import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


lowercase__      :    List[Any]          =     get_logger(__name__)

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     Tuple:
							if text.startswith(_lowerCAmelCase							):
														return text[len(_lowerCAmelCase							) :]
							return text  # or whatever

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     str:
							return {remove_prefix(_lowerCAmelCase       ,  '''model.'''							): v for k, v in sd.items()}

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     List[str]:
							lowerCAmelCase     		=		{}
							for k in state_dicts[0].keys():
														lowerCAmelCase     		=		[sd[k] for sd in state_dicts]
														lowerCAmelCase     		=		sum(_lowerCAmelCase							) / len(_lowerCAmelCase							)
														assert isinstance(_lowerCAmelCase       ,  torch.Tensor							)
														lowerCAmelCase     		=		new_t
							return new_sd

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     None:
							lowerCAmelCase     		=		AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase							)
							if os.path.isfile(_lowerCAmelCase							):
														lowerCAmelCase     		=		[pl_ckpt_path]
							else:
														assert os.path.isdir(_lowerCAmelCase							)
														lowerCAmelCase     		=		list(Path(_lowerCAmelCase							).glob('''*.ckpt'''							)							)
														assert ckpt_files, f"could not find any ckpt files inside the {pl_ckpt_path} directory"

							if len(_lowerCAmelCase							) > 1:
														logger.info(f"averaging the weights of {ckpt_files}"							)

							lowerCAmelCase     		=		[sanitize(torch.load(_lowerCAmelCase       ,  map_location='''cpu'''							)['''state_dict''']							) for x in ckpt_files]
							lowerCAmelCase     		=		average_state_dicts(_lowerCAmelCase							)

							lowerCAmelCase     		=		hf_model.load_state_dict(_lowerCAmelCase       ,  strict=_lowerCAmelCase							)
							assert not missing, f"missing keys: {missing}"
							hf_model.save_pretrained(_lowerCAmelCase							)
							try:
														lowerCAmelCase     		=		AutoTokenizer.from_pretrained(_lowerCAmelCase							)
														tok.save_pretrained(_lowerCAmelCase							)
							except Exception:
														pass
														# dont copy tokenizer if cant


if __name__ == "__main__":
	fire.Fire(convert_pl_to_hf)




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__)



