'''simple docstring'''





import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_     :   Optional[int] 				=				logging.get_logger(__name__)







def 			A__      (	snake_case_ :		Optional[int]      ):
 SCREAMING_SNAKE_CASE__:   str=      torch.load(_lowerCAmelCase       ,				map_location='''cpu'''      )
 if "model" in sd.keys():
  SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.load(_lowerCAmelCase       ,				map_location='''cpu'''      )["model"]

 # pop unnecessary weights
 SCREAMING_SNAKE_CASE__:   Optional[Any]=      [
     "decoder.version",
     "decoder.output_projection.weight",
 ]
 for key in keys_to_delete:
  if key in sd:
   sd.pop(_lowerCAmelCase      )

 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      {
     "decoder.project_in_dim.weight": "decoder.project_in.weight",
     "decoder.project_out_dim.weight": "decoder.project_out.weight",
     "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
     "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
 }
 for old_key, new_key in keys_to_rename.items():
  if old_key in sd:
   SCREAMING_SNAKE_CASE__:   Optional[Any]=      sd.pop(_lowerCAmelCase      )

 SCREAMING_SNAKE_CASE__:   Any=      list(sd.keys()      )
 for key in keys:
  if ".qkv_proj." in key:
   SCREAMING_SNAKE_CASE__:   Optional[Any]=      sd[key]
   # We split QKV in separate Q,K,V

   SCREAMING_SNAKE_CASE__:   int=      key.replace('''.qkv_proj.'''       ,				'''.q_proj.'''      )
   SCREAMING_SNAKE_CASE__:   List[Any]=      key.replace('''.qkv_proj.'''       ,				'''.k_proj.'''      )
   SCREAMING_SNAKE_CASE__:   Dict=      key.replace('''.qkv_proj.'''       ,				'''.v_proj.'''      )

   SCREAMING_SNAKE_CASE__:   Any=      value.shape[0]
   assert depth % 3 == 0
   # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
   # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
   SCREAMING_SNAKE_CASE__:   List[str]=      torch.split(_lowerCAmelCase       ,				depth // 3       ,				dim=0      )

   SCREAMING_SNAKE_CASE__:   str=      q
   SCREAMING_SNAKE_CASE__:   Dict=      k
   SCREAMING_SNAKE_CASE__:   int=      v
   del sd[key]

 return sd







@torch.no_grad()
def 			A__      (	snake_case_ :		List[Any]       ,				snake_case_ :		Dict       ,				snake_case_ :		List[str]=None      ):
 SCREAMING_SNAKE_CASE__:   Any=      load_checkpoint(_lowerCAmelCase      )

 if config is not None:
  SCREAMING_SNAKE_CASE__:   str=      OPTConfig.from_pretrained(_lowerCAmelCase      )
 else:
  SCREAMING_SNAKE_CASE__:   Any=      OPTConfig()

 SCREAMING_SNAKE_CASE__:   Optional[int]=      OPTModel(_lowerCAmelCase      ).half().eval()
 model.load_state_dict(_lowerCAmelCase      )

 # Check results
 Path(_lowerCAmelCase      ).mkdir(exist_ok=_lowerCAmelCase      )
 model.save_pretrained(_lowerCAmelCase      )


if __name__ == "__main__":
      lowercase_     :   List[str] 				=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--fairseq_path',
          type=str,
          help=(
              'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
              ' https://huggingface.co/models?other=opt_metasq'
          ),
      )
      parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
      parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
      lowercase_     :   str 				=				parser.parse_args()
      convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible






class 		_lowerCamelCase		(   unittest.TestCase    ):
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   List[Any]=      [
         '''safety_checker/pytorch_model.bin''',
         '''safety_checker/model.safetensors''',
         '''vae/diffusion_pytorch_model.bin''',
         '''vae/diffusion_pytorch_model.safetensors''',
         '''text_encoder/pytorch_model.bin''',
         '''text_encoder/model.safetensors''',
         '''unet/diffusion_pytorch_model.bin''',
         '''unet/diffusion_pytorch_model.safetensors''',
     ]
     self.assertTrue(is_safetensors_compatible(lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   List[str]=      [
         '''unet/diffusion_pytorch_model.bin''',
         '''unet/diffusion_pytorch_model.safetensors''',
     ]
     self.assertTrue(is_safetensors_compatible(lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [
         '''safety_checker/pytorch_model.bin''',
         '''safety_checker/model.safetensors''',
         '''vae/diffusion_pytorch_model.bin''',
         '''vae/diffusion_pytorch_model.safetensors''',
         '''text_encoder/pytorch_model.bin''',
         '''text_encoder/model.safetensors''',
         '''unet/diffusion_pytorch_model.bin''',
         # Removed: 'unet/diffusion_pytorch_model.safetensors',
     ]
     self.assertFalse(is_safetensors_compatible(lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   str=      [
         '''text_encoder/pytorch_model.bin''',
         '''text_encoder/model.safetensors''',
     ]
     self.assertTrue(is_safetensors_compatible(lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   List[str]=      [
         '''safety_checker/pytorch_model.bin''',
         '''safety_checker/model.safetensors''',
         '''vae/diffusion_pytorch_model.bin''',
         '''vae/diffusion_pytorch_model.safetensors''',
         '''text_encoder/pytorch_model.bin''',
         # Removed: 'text_encoder/model.safetensors',
         '''unet/diffusion_pytorch_model.bin''',
         '''unet/diffusion_pytorch_model.safetensors''',
     ]
     self.assertFalse(is_safetensors_compatible(lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [
         '''safety_checker/pytorch_model.fp16.bin''',
         '''safety_checker/model.fp16.safetensors''',
         '''vae/diffusion_pytorch_model.fp16.bin''',
         '''vae/diffusion_pytorch_model.fp16.safetensors''',
         '''text_encoder/pytorch_model.fp16.bin''',
         '''text_encoder/model.fp16.safetensors''',
         '''unet/diffusion_pytorch_model.fp16.bin''',
         '''unet/diffusion_pytorch_model.fp16.safetensors''',
     ]
     SCREAMING_SNAKE_CASE__:   Any=      '''fp16'''
     self.assertTrue(is_safetensors_compatible(lowerCAmelCase , variant=lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   List[str]=      [
         '''unet/diffusion_pytorch_model.fp16.bin''',
         '''unet/diffusion_pytorch_model.fp16.safetensors''',
     ]
     SCREAMING_SNAKE_CASE__:   Dict=      '''fp16'''
     self.assertTrue(is_safetensors_compatible(lowerCAmelCase , variant=lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     # pass variant but use the non-variant filenames
     SCREAMING_SNAKE_CASE__:   List[str]=      [
         '''unet/diffusion_pytorch_model.bin''',
         '''unet/diffusion_pytorch_model.safetensors''',
     ]
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      '''fp16'''
     self.assertTrue(is_safetensors_compatible(lowerCAmelCase , variant=lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   Dict=      [
         '''safety_checker/pytorch_model.fp16.bin''',
         '''safety_checker/model.fp16.safetensors''',
         '''vae/diffusion_pytorch_model.fp16.bin''',
         '''vae/diffusion_pytorch_model.fp16.safetensors''',
         '''text_encoder/pytorch_model.fp16.bin''',
         '''text_encoder/model.fp16.safetensors''',
         '''unet/diffusion_pytorch_model.fp16.bin''',
         # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
     ]
     SCREAMING_SNAKE_CASE__:   List[Any]=      '''fp16'''
     self.assertFalse(is_safetensors_compatible(lowerCAmelCase , variant=lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   int=      [
         '''text_encoder/pytorch_model.fp16.bin''',
         '''text_encoder/model.fp16.safetensors''',
     ]
     SCREAMING_SNAKE_CASE__:   Any=      '''fp16'''
     self.assertTrue(is_safetensors_compatible(lowerCAmelCase , variant=lowerCAmelCase   )   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     # pass variant but use the non-variant filenames
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [
         '''text_encoder/pytorch_model.bin''',
         '''text_encoder/model.safetensors''',
     ]
     SCREAMING_SNAKE_CASE__:   str=      '''fp16'''
     self.assertTrue(is_safetensors_compatible(lowerCAmelCase , variant=lowerCAmelCase   )   )



    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   Tuple=      [
         '''safety_checker/pytorch_model.fp16.bin''',
         '''safety_checker/model.fp16.safetensors''',
         '''vae/diffusion_pytorch_model.fp16.bin''',
         '''vae/diffusion_pytorch_model.fp16.safetensors''',
         '''text_encoder/pytorch_model.fp16.bin''',
         # 'text_encoder/model.fp16.safetensors',
         '''unet/diffusion_pytorch_model.fp16.bin''',
         '''unet/diffusion_pytorch_model.fp16.safetensors''',
     ]
     SCREAMING_SNAKE_CASE__:   str=      '''fp16'''
     self.assertFalse(is_safetensors_compatible(lowerCAmelCase , variant=lowerCAmelCase   )   )

