
"""simple docstring"""

import argparse

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


logging.set_verbosity_info()
def 		UpperCAmelCase					(		a_,						a_,						a_,						a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			List[str]		=    BertConfig.from_pretrained(
		    'bert-large-cased',						vocab_size=a_,						max_position_embeddings=512,						is_decoder=a_,						add_cross_attention=a_,						)
		lowerCamelCase     :			Any		=    bert_config.to_dict()
		del bert_config_dict["type_vocab_size"]
		lowerCamelCase     :			List[Any]		=    BertGenerationConfig(**a_			)
		if is_encoder:
				lowerCamelCase     :			Optional[int]		=    BertGenerationEncoder(a_			)
		else:
				lowerCamelCase     :			Optional[Any]		=    BertGenerationDecoder(a_			)
		print(F"""Building PyTorch model from configuration: {config}"""			)

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

		# Save pytorch-model
		print(F"""Save PyTorch model and config to {pytorch_dump_path}"""			)
		model.save_pretrained(a_			)


if __name__ == "__main__":
					_A            = 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=5_0_3_5_8, type=int, help='Vocab size of model')
					_A            = 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,
					)














"""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  (  UpperCAmelCase   , UpperCAmelCase   , unittest.TestCase       ):
    __magic_name__         =     IFImgaImgPipeline
    __magic_name__         =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
    __magic_name__         =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __magic_name__         =     PipelineTesterMixin.required_optional_params - {'''latents'''}





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      return self._get_dummy_components()





    def 				lowerCamelCase__      (							self	:					Dict				,		A	:					List[str]				,		A	:					Optional[Any]=0			):


      '''simple docstring'''




      if str(A			).startswith('mps'			):
        a     :			Union[str, Any]				=   torch.manual_seed(A			)
      else:
        a     :			Optional[int]				=   torch.Generator(device=A			).manual_seed(A			)

      a     :			Any				=   floats_tensor((1, 3, 3_2, 3_2)				,		rng=random.Random(A			)			).to(A			)

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

      return inputs





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      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 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)





    @unittest.skipIf(torch_device != 'cuda'				,		reason='float16 requires CUDA'			)
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      super().test_save_load_floataa(expected_max_diff=1E-1			)





    @unittest.skipIf(torch_device != 'cuda'				,		reason='float16 requires CUDA'			)
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      super().test_floataa_inference(expected_max_diff=1E-1			)





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      self._test_save_load_local()





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




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




