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


_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							get_logger(__name__)






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





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






def  UpperCAmelCase__(    lowerCamelCase_	):



			"""simple docstring"""





			return {remove_prefix(lowerCamelCase_				,						"model."	): v for k, v in sd.items()}






def  UpperCAmelCase__(    lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   {}
			for k in state_dicts[0].keys():
						_snake_case														=   [sd[k] for sd in state_dicts]
						_snake_case														=   sum(lowerCamelCase_	) / len(lowerCamelCase_	)
						assert isinstance(lowerCamelCase_				,						torch.Tensor	)
						_snake_case														=   new_t
			return new_sd






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_	)
			if os.path.isfile(lowerCamelCase_	):
						_snake_case														=   [pl_ckpt_path]
			else:
						assert os.path.isdir(lowerCamelCase_	)
						_snake_case														=   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}'	)

			_snake_case														=   [sanitize(torch.load(lowerCamelCase_				,						map_location="cpu"	)["state_dict"]	) for x in ckpt_files]
			_snake_case														=   average_state_dicts(lowerCamelCase_	)

			_snake_case							,			_snake_case														=   hf_model.load_state_dict(lowerCamelCase_				,						strict=lowerCamelCase_	)
			assert not missing, f'missing keys: {missing}'
			hf_model.save_pretrained(lowerCamelCase_	)
			try:
						_snake_case														=   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__)

