


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		['image_processor', 'tokenizer']
    __magic_name__        =		'LayoutLMv2ImageProcessor'
    __magic_name__        =		('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')
    def __init__(	self      , __snake_case=None      , __snake_case=None      , **__snake_case   ):
         if "feature_extractor" in kwargs:
              warnings.warn(
                  '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                  ''' instead.'''      , __snake_case      , )
              snake_case					=    kwargs.pop('''feature_extractor'''   )

         snake_case					=    image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
              raise ValueError('''You need to specify an `image_processor`.'''   )
         if tokenizer is None:
              raise ValueError('''You need to specify a `tokenizer`.'''   )

         super().__init__(__snake_case      , __snake_case   )
    def __call__(	self      , __snake_case      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = True      , __snake_case = False      , __snake_case = None      , __snake_case = None      , __snake_case = 0      , __snake_case = None      , __snake_case = None      , __snake_case = None      , __snake_case = False      , __snake_case = False      , __snake_case = False      , __snake_case = False      , __snake_case = True      , __snake_case = None      , **__snake_case      , ):
         # verify input
         if self.image_processor.apply_ocr and (boxes is not None):
              raise ValueError(
                  '''You cannot provide bounding boxes '''
                  '''if you initialized the image processor with apply_ocr set to True.'''   )

         if self.image_processor.apply_ocr and (word_labels is not None):
              raise ValueError(
                  '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''   )

         if return_overflowing_tokens is True and return_offsets_mapping is False:
              raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''   )

         # first, apply the image processor
         snake_case					=    self.image_processor(images=__snake_case      , return_tensors=__snake_case   )

         # second, apply the tokenizer
         if text is not None and self.image_processor.apply_ocr and text_pair is None:
              if isinstance(__snake_case      , __snake_case   ):
                   snake_case					=    [text]  # add batch dimension (as the image processor always adds a batch dimension)
              snake_case					=    features['''words''']

         snake_case					=    self.tokenizer(
             text=text if text is not None else features['''words''']      , text_pair=text_pair if text_pair is not None else None      , boxes=boxes if boxes is not None else features['''boxes''']      , word_labels=__snake_case      , add_special_tokens=__snake_case      , padding=__snake_case      , truncation=__snake_case      , max_length=__snake_case      , stride=__snake_case      , pad_to_multiple_of=__snake_case      , return_token_type_ids=__snake_case      , return_attention_mask=__snake_case      , return_overflowing_tokens=__snake_case      , return_special_tokens_mask=__snake_case      , return_offsets_mapping=__snake_case      , return_length=__snake_case      , verbose=__snake_case      , return_tensors=__snake_case      , **__snake_case      , )

         # add pixel values
         snake_case					=    features.pop('''pixel_values'''   )
         if return_overflowing_tokens is True:
              snake_case					=    self.get_overflowing_images(__snake_case      , encoded_inputs['''overflow_to_sample_mapping''']   )
         snake_case					=    images

         return encoded_inputs
    def 		a_      (	self      , __snake_case      , __snake_case   ):
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         snake_case					=    []
         for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx]   )

         if len(__snake_case   ) != len(__snake_case   ):
              raise ValueError(
                  '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                  F''' {len(__snake_case   )} and {len(__snake_case   )}'''   )

         return images_with_overflow
    def 		a_      (	self      , *__snake_case      , **__snake_case   ):
         return self.tokenizer.batch_decode(*__snake_case      , **__snake_case   )
    def 		a_      (	self      , *__snake_case      , **__snake_case   ):
         return self.tokenizer.decode(*__snake_case      , **__snake_case   )
    @property
    def 		a_      (	self   ):
         return ["input_ids", "bbox", "attention_mask", "image"]
    @property
    def 		a_      (	self   ):
         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''      , __snake_case      , )
         return self.image_processor_class




    @property
    def 		a_      (	self   ):
         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''      , __snake_case      , )
         return self.image_processor








import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_SCREAMING_SNAKE_CASE      :			Dict      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Any      				=   {
    "facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'detr'
    __magic_name__        =		['past_key_values']
    __magic_name__        =		{
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }
    def __init__(	self      , __snake_case=True      , __snake_case=None      , __snake_case=3      , __snake_case=1_0_0      , __snake_case=6      , __snake_case=2_0_4_8      , __snake_case=8      , __snake_case=6      , __snake_case=2_0_4_8      , __snake_case=8      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=True      , __snake_case="relu"      , __snake_case=2_5_6      , __snake_case=0.1      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=0.02      , __snake_case=1.0      , __snake_case=False      , __snake_case="sine"      , __snake_case="resnet50"      , __snake_case=True      , __snake_case=False      , __snake_case=1      , __snake_case=5      , __snake_case=2      , __snake_case=1      , __snake_case=1      , __snake_case=5      , __snake_case=2      , __snake_case=0.1      , **__snake_case      , ):
         if backbone_config is not None and use_timm_backbone:
              raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''   )

         if not use_timm_backbone:
              if backbone_config is None:
                   logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''   )
                   snake_case					=    CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']   )
              elif isinstance(__snake_case      , __snake_case   ):
                   snake_case					=    backbone_config.get('''model_type'''   )
                   snake_case					=    CONFIG_MAPPING[backbone_model_type]
                   snake_case					=    config_class.from_dict(__snake_case   )
              # set timm attributes to None
              snake_case			,  snake_case			,  snake_case					=    None, None, None

         snake_case					=    use_timm_backbone
         snake_case					=    backbone_config
         snake_case					=    num_channels
         snake_case					=    num_queries
         snake_case					=    d_model
         snake_case					=    encoder_ffn_dim
         snake_case					=    encoder_layers
         snake_case					=    encoder_attention_heads
         snake_case					=    decoder_ffn_dim
         snake_case					=    decoder_layers
         snake_case					=    decoder_attention_heads
         snake_case					=    dropout
         snake_case					=    attention_dropout
         snake_case					=    activation_dropout
         snake_case					=    activation_function
         snake_case					=    init_std
         snake_case					=    init_xavier_std
         snake_case					=    encoder_layerdrop
         snake_case					=    decoder_layerdrop
         snake_case					=    encoder_layers
         snake_case					=    auxiliary_loss
         snake_case					=    position_embedding_type
         snake_case					=    backbone
         snake_case					=    use_pretrained_backbone
         snake_case					=    dilation
         # Hungarian matcher
         snake_case					=    class_cost
         snake_case					=    bbox_cost
         snake_case					=    giou_cost
         # Loss coefficients
         snake_case					=    mask_loss_coefficient
         snake_case					=    dice_loss_coefficient
         snake_case					=    bbox_loss_coefficient
         snake_case					=    giou_loss_coefficient
         snake_case					=    eos_coefficient
         super().__init__(is_encoder_decoder=__snake_case      , **__snake_case   )
    @property
    def 		a_      (	self   ):
         return self.encoder_attention_heads
    @property
    def 		a_      (	self   ):
         return self.d_model
    @classmethod
    def 		a_      (	cls      , __snake_case      , **__snake_case   ):
         return cls(backbone_config=__snake_case      , **__snake_case   )




    def 		a_      (	self   ):
         snake_case					=    copy.deepcopy(self.__dict__   )
         if output["backbone_config"] is not None:
              snake_case					=    self.backbone_config.to_dict()
         snake_case					=    self.__class__.model_type
         return output






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		version.parse('1.11'     )
    @property
    def 		a_      (	self   ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ('''pixel_mask''', {0: '''batch'''}),
             ]   )
    @property
    def 		a_      (	self   ):
         return 1E-5




    @property
    def 		a_      (	self   ):
         return 1_2




