


'''simple docstring'''


import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu



class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  def        _a							(							self :       str ):

        '''simple docstring'''




        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





  @property
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        A_						:  List[Any]          =				1
        A_						:  Any          =				3
        A_						:  Optional[int]          =				(32, 32)

        A_						:  Optional[int]          =				floats_tensor((batch_size, num_channels) + sizes	,rng=random.Random(0 ) ).to(lowerCamelCase_ )
        return image





  @property
  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  str          =				UNetaDConditionModel(
            block_out_channels=(32, 64)	,layers_per_block=2	,sample_size=32	,in_channels=4	,out_channels=4	,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")	,cross_attention_dim=32	,)
        return model





  @property
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  Any          =				AutoencoderKL(
            block_out_channels=[32, 64]	,in_channels=3	,out_channels=3	,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]	,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]	,latent_channels=4	,)
        return model





  @property
  def        _a							(							self :       Any ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  Any          =				CLIPTextConfig(
            bos_token_id=0	,eos_token_id=2	,hidden_size=32	,intermediate_size=37	,layer_norm_eps=1e-05	,num_attention_heads=4	,num_hidden_layers=5	,pad_token_id=1	,vocab_size=1000	,)
        return CLIPTextModel(lowerCamelCase_ )





  @property
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        def extract(*_a :       Union[str, Any]	,**_a :       List[str] ):



              class 	__lowerCAmelCase		:


                '''simple docstring'''







                def __init__(							self :       Any ):

                      '''simple docstring'''




                      A_						:  Any          =				torch.ones([0] )





                def        _a							(							self :       Any	,_a :       Tuple ):

                      '''simple docstring'''




                      self.pixel_values.to(lowerCamelCase_ )
                      return self







              return Out()

        return extract





  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  Any          =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        A_						:  Any          =				self.dummy_cond_unet
        A_						:  int          =				DDIMScheduler(
            beta_start=0.00085	,beta_end=0.012	,beta_schedule="""scaled_linear"""	,clip_sample=lowerCamelCase_	,set_alpha_to_one=lowerCamelCase_	,)

        A_						:  Optional[int]          =				self.dummy_vae
        A_						:  Optional[int]          =				self.dummy_text_encoder
        A_						:  str          =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

        # make sure here that pndm scheduler skips prk
        A_						:  List[str]          =				StableDiffusionPipeline(
            unet=lowerCamelCase_	,scheduler=lowerCamelCase_	,vae=lowerCamelCase_	,text_encoder=lowerCamelCase_	,tokenizer=lowerCamelCase_	,safety_checker=lowerCamelCase_	,feature_extractor=self.dummy_extractor	,)
        A_						:  str          =				sd_pipe.to(lowerCamelCase_ )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )

        A_						:  Optional[int]          =				'''A painting of a squirrel eating a burger'''

        A_						:  Tuple          =				torch.Generator(device=lowerCamelCase_ ).manual_seed(0 )
        A_						:  List[Any]          =				sd_pipe([prompt]	,generator=lowerCamelCase_	,guidance_scale=6.0	,num_inference_steps=2	,output_type="""np""" )
        A_						:  Tuple          =				output.images

        A_						:  Tuple          =				torch.Generator(device=lowerCamelCase_ ).manual_seed(0 )
        A_						:  Union[str, Any]          =				sd_pipe(
            [prompt]	,generator=lowerCamelCase_	,guidance_scale=6.0	,num_inference_steps=2	,output_type="""np"""	,return_dict=lowerCamelCase_	,)[0]

        A_						:  Any          =				image[0, -3:, -3:, -1]
        A_						:  List[Any]          =				image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        A_						:  Optional[int]          =				np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864] )

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2





  def        _a							(							self :       int ):

        '''simple docstring'''




        A_						:  int          =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        A_						:  int          =				self.dummy_cond_unet
        A_						:  Optional[Any]          =				PNDMScheduler(skip_prk_steps=lowerCamelCase_ )
        A_						:  List[str]          =				self.dummy_vae
        A_						:  Optional[int]          =				self.dummy_text_encoder
        A_						:  int          =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

        # make sure here that pndm scheduler skips prk
        A_						:  Optional[int]          =				StableDiffusionPipeline(
            unet=lowerCamelCase_	,scheduler=lowerCamelCase_	,vae=lowerCamelCase_	,text_encoder=lowerCamelCase_	,tokenizer=lowerCamelCase_	,safety_checker=lowerCamelCase_	,feature_extractor=self.dummy_extractor	,)
        A_						:  List[Any]          =				sd_pipe.to(lowerCamelCase_ )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )

        A_						:  str          =				'''A painting of a squirrel eating a burger'''
        A_						:  Dict          =				torch.Generator(device=lowerCamelCase_ ).manual_seed(0 )
        A_						:  Optional[int]          =				sd_pipe([prompt]	,generator=lowerCamelCase_	,guidance_scale=6.0	,num_inference_steps=2	,output_type="""np""" )

        A_						:  List[Any]          =				output.images

        A_						:  Any          =				torch.Generator(device=lowerCamelCase_ ).manual_seed(0 )
        A_						:  str          =				sd_pipe(
            [prompt]	,generator=lowerCamelCase_	,guidance_scale=6.0	,num_inference_steps=2	,output_type="""np"""	,return_dict=lowerCamelCase_	,)[0]

        A_						:  Any          =				image[0, -3:, -3:, -1]
        A_						:  Optional[int]          =				image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        A_						:  Optional[int]          =				np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993] )

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2





  def        _a							(							self :       str ):

        '''simple docstring'''




        A_						:  str          =				StableDiffusionPipeline.from_pretrained(
            """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""	,safety_checker=lowerCamelCase_ )
        assert isinstance(lowerCamelCase_	,lowerCamelCase_ )
        assert isinstance(pipe.scheduler	,lowerCamelCase_ )
        assert pipe.safety_checker is None

        A_						:  List[str]          =				pipe("""example prompt"""	,num_inference_steps=2 ).images[0]
        assert image is not None

        # check that there's no error when saving a pipeline with one of the models being None
        with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(lowerCamelCase_ )
              A_						:  Dict          =				StableDiffusionPipeline.from_pretrained(lowerCamelCase_ )

        # sanity check that the pipeline still works
        assert pipe.safety_checker is None
        A_						:  List[str]          =				pipe("""example prompt"""	,num_inference_steps=2 ).images[0]
        assert image is not None





  @unittest.skipIf(torch_device != """cuda"""	,"""This test requires a GPU""" )
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  Dict          =				self.dummy_cond_unet
        A_						:  Tuple          =				PNDMScheduler(skip_prk_steps=lowerCamelCase_ )
        A_						:  Dict          =				self.dummy_vae
        A_						:  List[Any]          =				self.dummy_text_encoder
        A_						:  str          =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

        # put models in fp16
        A_						:  Dict          =				unet.half()
        A_						:  Optional[int]          =				vae.half()
        A_						:  Optional[Any]          =				bert.half()

        # make sure here that pndm scheduler skips prk
        A_						:  List[str]          =				StableDiffusionPipeline(
            unet=lowerCamelCase_	,scheduler=lowerCamelCase_	,vae=lowerCamelCase_	,text_encoder=lowerCamelCase_	,tokenizer=lowerCamelCase_	,safety_checker=lowerCamelCase_	,feature_extractor=self.dummy_extractor	,)
        A_						:  str          =				sd_pipe.to(lowerCamelCase_ )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )

        A_						:  str          =				'''A painting of a squirrel eating a burger'''
        A_						:  Dict          =				sd_pipe([prompt]	,num_inference_steps=2	,output_type="""np""" ).images

        assert image.shape == (1, 64, 64, 3)







@nightly
@require_torch_gpu
class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  def        _a							(							self :       int ):

        '''simple docstring'''




        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        A_						:  Tuple          =				StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""	,safety_checker=lowerCamelCase_ )
        A_						:  Optional[Any]          =				LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
        A_						:  Any          =				sd_pipe.to(lowerCamelCase_ )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )

        A_						:  int          =				(
            '''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
            ''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
            ''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
            ''' children from bahnhof zoo, detailed '''
        )
        A_						:  Union[str, Any]          =				4003660346
        A_						:  Optional[int]          =				7

        # without safety guidance (sld_guidance_scale = 0)
        A_						:  Any          =				torch.manual_seed(lowerCamelCase_ )
        A_						:  Dict          =				sd_pipe(
            [prompt]	,generator=lowerCamelCase_	,guidance_scale=lowerCamelCase_	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,sld_guidance_scale=0	,)

        A_						:  Union[str, Any]          =				output.images
        A_						:  List[str]          =				image[0, -3:, -3:, -1]
        A_						:  Any          =				[0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2

        # without safety guidance (strong configuration)
        A_						:  str          =				torch.manual_seed(lowerCamelCase_ )
        A_						:  Union[str, Any]          =				sd_pipe(
            [prompt]	,generator=lowerCamelCase_	,guidance_scale=lowerCamelCase_	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,sld_guidance_scale=2000	,sld_warmup_steps=7	,sld_threshold=0.025	,sld_momentum_scale=0.5	,sld_mom_beta=0.7	,)

        A_						:  str          =				output.images
        A_						:  List[str]          =				image[0, -3:, -3:, -1]
        A_						:  Any          =				[0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2





  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  Tuple          =				StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""	,safety_checker=lowerCamelCase_ )
        A_						:  str          =				LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
        A_						:  Dict          =				sd_pipe.to(lowerCamelCase_ )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )

        A_						:  int          =				'''padme amidala taking a bath artwork, safe for work, no nudity'''
        A_						:  List[Any]          =				2734971755
        A_						:  str          =				7

        A_						:  List[str]          =				torch.manual_seed(lowerCamelCase_ )
        A_						:  Optional[int]          =				sd_pipe(
            [prompt]	,generator=lowerCamelCase_	,guidance_scale=lowerCamelCase_	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,sld_guidance_scale=0	,)

        A_						:  Optional[int]          =				output.images
        A_						:  Dict          =				image[0, -3:, -3:, -1]
        A_						:  List[str]          =				[0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2

        A_						:  Dict          =				torch.manual_seed(lowerCamelCase_ )
        A_						:  Union[str, Any]          =				sd_pipe(
            [prompt]	,generator=lowerCamelCase_	,guidance_scale=lowerCamelCase_	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,sld_guidance_scale=2000	,sld_warmup_steps=7	,sld_threshold=0.025	,sld_momentum_scale=0.5	,sld_mom_beta=0.7	,)

        A_						:  List[Any]          =				output.images
        A_						:  List[str]          =				image[0, -3:, -3:, -1]
        A_						:  int          =				[0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2





  def        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" )
        A_						:  Optional[int]          =				sd_pipe.to(lowerCamelCase_ )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )

        A_						:  Tuple          =				(
            '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
            ''' leyendecker'''
        )
        A_						:  List[str]          =				1044355234
        A_						:  Any          =				12

        A_						:  int          =				torch.manual_seed(lowerCamelCase_ )
        A_						:  Any          =				sd_pipe(
            [prompt]	,generator=lowerCamelCase_	,guidance_scale=lowerCamelCase_	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,sld_guidance_scale=0	,)

        A_						:  Any          =				output.images
        A_						:  List[str]          =				image[0, -3:, -3:, -1]
        A_						:  Any          =				np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-7

        A_						:  int          =				torch.manual_seed(lowerCamelCase_ )
        A_						:  Union[str, Any]          =				sd_pipe(
            [prompt]	,generator=lowerCamelCase_	,guidance_scale=lowerCamelCase_	,num_inference_steps=50	,output_type="""np"""	,width=512	,height=512	,sld_guidance_scale=2000	,sld_warmup_steps=7	,sld_threshold=0.025	,sld_momentum_scale=0.5	,sld_mom_beta=0.7	,)

        A_						:  Union[str, Any]          =				output.images
        A_						:  Dict          =				image[0, -3:, -3:, -1]
        A_						:  str          =				np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561] )
        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2




'''simple docstring'''


import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
     from ...processing_utils import ProcessorMixin
     from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__magic_name__			=      logging.get_logger(__name__)

__magic_name__			=      {
    'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
    'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
    'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""owlvit_text_model"""






  def __init__(							self :       Union[str, Any]	,_a :       Any=49408	,_a :       Any=512	,_a :       Tuple=2048	,_a :       Dict=12	,_a :       Optional[int]=8	,_a :       Tuple=16	,_a :       Tuple="quick_gelu"	,_a :       Optional[Any]=1e-5	,_a :       List[Any]=0.0	,_a :       Optional[int]=0.02	,_a :       Dict=1.0	,_a :       Dict=0	,_a :       Any=49406	,_a :       Tuple=49407	,**_a :       List[Any]	,):

        '''simple docstring'''




        super().__init__(pad_token_id=_a	,bos_token_id=_a	,eos_token_id=_a	,**_a )

        A_						:  Tuple          =				vocab_size
        A_						:  int          =				hidden_size
        A_						:  Optional[int]          =				intermediate_size
        A_						:  Optional[int]          =				num_hidden_layers
        A_						:  Union[str, Any]          =				num_attention_heads
        A_						:  int          =				max_position_embeddings
        A_						:  str          =				hidden_act
        A_						:  Union[str, Any]          =				layer_norm_eps
        A_						:  Tuple          =				attention_dropout
        A_						:  Union[str, Any]          =				initializer_range
        A_						:  List[Any]          =				initializer_factor






  @classmethod
  def        _a							(							cls :       List[str]	,_a :       Union[str, os.PathLike]	,**_a :       str ):

        '''simple docstring'''




        cls._set_token_in_kwargs(_a )

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

        # get the text config dict if we are loading from OwlViTConfig
        if config_dict.get("""model_type""" ) == "owlvit":
              A_						:  Union[str, Any]          =				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(_a	,**_a )







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""owlvit_vision_model"""






  def __init__(							self :       List[Any]	,_a :       Optional[Any]=768	,_a :       Tuple=3072	,_a :       Dict=12	,_a :       int=12	,_a :       Dict=3	,_a :       Tuple=768	,_a :       int=32	,_a :       int="quick_gelu"	,_a :       List[Any]=1e-5	,_a :       Tuple=0.0	,_a :       List[Any]=0.02	,_a :       str=1.0	,**_a :       int	,):

        '''simple docstring'''




        super().__init__(**_a )

        A_						:  List[str]          =				hidden_size
        A_						:  Union[str, Any]          =				intermediate_size
        A_						:  Union[str, Any]          =				num_hidden_layers
        A_						:  Optional[Any]          =				num_attention_heads
        A_						:  int          =				num_channels
        A_						:  str          =				image_size
        A_						:  List[Any]          =				patch_size
        A_						:  int          =				hidden_act
        A_						:  List[Any]          =				layer_norm_eps
        A_						:  List[str]          =				attention_dropout
        A_						:  str          =				initializer_range
        A_						:  str          =				initializer_factor






  @classmethod
  def        _a							(							cls :       List[Any]	,_a :       Union[str, os.PathLike]	,**_a :       str ):

        '''simple docstring'''




        cls._set_token_in_kwargs(_a )

        A_   ,					A_						:  Optional[int]          =				cls.get_config_dict(_a	,**_a )

        # get the vision config dict if we are loading from OwlViTConfig
        if config_dict.get("""model_type""" ) == "owlvit":
              A_						:  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 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""owlvit"""
  a_  					=							True






  def __init__(							self :       Union[str, Any]	,_a :       List[str]=None	,_a :       List[str]=None	,_a :       Dict=512	,_a :       List[Any]=2.6592	,_a :       Optional[Any]=True	,**_a :       Optional[int]	,):

        '''simple docstring'''




        super().__init__(**_a )

        if text_config is None:
              A_						:  List[Any]          =				{}
              logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values.""" )

        if vision_config is None:
              A_						:  Tuple          =				{}
              logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values.""" )

        A_						:  Dict          =				OwlViTTextConfig(**_a )
        A_						:  Dict          =				OwlViTVisionConfig(**_a )

        A_						:  Any          =				projection_dim
        A_						:  Optional[int]          =				logit_scale_init_value
        A_						:  Optional[int]          =				return_dict
        A_						:  Dict          =				1.0






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

        '''simple docstring'''




        cls._set_token_in_kwargs(_a )

        A_   ,					A_						:  List[Any]          =				cls.get_config_dict(_a	,**_a )

        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 )






  @classmethod
  def        _a							(							cls :       int	,_a :       Dict	,_a :       Dict	,**_a :       List[str] ):

        '''simple docstring'''




        A_						:  str          =				{}
        A_						:  int          =				text_config
        A_						:  Union[str, Any]          =				vision_config

        return cls.from_dict(_a	,**_a )






  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




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







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  @property
  def        _a							(							self :       int ):

        '''simple docstring'''




        return OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
            ] )






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        return OrderedDict(
            [
                ("""logits_per_image""", {0: """batch"""}),
                ("""logits_per_text""", {0: """batch"""}),
                ("""text_embeds""", {0: """batch"""}),
                ("""image_embeds""", {0: """batch"""}),
            ] )






  @property
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        return 1e-4






  def        _a							(							self :       int	,_a :       "ProcessorMixin"	,_a :       int = -1	,_a :       int = -1	,_a :       Optional["TensorType"] = None	,):

        '''simple docstring'''




        A_						:  Any          =				super().generate_dummy_inputs(
            processor.tokenizer	,batch_size=_a	,seq_length=_a	,framework=_a )
        A_						:  Any          =				super().generate_dummy_inputs(
            processor.image_processor	,batch_size=_a	,framework=_a )
        return {**text_input_dict, **image_input_dict}






  @property
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        return 14
