
"""simple docstring"""




from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
  from fairscale.optim import OSS


A__   :					List[str]							=   logging.get_logger(__name__)

A__   :					Any							=   {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    'constant': get_constant_schedule,
    'constant_w_warmup': get_constant_schedule_with_warmup,
}


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       def __init__(     self ,				A_=None ,				A_=None ,				*A_ ,				**A_       )     ->	Tuple:





         """simple docstring"""




         super().__init__(*A_ ,				**A_       )

         if config is None:
           assert isinstance(self.model ,				A_       ), (
               "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
               f''' {self.model.__class__}'''
           )
           _lowercase:			Union[str, Any]       =     self.model.config
         else:
           _lowercase:			Union[str, Any]       =     config

         _lowercase:			int       =     data_args
         _lowercase:			str       =     self.config.tgt_vocab_size if isinstance(self.config ,				A_       ) else self.config.vocab_size

         if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
           assert self.config.pad_token_id is not None, (
               "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
               " calculation or doing label smoothing."
           )

         if self.config.pad_token_id is None and self.config.eos_token_id is not None:
           logger.warning(
               f'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
               ''' padding..'''       )

         if self.args.label_smoothing == 0:
           _lowercase:			Dict       =     torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id       )
         else:
           # dynamically import label_smoothed_nll_loss
           from utils import label_smoothed_nll_loss

           _lowercase:			Union[str, Any]       =     label_smoothed_nll_loss
       def 							lowercase_							(     self ,				A_       )     ->	Any:





         """simple docstring"""




         if self.optimizer is None:
           _lowercase:			List[str]       =     ['''bias''', '''LayerNorm.weight''']
           _lowercase:			Union[str, Any]       =     [
               {
                   '''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay       )],
                   '''weight_decay''': self.args.weight_decay,
               },
               {
                   '''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay       )],
                   '''weight_decay''': 0.0,
               },
           ]
           _lowercase:			List[Any]       =     Adafactor if self.args.adafactor else AdamW
           if self.args.adafactor:
             _lowercase:			List[str]       =     Adafactor
             _lowercase:			List[Any]       =     {'''scale_parameter''': False, '''relative_step''': False}
           else:
             _lowercase:			Tuple       =     AdamW
             _lowercase:			Dict       =     {
                 '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
                 '''eps''': self.args.adam_epsilon,
             }
           _lowercase:			List[Any]       =     self.args.learning_rate
           if self.sharded_ddp:
             _lowercase:			List[Any]       =     OSS(
                 params=A_ ,				optim=A_ ,				**A_ ,				)
           else:
             _lowercase:			List[str]       =     optimizer_cls(A_ ,				**A_       )

         if self.lr_scheduler is None:
           _lowercase:			Dict       =     self._get_lr_scheduler(A_       )
         else:  # ignoring --lr_scheduler
           logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''       )
       def 							lowercase_							(     self ,				A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			str       =     arg_to_scheduler[self.args.lr_scheduler]
         if self.args.lr_scheduler == "constant":
           _lowercase:			List[str]       =     schedule_func(self.optimizer       )
         elif self.args.lr_scheduler == "constant_w_warmup":
           _lowercase:			List[str]       =     schedule_func(self.optimizer ,				num_warmup_steps=self.args.warmup_steps       )
         else:
           _lowercase:			Union[str, Any]       =     schedule_func(
               self.optimizer ,				num_warmup_steps=self.args.warmup_steps ,				num_training_steps=A_       )
         return scheduler
       def 							lowercase_							(     self       )     ->	Optional[torch.utils.data.Sampler]:





         """simple docstring"""




         if isinstance(self.train_dataset ,				torch.utils.data.IterableDataset       ):
           return None
         elif is_torch_tpu_available():
           return get_tpu_sampler(self.train_dataset       )
         else:
           if self.args.sortish_sampler:
             self.train_dataset.make_sortish_sampler(
                 self.args.per_device_train_batch_size ,				distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) ,				)

           return (
               RandomSampler(self.train_dataset       )
               if self.args.local_rank == -1
               else DistributedSampler(self.train_dataset       )
           )
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_       )     ->	Optional[int]:





         """simple docstring"""




         if self.args.label_smoothing == 0:
           if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
             # force training to ignore pad token
             _lowercase:			List[str]       =     model(**A_ ,				use_cache=A_       )[0]
             _lowercase:			Optional[int]       =     self.loss_fn(logits.view(-1 ,				logits.shape[-1]       ) ,				labels.view(-1       )       )
           else:
             # compute usual loss via models
             _lowercase ,				_lowercase:			Tuple       =     model(**A_ ,				labels=A_ ,				use_cache=A_       )[:2]
         else:
           # compute label smoothed loss
           _lowercase:			int       =     model(**A_ ,				use_cache=A_       )[0]
           _lowercase:			Union[str, Any]       =     torch.nn.functional.log_softmax(A_ ,				dim=-1       )
           _lowercase ,				_lowercase:			str       =     self.loss_fn(A_ ,				A_ ,				self.args.label_smoothing ,				ignore_index=self.config.pad_token_id       )
         return loss, logits
       def 							lowercase_							(     self ,				A_ ,				A_       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Tuple       =     inputs.pop('''labels'''       )
         _lowercase ,				_lowercase:			Optional[int]       =     self._compute_loss(A_ ,				A_ ,				A_       )
         return loss
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ = None ,				)     ->	Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:





         """simple docstring"""




         _lowercase:			Tuple       =     self._prepare_inputs(A_       )

         _lowercase:			List[Any]       =     {
             '''max_length''': self.data_args.val_max_target_length
             if self.data_args is not None
             else self.config.max_length,
             '''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
         }

         if self.args.predict_with_generate and not self.args.prediction_loss_only:
           _lowercase:			int       =     self.model.generate(
               inputs['''input_ids'''] ,				attention_mask=inputs['''attention_mask'''] ,				**A_ ,				)
           # in case the batch is shorter than max length, the output should be padded
           if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
             _lowercase:			Dict       =     self._pad_tensors_to_max_len(A_ ,				gen_kwargs['''max_length''']       )

         _lowercase:			Union[str, Any]       =     inputs.pop('''labels'''       )
         with torch.no_grad():
           # compute loss on predict data
           _lowercase ,				_lowercase:			List[Any]       =     self._compute_loss(A_ ,				A_ ,				A_       )

         _lowercase:			str       =     loss.mean().detach()
         if self.args.prediction_loss_only:
           return (loss, None, None)

         _lowercase:			List[Any]       =     generated_tokens if self.args.predict_with_generate else logits

         if labels.shape[-1] < gen_kwargs["max_length"]:
           _lowercase:			Optional[int]       =     self._pad_tensors_to_max_len(A_ ,				gen_kwargs['''max_length''']       )

         return (loss, logits, labels)



       def 							lowercase_							(     self ,				A_ ,				A_       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			int       =     self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

         if pad_token_id is None:
           raise ValueError(
               '''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
               f''' padded to `max_length`={max_length}'''       )

         _lowercase:			Dict       =     pad_token_id * torch.ones(
             (tensor.shape[0], max_length) ,				dtype=tensor.dtype ,				device=tensor.device       )
         _lowercase:			List[Any]       =     tensor
         return padded_tensor


"""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  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase=0						):






  """simple docstring"""



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

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




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



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




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			List[Any]       =     {}

  # old versions did not store training args
  _lowercase:			int       =     input_state_dict.get('''args''' ,				_UpperCamelCase						)
  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))

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

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

  # The model.
  _lowercase:			Dict       =     input_state_dict['''model''']
  # The language model.
  _lowercase:			str       =     model['''language_model''']
  # The embeddings.
  _lowercase:			List[Any]       =     lm['''embedding''']

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

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

  # The transformer.
  _lowercase:			Optional[Any]       =     lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

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

  # The simple map of names for "automated" rules.
  _lowercase:			Any       =     {
      '''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.
    _lowercase:			str       =     layer_re.match(_UpperCamelCase						)

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

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

    # The name of the layer.
    _lowercase:			Dict       =     f'''transformer.h.{layer_idx}'''

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

      # Insert a "dummy" tensor for masked_bias.
      _lowercase:			Any       =     torch.tensor(-1e4 ,				dtype=torch.floataa						)
      _lowercase:			Tuple       =     masked_bias

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

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

    # Copy the bias.
    elif weight_or_bias == "bias":
      _lowercase:			List[Any]       =     megatron_to_transformers[op_name]
      _lowercase:			Optional[int]       =     val

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

  # The final layernorm.
  _lowercase:			str       =     transformer['''final_layernorm.weight''']
  _lowercase:			Dict       =     transformer['''final_layernorm.bias''']

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

  # It should be done!
  return output_state_dict




def  _lowerCAmelCase		(		):






  """simple docstring"""



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

  # Extract the basename.
  _lowercase:			Tuple       =     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:
        _lowercase:			str       =     torch.load(_UpperCamelCase ,				map_location='''cpu'''						)
  else:
    _lowercase:			Optional[int]       =     torch.load(args.path_to_checkpoint ,				map_location='''cpu'''						)

  _lowercase:			Dict       =     input_state_dict.get('''args''' ,				_UpperCamelCase						)

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

    # Spell out all parameters in case the defaults change.
    _lowercase:			List[str]       =     GPTaConfig(
        vocab_size=50_257 ,				n_positions=1_024 ,				n_embd=1_024 ,				n_layer=24 ,				n_head=16 ,				n_inner=4_096 ,				activation_function=_UpperCamelCase ,				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=_UpperCamelCase ,				summary_activation=_UpperCamelCase ,				summary_proj_to_labels=_UpperCamelCase ,				summary_first_dropout=0.1 ,				scale_attn_weights=_UpperCamelCase ,				use_cache=_UpperCamelCase ,				bos_token_id=50_256 ,				eos_token_id=50_256 ,				)
  else:
    _lowercase:			Optional[int]       =     GPTaConfig.from_json_file(args.config_file						)

  _lowercase:			str       =     ['''GPT2LMHeadModel''']

  # Convert.
  print('''Converting'''						)
  _lowercase:			Optional[int]       =     convert_megatron_checkpoint(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						)

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

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

  _lowercase:			List[Any]       =     AutoTokenizer.from_pretrained(_UpperCamelCase						)
  _lowercase:			Any       =     type(_UpperCamelCase						).__name__
  _lowercase:			Tuple       =     tokenizer_class

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

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

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


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

if __name__ == "__main__":
  main()

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