






'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__A							:							Union[str, Any]  							=		{
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Optional[Any]  							=		["""LayoutLMv2TokenizerFast"""]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							List[Any]  							=		["""LayoutLMv2FeatureExtractor"""]
 __A							:							List[Any]  							=		["""LayoutLMv2ImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:							Any  							=		[
     """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """LayoutLMv2ForQuestionAnswering""",
     """LayoutLMv2ForSequenceClassification""",
     """LayoutLMv2ForTokenClassification""",
     """LayoutLMv2Layer""",
     """LayoutLMv2Model""",
     """LayoutLMv2PreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
 from .processing_layoutlmva import LayoutLMvaProcessor
 from .tokenization_layoutlmva import LayoutLMvaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaLayer,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )
else:
 import sys

 __A							:							Tuple  							=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









'''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


__A							:							Union[str, Any]  							=		logging.get_logger(__name__)

__A							:							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 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"gpt_neo"
    lowerCAmelCase__      				=							["past_key_values"]
    lowerCAmelCase__      				=							{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}

    def __init__(					self     :     str				,     __lowerCamelCase     :     Optional[int]=50257				,     __lowerCamelCase     :     Optional[int]=2048				,     __lowerCamelCase     :     Optional[int]=2048				,     __lowerCamelCase     :     Any=24				,     __lowerCamelCase     :     List[Any]=[[["global", "local"], 12]]				,     __lowerCamelCase     :     Any=16				,     __lowerCamelCase     :     Any=None				,     __lowerCamelCase     :     Optional[Any]=256				,     __lowerCamelCase     :     List[Any]="gelu_new"				,     __lowerCamelCase     :     Any=0.0				,     __lowerCamelCase     :     List[Any]=0.0				,     __lowerCamelCase     :     Union[str, Any]=0.0				,     __lowerCamelCase     :     Any=0.1				,     __lowerCamelCase     :     Dict=1E-5				,     __lowerCamelCase     :     List[Any]=0.0_2				,     __lowerCamelCase     :     List[Any]=True				,     __lowerCamelCase     :     Optional[int]=50256				,     __lowerCamelCase     :     Tuple=50256				,     **__lowerCamelCase     :     Optional[int]				,     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     vocab_size
     lowerCamelCase__  =     max_position_embeddings
     lowerCamelCase__  =     hidden_size
     lowerCamelCase__  =     num_layers
     lowerCamelCase__  =     num_heads
     lowerCamelCase__  =     intermediate_size
     lowerCamelCase__  =     window_size
     lowerCamelCase__  =     activation_function
     lowerCamelCase__  =     resid_dropout
     lowerCamelCase__  =     embed_dropout
     lowerCamelCase__  =     attention_dropout
     lowerCamelCase__  =     classifier_dropout
     lowerCamelCase__  =     layer_norm_epsilon
     lowerCamelCase__  =     initializer_range
     lowerCamelCase__  =     use_cache

     lowerCamelCase__  =     bos_token_id
     lowerCamelCase__  =     eos_token_id

     lowerCamelCase__  =     attention_types
     lowerCamelCase__  =     self.expand_attention_types_params(__lowerCamelCase     )

     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=__lowerCamelCase				,     eos_token_id=__lowerCamelCase				,     **__lowerCamelCase     )




    @staticmethod
    def    a__				(					__lowerCamelCase     :     int     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     []
     for item in attention_types:
      for _ in range(item[1]     ):
       attentions.extend(item[0]     )
     return attentions






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 import torch

 lowerCamelCase__  =     input.size()
 lowerCamelCase__  =     len(lowercase__)
 lowerCamelCase__  =     shape[dimension]

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

 lowerCamelCase__  =     [slice(lowercase__)] * rank
 lowerCamelCase__  =     indices
 lowerCamelCase__  =     input[s]

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

 return sliced.permute(lowercase__)






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 import torch

 lowerCamelCase__  =     torch.arange(1				,     lowercase__)
 lowerCamelCase__  =     torch.remainder(lowercase__				,     lowercase__)
 lowerCamelCase__  =     remainders == 0
 lowerCamelCase__  =     candidates[divisor_indices]
 lowerCamelCase__  =     torch.max(lowercase__)
 return largest_divisor, torch.div(lowercase__				,     lowercase__				,     rounding_mode="floor")
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    @property
    def    a__				(					self     :     Any     )							->							Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     lowerCamelCase__  =     OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}     )
     if self.use_past:
      self.fill_with_past_key_values_(__lowerCamelCase				,     direction="inputs"     )
      lowerCamelCase__  =     {0: "batch", 1: "past_sequence + sequence"}
     else:
      lowerCamelCase__  =     {0: "batch", 1: "sequence"}

     return common_inputs

    @property
    def    a__				(					self     :     Any     )							->							int:

     '''simple docstring'''




     return self._config.num_heads

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     PreTrainedTokenizer				,     __lowerCamelCase     :     int = -1				,     __lowerCamelCase     :     int = -1				,     __lowerCamelCase     :     bool = False				,     __lowerCamelCase     :     Optional[TensorType] = None				,     )							->							Mapping[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     super(__lowerCamelCase				,     self     ).generate_dummy_inputs(
         __lowerCamelCase				,     batch_size=__lowerCamelCase				,     seq_length=__lowerCamelCase				,     is_pair=__lowerCamelCase				,     framework=__lowerCamelCase     )

     # We need to order the input in the way they appears in the forward()
     lowerCamelCase__  =     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

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

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

     return ordered_inputs




    @property
    def    a__				(					self     :     Optional[Any]     )							->							int:

     '''simple docstring'''




     return 13

