

"""simple docstring"""

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					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[int]    =						['image_processor', 'tokenizer']
       __UpperCamelCase					:      Any    =						'LayoutLMv2ImageProcessor'
       __UpperCamelCase					:      Any    =						('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')






       def __init__(   self			,					snake_case=None			,					snake_case=None			,					**snake_case   ):





        """simple docstring"""

        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			,					)
         lowerCAmelCase__   :				Dict 			=				kwargs.pop("feature_extractor"   )

        lowerCAmelCase__   :				Union[str, Any] 			=				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			,					):





        """simple docstring"""

        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
        lowerCAmelCase__   :				Any 			=				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   ):
          lowerCAmelCase__   :				int 			=				[text]  # add batch dimension (as the image processor always adds a batch dimension)
         lowerCAmelCase__   :				Optional[int] 			=				features["words"]

        lowerCAmelCase__   :				Tuple 			=				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
        lowerCAmelCase__   :				List[str] 			=				features.pop("pixel_values"   )
        if return_overflowing_tokens is True:
         lowerCAmelCase__   :				Any 			=				self.get_overflowing_images(snake_case			,					encoded_inputs["overflow_to_sample_mapping"]   )
        lowerCAmelCase__   :				Union[str, Any] 			=				images

        return encoded_inputs






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				[]
        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 			SCREAMING_SNAKE_CASE_	(   self			,					*snake_case			,					**snake_case   ):





        """simple docstring"""

        return self.tokenizer.batch_decode(*snake_case			,					**snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					*snake_case			,					**snake_case   ):





        """simple docstring"""

        return self.tokenizer.decode(*snake_case			,					**snake_case   )






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return ["input_ids", "bbox", "attention_mask", "image"]






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        warnings.warn(
            "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,					snake_case			,					)
        return self.image_processor









"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    {
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      str    =						'dpr'






       def __init__(   self			,					snake_case=30_522			,					snake_case=768			,					snake_case=12			,					snake_case=12			,					snake_case=3_072			,					snake_case="gelu"			,					snake_case=0.1			,					snake_case=0.1			,					snake_case=512			,					snake_case=2			,					snake_case=0.02			,					snake_case=1e-12			,					snake_case=0			,					snake_case="absolute"			,					snake_case = 0			,					**snake_case			,					):





        """simple docstring"""

        super().__init__(pad_token_id=snake_case			,					**snake_case   )

        lowerCAmelCase__   :				Dict 			=				vocab_size
        lowerCAmelCase__   :				int 			=				hidden_size
        lowerCAmelCase__   :				List[str] 			=				num_hidden_layers
        lowerCAmelCase__   :				List[Any] 			=				num_attention_heads
        lowerCAmelCase__   :				Any 			=				hidden_act
        lowerCAmelCase__   :				Any 			=				intermediate_size
        lowerCAmelCase__   :				str 			=				hidden_dropout_prob
        lowerCAmelCase__   :				List[str] 			=				attention_probs_dropout_prob
        lowerCAmelCase__   :				List[Any] 			=				max_position_embeddings
        lowerCAmelCase__   :				Optional[Any] 			=				type_vocab_size
        lowerCAmelCase__   :				Tuple 			=				initializer_range
        lowerCAmelCase__   :				Dict 			=				layer_norm_eps
        lowerCAmelCase__   :				Dict 			=				projection_dim
        lowerCAmelCase__   :				Any 			=				position_embedding_type






