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    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase		)    ->     List[Any]:



      '''simple docstring'''

      _lowerCamelCase           =					{'''small''': (512, 8, 24), '''medium''': (1024, 8, 36), '''large''': (1536, 16, 48)}
      _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase           =					MODELS[model_size]  # set model hyperparameters
      _lowerCamelCase           =					ImageGPTConfig(n_embd=__UpperCAmelCase			,    n_layer=__UpperCAmelCase			,    n_head=__UpperCAmelCase		)
      _lowerCamelCase           =					ImageGPTForCausalLM(__UpperCAmelCase		)

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

      # Save pytorch-model
      _lowerCamelCase           =					pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
      _lowerCamelCase           =					pytorch_dump_folder_path + '''/''' + CONFIG_NAME
      print(F'Save PyTorch model to {pytorch_weights_dump_path}'		)
      torch.save(model.state_dict()			,    __UpperCAmelCase		)
      print(F'Save configuration file to {pytorch_config_dump_path}'		)
      with open(__UpperCAmelCase			,    '''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
     )
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
snake_case__						      =			abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def    __magic_name__(	__UpperCAmelCase		)    ->     int:



      '''simple docstring'''

      from transformers.testing_utils import pytest_addoption_shared

      pytest_addoption_shared(__UpperCAmelCase		)




def    __magic_name__(	__UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

      from transformers.testing_utils import pytest_terminal_summary_main

      _lowerCamelCase           =					terminalreporter.config.getoption('''--make-reports'''		)
      if make_reports:
            pytest_terminal_summary_main(__UpperCAmelCase			,    id=__UpperCAmelCase		)