


'''simple docstring'''
from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__snake_case							      =     logging.get_logger(__name__)


__snake_case							      =     {
    "nielsr/canine-s": 2048,
}

# Unicode defines 1,114,112 total “codepoints”
__snake_case							      =     1114112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
__snake_case							      =     0

__snake_case							      =     0XE000
__snake_case							      =     0XE001
__snake_case							      =     0XE002
__snake_case							      =     0XE003
__snake_case							      =     0XE004

# Maps special codepoints to human-readable names.
__snake_case							      =     {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
__snake_case							      =     {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
class    lowercase	(							lowerCAmelCase_					):

		"""simple docstring"""

		_a     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




		def __init__(		self    ,   UpperCamelCase_=chr(__snake_case							)    ,   UpperCamelCase_=chr(__snake_case							)    ,   UpperCamelCase_=chr(__snake_case							)    ,   UpperCamelCase_=chr(__snake_case							)    ,   UpperCamelCase_=chr(__snake_case							)    ,   UpperCamelCase_=chr(__snake_case							)    ,   UpperCamelCase_=False    ,   UpperCamelCase_=2048    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= AddedToken(__snake_case    ,   lstrip=__snake_case    ,   rstrip=__snake_case							) if isinstance(__snake_case    ,   __snake_case							) else bos_token
							UpperCamelCase__   :List[str]												= AddedToken(__snake_case    ,   lstrip=__snake_case    ,   rstrip=__snake_case							) if isinstance(__snake_case    ,   __snake_case							) else eos_token
							UpperCamelCase__   :List[str]												= AddedToken(__snake_case    ,   lstrip=__snake_case    ,   rstrip=__snake_case							) if isinstance(__snake_case    ,   __snake_case							) else sep_token
							UpperCamelCase__   :Union[str, Any]												= AddedToken(__snake_case    ,   lstrip=__snake_case    ,   rstrip=__snake_case							) if isinstance(__snake_case    ,   __snake_case							) else cls_token
							UpperCamelCase__   :Dict												= AddedToken(__snake_case    ,   lstrip=__snake_case    ,   rstrip=__snake_case							) if isinstance(__snake_case    ,   __snake_case							) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							UpperCamelCase__   :List[str]												= AddedToken(__snake_case    ,   lstrip=__snake_case    ,   rstrip=__snake_case							) if isinstance(__snake_case    ,   __snake_case							) else mask_token

							super().__init__(
							    bos_token=__snake_case    ,   eos_token=__snake_case    ,   sep_token=__snake_case    ,   cls_token=__snake_case    ,   pad_token=__snake_case    ,   mask_token=__snake_case    ,   add_prefix_space=__snake_case    ,   model_max_length=__snake_case    ,   **__snake_case    ,   )

							# Creates a mapping for looking up the IDs of special symbols.
							UpperCamelCase__   :Optional[Any]												= {}
							for codepoint, name in SPECIAL_CODEPOINTS.items():
												UpperCamelCase__   :Tuple												= codepoint

							# Creates a mapping for looking up the string forms of special symbol IDs.
							UpperCamelCase__   :int												= {
							    codepoint: name for name, codepoint in self._special_codepoints.items()
							}

							UpperCamelCase__   :str												= UNICODE_VOCAB_SIZE
							UpperCamelCase__   :List[str]												= len(self._special_codepoints							)




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return self._unicode_vocab_size




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							return list(__snake_case							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							try:
												return ord(__snake_case							)
							except TypeError:
												raise ValueError(F'''invalid token: \'{token}\''''							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							try:
												if index in SPECIAL_CODEPOINTS:
																	return SPECIAL_CODEPOINTS[index]
												return chr(__snake_case							)
							except TypeError:
												raise ValueError(F'''invalid id: {index}'''							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							return "".join(__snake_case							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= [self.sep_token_id]
							UpperCamelCase__   :str												= [self.cls_token_id]

							UpperCamelCase__   :str												= cls + token_ids_a + sep
							if token_ids_a is not None:
												result += token_ids_a + sep
							return result




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = False							):

							'''simple docstring'''


							if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=__snake_case    ,   token_ids_a=__snake_case    ,   already_has_special_tokens=__snake_case							)

							UpperCamelCase__   :Union[str, Any]												= [1] + ([0] * len(__snake_case							)) + [1]
							if token_ids_a is not None:
												result += ([0] * len(__snake_case							)) + [1]
							return result




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= [self.sep_token_id]
							UpperCamelCase__   :Optional[int]												= [self.cls_token_id]

							UpperCamelCase__   :str												= len(cls + token_ids_a + sep							) * [0]
							if token_ids_a is not None:
												result += len(token_ids_a + sep							) * [1]
							return result




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None							):

							'''simple docstring'''


							return ()


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCAmelCase					:			List[str]  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Union[str, Any]  				=			{
    "Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'instructblip_vision_model'





			def __init__(		self					,  __snake_case=1408					,  __snake_case=6144					,  __snake_case=39					,  __snake_case=16					,  __snake_case=224					,  __snake_case=14					,  __snake_case="gelu"					,  __snake_case=1e-6					,  __snake_case=0.0					,  __snake_case=1e-10					,  __snake_case=True					,  **__snake_case					,  )   -> str:




						'''simple docstring'''
						super().__init__(**__snake_case					)

						__a        =hidden_size
						__a        =intermediate_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =patch_size
						__a        =image_size
						__a        =initializer_range
						__a        =attention_dropout
						__a        =layer_norm_eps
						__a        =hidden_act
						__a        =qkv_bias





			@classmethod
			def 		__magic_name__    (		cls					,  __snake_case					,  **__snake_case					)   -> "PretrainedConfig":




						'''simple docstring'''
						cls._set_token_in_kwargs(__snake_case					)

						__a      ,    __a        =cls.get_config_dict(__snake_case					,  **__snake_case					)

						# get the vision config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'					) == "instructblip":
									__a        =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(__snake_case					,  **__snake_case					)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'instructblip_qformer'





			def __init__(		self					,  __snake_case=3_0522					,  __snake_case=768					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=512					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0					,  __snake_case="absolute"					,  __snake_case=2					,  __snake_case=1408					,  **__snake_case					,  )   -> List[str]:




						'''simple docstring'''
						super().__init__(pad_token_id=__snake_case					,  **__snake_case					)

						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =hidden_act
						__a        =intermediate_size
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =position_embedding_type
						__a        =cross_attention_frequency
						__a        =encoder_hidden_size





			@classmethod
			def 		__magic_name__    (		cls					,  __snake_case					,  **__snake_case					)   -> "PretrainedConfig":




						'''simple docstring'''
						cls._set_token_in_kwargs(__snake_case					)

						__a      ,    __a        =cls.get_config_dict(__snake_case					,  **__snake_case					)

						# get the qformer config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'					) == "instructblip":
									__a        =config_dict['qformer_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(__snake_case					,  **__snake_case					)






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'instructblip'
			SCREAMING_SNAKE_CASE							   =						True





			def __init__(		self					,  __snake_case=None					,  __snake_case=None					,  __snake_case=None					,  __snake_case=32					,  **__snake_case					)   -> str:




						'''simple docstring'''
						super().__init__(**__snake_case					)

						if vision_config is None:
									__a        ={}
									logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'					)

						if qformer_config is None:
									__a        ={}
									logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'					)

						if text_config is None:
									__a        ={}
									logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'					)

						__a        =InstructBlipVisionConfig(**__snake_case					)
						__a        =InstructBlipQFormerConfig(**__snake_case					)
						__a        =text_config['model_type'] if 'model_type' in text_config else 'opt'
						__a        =CONFIG_MAPPING[text_model_type](**__snake_case					)

						__a        =self.text_config.tie_word_embeddings
						__a        =self.text_config.is_encoder_decoder

						__a        =num_query_tokens
						__a        =self.vision_config.hidden_size
						__a        =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
						__a        =1.0
						__a        =0.02





			@classmethod
			def 		__magic_name__    (		cls					,  __snake_case					,  __snake_case					,  __snake_case					,  **__snake_case					,  )   -> Optional[Any]:




						'''simple docstring'''
						return cls(
						    vision_config=vision_config.to_dict()					,  qformer_config=qformer_config.to_dict()					,  text_config=text_config.to_dict()					,  **__snake_case					,  )





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__a        =copy.deepcopy(self.__dict__					)
						__a        =self.vision_config.to_dict()
						__a        =self.qformer_config.to_dict()
						__a        =self.text_config.to_dict()
						__a        =self.__class__.model_type
						return output






