

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase	:     Optional[Any]		       = logging.get_logger(__name__)

_UpperCAmelCase	:     Dict		       = {
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}







class 				lowercase       ( lowercase_    ):
    __SCREAMING_SNAKE_CASE      :				List[Any]     =    '''xlm'''
    __SCREAMING_SNAKE_CASE      :				List[Any]     =    {
        '''hidden_size''': '''emb_dim''',
        '''num_attention_heads''': '''n_heads''',
        '''num_hidden_layers''': '''n_layers''',
        '''n_words''': '''vocab_size''',  # For backward compatibility
    }




    def __init__(      self   ,     snake_case=3_0145   ,     snake_case=2048   ,     snake_case=12   ,     snake_case=16   ,     snake_case=0.1   ,     snake_case=0.1   ,     snake_case=True   ,     snake_case=False   ,     snake_case=False   ,     snake_case=False   ,     snake_case=1   ,     snake_case=True   ,     snake_case=512   ,     snake_case=2048**-0.5   ,     snake_case=1e-1_2   ,     snake_case=0.02   ,     snake_case=0   ,     snake_case=1   ,     snake_case=2   ,     snake_case=3   ,     snake_case=5   ,     snake_case=True   ,     snake_case="first"   ,     snake_case=True   ,     snake_case=None   ,     snake_case=True   ,     snake_case=0.1   ,     snake_case=5   ,     snake_case=5   ,     snake_case=0   ,     snake_case=0   ,     snake_case=2   ,     snake_case=0   ,     **snake_case   ,     ):
           snake_case_           =					vocab_size
           snake_case_           =					emb_dim
           snake_case_           =					n_layers
           snake_case_           =					n_heads
           snake_case_           =					dropout
           snake_case_           =					attention_dropout
           snake_case_           =					gelu_activation
           snake_case_           =					sinusoidal_embeddings
           snake_case_           =					causal
           snake_case_           =					asm
           snake_case_           =					n_langs
           snake_case_           =					use_lang_emb
           snake_case_           =					layer_norm_eps
           snake_case_           =					bos_index
           snake_case_           =					eos_index
           snake_case_           =					pad_index
           snake_case_           =					unk_index
           snake_case_           =					mask_index
           snake_case_           =					is_encoder
           snake_case_           =					max_position_embeddings
           snake_case_           =					embed_init_std
           snake_case_           =					init_std
           snake_case_           =					summary_type
           snake_case_           =					summary_use_proj
           snake_case_           =					summary_activation
           snake_case_           =					summary_proj_to_labels
           snake_case_           =					summary_first_dropout
           snake_case_           =					start_n_top
           snake_case_           =					end_n_top
           snake_case_           =					mask_token_id
           snake_case_           =					lang_id

           if "n_words" in kwargs:
                  snake_case_           =					kwargs['n_words']

           super().__init__(pad_token_id=snake_case   ,     bos_token_id=snake_case   ,     **snake_case )







class 				lowercase       ( lowercase_    ):




    @property
    def 					a							(      self ):
           if self.task == "multiple-choice":
                  snake_case_           =					{0: 'batch', 1: 'choice', 2: 'sequence'}
           else:
                  snake_case_           =					{0: 'batch', 1: 'sequence'}
           return OrderedDict(
               [
                   ('input_ids', dynamic_axis),
                   ('attention_mask', dynamic_axis),
                   ('token_type_ids', dynamic_axis),
               ] )







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




