowerCamelCase			)
               logger.warning(
                   f"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"
                   f" {positive_arg}={kwargs[positive_arg]}"			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("tpu_name"     , self.tpu_name			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("device_idx"     , self.device_idx			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("eager_mode"     , self.eager_mode			)
       SCREAMING_SNAKE_CASE										=   kwargs.pop("use_xla"     , self.use_xla			)
       super().__init__(**__lowerCamelCase			)

   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={"help": "Name of TPU"}					,       )
   lowerCamelCase__				=      field(
       default=0					,       metadata={"help": "CPU / GPU device index. Defaults to 0."}					,       )
   lowerCamelCase__				=      field(default=__snake_case					,       metadata={"help": "Benchmark models in eager model."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
       }					,       )





   @cached_property
   def      _snake_case		(       self					:			Optional[int]			):
       requires_backends(self     , ["tf"]			)
       SCREAMING_SNAKE_CASE										=   None
       if self.tpu:
           try:
               if self.tpu_name:
                   SCREAMING_SNAKE_CASE										=   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name			)
               else:
                   SCREAMING_SNAKE_CASE										=   tf.distribute.cluster_resolver.TPUClusterResolver()
           except ValueError:
               SCREAMING_SNAKE_CASE										=   None
       return tpu





   @cached_property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       if self.is_tpu:
           tf.config.experimental_connect_to_cluster(self._setup_tpu			)
           tf.tpu.experimental.initialize_tpu_system(self._setup_tpu			)

           SCREAMING_SNAKE_CASE										=   tf.distribute.TPUStrategy(self._setup_tpu			)
       else:
           # currently no multi gpu is allowed
           if self.is_gpu:
               # TODO: Currently only single GPU is supported
               tf.config.set_visible_devices(self.gpu_list[self.device_idx]     , "GPU"			)
               SCREAMING_SNAKE_CASE										=   tf.distribute.OneDeviceStrategy(device=f"/gpu:{self.device_idx}"			)
           else:
               tf.config.set_visible_devices([]     , "GPU"			)  # disable GPU
               SCREAMING_SNAKE_CASE										=   tf.distribute.OneDeviceStrategy(device=f"/cpu:{self.device_idx}"			)

       return strategy





   @property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       return self._setup_tpu is not None





   @property
   def      _snake_case		(       self					:			Optional[Any]			):
       requires_backends(self     , ["tf"]			)
       return self._setup_strategy





   @property
   def      _snake_case		(       self					:			List[str]			):
       requires_backends(self     , ["tf"]			)
       return tf.config.list_physical_devices("GPU"			)





   @property
   def      _snake_case		(       self					:			Any			):
       requires_backends(self     , ["tf"]			)
       if self.cuda:
           return len(self.gpu_list			)
       return 0





   @property
   def      _snake_case		(       self					:			Dict			):
       return self.n_gpu > 0