





'''simple docstring'''







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_lowercase       						=			False
_lowercase       						=			True
_lowercase       						=			False


if __name__ == "__main__":
   _lowercase       						=			argparse.ArgumentParser()

   parser.add_argument(
       '--repo_path',
       default=None,
       type=str,
       required=True,
       help='The config json file corresponding to the architecture.',
   )

   parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

   _lowercase       						=			parser.parse_args()

   _lowercase       						=			{
       'image_size': 'sample_size',
       'num_res_blocks': 'layers_per_block',
       'block_channels': 'block_out_channels',
       'down_blocks': 'down_block_types',
       'up_blocks': 'up_block_types',
       'downscale_freq_shift': 'freq_shift',
       'resnet_num_groups': 'norm_num_groups',
       'resnet_act_fn': 'act_fn',
       'resnet_eps': 'norm_eps',
       'num_head_channels': 'attention_head_dim',
   }

   _lowercase       						=			{
       'time_steps': 'time_proj',
       'mid': 'mid_block',
       'downsample_blocks': 'down_blocks',
       'upsample_blocks': 'up_blocks',
   }

   _lowercase       						=			'' if has_file(args.repo_path, 'config.json') else 'unet'

   with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
      _lowercase       						=			reader.read()
      _lowercase       						=			json.loads(text)

   if do_only_config:
      for key in config_parameters_to_change.keys():
         config.pop(key, None)

   if has_file(args.repo_path, 'config.json'):
      _lowercase       						=			UNetaDModel(**config)
   else:
      _lowercase       						=			UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
      _lowercase       						=			class_name(**config)

   if do_only_config:
      model.save_config(os.path.join(args.repo_path, subfolder))

   _lowercase       						=			dict(model.config)

   if do_only_renaming:
      for key, value in config_parameters_to_change.items():
         if key in config:
            _lowercase       						=			config[key]
            del config[key]

      _lowercase       						=			[k.replace('UNetRes', '') for k in config['down_block_types']]
      _lowercase       						=			[k.replace('UNetRes', '') for k in config['up_block_types']]

   if do_only_weights:
      _lowercase       						=			torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

      _lowercase       						=			{}
      for param_key, param_value in state_dict.items():
         if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
            continue
         _lowercase       						=			False
         for key, new_key in key_parameters_to_change.items():
            if not has_changed and param_key.split('.')[0] == key:
               _lowercase       						=			param_value
               _lowercase       						=			True
         if not has_changed:
            _lowercase       						=			param_value

      model.load_state_dict(new_state_dict)
      model.save_pretrained(os.path.join(args.repo_path, subfolder))








'''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 			_lowercase						(       __a				,		__a				,		unittest.TestCase					):
     _UpperCAmelCase										=					IFInpaintingSuperResolutionPipeline
     _UpperCAmelCase										=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
     _UpperCAmelCase										=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}					)
     _UpperCAmelCase										=					PipelineTesterMixin.required_optional_params - {'''latents'''}







     def     UpperCamelCase			( self    ) ->	int:
      return self._get_superresolution_dummy_components()







     def     UpperCamelCase			( self    ,   A__    ,   A__=0    ) ->	Union[str, Any]:
      if str(A__    ).startswith('''mps'''    ):
       snake_case				=			torch.manual_seed(A__    )
      else:
       snake_case				=			torch.Generator(device=A__    ).manual_seed(A__    )

      snake_case				=			floats_tensor((1, 3, 16, 16)    ,   rng=random.Random(A__    )    ).to(A__    )
      snake_case				=			floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(A__    )    ).to(A__    )
      snake_case				=			floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(A__    )    ).to(A__    )

      snake_case				=			{
          '''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     UpperCamelCase			( self    ) ->	List[Any]:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      self._test_save_load_optional_components()







     @unittest.skipIf(torch_device != '''cuda'''    ,   reason='''float16 requires CUDA'''    )
     def     UpperCamelCase			( self    ) ->	List[str]:
      # 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     UpperCamelCase			( self    ) ->	int:
      self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      self._test_save_load_local()







     def     UpperCamelCase			( self    ) ->	Dict:
      self._test_inference_batch_single_identical(
          expected_max_diff=1e-2    ,   )

