"""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  _snake_case     (					unittest.TestCase		):






      def       lowerCamelCase__     (       self      :			Optional[Any]    ):
             __lowerCamelCase					:		Optional[int]		   =     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					:		str		   =     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					:		Tuple		   =     find_backend(
                 "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"    )
             self.assertEqual(UpperCAmelCase							,					"torch_and_transformers_and_onnx"    )






      def       lowerCamelCase__     (       self      :			Dict    ):
             __lowerCamelCase					:		Optional[Any]		   =     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       lowerCamelCase__     (       self      :			Any    ):
             __lowerCamelCase					:		Union[str, Any]		   =     create_dummy_object("CONSTANT"							,					"'torch'"    )
             self.assertEqual(UpperCAmelCase							,					"\nCONSTANT = None\n"    )

             __lowerCamelCase					:		Optional[Any]		   =     create_dummy_object("function"							,					"'torch'"    )
             self.assertEqual(
                 UpperCAmelCase							,					"\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"    )

             __lowerCamelCase					:		List[str]		   =     "\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       lowerCamelCase__     (       self      :			List[Any]    ):
             __lowerCamelCase					:		str		   =     "# 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					:		Union[str, Any]		   =     create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}    )
             self.assertEqual(dummy_files["torch"]							,					UpperCAmelCase    )
"""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_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	str						,   _lowerCamelCase:	Dict      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     TaConfig.from_json_file(_lowerCamelCase      )
							print(F"""Building PyTorch model from configuration: {config}"""      )
							__lowerCamelCase					:		Optional[int]		   =     TaForConditionalGeneration(_lowerCamelCase      )

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

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


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(
				    '''--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  =				parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)