






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 				__UpperCAmelCase		(__snake_case   ):
   __snake_case    :				Tuple									=							"""pix2struct_text_model"""
   __snake_case    :				Tuple									=							["""past_key_values"""]
   __snake_case    :				str									=							{
       """hidden_size""": """hidden_size""",
       """num_attention_heads""": """num_heads""",
       """num_hidden_layers""": """num_layers""",
   }







   def __init__(     self:  Union[str, Any]				,     UpperCAmelCase_:  Dict=50_244				,     UpperCAmelCase_:  Union[str, Any]=768				,     UpperCAmelCase_:  str=64				,     UpperCAmelCase_:  Tuple=2_048				,     UpperCAmelCase_:  Dict=12				,     UpperCAmelCase_:  Dict=12				,     UpperCAmelCase_:  Optional[Any]=32				,     UpperCAmelCase_:  Union[str, Any]=128				,     UpperCAmelCase_:  int=0.1				,     UpperCAmelCase_:  int=1E-6				,     UpperCAmelCase_:  int=1.0				,     UpperCAmelCase_:  int="gelu_new"				,     UpperCAmelCase_:  Tuple=0				,     UpperCAmelCase_:  Tuple=False				,     UpperCAmelCase_:  List[Any]=0				,     UpperCAmelCase_:  Union[str, Any]=1				,     UpperCAmelCase_:  Any=False				,     UpperCAmelCase_:  Dict=True				,     **UpperCAmelCase_:  List[str]				,     ):







         '''simple docstring'''




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

         _SCREAMING_SNAKE_CASE     				=  eos_token_id
         _SCREAMING_SNAKE_CASE     				=  decoder_start_token_id

         # for backwards compatibility
         _SCREAMING_SNAKE_CASE     				=  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 							UpperCamelCase				(     cls:  Any				,     UpperCAmelCase_:  Union[str, os.PathLike]				,     **UpperCAmelCase_:  str					):







         '''simple docstring'''




         cls._set_token_in_kwargs(UpperCamelCase__					)

         _SCREAMING_SNAKE_CASE     				=  cls.get_config_dict(UpperCamelCase__				,     **UpperCamelCase__					)

         # get the text config dict if we are loading from Pix2StructConfig
         if config_dict.get("""model_type"""					) == "pix2struct":
               _SCREAMING_SNAKE_CASE     				=  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 				__UpperCAmelCase		(__snake_case   ):
   __snake_case    :				int									=							"""pix2struct_vision_model"""







   def __init__(     self:  int				,     UpperCAmelCase_:  int=768				,     UpperCAmelCase_:  str=768				,     UpperCAmelCase_:  Optional[Any]=2_048				,     UpperCAmelCase_:  List[str]=64				,     UpperCAmelCase_:  Any=12				,     UpperCAmelCase_:  Tuple=12				,     UpperCAmelCase_:  Optional[int]="gelu_new"				,     UpperCAmelCase_:  str=1E-6				,     UpperCAmelCase_:  List[str]=0.0				,     UpperCAmelCase_:  List[str]=0.0				,     UpperCAmelCase_:  Tuple=1E-10				,     UpperCAmelCase_:  str=1.0				,     UpperCAmelCase_:  Optional[int]=4_096				,     UpperCAmelCase_:  Dict=32				,     UpperCAmelCase_:  Tuple=128				,     **UpperCAmelCase_:  str				,     ):







         '''simple docstring'''




         super().__init__(**UpperCamelCase__					)

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







   @classmethod
   def 							UpperCamelCase				(     cls:  List[Any]				,     UpperCAmelCase_:  Union[str, os.PathLike]				,     **UpperCAmelCase_:  Union[str, Any]					):







         '''simple docstring'''




         cls._set_token_in_kwargs(UpperCamelCase__					)

         _SCREAMING_SNAKE_CASE     				=  cls.get_config_dict(UpperCamelCase__				,     **UpperCamelCase__					)

         # get the vision config dict if we are loading from Pix2StructConfig
         if config_dict.get("""model_type"""					) == "pix2struct":
               _SCREAMING_SNAKE_CASE     				=  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 				__UpperCAmelCase		(__snake_case   ):
   __snake_case    :				Any									=							"""pix2struct"""
   __snake_case    :				Optional[Any]									=							True







   def __init__(     self:  Any				,     UpperCAmelCase_:  Tuple=None				,     UpperCAmelCase_:  Any=None				,     UpperCAmelCase_:  int=1.0				,     UpperCAmelCase_:  Tuple=0.02				,     UpperCAmelCase_:  List[Any]=False				,     UpperCAmelCase_:  int=False				,     UpperCAmelCase_:  Any=True				,     **UpperCAmelCase_:  int				,     ):







         '''simple docstring'''




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

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

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

         _SCREAMING_SNAKE_CASE     				=  PixaStructTextConfig(**UpperCamelCase__					)
         _SCREAMING_SNAKE_CASE     				=  PixaStructVisionConfig(**UpperCamelCase__					)

         _SCREAMING_SNAKE_CASE     				=  self.text_config.decoder_start_token_id
         _SCREAMING_SNAKE_CASE     				=  self.text_config.pad_token_id
         _SCREAMING_SNAKE_CASE     				=  self.text_config.eos_token_id

         _SCREAMING_SNAKE_CASE     				=  initializer_factor
         _SCREAMING_SNAKE_CASE     				=  initializer_range

         _SCREAMING_SNAKE_CASE     				=  self.initializer_range
         _SCREAMING_SNAKE_CASE     				=  self.initializer_range

         _SCREAMING_SNAKE_CASE     				=  is_vqa







   @classmethod
   def 							UpperCamelCase				(     cls:  Union[str, Any]				,     UpperCAmelCase_:  PixaStructTextConfig				,     UpperCAmelCase_:  PixaStructVisionConfig				,     **UpperCAmelCase_:  Union[str, Any]					):







         '''simple docstring'''




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







   def 							UpperCamelCase				(     self:  Optional[int]					):







         '''simple docstring'''




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









import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Optional[Any]									=							(UnCLIPScheduler,)







			def 							UpperCamelCase				(     self:  int				,     **UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  {
									    """num_train_timesteps""": 1_000,
									    """variance_type""": """fixed_small_log""",
									    """clip_sample""": True,
									    """clip_sample_range""": 1.0,
									    """prediction_type""": """epsilon""",
									}

									config.update(**UpperCAmelCase_					)
									return config







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									for timesteps in [1, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									for variance in ["fixed_small_log", "learned_range"]:
															self.check_over_configs(variance_type=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									for clip_sample_range in [1, 5, 10, 20]:
															self.check_over_configs(clip_sample_range=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(prediction_type=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									for time_step in [0, 500, 999]:
															for prev_timestep in [None, 5, 100, 250, 500, 750]:
																					if prev_timestep is not None and prev_timestep >= time_step:
																											continue

																					self.check_over_forward(time_step=UpperCAmelCase_				,     prev_timestep=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config(variance_type="""fixed_small_log"""					)
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0_000E-10					)					) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.0_54_96_25					)					) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.9_99_49_87					)					) < 1E-5







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config(variance_type="""learned_range"""					)
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  0.5

									assert scheduler._get_variance(1				,     predicted_variance=UpperCAmelCase_					) - -10.1_71_27_90 < 1E-5
									assert scheduler._get_variance(487				,     predicted_variance=UpperCAmelCase_					) - -5.7_99_80_52 < 1E-5
									assert scheduler._get_variance(999				,     predicted_variance=UpperCAmelCase_					) - -0.0_01_00_11 < 1E-5







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config()
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  scheduler.timesteps

									_SCREAMING_SNAKE_CASE     				=  self.dummy_model()
									_SCREAMING_SNAKE_CASE     				=  self.dummy_sample_deter
									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)

									for i, t in enumerate(UpperCAmelCase_					):
															# 1. predict noise residual
															_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     UpperCAmelCase_					)

															# 2. predict previous mean of sample x_t-1
															_SCREAMING_SNAKE_CASE     				=  scheduler.step(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     generator=UpperCAmelCase_					).prev_sample

															_SCREAMING_SNAKE_CASE     				=  pred_prev_sample

									_SCREAMING_SNAKE_CASE     				=  torch.sum(torch.abs(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  torch.mean(torch.abs(UpperCAmelCase_					)					)

									assert abs(result_sum.item() - 2_52.2_68_24_95					) < 1E-2
									assert abs(result_mean.item() - 0.3_28_47_43					) < 1E-3







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config()
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									scheduler.set_timesteps(25					)

									_SCREAMING_SNAKE_CASE     				=  scheduler.timesteps

									_SCREAMING_SNAKE_CASE     				=  self.dummy_model()
									_SCREAMING_SNAKE_CASE     				=  self.dummy_sample_deter
									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(0					)

									for i, t in enumerate(UpperCAmelCase_					):
															# 1. predict noise residual
															_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     UpperCAmelCase_					)

															if i + 1 == timesteps.shape[0]:
																					_SCREAMING_SNAKE_CASE     				=  None
															else:
																					_SCREAMING_SNAKE_CASE     				=  timesteps[i + 1]

															# 2. predict previous mean of sample x_t-1
															_SCREAMING_SNAKE_CASE     				=  scheduler.step(
															    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     prev_timestep=UpperCAmelCase_				,     generator=UpperCAmelCase_					).prev_sample

															_SCREAMING_SNAKE_CASE     				=  pred_prev_sample

									_SCREAMING_SNAKE_CASE     				=  torch.sum(torch.abs(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  torch.mean(torch.abs(UpperCAmelCase_					)					)

									assert abs(result_sum.item() - 2_58.2_04_49_83					) < 1E-2
									assert abs(result_mean.item() - 0.3_36_20_38					) < 1E-3







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									pass







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									pass

