


import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
			_lowerCamelCase :       Union[str, Any]     					=    []

			_lowerCamelCase :       Optional[Any]     					=    os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
			_lowerCamelCase :       Tuple     					=    glob.glob(pattern)
			# TODO: deal with TF/Flax too
			_lowerCamelCase :       Dict     					=    [
			    x for x in test_files if not (x.startswith('''test_modeling_tf_''') or x.startswith('''test_modeling_flax_'''))
			]

			for test_file in test_files:
						_lowerCamelCase :       Optional[Any]     					=    get_tester_classes(test_file)
						for tester_class in tester_classes:
									# A few tester classes don't have `parent` parameter in `__init__`.
									# TODO: deal this better
									try:
												_lowerCamelCase :       str     					=    tester_class(parent=None)
									except Exception:
												continue
									if hasattr(tester, '''get_config'''):
												_lowerCamelCase :       Dict     					=    tester.get_config()
												for k, v in config.to_dict().items():
															if isinstance(v, int):
																		_lowerCamelCase :       Union[str, Any]     					=    None
																		if k in ["vocab_size"]:
																					_lowerCamelCase :       int     					=    100
																		elif k in ["max_position_embeddings"]:
																					_lowerCamelCase :       List[str]     					=    128
																		elif k in ["hidden_size", "d_model"]:
																					_lowerCamelCase :       Any     					=    40
																		elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																					_lowerCamelCase :       Dict     					=    5
																		if target is not None and v > target:
																					failures.append(
																					    F'{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'
																					    F' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'
																					    F' than {target}.'
																					)

			if len(failures) > 0:
						raise Exception(F'There were {len(failures)} failures:\n' + '''\n'''.join(failures))

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  a_	( __lowercase     :			Optional[Any] ,      __lowercase     :			Union[str, Any] ,      __lowercase     :			str				)				->						Any:
			# Construct configuration depending on size
			_snake_case						=    {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_snake_case			,      _snake_case			,      _snake_case						=    MODELS[model_size]  # set model hyperparameters
			_snake_case						=    ImageGPTConfig(n_embd=__lowercase ,      n_layer=__lowercase ,      n_head=__lowercase				)
			_snake_case						=    ImageGPTForCausalLM(__lowercase				)

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

			# Save pytorch-model
			_snake_case						=    pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_snake_case						=    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__":
			_lowerCamelCase :       List[Any]     					=    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.'''
			)
			_lowerCamelCase :       Optional[Any]     					=    parser.parse_args()
			convert_imagegpt_checkpoint_to_pytorch(
			    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
			)