


'''simple docstring'''



import copy
import os
from typing import Union

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


__lowercase	:						Union[str, Any]       =	logging.get_logger(__name__)

__lowercase	:						List[str]       =	{
    'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
    'BridgeTower/bridgetower-base-itm-mlm': (
        'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'
    ),
}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    "bridgetower_vision_model"




	def __init__(   self						,				__a=768						,				__a=12						,				__a=3						,				__a=16						,				__a=288						,				__a=1						,				__a=1E-0_5						,				__a=False						,				__a=True						,				__a=False						,				**__a						,				):

						'''simple docstring'''





						super().__init__(**__a							)
						__a			:       Dict					    =  hidden_size
						__a			:       Any					    =  num_hidden_layers
						__a			:       Optional[int]					    =  num_channels
						__a			:       int					    =  patch_size
						__a			:       Any					    =  image_size
						__a			:       Union[str, Any]					    =  initializer_factor
						__a			:       Optional[int]					    =  layer_norm_eps
						__a			:       Tuple					    =  stop_gradient
						__a			:       int					    =  share_layernorm
						__a			:       str					    =  remove_last_layer





	@classmethod
	def       __UpperCAmelCase							(   cls						,				__a						,				**__a							):

						'''simple docstring'''





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

						if config_dict.get('model_type'							) == "bridgetower":
											__a			:       str					    =  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 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    "bridgetower_text_model"




	def __init__(   self						,				__a=5_0265						,				__a=768						,				__a=12						,				__a=12						,				__a=1						,				__a=3072						,				__a="gelu"						,				__a=0.1						,				__a=0.1						,				__a=514						,				__a=1						,				__a=1E-0_5						,				__a=1						,				__a=0						,				__a=2						,				__a="absolute"						,				__a=True						,				**__a						,				):

						'''simple docstring'''





						super().__init__(**__a							)

						__a			:       Union[str, Any]					    =  vocab_size
						__a			:       Any					    =  hidden_size
						__a			:       Optional[int]					    =  num_hidden_layers
						__a			:       List[Any]					    =  num_attention_heads
						__a			:       str					    =  hidden_act
						__a			:       Optional[int]					    =  initializer_factor
						__a			:       Optional[Any]					    =  intermediate_size
						__a			:       Any					    =  hidden_dropout_prob
						__a			:       str					    =  attention_probs_dropout_prob
						__a			:       Dict					    =  max_position_embeddings
						__a			:       int					    =  type_vocab_size
						__a			:       Any					    =  layer_norm_eps
						__a			:       Union[str, Any]					    =  position_embedding_type
						__a			:       Optional[int]					    =  use_cache
						__a			:       Dict					    =  pad_token_id
						__a			:       Tuple					    =  bos_token_id
						__a			:       int					    =  eos_token_id





	@classmethod
	def       __UpperCAmelCase							(   cls						,				__a						,				**__a							):

						'''simple docstring'''





						__a    ,				__a			:       Union[str, Any]					    =  cls.get_config_dict(__a						,				**__a							)

						if config_dict.get('model_type'							) == "bridgetower":
											__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 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    "bridgetower"




	def __init__(   self						,				__a=True						,				__a="gelu"						,				__a=768						,				__a=1						,				__a=1E-0_5						,				__a=False						,				__a="add"						,				__a=12						,				__a=6						,				__a=False						,				__a=False						,				__a=None						,				__a=None						,				**__a						,				):

						'''simple docstring'''





						__a			:       Dict					    =  kwargs.pop('text_config_dict'						,				__a							)
						__a			:       Tuple					    =  kwargs.pop('vision_config_dict'						,				__a							)

						super().__init__(**__a							)
						__a			:       int					    =  share_cross_modal_transformer_layers
						__a			:       Any					    =  hidden_act
						__a			:       str					    =  hidden_size
						__a			:       int					    =  initializer_factor
						__a			:       List[str]					    =  layer_norm_eps
						__a			:       Optional[int]					    =  share_link_tower_layers
						__a			:       List[Any]					    =  link_tower_type
						__a			:       Union[str, Any]					    =  num_attention_heads
						__a			:       Optional[Any]					    =  num_hidden_layers
						__a			:       int					    =  tie_word_embeddings
						__a			:       int					    =  init_layernorm_from_vision_encoder

						if text_config is None:
											__a			:       Union[str, Any]					    =  {}
											logger.info('`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'							)

						if vision_config is None:
											__a			:       Optional[Any]					    =  {}
											logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'							)

						__a			:       Optional[Any]					    =  BridgeTowerTextConfig(**__a							)
						__a			:       Optional[int]					    =  BridgeTowerVisionConfig(**__a							)




	@classmethod
	def       __UpperCAmelCase							(   cls						,				__a						,				__a						,				**__a							):

						'''simple docstring'''





						return cls(text_config=text_config.to_dict()						,				vision_config=vision_config.to_dict()						,				**__a							)





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  copy.deepcopy(self.__dict__							)
						__a			:       Any					    =  self.text_config.to_dict()
						__a			:       Union[str, Any]					    =  self.vision_config.to_dict()
						__a			:       Union[str, Any]					    =  self.__class__.model_type
						return output







'''simple docstring'''



import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
	import onnxruntime as ort





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




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  ort.SessionOptions()
						__a			:       Dict					    =  False
						return options





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo.png'							)
						__a			:       int					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'							)
						__a			:       Dict					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'							)

						# using the PNDM scheduler by default
						__a			:       str					    =  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
						    'CompVis/stable-diffusion-v1-4'						,				revision='onnx'						,				safety_checker=__a						,				feature_extractor=__a						,				provider=self.gpu_provider						,				sess_options=self.gpu_options						,				)
						pipe.set_progress_bar_config(disable=__a							)

						__a			:       Tuple					    =  'A red cat sitting on a park bench'

						__a			:       int					    =  np.random.RandomState(0							)
						__a			:       Tuple					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				strength=0.75						,				guidance_scale=7.5						,				num_inference_steps=15						,				generator=__a						,				output_type='np'						,				)

						__a			:       Tuple					    =  output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 1E-2



