

# 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_bert/convert_megatron_bert_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 MegatronBertConfig


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						Dict       ,      _SCREAMING_SNAKE_CASE    :						Tuple       ,      _SCREAMING_SNAKE_CASE    :						Any=0				):





    """simple docstring"""


    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(_SCREAMING_SNAKE_CASE				)

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


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						List[str]       ,      _SCREAMING_SNAKE_CASE    :						str       ,      _SCREAMING_SNAKE_CASE    :						List[Any]       ,      _SCREAMING_SNAKE_CASE    :						Any       ,      _SCREAMING_SNAKE_CASE    :						Dict				):





    """simple docstring"""


    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(*_SCREAMING_SNAKE_CASE				)
        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(*_SCREAMING_SNAKE_CASE				)
        SCREAMING_SNAKE_CASE_													=  param.transpose(0       ,      1				).contiguous()
    SCREAMING_SNAKE_CASE_													=  param.view(*_SCREAMING_SNAKE_CASE				)
    return param


def     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						List[Any]       ,      _SCREAMING_SNAKE_CASE    :						Dict       ,      _SCREAMING_SNAKE_CASE    :						str				):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  {}

    # old versions did not store training args
    SCREAMING_SNAKE_CASE_													=  input_state_dict.get('args'       ,      _SCREAMING_SNAKE_CASE				)
    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.tokenizer_type
        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 if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
        # pprint(config)

    # The number of heads.
    SCREAMING_SNAKE_CASE_													=  config.num_attention_heads
    # The hidden_size per head.
    SCREAMING_SNAKE_CASE_													=  config.hidden_size // heads
    # 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, :]
    # Store the word embeddings.
    SCREAMING_SNAKE_CASE_													=  word_embeddings

    # The position embeddings.
    SCREAMING_SNAKE_CASE_													=  embeddings['position_embeddings']['weight']
    assert pos_embeddings.size(0				) == config.max_position_embeddings and pos_embeddings.size(1				) == config.hidden_size
    # Store the position embeddings.
    SCREAMING_SNAKE_CASE_													=  pos_embeddings

    # The token-type embeddings.
    SCREAMING_SNAKE_CASE_													=  embeddings['tokentype_embeddings']['weight']
    # Store the position embeddings.
    SCREAMING_SNAKE_CASE_													=  tokentype_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': '.attention.output.dense.',
        'self_attention.dense': '.attention.output.dense.',
        'mlp.dense_h_to_4h': '.intermediate.dense.',
        'mlp.dense_4h_to_h': '.output.dense.',
    }

    # Keep track of the attention/query/value tensor.
    SCREAMING_SNAKE_CASE_													=  None

    # Extract the layers.
    for key, val in transformer.items():
        # Match the name.
        SCREAMING_SNAKE_CASE_													=  layer_re.match(_SCREAMING_SNAKE_CASE				)

        # 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"""bert.encoder.layer.{layer_idx}"""

        # For layernorm(s), simply store the layer norm.
        if op_name.endswith('layernorm'				):
            SCREAMING_SNAKE_CASE_													=  'attention.ln' if op_name.startswith('input'				) else 'ln'
            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":
            # Make sure the QKV pointer is nil.
            assert attention_qkv_weight is None, ""

            SCREAMING_SNAKE_CASE_													=  fix_query_key_value_ordering(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      3       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)
            # Store the tensor as we need the bias as well to interleave QKV and biases.
            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":
            # Make sure we read the weight tensor.
            assert attention_qkv_weight is not None, ""

            # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
            SCREAMING_SNAKE_CASE_													=  attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
            SCREAMING_SNAKE_CASE_													=  attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
            SCREAMING_SNAKE_CASE_													=  attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

            SCREAMING_SNAKE_CASE_													=  fix_query_key_value_ordering(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      3       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)
            # Split the bias.
            SCREAMING_SNAKE_CASE_													=  out_val[0 * config.hidden_size : 1 * config.hidden_size]
            SCREAMING_SNAKE_CASE_													=  out_val[1 * config.hidden_size : 2 * config.hidden_size]
            SCREAMING_SNAKE_CASE_													=  out_val[2 * config.hidden_size : 3 * config.hidden_size]

            # Store.
            SCREAMING_SNAKE_CASE_													=  q
            SCREAMING_SNAKE_CASE_													=  q_bias
            SCREAMING_SNAKE_CASE_													=  k
            SCREAMING_SNAKE_CASE_													=  k_bias
            SCREAMING_SNAKE_CASE_													=  v
            SCREAMING_SNAKE_CASE_													=  v_bias

            # Clear the stored tensor.
            SCREAMING_SNAKE_CASE_													=  None

        # Copy weights and biases as is.
        elif weight_or_bias in ["weight", "bias"]:
            SCREAMING_SNAKE_CASE_													=  megatron_to_transformers[op_name]
            SCREAMING_SNAKE_CASE_													=  val

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

    # The pooler.
    SCREAMING_SNAKE_CASE_													=  lm['pooler']

    # Store the matrix and the bias.
    SCREAMING_SNAKE_CASE_													=  pooler['dense.weight']
    SCREAMING_SNAKE_CASE_													=  pooler['dense.bias']

    # The LM head from Megatron (for RACE).
    SCREAMING_SNAKE_CASE_													=  model['lm_head']

    # The transform matrix.
    SCREAMING_SNAKE_CASE_													=  lm_head['dense.weight']
    SCREAMING_SNAKE_CASE_													=  lm_head['dense.bias']

    # The transform LN.
    SCREAMING_SNAKE_CASE_													=  lm_head['layernorm.weight']
    SCREAMING_SNAKE_CASE_													=  lm_head['layernorm.bias']

    # For the decoder, we replicate the weights.
    SCREAMING_SNAKE_CASE_													=  word_embeddings
    SCREAMING_SNAKE_CASE_													=  lm_head['bias']

    # The classifier from Megatron (for MLNI).
    SCREAMING_SNAKE_CASE_													=  model['binary_head']

    # Store the classifier.
    SCREAMING_SNAKE_CASE_													=  binary_head['weight']
    SCREAMING_SNAKE_CASE_													=  binary_head['bias']

    # It should be done!
    return output_state_dict


def     _UpperCAmelCase				(     ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  argparse.ArgumentParser()
    parser.add_argument('--print-checkpoint-structure'       ,      action='store_true'				)
    parser.add_argument('path_to_checkpoint'       ,      type=_SCREAMING_SNAKE_CASE       ,      help='Path to the ZIP file containing the checkpoint'				)
    parser.add_argument(
        '--config_file'       ,      default=''       ,      type=_SCREAMING_SNAKE_CASE       ,      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(_SCREAMING_SNAKE_CASE       ,      map_location='cpu'				)
    else:
        SCREAMING_SNAKE_CASE_													=  torch.load(args.path_to_checkpoint       ,      map_location='cpu'				)

    if args.config_file == "":
        # Default config of megatron-bert 345m
        SCREAMING_SNAKE_CASE_													=  MegatronBertConfig()

        # different megatron-bert-*-345m models have different vocab sizes, so override the default
        # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
        SCREAMING_SNAKE_CASE_													=  input_state_dict['model']['lm_head']['bias'].numel()
    else:
        SCREAMING_SNAKE_CASE_													=  MegatronBertConfig.from_json_file(args.config_file				)

    # Convert.
    print('Converting'				)
    SCREAMING_SNAKE_CASE_													=  convert_megatron_checkpoint(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE				)

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

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

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


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

if __name__ == "__main__":
       main()

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__     :					int								=			{
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					str								=			["CamembertTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Any								=			["CamembertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Union[str, Any]								=			[
           "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "CamembertForCausalLM",
           "CamembertForMaskedLM",
           "CamembertForMultipleChoice",
           "CamembertForQuestionAnswering",
           "CamembertForSequenceClassification",
           "CamembertForTokenClassification",
           "CamembertModel",
           "CamembertPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Optional[Any]								=			[
           "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFCamembertForCausalLM",
           "TFCamembertForMaskedLM",
           "TFCamembertForMultipleChoice",
           "TFCamembertForQuestionAnswering",
           "TFCamembertForSequenceClassification",
           "TFCamembertForTokenClassification",
           "TFCamembertModel",
           "TFCamembertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert import CamembertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert_fast import CamembertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_camembert import (
                  CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CamembertForCausalLM,
                  CamembertForMaskedLM,
                  CamembertForMultipleChoice,
                  CamembertForQuestionAnswering,
                  CamembertForSequenceClassification,
                  CamembertForTokenClassification,
                  CamembertModel,
                  CamembertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_camembert import (
                  TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCamembertForCausalLM,
                  TFCamembertForMaskedLM,
                  TFCamembertForMultipleChoice,
                  TFCamembertForQuestionAnswering,
                  TFCamembertForSequenceClassification,
                  TFCamembertForTokenClassification,
                  TFCamembertModel,
                  TFCamembertPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase__     :					List[str]								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


