



'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin



@flax.struct.dataclass
class 		lowerCamelCase__      :




					'''simple docstring'''



					A_						    =						None
					A_						    =						None
					A_						    =						None  # sigma(t_i)




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	Optional[int]					)  ->       Optional[int]:



						'''simple docstring'''





						return cls()






@dataclass
class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						42
					A_						    =						42
					A_						    =						42






class 		lowerCamelCase__      (						A	,   A     ):




					'''simple docstring'''



					@property
					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       str:



						'''simple docstring'''





						return True




					@register_to_config
					def __init__(  self				:	Union[str, Any]     ,  UpperCamelCase_				:	float = 0.02     ,  UpperCamelCase_				:	float = 100     ,  UpperCamelCase_				:	float = 1.0_07     ,  UpperCamelCase_				:	float = 80     ,  UpperCamelCase_				:	float = 0.05     ,  UpperCamelCase_				:	float = 50     ,  )  ->       Union[str, Any]:



						'''simple docstring'''





						pass




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Tuple:



						'''simple docstring'''





						return KarrasVeSchedulerState.create()




					def 						__UpperCAmelCase     (  self				:	Optional[Any]     ,  UpperCamelCase_				:	KarrasVeSchedulerState     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	Tuple = ()					)  ->       KarrasVeSchedulerState:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		jnp.arange(0     ,  UpperCamelCase_					)[::-1].copy()
						_lowercase					:       List[str]       =		[
						    (
						        self.config.sigma_max**2
						        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
						    )
						    for i in timesteps
						]

						return state.replace(
						    num_inference_steps=UpperCamelCase_     ,  schedule=jnp.array(UpperCamelCase_     ,  dtype=jnp.floataa					)     ,  timesteps=UpperCamelCase_     ,  )




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	KarrasVeSchedulerState     ,  UpperCamelCase_				:	jnp.ndarray     ,  UpperCamelCase_				:	float     ,  UpperCamelCase_				:	random.KeyArray     ,  )  ->       Tuple[jnp.ndarray, float]:



						'''simple docstring'''





						if self.config.s_min <= sigma <= self.config.s_max:
							_lowercase					:       List[str]       =		min(self.config.s_churn / state.num_inference_steps     ,  2**0.5 - 1					)
						else:
							_lowercase					:       Any       =		0

						# sample eps ~ N(0, S_noise^2 * I)
						_lowercase					:       Any       =		random.split(UpperCamelCase_     ,  num=1					)
						_lowercase					:       str       =		self.config.s_noise * random.normal(key=UpperCamelCase_     ,  shape=sample.shape					)
						_lowercase					:       List[str]       =		sigma + gamma * sigma
						_lowercase					:       Optional[int]       =		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

						return sample_hat, sigma_hat




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	KarrasVeSchedulerState     ,  UpperCamelCase_				:	jnp.ndarray     ,  UpperCamelCase_				:	float     ,  UpperCamelCase_				:	float     ,  UpperCamelCase_				:	jnp.ndarray     ,  UpperCamelCase_				:	bool = True     ,  )  ->       Union[FlaxKarrasVeOutput, Tuple]:



						'''simple docstring'''





						_lowercase					:       int       =		sample_hat + sigma_hat * model_output
						_lowercase					:       Dict       =		(sample_hat - pred_original_sample) / sigma_hat
						_lowercase					:       Union[str, Any]       =		sample_hat + (sigma_prev - sigma_hat) * derivative

						if not return_dict:
							return (sample_prev, derivative, state)

						return FlaxKarrasVeOutput(prev_sample=UpperCamelCase_     ,  derivative=UpperCamelCase_     ,  state=UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	KarrasVeSchedulerState     ,  UpperCamelCase_				:	jnp.ndarray     ,  UpperCamelCase_				:	float     ,  UpperCamelCase_				:	float     ,  UpperCamelCase_				:	jnp.ndarray     ,  UpperCamelCase_				:	jnp.ndarray     ,  UpperCamelCase_				:	jnp.ndarray     ,  UpperCamelCase_				:	bool = True     ,  )  ->       Union[FlaxKarrasVeOutput, Tuple]:



						'''simple docstring'''





						_lowercase					:       List[str]       =		sample_prev + sigma_prev * model_output
						_lowercase					:       Optional[Any]       =		(sample_prev - pred_original_sample) / sigma_prev
						_lowercase					:       Tuple       =		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

						if not return_dict:
							return (sample_prev, derivative, state)

						return FlaxKarrasVeOutput(prev_sample=UpperCamelCase_     ,  derivative=UpperCamelCase_     ,  state=UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Optional[Any]     ,  UpperCamelCase_				:	KarrasVeSchedulerState     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	Any					)  ->       Tuple:



						'''simple docstring'''





						raise NotImplementedError()





'''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				:    int     						=logging.get_logger(__name__)

_A				:    Union[str, Any]     						={
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""instructblip_vision_model"""




					def __init__(  self				:	Union[str, Any]     ,  UpperCamelCase_				:	str=1408     ,  UpperCamelCase_				:	Tuple=6144     ,  UpperCamelCase_				:	Union[str, Any]=39     ,  UpperCamelCase_				:	Optional[Any]=16     ,  UpperCamelCase_				:	str=224     ,  UpperCamelCase_				:	Dict=14     ,  UpperCamelCase_				:	Dict="gelu"     ,  UpperCamelCase_				:	int=1E-6     ,  UpperCamelCase_				:	int=0.0     ,  UpperCamelCase_				:	List[str]=1E-10     ,  UpperCamelCase_				:	str=True     ,  **UpperCamelCase_				:	Dict     ,  )  ->       Any:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)

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




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	List[Any]     ,  UpperCamelCase_				:	Union[str, os.PathLike]     ,  **UpperCamelCase_				:	List[str]					)  ->       "PretrainedConfig":



						'''simple docstring'''





						cls._set_token_in_kwargs(UpperCamelCase_					)

						_lowercase  ,    _lowercase					:       Tuple       =		cls.get_config_dict(UpperCamelCase_     ,  **UpperCamelCase_					)

						# get the vision config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'					) == "instructblip":
							_lowercase					:       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(UpperCamelCase_     ,  **UpperCamelCase_					)






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""instructblip_qformer"""




					def __init__(  self				:	Tuple     ,  UpperCamelCase_				:	Union[str, Any]=3_0522     ,  UpperCamelCase_				:	Union[str, Any]=768     ,  UpperCamelCase_				:	Tuple=12     ,  UpperCamelCase_				:	Optional[Any]=12     ,  UpperCamelCase_				:	List[str]=3072     ,  UpperCamelCase_				:	List[str]="gelu"     ,  UpperCamelCase_				:	Union[str, Any]=0.1     ,  UpperCamelCase_				:	List[str]=0.1     ,  UpperCamelCase_				:	Any=512     ,  UpperCamelCase_				:	Union[str, Any]=0.02     ,  UpperCamelCase_				:	List[Any]=1E-12     ,  UpperCamelCase_				:	Optional[Any]=0     ,  UpperCamelCase_				:	str="absolute"     ,  UpperCamelCase_				:	List[Any]=2     ,  UpperCamelCase_				:	Any=1408     ,  **UpperCamelCase_				:	Dict     ,  )  ->       Any:



						'''simple docstring'''





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

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




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	List[Any]     ,  UpperCamelCase_				:	Union[str, os.PathLike]     ,  **UpperCamelCase_				:	List[str]					)  ->       "PretrainedConfig":



						'''simple docstring'''





						cls._set_token_in_kwargs(UpperCamelCase_					)

						_lowercase  ,    _lowercase					:       List[str]       =		cls.get_config_dict(UpperCamelCase_     ,  **UpperCamelCase_					)

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






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""instructblip"""
					A_						    =						True




					def __init__(  self				:	Any     ,  UpperCamelCase_				:	List[Any]=None     ,  UpperCamelCase_				:	Tuple=None     ,  UpperCamelCase_				:	Dict=None     ,  UpperCamelCase_				:	Union[str, Any]=32     ,  **UpperCamelCase_				:	int					)  ->       List[str]:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)

						if vision_config is None:
							_lowercase					:       Any       =		{}
							logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'					)

						if qformer_config is None:
							_lowercase					:       List[Any]       =		{}
							logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'					)

						if text_config is None:
							_lowercase					:       List[Any]       =		{}
							logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'					)

						_lowercase					:       List[Any]       =		InstructBlipVisionConfig(**UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		InstructBlipQFormerConfig(**UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		text_config['model_type'] if 'model_type' in text_config else 'opt'
						_lowercase					:       int       =		CONFIG_MAPPING[text_model_type](**UpperCamelCase_					)

						_lowercase					:       str       =		self.text_config.tie_word_embeddings
						_lowercase					:       int       =		self.text_config.is_encoder_decoder

						_lowercase					:       Tuple       =		num_query_tokens
						_lowercase					:       str       =		self.vision_config.hidden_size
						_lowercase					:       Union[str, Any]       =		self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
						_lowercase					:       List[Any]       =		1.0
						_lowercase					:       int       =		0.02




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	Tuple     ,  UpperCamelCase_				:	InstructBlipVisionConfig     ,  UpperCamelCase_				:	InstructBlipQFormerConfig     ,  UpperCamelCase_				:	PretrainedConfig     ,  **UpperCamelCase_				:	Dict     ,  )  ->       List[str]:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       List[str]:



						'''simple docstring'''





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