

"""simple docstring"""




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
				from ...onnx.config import PatchingSpec
				from ...tokenization_utils_base import PreTrainedTokenizerBase


_lowercase   :	List[str]												=						logging.get_logger(__name__)

_lowercase   :	Tuple												=						{
    'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json',
    'allenai/longformer-large-4096': 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json',
    'allenai/longformer-large-4096-finetuned-triviaqa': (
        'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'
    ),
    'allenai/longformer-base-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
    'allenai/longformer-large-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
}
class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: Dict					=					"longformer"



	def __init__(  self     :       List[str]			,    _lowercase     :       Union[List[int], int] = 5_12			,    _lowercase     :       int = 2			,    _lowercase     :       int = 1			,    _lowercase     :       int = 0			,    _lowercase     :       int = 2			,    _lowercase     :       int = 3_05_22			,    _lowercase     :       int = 7_68			,    _lowercase     :       int = 12			,    _lowercase     :       int = 12			,    _lowercase     :       int = 30_72			,    _lowercase     :       str = "gelu"			,    _lowercase     :       float = 0.1			,    _lowercase     :       float = 0.1			,    _lowercase     :       int = 5_12			,    _lowercase     :       int = 2			,    _lowercase     :       float = 0.02			,    _lowercase     :       float = 1E-12			,    _lowercase     :       bool = False			,    **_lowercase     :       List[str]			,    ):
		super().__init__(pad_token_id=_lowercase			,    **_lowercase						)

		__UpperCAmelCase      		= attention_window
		__UpperCAmelCase      		= sep_token_id
		__UpperCAmelCase      		= bos_token_id
		__UpperCAmelCase      		= eos_token_id
		__UpperCAmelCase      		= vocab_size
		__UpperCAmelCase      		= hidden_size
		__UpperCAmelCase      		= num_hidden_layers
		__UpperCAmelCase      		= num_attention_heads
		__UpperCAmelCase      		= hidden_act
		__UpperCAmelCase      		= intermediate_size
		__UpperCAmelCase      		= hidden_dropout_prob
		__UpperCAmelCase      		= attention_probs_dropout_prob
		__UpperCAmelCase      		= max_position_embeddings
		__UpperCAmelCase      		= type_vocab_size
		__UpperCAmelCase      		= initializer_range
		__UpperCAmelCase      		= layer_norm_eps
		__UpperCAmelCase      		= onnx_export






class      _UpperCAmelCase   (							_lowerCAmelCase       ):



	def __init__(  self     :       List[str]			,    _lowercase     :       "PretrainedConfig"			,    _lowercase     :       str = "default"			,    _lowercase     :       "List[PatchingSpec]" = None						):
		super().__init__(_lowercase			,    _lowercase			,    _lowercase						)
		__UpperCAmelCase      		= True



	@property
	def        a  (  self     :       Dict						):
		if self.task == "multiple-choice":
			__UpperCAmelCase      		= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
		else:
			__UpperCAmelCase      		= {0: '''batch''', 1: '''sequence'''}
		return OrderedDict(
		    [
		        ('''input_ids''', dynamic_axis),
		        ('''attention_mask''', dynamic_axis),
		        ('''global_attention_mask''', dynamic_axis),
		    ]						)



	@property
	def        a  (  self     :       int						):
		__UpperCAmelCase      		= super().outputs
		if self.task == "default":
			__UpperCAmelCase      		= {0: '''batch'''}
		return outputs



	@property
	def        a  (  self     :       Any						):
		return 1E-4



	@property
	def        a  (  self     :       Optional[Any]						):
		# needs to be >= 14 to support tril operator
		return max(super().default_onnx_opset			,    14						)



	def        a  (  self     :       List[str]			,    _lowercase     :       "PreTrainedTokenizerBase"			,    _lowercase     :       int = -1			,    _lowercase     :       int = -1			,    _lowercase     :       bool = False			,    _lowercase     :       Optional[TensorType] = None			,    ):
		__UpperCAmelCase      		= super().generate_dummy_inputs(
		    preprocessor=_lowercase			,    batch_size=_lowercase			,    seq_length=_lowercase			,    is_pair=_lowercase			,    framework=_lowercase						)
		import torch

		# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
		# makes the export fail randomly
		__UpperCAmelCase      		= torch.zeros_like(inputs['''input_ids''']						)
		# make every second token global
		__UpperCAmelCase      		= 1

		return inputs




import copy
import os
from typing import Union

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


__a	 =      logging.get_logger(__name__)

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






class 	__lowercase					(	__snake_case	):
      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    :       Optional[Any]       ,							__lowerCamelCase    :       Optional[int]=5_0_2_4_4       ,							__lowerCamelCase    :       int=7_6_8       ,							__lowerCamelCase    :       Union[str, Any]=6_4       ,							__lowerCamelCase    :       Dict=2_0_4_8       ,							__lowerCamelCase    :       int=1_2       ,							__lowerCamelCase    :       Any=1_2       ,							__lowerCamelCase    :       Union[str, Any]=3_2       ,							__lowerCamelCase    :       Any=1_2_8       ,							__lowerCamelCase    :       Union[str, Any]=0.1       ,							__lowerCamelCase    :       str=1e-6       ,							__lowerCamelCase    :       int=1.0       ,							__lowerCamelCase    :       Optional[int]="gelu_new"       ,							__lowerCamelCase    :       int=0       ,							__lowerCamelCase    :       int=False       ,							__lowerCamelCase    :       Optional[Any]=0       ,							__lowerCamelCase    :       Dict=1       ,							__lowerCamelCase    :       List[Any]=False       ,							__lowerCamelCase    :       Optional[Any]=True       ,							**__lowerCamelCase    :       Dict       ,							)     -> int:






         """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=__lowerCamelCase       ,							eos_token_id=__lowerCamelCase       ,							decoder_start_token_id=__lowerCamelCase       ,							tie_word_embeddings=__lowerCamelCase       ,							is_decoder=__lowerCamelCase       ,							**__lowerCamelCase       ,							)





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






         """simple docstring"""







         cls._set_token_in_kwargs(__lowerCamelCase							)

         UpperCAmelCase					,	UpperCAmelCase      					=  cls.get_config_dict(__lowerCamelCase       ,							**__lowerCamelCase							)

         # 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(__lowerCamelCase       ,							**__lowerCamelCase							)






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							'''pix2struct_vision_model'''





      def __init__(		self    :       str       ,							__lowerCamelCase    :       Any=7_6_8       ,							__lowerCamelCase    :       Dict=7_6_8       ,							__lowerCamelCase    :       List[Any]=2_0_4_8       ,							__lowerCamelCase    :       List[Any]=6_4       ,							__lowerCamelCase    :       Union[str, Any]=1_2       ,							__lowerCamelCase    :       Optional[int]=1_2       ,							__lowerCamelCase    :       Tuple="gelu_new"       ,							__lowerCamelCase    :       Tuple=1e-6       ,							__lowerCamelCase    :       Any=0.0       ,							__lowerCamelCase    :       List[Any]=0.0       ,							__lowerCamelCase    :       int=1e-1_0       ,							__lowerCamelCase    :       Union[str, Any]=1.0       ,							__lowerCamelCase    :       Any=4_0_9_6       ,							__lowerCamelCase    :       Dict=3_2       ,							__lowerCamelCase    :       Any=1_2_8       ,							**__lowerCamelCase    :       Optional[int]       ,							)     -> Optional[Any]:






         """simple docstring"""







         super().__init__(**__lowerCamelCase							)

         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 							_lowercase					(		cls    :       Any       ,							__lowerCamelCase    :       Union[str, os.PathLike]       ,							**__lowerCamelCase    :       int							)     -> "PretrainedConfig":






         """simple docstring"""







         cls._set_token_in_kwargs(__lowerCamelCase							)

         UpperCAmelCase					,	UpperCAmelCase      					=  cls.get_config_dict(__lowerCamelCase       ,							**__lowerCamelCase							)

         # 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(__lowerCamelCase       ,							**__lowerCamelCase							)






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							'''pix2struct'''
      UpperCamelCase										=							True





      def __init__(		self    :       str       ,							__lowerCamelCase    :       str=None       ,							__lowerCamelCase    :       Dict=None       ,							__lowerCamelCase    :       Optional[Any]=1.0       ,							__lowerCamelCase    :       str=0.02       ,							__lowerCamelCase    :       Any=False       ,							__lowerCamelCase    :       Tuple=False       ,							__lowerCamelCase    :       str=True       ,							**__lowerCamelCase    :       Dict       ,							)     -> Optional[Any]:






         """simple docstring"""







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

         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(**__lowerCamelCase							)
         UpperCAmelCase      					=  PixaStructVisionConfig(**__lowerCamelCase							)

         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 							_lowercase					(		cls    :       List[Any]       ,							__lowerCamelCase    :       PixaStructTextConfig       ,							__lowerCamelCase    :       PixaStructVisionConfig       ,							**__lowerCamelCase    :       Dict							)     -> Union[str, Any]:






         """simple docstring"""







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





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



