

"""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_					:	Union[str, Any]							  =						logging.get_logger(__name__)

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



class 					a						(       UpperCamelCase_				):



				"""simple docstring"""



				UpperCAmelCase						 =					"instructblip_vision_model"

				def __init__(     self: List[Any]    ,						UpperCamelCase: Dict=14_08    ,						UpperCamelCase: Tuple=61_44    ,						UpperCamelCase: str=39    ,						UpperCamelCase: int=16    ,						UpperCamelCase: str=2_24    ,						UpperCamelCase: Any=14    ,						UpperCamelCase: Dict="gelu"    ,						UpperCamelCase: List[Any]=1e-6    ,						UpperCamelCase: Any=0.0    ,						UpperCamelCase: List[Any]=1e-1_0    ,						UpperCamelCase: Union[str, Any]=True    ,						**UpperCamelCase: Tuple    ,						):






									"""simple docstring"""


									super().__init__(**__A       )

									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 				UpperCamelCase	(     cls: List[str]    ,						UpperCamelCase: Union[str, os.PathLike]    ,						**UpperCamelCase: Optional[Any]       ):






									"""simple docstring"""


									cls._set_token_in_kwargs(__A       )

									A__										=  cls.get_config_dict(__A    ,						**__A       )

									# 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(__A    ,						**__A       )



class 					a						(       UpperCamelCase_				):



				"""simple docstring"""



				UpperCAmelCase						 =					"instructblip_qformer"

				def __init__(     self: Any    ,						UpperCamelCase: Union[str, Any]=3_05_22    ,						UpperCamelCase: Union[str, Any]=7_68    ,						UpperCamelCase: Optional[int]=12    ,						UpperCamelCase: Dict=12    ,						UpperCamelCase: Dict=30_72    ,						UpperCamelCase: List[str]="gelu"    ,						UpperCamelCase: Union[str, Any]=0.1    ,						UpperCamelCase: Tuple=0.1    ,						UpperCamelCase: Any=5_12    ,						UpperCamelCase: Optional[int]=0.02    ,						UpperCamelCase: List[str]=1e-1_2    ,						UpperCamelCase: Any=0    ,						UpperCamelCase: Optional[Any]="absolute"    ,						UpperCamelCase: str=2    ,						UpperCamelCase: Any=14_08    ,						**UpperCamelCase: List[str]    ,						):






									"""simple docstring"""


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

									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 				UpperCamelCase	(     cls: List[Any]    ,						UpperCamelCase: Union[str, os.PathLike]    ,						**UpperCamelCase: Optional[int]       ):






									"""simple docstring"""


									cls._set_token_in_kwargs(__A       )

									A__										=  cls.get_config_dict(__A    ,						**__A       )

									# 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(__A    ,						**__A       )



class 					a						(       UpperCamelCase_				):



				"""simple docstring"""



				UpperCAmelCase						 =					"instructblip"
				UpperCAmelCase						 =					True

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






									"""simple docstring"""


									super().__init__(**__A       )

									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(**__A       )
									A__										=  InstructBlipQFormerConfig(**__A       )
									A__										=  text_config["model_type"] if "model_type" in text_config else "opt"
									A__										=  CONFIG_MAPPING[text_model_type](**__A       )

									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 				UpperCamelCase	(     cls: List[str]    ,						UpperCamelCase: InstructBlipVisionConfig    ,						UpperCamelCase: InstructBlipQFormerConfig    ,						UpperCamelCase: PretrainedConfig    ,						**UpperCamelCase: int    ,						):






									"""simple docstring"""


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





				def 				UpperCamelCase	(     self: Optional[int]       ):






									"""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









"""simple docstring"""



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE_					:	List[str]							  =						{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_					:	List[Any]							  =						[
     'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MraForMaskedLM',
     'MraForMultipleChoice',
     'MraForQuestionAnswering',
     'MraForSequenceClassification',
     'MraForTokenClassification',
     'MraLayer',
     'MraModel',
     'MraPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mra import (
      MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraLayer,
      MraModel,
      MraPreTrainedModel,
  )
else:
 import sys

 SCREAMING_SNAKE_CASE_					:	List[str]							  =						_LazyModule(__name__, globals()['__file__'], _import_structure)






