import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig





def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Tuple=0	):
 # Format the message.
 if name is None:
  UpperCamelCase_					:						str =					None
 else:
  UpperCamelCase_					:						Optional[int] =					'''.''' * max(0  ,  spaces - 2	) + '''# {:''' + str(50 - spaces	) + '''s}'''
  UpperCamelCase_					:						Optional[Any] =					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    __lowercase     (						lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     List[str]  ,  lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Tuple	):
 # 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 GPT2.
 UpperCamelCase_					:						Tuple =					param.size()
 if checkpoint_version == 1.0:
  # version 1.0 stores [num_heads * hidden_size * num_splits, :]
  UpperCamelCase_					:						str =					(num_heads, hidden_size, num_splits) + input_shape[1:]
  UpperCamelCase_					:						Any =					param.view(*_a	)
  UpperCamelCase_					:						Tuple =					param.transpose(0  ,  2	)
  UpperCamelCase_					:						int =					param.transpose(1  ,  2	).contiguous()
 elif checkpoint_version >= 2.0:
  # other versions store [num_heads * num_splits * hidden_size, :]
  UpperCamelCase_					:						Tuple =					(num_heads, num_splits, hidden_size) + input_shape[1:]
  UpperCamelCase_					:						Any =					param.view(*_a	)
  UpperCamelCase_					:						Optional[Any] =					param.transpose(0  ,  1	).contiguous()
 UpperCamelCase_					:						Union[str, Any] =					param.view(*_a	)
 return param





def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     Optional[Any]	):
 # The converted output model.
 UpperCamelCase_					:						List[str] =					{}

 # old versions did not store training args
 UpperCamelCase_					:						List[str] =					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_					:						int =					ds_args.padded_vocab_size
  UpperCamelCase_					:						List[Any] =					ds_args.max_position_embeddings
  UpperCamelCase_					:						List[Any] =					ds_args.hidden_size
  UpperCamelCase_					:						str =					ds_args.num_layers
  UpperCamelCase_					:						int =					ds_args.num_attention_heads
  UpperCamelCase_					:						int =					ds_args.ffn_hidden_size
  # pprint(config)

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

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

 # The word embeddings.
 UpperCamelCase_					:						Dict =					embeddings['''word_embeddings''']['''weight''']
 # Truncate the embedding table to vocab_size rows.
 UpperCamelCase_					:						List[str] =					word_embeddings[: config.vocab_size, :]
 UpperCamelCase_					:						int =					word_embeddings

 # The position embeddings.
 UpperCamelCase_					:						List[str] =					embeddings['''position_embeddings''']['''weight''']
 # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
 UpperCamelCase_					:						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.
 UpperCamelCase_					:						Dict =					pos_embeddings

 # The transformer.
 UpperCamelCase_					:						Union[str, Any] =					lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

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

 # The simple map of names for "automated" rules.
 UpperCamelCase_					:						Dict =					{
     '''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.
  UpperCamelCase_					:						Union[str, Any] =					layer_re.match(_a	)

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

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

  # The name of the layer.
  UpperCamelCase_					:						Any =					F"transformer.h.{layer_idx}"

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

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

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

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

  # Copy the bias.
  elif weight_or_bias == "bias":
   UpperCamelCase_					:						Optional[int] =					megatron_to_transformers[op_name]
   UpperCamelCase_					:						Union[str, Any] =					val

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

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

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

 # It should be done!
 return output_state_dict





def    __lowercase     (						):
 # Create the argument parser.
 UpperCamelCase_					:						Dict =					argparse.ArgumentParser()
 parser.add_argument('--print-checkpoint-structure'  ,  action='store_true'	)
 parser.add_argument(
     'path_to_checkpoint'  ,  type=_a  ,  help='Path to the checkpoint file (.zip archive or direct .pt file)'  ,  )
 parser.add_argument(
     '--config_file'  ,  default=''  ,  type=_a  ,  help='An optional config json file describing the pre-trained model.'  ,  )
 UpperCamelCase_					:						List[str] =					parser.parse_args()

 # Extract the basename.
 UpperCamelCase_					:						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:
    UpperCamelCase_					:						str =					torch.load(_a  ,  map_location='cpu'	)
 else:
  UpperCamelCase_					:						Any =					torch.load(args.path_to_checkpoint  ,  map_location='cpu'	)

 UpperCamelCase_					:						Tuple =					input_state_dict.get('args'  ,  _a	)

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

  # Spell out all parameters in case the defaults change.
  UpperCamelCase_					:						str =					GPTaConfig(
      vocab_size=50257  ,  n_positions=1024  ,  n_embd=1024  ,  n_layer=24  ,  n_head=16  ,  n_inner=4096  ,  activation_function=_a  ,  resid_pdrop=0.1  ,  embd_pdrop=0.1  ,  attn_pdrop=0.1  ,  layer_norm_epsilon=1e-5  ,  initializer_range=0.0_2  ,  summary_type='cls_index'  ,  summary_use_proj=_a  ,  summary_activation=_a  ,  summary_proj_to_labels=_a  ,  summary_first_dropout=0.1  ,  scale_attn_weights=_a  ,  use_cache=_a  ,  bos_token_id=50256  ,  eos_token_id=50256  ,  )
 else:
  UpperCamelCase_					:						List[Any] =					GPTaConfig.from_json_file(args.config_file	)

 UpperCamelCase_					:						str =					['''GPT2LMHeadModel''']

 # Convert.
 print('Converting'	)
 UpperCamelCase_					:						Union[str, Any] =					convert_megatron_checkpoint(_a  ,  _a  ,  _a	)

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

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

 UpperCamelCase_					:						Optional[int] =					AutoTokenizer.from_pretrained(_a	)
 UpperCamelCase_					:						Optional[int] =					type(_a	).__name__
 UpperCamelCase_					:						List[str] =					tokenizer_class

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

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

 # Store the state_dict to file.
 UpperCamelCase_					:						Any =					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"""

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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class        _UpperCAmelCase       (			lowerCAmelCase__):

 def __init__(     self		:       Any  ,     lowercase_		:       TransformeraDModel  ,     lowercase_		:       AutoencoderKL  ,     lowercase_		:       KarrasDiffusionSchedulers  ,     lowercase_		:       Optional[Dict[int, str]] = None  ,     ):
     super().__init__()
     self.register_modules(transformer=lowercase_  ,     vae=lowercase_  ,     scheduler=lowercase_				)

     # create a imagenet -> id dictionary for easier use
     snake_case_ :     Tuple				=	{}
     if idalabel is not None:
         for key, value in idalabel.items():
             for label in value.split(''','''				):
                 snake_case_ :     str				=	int(lowercase_				)
         snake_case_ :     Any				=	dict(sorted(self.labels.items()				)				)

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Union[str, List[str]]				):

     if not isinstance(lowercase_  ,     lowercase_				):
         snake_case_ :     Tuple				=	list(lowercase_				)

     for l in label:
         if l not in self.labels:
             raise ValueError(
                 f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."				)

     return [self.labels[l] for l in label]






 @torch.no_grad()
 def __call__(     self		:       Optional[int]  ,     lowercase_		:       List[int]  ,     lowercase_		:       float = 4.0  ,     lowercase_		:       Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     lowercase_		:       int = 50  ,     lowercase_		:       Optional[str] = "pil"  ,     lowercase_		:       bool = True  ,     ):
     snake_case_ :     Any				=	len(lowercase_				)
     snake_case_ :     List[str]				=	self.transformer.config.sample_size
     snake_case_ :     Union[str, Any]				=	self.transformer.config.in_channels

     snake_case_ :     str				=	randn_tensor(
         shape=(batch_size, latent_channels, latent_size, latent_size)  ,     generator=lowercase_  ,     device=self.device  ,     dtype=self.transformer.dtype  ,     )
     snake_case_ :     Optional[Any]				=	torch.cat([latents] * 2				) if guidance_scale > 1 else latents

     snake_case_ :     Optional[int]				=	torch.tensor(lowercase_  ,     device=self.device				).reshape(-1				)
     snake_case_ :     Dict				=	torch.tensor([1000] * batch_size  ,     device=self.device				)
     snake_case_ :     Tuple				=	torch.cat([class_labels, class_null]  ,     0				) if guidance_scale > 1 else class_labels

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

     for t in self.progress_bar(self.scheduler.timesteps				):
         if guidance_scale > 1:
             snake_case_ :     List[Any]				=	latent_model_input[: len(lowercase_				) // 2]
             snake_case_ :     Union[str, Any]				=	torch.cat([half, half]  ,     dim=0				)
         snake_case_ :     Optional[Any]				=	self.scheduler.scale_model_input(lowercase_  ,     lowercase_				)

         snake_case_ :     int				=	t
         if not torch.is_tensor(lowercase_				):
             # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
             # This would be a good case for the `match` statement (Python 3.10+)
             snake_case_ :     Tuple				=	latent_model_input.device.type == '''mps'''
             if isinstance(lowercase_  ,     lowercase_				):
                 snake_case_ :     List[str]				=	torch.floataa if is_mps else torch.floataa
             else:
                 snake_case_ :     Optional[int]				=	torch.intaa if is_mps else torch.intaa
             snake_case_ :     List[Any]				=	torch.tensor([timesteps]  ,     dtype=lowercase_  ,     device=latent_model_input.device				)
         elif len(timesteps.shape				) == 0:
             snake_case_ :     str				=	timesteps[None].to(latent_model_input.device				)
         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         snake_case_ :     Tuple				=	timesteps.expand(latent_model_input.shape[0]				)
         # predict noise model_output
         snake_case_ :     List[Any]				=	self.transformer(
             lowercase_  ,     timestep=lowercase_  ,     class_labels=lowercase_				).sample

         # perform guidance
         if guidance_scale > 1:
             snake_case_,	snake_case_ :     Dict				=	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
             snake_case_,	snake_case_ :     Any				=	torch.split(lowercase_  ,     len(lowercase_				) // 2  ,     dim=0				)

             snake_case_ :     int				=	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
             snake_case_ :     str				=	torch.cat([half_eps, half_eps]  ,     dim=0				)

             snake_case_ :     List[Any]				=	torch.cat([eps, rest]  ,     dim=1				)

         # learned sigma
         if self.transformer.config.out_channels // 2 == latent_channels:
             snake_case_,	snake_case_ :     Optional[Any]				=	torch.split(lowercase_  ,     lowercase_  ,     dim=1				)
         else:
             snake_case_ :     List[str]				=	noise_pred

         # compute previous image: x_t -> x_t-1
         snake_case_ :     int				=	self.scheduler.step(lowercase_  ,     lowercase_  ,     lowercase_				).prev_sample

     if guidance_scale > 1:
         snake_case_,	snake_case_ :     Optional[Any]				=	latent_model_input.chunk(2  ,     dim=0				)
     else:
         snake_case_ :     Dict				=	latent_model_input

     snake_case_ :     Union[str, Any]				=	1 / self.vae.config.scaling_factor * latents
     snake_case_ :     Tuple				=	self.vae.decode(lowercase_				).sample

     snake_case_ :     str				=	(samples / 2 + 0.5).clamp(0  ,     1				)

     # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
     snake_case_ :     Union[str, Any]				=	samples.cpu().permute(0  ,     2  ,     3  ,     1				).float().numpy()

     if output_type == "pil":
         snake_case_ :     Union[str, Any]				=	self.numpy_to_pil(lowercase_				)

     if not return_dict:
         return (samples,)

     return ImagePipelineOutput(images=lowercase_				)





