


'''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


UpperCAmelCase_ :     Any								=      logging.get_logger(__name__)

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

class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		Any    							=						'''instructblip_vision_model'''


 def __init__(      self      ,      __lowercase=1_408      ,      __lowercase=6_144      ,      __lowercase=39      ,      __lowercase=16      ,      __lowercase=224      ,      __lowercase=14      ,      __lowercase="gelu"      ,      __lowercase=1E-6      ,      __lowercase=0.0      ,      __lowercase=1E-10      ,      __lowercase=True      ,      **__lowercase      ,      ):

  """simple docstring"""







  super().__init__(**__lowercase						)

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







 @classmethod
 def  snake_case__   (      cls      ,      __lowercase      ,      **__lowercase						):

  """simple docstring"""







  cls._set_token_in_kwargs(__lowercase						)

  __A     ,__A  :		List[Any]       =			cls.get_config_dict(__lowercase      ,      **__lowercase						)

  # get the vision config dict if we are loading from InstructBlipConfig
  if config_dict.get('model_type'						) == "instructblip":
   __A  :		Union[str, Any]       =			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(__lowercase      ,      **__lowercase						)


class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		Tuple    							=						'''instructblip_qformer'''


 def __init__(      self      ,      __lowercase=30_522      ,      __lowercase=768      ,      __lowercase=12      ,      __lowercase=12      ,      __lowercase=3_072      ,      __lowercase="gelu"      ,      __lowercase=0.1      ,      __lowercase=0.1      ,      __lowercase=512      ,      __lowercase=0.0_2      ,      __lowercase=1E-12      ,      __lowercase=0      ,      __lowercase="absolute"      ,      __lowercase=2      ,      __lowercase=1_408      ,      **__lowercase      ,      ):

  """simple docstring"""







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

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







 @classmethod
 def  snake_case__   (      cls      ,      __lowercase      ,      **__lowercase						):

  """simple docstring"""







  cls._set_token_in_kwargs(__lowercase						)

  __A     ,__A  :		int       =			cls.get_config_dict(__lowercase      ,      **__lowercase						)

  # get the qformer config dict if we are loading from InstructBlipConfig
  if config_dict.get('model_type'						) == "instructblip":
   __A  :		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(__lowercase      ,      **__lowercase						)


class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		Optional[int]    							=						'''instructblip'''
 __lowercase				:		Dict    							=						True


 def __init__(      self      ,      __lowercase=None      ,      __lowercase=None      ,      __lowercase=None      ,      __lowercase=32      ,      **__lowercase						):

  """simple docstring"""







  super().__init__(**__lowercase						)

  if vision_config is None:
   __A  :		List[str]       =			{}
   logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'						)

  if qformer_config is None:
   __A  :		Dict       =			{}
   logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'						)

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

  __A  :		List[Any]       =			InstructBlipVisionConfig(**__lowercase						)
  __A  :		Any       =			InstructBlipQFormerConfig(**__lowercase						)
  __A  :		Any       =			text_config['model_type'] if 'model_type' in text_config else 'opt'
  __A  :		Dict       =			CONFIG_MAPPING[text_model_type](**__lowercase						)

  __A  :		Tuple       =			self.text_config.tie_word_embeddings
  __A  :		Dict       =			self.text_config.is_encoder_decoder

  __A  :		int       =			num_query_tokens
  __A  :		List[Any]       =			self.vision_config.hidden_size
  __A  :		List[Any]       =			self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
  __A  :		Any       =			1.0
  __A  :		Optional[Any]       =			0.0_2


 @classmethod
 def  snake_case__   (      cls      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      **__lowercase      ,      ):

  """simple docstring"""







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







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			copy.deepcopy(self.__dict__						)
  __A  :		Optional[int]       =			self.vision_config.to_dict()
  __A  :		str       =			self.qformer_config.to_dict()
  __A  :		int       =			self.text_config.to_dict()
  __A  :		List[Any]       =			self.__class__.model_type
  return output










'''simple docstring'''
from __future__ import annotations

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Dict       =			order

  # a_{0} ... a_{k}
  __A  :		str       =			[1.0] + [0.0] * order
  # b_{0} ... b_{k}
  __A  :		Any       =			[1.0] + [0.0] * order

  # x[n-1] ... x[n-k]
  __A  :		Optional[int]       =			[0.0] * self.order
  # y[n-1] ... y[n-k]
  __A  :		Dict       =			[0.0] * self.order


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if len(__lowercase						) < self.order:
   __A  :		List[Any]       =			[1.0, *a_coeffs]

  if len(__lowercase						) != self.order + 1:
   __A  :		Tuple       =			(
       F"""Expected a_coeffs to have {self.order + 1} elements """
       F"""for {self.order}-order filter, got {len(__lowercase						)}"""
   )
   raise ValueError(__lowercase						)

  if len(__lowercase						) != self.order + 1:
   __A  :		List[Any]       =			(
       F"""Expected b_coeffs to have {self.order + 1} elements """
       F"""for {self.order}-order filter, got {len(__lowercase						)}"""
   )
   raise ValueError(__lowercase						)

  __A  :		Optional[int]       =			a_coeffs
  __A  :		Optional[int]       =			b_coeffs







 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			0.0

  # Start at index 1 and do index 0 at the end.
  for i in range(1      ,      self.order + 1						):
   result += (
       self.b_coeffs[i] * self.input_history[i - 1]
       - self.a_coeffs[i] * self.output_history[i - 1]
   )

  __A  :		Optional[int]       =			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

  __A  :		List[str]       =			self.input_history[:-1]
  __A  :		Optional[int]       =			self.output_history[:-1]

  __A  :		Optional[int]       =			sample
  __A  :		Dict       =			result

  return result






