

'''simple docstring'''



import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def 						__lowerCamelCase	(       __snake_case					:		Dict,		__snake_case					:		str,		__snake_case					:		Tuple,		__snake_case					:		Tuple	)	->							int:






						"""simple docstring"""





						A__					:       Optional[Any]     =s.rsplit(__snake_case,		__snake_case	)
						return new.join(__snake_case	)
def 						__lowerCamelCase	(       __snake_case					:		Optional[int]	)	->							Tuple:






						"""simple docstring"""





						return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()	)
def 						__lowerCamelCase	(       __snake_case					:		Optional[int]	)	->							str:






						"""simple docstring"""





						A__					:       int     ={}

						A__					:       Any     =["""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:
																								A__					:       Dict     =key.replace(f"{group_key}.",		f"{group_key}.group."	)

												if "res_path" in key:
																		A__					:       List[str]     =key.replace("""res_path.""",		"""res_path.path."""	)

												if key.endswith(""".w"""	):
																		A__					:       Optional[int]     =rreplace(__snake_case,		""".w""",		""".weight""",		1	)
												if key.endswith(""".b"""	):
																		A__					:       int     =rreplace(__snake_case,		""".b""",		""".bias""",		1	)

												A__					:       str     =value.float()

						return upgrade
@torch.no_grad()
def 						__lowerCamelCase	(       __snake_case					:		List[Any],		__snake_case					:		Dict,		__snake_case					:		Dict=None,		__snake_case					:		int=True	)	->							Union[str, Any]:






						"""simple docstring"""





						from dall_e import Encoder

						A__					:       List[str]     =Encoder()
						if os.path.exists(__snake_case	):
												A__					:       Union[str, Any]     =torch.load(__snake_case	)
						else:
												A__					:       Union[str, Any]     =torch.hub.load_state_dict_from_url(__snake_case	)

						if isinstance(__snake_case,		__snake_case	):
												A__					:       List[Any]     =ckpt.state_dict()
						encoder.load_state_dict(__snake_case	)

						if config_path is not None:
												A__					:       List[Any]     =FlavaImageCodebookConfig.from_pretrained(__snake_case	)
						else:
												A__					:       Union[str, Any]     =FlavaImageCodebookConfig()

						A__					:       Optional[Any]     =FlavaImageCodebook(__snake_case	).eval()
						A__					:       Any     =encoder.state_dict()

						A__					:       Optional[Any]     =upgrade_state_dict(__snake_case	)
						hf_model.load_state_dict(__snake_case	)
						A__					:       Optional[int]     =hf_model.state_dict()
						A__					:       str     =count_parameters(__snake_case	)
						A__					:       List[Any]     =count_parameters(__snake_case	)

						assert torch.allclose(__snake_case,		__snake_case,		atol=1E-3	)

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


if __name__ == "__main__":
						__snake_case			:     List[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			:     Dict										=      parser.parse_args()

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




'''simple docstring'''



import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	['image_processor', 'tokenizer']
			__snake_case        =	'FlavaImageProcessor'
			__snake_case        =	('BertTokenizer', 'BertTokenizerFast')


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





									'''simple docstring'''
									A__					:       Any     =None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""		,      lowerCAmelCase_		,      )
															A__					:       Optional[Any]     =kwargs.pop("""feature_extractor"""			)

									A__					:       List[Any]     =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__(lowerCAmelCase_		,      lowerCAmelCase_			)
									A__					:       int     =self.image_processor


			def __call__(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						Optional[ImageInput] = None		,      lowerCAmelCase_	:						Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Union[bool, str, PaddingStrategy] = False		,      lowerCAmelCase_	:						Union[bool, str, TruncationStrategy] = False		,      lowerCAmelCase_	:						Optional[int] = None		,      lowerCAmelCase_	:						int = 0		,      lowerCAmelCase_	:						Optional[int] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Optional[Union[str, TensorType]] = None		,      **lowerCAmelCase_	:						Optional[int]		,      )      ->		str:





									'''simple docstring'''

									if text is None and images is None:
															raise ValueError("""You have to specify either text or images. Both cannot be none."""			)

									if text is not None:
															A__					:       int     =self.tokenizer(
															    text=lowerCAmelCase_		,      add_special_tokens=lowerCAmelCase_		,      padding=lowerCAmelCase_		,      truncation=lowerCAmelCase_		,      max_length=lowerCAmelCase_		,      stride=lowerCAmelCase_		,      pad_to_multiple_of=lowerCAmelCase_		,      return_token_type_ids=lowerCAmelCase_		,      return_attention_mask=lowerCAmelCase_		,      return_overflowing_tokens=lowerCAmelCase_		,      return_special_tokens_mask=lowerCAmelCase_		,      return_offsets_mapping=lowerCAmelCase_		,      return_length=lowerCAmelCase_		,      verbose=lowerCAmelCase_		,      return_tensors=lowerCAmelCase_		,      **lowerCAmelCase_		,      )
									if images is not None:
															A__					:       List[str]     =self.image_processor(
															    lowerCAmelCase_		,      return_image_mask=lowerCAmelCase_		,      return_codebook_pixels=lowerCAmelCase_		,      return_tensors=lowerCAmelCase_		,      **lowerCAmelCase_		,      )

									if text is not None and images is not None:
															encoding.update(lowerCAmelCase_			)
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**lowerCAmelCase_			)		,      tensor_type=lowerCAmelCase_			)


			def lowercase__						(				self	:						Any		,      *lowerCAmelCase_	:						str		,      **lowerCAmelCase_	:						Optional[int]			)      ->		str:





									'''simple docstring'''
									return self.tokenizer.batch_decode(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						List[Any]		,      *lowerCAmelCase_	:						List[Any]		,      **lowerCAmelCase_	:						Any			)      ->		Tuple:





									'''simple docstring'''
									return self.tokenizer.decode(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			@property
			def lowercase__						(				self	:						Tuple			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Any     =self.tokenizer.model_input_names
									A__					:       Optional[Any]     =self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


			@property
			def lowercase__						(				self	:						Tuple			)      ->		int:





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



			@property
			def lowercase__						(				self	:						Tuple			)      ->		Dict:





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

