





import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       # Initialise PyTorch model
       _UpperCamelCase														=    json.load(open(lowerCAmelCase     ,							'''r'''       )       )
       _UpperCamelCase														=    GPTNeoConfig(
           hidden_size=config_json['''n_embd''']     ,							num_layers=config_json['''n_layer''']     ,							num_heads=config_json['''n_head''']     ,							attention_types=config_json['''attention_types''']     ,							max_position_embeddings=config_json['''n_positions''']     ,							resid_dropout=config_json['''res_dropout''']     ,							embed_dropout=config_json['''embed_dropout''']     ,							attention_dropout=config_json['''attn_dropout''']     ,							)
       print(f'''Building PyTorch model from configuration: {config}'''       )
       _UpperCamelCase														=    GPTNeoForCausalLM(lowerCAmelCase       )

       # Load weights from tf checkpoint
       load_tf_weights_in_gpt_neo(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )

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


if __name__ == "__main__":
       lowercase     :			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 mesh-tf 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."""
       )
       lowercase     :			Optional[int]     						=	parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)













import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
       from PIL import Image

class 			__A(    unittest.TestCase    ,      __UpperCAmelCase			):

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    load_tool('''image-question-answering'''   )
            self.tool.setup()
            _UpperCamelCase														=    load_tool('''image-question-answering''',							remote=A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.tool(A,							'''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.remote_tool(A,							'''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.tool(image=A,							question='''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.remote_tool(image=A,							question='''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )






