


import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig







def 						lowerCamelCase(   a__	,a__	,a__	,a__):
				_SCREAMING_SNAKE_CASE			  =s.rsplit(a__	,a__)
				return new.join(a__)







def 						lowerCamelCase(   a__):
				# encoder.embeddings are double copied in original FLAVA
				return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items())







def 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE			  ={}

				_SCREAMING_SNAKE_CASE			  =['''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:
																_SCREAMING_SNAKE_CASE			  =key.replace(f"{group_key}."	,f"{group_key}.group.")

								if "res_path" in key:
												_SCREAMING_SNAKE_CASE			  =key.replace('''res_path.'''	,'''res_path.path.''')

								if key.endswith('''.w'''):
												_SCREAMING_SNAKE_CASE			  =rreplace(a__	,'''.w'''	,'''.weight'''	,1)
								if key.endswith('''.b'''):
												_SCREAMING_SNAKE_CASE			  =rreplace(a__	,'''.b'''	,'''.bias'''	,1)

								_SCREAMING_SNAKE_CASE			  =value.float()

				return upgrade







@torch.no_grad()
def 						lowerCamelCase(   a__	,a__	,a__=None	,a__=True):
				from dall_e import Encoder

				_SCREAMING_SNAKE_CASE			  =Encoder()
				if os.path.exists(a__):
								_SCREAMING_SNAKE_CASE			  =torch.load(a__)
				else:
								_SCREAMING_SNAKE_CASE			  =torch.hub.load_state_dict_from_url(a__)

				if isinstance(a__	,a__):
								_SCREAMING_SNAKE_CASE			  =ckpt.state_dict()
				encoder.load_state_dict(a__)

				if config_path is not None:
								_SCREAMING_SNAKE_CASE			  =FlavaImageCodebookConfig.from_pretrained(a__)
				else:
								_SCREAMING_SNAKE_CASE			  =FlavaImageCodebookConfig()

				_SCREAMING_SNAKE_CASE			  =FlavaImageCodebook(a__).eval()
				_SCREAMING_SNAKE_CASE			  =encoder.state_dict()

				_SCREAMING_SNAKE_CASE			  =upgrade_state_dict(a__)
				hf_model.load_state_dict(a__)
				_SCREAMING_SNAKE_CASE			  =hf_model.state_dict()
				_SCREAMING_SNAKE_CASE			  =count_parameters(a__)
				_SCREAMING_SNAKE_CASE			  =count_parameters(a__)

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

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


if __name__ == "__main__":
							snake_case_						:			Any													=							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''')
							snake_case_						:			List[str]													=							parser.parse_args()

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



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						["image_processor", "tokenizer"]
						UpperCAmelCase             =						"ViTImageProcessor"
						UpperCAmelCase             =						("CLIPTokenizer", "CLIPTokenizerFast")




						def __init__(       self					:      int				,    _a					:      int=None				,    _a					:      Optional[int]=None				,    **_a					:      str  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
														    ''' instead.'''				,    _a				,    )
														_SCREAMING_SNAKE_CASE			  =kwargs.pop('''feature_extractor'''  )

										_SCREAMING_SNAKE_CASE			  =image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError('''You need to specify an `image_processor`.'''  )
										if tokenizer is None:
														raise ValueError('''You need to specify a `tokenizer`.'''  )

										super().__init__(_a				,    _a  )




						def __call__(       self					:      List[str]				,    _a					:      Tuple=None				,    _a					:      Optional[Any]=None				,    _a					:      List[Any]=None				,    _a					:      List[Any]=None				,    **_a					:      List[Any]  )							->     Optional[Any]:


										"""simple docstring"""





										if text is None and visual_prompt is None and images is None:
														raise ValueError('''You have to specify either text, visual prompt or images.'''  )

										if text is not None and visual_prompt is not None:
														raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''  )

										if text is not None:
														_SCREAMING_SNAKE_CASE			  =self.tokenizer(_a				,    return_tensors=_a				,    **_a  )

										if visual_prompt is not None:
														_SCREAMING_SNAKE_CASE			  =self.image_processor(_a				,    return_tensors=_a				,    **_a  )

										if images is not None:
														_SCREAMING_SNAKE_CASE			  =self.image_processor(_a				,    return_tensors=_a				,    **_a  )

										if visual_prompt is not None and images is not None:
														_SCREAMING_SNAKE_CASE			  ={
														    '''pixel_values''': image_features.pixel_values,
														    '''conditional_pixel_values''': prompt_features.pixel_values,
														}
														return encoding
										elif text is not None and images is not None:
														_SCREAMING_SNAKE_CASE			  =image_features.pixel_values
														return encoding
										elif text is not None:
														return encoding
										elif visual_prompt is not None:
														_SCREAMING_SNAKE_CASE			  ={
														    '''conditional_pixel_values''': prompt_features.pixel_values,
														}
														return encoding
										else:
														return BatchEncoding(data=dict(**_a  )				,    tensor_type=_a  )




						def 							__UpperCamelCase				(       self					:      Any				,    *_a					:      str				,    **_a					:      List[Any]  )							->     Tuple:


										"""simple docstring"""





										return self.tokenizer.batch_decode(*_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    *_a					:      str				,    **_a					:      List[Any]  )							->     List[Any]:


										"""simple docstring"""





										return self.tokenizer.decode(*_a				,    **_a  )




						@property
						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     int:


										"""simple docstring"""





										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,    _a				,    )
										return self.image_processor_class




						@property
						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Union[str, Any]:


										"""simple docstring"""





										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,    _a				,    )
										return self.image_processor