




import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class       A      :
     """simple docstring"""

     def __init__(     self	: Tuple						,	A__	: Any						,	A__	: Union[str, Any]=sys.maxsize      )					->							Optional[int]:
           lowerCamelCase__   	=					'bilinear'
           lowerCamelCase__   	=					max_size
           lowerCamelCase__   	=					short_edge_length




     def __call__(     self	: Optional[Any]						,	A__	: List[Any]      )					->							Dict:
           lowerCamelCase__   	=					[]
           for img in imgs:
                 lowerCamelCase__	,		lowerCamelCase__   	=					img.shape[:2]
                 # later: provide list and randomly choose index for resize
                 lowerCamelCase__   	=					np.random.randint(self.short_edge_length[0]						,	self.short_edge_length[1] + 1      )
                 if size == 0:
                       return img
                 lowerCamelCase__   	=					size * 1.0 / min(A__						,	A__      )
                 if h < w:
                       lowerCamelCase__	,		lowerCamelCase__   	=					size, scale * w
                 else:
                       lowerCamelCase__	,		lowerCamelCase__   	=					scale * h, size
                 if max(A__						,	A__      ) > self.max_size:
                       lowerCamelCase__   	=					self.max_size * 1.0 / max(A__						,	A__      )
                       lowerCamelCase__   	=					newh * scale
                       lowerCamelCase__   	=					neww * scale
                 lowerCamelCase__   	=					int(neww + 0.5      )
                 lowerCamelCase__   	=					int(newh + 0.5      )

                 if img.dtype == np.uinta:
                       lowerCamelCase__   	=					Image.fromarray(A__      )
                       lowerCamelCase__   	=					pil_image.resize((neww, newh)						,	PILImageResampling.BILINEAR      )
                       lowerCamelCase__   	=					np.asarray(A__      )
                 else:
                       lowerCamelCase__   	=					img.permute(2						,	0						,	1      ).unsqueeze(0      )  # 3, 0, 1)  # hw(c) -> nchw
                       lowerCamelCase__   	=					nn.functional.interpolate(
                           A__						,	(newh, neww)						,	mode=self.interp_method						,	align_corners=A__      ).squeeze(0      )
                 img_augs.append(A__      )

           return img_augs






class       A      :
     """simple docstring"""

     def __init__(     self	: Union[str, Any]						,	A__	: int      )					->							int:
           lowerCamelCase__   	=					ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]						,	cfg.INPUT.MAX_SIZE_TEST      )
           lowerCamelCase__   	=					cfg.INPUT.FORMAT
           lowerCamelCase__   	=					cfg.SIZE_DIVISIBILITY
           lowerCamelCase__   	=					cfg.PAD_VALUE
           lowerCamelCase__   	=					cfg.INPUT.MAX_SIZE_TEST
           lowerCamelCase__   	=					cfg.MODEL.DEVICE
           lowerCamelCase__   	=					torch.tensor(cfg.MODEL.PIXEL_STD      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )						,	1						,	1      )
           lowerCamelCase__   	=					torch.tensor(cfg.MODEL.PIXEL_MEAN      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )						,	1						,	1      )
           lowerCamelCase__   	=					lambda A__      : (x - self.pixel_mean) / self.pixel_std




     def 			__snake_case (     self	: List[str]						,	A__	: Any      )					->							Dict:
           lowerCamelCase__   	=					tuple(max(A__      ) for s in zip(*[img.shape for img in images]      )      )
           lowerCamelCase__   	=					[im.shape[-2:] for im in images]
           lowerCamelCase__   	=					[
               nn.functional.pad(
                   A__						,	[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]						,	value=self.pad_value						,	)
               for size, im in zip(A__						,	A__      )
           ]

           return torch.stack(A__      ), torch.tensor(A__      )




     def __call__(     self	: Tuple						,	A__	: Optional[int]						,	A__	: Tuple=False      )					->							Union[str, Any]:
           with torch.no_grad():
                 if not isinstance(A__						,	A__      ):
                       lowerCamelCase__   	=					[images]
                 if single_image:
                       assert len(A__      ) == 1
                 for i in range(len(A__      )      ):
                       if isinstance(images[i]						,	torch.Tensor      ):
                             images.insert(A__						,	images.pop(A__      ).to(self.device      ).float()      )
                       elif not isinstance(images[i]						,	torch.Tensor      ):
                             images.insert(
                                 A__						,	torch.as_tensor(img_tensorize(images.pop(A__      )						,	input_format=self.input_format      )      )
                                 .to(self.device      )
                                 .float()						,	)
            # resize smallest edge
                 lowerCamelCase__   	=					torch.tensor([im.shape[:2] for im in images]      )
                 lowerCamelCase__   	=					self.aug(A__      )
                 # transpose images and convert to torch tensors
                 # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
                 # now normalize before pad to avoid useless arithmetic
                 lowerCamelCase__   	=					[self.normalizer(A__      ) for x in images]
                 # now pad them to do the following operations
                 lowerCamelCase__	,		lowerCamelCase__   	=					self.pad(A__      )
                 # Normalize

                 if self.size_divisibility > 0:
                       raise NotImplementedError()
                 # pad
                 lowerCamelCase__   	=					torch.true_divide(A__						,	A__      )
                 if single_image:
                       return images[0], sizes[0], scales_yx[0]
                 else:
                       return images, sizes, scales_yx

def       lowerCAmelCase_      (	lowerCamelCase:							List[str]  ,					lowerCamelCase:							Optional[Any]    )       ->      Tuple:


      '''simple docstring'''




      boxes[:, 0::2] *= scale_yx[:, 1]
      boxes[:, 1::2] *= scale_yx[:, 0]
      return boxes


def       lowerCAmelCase_      (	lowerCamelCase:							List[str]  ,					lowerCamelCase:							Tuple[int, int]    )       ->      List[Any]:


      '''simple docstring'''




      assert torch.isfinite(lowerCamelCase    ).all(), "Box tensor contains infinite or NaN!"
      lowerCamelCase__	,		lowerCamelCase__   	=					box_size
      tensor[:, 0].clamp_(min=0  ,					max=lowerCamelCase    )
      tensor[:, 1].clamp_(min=0  ,					max=lowerCamelCase    )
      tensor[:, 2].clamp_(min=0  ,					max=lowerCamelCase    )
      tensor[:, 3].clamp_(min=0  ,					max=lowerCamelCase    )





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

def       lowerCAmelCase_      (	lowerCamelCase:							str  ,					lowerCamelCase:							List[str]  ,					lowerCamelCase:							List[Any]=0    )       ->      Dict:


      '''simple docstring'''




      if name is None:
            lowerCamelCase__   	=					None
      else:
            lowerCamelCase__   	=					'.' * max(0  ,					spaces - 2    ) + '# {:' + str(50 - spaces    ) + 's}'
            lowerCamelCase__   	=					fmt.format(lowerCamelCase    )

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

def       lowerCAmelCase_      (	lowerCamelCase:							Tuple  ,					lowerCamelCase:							Dict  ,					lowerCamelCase:							List[str]  ,					lowerCamelCase:							Any    )       ->      Optional[int]:


      '''simple docstring'''




      lowerCamelCase__   	=					param.size()
      # other versions store [num_heads * num_splits * hidden_size, :]
      lowerCamelCase__   	=					(num_heads, num_splits, hidden_size) + input_shape[1:]
      lowerCamelCase__   	=					param.view(*lowerCamelCase    )
      lowerCamelCase__   	=					param.transpose(0  ,					1    ).contiguous()
      lowerCamelCase__   	=					param.view(*lowerCamelCase    )
      return param

def       lowerCAmelCase_      (	lowerCamelCase:							List[str]  ,					lowerCamelCase:							int    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					config.n_positions
      lowerCamelCase__   	=					config.n_layer
      lowerCamelCase__   	=					config.vocab_size
      lowerCamelCase__   	=					config.n_head
      lowerCamelCase__   	=					config.n_embd // config.n_head

      lowerCamelCase__   	=					sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
      lowerCamelCase__   	=					{
          'transformer.wte.weight': word_embeddings,
          'transformer.wpe.weight': sd_megatron['model.language_model.embedding.position_embeddings.weight'],
          'transformer.ln_f.weight': sd_megatron['model.language_model.encoder.final_layernorm.weight'],
          'transformer.ln_f.bias': sd_megatron['model.language_model.encoder.final_layernorm.bias'],
      }

      lowerCamelCase__   	=					'model.language_model.encoder.layers.'
      for i in range(lowerCamelCase    ):
            lowerCamelCase__   	=					torch.tril(torch.ones((n_positions, n_positions)  ,					dtype=torch.bool    )    )
            lowerCamelCase__   	=					causal_mask.view(1  ,					1  ,					lowerCamelCase  ,					lowerCamelCase    )
            lowerCamelCase__   	=					causal_mask
            lowerCamelCase__   	=					torch.tensor(-1e4  ,					dtype=torch.bfloataa    )

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
            lowerCamelCase__   	=					fix_query_key_value_ordering(lowerCamelCase  ,					3  ,					lowerCamelCase  ,					lowerCamelCase    )
            lowerCamelCase__   	=					vala.transpose(0  ,					1    ).contiguous()

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
            lowerCamelCase__   	=					fix_query_key_value_ordering(lowerCamelCase  ,					3  ,					lowerCamelCase  ,					lowerCamelCase    )
            lowerCamelCase__   	=					vala

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.dense.weight"""].transpose(
                0  ,					1    )
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.dense.bias"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.post_attention_layernorm.weight"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.post_attention_layernorm.bias"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.weight"""].transpose(0  ,					1    )
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.bias"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.weight"""].transpose(
                0  ,					1    )
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.bias"""]

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

      return sd_hf

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[int]  ,					lowerCamelCase:							List[Any]    )       ->      Tuple:


      '''simple docstring'''




      lowerCamelCase__   	=					6_40_00
      lowerCamelCase__   	=					config_megatron['encoder_seq_length']
      lowerCamelCase__   	=					config_megatron['hidden_size']
      lowerCamelCase__   	=					config_megatron['num_layers']
      lowerCamelCase__   	=					config_megatron['num_attention_heads']
      lowerCamelCase__   	=					config_megatron['ffn_hidden_size']
      lowerCamelCase__   	=					'gelu'
      lowerCamelCase__   	=					0.1
      lowerCamelCase__   	=					0.1
      lowerCamelCase__   	=					0.1
      lowerCamelCase__   	=					config_megatron['layernorm_epsilon']  # 1e-5
      lowerCamelCase__   	=					config_megatron['init_method_std']  # 0.02
      lowerCamelCase__   	=					config_megatron['apply_query_key_layer_scaling']  # True
      lowerCamelCase__   	=					True
      lowerCamelCase__   	=					True

      # This identifies the 6.7B (7B) model which uses a different tokenizer
      if config_megatron["hidden_size"] == 40_96:
            lowerCamelCase__   	=					1  # <|endoftext|>
            lowerCamelCase__   	=					1  # <|endoftext|>
            lowerCamelCase__   	=					0  # <unk>
      else:
            lowerCamelCase__   	=					2  # <s>
            lowerCamelCase__   	=					3  # <|endoftext|>
            lowerCamelCase__   	=					0  # <pad>

      return config_hf

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[Any]    )       ->      Dict:


      '''simple docstring'''




      print(lowerCamelCase    )

      lowerCamelCase__   	=					args.checkpoint_path
      lowerCamelCase__   	=					args.save_path
      if isfile(lowerCamelCase    ):
            raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}"""    )

      # Load the model.
      lowerCamelCase__   	=					torch.load(lowerCamelCase  ,					map_location='cpu'    )

      # Load the config.
      lowerCamelCase__   	=					checkpoint['hyper_parameters']['cfg']
      lowerCamelCase__   	=					GPTaConfig()
      lowerCamelCase__   	=					copy_config(config_hf=lowerCamelCase  ,					config_megatron=lowerCamelCase    )
      lowerCamelCase__   	=					['GPT2LMHeadModel']

      lowerCamelCase__   	=					checkpoint['state_dict']

      # Convert.
      print('Converting'    )
      lowerCamelCase__   	=					convert_megatron_checkpoint(lowerCamelCase  ,					lowerCamelCase    )

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

      lowerCamelCase__   	=					'GPTSw3Tokenizer'

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

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


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE     : Optional[int]      =argparse.ArgumentParser()
      parser.add_argument(
          """--checkpoint_path""",
          type=str,
          required=True,
          help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
      )
      parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
      parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
      __SCREAMING_SNAKE_CASE     : Tuple      =parser.parse_args()
      main(_args)