





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		int:






				"""simple docstring"""


				if "model" in orig_key:
								snake_case_		:       Optional[Any]           =    orig_key.replace('''model.'''					,							''''''					)
				if "norm1" in orig_key:
								snake_case_		:       Optional[int]           =    orig_key.replace('''norm1'''					,							'''attention.output.LayerNorm'''					)
				if "norm2" in orig_key:
								snake_case_		:       Optional[int]           =    orig_key.replace('''norm2'''					,							'''output.LayerNorm'''					)
				if "norm" in orig_key:
								snake_case_		:       List[str]           =    orig_key.replace('''norm'''					,							'''LayerNorm'''					)
				if "transformer" in orig_key:
								snake_case_		:       List[str]           =    orig_key.split('''.'''					)[0].split('''_'''					)[-1]
								snake_case_		:       Optional[Any]           =    orig_key.replace(f'''transformer_{layer_num}'''					,							f'''encoder.layer.{layer_num}'''					)
				if "mha.attn" in orig_key:
								snake_case_		:       Dict           =    orig_key.replace('''mha.attn'''					,							'''attention.self'''					)
				if "mha" in orig_key:
								snake_case_		:       Any           =    orig_key.replace('''mha'''					,							'''attention'''					)
				if "W_q" in orig_key:
								snake_case_		:       Tuple           =    orig_key.replace('''W_q'''					,							'''self.query'''					)
				if "W_k" in orig_key:
								snake_case_		:       List[str]           =    orig_key.replace('''W_k'''					,							'''self.key'''					)
				if "W_v" in orig_key:
								snake_case_		:       Optional[int]           =    orig_key.replace('''W_v'''					,							'''self.value'''					)
				if "ff1" in orig_key:
								snake_case_		:       Any           =    orig_key.replace('''ff1'''					,							'''intermediate.dense'''					)
				if "ff2" in orig_key:
								snake_case_		:       Dict           =    orig_key.replace('''ff2'''					,							'''output.dense'''					)
				if "ff" in orig_key:
								snake_case_		:       Union[str, Any]           =    orig_key.replace('''ff'''					,							'''output.dense'''					)
				if "mlm_class" in orig_key:
								snake_case_		:       Optional[Any]           =    orig_key.replace('''mlm.mlm_class'''					,							'''cls.predictions.decoder'''					)
				if "mlm" in orig_key:
								snake_case_		:       Dict           =    orig_key.replace('''mlm'''					,							'''cls.predictions.transform'''					)
				if "cls" not in orig_key:
								snake_case_		:       Optional[int]           =    '''yoso.''' + orig_key

				return orig_key






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		Any:






				"""simple docstring"""


				for key in orig_state_dict.copy().keys():
								snake_case_		:       Union[str, Any]           =    orig_state_dict.pop(_UpperCamelCase					)

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												snake_case_		:       Dict           =    val

				snake_case_		:       int           =    orig_state_dict['''cls.predictions.decoder.bias''']
				snake_case_		:       Optional[int]           =    torch.arange(_UpperCamelCase					).expand((1, -1)					) + 2

				return orig_state_dict






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					)						->		List[str]:






				"""simple docstring"""


				snake_case_		:       Tuple           =    torch.load(_UpperCamelCase					,							map_location='''cpu'''					)['''model_state_dict''']
				snake_case_		:       Union[str, Any]           =    YosoConfig.from_json_file(_UpperCamelCase					)
				snake_case_		:       Tuple           =    YosoForMaskedLM(_UpperCamelCase					)

				snake_case_		:       Any           =    convert_checkpoint_helper(config.max_position_embeddings					,							_UpperCamelCase					)

				print(model.load_state_dict(_UpperCamelCase					)					)
				model.eval()
				model.save_pretrained(_UpperCamelCase					)

				print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''					)


if __name__ == "__main__":
	lowerCAmelCase_        =							argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
	)
	parser.add_argument(
	    '''--config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help='''The json file for YOSO model config.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	lowerCAmelCase_        =							parser.parse_args()
	convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


"""simple docstring"""



import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class 				a			(					lowercase						,		lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   int           =     IFImgaImgSuperResolutionPipeline
      UpperCamelCase							:   Union[str, Any]           =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
      UpperCamelCase							:   Tuple           =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""}      )
      UpperCamelCase							:   Union[str, Any]           =     PipelineTesterMixin.required_optional_params - {"""latents"""}



      def  __snake_case							(   self      ):
          return self._get_superresolution_dummy_components()



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=0      ):
          if str(UpperCamelCase_      ).startswith('mps'      ):
              UpperCAmelCase__	:      Optional[int]     							=					torch.manual_seed(UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      int     							=					torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )

          UpperCAmelCase__	:      Union[str, Any]     							=					floats_tensor((1, 3, 32, 32)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					floats_tensor((1, 3, 16, 16)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )

          UpperCAmelCase__	:      int     							=					{
              'prompt': 'A painting of a squirrel eating a burger',
              'image': image,
              'original_image': original_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  __snake_case							(   self      ):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )



      def  __snake_case							(   self      ):
          self._test_save_load_optional_components()



      @unittest.skipIf(torch_device != 'cuda'     ,	reason='float16 requires CUDA'      )
      def  __snake_case							(   self      ):
          # 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  __snake_case							(   self      ):
          self._test_attention_slicing_forward_pass(expected_max_diff=1E-2      )



      def  __snake_case							(   self      ):
          self._test_save_load_local()



      def  __snake_case							(   self      ):
          self._test_inference_batch_single_identical(
              expected_max_diff=1E-2     ,	)

