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        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     Union[str, Any]:
							# Construct configuration depending on size
							lowerCAmelCase     		=		{'''small''': (5_1_2, 8, 2_4), '''medium''': (1_0_2_4, 8, 3_6), '''large''': (1_5_3_6, 1_6, 4_8)}
							lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		MODELS[model_size]  # set model hyperparameters
							lowerCAmelCase     		=		ImageGPTConfig(n_embd=snake_case__       ,  n_layer=snake_case__       ,  n_head=snake_case__							)
							lowerCAmelCase     		=		ImageGPTForCausalLM(snake_case__							)

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

							# 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()       ,  snake_case__							)
							print(f"Save configuration file to {pytorch_config_dump_path}"							)
							with open(snake_case__       ,  '''w'''       ,  encoding='''utf-8'''							) as f:
														f.write(config.to_json_string()							)


if __name__ == "__main__":
	lowercase__      :    Dict          =     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.'''
	)
	lowercase__      :    str          =     parser.parse_args()
	convert_imagegpt_checkpoint_to_pytorch(
	    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
	)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__      :    Dict          =     {
    '''configuration_sam''': [
        '''SAM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SamConfig''',
        '''SamMaskDecoderConfig''',
        '''SamPromptEncoderConfig''',
        '''SamVisionConfig''',
    ],
    '''processing_sam''': ['''SamProcessor'''],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__      :    Dict          =     [
	    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SamModel''',
	    '''SamPreTrainedModel''',
	]
try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__      :    str          =     [
	    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFSamModel''',
	    '''TFSamPreTrainedModel''',
	]
try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__      :    Dict          =     ['''SamImageProcessor''']


if TYPE_CHECKING:
	from .configuration_sam import (
	    SAM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SamConfig,
	    SamMaskDecoderConfig,
	    SamPromptEncoderConfig,
	    SamVisionConfig,
	)
	from .processing_sam import SamProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST, SamModel, SamPreTrainedModel

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_sam import TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST, TFSamModel, TFSamPreTrainedModel

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_sam import SamImageProcessor

else:
	import sys

	lowercase__      :    Dict          =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



