


'''simple docstring'''

import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     str		):





	"""simple docstring"""







	__magic_name__	:   List[Any]					     =					s.rsplit(lowerCAmelCase		,		lowerCAmelCase		)
	return new.join(lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()		)





def lowerCamelCase      (      lowerCAmelCase    :     Dict		):





	"""simple docstring"""







	__magic_name__	:   Optional[Any]					     =					{}

	__magic_name__	:   Dict					     =					['group_1', 'group_2', 'group_3', 'group_4']
	for key, value in state_dict.items():
		for group_key in group_keys:
			if group_key in key:
				__magic_name__	:   List[Any]					     =					key.replace(f'{group_key}.'		,		f'{group_key}.group.'		)

		if "res_path" in key:
			__magic_name__	:   Optional[Any]					     =					key.replace('res_path.'		,		'res_path.path.'		)

		if key.endswith('.w'		):
			__magic_name__	:   List[str]					     =					rreplace(lowerCAmelCase		,		'.w'		,		'.weight'		,		1		)
		if key.endswith('.b'		):
			__magic_name__	:   Tuple					     =					rreplace(lowerCAmelCase		,		'.b'		,		'.bias'		,		1		)

		__magic_name__	:   Tuple					     =					value.float()

	return upgrade





@torch.no_grad()
def lowerCamelCase      (      lowerCAmelCase    :     int		,		lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     Dict=None		,		lowerCAmelCase    :     Dict=True		):





	"""simple docstring"""







	from dall_e import Encoder

	__magic_name__	:   Union[str, Any]					     =					Encoder()
	if os.path.exists(lowerCAmelCase		):
		__magic_name__	:   Optional[Any]					     =					torch.load(lowerCAmelCase		)
	else:
		__magic_name__	:   int					     =					torch.hub.load_state_dict_from_url(lowerCAmelCase		)

	if isinstance(lowerCAmelCase		,		lowerCAmelCase		):
		__magic_name__	:   Tuple					     =					ckpt.state_dict()
	encoder.load_state_dict(lowerCAmelCase		)

	if config_path is not None:
		__magic_name__	:   Union[str, Any]					     =					FlavaImageCodebookConfig.from_pretrained(lowerCAmelCase		)
	else:
		__magic_name__	:   Any					     =					FlavaImageCodebookConfig()

	__magic_name__	:   Union[str, Any]					     =					FlavaImageCodebook(lowerCAmelCase		).eval()
	__magic_name__	:   Union[str, Any]					     =					encoder.state_dict()

	__magic_name__	:   List[str]					     =					upgrade_state_dict(lowerCAmelCase		)
	hf_model.load_state_dict(lowerCAmelCase		)
	__magic_name__	:   List[Any]					     =					hf_model.state_dict()
	__magic_name__	:   Union[str, Any]					     =					count_parameters(lowerCAmelCase		)
	__magic_name__	:   Dict					     =					count_parameters(lowerCAmelCase		)

	assert torch.allclose(lowerCAmelCase		,		lowerCAmelCase		,		atol=1e-3		)

	if save_checkpoint:
		hf_model.save_pretrained(lowerCAmelCase		)
	else:
		return hf_state_dict


if __name__ == "__main__":
							lowerCAmelCase				:List[str]        =      argparse.ArgumentParser()
							parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
							parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to flava checkpoint''')
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
							lowerCAmelCase				:Tuple        =      parser.parse_args()

							convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)



'''simple docstring'''

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
							from PIL import Image
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      Any				   =    """Salesforce/blip-image-captioning-base"""
		A_     :      Any				   =    (
		    """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
		    """image to caption, and returns a text that contains the description in English."""
		)
		A_     :      List[str]				   =    """image_captioner"""
		A_     :      Optional[Any]				   =    AutoModelForVisionaSeq

		A_     :      Any				   =    ["""image"""]
		A_     :      Union[str, Any]				   =    ["""text"""]







		def __init__( self		:						Dict  ,					*_A		:						Dict  ,					**_A		:						Any      )  ->			Dict:
			requires_backends(self  ,					['vision']      )
			super().__init__(*_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						"Image"      )  ->			Union[str, Any]:
			return self.pre_processor(images=_A  ,					return_tensors='pt'      )







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						str      )  ->			List[Any]:
			return self.model.generate(**_A      )







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						Optional[int]      )  ->			Union[str, Any]:
			return self.pre_processor.batch_decode(_A  ,					skip_special_tokens=_A      )[0].strip()