

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


_UpperCAmelCase	:     Dict		       = logging.get_logger(__name__)

_UpperCAmelCase	:     Dict		       = {
    """salesforce/blip2-opt-2.7b""": """https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json""",
}







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    '''blip_2_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=0.0_00_01   ,     snake_case=0.0   ,     snake_case=1e-1_0   ,     snake_case=True   ,     **snake_case   ,     ):
											super().__init__(**snake_case )

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




				@classmethod
				def 					a							(      cls   ,     snake_case   ,     **snake_case ):
											cls._set_token_in_kwargs(snake_case )

											snake_case_       ,					snake_case_           =					cls.get_config_dict(snake_case   ,     **snake_case )

											# get the vision config dict if we are loading from Blip2Config
											if config_dict.get('model_type' ) == "blip-2":
																		snake_case_           =					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 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Any     =    '''blip_2_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-1_2   ,     snake_case=0   ,     snake_case="absolute"   ,     snake_case=2   ,     snake_case=1408   ,     **snake_case   ,     ):
											super().__init__(pad_token_id=snake_case   ,     **snake_case )

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




				@classmethod
				def 					a							(      cls   ,     snake_case   ,     **snake_case ):
											cls._set_token_in_kwargs(snake_case )

											snake_case_       ,					snake_case_           =					cls.get_config_dict(snake_case   ,     **snake_case )

											# get the qformer config dict if we are loading from Blip2Config
											if config_dict.get('model_type' ) == "blip-2":
																		snake_case_           =					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 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    '''blip-2'''
				__SCREAMING_SNAKE_CASE      :				Dict     =    True




				def __init__(      self   ,     snake_case=None   ,     snake_case=None   ,     snake_case=None   ,     snake_case=32   ,     **snake_case ):
											super().__init__(**snake_case )

											if vision_config is None:
																		snake_case_           =					{}
																		logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.' )

											if qformer_config is None:
																		snake_case_           =					{}
																		logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.' )

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

											snake_case_           =					BlipaVisionConfig(**snake_case )
											snake_case_           =					BlipaQFormerConfig(**snake_case )
											snake_case_           =					text_config['model_type'] if 'model_type' in text_config else 'opt'
											snake_case_           =					CONFIG_MAPPING[text_model_type](**snake_case )

											snake_case_           =					self.text_config.tie_word_embeddings
											snake_case_           =					self.text_config.is_encoder_decoder

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




				@classmethod
				def 					a							(      cls   ,     snake_case   ,     snake_case   ,     snake_case   ,     **snake_case   ,     ):
											return cls(
											    vision_config=vision_config.to_dict()   ,     qformer_config=qformer_config.to_dict()   ,     text_config=text_config.to_dict()   ,     **snake_case   ,     )




				def 					a							(      self ):
											snake_case_           =					copy.deepcopy(self.__dict__ )
											snake_case_           =					self.vision_config.to_dict()
											snake_case_           =					self.qformer_config.to_dict()
											snake_case_           =					self.text_config.to_dict()
											snake_case_           =					self.__class__.model_type
											return output







import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				int     =    PhobertTokenizer
				__SCREAMING_SNAKE_CASE      :				int     =    False




				def 					a							(      self ):
											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											snake_case_           =					['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
											snake_case_           =					dict(zip(snake_case   ,     range(len(snake_case ) ) ) )
											snake_case_           =					['#version: 0.2', 'l à</w>']
											snake_case_           =					{'unk_token': '<unk>'}

											snake_case_           =					os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES['vocab_file'] )
											snake_case_           =					os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES['merges_file'] )

											with open(self.vocab_file   ,     'w'   ,     encoding='utf-8' ) as fp:
																		for token in vocab_tokens:
																									fp.write(F'''{token} {vocab_tokens[token]}\n''' )
											with open(self.merges_file   ,     'w'   ,     encoding='utf-8' ) as fp:
																		fp.write('\n'.join(snake_case ) )




				def 					a							(      self   ,     **snake_case ):
											kwargs.update(self.special_tokens_map )
											return PhobertTokenizer.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     snake_case ):
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
											return input_text, output_text




				def 					a							(      self ):
											snake_case_           =					PhobertTokenizer(self.vocab_file   ,     self.merges_file   ,     **self.special_tokens_map )
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
											snake_case_           =					tokenizer.tokenize(snake_case )
											print(snake_case )
											self.assertListEqual(snake_case   ,     snake_case )

											snake_case_           =					tokens + [tokenizer.unk_token]

											snake_case_           =					[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case )   ,     snake_case )




