






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      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

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

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

		# Save pytorch-model
		__A    =							pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
		__A    =							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_   :	Tuple							      =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_   :	str							      =parser.parse_args()
					convert_imagegpt_checkpoint_to_pytorch(
					    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
					)







'''simple docstring'''


import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
     import torch_xla.core.xla_model as xm  # noqa: F401

     UpperCamelCase			     =					True
except ImportError:
     UpperCamelCase			     =					False


# Cache this result has it's a C FFI call which can be pretty time-consuming
UpperCamelCase			     =					torch.distributed.is_available()
def 					SCREAMING_SNAKE_CASE(   __lowercase	)			-> Optional[int]:
     # Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
     A:		Optional[Any]          =		importlib.util.find_spec(__lowercase	) is not None
     if package_exists:
          try:
               A:		Dict          =		importlib.metadata.metadata(__lowercase	)
               return True
          except importlib.metadata.PackageNotFoundError:
               return False
def 					SCREAMING_SNAKE_CASE(   )			-> bool:
     return _torch_distributed_available
def 					SCREAMING_SNAKE_CASE(   )			-> Optional[int]:
     try:
          pass
     except ImportError:
          print(
              '''Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'''
              ''' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'''
              ''' Bindings for PyTorch*.'''	)
     return (
         importlib.util.find_spec('''torch_ccl'''	) is not None
         or importlib.util.find_spec('''oneccl_bindings_for_pytorch'''	) is not None
     )
def 					SCREAMING_SNAKE_CASE(   )			-> List[str]:
     return importlib.metadata.version('''oneccl_bind_pt'''	)
def 					SCREAMING_SNAKE_CASE(   )			-> Union[str, Any]:
     return _is_package_available('''transformer_engine'''	)
@lru_cache
def 					SCREAMING_SNAKE_CASE(   __lowercase=True	)			-> Union[str, Any]:
     # Due to bugs on the amp series GPUs, we disable torch-xla on them
     if torch.cuda.is_available():
          return False
     if _tpu_available and check_device:
          try:
               # Will raise a RuntimeError if no XLA configuration is found
               A:		Tuple          =		xm.xla_device()
               return True
          except RuntimeError:
               return False
     return _tpu_available
def 					SCREAMING_SNAKE_CASE(   )			-> List[str]:
     return _is_package_available('''deepspeed'''	)
def 					SCREAMING_SNAKE_CASE(   __lowercase=False	)			-> Any:
     if is_tpu_available():
          return not ignore_tpu
     if torch.cuda.is_available():
          return torch.cuda.is_bfaa_supported()
     if is_npu_available():
          return False
     return True
def 					SCREAMING_SNAKE_CASE(   )			-> str:
     A:		List[str]          =		_is_package_available('''bitsandbytes'''	)
     if package_exists:
          A:		Union[str, Any]          =		version.parse(importlib.metadata.version('''bitsandbytes'''	)	)
          return compare_versions(__lowercase		,				'''>='''		,				'''0.39.0'''	)
     return False
def 					SCREAMING_SNAKE_CASE(   )			-> str:
     A:		int          =		_is_package_available('''bitsandbytes'''	)
     if package_exists:
          A:		Tuple          =		version.parse(importlib.metadata.version('''bitsandbytes'''	)	)
          return compare_versions(__lowercase		,				'''>='''		,				'''0.37.2'''	)
     return False
def 					SCREAMING_SNAKE_CASE(   )			-> int:
     return _is_package_available('''bitsandbytes'''	)
def 					SCREAMING_SNAKE_CASE(   )			-> Optional[int]:
     if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM'''		,				'''False'''	)	) == 1:
          A:		Any          =		importlib.util.find_spec('''megatron'''	) is not None
          if package_exists:
               try:
                    A:		str          =		parse(importlib.metadata.version('''megatron-lm'''	)	)
                    return compare_versions(__lowercase		,				'''>='''		,				'''2.2.0'''	)
               except Exception as e:
                    warnings.warn(F"""Parse Megatron version failed. Exception:{e}"""	)
                    return False
def 					SCREAMING_SNAKE_CASE(   )			-> Any:
     return _is_package_available('''safetensors'''	)
def 					SCREAMING_SNAKE_CASE(   )			-> Tuple:
     return _is_package_available('''transformers'''	)
def 					SCREAMING_SNAKE_CASE(   )			-> Union[str, Any]:
     return _is_package_available('''datasets'''	)
def 					SCREAMING_SNAKE_CASE(   )			-> Union[str, Any]:
     return _is_package_available('''aim'''	)
def 					SCREAMING_SNAKE_CASE(   )			-> List[Any]:
     return _is_package_available('''tensorboard'''	) or _is_package_available('''tensorboardX'''	)
def 					SCREAMING_SNAKE_CASE(   )			-> Dict:
     return _is_package_available('''wandb'''	)
def 					SCREAMING_SNAKE_CASE(   )			-> Optional[Any]:
     return _is_package_available('''comet_ml'''	)
def 					SCREAMING_SNAKE_CASE(   )			-> Any:
     return _is_package_available('''boto3'''	)
def 					SCREAMING_SNAKE_CASE(   )			-> str:
     if _is_package_available('''rich'''	):
          if "ACCELERATE_DISABLE_RICH" in os.environ:
               warnings.warn(
                   '''`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'''	)
               return not parse_flag_from_env('''ACCELERATE_DISABLE_RICH'''		,				__lowercase	)
          return parse_flag_from_env('''ACCELERATE_ENABLE_RICH'''		,				__lowercase	)
     return False
def 					SCREAMING_SNAKE_CASE(   )			-> int:
     return _is_package_available('''sagemaker'''	)
def 					SCREAMING_SNAKE_CASE(   )			-> Union[str, Any]:
     return _is_package_available('''tqdm'''	)
def 					SCREAMING_SNAKE_CASE(   )			-> str:
     return _is_package_available('''mlflow'''	)
def 					SCREAMING_SNAKE_CASE(   )			-> int:
     return is_torch_version('''>='''		,				'''1.12'''	) and torch.backends.mps.is_available() and torch.backends.mps.is_built()
def 					SCREAMING_SNAKE_CASE(   )			-> str:
     def get_major_and_minor_from_version(__lowercase	):
          return str(version.parse(__lowercase	).major	) + "." + str(version.parse(__lowercase	).minor	)

     A:		int          =		importlib.metadata.version('''torch'''	)
     if importlib.util.find_spec('''intel_extension_for_pytorch'''	) is None:
          return False
     A:		Optional[Any]          =		'''N/A'''
     try:
          A:		List[Any]          =		importlib.metadata.version('''intel_extension_for_pytorch'''	)
     except importlib.metadata.PackageNotFoundError:
          return False
     A:		Optional[int]          =		get_major_and_minor_from_version(_torch_version	)
     A:		List[str]          =		get_major_and_minor_from_version(_ipex_version	)
     if torch_major_and_minor != ipex_major_and_minor:
          warnings.warn(
              F"""Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"""
              F""" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."""	)
          return False
     return True
@lru_cache
def 					SCREAMING_SNAKE_CASE(   __lowercase=False	)			-> Optional[Any]:
     if importlib.util.find_spec('''torch'''	) is None or importlib.util.find_spec('''torch_npu'''	) is None:
          return False

     import torch
     import torch_npu  # noqa: F401

     if check_device:
          try:
               # Will raise a RuntimeError if no NPU is found
               A:		Tuple          =		torch.npu.device_count()
               return torch.npu.is_available()
          except RuntimeError:
               return False
     return hasattr(__lowercase		,				'''npu'''	) and torch.npu.is_available()


@lru_cache
def 					SCREAMING_SNAKE_CASE(   __lowercase=False	)			-> Tuple:
     if not parse_flag_from_env('''ACCELERATE_USE_XPU'''		,				default=__lowercase	):
          return False
     if is_ipex_available():
          import torch

          if is_torch_version('''<='''		,				'''1.12'''	):
               return False
     else:
          return False

     import intel_extension_for_pytorch  # noqa: F401

     if check_device:
          try:
               # Will raise a RuntimeError if no XPU  is found
               A:		str          =		torch.xpu.device_count()
               return torch.xpu.is_available()
          except RuntimeError:
               return False
     return hasattr(__lowercase		,				'''xpu'''	) and torch.xpu.is_available()






