




"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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 							snake_case_ (				lowerCamelCase_  ,   lowerCamelCase_  ,   unittest.TestCase ):







			"""simple docstring"""




			A_										=					IFImgaImgPipeline
			A_										=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
			A_										=					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			A_										=					PipelineTesterMixin.required_optional_params - {'''latents'''}







			def      UpperCAmelCase__ (		self)							->      Optional[Any]:
							return self._get_dummy_components()







			def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_=0)							->      Tuple:
							if str(lowerCamelCase_).startswith('''mps'''):
											UpperCamelCase										=  torch.manual_seed(lowerCamelCase_)
							else:
											UpperCamelCase										=  torch.Generator(device=lowerCamelCase_).manual_seed(lowerCamelCase_)

							UpperCamelCase										=  floats_tensor((1, 3, 3_2, 3_2)					,				rng=random.Random(lowerCamelCase_)).to(lowerCamelCase_)

							UpperCamelCase										=  {
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}

							return inputs







			def      UpperCAmelCase__ (		self)							->      List[Any]:
							self._test_save_load_optional_components()







			@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)







			@unittest.skipIf(torch_device != '''cuda'''					,				reason='''float16 requires CUDA''')
			def      UpperCAmelCase__ (		self)							->      Dict:
							# 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)







			@unittest.skipIf(torch_device != '''cuda'''					,				reason='''float16 requires CUDA''')
			def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
							super().test_floataa_inference(expected_max_diff=1e-1)







			def      UpperCAmelCase__ (		self)							->      Tuple:
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)







			def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
							self._test_save_load_local()







			def      UpperCAmelCase__ (		self)							->      Any:
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2					,				)





"""simple docstring"""







import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  BertConfig.from_pretrained(
        '''bert-large-cased''' ,vocab_size=_lowercase ,max_position_embeddings=512 ,is_decoder=_lowercase ,add_cross_attention=_lowercase ,)
    UpperCamelCase										=  bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    UpperCamelCase										=  BertGenerationConfig(**_lowercase						)
    if is_encoder:
        UpperCamelCase										=  BertGenerationEncoder(_lowercase						)
    else:
        UpperCamelCase										=  BertGenerationDecoder(_lowercase						)
    print(f'Building PyTorch model from configuration: {config}'						)

    # Load weights from tf checkpoint
    load_tf_weights_in_bert_generation(
        _lowercase ,_lowercase ,model_class='''bert''' ,is_encoder_named_decoder=_lowercase ,is_encoder=_lowercase ,)

    # Save pytorch-model
    print(f'Save PyTorch model and config to {pytorch_dump_path}'						)
    model.save_pretrained(_lowercase						)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_      =					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  parser.add_argument(
      '--is_encoder_named_decoder',
      action='store_true',
      help='If decoder has to be renamed to encoder in PyTorch model.',
  )
  parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
  parser.add_argument('--vocab_size', default=50358, type=int, help='Vocab size of model')
  SCREAMING_SNAKE_CASE_      =					parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_hub_path,
      args.pytorch_dump_path,
      args.is_encoder_named_decoder,
      args.vocab_size,
      is_encoder=args.is_encoder,
  )