
"""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


_A			:       Tuple   		=       logging.get_logger(__name__)

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

class 		a__						(      a_						):
   __lowerCAmelCase            =				"""instructblip_vision_model"""
   def __init__(			self	,    _a=1_408	,    _a=6_144	,    _a=39	,    _a=16	,    _a=224	,    _a=14	,    _a="gelu"	,    _a=1E-6	,    _a=0.0	,    _a=1E-10	,    _a=True	,    **_a	,    ):
          super().__init__(**_a				)

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

   @classmethod
   def 			__magic_name__			(			cls	,    _a	,    **_a				):
          cls._set_token_in_kwargs(_a				)

          lowercase		,    lowercase    :				int       		=							cls.get_config_dict(_a	,    **_a				)

          # get the vision config dict if we are loading from InstructBlipConfig
          if config_dict.get("model_type"				) == "instructblip":
                 lowercase    :				List[str]       		=							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__						(      a_						):
   __lowerCAmelCase            =				"""instructblip_qformer"""
   def __init__(			self	,    _a=30_522	,    _a=768	,    _a=12	,    _a=12	,    _a=3_072	,    _a="gelu"	,    _a=0.1	,    _a=0.1	,    _a=512	,    _a=0.0_2	,    _a=1E-12	,    _a=0	,    _a="absolute"	,    _a=2	,    _a=1_408	,    **_a	,    ):
          super().__init__(pad_token_id=_a	,    **_a				)

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

   @classmethod
   def 			__magic_name__			(			cls	,    _a	,    **_a				):
          cls._set_token_in_kwargs(_a				)

          lowercase		,    lowercase    :				Union[str, Any]       		=							cls.get_config_dict(_a	,    **_a				)

          # get the qformer config dict if we are loading from InstructBlipConfig
          if config_dict.get("model_type"				) == "instructblip":
                 lowercase    :				int       		=							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__						(      a_						):
   __lowerCAmelCase            =				"""instructblip"""
   __lowerCAmelCase            =				True
   def __init__(			self	,    _a=None	,    _a=None	,    _a=None	,    _a=32	,    **_a				):
          super().__init__(**_a				)

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

          if qformer_config is None:
                 lowercase    :				List[str]       		=							{}
                 logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."				)

          if text_config is None:
                 lowercase    :				Union[str, Any]       		=							{}
                 logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)."				)

          lowercase    :				Union[str, Any]       		=							InstructBlipVisionConfig(**_a				)
          lowercase    :				Any       		=							InstructBlipQFormerConfig(**_a				)
          lowercase    :				Tuple       		=							text_config["model_type"] if "model_type" in text_config else "opt"
          lowercase    :				Optional[int]       		=							CONFIG_MAPPING[text_model_type](**_a				)

          lowercase    :				Optional[int]       		=							self.text_config.tie_word_embeddings
          lowercase    :				int       		=							self.text_config.is_encoder_decoder

          lowercase    :				List[str]       		=							num_query_tokens
          lowercase    :				List[str]       		=							self.vision_config.hidden_size
          lowercase    :				Dict       		=							self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
          lowercase    :				Any       		=							1.0
          lowercase    :				Tuple       		=							0.0_2
   @classmethod
   def 			__magic_name__			(			cls	,    _a	,    _a	,    _a	,    **_a	,    ):
          return cls(
              vision_config=vision_config.to_dict()	,    qformer_config=qformer_config.to_dict()	,    text_config=text_config.to_dict()	,    **_a	,    )

   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							copy.deepcopy(self.__dict__				)
          lowercase    :				Dict       		=							self.vision_config.to_dict()
          lowercase    :				Dict       		=							self.qformer_config.to_dict()
          lowercase    :				Optional[Any]       		=							self.text_config.to_dict()
          lowercase    :				List[Any]       		=							self.__class__.model_type
          return output




"""simple docstring"""




# Copyright 2023 The HuggingFace Inc. 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.
import re

from ..utils import cached_file


# docstyle-ignore
_A			:       Optional[int]   		=       """
Human: <<task>>

Assistant: """


_A			:       List[Any]   		=       """huggingface-tools/default-prompts"""
_A			:       Optional[int]   		=       {"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}


def   __magic_name__	(      __snake_case				:       int     , __snake_case				:       List[Any]     , __snake_case				:       Dict="run"  ) ->				Union[str, Any]:
       if prompt_or_repo_id is None:
              lowercase    :				List[Any]       		=							DEFAULT_PROMPTS_REPO

       # prompt is considered a repo ID when it does not contain any kind of space
       if re.search("\\s"     , __snake_case  ) is not None:
              return prompt_or_repo_id

       lowercase    :				Optional[int]       		=							cached_file(
           __snake_case     , PROMPT_FILES[mode]     , repo_type="dataset"     , user_agent={"agent": agent_name}  )
       with open(__snake_case     , "r"     , encoding="utf-8"  ) as f:
              return f.read()


