
import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


_lowerCamelCase :       Any     					=    logging.get_logger(__name__)

if is_tf_available():
			import tensorflow as tf




@dataclass
class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Optional[Any]					     =      "tf"
	_UpperCAmelCase :	Optional[str]					     =      field(
	    default=UpperCAmelCase							,metadata={"help": "Name of TPU"}							,)

	_UpperCAmelCase :	Optional[str]					     =      field(
	    default=UpperCAmelCase							,metadata={"help": "Zone of TPU"}							,)

	_UpperCAmelCase :	Optional[str]					     =      field(
	    default=UpperCAmelCase							,metadata={"help": "Name of Cloud TPU-enabled project"}							,)

	_UpperCAmelCase :	float					     =      field(
	    default=1.0							,metadata={"help": "Power for the Polynomial decay LR scheduler."}							,)

	_UpperCAmelCase :	bool					     =      field(default=UpperCAmelCase							,metadata={"help": "Whether to activate the XLA compilation or not"}      )



	@cached_property
	def 				A							(						self			:					Dict			):


				'''simple docstring'''


				requires_backends(self    ,						['tf']			)
				logger.info('Tensorflow: setting up strategy'			)

				_snake_case						=    tf.config.list_physical_devices('GPU'			)

				# Set to float16 at first
				if self.fpaa:
							tf.keras.mixed_precision.set_global_policy('mixed_float16'			)

				if self.no_cuda:
							_snake_case						=    tf.distribute.OneDeviceStrategy(device='/cpu:0'			)
				else:
							try:
										if self.tpu_name:
													_snake_case						=    tf.distribute.cluster_resolver.TPUClusterResolver(
													    self.tpu_name    ,						zone=self.tpu_zone    ,						project=self.gcp_project			)
										else:
													_snake_case						=    tf.distribute.cluster_resolver.TPUClusterResolver()
							except ValueError:
										if self.tpu_name:
													raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''			)
										else:
													_snake_case						=    None

							if tpu:
										# Set to bfloat16 in case of TPU
										if self.fpaa:
													tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'			)

										tf.config.experimental_connect_to_cluster(lowercase			)
										tf.tpu.experimental.initialize_tpu_system(lowercase			)

										_snake_case						=    tf.distribute.TPUStrategy(lowercase			)

							elif len(lowercase			) == 0:
										_snake_case						=    tf.distribute.OneDeviceStrategy(device='/cpu:0'			)
							elif len(lowercase			) == 1:
										_snake_case						=    tf.distribute.OneDeviceStrategy(device='/gpu:0'			)
							elif len(lowercase			) > 1:
										# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
										_snake_case						=    tf.distribute.MirroredStrategy()
							else:
										raise ValueError('Cannot find the proper strategy, please check your environment properties.'			)

				return strategy



	@property
	def 				A							(						self			:					int			):


				'''simple docstring'''


				requires_backends(self    ,						['tf']			)
				return self._setup_strategy



	@property
	def 				A							(						self			:					str			):


				'''simple docstring'''


				requires_backends(self    ,						['tf']			)
				return self._setup_strategy.num_replicas_in_sync



	@property
	def 				A							(						self			:					Dict			):


				'''simple docstring'''


				return False  # TF Logging is handled by Keras not the Trainer



	@property
	def 				A							(						self			:					Tuple			):


				'''simple docstring'''


				if self.per_gpu_train_batch_size:
							logger.warning(
							    'Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '
							    'version. Using `--per_device_train_batch_size` is preferred.'			)
				_snake_case						=    self.per_gpu_train_batch_size or self.per_device_train_batch_size
				return per_device_batch_size * self.n_replicas



	@property
	def 				A							(						self			:					Dict			):


				'''simple docstring'''


				if self.per_gpu_eval_batch_size:
							logger.warning(
							    'Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '
							    'version. Using `--per_device_eval_batch_size` is preferred.'			)
				_snake_case						=    self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
				return per_device_batch_size * self.n_replicas





	@property
	def 				A							(						self			:					str			):


				'''simple docstring'''


				requires_backends(self    ,						['tf']			)
				warnings.warn(
				    'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'    ,						lowercase    ,						)
				return self._setup_strategy.num_replicas_in_sync

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
			)