






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__      :			str							=		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      :			str							=		{
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}
class 						lowerCamelCase_				(      UpperCamelCase_ ):
    a__					       =  '''layoutlmv3'''







    def __init__(						self       ,					__lowerCAmelCase=5_0_2_6_5       ,					__lowerCAmelCase=7_6_8       ,					__lowerCAmelCase=1_2       ,					__lowerCAmelCase=1_2       ,					__lowerCAmelCase=3_0_7_2       ,					__lowerCAmelCase="gelu"       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=5_1_2       ,					__lowerCAmelCase=2       ,					__lowerCAmelCase=0.02       ,					__lowerCAmelCase=1E-5       ,					__lowerCAmelCase=1       ,					__lowerCAmelCase=0       ,					__lowerCAmelCase=2       ,					__lowerCAmelCase=1_0_2_4       ,					__lowerCAmelCase=1_2_8       ,					__lowerCAmelCase=1_2_8       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=3_2       ,					__lowerCAmelCase=1_2_8       ,					__lowerCAmelCase=6_4       ,					__lowerCAmelCase=2_5_6       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=2_2_4       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=1_6       ,					__lowerCAmelCase=None       ,					**__lowerCAmelCase       ,					):
        """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__       ,					)
        __magic_name__		:Union[str, Any]        =      max_ad_position_embeddings
        __magic_name__		:List[Any]        =      coordinate_size
        __magic_name__		:List[str]        =      shape_size
        __magic_name__		:Optional[int]        =      has_relative_attention_bias
        __magic_name__		:List[Any]        =      rel_pos_bins
        __magic_name__		:str        =      max_rel_pos
        __magic_name__		:Any        =      has_spatial_attention_bias
        __magic_name__		:Union[str, Any]        =      rel_ad_pos_bins
        __magic_name__		:Union[str, Any]        =      max_rel_ad_pos
        __magic_name__		:Union[str, Any]        =      text_embed
        __magic_name__		:List[str]        =      visual_embed
        __magic_name__		:Optional[Any]        =      input_size
        __magic_name__		:Optional[Any]        =      num_channels
        __magic_name__		:List[Any]        =      patch_size
        __magic_name__		:List[Any]        =      classifier_dropout







class 						lowerCamelCase_				(      UpperCamelCase_ ):
    a__					       =  version.parse('''1.12''' )







    @property
    def 			A	(						self     ):
        """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 			A	(						self     ):
        """simple docstring"""
        return 1E-5







    @property
    def 			A	(						self     ):
        """simple docstring"""
        return 1_2







    def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase = -1       ,					__lowerCAmelCase = -1       ,					__lowerCAmelCase = False       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = 3       ,					__lowerCAmelCase = 4_0       ,					__lowerCAmelCase = 4_0       ,					):
        """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
        __magic_name__		:Any        =      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
        __magic_name__		:List[Any]        =      processor.tokenizer.num_special_tokens_to_add(UpperCamelCase__     )
        __magic_name__		:Tuple        =      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
        __magic_name__		:Union[str, Any]        =      [[''' '''.join([processor.tokenizer.unk_token]     ) * seq_length]] * batch_size

        # Generate dummy bounding boxes
        __magic_name__		:Any        =      [[[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)
        __magic_name__		:List[Any]        =      self._generate_dummy_images(UpperCamelCase__       ,					UpperCamelCase__       ,					UpperCamelCase__       ,					UpperCamelCase__     )

        __magic_name__		:Optional[int]        =      dict(
            processor(
                UpperCamelCase__       ,					text=UpperCamelCase__       ,					boxes=UpperCamelCase__       ,					return_tensors=UpperCamelCase__       ,					)     )

        return inputs














from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url

@pytest.mark.parametrize('''repo_id''',  ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''',  ['''filename.csv''', '''filename with blanks.csv'''] )
@pytest.mark.parametrize('''revision''',  [None, '''v2'''] )
def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Tuple        =      hf_hub_url(repo_id=snake_case,  path=snake_case,  revision=snake_case )
				assert url == f'''https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(snake_case )}'''






