


'''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 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					["image_processor", "tokenizer"]
 UpperCAmelCase				=					"LayoutLMv2ImageProcessor"
 UpperCAmelCase				=					("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")


 def __init__(      self							:	Optional[int]			,       __lowerCamelCase							:	Union[str, Any]=None			,       __lowerCamelCase							:	Optional[Any]=None			,       **__lowerCamelCase							:	Tuple ):
    if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.'''			,       __lowerCamelCase			,       )
       UpperCAmelCase__	:Tuple      =      kwargs.pop('''feature_extractor''' )

    UpperCAmelCase__	:Tuple      =      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__(__lowerCamelCase			,       __lowerCamelCase )


 def __call__(      self							:	Optional[Any]			,       __lowerCamelCase							:	Tuple			,       __lowerCamelCase							:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,       __lowerCamelCase							:	Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,       __lowerCamelCase							:	Union[List[List[int]], List[List[List[int]]]] = None			,       __lowerCamelCase							:	Optional[Union[List[int], List[List[int]]]] = None			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Union[bool, str, PaddingStrategy] = False			,       __lowerCamelCase							:	Union[bool, str, TruncationStrategy] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[bool] = None			,       __lowerCamelCase							:	Optional[bool] = None			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Optional[Union[str, TensorType]] = None			,       **__lowerCamelCase							:	List[str]			,       ):
    # 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
    UpperCAmelCase__	:Optional[Any]      =      self.image_processor(images=__lowerCamelCase			,       return_tensors=__lowerCamelCase )

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

    UpperCAmelCase__	:Dict      =      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=__lowerCamelCase			,       add_special_tokens=__lowerCamelCase			,       padding=__lowerCamelCase			,       truncation=__lowerCamelCase			,       max_length=__lowerCamelCase			,       stride=__lowerCamelCase			,       pad_to_multiple_of=__lowerCamelCase			,       return_token_type_ids=__lowerCamelCase			,       return_attention_mask=__lowerCamelCase			,       return_overflowing_tokens=__lowerCamelCase			,       return_special_tokens_mask=__lowerCamelCase			,       return_offsets_mapping=__lowerCamelCase			,       return_length=__lowerCamelCase			,       verbose=__lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase			,       )

    # add pixel values
    UpperCAmelCase__	:Union[str, Any]      =      features.pop('''pixel_values''' )
    if return_overflowing_tokens is True:
       UpperCAmelCase__	:Optional[int]      =      self.get_overflowing_images(__lowerCamelCase			,       encoded_inputs['''overflow_to_sample_mapping'''] )
    UpperCAmelCase__	:Any      =      images

    return encoded_inputs


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	List[Any]			,       __lowerCamelCase							:	Optional[int] ):
    # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
    UpperCAmelCase__	:Optional[int]      =      []
    for sample_idx in overflow_to_sample_mapping:
       images_with_overflow.append(images[sample_idx] )

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

    return images_with_overflow


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       *__lowerCamelCase							:	Optional[int]			,       **__lowerCamelCase							:	Union[str, Any] ):
    return self.tokenizer.batch_decode(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple			,       *__lowerCamelCase							:	Any			,       **__lowerCamelCase							:	List[Any] ):
    return self.tokenizer.decode(*__lowerCamelCase			,       **__lowerCamelCase )


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any] ):
    return ["input_ids", "bbox", "attention_mask", "image"]


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,       __lowerCamelCase			,       )
    return self.image_processor_class





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,       __lowerCamelCase			,       )
    return self.image_processor







'''simple docstring'''





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 				UpperCAmelCase			(			nn.Module						):


 def __init__(      self							:	int			,       __lowerCamelCase							:	int = 1_6			,       __lowerCamelCase							:	int = 8_8			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	float = 0.0			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "geglu"			,       __lowerCamelCase							:	Optional[int] = None			,       ):
    super().__init__()
    UpperCAmelCase__	:Optional[int]      =      nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=__lowerCamelCase			,       attention_head_dim=__lowerCamelCase			,       in_channels=__lowerCamelCase			,       num_layers=__lowerCamelCase			,       dropout=__lowerCamelCase			,       norm_num_groups=__lowerCamelCase			,       cross_attention_dim=__lowerCamelCase			,       attention_bias=__lowerCamelCase			,       sample_size=__lowerCamelCase			,       num_vector_embeds=__lowerCamelCase			,       activation_fn=__lowerCamelCase			,       num_embeds_ada_norm=__lowerCamelCase			,       )
            for _ in range(2 )
        ] )

    # Variables that can be set by a pipeline:

    # The ratio of transformer1 to transformer2's output states to be combined during inference
    UpperCAmelCase__	:Tuple      =      0.5

    # The shape of `encoder_hidden_states` is expected to be
    # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
    UpperCAmelCase__	:int      =      [7_7, 2_5_7]

    # Which transformer to use to encode which condition.
    # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
    UpperCAmelCase__	:Any      =      [1, 0]





 def        __SCREAMING_SNAKE_CASE					(      self							:	str			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	int=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	bool = True			,       ):
    UpperCAmelCase__	:Optional[int]      =      hidden_states

    UpperCAmelCase__	:Dict      =      []
    UpperCAmelCase__	:Tuple      =      0
    # attention_mask is not used yet
    for i in range(2 ):
       # for each of the two transformers, pass the corresponding condition tokens
       UpperCAmelCase__	:List[Any]      =      encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
       UpperCAmelCase__	:Dict      =      self.transformer_index_for_condition[i]
       UpperCAmelCase__	:Tuple      =      self.transformers[transformer_index](
           __lowerCamelCase			,       encoder_hidden_states=__lowerCamelCase			,       timestep=__lowerCamelCase			,       cross_attention_kwargs=__lowerCamelCase			,       return_dict=__lowerCamelCase			,       )[0]
       encoded_states.append(encoded_state - input_states )
       tokens_start += self.condition_lengths[i]

    UpperCAmelCase__	:Optional[int]      =      encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
    UpperCAmelCase__	:Optional[Any]      =      output_states + input_states

    if not return_dict:
       return (output_states,)

    return TransformeraDModelOutput(sample=__lowerCamelCase )



