






"""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_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			(  A    ,       A    ,       A=0			):
   # Format the message.
   if name is None:
      UpperCamelCase_     =None
   else:
      UpperCamelCase_     ="." * max(0    ,       spaces - 2			) + "# {:" + str(50 - spaces			) + "s}"
      UpperCamelCase_     =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 						_UpperCamelCase			(  A    ,       A    ,       A    ,       A    ,       A			):
   # 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 BERT.
   UpperCamelCase_     =param.size()
   if checkpoint_version == 1.0:
      # version 1.0 stores [num_heads * hidden_size * num_splits, :]
      UpperCamelCase_     =(num_heads, hidden_size, num_splits) + input_shape[1:]
      UpperCamelCase_     =param.view(*A			)
      UpperCamelCase_     =param.transpose(0    ,       2			)
      UpperCamelCase_     =param.transpose(1    ,       2			).contiguous()
   elif checkpoint_version >= 2.0:
      # other versions store [num_heads * num_splits * hidden_size, :]
      UpperCamelCase_     =(num_heads, num_splits, hidden_size) + input_shape[1:]
      UpperCamelCase_     =param.view(*A			)
      UpperCamelCase_     =param.transpose(0    ,       1			).contiguous()
   UpperCamelCase_     =param.view(*A			)
   return param




def 						_UpperCamelCase			(  A    ,       A    ,       A			):
   # The converted output model.
   UpperCamelCase_     ={}

   # old versions did not store training args
   UpperCamelCase_     =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))

      UpperCamelCase_     =ds_args.tokenizer_type
      UpperCamelCase_     =ds_args.padded_vocab_size
      UpperCamelCase_     =ds_args.max_position_embeddings
      UpperCamelCase_     =ds_args.hidden_size
      UpperCamelCase_     =ds_args.num_layers
      UpperCamelCase_     =ds_args.num_attention_heads
      UpperCamelCase_     =ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
      # pprint(config)

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

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

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

   # The position embeddings.
   UpperCamelCase_     =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.
   UpperCamelCase_     =pos_embeddings

   # The token-type embeddings.
   UpperCamelCase_     =embeddings["tokentype_embeddings"]["weight"]
   # Store the position embeddings.
   UpperCamelCase_     =tokentype_embeddings

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

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

   # The simple map of names for "automated" rules.
   UpperCamelCase_     ={
       "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.
   UpperCamelCase_     =None

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

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

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

      # The name of the layer.
      UpperCamelCase_     =f"""bert.encoder.layer.{layer_idx}"""

      # For layernorm(s), simply store the layer norm.
      if op_name.endswith("layernorm"			):
         UpperCamelCase_     ="attention.ln" if op_name.startswith("input"			) else "ln"
         UpperCamelCase_     =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, ""

         UpperCamelCase_     =fix_query_key_value_ordering(A    ,       A    ,       3    ,       A    ,       A			)
         # Store the tensor as we need the bias as well to interleave QKV and biases.
         UpperCamelCase_     =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.
         UpperCamelCase_     =attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
         UpperCamelCase_     =attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
         UpperCamelCase_     =attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

         # Clear the stored tensor.
         UpperCamelCase_     =None

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

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

   # The pooler.
   UpperCamelCase_     =lm["pooler"]

   # Store the matrix and the bias.
   UpperCamelCase_     =pooler["dense.weight"]
   UpperCamelCase_     =pooler["dense.bias"]

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

   # The transform matrix.
   UpperCamelCase_     =lm_head["dense.weight"]
   UpperCamelCase_     =lm_head["dense.bias"]

   # The transform LN.
   UpperCamelCase_     =lm_head["layernorm.weight"]
   UpperCamelCase_     =lm_head["layernorm.bias"]

   # For the decoder, we replicate the weights.
   UpperCamelCase_     =word_embeddings
   UpperCamelCase_     =lm_head["bias"]

   # The classifier from Megatron (for MLNI).
   UpperCamelCase_     =model["binary_head"]

   # Store the classifier.
   UpperCamelCase_     =binary_head["weight"]
   UpperCamelCase_     =binary_head["bias"]

   # It should be done!
   return output_state_dict




def 						_UpperCamelCase			(  ):
   # Create the argument parser.
   UpperCamelCase_     =argparse.ArgumentParser()
   parser.add_argument("--print-checkpoint-structure"    ,       action="store_true"			)
   parser.add_argument("path_to_checkpoint"    ,       type=A    ,       help="Path to the ZIP file containing the checkpoint"			)
   parser.add_argument(
       "--config_file"    ,       default=""    ,       type=A    ,       help="An optional config json file describing the pre-trained model."    ,       )
   UpperCamelCase_     =parser.parse_args()

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

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

   # Convert.
   print("Converting"			)
   UpperCamelCase_     =convert_megatron_checkpoint(A    ,       A    ,       A			)

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

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

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


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

if __name__ == "__main__":
    main()

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








"""simple docstring"""






import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


A_			  =    {512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"}




def 						_UpperCamelCase			(  A			):
   UpperCamelCase_     =f"""pretrained_models/{model_name}"""
   if not os.path.isfile(A			):
      os.makedirs("pretrained_models"    ,       exist_ok=A			)
      UpperCamelCase_     =f"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
      download_url(A    ,       "pretrained_models"			)
   UpperCamelCase_     =torch.load(A    ,       map_location=lambda A    ,       A			: storage			)
   return model




def 						_UpperCamelCase			(  A			):
   UpperCamelCase_     =download_model(pretrained_models[args.image_size]			)

   UpperCamelCase_     =state_dict["x_embedder.proj.weight"]
   UpperCamelCase_     =state_dict["x_embedder.proj.bias"]
   state_dict.pop("x_embedder.proj.weight"			)
   state_dict.pop("x_embedder.proj.bias"			)

   for depth in range(28			):
      UpperCamelCase_     =state_dict[
          "t_embedder.mlp.0.weight"
      ]
      UpperCamelCase_     =state_dict[
          "t_embedder.mlp.0.bias"
      ]
      UpperCamelCase_     =state_dict[
          "t_embedder.mlp.2.weight"
      ]
      UpperCamelCase_     =state_dict[
          "t_embedder.mlp.2.bias"
      ]
      UpperCamelCase_     =state_dict[
          "y_embedder.embedding_table.weight"
      ]

      UpperCamelCase_     =state_dict[
          f"""blocks.{depth}.adaLN_modulation.1.weight"""
      ]
      UpperCamelCase_     =state_dict[
          f"""blocks.{depth}.adaLN_modulation.1.bias"""
      ]

      UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_     =torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.weight"""]    ,       3    ,       dim=0			)
      UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_     =torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.bias"""]    ,       3    ,       dim=0			)

      UpperCamelCase_     =q
      UpperCamelCase_     =q_bias
      UpperCamelCase_     =k
      UpperCamelCase_     =k_bias
      UpperCamelCase_     =v
      UpperCamelCase_     =v_bias

      UpperCamelCase_     =state_dict[
          f"""blocks.{depth}.attn.proj.weight"""
      ]
      UpperCamelCase_     =state_dict[f"""blocks.{depth}.attn.proj.bias"""]

      UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc1.weight"""]
      UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc1.bias"""]
      UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc2.weight"""]
      UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc2.bias"""]

      state_dict.pop(f"""blocks.{depth}.attn.qkv.weight"""			)
      state_dict.pop(f"""blocks.{depth}.attn.qkv.bias"""			)
      state_dict.pop(f"""blocks.{depth}.attn.proj.weight"""			)
      state_dict.pop(f"""blocks.{depth}.attn.proj.bias"""			)
      state_dict.pop(f"""blocks.{depth}.mlp.fc1.weight"""			)
      state_dict.pop(f"""blocks.{depth}.mlp.fc1.bias"""			)
      state_dict.pop(f"""blocks.{depth}.mlp.fc2.weight"""			)
      state_dict.pop(f"""blocks.{depth}.mlp.fc2.bias"""			)
      state_dict.pop(f"""blocks.{depth}.adaLN_modulation.1.weight"""			)
      state_dict.pop(f"""blocks.{depth}.adaLN_modulation.1.bias"""			)

   state_dict.pop("t_embedder.mlp.0.weight"			)
   state_dict.pop("t_embedder.mlp.0.bias"			)
   state_dict.pop("t_embedder.mlp.2.weight"			)
   state_dict.pop("t_embedder.mlp.2.bias"			)
   state_dict.pop("y_embedder.embedding_table.weight"			)

   UpperCamelCase_     =state_dict["final_layer.adaLN_modulation.1.weight"]
   UpperCamelCase_     =state_dict["final_layer.adaLN_modulation.1.bias"]
   UpperCamelCase_     =state_dict["final_layer.linear.weight"]
   UpperCamelCase_     =state_dict["final_layer.linear.bias"]

   state_dict.pop("final_layer.linear.weight"			)
   state_dict.pop("final_layer.linear.bias"			)
   state_dict.pop("final_layer.adaLN_modulation.1.weight"			)
   state_dict.pop("final_layer.adaLN_modulation.1.bias"			)

   # DiT XL/2
   UpperCamelCase_     =TransformeraDModel(
       sample_size=args.image_size // 8    ,       num_layers=28    ,       attention_head_dim=72    ,       in_channels=4    ,       out_channels=8    ,       patch_size=2    ,       attention_bias=A    ,       num_attention_heads=16    ,       activation_fn="gelu-approximate"    ,       num_embeds_ada_norm=1_000    ,       norm_type="ada_norm_zero"    ,       norm_elementwise_affine=A    ,       )
   transformer.load_state_dict(A    ,       strict=A			)

   UpperCamelCase_     =DDIMScheduler(
       num_train_timesteps=1_000    ,       beta_schedule="linear"    ,       prediction_type="epsilon"    ,       clip_sample=A    ,       )

   UpperCamelCase_     =AutoencoderKL.from_pretrained(args.vae_model			)

   UpperCamelCase_     =DiTPipeline(transformer=A    ,       vae=A    ,       scheduler=A			)

   if args.save:
      pipeline.save_pretrained(args.checkpoint_path			)


if __name__ == "__main__":
    A_			  =    argparse.ArgumentParser()

    parser.add_argument(
        "--image_size",
        default=256,
        type=int,
        required=False,
        help="Image size of pretrained model, either 256 or 512.",
    )
    parser.add_argument(
        "--vae_model",
        default="stabilityai/sd-vae-ft-ema",
        type=str,
        required=False,
        help="Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.",
    )
    parser.add_argument(
        "--save", default=True, type=bool, required=False, help="Whether to save the converted pipeline or not."
    )
    parser.add_argument(
        "--checkpoint_path", default=None, type=str, required=True, help="Path to the output pipeline."
    )

    A_			  =    parser.parse_args()
    main(args)
