




'''simple docstring'''

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a  :				Optional[int]									=  logging.get_logger(__name__)

a  :				Dict									=  {
    'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json',
    'Salesforce/blip-vqa-capfit-large': (
        'https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-base': (
        'https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-large': (
        'https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'
    ),
    'Salesforce/blip-itm-base-coco': 'https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json',
    'Salesforce/blip-itm-large-coco': 'https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json',
    'Salesforce/blip-itm-base-flikr': 'https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json',
    'Salesforce/blip-itm-large-flikr': (
        'https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'
    ),
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"blip_text_model"


				def __init__(		self    :					Dict       ,				lowercase_    :					Tuple=3_0524       ,				lowercase_    :					Any=768       ,				lowercase_    :					Optional[Any]=768       ,				lowercase_    :					int=3072       ,				lowercase_    :					Tuple=768       ,				lowercase_    :					List[Any]=12       ,				lowercase_    :					Union[str, Any]=8       ,				lowercase_    :					Optional[Any]=512       ,				lowercase_    :					Optional[int]="gelu"       ,				lowercase_    :					int=1e-12       ,				lowercase_    :					Union[str, Any]=0.0       ,				lowercase_    :					str=0.0       ,				lowercase_    :					int=0.02       ,				lowercase_    :					int=3_0522       ,				lowercase_    :					Any=2       ,				lowercase_    :					Any=0       ,				lowercase_    :					int=102       ,				lowercase_    :					List[Any]=True       ,				lowercase_    :					Optional[int]=True       ,				**lowercase_    :					Optional[int]       ,				):
									super().__init__(
									    pad_token_id=lowercase_       ,				bos_token_id=lowercase_       ,				eos_token_id=lowercase_       ,				sep_token_id=lowercase_       ,				**lowercase_       ,				)

									snake_case_									=				vocab_size
									snake_case_									=				hidden_size
									snake_case_									=				encoder_hidden_size
									snake_case_									=				intermediate_size
									snake_case_									=				projection_dim
									snake_case_									=				hidden_dropout_prob
									snake_case_									=				num_hidden_layers
									snake_case_									=				num_attention_heads
									snake_case_									=				max_position_embeddings
									snake_case_									=				layer_norm_eps
									snake_case_									=				hidden_act
									snake_case_									=				initializer_range
									snake_case_									=				attention_probs_dropout_prob
									snake_case_									=				is_decoder
									snake_case_									=				use_cache


				@classmethod
				def   A_ (		cls    :					str       ,				lowercase_    :					Union[str, os.PathLike]       ,				**lowercase_    :					int					):
									cls._set_token_in_kwargs(lowercase_					)

									snake_case_			,snake_case_									=				cls.get_config_dict(lowercase_       ,				**lowercase_					)

									# get the text config dict if we are loading from BlipConfig
									if config_dict.get('''model_type'''					) == "blip":
														snake_case_									=				config_dict['''text_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 						a				(   _lowerCamelCase     ):
				snake_case_      =		"blip_vision_model"


				def __init__(		self    :					int       ,				lowercase_    :					Dict=768       ,				lowercase_    :					Tuple=3072       ,				lowercase_    :					Optional[Any]=512       ,				lowercase_    :					Union[str, Any]=12       ,				lowercase_    :					Optional[Any]=12       ,				lowercase_    :					Any=384       ,				lowercase_    :					str=16       ,				lowercase_    :					Union[str, Any]="gelu"       ,				lowercase_    :					Tuple=1e-5       ,				lowercase_    :					Optional[int]=0.0       ,				lowercase_    :					Optional[int]=1e-10       ,				**lowercase_    :					Optional[Any]       ,				):
									super().__init__(**lowercase_					)

									snake_case_									=				hidden_size
									snake_case_									=				intermediate_size
									snake_case_									=				projection_dim
									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


				@classmethod
				def   A_ (		cls    :					Optional[Any]       ,				lowercase_    :					Union[str, os.PathLike]       ,				**lowercase_    :					int					):
									cls._set_token_in_kwargs(lowercase_					)

									snake_case_			,snake_case_									=				cls.get_config_dict(lowercase_       ,				**lowercase_					)

									# get the vision config dict if we are loading from BlipConfig
									if config_dict.get('''model_type'''					) == "blip":
														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(lowercase_       ,				**lowercase_					)







class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"blip"
				snake_case_      =		True


				def __init__(		self    :					str       ,				lowercase_    :					str=None       ,				lowercase_    :					List[str]=None       ,				lowercase_    :					List[str]=512       ,				lowercase_    :					int=2.6592       ,				lowercase_    :					List[Any]=256       ,				**lowercase_    :					List[str]       ,				):
									super().__init__(**lowercase_					)

									if text_config is None:
														snake_case_									=				{}
														logger.info('''`text_config` is `None`. Initializing the `BlipTextConfig` with default values.'''					)

									if vision_config is None:
														snake_case_									=				{}
														logger.info('''`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.'''					)

									snake_case_									=				BlipTextConfig(**lowercase_					)
									snake_case_									=				BlipVisionConfig(**lowercase_					)

									snake_case_									=				self.vision_config.hidden_size

									snake_case_									=				projection_dim
									snake_case_									=				logit_scale_init_value
									snake_case_									=				1.0
									snake_case_									=				0.02
									snake_case_									=				image_text_hidden_size


				@classmethod
				def   A_ (		cls    :					int       ,				lowercase_    :					BlipTextConfig       ,				lowercase_    :					BlipVisionConfig       ,				**lowercase_    :					List[str]					):
									return cls(text_config=text_config.to_dict()       ,				vision_config=vision_config.to_dict()       ,				**lowercase_					)


				def   A_ (		self    :					str					):
									snake_case_									=				copy.deepcopy(self.__dict__					)
									snake_case_									=				self.text_config.to_dict()
									snake_case_									=				self.vision_config.to_dict()
									snake_case_									=				self.__class__.model_type
									return output








from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


__lowerCamelCase       :     str   =     logging.get_logger(__name__)  # pylint: disable=invalid-name



class 					A__   (       __snake_case			):





      def __init__( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       super().__init__()
       self.register_modules(unet=A_       ,							scheduler=A_   )





      @torch.no_grad()
      def __call__( self       ,							A_ = 1       ,							A_ = 100       ,							A_ = None       ,							A_ = None       ,							A_ = True       ,							):
       '''simple docstring'''




       if audio_length_in_s is None:
        UpperCamelCase	:					str       	=   self.unet.config.sample_size / self.unet.config.sample_rate

       UpperCamelCase	:					Optional[Any]       	=   audio_length_in_s * self.unet.config.sample_rate

       UpperCamelCase	:					Any       	=   2 ** len(self.unet.up_blocks   )
       if sample_size < 3 * down_scale_factor:
        raise ValueError(
            F"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
            F""" {3 * down_scale_factor / self.unet.config.sample_rate}."""   )

       UpperCamelCase	:					Union[str, Any]       	=   int(A_   )
       if sample_size % down_scale_factor != 0:
        UpperCamelCase	:					List[str]       	=   (
            (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
        ) * down_scale_factor
        logger.info(
            F"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
            F""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
            " process."   )
       UpperCamelCase	:					Any       	=   int(A_   )

       UpperCamelCase	:					Union[str, Any]       	=   next(iter(self.unet.parameters()   )   ).dtype
       UpperCamelCase	:					Optional[int]       	=   (batch_size, self.unet.config.in_channels, sample_size)
       if isinstance(A_       ,							A_   ) and len(A_   ) != batch_size:
        raise ValueError(
            F"""You have passed a list of generators of length {len(A_   )}, but requested an effective batch"""
            F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""   )

       UpperCamelCase	:					Optional[Any]       	=   randn_tensor(A_       ,							generator=A_       ,							device=self.device       ,							dtype=A_   )

       # set step values
       self.scheduler.set_timesteps(A_       ,							device=audio.device   )
       UpperCamelCase	:					Optional[int]       	=   self.scheduler.timesteps.to(A_   )

       for t in self.progress_bar(self.scheduler.timesteps   ):
        # 1. predict noise model_output
        UpperCamelCase	:					Dict       	=   self.unet(A_       ,							A_   ).sample

        # 2. compute previous image: x_t -> t_t-1
        UpperCamelCase	:					int       	=   self.scheduler.step(A_       ,							A_       ,							A_   ).prev_sample

       UpperCamelCase	:					Optional[Any]       	=   audio.clamp(-1       ,							1   ).float().cpu().numpy()

       UpperCamelCase	:					Dict       	=   audio[:, :, :original_sample_size]

       if not return_dict:
        return (audio,)

       return AudioPipelineOutput(audios=A_   )
