


'''simple docstring'''
import copy
import os
from typing import Union

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


__snake_case				 =	logging.get_logger(__name__)

__snake_case				 =	{
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}
class   lowercase      (				A__   ):

		"""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       ,  UpperCamelCase_=50244       ,  UpperCamelCase_=768       ,  UpperCamelCase_=64       ,  UpperCamelCase_=2048       ,  UpperCamelCase_=12       ,  UpperCamelCase_=12       ,  UpperCamelCase_=32       ,  UpperCamelCase_=128       ,  UpperCamelCase_=0.1       ,  UpperCamelCase_=1e-6       ,  UpperCamelCase_=1.0       ,  UpperCamelCase_="gelu_new"       ,  UpperCamelCase_=0       ,  UpperCamelCase_=False       ,  UpperCamelCase_=0       ,  UpperCamelCase_=1       ,  UpperCamelCase_=False       ,  UpperCamelCase_=True       ,  **UpperCamelCase_       ,  ):

							'''simple docstring'''


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

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

							# for backwards compatibility
							UpperCamelCase__  :Optional[int]				   =					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        lowerCAmelCase__							(						cls       ,  UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


							cls._set_token_in_kwargs(UpperCamelCase_				)

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

							# get the text config dict if we are loading from Pix2StructConfig
							if config_dict.get('''model_type'''				) == "pix2struct":
												UpperCamelCase__  :Optional[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   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							'pix2struct_vision_model'




		def __init__(						self       ,  UpperCamelCase_=768       ,  UpperCamelCase_=768       ,  UpperCamelCase_=2048       ,  UpperCamelCase_=64       ,  UpperCamelCase_=12       ,  UpperCamelCase_=12       ,  UpperCamelCase_="gelu_new"       ,  UpperCamelCase_=1e-6       ,  UpperCamelCase_=0.0       ,  UpperCamelCase_=0.0       ,  UpperCamelCase_=1e-10       ,  UpperCamelCase_=1.0       ,  UpperCamelCase_=4096       ,  UpperCamelCase_=32       ,  UpperCamelCase_=128       ,  **UpperCamelCase_       ,  ):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_				)

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




		@classmethod
		def        lowerCAmelCase__							(						cls       ,  UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


							cls._set_token_in_kwargs(UpperCamelCase_				)

							UpperCamelCase__   ,							UpperCamelCase__  :Any				   =					cls.get_config_dict(UpperCamelCase_       ,  **UpperCamelCase_				)

							# get the vision config dict if we are loading from Pix2StructConfig
							if config_dict.get('''model_type'''				) == "pix2struct":
												UpperCamelCase__  :Union[str, Any]				   =					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   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							'pix2struct'
		_a       					=							True




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

							'''simple docstring'''


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

							if text_config is None:
												UpperCamelCase__  :Union[str, Any]				   =					{}
												logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.'''				)

							if vision_config is None:
												UpperCamelCase__  :List[str]				   =					{}
												logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.'''				)

							UpperCamelCase__  :Dict				   =					PixaStructTextConfig(**UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					PixaStructVisionConfig(**UpperCamelCase_				)

							UpperCamelCase__  :Tuple				   =					self.text_config.decoder_start_token_id
							UpperCamelCase__  :Any				   =					self.text_config.pad_token_id
							UpperCamelCase__  :Tuple				   =					self.text_config.eos_token_id

							UpperCamelCase__  :List[str]				   =					initializer_factor
							UpperCamelCase__  :Union[str, Any]				   =					initializer_range

							UpperCamelCase__  :Union[str, Any]				   =					self.initializer_range
							UpperCamelCase__  :Optional[int]				   =					self.initializer_range

							UpperCamelCase__  :Optional[Any]				   =					is_vqa




		@classmethod
		def        lowerCAmelCase__							(						cls       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					copy.deepcopy(self.__dict__				)
							UpperCamelCase__  :List[str]				   =					self.text_config.to_dict()
							UpperCamelCase__  :List[Any]				   =					self.vision_config.to_dict()
							UpperCamelCase__  :List[str]				   =					self.__class__.model_type
							return output



'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				 =	logging.getLogger(__name__)
@dataclass
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							field(
		    default=0.0      ,						metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'Whether to SortishSamler or not.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'whether to use adafactor'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'Dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Attention dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default='linear'      ,						metadata={'help': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()   )}'''}      ,						)