
'''simple docstring'''






import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class  UpperCAmelCase__						(       A						,	A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		IFInpaintingSuperResolutionPipeline
    lowerCAmelCase_				    =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
    lowerCAmelCase_				    =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'} )
    lowerCAmelCase_				    =		PipelineTesterMixin.required_optional_params - {'latents'}



    def   lowerCamelCase_  (	self      :       int     ):
          return self._get_superresolution_dummy_components()



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       int,__A      :       Optional[int]=0     ):
          if str(__A     ).startswith("mps"     ):
                _lowerCamelCase				:   List[str]								=   torch.manual_seed(__A     )
          else:
                _lowerCamelCase				:   str								=   torch.Generator(device=__A     ).manual_seed(__A     )

          _lowerCamelCase				:   List[Any]								=   floats_tensor((1, 3, 1_6, 1_6),rng=random.Random(__A     )     ).to(__A     )
          _lowerCamelCase				:   int								=   floats_tensor((1, 3, 3_2, 3_2),rng=random.Random(__A     )     ).to(__A     )
          _lowerCamelCase				:   Optional[int]								=   floats_tensor((1, 3, 3_2, 3_2),rng=random.Random(__A     )     ).to(__A     )

          _lowerCamelCase				:   List[Any]								=   {
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "original_image": original_image,
              "mask_image": mask_image,
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }

          return inputs



    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available(),reason="XFormers attention is only available with CUDA and `xformers` installed",)
    def   lowerCamelCase_  (	self      :       Tuple     ):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3     )



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          self._test_save_load_optional_components()



    @unittest.skipIf(torch_device != "cuda",reason="float16 requires CUDA"     )
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
          super().test_save_load_floataa(expected_max_diff=1e-1     )



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2     )



    def   lowerCamelCase_  (	self      :       Dict     ):
          self._test_save_load_local()



    def   lowerCamelCase_  (	self      :       Tuple     ):
          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2,)

'''simple docstring'''






import argparse
from pathlib import Path

import torch

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


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







def 		A_       (	_lowerCAmelCase   :    Optional[int]							):



      """simple docstring"""




      _lowerCamelCase				:   str								=   torch.load(_lowerCAmelCase				,	map_location="cpu"							)
      if "model" in sd.keys():
            _lowerCamelCase				:   Optional[Any]								=   torch.load(_lowerCAmelCase				,	map_location="cpu"							)["model"]

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

      _lowerCamelCase				:   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:
                  _lowerCamelCase				:   Optional[Any]								=   sd.pop(_lowerCAmelCase							)

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

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

                  _lowerCamelCase				:   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
                  _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   List[str]								=   torch.split(_lowerCAmelCase				,	depth // 3				,	dim=0							)

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

      return sd







@torch.no_grad()
def 		A_       (	_lowerCAmelCase   :    List[Any]				,	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    List[str]=None							):



      """simple docstring"""




      _lowerCamelCase				:   Any								=   load_checkpoint(_lowerCAmelCase							)

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

      _lowerCamelCase				:   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__":
 UpperCAmelCase_       :   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.')
 UpperCAmelCase_       :   str   =       parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)