



"""simple docstring"""






import copy
import os
from typing import Union

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


_lowercase			:			int										=						logging.get_logger(__name__)

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

class 	__SCREAMING_SNAKE_CASE     (				UpperCamelCase__       ):


					'''simple docstring'''





					_a             =							'''pix2struct_text_model'''
					_a             =							['''past_key_values''']
					_a             =							{
					    '''hidden_size''': '''hidden_size''',
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}



					def __init__(	self							:  List[str],  lowerCamelCase							:  int=5_0244,  lowerCamelCase							:  Tuple=768,  lowerCamelCase							:  Optional[Any]=64,  lowerCamelCase							:  str=2048,  lowerCamelCase							:  Dict=12,  lowerCamelCase							:  Union[str, Any]=12,  lowerCamelCase							:  int=32,  lowerCamelCase							:  Tuple=128,  lowerCamelCase							:  Dict=0.1,  lowerCamelCase							:  List[Any]=1E-6,  lowerCamelCase							:  List[Any]=1.0,  lowerCamelCase							:  List[Any]="gelu_new",  lowerCamelCase							:  str=0,  lowerCamelCase							:  Tuple=False,  lowerCamelCase							:  Dict=0,  lowerCamelCase							:  Tuple=1,  lowerCamelCase							:  str=False,  lowerCamelCase							:  int=True,  **lowerCamelCase							:  str,  )-> Optional[Any]:
								lowerCamelCase__		:		Union[str, Any]		     =vocab_size
								lowerCamelCase__		:		Optional[int]		     =hidden_size
								lowerCamelCase__		:		Tuple		     =d_kv
								lowerCamelCase__		:		Optional[Any]		     =d_ff
								lowerCamelCase__		:		Optional[int]		     =num_layers
								lowerCamelCase__		:		Union[str, Any]		     =num_heads
								lowerCamelCase__		:		Tuple		     =relative_attention_num_buckets
								lowerCamelCase__		:		Tuple		     =relative_attention_max_distance
								lowerCamelCase__		:		Optional[Any]		     =dropout_rate
								lowerCamelCase__		:		Optional[int]		     =layer_norm_epsilon
								lowerCamelCase__		:		List[str]		     =initializer_factor
								lowerCamelCase__		:		Optional[int]		     =use_cache

								lowerCamelCase__		:		Optional[int]		     =eos_token_id
								lowerCamelCase__		:		List[str]		     =decoder_start_token_id

								# for backwards compatibility
								lowerCamelCase__		:		List[Any]		     =dense_act_fn

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




					@classmethod
					def 		snake_case	(	cls							:  Dict,  lowerCamelCase							:  Union[str, os.PathLike],  **lowerCamelCase							:  Union[str, Any]					)-> "PretrainedConfig":
								cls._set_token_in_kwargs(UpperCamelCase_					)

								lowerCamelCase__     ,       lowerCamelCase__		:		Tuple		     =cls.get_config_dict(UpperCamelCase_,  **UpperCamelCase_					)

								# get the text config dict if we are loading from Pix2StructConfig
								if config_dict.get('''model_type'''					) == "pix2struct":
											lowerCamelCase__		:		List[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(UpperCamelCase_,  **UpperCamelCase_					)





class 	__SCREAMING_SNAKE_CASE     (				UpperCamelCase__       ):


					'''simple docstring'''





					_a             =							'''pix2struct_vision_model'''



					def __init__(	self							:  Dict,  lowerCamelCase							:  int=768,  lowerCamelCase							:  Any=768,  lowerCamelCase							:  Dict=2048,  lowerCamelCase							:  Dict=64,  lowerCamelCase							:  List[str]=12,  lowerCamelCase							:  Tuple=12,  lowerCamelCase							:  Tuple="gelu_new",  lowerCamelCase							:  int=1E-6,  lowerCamelCase							:  Dict=0.0,  lowerCamelCase							:  Union[str, Any]=0.0,  lowerCamelCase							:  int=1E-10,  lowerCamelCase							:  Optional[Any]=1.0,  lowerCamelCase							:  Union[str, Any]=4096,  lowerCamelCase							:  Optional[int]=32,  lowerCamelCase							:  Optional[int]=128,  **lowerCamelCase							:  Union[str, Any],  )-> List[Any]:
								super().__init__(**UpperCamelCase_					)

								lowerCamelCase__		:		Any		     =hidden_size
								lowerCamelCase__		:		Tuple		     =patch_embed_hidden_size
								lowerCamelCase__		:		Union[str, Any]		     =d_ff
								lowerCamelCase__		:		List[str]		     =dropout_rate
								lowerCamelCase__		:		int		     =num_hidden_layers
								lowerCamelCase__		:		Optional[int]		     =num_attention_heads
								lowerCamelCase__		:		List[Any]		     =initializer_range
								lowerCamelCase__		:		str		     =initializer_factor
								lowerCamelCase__		:		Any		     =attention_dropout
								lowerCamelCase__		:		Optional[Any]		     =layer_norm_eps
								lowerCamelCase__		:		str		     =dense_act_fn
								lowerCamelCase__		:		Tuple		     =seq_len
								lowerCamelCase__		:		List[str]		     =relative_attention_num_buckets
								lowerCamelCase__		:		Optional[int]		     =relative_attention_max_distance
								lowerCamelCase__		:		Tuple		     =d_kv




					@classmethod
					def 		snake_case	(	cls							:  str,  lowerCamelCase							:  Union[str, os.PathLike],  **lowerCamelCase							:  Any					)-> "PretrainedConfig":
								cls._set_token_in_kwargs(UpperCamelCase_					)

								lowerCamelCase__     ,       lowerCamelCase__		:		int		     =cls.get_config_dict(UpperCamelCase_,  **UpperCamelCase_					)

								# get the vision config dict if we are loading from Pix2StructConfig
								if config_dict.get('''model_type'''					) == "pix2struct":
											lowerCamelCase__		:		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(UpperCamelCase_,  **UpperCamelCase_					)





class 	__SCREAMING_SNAKE_CASE     (				UpperCamelCase__       ):


					'''simple docstring'''





					_a             =							'''pix2struct'''
					_a             =							True



					def __init__(	self							:  List[Any],  lowerCamelCase							:  Union[str, Any]=None,  lowerCamelCase							:  Dict=None,  lowerCamelCase							:  int=1.0,  lowerCamelCase							:  Any=0.02,  lowerCamelCase							:  List[Any]=False,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  Tuple=True,  **lowerCamelCase							:  Optional[int],  )-> Optional[int]:
								super().__init__(tie_word_embeddings=UpperCamelCase_,  is_encoder_decoder=UpperCamelCase_,  **UpperCamelCase_					)

								if text_config is None:
											lowerCamelCase__		:		List[Any]		     ={}
											logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.'''					)

								if vision_config is None:
											lowerCamelCase__		:		Union[str, Any]		     ={}
											logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.'''					)

								lowerCamelCase__		:		List[Any]		     =PixaStructTextConfig(**UpperCamelCase_					)
								lowerCamelCase__		:		int		     =PixaStructVisionConfig(**UpperCamelCase_					)

								lowerCamelCase__		:		List[str]		     =self.text_config.decoder_start_token_id
								lowerCamelCase__		:		List[Any]		     =self.text_config.pad_token_id
								lowerCamelCase__		:		Dict		     =self.text_config.eos_token_id

								lowerCamelCase__		:		Optional[Any]		     =initializer_factor
								lowerCamelCase__		:		Any		     =initializer_range

								lowerCamelCase__		:		List[Any]		     =self.initializer_range
								lowerCamelCase__		:		Tuple		     =self.initializer_range

								lowerCamelCase__		:		Dict		     =is_vqa



					@classmethod
					def 		snake_case	(	cls							:  Optional[Any],  lowerCamelCase							:  PixaStructTextConfig,  lowerCamelCase							:  PixaStructVisionConfig,  **lowerCamelCase							:  int					)-> Optional[Any]:
								return cls(text_config=text_config.to_dict(),  vision_config=vision_config.to_dict(),  **UpperCamelCase_					)




					def 		snake_case	(	self							:  Any					)-> Dict:
								lowerCamelCase__		:		Dict		     =copy.deepcopy(self.__dict__					)
								lowerCamelCase__		:		Dict		     =self.text_config.to_dict()
								lowerCamelCase__		:		Any		     =self.vision_config.to_dict()
								lowerCamelCase__		:		Tuple		     =self.__class__.model_type
								return output









"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_lowercase			:			Tuple										=						False

class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					pass





@slow
@require_torch_gpu
class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  List[Any]					)-> Dict:
								lowerCamelCase__		:		str		     =VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					)
								pipe.to(lowerCamelCase					)
								pipe.set_progress_bar_config(disable=lowerCamelCase					)

								lowerCamelCase__		:		int		     =load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''					)
								lowerCamelCase__		:		Dict		     =torch.manual_seed(0					)
								lowerCamelCase__		:		str		     =pipe(
								    image=lowerCamelCase,  generator=lowerCamelCase,  guidance_scale=7.5,  num_inference_steps=50,  output_type='''numpy''',  ).images

								lowerCamelCase__		:		Dict		     =image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								lowerCamelCase__		:		List[Any]		     =np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]					)

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




