


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		:Dict     						=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:str     						=   {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}


class 			__lowerCAmelCase   (       _a    ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE									=				"""layoutlmv3"""



  def __init__(   self				:				List[Any]			, _lowerCAmelCase				:				Optional[Any]=5_0_2_6_5			, _lowerCAmelCase				:				Dict=7_6_8			, _lowerCAmelCase				:				Any=1_2			, _lowerCAmelCase				:				int=1_2			, _lowerCAmelCase				:				str=3_0_7_2			, _lowerCAmelCase				:				List[Any]="gelu"			, _lowerCAmelCase				:				List[str]=0.1			, _lowerCAmelCase				:				Optional[int]=0.1			, _lowerCAmelCase				:				Optional[Any]=5_1_2			, _lowerCAmelCase				:				Optional[int]=2			, _lowerCAmelCase				:				Union[str, Any]=0.02			, _lowerCAmelCase				:				Union[str, Any]=1e-5			, _lowerCAmelCase				:				Any=1			, _lowerCAmelCase				:				Optional[int]=0			, _lowerCAmelCase				:				List[Any]=2			, _lowerCAmelCase				:				Dict=1_0_2_4			, _lowerCAmelCase				:				List[Any]=1_2_8			, _lowerCAmelCase				:				str=1_2_8			, _lowerCAmelCase				:				Optional[Any]=True			, _lowerCAmelCase				:				str=3_2			, _lowerCAmelCase				:				List[Any]=1_2_8			, _lowerCAmelCase				:				str=6_4			, _lowerCAmelCase				:				List[str]=2_5_6			, _lowerCAmelCase				:				Dict=True			, _lowerCAmelCase				:				List[Any]=True			, _lowerCAmelCase				:				Tuple=True			, _lowerCAmelCase				:				Tuple=2_2_4			, _lowerCAmelCase				:				Tuple=3			, _lowerCAmelCase				:				Dict=1_6			, _lowerCAmelCase				:				Union[str, Any]=None			, **_lowerCAmelCase				:				Optional[Any]			, )      ->       List[str]:




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






class 			__lowerCAmelCase   (       _a    ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE									=				version.parse('1.12'    )



  @property
  def     lowerCAmelCase__     (   self				:				Optional[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				:				str     )      ->       List[Any]:




     """simple docstring"""
     return 1e-5



  @property
  def     lowerCAmelCase__     (   self				:				Dict     )      ->       Tuple:




     """simple docstring"""
     return 1_2



  def     lowerCAmelCase__     (   self				:				Dict			, _lowerCAmelCase				:				"ProcessorMixin"			, _lowerCAmelCase				:				int = -1			, _lowerCAmelCase				:				int = -1			, _lowerCAmelCase				:				bool = False			, _lowerCAmelCase				:				Optional["TensorType"] = None			, _lowerCAmelCase				:				int = 3			, _lowerCAmelCase				:				int = 4_0			, _lowerCAmelCase				:				int = 4_0			, )      ->       List[Any]:




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

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

     # Generate dummy bounding boxes
     snake_case_    			=    [[[4_8, 8_4, 7_3, 1_2_8]]] * 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)
     snake_case_    			=    self._generate_dummy_images(__lowerCamelCase			, __lowerCamelCase			, __lowerCamelCase			, __lowerCamelCase     )

     snake_case_    			=    dict(
         processor(
             __lowerCamelCase			, text=__lowerCamelCase			, boxes=__lowerCamelCase			, return_tensors=__lowerCamelCase			, )     )

     return inputs












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

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

@dataclass(frozen=_a      )
class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     str     	=  field(default="""automatic-speech-recognition"""		,   metadata={"""include_in_asdict_even_if_is_default""": True}      )
	snake_case__							:     ClassVar[Features]     	=  Features({"""audio""": Audio()}      )
	snake_case__							:     ClassVar[Features]     	=  Features({"""transcription""": Value("""string"""      )}      )
	snake_case__							:     str     	=  "audio"
	snake_case__							:     str     	=  "transcription"





	def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					Dict	):
		if self.audio_column not in features:
			raise ValueError(F"""Column {self.audio_column} is not present in features."""	)
		if not isinstance(features[self.audio_column] ,  __lowerCamelCase	):
			raise ValueError(F"""Column {self.audio_column} is not an Audio type."""	)
		UpperCamelCase     :int          =	copy.deepcopy(self	)
		UpperCamelCase     :Any          =	self.input_schema.copy()
		UpperCamelCase     :List[str]          =	features[self.audio_column]
		UpperCamelCase     :List[Any]          =	input_schema
		return task_template





	@property
	def 						_A (		self				:					Optional[int]	):
		return {self.audio_column: "audio", self.transcription_column: "transcription"}
