



import copy
import os
from typing import Union

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


UpperCAmelCase__     = logging.get_logger(__name__)

UpperCAmelCase__     = {
    "google/pix2struct-textcaps-base": (
        "https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"
    ),
}







class     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  '''pix2struct_text_model'''
			UpperCamelCase           =  ['''past_key_values''']
			UpperCamelCase           =  {
			    '''hidden_size''': '''hidden_size''',
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}



			def __init__(		self     :						Dict				,	A     :						List[Any]=5_02_44				,	A     :						str=7_68				,	A     :						List[Any]=64				,	A     :						Union[str, Any]=20_48				,	A     :						List[Any]=12				,	A     :						Tuple=12				,	A     :						Tuple=32				,	A     :						List[Any]=1_28				,	A     :						List[Any]=0.1				,	A     :						Optional[Any]=1E-6				,	A     :						Tuple=1.0				,	A     :						List[Any]="gelu_new"				,	A     :						Optional[int]=0				,	A     :						Any=False				,	A     :						List[str]=0				,	A     :						List[Any]=1				,	A     :						Union[str, Any]=False				,	A     :						Optional[int]=True				,	**A     :						Any				,	)     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     vocab_size
						_UpperCAmelCase	 =     hidden_size
						_UpperCAmelCase	 =     d_kv
						_UpperCAmelCase	 =     d_ff
						_UpperCAmelCase	 =     num_layers
						_UpperCAmelCase	 =     num_heads
						_UpperCAmelCase	 =     relative_attention_num_buckets
						_UpperCAmelCase	 =     relative_attention_max_distance
						_UpperCAmelCase	 =     dropout_rate
						_UpperCAmelCase	 =     layer_norm_epsilon
						_UpperCAmelCase	 =     initializer_factor
						_UpperCAmelCase	 =     use_cache

						_UpperCAmelCase	 =     eos_token_id
						_UpperCAmelCase	 =     decoder_start_token_id

						# for backwards compatibility
						_UpperCAmelCase	 =     dense_act_fn

						super().__init__(
						    pad_token_id=A				,	eos_token_id=A				,	decoder_start_token_id=A				,	tie_word_embeddings=A				,	is_decoder=A				,	**A				,	)



			@classmethod
			def 					_lowerCamelCase (		cls     :						List[str]				,	A     :						Union[str, os.PathLike]				,	**A     :						List[str])     ->					"PretrainedConfig":



						"""simple docstring"""






						cls._set_token_in_kwargs(A)

						_UpperCAmelCase ,  _UpperCAmelCase	 =     cls.get_config_dict(A				,	**A)

						# get the text config dict if we are loading from Pix2StructConfig
						if config_dict.get('model_type') == "pix2struct":
									_UpperCAmelCase	 =     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					(    A		):
			UpperCamelCase           =  '''pix2struct_vision_model'''



			def __init__(		self     :						Tuple				,	A     :						str=7_68				,	A     :						Dict=7_68				,	A     :						Optional[Any]=20_48				,	A     :						Any=64				,	A     :						Dict=12				,	A     :						Tuple=12				,	A     :						Tuple="gelu_new"				,	A     :						Optional[int]=1E-6				,	A     :						List[str]=0.0				,	A     :						Optional[int]=0.0				,	A     :						Optional[int]=1E-10				,	A     :						Any=1.0				,	A     :						List[str]=40_96				,	A     :						List[str]=32				,	A     :						List[str]=1_28				,	**A     :						Union[str, Any]				,	)     ->					List[str]:



						"""simple docstring"""






						super().__init__(**A)

						_UpperCAmelCase	 =     hidden_size
						_UpperCAmelCase	 =     patch_embed_hidden_size
						_UpperCAmelCase	 =     d_ff
						_UpperCAmelCase	 =     dropout_rate
						_UpperCAmelCase	 =     num_hidden_layers
						_UpperCAmelCase	 =     num_attention_heads
						_UpperCAmelCase	 =     initializer_range
						_UpperCAmelCase	 =     initializer_factor
						_UpperCAmelCase	 =     attention_dropout
						_UpperCAmelCase	 =     layer_norm_eps
						_UpperCAmelCase	 =     dense_act_fn
						_UpperCAmelCase	 =     seq_len
						_UpperCAmelCase	 =     relative_attention_num_buckets
						_UpperCAmelCase	 =     relative_attention_max_distance
						_UpperCAmelCase	 =     d_kv



			@classmethod
			def 					_lowerCamelCase (		cls     :						str				,	A     :						Union[str, os.PathLike]				,	**A     :						Union[str, Any])     ->					"PretrainedConfig":



						"""simple docstring"""






						cls._set_token_in_kwargs(A)

						_UpperCAmelCase ,  _UpperCAmelCase	 =     cls.get_config_dict(A				,	**A)

						# get the vision config dict if we are loading from Pix2StructConfig
						if config_dict.get('model_type') == "pix2struct":
									_UpperCAmelCase	 =     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					(    A		):
			UpperCamelCase           =  '''pix2struct'''
			UpperCamelCase           =  True



			def __init__(		self     :						Union[str, Any]				,	A     :						Union[str, Any]=None				,	A     :						Tuple=None				,	A     :						Union[str, Any]=1.0				,	A     :						Tuple=0.0_2				,	A     :						Any=False				,	A     :						int=False				,	A     :						Optional[int]=True				,	**A     :						Tuple				,	)     ->					int:



						"""simple docstring"""






						super().__init__(tie_word_embeddings=A				,	is_encoder_decoder=A				,	**A)

						if text_config is None:
									_UpperCAmelCase	 =     {}
									logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.')

						if vision_config is None:
									_UpperCAmelCase	 =     {}
									logger.info('vision_config is None. Initializing the Pix2StructVisionConfig with default values.')

						_UpperCAmelCase	 =     PixaStructTextConfig(**A)
						_UpperCAmelCase	 =     PixaStructVisionConfig(**A)

						_UpperCAmelCase	 =     self.text_config.decoder_start_token_id
						_UpperCAmelCase	 =     self.text_config.pad_token_id
						_UpperCAmelCase	 =     self.text_config.eos_token_id

						_UpperCAmelCase	 =     initializer_factor
						_UpperCAmelCase	 =     initializer_range

						_UpperCAmelCase	 =     self.initializer_range
						_UpperCAmelCase	 =     self.initializer_range

						_UpperCAmelCase	 =     is_vqa



			@classmethod
			def 					_lowerCamelCase (		cls     :						List[Any]				,	A     :						PixaStructTextConfig				,	A     :						PixaStructVisionConfig				,	**A     :						str)     ->					str:



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     copy.deepcopy(self.__dict__)
						_UpperCAmelCase	 =     self.text_config.to_dict()
						_UpperCAmelCase	 =     self.vision_config.to_dict()
						_UpperCAmelCase	 =     self.__class__.model_type
						return output





import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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     __lowerCAmelCase					(    A       ,		A       ,		unittest.TestCase		):
			UpperCamelCase           =  IFInpaintingPipeline
			UpperCamelCase           =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
			UpperCamelCase           =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			UpperCamelCase           =  PipelineTesterMixin.required_optional_params - {'''latents'''}



			def 					_lowerCamelCase (		self     :						List[str])     ->					Tuple:



						"""simple docstring"""






						return self._get_dummy_components()



			def 					_lowerCamelCase (		self     :						Any				,	A     :						int				,	A     :						Dict=0)     ->					Tuple:



						"""simple docstring"""






						if str(A).startswith('mps'):
									_UpperCAmelCase	 =     torch.manual_seed(A)
						else:
									_UpperCAmelCase	 =     torch.Generator(device=A).manual_seed(A)

						_UpperCAmelCase	 =     floats_tensor((1, 3, 32, 32)				,	rng=random.Random(A)).to(A)
						_UpperCAmelCase	 =     floats_tensor((1, 3, 32, 32)				,	rng=random.Random(A)).to(A)

						_UpperCAmelCase	 =     {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'image': 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 					_lowerCamelCase (		self     :						List[str])     ->					Union[str, Any]:



						"""simple docstring"""






						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)



			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					Optional[int]:



						"""simple docstring"""






						self._test_save_load_optional_components()



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



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Tuple:



						"""simple docstring"""






						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)



			def 					_lowerCamelCase (		self     :						str)     ->					List[str]:



						"""simple docstring"""






						self._test_save_load_local()



			def 					_lowerCamelCase (		self     :						int)     ->					Tuple:



						"""simple docstring"""






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