




import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[int]    =					VQModel
      lowerCAmelCase__			: Optional[Any]    =					"sample"







      @property
      def       snake_case	(     self					: Any ,       snake_case					: Dict=(32, 32)				):
          __UpperCamelCase  		=						4
          __UpperCamelCase  		=						3

          __UpperCamelCase  		=						floats_tensor((batch_size, num_channels) + sizes				).to(snake_case				)

          return {"sample": image}







      @property
      def       snake_case	(     self					: Tuple				):
          return (3, 32, 32)







      @property
      def       snake_case	(     self					: Tuple				):
          return (3, 32, 32)







      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase  		=						{
              '''block_out_channels''': [32, 64],
              '''in_channels''': 3,
              '''out_channels''': 3,
              '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
              '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
              '''latent_channels''': 3,
          }
          __UpperCamelCase  		=						self.dummy_input
          return init_dict, inputs_dict







      def       snake_case	(     self					: List[str]				):
          pass







      def       snake_case	(     self					: Dict				):
          pass







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase		,      __UpperCamelCase  		=						VQModel.from_pretrained('''fusing/vqgan-dummy''' ,       output_loading_info=snake_case				)
          self.assertIsNotNone(snake_case				)
          self.assertEqual(len(loading_info['''missing_keys''']				) ,       0				)

          model.to(snake_case				)
          __UpperCamelCase  		=						model(**self.dummy_input				)

          assert image is not None, "Make sure output is not None"







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						VQModel.from_pretrained('''fusing/vqgan-dummy'''				)
          model.to(snake_case				).eval()

          torch.manual_seed(0				)
          if torch.cuda.is_available():
              torch.cuda.manual_seed_all(0				)

          __UpperCamelCase  		=						torch.randn(1 ,       model.config.in_channels ,       model.config.sample_size ,       model.config.sample_size				)
          __UpperCamelCase  		=						image.to(snake_case				)
          with torch.no_grad():
              __UpperCamelCase  		=						model(snake_case				).sample

          __UpperCamelCase  		=						output[0, -1, -3:, -3:].flatten().cpu()
          # fmt: off
          __UpperCamelCase  		=						torch.tensor([-0.01_53, -0.40_44, -0.18_80, -0.51_61, -0.24_18, -0.40_72, -0.16_12, -0.06_33, -0.01_43]				)
          # fmt: on
          self.assertTrue(torch.allclose(snake_case ,       snake_case ,       atol=1E-3				)				)












import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
a_   		=     logging.get_logger(__name__)




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )
    if "model" in sd.keys():
        __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )['''model''']

    # pop unnecessary weights
    __UpperCamelCase  		=						[
        '''decoder.version''',
        '''decoder.output_projection.weight''',
    ]
    for key in keys_to_delete:
        if key in sd:
            sd.pop(lowercase_      )

    __UpperCamelCase  		=						{
        '''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:
            __UpperCamelCase  		=						sd.pop(lowercase_      )

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

            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.q_proj.'''      )
            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.k_proj.'''      )
            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.v_proj.'''      )

            __UpperCamelCase  		=						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
            __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						torch.split(lowercase_					,     depth // 3					,     dim=0      )

            __UpperCamelCase  		=						q
            __UpperCamelCase  		=						k
            __UpperCamelCase  		=						v
            del sd[key]

    return sd




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						load_checkpoint(lowercase_      )

    if config is not None:
        __UpperCamelCase  		=						OPTConfig.from_pretrained(lowercase_      )
    else:
        __UpperCamelCase  		=						OPTConfig()

    __UpperCamelCase  		=						OPTModel(lowercase_      ).half().eval()
    model.load_state_dict(lowercase_      )

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


if __name__ == "__main__":
 a_   		=     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.")
 a_   		=     parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)






