


"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase_       :       Dict      						=  {
    """configuration_pix2struct""": [
        """PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Pix2StructConfig""",
        """Pix2StructTextConfig""",
        """Pix2StructVisionConfig""",
    ],
    """processing_pix2struct""": ["""Pix2StructProcessor"""],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[Any]      						=  ["""Pix2StructImageProcessor"""]


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[str]      						=  [
           """PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """Pix2StructPreTrainedModel""",
           """Pix2StructForConditionalGeneration""",
           """Pix2StructVisionModel""",
           """Pix2StructTextModel""",
       ]

if TYPE_CHECKING:
       from .configuration_pixastruct import (
           PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           PixaStructConfig,
           PixaStructTextConfig,
           PixaStructVisionConfig,
       )
       from .processing_pixastruct import PixaStructProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_pixastruct import PixaStructImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_pixastruct import (
                  PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PixaStructForConditionalGeneration,
                  PixaStructPreTrainedModel,
                  PixaStructTextModel,
                  PixaStructVisionModel,
              )

else:
       import sys

       lowerCamelCase_       :       Any      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase_       :       str      						=  logging.get_logger(__name__)

lowerCamelCase_       :       Optional[int]      						=  {
    """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/config.json""",
    """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/config.json""",
    """xlm-roberta-large-finetuned-conll02-dutch""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll02-spanish""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-english""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-german""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"""
    ),
}






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  "xlm-roberta"


 def __init__(		self			,   __A=3_0522			,   __A=768			,   __A=12			,   __A=12			,   __A=3072			,   __A="gelu"			,   __A=0.1			,   __A=0.1			,   __A=512			,   __A=2			,   __A=0.02			,   __A=1E-1_2			,   __A=1			,   __A=0			,   __A=2			,   __A="absolute"			,   __A=True			,   __A=None			,   **__A			,   ) ->					str:
     super().__init__(pad_token_id=__A			,   bos_token_id=__A			,   eos_token_id=__A			,   **__A						)

     a					=vocab_size
     a					=hidden_size
     a					=num_hidden_layers
     a					=num_attention_heads
     a					=hidden_act
     a					=intermediate_size
     a					=hidden_dropout_prob
     a					=attention_probs_dropout_prob
     a					=max_position_embeddings
     a					=type_vocab_size
     a					=initializer_range
     a					=layer_norm_eps
     a					=position_embedding_type
     a					=use_cache
     a					=classifier_dropout






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




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