'''simple docstring'''





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE_:	int    =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:	Tuple    ={
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}




class 			__A (					UpperCamelCase__      ):
    a__			:		str			  =    """gpt_neo"""
    a__			:		Union[str, Any]			  =    ["""past_key_values"""]
    a__			:		List[str]			  =    {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



    def __init__(self       :       str  ,     __a       :       List[Any]=50257  ,     __a       :       Tuple=2048  ,     __a       :       Optional[Any]=2048  ,     __a       :       Dict=24  ,     __a       :       List[Any]=[[["global", "local"], 12]]  ,     __a       :       List[Any]=16  ,     __a       :       Optional[int]=None  ,     __a       :       Tuple=256  ,     __a       :       Optional[Any]="gelu_new"  ,     __a       :       List[str]=0.0  ,     __a       :       int=0.0  ,     __a       :       Any=0.0  ,     __a       :       List[Any]=0.1  ,     __a       :       Optional[int]=1E-5  ,     __a       :       List[Any]=0.02  ,     __a       :       str=True  ,     __a       :       int=50256  ,     __a       :       int=50256  ,     **__a       :       Dict  ,     ):
         UpperCAmelCase_			       =  vocab_size
         UpperCAmelCase_			       =  max_position_embeddings
         UpperCAmelCase_			       =  hidden_size
         UpperCAmelCase_			       =  num_layers
         UpperCAmelCase_			       =  num_heads
         UpperCAmelCase_			       =  intermediate_size
         UpperCAmelCase_			       =  window_size
         UpperCAmelCase_			       =  activation_function
         UpperCAmelCase_			       =  resid_dropout
         UpperCAmelCase_			       =  embed_dropout
         UpperCAmelCase_			       =  attention_dropout
         UpperCAmelCase_			       =  classifier_dropout
         UpperCAmelCase_			       =  layer_norm_epsilon
         UpperCAmelCase_			       =  initializer_range
         UpperCAmelCase_			       =  use_cache

         UpperCAmelCase_			       =  bos_token_id
         UpperCAmelCase_			       =  eos_token_id

         UpperCAmelCase_			       =  attention_types
         UpperCAmelCase_			       =  self.expand_attention_types_params(__a					)

         if len(self.attention_layers					) != self.num_layers:
              raise ValueError(
                  "Configuration for convolutional module is incorrect. "
                  "It is required that `len(config.attention_layers)` == `config.num_layers` "
                  f"""but is `len(config.attention_layers) = {len(self.attention_layers					)}`, """
                  f"""`config.num_layers = {self.num_layers}`. """
                  "`config.attention_layers` is prepared using `config.attention_types`. "
                  "Please verify the value of `config.attention_types` argument."					)

         super().__init__(bos_token_id=__a  ,     eos_token_id=__a  ,     **__a					)






    @staticmethod
    def    _lowercase		(__a       :       int					):
         UpperCAmelCase_			       =  []
         for item in attention_types:
              for _ in range(item[1]					):
                   attentions.extend(item[0]					)
         return attentions





def       lowerCAmelCase_							(   snake_case_			:					str   ,       snake_case_			:					str   ,       snake_case_			:					Union[str, Any]   ,       snake_case_			:					List[Any]     )       -> Tuple:


     '''simple docstring'''




     import torch

     UpperCAmelCase_			       =  input.size()
     UpperCAmelCase_			       =  len(snake_case_     )
     UpperCAmelCase_			       =  shape[dimension]

     UpperCAmelCase_			       =  torch.arange(0   ,       snake_case_   ,       snake_case_     )
     UpperCAmelCase_			       =  torch.div(sizedim - size   ,       snake_case_   ,       rounding_mode="floor"     ) + 1
     UpperCAmelCase_			       =  torch.arange(snake_case_     ) + low_indices[:min_length][:, None]

     UpperCAmelCase_			       =  [slice(snake_case_     )] * rank
     UpperCAmelCase_			       =  indices
     UpperCAmelCase_			       =  input[s]

     UpperCAmelCase_			       =  list(range(0   ,       rank + 1     )     )
     perm.append(perm.pop(dimension + 1     )     )

     return sliced.permute(snake_case_     )





def       lowerCAmelCase_							(   snake_case_			:					Optional[int]   ,       snake_case_			:					str     )       -> str:


     '''simple docstring'''




     import torch

     UpperCAmelCase_			       =  torch.arange(1   ,       snake_case_     )
     UpperCAmelCase_			       =  torch.remainder(snake_case_   ,       snake_case_     )
     UpperCAmelCase_			       =  remainders == 0
     UpperCAmelCase_			       =  candidates[divisor_indices]
     UpperCAmelCase_			       =  torch.max(snake_case_     )
     return largest_divisor, torch.div(snake_case_   ,       snake_case_   ,       rounding_mode="floor"     )






class 			__A (					UpperCamelCase__      ):



    @property
    def    _lowercase		(self       :       Union[str, Any]					):
         UpperCAmelCase_			       =  OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}					)
         if self.use_past:
              self.fill_with_past_key_values_(__a  ,     direction="inputs"					)
              UpperCAmelCase_			       =  {0: "batch", 1: "past_sequence + sequence"}
         else:
              UpperCAmelCase_			       =  {0: "batch", 1: "sequence"}

         return common_inputs



    @property
    def    _lowercase		(self       :       Tuple					):
         return self._config.num_heads



    def    _lowercase		(self       :       int  ,     __a       :       PreTrainedTokenizer  ,     __a       :       int = -1  ,     __a       :       int = -1  ,     __a       :       bool = False  ,     __a       :       Optional[TensorType] = None  ,     ):
         UpperCAmelCase_			       =  super(__a  ,     self					).generate_dummy_inputs(
             __a  ,     batch_size=__a  ,     seq_length=__a  ,     is_pair=__a  ,     framework=__a					)

         # We need to order the input in the way they appears in the forward()
         UpperCAmelCase_			       =  OrderedDict({"input_ids": common_inputs["input_ids"]}					)

         # Need to add the past_keys
         if self.use_past:
              if not is_torch_available():
                   raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."					)
              else:
                   import torch

                   UpperCAmelCase_       ,					UpperCAmelCase_			       =  common_inputs["input_ids"].shape
                   # Not using the same length for past_key_values
                   UpperCAmelCase_			       =  seqlen + 2
                   UpperCAmelCase_			       =  (
                       batch,
                       self.num_attention_heads,
                       past_key_values_length,
                       self._config.hidden_size // self.num_attention_heads,
                   )
                   UpperCAmelCase_			       =  [
                       (torch.zeros(__a					), torch.zeros(__a					)) for _ in range(self.num_layers					)
                   ]

         UpperCAmelCase_			       =  common_inputs["attention_mask"]
         if self.use_past:
              UpperCAmelCase_			       =  ordered_inputs["attention_mask"].dtype
              UpperCAmelCase_			       =  torch.cat(
                  [ordered_inputs["attention_mask"], torch.ones(__a  ,     __a  ,     dtype=__a					)]  ,     dim=1					)

         return ordered_inputs






    @property
    def    _lowercase		(self       :       Dict					):
         return 13












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

from packaging import version

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


if TYPE_CHECKING:
    from ...processing_utils import ProcessorMixin
    from ...utils import TensorType


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =  {
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Tuple					='''layoutlmv3'''





       def __init__(				self	:Optional[int],      snake_case	:int=5_0265,      snake_case	:int=768,      snake_case	:Any=12,      snake_case	:Union[str, Any]=12,      snake_case	:List[str]=3072,      snake_case	:List[str]="gelu",      snake_case	:List[str]=0.1,      snake_case	:Optional[int]=0.1,      snake_case	:Optional[int]=512,      snake_case	:Tuple=2,      snake_case	:Optional[Any]=0.0_2,      snake_case	:Optional[int]=1e-5,      snake_case	:Union[str, Any]=1,      snake_case	:Dict=0,      snake_case	:Tuple=2,      snake_case	:Tuple=1024,      snake_case	:Optional[Any]=128,      snake_case	:Optional[Any]=128,      snake_case	:List[str]=True,      snake_case	:str=32,      snake_case	:Optional[int]=128,      snake_case	:Dict=64,      snake_case	:List[str]=256,      snake_case	:Optional[Any]=True,      snake_case	:Optional[Any]=True,      snake_case	:Dict=True,      snake_case	:Optional[Any]=224,      snake_case	:int=3,      snake_case	:int=16,      snake_case	:str=None,      **snake_case	:Dict,      ):



          """simple docstring"""

          super().__init__(
              vocab_size=snake_case,      hidden_size=snake_case,      num_hidden_layers=snake_case,      num_attention_heads=snake_case,      intermediate_size=snake_case,      hidden_act=snake_case,      hidden_dropout_prob=snake_case,      attention_probs_dropout_prob=snake_case,      max_position_embeddings=snake_case,      type_vocab_size=snake_case,      initializer_range=snake_case,      layer_norm_eps=snake_case,      pad_token_id=snake_case,      bos_token_id=snake_case,      eos_token_id=snake_case,      **snake_case,      )
          _lowercase              =max_ad_position_embeddings
          _lowercase              =coordinate_size
          _lowercase              =shape_size
          _lowercase              =has_relative_attention_bias
          _lowercase              =rel_pos_bins
          _lowercase              =max_rel_pos
          _lowercase              =has_spatial_attention_bias
          _lowercase              =rel_ad_pos_bins
          _lowercase              =max_rel_ad_pos
          _lowercase              =text_embed
          _lowercase              =visual_embed
          _lowercase              =input_size
          _lowercase              =num_channels
          _lowercase              =patch_size
          _lowercase              =classifier_dropout






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Optional[Any]					=version.parse('''1.12''' )





       @property
       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          if self.task in ["question-answering", "sequence-classification"]:
             return OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'sequence'}),
                     ('bbox', {0: 'batch', 1: 'sequence'}),
                     ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                 ])
          else:
             return OrderedDict(
                 [
                     ('input_ids', {0: 'batch', 1: 'sequence'}),
                     ('bbox', {0: 'batch', 1: 'sequence'}),
                     ('attention_mask', {0: 'batch', 1: 'sequence'}),
                     ('pixel_values', {0: 'batch', 1: 'num_channels'}),
                 ])





       @property
       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          return 1e-5





       @property
       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          return 12





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:"ProcessorMixin",      snake_case	:int = -1,      snake_case	:int = -1,      snake_case	:bool = False,      snake_case	:Optional["TensorType"] = None,      snake_case	:int = 3,      snake_case	:int = 40,      snake_case	:int = 40,      ):



          """simple docstring"""

          setattr(processor.image_processor,      'apply_ocr',      snake_case)

          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          _lowercase              =compute_effective_axis_dimension(
              snake_case,      fixed_dimension=OnnxConfig.default_fixed_batch,      num_token_to_add=0)
          # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
          _lowercase              =processor.tokenizer.num_special_tokens_to_add(snake_case)
          _lowercase              =compute_effective_axis_dimension(
              snake_case,      fixed_dimension=OnnxConfig.default_fixed_sequence,      num_token_to_add=snake_case)
          # Generate dummy inputs according to compute batch and sequence
          _lowercase              =[[' '.join([processor.tokenizer.unk_token]) * seq_length]] * batch_size

          # Generate dummy bounding boxes
          _lowercase              =[[[48, 84, 73, 128]]] * batch_size

          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
          _lowercase              =self._generate_dummy_images(snake_case,      snake_case,      snake_case,      snake_case)

          _lowercase              =dict(
              processor(
                  snake_case,      text=snake_case,      boxes=snake_case,      return_tensors=snake_case,      ))

          return inputs


