
'''simple docstring'''







# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")






class  lowerCAmelCase__   :






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = False		):


											"""simple docstring"""





											lowercase_	:     int													=       scheduler
											lowercase_	:     Optional[int]													=       optimizers if isinstance(__SCREAMING_SNAKE_CASE				,							(list, tuple)		) else [optimizers]
											lowercase_	:     Optional[Any]													=       split_batches
											lowercase_	:     Union[str, Any]													=       step_with_optimizer
											lowercase_	:     List[Any]													=       GradientState()






					def 	_snake_case    (   self				,							*__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if not self.step_with_optimizer:
																	# No link between scheduler and optimizer -> just step
																	self.scheduler.step(*__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)
																	return

											# Otherwise, first make sure the optimizer was stepped.
											if not self.gradient_state.sync_gradients:
																	if self.gradient_state.adjust_scheduler:
																							self.scheduler._step_count += 1
																	return

											for opt in self.optimizers:
																	if opt.step_was_skipped:
																							return
											if self.split_batches:
																	# Split batches -> the training dataloader batch size is not changed so one step per training step
																	self.scheduler.step(*__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)
											else:
																	# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
																	# num_processes steps per training step
																	lowercase_	:     Union[str, Any]													=       AcceleratorState().num_processes
																	for _ in range(__SCREAMING_SNAKE_CASE		):
																							# Special case when using OneCycle and `drop_last` was not used
																							if hasattr(self.scheduler				,							'''total_steps'''		):
																													if self.scheduler._step_count <= self.scheduler.total_steps:
																																			self.scheduler.step(*__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)
																							else:
																													self.scheduler.step(*__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.scheduler.get_last_lr()






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.scheduler.state_dict()






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											self.scheduler.load_state_dict(__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.scheduler.get_lr()






					def 	_snake_case    (   self				,							*__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self.scheduler.print_lr(*__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)





'''simple docstring'''







import copy
import os
from typing import Union

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


_lowercase :  Optional[Any]    							=       logging.get_logger(__name__)

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






class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     '''pix2struct_text_model'''
					lowerCAmelCase_        =     ['''past_key_values''']
					lowerCAmelCase_        =     {
					    '''hidden_size''': '''hidden_size''',
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=5_02_44				,							__SCREAMING_SNAKE_CASE=7_68				,							__SCREAMING_SNAKE_CASE=64				,							__SCREAMING_SNAKE_CASE=20_48				,							__SCREAMING_SNAKE_CASE=12				,							__SCREAMING_SNAKE_CASE=12				,							__SCREAMING_SNAKE_CASE=32				,							__SCREAMING_SNAKE_CASE=1_28				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE=1E-6				,							__SCREAMING_SNAKE_CASE=1.0				,							__SCREAMING_SNAKE_CASE="gelu_new"				,							__SCREAMING_SNAKE_CASE=0				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=0				,							__SCREAMING_SNAKE_CASE=1				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=True				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     Any													=       vocab_size
											lowercase_	:     Tuple													=       hidden_size
											lowercase_	:     Optional[Any]													=       d_kv
											lowercase_	:     List[str]													=       d_ff
											lowercase_	:     List[str]													=       num_layers
											lowercase_	:     Optional[Any]													=       num_heads
											lowercase_	:     Union[str, Any]													=       relative_attention_num_buckets
											lowercase_	:     Optional[int]													=       relative_attention_max_distance
											lowercase_	:     Union[str, Any]													=       dropout_rate
											lowercase_	:     Dict													=       layer_norm_epsilon
											lowercase_	:     Dict													=       initializer_factor
											lowercase_	:     List[Any]													=       use_cache

											lowercase_	:     Optional[int]													=       eos_token_id
											lowercase_	:     Optional[int]													=       decoder_start_token_id

											# for backwards compatibility
											lowercase_	:     Any													=       dense_act_fn

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






					@classmethod
					def 	_snake_case    (   cls				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE		)

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

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







class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     '''pix2struct_vision_model'''






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=7_68				,							__SCREAMING_SNAKE_CASE=7_68				,							__SCREAMING_SNAKE_CASE=20_48				,							__SCREAMING_SNAKE_CASE=64				,							__SCREAMING_SNAKE_CASE=12				,							__SCREAMING_SNAKE_CASE=12				,							__SCREAMING_SNAKE_CASE="gelu_new"				,							__SCREAMING_SNAKE_CASE=1E-6				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=1E-1_0				,							__SCREAMING_SNAKE_CASE=1.0				,							__SCREAMING_SNAKE_CASE=40_96				,							__SCREAMING_SNAKE_CASE=32				,							__SCREAMING_SNAKE_CASE=1_28				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											super().__init__(**__SCREAMING_SNAKE_CASE		)

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






					@classmethod
					def 	_snake_case    (   cls				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE		)

											lowercase_						, lowercase_	:     str													=       cls.get_config_dict(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)

											# get the vision config dict if we are loading from Pix2StructConfig
											if config_dict.get('''model_type'''		) == "pix2struct":
																	lowercase_	:     Optional[int]													=       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(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)







class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     '''pix2struct'''
					lowerCAmelCase_        =     True






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=1.0				,							__SCREAMING_SNAKE_CASE=0.02				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=True				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





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

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

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

											lowercase_	:     str													=       PixaStructTextConfig(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Tuple													=       PixaStructVisionConfig(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[Any]													=       self.text_config.decoder_start_token_id
											lowercase_	:     Union[str, Any]													=       self.text_config.pad_token_id
											lowercase_	:     Union[str, Any]													=       self.text_config.eos_token_id

											lowercase_	:     int													=       initializer_factor
											lowercase_	:     Any													=       initializer_range

											lowercase_	:     str													=       self.initializer_range
											lowercase_	:     str													=       self.initializer_range

											lowercase_	:     int													=       is_vqa






					@classmethod
					def 	_snake_case    (   cls				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       copy.deepcopy(self.__dict__		)
											lowercase_	:     Any													=       self.text_config.to_dict()
											lowercase_	:     Optional[Any]													=       self.vision_config.to_dict()
											lowercase_	:     Optional[int]													=       self.__class__.model_type
											return output



