
'''simple docstring'''


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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



def  __snake_case     (    _UpperCAmelCase					:       Dict):
   UpperCamelCase     , UpperCamelCase       =  image.size
   UpperCamelCase     , UpperCamelCase       =  (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
   UpperCamelCase       =  image.resize((w, h),      resample=PIL_INTERPOLATION['''lanczos'''])
   UpperCamelCase       =  np.array(_UpperCAmelCase).astype(np.floataa) / 2_5_5.0
   UpperCamelCase       =  image[None].transpose(0,      3,      1,      2)
   UpperCamelCase       =  torch.from_numpy(_UpperCAmelCase)
   return 2.0 * image - 1.0


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   ):

      '''simple docstring'''







      super().__init__()
      self.register_modules(vqvae=lowerCamelCase__ ,   unet=lowerCamelCase__ ,   scheduler=lowerCamelCase__   )







   @torch.no_grad()
   def __call__(			self ,   lowerCamelCase__ = None ,   lowerCamelCase__ = 1 ,   lowerCamelCase__ = 1_0_0 ,   lowerCamelCase__ = 0.0 ,   lowerCamelCase__ = None ,   lowerCamelCase__ = "pil" ,   lowerCamelCase__ = True ,   ):

      '''simple docstring'''







      if isinstance(lowerCamelCase__ ,   PIL.Image.Image   ):
         UpperCamelCase       =  1
      elif isinstance(lowerCamelCase__ ,   torch.Tensor   ):
         UpperCamelCase       =  image.shape[0]
      else:
         raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCamelCase__   )}'   )

      if isinstance(lowerCamelCase__ ,   PIL.Image.Image   ):
         UpperCamelCase       =  preprocess(lowerCamelCase__   )

      UpperCamelCase     , UpperCamelCase       =  image.shape[-2:]

      # in_channels should be 6: 3 for latents, 3 for low resolution image
      UpperCamelCase       =  (batch_size, self.unet.config.in_channels // 2, height, width)
      UpperCamelCase       =  next(self.unet.parameters()   ).dtype

      UpperCamelCase       =  randn_tensor(lowerCamelCase__ ,   generator=lowerCamelCase__ ,   device=self.device ,   dtype=lowerCamelCase__   )

      UpperCamelCase       =  image.to(device=self.device ,   dtype=lowerCamelCase__   )

      # set timesteps and move to the correct device
      self.scheduler.set_timesteps(lowerCamelCase__ ,   device=self.device   )
      UpperCamelCase       =  self.scheduler.timesteps

      # scale the initial noise by the standard deviation required by the scheduler
      UpperCamelCase       =  latents * self.scheduler.init_noise_sigma

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      UpperCamelCase       =  '''eta''' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
      UpperCamelCase       =  {}
      if accepts_eta:
         UpperCamelCase       =  eta

      for t in self.progress_bar(lowerCamelCase__   ):
         # concat latents and low resolution image in the channel dimension.
         UpperCamelCase       =  torch.cat([latents, image] ,   dim=1   )
         UpperCamelCase       =  self.scheduler.scale_model_input(lowerCamelCase__ ,   lowerCamelCase__   )
         # predict the noise residual
         UpperCamelCase       =  self.unet(lowerCamelCase__ ,   lowerCamelCase__   ).sample
         # compute the previous noisy sample x_t -> x_t-1
         UpperCamelCase       =  self.scheduler.step(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   **lowerCamelCase__   ).prev_sample

      # decode the image latents with the VQVAE
      UpperCamelCase       =  self.vqvae.decode(lowerCamelCase__   ).sample
      UpperCamelCase       =  torch.clamp(lowerCamelCase__ ,   -1.0 ,   1.0   )
      UpperCamelCase       =  image / 2 + 0.5
      UpperCamelCase       =  image.cpu().permute(0 ,   2 ,   3 ,   1   ).numpy()

      if output_type == "pil":
         UpperCamelCase       =  self.numpy_to_pil(lowerCamelCase__   )

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=lowerCamelCase__   )







'''simple docstring'''


import argparse
import json
import os
import re

import torch

from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging


logging.set_verbosity_info()

snake_case_ :  Tuple     =					[
    'word_embeddings_layernorm.weight',
    'word_embeddings_layernorm.bias',
    'input_layernorm.weight',
    'input_layernorm.bias',
    'post_attention_layernorm.weight',
    'post_attention_layernorm.bias',
    'self_attention.dense.bias',
    'mlp.dense_4h_to_h.bias',
    'ln_f.weight',
    'ln_f.bias',
]

snake_case_ :  Union[str, Any]     =					[
    'mlp.dense_4h_to_h.weight',
    'self_attention.dense.weight',
]



def  __snake_case     (    _UpperCAmelCase					:       List[str],      _UpperCAmelCase					:       List[str]):
   UpperCamelCase       =  {
       '''word_embeddings.weight''': '''word_embeddings.weight''',
       '''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
       '''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
       '''weight''': '''ln_f.weight''',
       '''bias''': '''ln_f.bias''',
   }

   if key in layer_rename_map:
      return layer_rename_map[key]

   # Handle transformer blocks
   UpperCamelCase       =  int(re.match(R'''.*layer_(\d*).*''',      _UpperCAmelCase)[1])
   layer_number -= 3
   return f'h.{layer_number}.' + key



def  __snake_case     (    _UpperCAmelCase					:       str):
   if dtype == torch.bool:
      return 1 / 8
   UpperCamelCase       =  re.search(R'''[^\d](\d+)$''',      str(_UpperCAmelCase))
   if bit_search is None:
      raise ValueError(f'`dtype` is not a valid dtype: {dtype}.')
   UpperCamelCase       =  int(bit_search.groups()[0])
   return bit_size // 8



def  __snake_case     (    _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Optional[Any],      _UpperCAmelCase					:       str,      _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       Optional[int]):
   # Construct model
   if bloom_config_file == "":
      UpperCamelCase       =  BloomConfig()
   else:
      UpperCamelCase       =  BloomConfig.from_json_file(_UpperCAmelCase)

   if shard_model:
      UpperCamelCase       =  os.listdir(_UpperCAmelCase)
      UpperCamelCase       =  sorted(filter(lambda _UpperCAmelCase: s.startswith('''layer''') and "model_00" in s,      _UpperCAmelCase))

      UpperCamelCase       =  {'''weight_map''': {}, '''metadata''': {}}
      UpperCamelCase       =  0

      UpperCamelCase       =  None

      UpperCamelCase       =  BloomConfig()

      for j, file in enumerate(_UpperCAmelCase):
         print('''Processing file: {}'''.format(_UpperCAmelCase))
         UpperCamelCase       =  None

         for i in range(_UpperCAmelCase):
            # load all TP files
            UpperCamelCase       =  file.replace('''model_00''',      f'model_0{i}')
            UpperCamelCase       =  torch.load(os.path.join(_UpperCAmelCase,      _UpperCAmelCase),      map_location='''cpu''')

            # Rename keys in the transformers names
            UpperCamelCase       =  list(temp.keys())
            for key in keys:
               UpperCamelCase       =  temp.pop(_UpperCAmelCase)

            if tensors is None:
               UpperCamelCase       =  temp
            else:
               for key in tensors.keys():
                  if any(key.endswith(_UpperCAmelCase) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
                     # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
                     tensors[key] += temp[key]
                  else:
                     # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
                     UpperCamelCase       =  1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN) else 0
                     # We concatenate these weights accross TP ranks
                     UpperCamelCase       =  torch.cat([tensors[key], temp[key]],      dim=_UpperCAmelCase)

            # Divide by the number of TP the weights we want to average
         for key in tensors.keys():
            if any(key.endswith(_UpperCAmelCase) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
               UpperCamelCase       =  tensors[key] / pretraining_tp
         torch.save(
             _UpperCAmelCase,      os.path.join(
                 _UpperCAmelCase,      '''pytorch_model_{}-of-{}.bin'''.format(str(j + 1).zfill(5),      str(len(_UpperCAmelCase)).zfill(5)),      ),      )

         for key in tensors.keys():
            UpperCamelCase       =  tensors[key]
            total_size += value.numel() * get_dtype_size(value.dtype)
            if key not in index_dict["weight_map"]:
               UpperCamelCase       =  '''pytorch_model_{}-of-{}.bin'''.format(
                   str(j + 1).zfill(5),      str(len(_UpperCAmelCase)).zfill(5))

      UpperCamelCase       =  BloomConfig()
      UpperCamelCase       =  pytorch_dump_folder_path + '''/''' + CONFIG_NAME
      UpperCamelCase       =  total_size
      with open(_UpperCAmelCase,      '''w''',      encoding='''utf-8''') as f:
         f.write(config.to_json_string())
      with open(os.path.join(_UpperCAmelCase,      WEIGHTS_NAME + '''.index.json'''),      '''w''',      encoding='''utf-8''') as f:
         UpperCamelCase       =  json.dumps(_UpperCAmelCase,      indent=2,      sort_keys=_UpperCAmelCase) + '''\n'''
         f.write(_UpperCAmelCase)
   else:
      UpperCamelCase       =  BloomModel(_UpperCAmelCase)

      UpperCamelCase       =  os.listdir(_UpperCAmelCase)
      UpperCamelCase       =  sorted(filter(lambda _UpperCAmelCase: s.startswith('''layer''') and "model_00" in s,      _UpperCAmelCase))

      UpperCamelCase       =  None
      for i, file in enumerate(_UpperCAmelCase):
         UpperCamelCase       =  None
         for i in range(_UpperCAmelCase):
            # load all TP files
            UpperCamelCase       =  file.replace('''model_00''',      f'model_0{i}')
            UpperCamelCase       =  torch.load(os.path.join(_UpperCAmelCase,      _UpperCAmelCase),      map_location='''cpu''')

            # Rename keys in the transformers names
            UpperCamelCase       =  list(temp.keys())
            for key in keys:
               UpperCamelCase       =  temp.pop(_UpperCAmelCase)

            if tensors is None:
               UpperCamelCase       =  temp
            else:
               for key in tensors.keys():
                  # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
                  if any(key.endswith(_UpperCAmelCase) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
                     tensors[key] += temp[key]
                  else:
                     # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
                     UpperCamelCase       =  1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN) else 0
                     # We concatenate these weights accross TP ranks
                     UpperCamelCase       =  torch.cat([tensors[key], temp[key]],      dim=_UpperCAmelCase)

            # Divide by the number of TP the weights we want to average
         for key in tensors.keys():
            if any(key.endswith(_UpperCAmelCase) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
               UpperCamelCase       =  tensors[key] / pretraining_tp

         UpperCamelCase       =  model.load_state_dict(_UpperCAmelCase,      strict=_UpperCAmelCase)
         assert not other_keys.unexpected_keys, f'The keys {other_keys.unexpected_keys} are unexpected'
         if missing_keys is None:
            UpperCamelCase       =  set(other_keys.missing_keys)
         else:
            UpperCamelCase       =  missing_keys.intersection(set(other_keys.missing_keys))

      assert not missing_keys, f'The keys {missing_keys} are missing'

      # Save pytorch-model
      os.makedirs(_UpperCAmelCase,      exist_ok=_UpperCAmelCase)
      UpperCamelCase       =  pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
      UpperCamelCase       =  pytorch_dump_folder_path + '''/''' + CONFIG_NAME
      print(f'Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}')
      if config.torch_dtype is not None:
         UpperCamelCase       =  model.to(config.torch_dtype)
      torch.save(model.state_dict(),      _UpperCAmelCase)
      print(f'Save configuration file to {pytorch_config_dump_path}')
      with open(_UpperCAmelCase,      '''w''',      encoding='''utf-8''') as f:
         f.write(config.to_json_string())


if __name__ == "__main__":
   snake_case_ :  Tuple     =					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--bloom_checkpoint_path',
       default=None,
       type=str,
       required=True,
       help='Path to the Megatron-LM checkpoint path.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   parser.add_argument(
       '--bloom_config_file',
       default='',
       type=str,
       help=(
           'An optional config json file corresponding to the pre-trained model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--shard_model',
       action='store_true',
       help='An optional setting to shard the output model \nThis enables sharding the converted checkpoint',
   )
   parser.add_argument(
       '--pretraining_tp',
       default=4,
       type=int,
       help='Pretraining TP rank that has been used when training the model in Megatron-LM \n',
   )
   snake_case_ :  List[str]     =					parser.parse_args()
   convert_bloom_checkpoint_to_pytorch(
       args.bloom_checkpoint_path,
       args.bloom_config_file,
       args.pytorch_dump_folder_path,
       args.shard_model,
       args.pretraining_tp,
   )





