



"""simple docstring"""






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase			:			Any										=						logging.get_logger(__name__)

_lowercase			:			Optional[Any]										=						{
    'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json',
    'Salesforce/blip-vqa-capfit-large': (
        'https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-base': (
        'https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-large': (
        'https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'
    ),
    'Salesforce/blip-itm-base-coco': 'https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json',
    'Salesforce/blip-itm-large-coco': 'https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json',
    'Salesforce/blip-itm-base-flikr': 'https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json',
    'Salesforce/blip-itm-large-flikr': (
        'https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'
    ),
}

class 	__SCREAMING_SNAKE_CASE     (				lowerCamelCase__       ):


					'''simple docstring'''





					_a             =							'blip_text_model'



					def __init__(	self							:  int,  lowerCamelCase							:  List[str]=3_0524,  lowerCamelCase							:  Tuple=768,  lowerCamelCase							:  Optional[int]=768,  lowerCamelCase							:  Dict=3072,  lowerCamelCase							:  Any=768,  lowerCamelCase							:  Tuple=12,  lowerCamelCase							:  List[Any]=8,  lowerCamelCase							:  str=512,  lowerCamelCase							:  Any="gelu",  lowerCamelCase							:  int=1E-12,  lowerCamelCase							:  Optional[Any]=0.0,  lowerCamelCase							:  Tuple=0.0,  lowerCamelCase							:  str=0.02,  lowerCamelCase							:  int=3_0522,  lowerCamelCase							:  List[str]=2,  lowerCamelCase							:  Optional[int]=0,  lowerCamelCase							:  Any=102,  lowerCamelCase							:  Dict=True,  lowerCamelCase							:  Dict=True,  **lowerCamelCase							:  Union[str, Any],  )-> Optional[Any]:
								super().__init__(
								    pad_token_id=__lowerCamelCase,  bos_token_id=__lowerCamelCase,  eos_token_id=__lowerCamelCase,  sep_token_id=__lowerCamelCase,  **__lowerCamelCase,  )

								lowerCamelCase__		:		Any		     =vocab_size
								lowerCamelCase__		:		List[Any]		     =hidden_size
								lowerCamelCase__		:		Optional[int]		     =encoder_hidden_size
								lowerCamelCase__		:		Tuple		     =intermediate_size
								lowerCamelCase__		:		Any		     =projection_dim
								lowerCamelCase__		:		Optional[Any]		     =hidden_dropout_prob
								lowerCamelCase__		:		Tuple		     =num_hidden_layers
								lowerCamelCase__		:		Any		     =num_attention_heads
								lowerCamelCase__		:		Optional[int]		     =max_position_embeddings
								lowerCamelCase__		:		Optional[Any]		     =layer_norm_eps
								lowerCamelCase__		:		Any		     =hidden_act
								lowerCamelCase__		:		Optional[int]		     =initializer_range
								lowerCamelCase__		:		Dict		     =attention_probs_dropout_prob
								lowerCamelCase__		:		Dict		     =is_decoder
								lowerCamelCase__		:		List[Any]		     =use_cache




					@classmethod
					def 		snake_case	(	cls							:  Any,  lowerCamelCase							:  int,  **lowerCamelCase							:  Any					)-> "PretrainedConfig":
								cls._set_token_in_kwargs(__lowerCamelCase					)

								lowerCamelCase__		:		Dict		     =cls.get_config_dict(__lowerCamelCase,  **__lowerCamelCase					)

								# get the text config dict if we are loading from BlipConfig
								if config_dict.get('''model_type'''					) == "blip":
											lowerCamelCase__		:		Tuple		     =config_dict['''text_config''']

								if "model_type" in config_dict and hasattr(cls,  '''model_type'''					) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
											    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''					)

								return cls.from_dict(__lowerCamelCase,  **__lowerCamelCase					)





class 	__SCREAMING_SNAKE_CASE     (				lowerCamelCase__       ):


					'''simple docstring'''





					_a             =							'blip_vision_model'



					def __init__(	self							:  List[str],  lowerCamelCase							:  Dict=768,  lowerCamelCase							:  Optional[Any]=3072,  lowerCamelCase							:  List[str]=512,  lowerCamelCase							:  List[Any]=12,  lowerCamelCase							:  Any=12,  lowerCamelCase							:  List[Any]=384,  lowerCamelCase							:  Optional[int]=16,  lowerCamelCase							:  Optional[Any]="gelu",  lowerCamelCase							:  str=1E-5,  lowerCamelCase							:  Dict=0.0,  lowerCamelCase							:  Any=1E-10,  **lowerCamelCase							:  Optional[int],  )-> Optional[Any]:
								super().__init__(**__lowerCamelCase					)

								lowerCamelCase__		:		str		     =hidden_size
								lowerCamelCase__		:		List[Any]		     =intermediate_size
								lowerCamelCase__		:		Optional[int]		     =projection_dim
								lowerCamelCase__		:		Union[str, Any]		     =num_hidden_layers
								lowerCamelCase__		:		Union[str, Any]		     =num_attention_heads
								lowerCamelCase__		:		Optional[int]		     =patch_size
								lowerCamelCase__		:		Tuple		     =image_size
								lowerCamelCase__		:		Union[str, Any]		     =initializer_range
								lowerCamelCase__		:		List[str]		     =attention_dropout
								lowerCamelCase__		:		int		     =layer_norm_eps
								lowerCamelCase__		:		Dict		     =hidden_act




					@classmethod
					def 		snake_case	(	cls							:  int,  lowerCamelCase							:  Optional[int],  **lowerCamelCase							:  Optional[Any]					)-> "PretrainedConfig":
								cls._set_token_in_kwargs(__lowerCamelCase					)

								lowerCamelCase__		:		Any		     =cls.get_config_dict(__lowerCamelCase,  **__lowerCamelCase					)

								# get the vision config dict if we are loading from BlipConfig
								if config_dict.get('''model_type'''					) == "blip":
											lowerCamelCase__		:		int		     =config_dict['''vision_config''']

								if "model_type" in config_dict and hasattr(cls,  '''model_type'''					) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
											    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''					)

								return cls.from_dict(__lowerCamelCase,  **__lowerCamelCase					)





class 	__SCREAMING_SNAKE_CASE     (				lowerCamelCase__       ):


					'''simple docstring'''





					_a             =							'blip'
					_a             =							True



					def __init__(	self							:  List[Any],  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Union[str, Any]=None,  lowerCamelCase							:  Dict=512,  lowerCamelCase							:  int=2.6_592,  lowerCamelCase							:  Optional[int]=256,  **lowerCamelCase							:  str,  )-> List[str]:
								super().__init__(**__lowerCamelCase					)

								if text_config is None:
											lowerCamelCase__		:		List[Any]		     ={}
											logger.info('''`text_config` is `None`. Initializing the `BlipTextConfig` with default values.'''					)

								if vision_config is None:
											lowerCamelCase__		:		List[Any]		     ={}
											logger.info('''`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.'''					)

								lowerCamelCase__		:		List[Any]		     =BlipTextConfig(**__lowerCamelCase					)
								lowerCamelCase__		:		int		     =BlipVisionConfig(**__lowerCamelCase					)

								lowerCamelCase__		:		Optional[int]		     =self.vision_config.hidden_size

								lowerCamelCase__		:		Any		     =projection_dim
								lowerCamelCase__		:		Dict		     =logit_scale_init_value
								lowerCamelCase__		:		int		     =1.0
								lowerCamelCase__		:		Tuple		     =0.02
								lowerCamelCase__		:		Union[str, Any]		     =image_text_hidden_size



					@classmethod
					def 		snake_case	(	cls							:  Optional[int],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Optional[int],  **lowerCamelCase							:  Tuple					)-> List[Any]:
								return cls(text_config=text_config.to_dict(),  vision_config=vision_config.to_dict(),  **__lowerCamelCase					)




					def 		snake_case	(	self							:  Optional[Any]					)-> Dict:
								lowerCamelCase__		:		Tuple		     =copy.deepcopy(self.__dict__					)
								lowerCamelCase__		:		int		     =self.text_config.to_dict()
								lowerCamelCase__		:		Tuple		     =self.vision_config.to_dict()
								lowerCamelCase__		:		List[Any]		     =self.__class__.model_type
								return output









"""simple docstring"""






import os
from collections import deque

import torch
from torch.utils.data import Dataset

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					def __init__(	self							:  List[Any],  lowerCamelCase							:  Dict="",  lowerCamelCase							:  Tuple="train"					)-> Dict:
								assert os.path.isdir(lowerCamelCase					)

								lowerCamelCase__		:		Optional[Any]		     =[]
								lowerCamelCase__		:		Dict		     =os.listdir(lowerCamelCase					)
								for story_filename in story_filenames_list:
											if "summary" in story_filename:
														continue
											lowerCamelCase__		:		Optional[int]		     =os.path.join(lowerCamelCase,  lowerCamelCase					)
											if not os.path.isfile(lowerCamelCase					):
														continue
											self.documents.append(lowerCamelCase					)



					def __len__(	self							:  Optional[Any]					)-> List[str]:
								return len(self.documents					)




					def __getitem__(	self							:  List[str],  lowerCamelCase							:  Dict					)-> str:
								lowerCamelCase__		:		int		     =self.documents[idx]
								lowerCamelCase__		:		List[Any]		     =document_path.split('''/'''					)[-1]
								with open(lowerCamelCase,  encoding='''utf-8'''					) as source:
											lowerCamelCase__		:		Optional[int]		     =source.read()
											lowerCamelCase__     ,       lowerCamelCase__		:		List[Any]		     =process_story(lowerCamelCase					)
								return document_name, story_lines, summary_lines






def 					snake_case__       (		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[str]		     =list(filter(lambda __lowerCamelCase     : len(__lowerCamelCase     ) != 0							,		[line.strip() for line in raw_story.split('''\n'''     )]     )     )

			# for some unknown reason some lines miss a period, add it
			lowerCamelCase__		:		Dict		     =[_add_missing_period(__lowerCamelCase     ) for line in nonempty_lines]

			# gather article lines
			lowerCamelCase__		:		Union[str, Any]		     =[]
			lowerCamelCase__		:		Optional[Any]		     =deque(__lowerCamelCase     )
			while True:
						try:
									lowerCamelCase__		:		Tuple		     =lines.popleft()
									if element.startswith('''@highlight'''     ):
												break
									story_lines.append(__lowerCamelCase     )
						except IndexError:
									# if "@highlight" is absent from the file we pop
									# all elements until there is None, raising an exception.
									return story_lines, []

    # gather summary lines
			lowerCamelCase__		:		Dict		     =list(filter(lambda __lowerCamelCase     : not t.startswith('''@highlight'''     )							,		__lowerCamelCase     )     )

			return story_lines, summary_lines






def 					snake_case__       (		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     =['''.''', '''!''', '''?''', '''...''', '''\'''', '''`''', '''"''', '''\u2019''', '''\u2019''', ''')''']
			if line.startswith('''@highlight'''     ):
						return line
			if line[-1] in END_TOKENS:
						return line
			return line + "."






def 					snake_case__       (		__lowerCamelCase    :   Dict							,		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   List[Any]     ):
			"""simple docstring"""
			if len(__lowerCamelCase     ) > block_size:
						return sequence[:block_size]
			else:
						sequence.extend([pad_token_id] * (block_size - len(__lowerCamelCase     ))     )
						return sequence






def 					snake_case__       (		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			lowerCamelCase__		:		int		     =torch.ones_like(__lowerCamelCase     )
			lowerCamelCase__		:		Any		     =sequence == pad_token_id
			lowerCamelCase__		:		List[str]		     =0
			return mask






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Optional[Any]							,		__lowerCamelCase    :   Optional[Any]     ):
			"""simple docstring"""
			lowerCamelCase__		:		Dict		     =[tokenizer.encode(__lowerCamelCase     ) for line in story_lines]
			lowerCamelCase__		:		List[Any]		     =[token for sentence in story_lines_token_ids for token in sentence]
			lowerCamelCase__		:		List[Any]		     =[tokenizer.encode(__lowerCamelCase     ) for line in summary_lines]
			lowerCamelCase__		:		Optional[int]		     =[token for sentence in summary_lines_token_ids for token in sentence]

			return story_token_ids, summary_token_ids






def 					snake_case__       (		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Optional[int]     ):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     =[]
			for sequence in batch:
						lowerCamelCase__		:		Optional[int]		     =-1
						lowerCamelCase__		:		List[str]		     =[]
						for s in sequence:
									if s == separator_token_id:
												sentence_num += 1
									embeddings.append(sentence_num % 2     )
						batch_embeddings.append(__lowerCamelCase     )
			return torch.tensor(__lowerCamelCase     )




