





"""simple docstring"""





import os
import sys
import unittest


__A											=		os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__A											=		os.path.join(git_repo_path, """src""", """diffusers""")






class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							find_backend('    if not is_torch_available():'      )
         self.assertEqual(__UpperCAmelCase    , 'torch'      )

         # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
         # self.assertEqual(backend_with_underscore, "tensorflow_text")

         lowerCAmelCase__			:int											=							find_backend('    if not (is_torch_available() and is_transformers_available()):'      )
         self.assertEqual(__UpperCAmelCase    , 'torch_and_transformers'      )

         # double_backend_with_underscore = find_backend(
         #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
         # )
         # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

         lowerCAmelCase__			:Union[str, Any]											=							find_backend(
             '    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'      )
         self.assertEqual(__UpperCAmelCase    , 'torch_and_transformers_and_onnx'      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							read_init()
         # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
         self.assertIn('torch'    , __UpperCAmelCase      )
         self.assertIn('torch_and_transformers'    , __UpperCAmelCase      )
         self.assertIn('flax_and_transformers'    , __UpperCAmelCase      )
         self.assertIn('torch_and_transformers_and_onnx'    , __UpperCAmelCase      )

         # Likewise, we can't assert on the exact content of a key
         self.assertIn('UNet2DModel'    , objects['torch']      )
         self.assertIn('FlaxUNet2DConditionModel'    , objects['flax']      )
         self.assertIn('StableDiffusionPipeline'    , objects['torch_and_transformers']      )
         self.assertIn('FlaxStableDiffusionPipeline'    , objects['flax_and_transformers']      )
         self.assertIn('LMSDiscreteScheduler'    , objects['torch_and_scipy']      )
         self.assertIn('OnnxStableDiffusionPipeline'    , objects['torch_and_transformers_and_onnx']      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							create_dummy_object('CONSTANT'    , '\'torch\''      )
         self.assertEqual(__UpperCAmelCase    , '\nCONSTANT = None\n'      )

         lowerCAmelCase__			:List[str]											=							create_dummy_object('function'    , '\'torch\''      )
         self.assertEqual(
             __UpperCAmelCase    , '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'      )

         lowerCAmelCase__			:Any											=							'\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n'
         lowerCAmelCase__			:Optional[int]											=							create_dummy_object('FakeClass'    , '\'torch\''      )
         self.assertEqual(__UpperCAmelCase    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							'# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n'
         lowerCAmelCase__			:int											=							create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}      )
         self.assertEqual(dummy_files['torch']    , __UpperCAmelCase      )







"""simple docstring"""





import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Dict:





  """simple docstring"""


  lowerCAmelCase__			:str											=							BertConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
  print(F"Building PyTorch model from configuration: {config}"			)
  lowerCAmelCase__			:int											=							BertForPreTraining(_SCREAMING_SNAKE_CASE			)

  # Load weights from tf checkpoint
  load_tf_weights_in_bert(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

  # Save pytorch-model
  print(F"Save PyTorch model to {pytorch_dump_path}"			)
  torch.save(model.state_dict()     ,   _SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
  __A											=		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(
      """--bert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained BERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __A											=		parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
