code: 
  
  
  
  
  
  
  """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()
  
  ####################################################################################################
  
style_context: 
  
  
  
  
  
  
  """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)
  