
"""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__      :	str          =    logging.get_logger(__name__)

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







class   _lowercase						(       _UpperCAmelCase				):


     '''simple docstring'''





     _A								=							'instructblip_vision_model'



     def __init__(      self ,			__UpperCamelCase=14_08 ,			__UpperCamelCase=61_44 ,			__UpperCamelCase=39 ,			__UpperCamelCase=16 ,			__UpperCamelCase=2_24 ,			__UpperCamelCase=14 ,			__UpperCamelCase="gelu" ,			__UpperCamelCase=1E-6 ,			__UpperCamelCase=0.0 ,			__UpperCamelCase=1E-10 ,			__UpperCamelCase=True ,			**__UpperCamelCase ,			)->					int:
          super().__init__(**__UpperCamelCase  )

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



     @classmethod
     def  lowerCAmelCase__       (      cls ,			__UpperCamelCase ,			**__UpperCamelCase  )->					"PretrainedConfig":
          cls._set_token_in_kwargs(__UpperCamelCase  )

          UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						cls.get_config_dict(__UpperCamelCase ,			**__UpperCamelCase  )

          # get the vision config dict if we are loading from InstructBlipConfig
          if config_dict.get("model_type"  ) == "instructblip":
               UpperCAmelCase__							: Tuple 							=						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   _lowercase						(       _UpperCAmelCase				):


     '''simple docstring'''





     _A								=							'instructblip_qformer'



     def __init__(      self ,			__UpperCamelCase=3_05_22 ,			__UpperCamelCase=7_68 ,			__UpperCamelCase=12 ,			__UpperCamelCase=12 ,			__UpperCamelCase=30_72 ,			__UpperCamelCase="gelu" ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=5_12 ,			__UpperCamelCase=0.02 ,			__UpperCamelCase=1E-12 ,			__UpperCamelCase=0 ,			__UpperCamelCase="absolute" ,			__UpperCamelCase=2 ,			__UpperCamelCase=14_08 ,			**__UpperCamelCase ,			)->					Union[str, Any]:
          super().__init__(pad_token_id=__UpperCamelCase ,			**__UpperCamelCase  )

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



     @classmethod
     def  lowerCAmelCase__       (      cls ,			__UpperCamelCase ,			**__UpperCamelCase  )->					"PretrainedConfig":
          cls._set_token_in_kwargs(__UpperCamelCase  )

          UpperCAmelCase__							,	UpperCAmelCase__							: 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":
               UpperCAmelCase__							: str 							=						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   _lowercase						(       _UpperCAmelCase				):


     '''simple docstring'''





     _A								=							'instructblip'
     _A								=							True



     def __init__(      self ,			__UpperCamelCase=None ,			__UpperCamelCase=None ,			__UpperCamelCase=None ,			__UpperCamelCase=32 ,			**__UpperCamelCase  )->					Union[str, Any]:
          super().__init__(**__UpperCamelCase  )

          if vision_config is None:
               UpperCAmelCase__							: List[Any] 							=						{}
               logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values."  )

          if qformer_config is None:
               UpperCAmelCase__							: Tuple 							=						{}
               logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."  )

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

          UpperCAmelCase__							: Tuple 							=						InstructBlipVisionConfig(**__UpperCamelCase  )
          UpperCAmelCase__							: str 							=						InstructBlipQFormerConfig(**__UpperCamelCase  )
          UpperCAmelCase__							: Any 							=						text_config["model_type"] if "model_type" in text_config else "opt"
          UpperCAmelCase__							: Dict 							=						CONFIG_MAPPING[text_model_type](**__UpperCamelCase  )

          UpperCAmelCase__							: Any 							=						self.text_config.tie_word_embeddings
          UpperCAmelCase__							: Union[str, Any] 							=						self.text_config.is_encoder_decoder

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



     @classmethod
     def  lowerCAmelCase__       (      cls ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			**__UpperCamelCase ,			)->					Dict:
          return cls(
              vision_config=vision_config.to_dict() ,			qformer_config=qformer_config.to_dict() ,			text_config=text_config.to_dict() ,			**__UpperCamelCase ,			)



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: str 							=						copy.deepcopy(self.__dict__  )
          UpperCAmelCase__							: Optional[Any] 							=						self.vision_config.to_dict()
          UpperCAmelCase__							: str 							=						self.qformer_config.to_dict()
          UpperCAmelCase__							: str 							=						self.text_config.to_dict()
          UpperCAmelCase__							: Tuple 							=						self.__class__.model_type
          return output





"""simple docstring"""




import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort







class   _lowercase						(       lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





     pass







@nightly
@require_onnxruntime
@require_torch_gpu
class   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return (
              "CUDAExecutionProvider",
              {
                  "gpu_mem_limit": "15000000000",  # 15GB
                  "arena_extend_strategy": "kSameAsRequested",
              },
          )



     @property
     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: Tuple 							=						ort.SessionOptions()
          UpperCAmelCase__							: List[str] 							=						False
          return options



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: Union[str, Any] 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo.png"  )
          UpperCAmelCase__							: int 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"  )
          UpperCAmelCase__							: str 							=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			revision="onnx" ,			safety_checker=__UpperCamelCase ,			feature_extractor=__UpperCamelCase ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
          pipe.set_progress_bar_config(disable=__UpperCamelCase  )

          UpperCAmelCase__							: int 							=						"A red cat sitting on a park bench"

          UpperCAmelCase__							: Tuple 							=						np.random.RandomState(0  )
          UpperCAmelCase__							: Any 							=						pipe(
              prompt=__UpperCamelCase ,			image=__UpperCamelCase ,			mask_image=__UpperCamelCase ,			guidance_scale=7.5 ,			num_inference_steps=10 ,			generator=__UpperCamelCase ,			output_type="np" ,			)
          UpperCAmelCase__							: Tuple 							=						output.images
          UpperCAmelCase__							: Dict 							=						images[0, 2_55:2_58, 2_55:2_58, -1]

          assert images.shape == (1, 5_12, 5_12, 3)
          UpperCAmelCase__							: Union[str, Any] 							=						np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]  )

          assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3



     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: Tuple 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo.png"  )
          UpperCAmelCase__							: Tuple 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"  )
          UpperCAmelCase__							: Optional[Any] 							=						LMSDiscreteScheduler.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			subfolder="scheduler" ,			revision="onnx"  )
          UpperCAmelCase__							: Optional[Any] 							=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			revision="onnx" ,			scheduler=__UpperCamelCase ,			safety_checker=__UpperCamelCase ,			feature_extractor=__UpperCamelCase ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
          pipe.set_progress_bar_config(disable=__UpperCamelCase  )

          UpperCAmelCase__							: int 							=						"A red cat sitting on a park bench"

          UpperCAmelCase__							: List[str] 							=						np.random.RandomState(0  )
          UpperCAmelCase__							: str 							=						pipe(
              prompt=__UpperCamelCase ,			image=__UpperCamelCase ,			mask_image=__UpperCamelCase ,			guidance_scale=7.5 ,			num_inference_steps=20 ,			generator=__UpperCamelCase ,			output_type="np" ,			)
          UpperCAmelCase__							: List[str] 							=						output.images
          UpperCAmelCase__							: List[Any] 							=						images[0, 2_55:2_58, 2_55:2_58, -1]

          assert images.shape == (1, 5_12, 5_12, 3)
          UpperCAmelCase__							: int 							=						np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]  )

          assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3



