



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase	:   Tuple    =   logging.get_logger(__name__)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  RobertaPreLayerNormConfig.from_pretrained(
				    snake_case__    ,			architectures=['RobertaPreLayerNormForMaskedLM']					)

				# convert state_dict
				lowerCAmelCase__		  =  torch.load(hf_hub_download(repo_id=snake_case__    ,			filename='pytorch_model.bin'					)					)
				lowerCAmelCase__		  =  {}
				for tensor_key, tensor_value in original_state_dict.items():
								# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
								if tensor_key.startswith('roberta.'					):
												lowerCAmelCase__		  =  'roberta_prelayernorm.' + tensor_key[len('roberta.'					) :]

								# The original implementation contains weights which are not used, remove them from the state_dict
								if tensor_key.endswith('.self.LayerNorm.weight'					) or tensor_key.endswith('.self.LayerNorm.bias'					):
												continue

								lowerCAmelCase__		  =  tensor_value

				lowerCAmelCase__		  =  RobertaPreLayerNormForMaskedLM.from_pretrained(
				    pretrained_model_name_or_path=snake_case__    ,			config=snake_case__    ,			state_dict=snake_case__					)
				model.save_pretrained(snake_case__					)

				# convert tokenizer
				lowerCAmelCase__		  =  AutoTokenizer.from_pretrained(snake_case__					)
				tokenizer.save_pretrained(snake_case__					)


if __name__ == "__main__":
	_lowerCAmelCase	:   Union[str, Any]    =   argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--checkpoint-repo",
	    default=None,
	    type=str,
	    required=True,
	    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
	)
	_lowerCAmelCase	:   Dict    =   parser.parse_args()
	convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase	:   Any    =   {
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   str    =   ["ChineseCLIPFeatureExtractor"]
	_lowerCAmelCase	:   Any    =   ["ChineseCLIPImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   Optional[int]    =   [
	    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "ChineseCLIPModel",
	    "ChineseCLIPPreTrainedModel",
	    "ChineseCLIPTextModel",
	    "ChineseCLIPVisionModel",
	]

if TYPE_CHECKING:
	from .configuration_chinese_clip import (
	    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    ChineseCLIPConfig,
	    ChineseCLIPOnnxConfig,
	    ChineseCLIPTextConfig,
	    ChineseCLIPVisionConfig,
	)
	from .processing_chinese_clip import ChineseCLIPProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_chinese_clip import (
		    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ChineseCLIPModel,
		    ChineseCLIPPreTrainedModel,
		    ChineseCLIPTextModel,
		    ChineseCLIPVisionModel,
		)

else:
	import sys

	_lowerCAmelCase	:   Any    =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
