



'''simple docstring'''



import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
@require_flax
class        UpperCAmelCase__  :



 def    __lowerCamelCase				(  self			)  ->							List[Any]:
  __UpperCamelCase      ,		__UpperCamelCase				      =  self.prepare_init_args_and_inputs_for_common()

  __UpperCamelCase				      =  self.model_class(**lowercase			)
  __UpperCamelCase				      =  model.init(inputs_dict["""prng_key"""]		,	inputs_dict["""sample"""]			)
  jax.lax.stop_gradient(lowercase			)

  __UpperCamelCase				      =  model.apply(lowercase		,	inputs_dict["""sample"""]			)

  if isinstance(lowercase		,	lowercase			):
   __UpperCamelCase				      =  output.sample

  self.assertIsNotNone(lowercase			)
  __UpperCamelCase				      =  inputs_dict["""sample"""].shape
  self.assertEqual(output.shape		,	lowercase		,	"""Input and output shapes do not match"""			)



 def    __lowerCamelCase				(  self			)  ->							Tuple:
  __UpperCamelCase      ,		__UpperCamelCase				      =  self.prepare_init_args_and_inputs_for_common()

  __UpperCamelCase				      =  1_6
  __UpperCamelCase				      =  (1_6, 3_2)

  __UpperCamelCase				      =  self.model_class(**lowercase			)
  __UpperCamelCase				      =  model.init(inputs_dict["""prng_key"""]		,	inputs_dict["""sample"""]			)
  jax.lax.stop_gradient(lowercase			)

  __UpperCamelCase				      =  model.apply(lowercase		,	inputs_dict["""sample"""]			)

  if isinstance(lowercase		,	lowercase			):
   __UpperCamelCase				      =  output.sample

  self.assertIsNotNone(lowercase			)
  __UpperCamelCase				      =  inputs_dict["""sample"""].shape
  self.assertEqual(output.shape		,	lowercase		,	"""Input and output shapes do not match"""			)




 def    __lowerCamelCase				(  self			)  ->							Optional[int]:
  __UpperCamelCase				      =  """kwargs""" in inspect.signature(self.model_class.__init__			).parameters
  __UpperCamelCase				      =  len(self.model_class._deprecated_kwargs			) > 0

  if has_kwarg_in_model_class and not has_deprecated_kwarg:
   raise ValueError(
       f"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
       """ under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"""
       """ no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="""
       """ [<deprecated_argument>]`"""			)

  if not has_kwarg_in_model_class and has_deprecated_kwarg:
   raise ValueError(
       f"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
       """ under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"""
       f" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
       """ from `_deprecated_kwargs = [<deprecated_argument>]`"""			)










'''simple docstring'''



import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()







def _lowercase   (  __A							,__A							,__A       ):



 '''simple docstring'''



 __UpperCamelCase				      =  TaConfig.from_json_file(__A       )
 print(f"Building PyTorch model from configuration: {config}"       )
 __UpperCamelCase				      =  TaForConditionalGeneration(__A       )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(__A							,__A							,__A       )

 # Save pytorch-model
 print(f"Save PyTorch model to {pytorch_dump_path}"       )
 model.save_pretrained(__A       )


if __name__ == "__main__":
 a__						:			int										=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
 )
 parser.add_argument(
     '--config_file',
     default=None,
     type=str,
     required=True,
     help=(
         'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
     ),
 )
 parser.add_argument(
     '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 a__						:			Optional[Any]										=			parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





