




"""simple docstring"""


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


_A													=      logging.get_logger(__name__)

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


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :int     				=     "layoutlmv3"






   def __init__(		self       :		Tuple   ,     UpperCamelCase       :		Any=5_02_65   ,     UpperCamelCase       :		List[Any]=7_68   ,     UpperCamelCase       :		List[str]=12   ,     UpperCamelCase       :		Optional[int]=12   ,     UpperCamelCase       :		Dict=30_72   ,     UpperCamelCase       :		Any="gelu"   ,     UpperCamelCase       :		Optional[int]=0.1   ,     UpperCamelCase       :		str=0.1   ,     UpperCamelCase       :		Optional[int]=5_12   ,     UpperCamelCase       :		List[str]=2   ,     UpperCamelCase       :		int=0.02   ,     UpperCamelCase       :		List[Any]=1E-5   ,     UpperCamelCase       :		int=1   ,     UpperCamelCase       :		str=0   ,     UpperCamelCase       :		str=2   ,     UpperCamelCase       :		Optional[Any]=10_24   ,     UpperCamelCase       :		str=1_28   ,     UpperCamelCase       :		Optional[Any]=1_28   ,     UpperCamelCase       :		Tuple=True   ,     UpperCamelCase       :		List[Any]=32   ,     UpperCamelCase       :		Any=1_28   ,     UpperCamelCase       :		List[str]=64   ,     UpperCamelCase       :		Optional[int]=2_56   ,     UpperCamelCase       :		Union[str, Any]=True   ,     UpperCamelCase       :		str=True   ,     UpperCamelCase       :		List[str]=True   ,     UpperCamelCase       :		List[Any]=2_24   ,     UpperCamelCase       :		Optional[Any]=3   ,     UpperCamelCase       :		str=16   ,     UpperCamelCase       :		List[str]=None   ,     **UpperCamelCase       :		Union[str, Any]   ,     )			->						Tuple:

         """simple docstring"""
         super().__init__(
             vocab_size=UpperCamelCase   ,     hidden_size=UpperCamelCase   ,     num_hidden_layers=UpperCamelCase   ,     num_attention_heads=UpperCamelCase   ,     intermediate_size=UpperCamelCase   ,     hidden_act=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   ,     pad_token_id=UpperCamelCase   ,     bos_token_id=UpperCamelCase   ,     eos_token_id=UpperCamelCase   ,     **UpperCamelCase   ,     )
         lowerCAmelCase__      :			Tuple   =	max_ad_position_embeddings
         lowerCAmelCase__      :			Any   =	coordinate_size
         lowerCAmelCase__      :			Dict   =	shape_size
         lowerCAmelCase__      :			str   =	has_relative_attention_bias
         lowerCAmelCase__      :			Optional[int]   =	rel_pos_bins
         lowerCAmelCase__      :			Dict   =	max_rel_pos
         lowerCAmelCase__      :			Union[str, Any]   =	has_spatial_attention_bias
         lowerCAmelCase__      :			List[str]   =	rel_ad_pos_bins
         lowerCAmelCase__      :			Optional[Any]   =	max_rel_ad_pos
         lowerCAmelCase__      :			str   =	text_embed
         lowerCAmelCase__      :			List[str]   =	visual_embed
         lowerCAmelCase__      :			Tuple   =	input_size
         lowerCAmelCase__      :			List[Any]   =	num_channels
         lowerCAmelCase__      :			List[Any]   =	patch_size
         lowerCAmelCase__      :			Dict   =	classifier_dropout






class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Optional[int]     				=     version.parse("1.12"						)






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

         """simple docstring"""
         # The order of inputs is different for question answering and sequence classification
         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 	_lowerCAmelCase   (		self       :		Tuple   )			->						float:

         """simple docstring"""
         return 1E-5






   @property
   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						int:

         """simple docstring"""
         return 12






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		"ProcessorMixin"   ,     UpperCamelCase       :		int = -1   ,     UpperCamelCase       :		int = -1   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		Optional["TensorType"] = None   ,     UpperCamelCase       :		int = 3   ,     UpperCamelCase       :		int = 40   ,     UpperCamelCase       :		int = 40   ,     )			->						Mapping[str, Any]:

         """simple docstring"""
         setattr(processor.image_processor   ,     """apply_ocr"""   ,     UpperCamelCase   )

         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         lowerCAmelCase__      :			str   =	compute_effective_axis_dimension(
             UpperCamelCase   ,     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
         lowerCAmelCase__      :			Tuple   =	processor.tokenizer.num_special_tokens_to_add(UpperCamelCase   )
         lowerCAmelCase__      :			List[str]   =	compute_effective_axis_dimension(
             UpperCamelCase   ,     fixed_dimension=OnnxConfig.default_fixed_sequence   ,     num_token_to_add=UpperCamelCase   )
         # Generate dummy inputs according to compute batch and sequence
         lowerCAmelCase__      :			List[Any]   =	[[""" """.join([processor.tokenizer.unk_token]   ) * seq_length]] * batch_size

         # Generate dummy bounding boxes
         lowerCAmelCase__      :			Tuple   =	[[[48, 84, 73, 1_28]]] * 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)
         lowerCAmelCase__      :			Union[str, Any]   =	self._generate_dummy_images(UpperCamelCase   ,     UpperCamelCase   ,     UpperCamelCase   ,     UpperCamelCase   )

         lowerCAmelCase__      :			str   =	dict(
             processor(
                 UpperCamelCase   ,     text=UpperCamelCase   ,     boxes=UpperCamelCase   ,     return_tensors=UpperCamelCase   ,     )   )

         return inputs











"""simple docstring"""


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate


@dataclass(frozen=a_						)
class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :str     				=     field(default="audio-classification"       ,	metadata={"include_in_asdict_even_if_is_default": True}						)
   _lowerCamelCase    :ClassVar[Features]     				=     Features({"audio": Audio()}						)
   _lowerCamelCase    :ClassVar[Features]     				=     Features({"labels": ClassLabel}						)
   _lowerCamelCase    :str     				=     "audio"
   _lowerCamelCase    :str     				=     "labels"






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		Optional[int]   )			->						Union[str, Any]:

         """simple docstring"""
         if self.label_column not in features:
               raise ValueError(f"""Column {self.label_column} is not present in features."""   )
         if not isinstance(features[self.label_column]   ,     UpperCamelCase   ):
               raise ValueError(f"""Column {self.label_column} is not a ClassLabel."""   )
         lowerCAmelCase__      :			str   =	copy.deepcopy(self   )
         lowerCAmelCase__      :			Optional[int]   =	self.label_schema.copy()
         lowerCAmelCase__      :			List[Any]   =	features[self.label_column]
         lowerCAmelCase__      :			Optional[int]   =	label_schema
         return task_template






   @property
   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Dict[str, str]:

         """simple docstring"""
         return {
             self.audio_column: "audio",
             self.label_column: "labels",
         }





