





"""simple docstring"""




import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=0   ):






  """simple docstring"""





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

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





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Tuple									=    param.size()
  # other versions store [num_heads * num_splits * hidden_size, :]
  A_    :			Optional[Any]									=    (num_heads, num_splits, hidden_size) + input_shape[1:]
  A_    :			int									=    param.view(*_UpperCAmelCase   )
  A_    :			Optional[int]									=    param.transpose(0					,   1   ).contiguous()
  A_    :			int									=    param.view(*_UpperCAmelCase   )
  return param





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			List[str]									=    config.n_positions
  A_    :			Any									=    config.n_layer
  A_    :			str									=    config.vocab_size
  A_    :			int									=    config.n_head
  A_    :			Union[str, Any]									=    config.n_embd // config.n_head

  A_    :			Dict									=    sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
  A_    :			str									=    {
      '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'],
  }

  A_    :			List[str]									=    'model.language_model.encoder.layers.'
  for i in range(_UpperCAmelCase   ):
    A_    :			List[Any]									=    torch.tril(torch.ones((n_positions, n_positions)					,   dtype=torch.bool   )   )
    A_    :			Dict									=    causal_mask.view(1					,   1					,   _UpperCAmelCase					,   _UpperCAmelCase   )
    A_    :			str									=    causal_mask
    A_    :			Optional[Any]									=    torch.tensor(-1E4					,   dtype=torch.bfloataa   )

    A_    :			Optional[Any]									=    sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
    A_    :			Optional[int]									=    sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

    A_    :			str									=    sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
    A_    :			Optional[int]									=    fix_query_key_value_ordering(_UpperCAmelCase					,   3					,   _UpperCAmelCase					,   _UpperCAmelCase   )
    A_    :			int									=    vala.transpose(0					,   1   ).contiguous()

    A_    :			int									=    sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
    A_    :			List[Any]									=    fix_query_key_value_ordering(_UpperCAmelCase					,   3					,   _UpperCAmelCase					,   _UpperCAmelCase   )
    A_    :			str									=    vala

    A_    :			Dict									=    sd_megatron[f"""{pf}{i}.self_attention.dense.weight"""].transpose(
        0					,   1   )
    A_    :			Optional[int]									=    sd_megatron[f"""{pf}{i}.self_attention.dense.bias"""]
    A_    :			Union[str, Any]									=    sd_megatron[f"""{pf}{i}.post_attention_layernorm.weight"""]
    A_    :			Tuple									=    sd_megatron[f"""{pf}{i}.post_attention_layernorm.bias"""]
    A_    :			Optional[Any]									=    sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.weight"""].transpose(0					,   1   )
    A_    :			Any									=    sd_megatron[f"""{pf}{i}.mlp.dense_h_to_4h.bias"""]
    A_    :			Any									=    sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.weight"""].transpose(
        0					,   1   )
    A_    :			List[Any]									=    sd_megatron[f"""{pf}{i}.mlp.dense_4h_to_h.bias"""]

  # For LM head, transformers' wants the matrix to weight embeddings.
  A_    :			List[str]									=    word_embeddings

  return sd_hf





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Union[str, Any]									=    64000
  A_    :			List[Any]									=    config_megatron['encoder_seq_length']
  A_    :			List[str]									=    config_megatron['hidden_size']
  A_    :			List[str]									=    config_megatron['num_layers']
  A_    :			List[Any]									=    config_megatron['num_attention_heads']
  A_    :			List[Any]									=    config_megatron['ffn_hidden_size']
  A_    :			Optional[int]									=    'gelu'
  A_    :			List[Any]									=    0.1
  A_    :			List[Any]									=    0.1
  A_    :			Tuple									=    0.1
  A_    :			int									=    config_megatron['layernorm_epsilon']  # 1e-5
  A_    :			int									=    config_megatron['init_method_std']  # 0.02
  A_    :			List[str]									=    config_megatron['apply_query_key_layer_scaling']  # True
  A_    :			int									=    True
  A_    :			Union[str, Any]									=    True

  # This identifies the 6.7B (7B) model which uses a different tokenizer
  if config_megatron["hidden_size"] == 4096:
    A_    :			List[str]									=    1  # <|endoftext|>
    A_    :			str									=    1  # <|endoftext|>
    A_    :			Any									=    0  # <unk>
  else:
    A_    :			Any									=    2  # <s>
    A_    :			Optional[Any]									=    3  # <|endoftext|>
    A_    :			int									=    0  # <pad>

  return config_hf





def 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





  print(_UpperCAmelCase   )

  A_    :			List[str]									=    args.checkpoint_path
  A_    :			Union[str, Any]									=    args.save_path
  if isfile(_UpperCAmelCase   ):
    raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}"""   )

  # Load the model.
  A_    :			List[Any]									=    torch.load(_UpperCAmelCase					,   map_location='cpu'   )

  # Load the config.
  A_    :			Any									=    checkpoint['hyper_parameters']['cfg']
  A_    :			Union[str, Any]									=    GPTaConfig()
  A_    :			Union[str, Any]									=    copy_config(config_hf=_UpperCAmelCase					,   config_megatron=_UpperCAmelCase   )
  A_    :			Optional[Any]									=    ['GPT2LMHeadModel']

  A_    :			Tuple									=    checkpoint['state_dict']

  # Convert.
  print('Converting'   )
  A_    :			int									=    convert_megatron_checkpoint(_UpperCAmelCase					,   _UpperCAmelCase   )

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

  A_    :			Optional[int]									=    'GPTSw3Tokenizer'

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

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


if __name__ == "__main__":
   lowerCamelCase_	:		List[Any]         =   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')
   lowerCamelCase_	:		str         =   parser.parse_args()
   main(_args)






"""simple docstring"""




import warnings
from typing import List, Optional, Union

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

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					Union[str, Any]             =  CONFIG_NAME

      @register_to_config
      def __init__(	self   , snake_case_ = True   , snake_case_ = 8   , snake_case_ = "lanczos"   , snake_case_ = True   , snake_case_ = False   , ):

        """simple docstring"""

        super().__init__()

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        if images.ndim == 3:
          A_    :			int									=    images[None, ...]
        A_    :			Union[str, Any]									=    (images * 2_5_5).round().astype('uint8'    )
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          A_    :			List[str]									=    [Image.fromarray(image.squeeze()   , mode='L'    ) for image in images]
        else:
          A_    :			Tuple									=    [Image.fromarray(snake_case_    ) for image in images]

        return pil_images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        if not isinstance(snake_case_   , snake_case_    ):
          A_    :			int									=    [images]
        A_    :			int									=    [np.array(snake_case_    ).astype(np.floataa    ) / 2_55.0 for image in images]
        A_    :			Tuple									=    np.stack(snake_case_   , axis=0    )

        return images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        if images.ndim == 3:
          A_    :			Any									=    images[..., None]

        A_    :			Dict									=    torch.from_numpy(images.transpose(0   , 3   , 1   , 2    )    )
        return images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    images.cpu().permute(0   , 2   , 3   , 1    ).float().numpy()
        return images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        return 2.0 * images - 1.0

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        return (images / 2 + 0.5).clamp(0   , 1    )

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        A_    :			List[Any]									=    image.convert('RGB'    )
        return image

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None   , snake_case_ = None   , ):

        """simple docstring"""

        if height is None:
          A_    :			int									=    image.height
        if width is None:
          A_    :			List[Any]									=    image.width

        A_		,			A_    :			Dict									=    (
            x - x % self.config.vae_scale_factor for x in (width, height)
        )  # resize to integer multiple of vae_scale_factor
        A_    :			Any									=    image.resize((width, height)   , resample=PIL_INTERPOLATION[self.config.resample]    )
        return image

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None   , snake_case_ = None   , ):

        """simple docstring"""

        A_    :			Dict									=    (PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(snake_case_   , snake_case_    ):
          A_    :			Dict									=    [image]
        elif not (isinstance(snake_case_   , snake_case_    ) and all(isinstance(snake_case_   , snake_case_    ) for i in image    )):
          raise ValueError(
              F"""Input is in incorrect format: {[type(snake_case_    ) for i in image]}. Currently, we only support {", ".join(snake_case_    )}"""    )

        if isinstance(image[0]   , PIL.Image.Image    ):
          if self.config.do_convert_rgb:
            A_    :			Dict									=    [self.convert_to_rgb(snake_case_    ) for i in image]
          if self.config.do_resize:
            A_    :			Any									=    [self.resize(snake_case_   , snake_case_   , snake_case_    ) for i in image]
          A_    :			Optional[int]									=    self.pil_to_numpy(snake_case_    )  # to np
          A_    :			Dict									=    self.numpy_to_pt(snake_case_    )  # to pt

        elif isinstance(image[0]   , np.ndarray    ):
          A_    :			Optional[Any]									=    np.concatenate(snake_case_   , axis=0    ) if image[0].ndim == 4 else np.stack(snake_case_   , axis=0    )
          A_    :			Optional[Any]									=    self.numpy_to_pt(snake_case_    )
          A_		,			A_		,			A_		,			A_    :			Tuple									=    image.shape
          if self.config.do_resize and (
              height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
          ):
            raise ValueError(
                F"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
                F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""    )

        elif isinstance(image[0]   , torch.Tensor    ):
          A_    :			int									=    torch.cat(snake_case_   , axis=0    ) if image[0].ndim == 4 else torch.stack(snake_case_   , axis=0    )
          A_		,			A_		,			A_		,			A_    :			str									=    image.shape

          # don't need any preprocess if the image is latents
          if channel == 4:
            return image

          if self.config.do_resize and (
              height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
          ):
            raise ValueError(
                F"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
                F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""    )

        # expected range [0,1], normalize to [-1,1]
        A_    :			Optional[Any]									=    self.config.do_normalize
        if image.min() < 0:
          warnings.warn(
              'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
              F"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""   , snake_case_   , )
          A_    :			Optional[int]									=    False

        if do_normalize:
          A_    :			Any									=    self.normalize(snake_case_    )

        return image

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = "pil"   , snake_case_ = None   , ):

        """simple docstring"""

        if not isinstance(snake_case_   , torch.Tensor    ):
          raise ValueError(
              F"""Input for postprocessing is in incorrect format: {type(snake_case_    )}. We only support pytorch tensor"""    )
        if output_type not in ["latent", "pt", "np", "pil"]:
          A_    :			Optional[Any]									=    (
              F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
              '`pil`, `np`, `pt`, `latent`'
          )
          deprecate('Unsupported output_type'   , '1.0.0'   , snake_case_   , standard_warn=snake_case_    )
          A_    :			Dict									=    'np'

        if output_type == "latent":
          return image

        if do_denormalize is None:
          A_    :			str									=    [self.config.do_normalize] * image.shape[0]

        A_    :			List[Any]									=    torch.stack(
            [self.denormalize(image[i]    ) if do_denormalize[i] else image[i] for i in range(image.shape[0]    )]    )

        if output_type == "pt":
          return image

        A_    :			Optional[Any]									=    self.pt_to_numpy(snake_case_    )

        if output_type == "np":
          return image

        if output_type == "pil":
          return self.numpy_to_pil(snake_case_    )


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					str             =  CONFIG_NAME

      @register_to_config
      def __init__(	self   , snake_case_ = True   , snake_case_ = 8   , snake_case_ = "lanczos"   , snake_case_ = True   , ):

        """simple docstring"""

        super().__init__()

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        if images.ndim == 3:
          A_    :			int									=    images[None, ...]
        A_    :			Optional[int]									=    (images * 2_5_5).round().astype('uint8'    )
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          A_    :			Optional[int]									=    [Image.fromarray(image.squeeze()   , mode='L'    ) for image in images]
        else:
          A_    :			Optional[Any]									=    [Image.fromarray(image[:, :, :3]    ) for image in images]

        return pil_images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        return image[:, :, 1] * 2**8 + image[:, :, 2]

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        if images.ndim == 3:
          A_    :			Union[str, Any]									=    images[None, ...]
        A_    :			Tuple									=    images[:, :, :, 3:]
        if images.shape[-1] == 6:
          A_    :			Dict									=    (images_depth * 2_5_5).round().astype('uint8'    )
          A_    :			List[Any]									=    [
              Image.fromarray(self.rgblike_to_depthmap(snake_case_    )   , mode='I;16'    ) for image_depth in images_depth
          ]
        elif images.shape[-1] == 4:
          A_    :			Optional[int]									=    (images_depth * 6_55_35.0).astype(np.uintaa    )
          A_    :			Union[str, Any]									=    [Image.fromarray(snake_case_   , mode='I;16'    ) for image_depth in images_depth]
        else:
          raise Exception('Not supported'    )

        return pil_images

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = "pil"   , snake_case_ = None   , ):

        """simple docstring"""

        if not isinstance(snake_case_   , torch.Tensor    ):
          raise ValueError(
              F"""Input for postprocessing is in incorrect format: {type(snake_case_    )}. We only support pytorch tensor"""    )
        if output_type not in ["latent", "pt", "np", "pil"]:
          A_    :			Optional[int]									=    (
              F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
              '`pil`, `np`, `pt`, `latent`'
          )
          deprecate('Unsupported output_type'   , '1.0.0'   , snake_case_   , standard_warn=snake_case_    )
          A_    :			Any									=    'np'

        if do_denormalize is None:
          A_    :			int									=    [self.config.do_normalize] * image.shape[0]

        A_    :			List[str]									=    torch.stack(
            [self.denormalize(image[i]    ) if do_denormalize[i] else image[i] for i in range(image.shape[0]    )]    )

        A_    :			Optional[Any]									=    self.pt_to_numpy(snake_case_    )

        if output_type == "np":
          if image.shape[-1] == 6:
            A_    :			Dict									=    np.stack([self.rgblike_to_depthmap(im[:, :, 3:]    ) for im in image]   , axis=0    )
          else:
            A_    :			str									=    image[:, :, :, 3:]
          return image[:, :, :, :3], image_depth

        if output_type == "pil":
          return self.numpy_to_pil(snake_case_    ), self.numpy_to_depth(snake_case_    )
        else:
          raise Exception(F"""This type {output_type} is not supported"""    )