




# 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 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase=0							)			->		List[Any]:





  """simple docstring"""





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





  """simple docstring"""





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


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		str:





  """simple docstring"""





  _lowercase: Optional[int]						     =    {}

  # old versions did not store training args
  _lowercase: str						     =    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: List[Any]						     =    ds_args.tokenizer_type
    _lowercase: Union[str, Any]						     =    ds_args.padded_vocab_size
    _lowercase: Optional[int]						     =    ds_args.max_position_embeddings
    _lowercase: Union[str, Any]						     =    ds_args.hidden_size
    _lowercase: Any						     =    ds_args.num_layers
    _lowercase: Any						     =    ds_args.num_attention_heads
    _lowercase: int						     =    ds_args.ffn_hidden_size if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
    # pprint(config)

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

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

  # The word embeddings.
  _lowercase: int						     =    embeddings['word_embeddings']['weight']
  # Truncate the embedding table to vocab_size rows.
  _lowercase: Any						     =    word_embeddings[: config.vocab_size, :]
  # Store the word embeddings.
  _lowercase: Union[str, Any]						     =    word_embeddings

  # The position embeddings.
  _lowercase: List[Any]						     =    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.
  _lowercase: Dict						     =    pos_embeddings

  # The token-type embeddings.
  _lowercase: Any						     =    embeddings['tokentype_embeddings']['weight']
  # Store the position embeddings.
  _lowercase: Optional[int]						     =    tokentype_embeddings

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

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

  # The simple map of names for "automated" rules.
  _lowercase: List[str]						     =    {
      '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.
  _lowercase: int						     =    None

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

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

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

    # The name of the layer.
    _lowercase: int						     =    F'bert.encoder.layer.{layer_idx}'

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith('layernorm'							):
      _lowercase: Optional[Any]						     =    'attention.ln' if op_name.startswith('input'							) else 'ln'
      _lowercase: Dict						     =    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, ""

      _lowercase: Dict						     =    fix_query_key_value_ordering(UpperCamelCase					,     UpperCamelCase					,     3					,     UpperCamelCase					,     UpperCamelCase							)
      # Store the tensor as we need the bias as well to interleave QKV and biases.
      _lowercase: 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":
      # 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.
      _lowercase: Tuple						     =    attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
      _lowercase: Dict						     =    attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
      _lowercase: Any						     =    attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

      _lowercase: List[str]						     =    fix_query_key_value_ordering(UpperCamelCase					,     UpperCamelCase					,     3					,     UpperCamelCase					,     UpperCamelCase							)
      # Split the bias.
      _lowercase: Union[str, Any]						     =    out_val[0 * config.hidden_size : 1 * config.hidden_size]
      _lowercase: Dict						     =    out_val[1 * config.hidden_size : 2 * config.hidden_size]
      _lowercase: List[str]						     =    out_val[2 * config.hidden_size : 3 * config.hidden_size]

      # Store.
      _lowercase: List[str]						     =    q
      _lowercase: Tuple						     =    q_bias
      _lowercase: Optional[Any]						     =    k
      _lowercase: List[str]						     =    k_bias
      _lowercase: List[str]						     =    v
      _lowercase: List[str]						     =    v_bias

      # Clear the stored tensor.
      _lowercase: Union[str, Any]						     =    None

    # Copy weights and biases as is.
    elif weight_or_bias in ["weight", "bias"]:
      _lowercase: Union[str, Any]						     =    megatron_to_transformers[op_name]
      _lowercase: List[str]						     =    val

    # The final layernorm.
  _lowercase: int						     =    transformer['final_layernorm.weight']
  _lowercase: Optional[Any]						     =    transformer['final_layernorm.bias']

  # The pooler.
  _lowercase: Union[str, Any]						     =    lm['pooler']

  # Store the matrix and the bias.
  _lowercase: List[str]						     =    pooler['dense.weight']
  _lowercase: int						     =    pooler['dense.bias']

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

  # The transform matrix.
  _lowercase: Any						     =    lm_head['dense.weight']
  _lowercase: Any						     =    lm_head['dense.bias']

  # The transform LN.
  _lowercase: int						     =    lm_head['layernorm.weight']
  _lowercase: int						     =    lm_head['layernorm.bias']

  # For the decoder, we replicate the weights.
  _lowercase: Dict						     =    word_embeddings
  _lowercase: Optional[int]						     =    lm_head['bias']

  # The classifier from Megatron (for MLNI).
  _lowercase: List[Any]						     =    model['binary_head']

  # Store the classifier.
  _lowercase: Union[str, Any]						     =    binary_head['weight']
  _lowercase: str						     =    binary_head['bias']

  # It should be done!
  return output_state_dict


def 		_lowerCamelCase				(					)			->		Any:





  """simple docstring"""





  _lowercase: Tuple						     =    argparse.ArgumentParser()
  parser.add_argument('--print-checkpoint-structure'					,     action='store_true'							)
  parser.add_argument('path_to_checkpoint'					,     type=UpperCamelCase					,     help='Path to the ZIP file containing the checkpoint'							)
  parser.add_argument(
      '--config_file'					,     default=''					,     type=UpperCamelCase					,     help='An optional config json file describing the pre-trained model.'					,     )
  _lowercase: Optional[Any]						     =    parser.parse_args()

  # Extract the basename.
  _lowercase: List[Any]						     =    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: Tuple						     =    torch.load(UpperCamelCase					,     map_location='cpu'							)
  else:
    _lowercase: Optional[Any]						     =    torch.load(args.path_to_checkpoint					,     map_location='cpu'							)

  if args.config_file == "":
    # Default config of megatron-bert 345m
    _lowercase: Optional[int]						     =    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
    _lowercase: Any						     =    input_state_dict['model']['lm_head']['bias'].numel()
  else:
    _lowercase: Optional[Any]						     =    MegatronBertConfig.from_json_file(args.config_file							)

  # Convert.
  print('Converting'							)
  _lowercase: str						     =    convert_megatron_checkpoint(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)

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

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

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






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      UpperCAmelCase						(			A   ):






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Tuple     )->			Any:
     super().__init__()
     self.register_modules(unet=UpperCamelCase_						,					scheduler=UpperCamelCase_     )






   @torch.no_grad()
   def __call__(	self   :       Optional[int]						,					UpperCamelCase_   :       int = 1						,					UpperCamelCase_   :       Optional[torch.Generator] = None						,					UpperCamelCase_   :       int = 50						,					UpperCamelCase_   :       Optional[str] = "pil"						,					UpperCamelCase_   :       bool = True						,					**UpperCamelCase_   :       Dict						,					)->			Union[ImagePipelineOutput, Tuple]:
     _lowercase: List[str]						     =    torch.randn(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,					generator=UpperCamelCase_						,					)
     _lowercase: str						     =    image.to(self.device     )

     # set step values
     self.scheduler.set_timesteps(UpperCamelCase_     )

     for t in self.progress_bar(self.scheduler.timesteps     ):
       # 1. predict noise model_output
       _lowercase: int						     =    self.unet(UpperCamelCase_						,					UpperCamelCase_     ).sample

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       _lowercase: List[Any]						     =    self.scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ).prev_sample

     _lowercase: str						     =    (image / 2 + 0.5).clamp(0						,					1     )
     _lowercase: List[str]						     =    image.cpu().permute(0						,					2						,					3						,					1     ).numpy()
     if output_type == "pil":
       _lowercase: Any						     =    self.numpy_to_pil(UpperCamelCase_     )

     if not return_dict:
       return (image,), "This is a local test"

     return ImagePipelineOutput(images=UpperCamelCase_     ), "This is a local test"
