






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


UpperCamelCase											=      get_logger(__name__)




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      Dict:





 if text.startswith(lowerCamelCase_				):
  return text[len(lowerCamelCase_				) :]
 return text  # or whatever




def       _a   (							lowerCamelCase__				)  ->      List[Any]:
 return {remove_prefix(lowerCamelCase_ ,			'model.'				): v for k, v in sd.items()}




def       _a   (							lowerCamelCase__				)  ->      Dict:
 lowerCamelCase_							:    Tuple							=     {}
 for k in state_dicts[0].keys():
  lowerCamelCase_							:    Any							=     [sd[k] for sd in state_dicts]
  lowerCamelCase_							:    Union[str, Any]							=     sum(lowerCamelCase_				) / len(lowerCamelCase_				)
  assert isinstance(lowerCamelCase_ ,			torch.Tensor				)
  lowerCamelCase_							:    int							=     new_t
 return new_sd




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      Union[str, Any]:
 lowerCamelCase_							:    Optional[int]							=     AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_				)
 if os.path.isfile(lowerCamelCase_				):
  lowerCamelCase_							:    Any							=     [pl_ckpt_path]
 else:
  assert os.path.isdir(lowerCamelCase_				)
  lowerCamelCase_							:    Optional[Any]							=     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_							:    Optional[int]							=     [sanitize(torch.load(lowerCamelCase_ ,			map_location='cpu'				)['state_dict']				) for x in ckpt_files]
 lowerCamelCase_							:    Tuple							=     average_state_dicts(lowerCamelCase_				)

 lowerCamelCase_		,      lowerCamelCase_							:    Tuple							=     hf_model.load_state_dict(lowerCamelCase_ ,			strict=lowerCamelCase_				)
 assert not missing, F'missing keys: {missing}'
 hf_model.save_pretrained(lowerCamelCase_				)
 try:
  lowerCamelCase_							:    Optional[int]							=     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,
)


_SCREAMING_SNAKE_CASE:    Optional[Any]     						=							{
    '''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:
			_SCREAMING_SNAKE_CASE:    Dict     						=							[
			    '''SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''SamModel''',
			    '''SamPreTrainedModel''',
			]
try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    Optional[Any]     						=							[
			    '''TF_SAM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFSamModel''',
			    '''TFSamPreTrainedModel''',
			]
try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_SCREAMING_SNAKE_CASE:    List[Any]     						=							['''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

			_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

