import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	Optional[int]:






      '''simple docstring'''





      UpperCAmelCase     :			Optional[int]     	={'''small''': (5_12, 8, 24), '''medium''': (10_24, 8, 36), '''large''': (15_36, 16, 48)}
      UpperCAmelCase     :			Tuple     	=MODELS[model_size]  # set model hyperparameters
      UpperCAmelCase     :			Union[str, Any]     	=ImageGPTConfig(n_embd=__lowercase		,       n_layer=__lowercase		,       n_head=__lowercase    )
      UpperCAmelCase     :			Any     	=ImageGPTForCausalLM(__lowercase    )

      # Load weights from numpy
      load_tf_weights_in_imagegpt(__lowercase		,       __lowercase		,       __lowercase    )

      # Save pytorch-model
      UpperCAmelCase     :			Union[str, Any]     	=pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
      UpperCAmelCase     :			Optional[Any]     	=pytorch_dump_folder_path + '''/''' + CONFIG_NAME
      print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''    )
      torch.save(model.state_dict()		,       __lowercase    )
      print(f'''Save configuration file to {pytorch_config_dump_path}'''    )
      with open(__lowercase		,       '''w'''		,       encoding='''utf-8'''    ) as f:
            f.write(config.to_json_string()    )


if __name__ == "__main__":
     __snake_case         =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--imagegpt_checkpoint_path''',
         default=None,
         type=str,
         required=True,
         help='''Path to the TensorFlow checkpoint path.''',
     )
     parser.add_argument(
         '''--model_size''',
         default=None,
         type=str,
         required=True,
         help='''Size of the model (can be either \'small\', \'medium\' or \'large\').''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     __snake_case         =     parser.parse_args()
     convert_imagegpt_checkpoint_to_pytorch(
         args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
     )




import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
       import tensorflow as tf

       from transformers.activations_tf import get_tf_activation





@require_tf
class 			lowerCAmelCase__(     unittest.TestCase    ):






    '''simple docstring'''





    def        _lowerCamelCase			(						self					: Optional[int]						):





       '''simple docstring'''
       UpperCAmelCase_				:   str							= tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]						)
       UpperCAmelCase_				:   List[Any]							= get_tf_activation('''gelu'''						)
       UpperCAmelCase_				:   Any							= get_tf_activation('''gelu_10'''						)

       UpperCAmelCase_				:   int							= gelu(__snake_case						)
       UpperCAmelCase_				:   Union[str, Any]							= geluaa(__snake_case						)

       UpperCAmelCase_				:   Union[str, Any]							= tf.where(y_gelu_aa < 10.0							, 1.0							, 0.0						)

       self.assertEqual(tf.math.reduce_max(__snake_case						).numpy().item()							, 10.0						)
       self.assertTrue(np.allclose(y_gelu * clipped_mask							, y_gelu_aa * clipped_mask						)						)





    def        _lowerCamelCase			(						self					: Dict						):





       '''simple docstring'''
       get_tf_activation('''gelu'''						)
       get_tf_activation('''gelu_10'''						)
       get_tf_activation('''gelu_fast'''						)
       get_tf_activation('''gelu_new'''						)
       get_tf_activation('''glu'''						)
       get_tf_activation('''mish'''						)
       get_tf_activation('''quick_gelu'''						)
       get_tf_activation('''relu'''						)
       get_tf_activation('''sigmoid'''						)
       get_tf_activation('''silu'''						)
       get_tf_activation('''swish'''						)
       get_tf_activation('''tanh'''						)
       with self.assertRaises(__snake_case						):
          get_tf_activation('''bogus'''						)
       with self.assertRaises(__snake_case						):
          get_tf_activation(__snake_case						)