
"""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       =  logging.get_logger(__name__)

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




class 			_SCREAMING_SNAKE_CASE					(			A__							):
			UpperCAmelCase_  :List[str]				 =						"instructblip_vision_model"






			def __init__(					self       ,   __A=1408       ,   __A=6144       ,   __A=39       ,   __A=16       ,   __A=224       ,   __A=14       ,   __A="gelu"       ,   __A=1E-6       ,   __A=0.0       ,   __A=1E-10       ,   __A=True       ,   **__A       ,   )			->     Dict:
						super().__init__(**__A       )

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






			@classmethod
			def  __lowerCAmelCase (					cls       ,   __A       ,   **__A       )			->     "PretrainedConfig":
						cls._set_token_in_kwargs(__A       )

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

						# get the vision config dict if we are loading from InstructBlipConfig
						if config_dict.get("""model_type"""       ) == "instructblip":
									lowerCAmelCase_    :Optional[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(__A       ,   **__A       )




class 			_SCREAMING_SNAKE_CASE					(			A__							):
			UpperCAmelCase_  :Optional[int]				 =						"instructblip_qformer"






			def __init__(					self       ,   __A=3_0522       ,   __A=768       ,   __A=12       ,   __A=12       ,   __A=3072       ,   __A="gelu"       ,   __A=0.1       ,   __A=0.1       ,   __A=512       ,   __A=0.0_2       ,   __A=1E-12       ,   __A=0       ,   __A="absolute"       ,   __A=2       ,   __A=1408       ,   **__A       ,   )			->     List[str]:
						super().__init__(pad_token_id=__A       ,   **__A       )

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






			@classmethod
			def  __lowerCAmelCase (					cls       ,   __A       ,   **__A       )			->     "PretrainedConfig":
						cls._set_token_in_kwargs(__A       )

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

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




class 			_SCREAMING_SNAKE_CASE					(			A__							):
			UpperCAmelCase_  :List[str]				 =						"instructblip"
			UpperCAmelCase_  :Optional[int]				 =						True






			def __init__(					self       ,   __A=None       ,   __A=None       ,   __A=None       ,   __A=32       ,   **__A       )			->     str:
						super().__init__(**__A       )

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

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

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

						lowerCAmelCase_    :str			     =  InstructBlipVisionConfig(**__A       )
						lowerCAmelCase_    :Any			     =  InstructBlipQFormerConfig(**__A       )
						lowerCAmelCase_    :Optional[Any]			     =  text_config["""model_type"""] if """model_type""" in text_config else """opt"""
						lowerCAmelCase_    :Tuple			     =  CONFIG_MAPPING[text_model_type](**__A       )

						lowerCAmelCase_    :Tuple			     =  self.text_config.tie_word_embeddings
						lowerCAmelCase_    :Tuple			     =  self.text_config.is_encoder_decoder

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






			@classmethod
			def  __lowerCAmelCase (					cls       ,   __A       ,   __A       ,   __A       ,   **__A       ,   )			->     Tuple:
						return cls(
						    vision_config=vision_config.to_dict()       ,   qformer_config=qformer_config.to_dict()       ,   text_config=text_config.to_dict()       ,   **__A       ,   )






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









import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 						__magic_name__			(							lowerCamelCase__		,       lowerCamelCase__		,       unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__ :	List[Any]    =   IFInpaintingSuperResolutionPipeline
   lowerCamelCase__ :	str    =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
   lowerCamelCase__ :	List[Any]    =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}							)
   lowerCamelCase__ :	str    =   PipelineTesterMixin.required_optional_params - {'latents'}



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     return self._get_superresolution_dummy_components()



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_=0       )							->    Tuple:






     """simple docstring"""






     if str(lowercase_       ).startswith('''mps'''       ):
       a__  =torch.manual_seed(lowercase_       )
     else:
       a__  =torch.Generator(device=lowercase_       ).manual_seed(lowercase_       )

     a__  =floats_tensor((1, 3, 16, 16),    rng=random.Random(lowercase_       )       ).to(lowercase_       )
     a__  =floats_tensor((1, 3, 32, 32),    rng=random.Random(lowercase_       )       ).to(lowercase_       )
     a__  =floats_tensor((1, 3, 32, 32),    rng=random.Random(lowercase_       )       ).to(lowercase_       )

     a__  ={
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': image,
         '''original_image''': original_image,
         '''mask_image''': mask_image,
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''output_type''': '''numpy''',
     }

     return inputs



   @unittest.skipIf(
       torch_device != '''cuda''' or not is_xformers_available(),    reason='''XFormers attention is only available with CUDA and `xformers` installed''',    )
   def  _UpperCAmelCase (				self       )							->    List[str]:






     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     self._test_save_load_optional_components()



   @unittest.skipIf(torch_device != '''cuda''',    reason='''float16 requires CUDA'''       )
   def  _UpperCAmelCase (				self       )							->    List[Any]:






     """simple docstring"""






     super().test_save_load_floataa(expected_max_diff=1E-1       )



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     self._test_save_load_local()



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     self._test_inference_batch_single_identical(
         expected_max_diff=1E-2,    )
