

import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class   _a					(			snake_case_     ,				unittest.TestCase				):




      """simple docstring"""

      _lowerCamelCase       :			List[str]      		=       RobertaTokenizer
      _lowerCamelCase       :			Tuple      		=       RobertaTokenizerFast
      _lowerCamelCase       :			str      		=       True
      _lowerCamelCase       :			Tuple      		=       {'cls_token': '<s>'}





      def 		__A	(   self							:				Any	):
            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            A_						  =				[
                "l",
                "o",
                "w",
                "e",
                "r",
                "s",
                "t",
                "i",
                "d",
                "n",
                "\u0120",
                "\u0120l",
                "\u0120n",
                "\u0120lo",
                "\u0120low",
                "er",
                "\u0120lowest",
                "\u0120newer",
                "\u0120wider",
                "<unk>",
            ]
            A_						  =				dict(zip(UpperCAmelCase						,			range(len(UpperCAmelCase	)	)	)	)
            A_						  =				["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
            A_						  =				{"unk_token": "<unk>"}

            A_						  =				os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES["vocab_file"]	)
            A_						  =				os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES["merges_file"]	)
            with open(self.vocab_file						,			"w"						,			encoding="utf-8"	) as fp:
                  fp.write(json.dumps(UpperCAmelCase	) + "\n"	)
            with open(self.merges_file						,			"w"						,			encoding="utf-8"	) as fp:
                  fp.write("\n".join(UpperCAmelCase	)	)





      def 		__A	(   self							:				Any						,			**UpperCAmelCase							:				Union[str, Any]	):
            kwargs.update(self.special_tokens_map	)
            return self.tokenizer_class.from_pretrained(self.tmpdirname						,			**UpperCAmelCase	)





      def 		__A	(   self							:				List[Any]						,			**UpperCAmelCase							:				List[str]	):
            kwargs.update(self.special_tokens_map	)
            return RobertaTokenizerFast.from_pretrained(self.tmpdirname						,			**UpperCAmelCase	)





      def 		__A	(   self							:				str						,			UpperCAmelCase							:				Optional[int]	):
            A_						  =				"lower newer"
            A_						  =				"lower newer"
            return input_text, output_text





      def 		__A	(   self							:				Union[str, Any]	):
            A_						  =				self.tokenizer_class(self.vocab_file						,			self.merges_file						,			**self.special_tokens_map	)
            A_						  =				"lower newer"
            A_						  =				["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
            A_						  =				tokenizer.tokenize(UpperCAmelCase	)  # , add_prefix_space=True)
            self.assertListEqual(UpperCAmelCase						,			UpperCAmelCase	)

            A_						  =				tokens + [tokenizer.unk_token]
            A_						  =				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase	)						,			UpperCAmelCase	)





      def 		__A	(   self							:				Union[str, Any]	):
            A_						  =				self.get_tokenizer()

            self.assertListEqual(tokenizer.encode("Hello world!"						,			add_special_tokens=UpperCAmelCase	)						,			[0, 31414, 232, 328, 2]	)
            self.assertListEqual(
                tokenizer.encode("Hello world! cécé herlolip 418"						,			add_special_tokens=UpperCAmelCase	)						,			[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]						,			)





      @slow
      def 		__A	(   self							:				int	):
            A_						  =				self.tokenizer_class.from_pretrained("roberta-base"	)

            A_						  =				tokenizer.encode("sequence builders"						,			add_special_tokens=UpperCAmelCase	)
            A_						  =				tokenizer.encode("multi-sequence build"						,			add_special_tokens=UpperCAmelCase	)

            A_						  =				tokenizer.encode(
                "sequence builders"						,			add_special_tokens=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase	)
            A_						  =				tokenizer.encode(
                "sequence builders"						,			"multi-sequence build"						,			add_special_tokens=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase	)

            A_						  =				tokenizer.build_inputs_with_special_tokens(UpperCAmelCase	)
            A_						  =				tokenizer.build_inputs_with_special_tokens(UpperCAmelCase						,			UpperCAmelCase	)

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode





      def 		__A	(   self							:				Tuple	):
            A_						  =				self.get_tokenizer()

            A_						  =				"Encode this sequence."
            A_						  =				tokenizer.byte_encoder[" ".encode("utf-8"	)[0]]

            # Testing encoder arguments
            A_						  =				tokenizer.encode(UpperCAmelCase						,			add_special_tokens=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase	)
            A_						  =				tokenizer.convert_ids_to_tokens(encoded[0]	)[0]
            self.assertNotEqual(UpperCAmelCase						,			UpperCAmelCase	)

            A_						  =				tokenizer.encode(UpperCAmelCase						,			add_special_tokens=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase	)
            A_						  =				tokenizer.convert_ids_to_tokens(encoded[0]	)[0]
            self.assertEqual(UpperCAmelCase						,			UpperCAmelCase	)

            tokenizer.add_special_tokens({"bos_token": "<s>"}	)
            A_						  =				tokenizer.encode(UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
            A_						  =				tokenizer.convert_ids_to_tokens(encoded[1]	)[0]
            self.assertNotEqual(UpperCAmelCase						,			UpperCAmelCase	)

            # Testing spaces after special tokens
            A_						  =				"<mask>"
            tokenizer.add_special_tokens(
                {"mask_token": AddedToken(UpperCAmelCase						,			lstrip=UpperCAmelCase						,			rstrip=UpperCAmelCase	)}	)  # mask token has a left space
            A_						  =				tokenizer.convert_tokens_to_ids(UpperCAmelCase	)

            A_						  =				"Encode <mask> sequence"
            A_						  =				"Encode <mask>sequence"

            A_						  =				tokenizer.encode(UpperCAmelCase	)
            A_						  =				encoded.index(UpperCAmelCase	)
            A_						  =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]	)[0]
            self.assertEqual(UpperCAmelCase						,			UpperCAmelCase	)

            A_						  =				tokenizer.encode(UpperCAmelCase	)
            A_						  =				encoded.index(UpperCAmelCase	)
            A_						  =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]	)[0]
            self.assertNotEqual(UpperCAmelCase						,			UpperCAmelCase	)





      def 		__A	(   self							:				Any	):
            pass





      def 		__A	(   self							:				Optional[int]	):
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                        A_						  =				self.rust_tokenizer_class.from_pretrained(UpperCAmelCase						,			**UpperCAmelCase	)
                        A_						  =				self.tokenizer_class.from_pretrained(UpperCAmelCase						,			**UpperCAmelCase	)
                        A_						  =				"A, <mask> AllenNLP sentence."
                        A_						  =				tokenizer_r.encode_plus(UpperCAmelCase						,			add_special_tokens=UpperCAmelCase						,			return_token_type_ids=UpperCAmelCase	)
                        A_						  =				tokenizer_p.encode_plus(UpperCAmelCase						,			add_special_tokens=UpperCAmelCase						,			return_token_type_ids=UpperCAmelCase	)

                        # token_type_ids should put 0 everywhere
                        self.assertEqual(sum(tokens_r["token_type_ids"]	)						,			sum(tokens_p["token_type_ids"]	)	)

                        # attention_mask should put 1 everywhere, so sum over length should be 1
                        self.assertEqual(
                            sum(tokens_r["attention_mask"]	) / len(tokens_r["attention_mask"]	)						,			sum(tokens_p["attention_mask"]	) / len(tokens_p["attention_mask"]	)						,			)

                        A_						  =				tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
                        A_						  =				tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]	)

                        # Rust correctly handles the space before the mask while python doesnt
                        self.assertSequenceEqual(tokens_p["input_ids"]						,			[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)
                        self.assertSequenceEqual(tokens_r["input_ids"]						,			[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)

                        self.assertSequenceEqual(
                            UpperCAmelCase						,			["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
                        self.assertSequenceEqual(
                            UpperCAmelCase						,			["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)





      def 		__A	(   self							:				Union[str, Any]	):
            for trim_offsets, add_prefix_space in itertools.product([True, False]						,			repeat=2	):
                  A_						  =				self.rust_tokenizer_class.from_pretrained(
                      self.tmpdirname						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)

                  A_						  =				json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()	)
                  A_						  =				json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()	)

                  self.assertEqual(pre_tokenizer_state["add_prefix_space"]						,			UpperCAmelCase	)

                  self.assertEqual(post_processor_state["add_prefix_space"]						,			UpperCAmelCase	)
                  self.assertEqual(post_processor_state["trim_offsets"]						,			UpperCAmelCase	)







      def 		__A	(   self							:				List[Any]	):
            # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
            # `trim_offsets`
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                        A_						  =				"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                        A_						  =				f'''{text_of_1_token} {text_of_1_token}'''

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(len(UpperCAmelCase	) + 1, len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(len(UpperCAmelCase	) + 1, len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(len(UpperCAmelCase	), len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(len(UpperCAmelCase	), len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				f''' {text}'''

                        # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                        #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                        # )
                        # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                        # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                        # self.assertEqual(
                        #     encoding.offset_mapping[1],
                        #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                        # )

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(1, 1 + len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(1 + len(UpperCAmelCase	) + 1, 1 + len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, 1 + len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(1 + len(UpperCAmelCase	), 1 + len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			add_prefix_space=UpperCAmelCase						,			trim_offsets=UpperCAmelCase	)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, 1 + len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(1 + len(UpperCAmelCase	), 1 + len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

"""simple docstring"""






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


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							=     {
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}





class 				a__					(						A__       ):
     UpperCAmelCase__         =      '''instructblip_vision_model'''



     def __init__(							self :int      ,  _lowerCamelCase :Optional[int]=1_408      ,  _lowerCamelCase :int=6_144      ,  _lowerCamelCase :str=39      ,  _lowerCamelCase :Tuple=16      ,  _lowerCamelCase :List[Any]=224      ,  _lowerCamelCase :int=14      ,  _lowerCamelCase :int="gelu"      ,  _lowerCamelCase :Optional[Any]=1E-6      ,  _lowerCamelCase :str=0.0      ,  _lowerCamelCase :Optional[Any]=1E-1_0      ,  _lowerCamelCase :str=True      ,  **_lowerCamelCase :Optional[Any]      ,  ):







      '''simple docstring'''



      super().__init__(**_lowerCamelCase   )

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




     @classmethod
     def  lowerCamelCase_						(							cls :Optional[Any]      ,  _lowerCamelCase :Union[str, os.PathLike]      ,  **_lowerCamelCase :Any   ):







      '''simple docstring'''



      cls._set_token_in_kwargs(_lowerCamelCase   )

      UpperCamelCase_	,     UpperCamelCase_     :					List[str]   	=cls.get_config_dict(_lowerCamelCase      ,  **_lowerCamelCase   )

      # get the vision config dict if we are loading from InstructBlipConfig
      if config_dict.get('model_type'   ) == "instructblip":
       UpperCamelCase_     :					Tuple   	=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 				a__					(						A__       ):
     UpperCAmelCase__         =      '''instructblip_qformer'''



     def __init__(							self :Dict      ,  _lowerCamelCase :List[Any]=30_522      ,  _lowerCamelCase :Optional[int]=768      ,  _lowerCamelCase :Tuple=12      ,  _lowerCamelCase :List[str]=12      ,  _lowerCamelCase :int=3_072      ,  _lowerCamelCase :Tuple="gelu"      ,  _lowerCamelCase :Dict=0.1      ,  _lowerCamelCase :List[Any]=0.1      ,  _lowerCamelCase :Optional[int]=512      ,  _lowerCamelCase :List[Any]=0.02      ,  _lowerCamelCase :int=1E-1_2      ,  _lowerCamelCase :str=0      ,  _lowerCamelCase :Dict="absolute"      ,  _lowerCamelCase :Optional[int]=2      ,  _lowerCamelCase :Union[str, Any]=1_408      ,  **_lowerCamelCase :Tuple      ,  ):







      '''simple docstring'''



      super().__init__(pad_token_id=_lowerCamelCase      ,  **_lowerCamelCase   )

      UpperCamelCase_     :					Optional[Any]   	=vocab_size
      UpperCamelCase_     :					Tuple   	=hidden_size
      UpperCamelCase_     :					Union[str, Any]   	=num_hidden_layers
      UpperCamelCase_     :					List[str]   	=num_attention_heads
      UpperCamelCase_     :					List[str]   	=hidden_act
      UpperCamelCase_     :					Tuple   	=intermediate_size
      UpperCamelCase_     :					Optional[Any]   	=hidden_dropout_prob
      UpperCamelCase_     :					List[str]   	=attention_probs_dropout_prob
      UpperCamelCase_     :					List[str]   	=max_position_embeddings
      UpperCamelCase_     :					List[str]   	=initializer_range
      UpperCamelCase_     :					str   	=layer_norm_eps
      UpperCamelCase_     :					int   	=position_embedding_type
      UpperCamelCase_     :					Tuple   	=cross_attention_frequency
      UpperCamelCase_     :					List[Any]   	=encoder_hidden_size




     @classmethod
     def  lowerCamelCase_						(							cls :Optional[Any]      ,  _lowerCamelCase :Union[str, os.PathLike]      ,  **_lowerCamelCase :List[Any]   ):







      '''simple docstring'''



      cls._set_token_in_kwargs(_lowerCamelCase   )

      UpperCamelCase_	,     UpperCamelCase_     :					Optional[Any]   	=cls.get_config_dict(_lowerCamelCase      ,  **_lowerCamelCase   )

      # get the qformer config dict if we are loading from InstructBlipConfig
      if config_dict.get('model_type'   ) == "instructblip":
       UpperCamelCase_     :					List[Any]   	=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(_lowerCamelCase      ,  **_lowerCamelCase   )





class 				a__					(						A__       ):
     UpperCAmelCase__         =      '''instructblip'''
     UpperCAmelCase__         =      True



     def __init__(							self :str      ,  _lowerCamelCase :Optional[Any]=None      ,  _lowerCamelCase :List[str]=None      ,  _lowerCamelCase :List[Any]=None      ,  _lowerCamelCase :Union[str, Any]=32      ,  **_lowerCamelCase :Tuple   ):







      '''simple docstring'''



      super().__init__(**_lowerCamelCase   )

      if vision_config is None:
       UpperCamelCase_     :					Optional[int]   	={}
       logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'   )

      if qformer_config is None:
       UpperCamelCase_     :					Union[str, Any]   	={}
       logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'   )

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

      UpperCamelCase_     :					Any   	=InstructBlipVisionConfig(**_lowerCamelCase   )
      UpperCamelCase_     :					Optional[Any]   	=InstructBlipQFormerConfig(**_lowerCamelCase   )
      UpperCamelCase_     :					str   	=text_config['model_type'] if 'model_type' in text_config else 'opt'
      UpperCamelCase_     :					int   	=CONFIG_MAPPING[text_model_type](**_lowerCamelCase   )

      UpperCamelCase_     :					Tuple   	=self.text_config.tie_word_embeddings
      UpperCamelCase_     :					Any   	=self.text_config.is_encoder_decoder

      UpperCamelCase_     :					List[Any]   	=num_query_tokens
      UpperCamelCase_     :					Tuple   	=self.vision_config.hidden_size
      UpperCamelCase_     :					Tuple   	=self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
      UpperCamelCase_     :					int   	=1.0
      UpperCamelCase_     :					List[str]   	=0.02



     @classmethod
     def  lowerCamelCase_						(							cls :Optional[Any]      ,  _lowerCamelCase :InstructBlipVisionConfig      ,  _lowerCamelCase :InstructBlipQFormerConfig      ,  _lowerCamelCase :PretrainedConfig      ,  **_lowerCamelCase :Dict      ,  ):







      '''simple docstring'''



      return cls(
          vision_config=vision_config.to_dict()      ,  qformer_config=qformer_config.to_dict()      ,  text_config=text_config.to_dict()      ,  **_lowerCamelCase      ,  )




     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=copy.deepcopy(self.__dict__   )
      UpperCamelCase_     :					Dict   	=self.vision_config.to_dict()
      UpperCamelCase_     :					Tuple   	=self.qformer_config.to_dict()
      UpperCamelCase_     :					List[str]   	=self.text_config.to_dict()
      UpperCamelCase_     :					List[str]   	=self.__class__.model_type
      return output
