


"""simple docstring"""



import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]						,							lowercase  :  int						,							lowercase  :  Dict=0 )	->						Any:


    """simple docstring"""





    if name is None:
        snake_case    :      List[Any]      					=    None
    else:
        snake_case    :      int      					=    '''.''' * max(0						,							spaces - 2 ) + '''# {:''' + str(50 - spaces ) + '''s}'''
        snake_case    :      Tuple      					=    fmt.format(_snake_case )

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







def   __lowerCAmelCase						(						lowercase  :  Dict						,							lowercase  :  Any						,							lowercase  :  int						,							lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int] )	->						Optional[int]:


    """simple docstring"""





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







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  List[str]						,							lowercase  :  Tuple )	->						Union[str, Any]:


    """simple docstring"""





    snake_case    :      List[Any]      					=    {}

    # old versions did not store training args
    snake_case    :      Any      					=    input_state_dict.get("args"						,							_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))

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

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

    # The model.
    snake_case    :      List[str]      					=    input_state_dict['''model''']
    # The language model.
    snake_case    :      Optional[Any]      					=    model['''language_model''']
    # The embeddings.
    snake_case    :      Union[str, Any]      					=    lm['''embedding''']

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

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

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

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

    # The simple map of names for "automated" rules.
    snake_case    :      int      					=    {
        '''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.
        snake_case    :      int      					=    layer_re.match(_snake_case )

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

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

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

        # For layernorm(s), simply store the layer norm.
        if op_name.endswith("layernorm" ):
            snake_case    :      Dict      					=    '''ln_1''' if op_name.startswith("input" ) else '''ln_2'''
            snake_case    :      Optional[int]      					=    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.
            snake_case    :      List[Any]      					=    torch.tril(torch.ones((n_positions, n_positions)						,							dtype=torch.floataa ) ).view(
                1						,							1						,							_snake_case						,							_snake_case )
            snake_case    :      List[str]      					=    causal_mask

            # Insert a "dummy" tensor for masked_bias.
            snake_case    :      Optional[Any]      					=    torch.tensor(-1e4						,							dtype=torch.floataa )
            snake_case    :      Optional[Any]      					=    masked_bias

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

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

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

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

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

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

    # It should be done!
    return output_state_dict







def   __lowerCAmelCase						(						)	->						Tuple:


    """simple docstring"""





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

    # Extract the basename.
    snake_case    :      Optional[int]      					=    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:
                snake_case    :      List[Any]      					=    torch.load(_snake_case						,							map_location="cpu" )
    else:
        snake_case    :      int      					=    torch.load(args.path_to_checkpoint						,							map_location="cpu" )

    snake_case    :      List[str]      					=    input_state_dict.get("args"						,							_snake_case )

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

        # Spell out all parameters in case the defaults change.
        snake_case    :      Optional[int]      					=    GPTaConfig(
            vocab_size=5_0257						,							n_positions=1024						,							n_embd=1024						,							n_layer=24						,							n_head=16						,							n_inner=4096						,							activation_function=_snake_case						,							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=_snake_case						,							summary_activation=_snake_case						,							summary_proj_to_labels=_snake_case						,							summary_first_dropout=0.1						,							scale_attn_weights=_snake_case						,							use_cache=_snake_case						,							bos_token_id=5_0256						,							eos_token_id=5_0256						,							)
    else:
        snake_case    :      Any      					=    GPTaConfig.from_json_file(args.config_file )

    snake_case    :      Optional[Any]      					=    ['''GPT2LMHeadModel''']

    # Convert.
    print("Converting" )
    snake_case    :      int      					=    convert_megatron_checkpoint(_snake_case						,							_snake_case						,							_snake_case )

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

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

    snake_case    :      Optional[Any]      					=    AutoTokenizer.from_pretrained(_snake_case )
    snake_case    :      Dict      					=    type(_snake_case ).__name__
    snake_case    :      Dict      					=    tokenizer_class

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

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

    # Store the state_dict to file.
    snake_case    :      Optional[Any]      					=    os.path.join(_snake_case						,							"pytorch_model.bin" )
    print(F'Saving checkpoint to "{output_checkpoint_file}"' )
    torch.save(_snake_case						,							_snake_case )


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

if __name__ == "__main__":
  main()

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









"""simple docstring"""





import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 						snake_case_(				a__	):






     def __init__(		self     :    int    , UpperCamelCase_     :    VQModel    , UpperCamelCase_     :    UNetaDModel    , UpperCamelCase_     :    DDIMScheduler ):
           super().__init__()
           self.register_modules(vqvae=UpperCamelCase_    , unet=UpperCamelCase_    , scheduler=UpperCamelCase_ )






     @torch.no_grad()
     def __call__(		self     :    Union[str, Any]    , UpperCamelCase_     :    int = 1    , UpperCamelCase_     :    Optional[Union[torch.Generator, List[torch.Generator]]] = None    , UpperCamelCase_     :    float = 0.0    , UpperCamelCase_     :    int = 5_0    , UpperCamelCase_     :    Optional[str] = "pil"    , UpperCamelCase_     :    bool = True    , **UpperCamelCase_     :    Optional[int]    , ):
           lowerCAmelCase    :		Dict				=      randn_tensor(
               (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    , generator=UpperCamelCase_    , )
           lowerCAmelCase    :		Optional[int]				=      latents.to(self.device )

           # scale the initial noise by the standard deviation required by the scheduler
           lowerCAmelCase    :		List[str]				=      latents * self.scheduler.init_noise_sigma

           self.scheduler.set_timesteps(UpperCamelCase_ )

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           lowerCAmelCase    :		Any				=      '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )

           lowerCAmelCase    :		List[str]				=      {}
           if accepts_eta:
                 lowerCAmelCase    :		List[Any]				=      eta

           for t in self.progress_bar(self.scheduler.timesteps ):
                 lowerCAmelCase    :		List[str]				=      self.scheduler.scale_model_input(UpperCamelCase_    , UpperCamelCase_ )
                 # predict the noise residual
                 lowerCAmelCase    :		Tuple				=      self.unet(UpperCamelCase_    , UpperCamelCase_ ).sample
                 # compute the previous noisy sample x_t -> x_t-1
                 lowerCAmelCase    :		Optional[Any]				=      self.scheduler.step(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , **UpperCamelCase_ ).prev_sample

           # decode the image latents with the VAE
           lowerCAmelCase    :		Dict				=      self.vqvae.decode(UpperCamelCase_ ).sample

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

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=UpperCamelCase_ )


