




'''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 	SCREAMING_SNAKE_CASE(    __A					):
       snake_case_			:					Union[str, Any]   = ["""image_processor""", """tokenizer"""]
       snake_case_			:					Optional[int]   = """LayoutLMv2ImageProcessor"""
       snake_case_			:					List[str]   = ("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")

       def __init__(   self ,		lowerCamelCase__=None ,		lowerCamelCase__=None ,		**lowerCamelCase__					)   -> Union[str, Any]:

           """simple docstring"""



           __lowercase          =						None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                   """ instead.""" ,		lowerCamelCase__ ,		)
               __lowercase          =						kwargs.pop("""feature_extractor"""					)

           __lowercase          =						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 ,		lowerCamelCase__ ,		lowerCamelCase__ = None ,		lowerCamelCase__ = None ,		lowerCamelCase__ = None ,		lowerCamelCase__ = None ,		lowerCamelCase__ = True ,		lowerCamelCase__ = False ,		lowerCamelCase__ = False ,		lowerCamelCase__ = None ,		lowerCamelCase__ = 0 ,		lowerCamelCase__ = None ,		lowerCamelCase__ = None ,		lowerCamelCase__ = None ,		lowerCamelCase__ = False ,		lowerCamelCase__ = False ,		lowerCamelCase__ = False ,		lowerCamelCase__ = False ,		lowerCamelCase__ = True ,		lowerCamelCase__ = None ,		**lowerCamelCase__ ,		)   -> BatchEncoding:

           """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
           __lowercase          =						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__					):
                   __lowercase          =						[text]  # add batch dimension (as the image processor always adds a batch dimension)
               __lowercase          =						features["""words"""]

           __lowercase          =						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
           __lowercase          =						features.pop("""pixel_values"""					)
           if return_overflowing_tokens is True:
               __lowercase          =						self.get_overflowing_images(lowerCamelCase__ ,		encoded_inputs["""overflow_to_sample_mapping"""]					)
           __lowercase          =						images

           return encoded_inputs

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__					)   -> List[str]:

           """simple docstring"""



           __lowercase          =						[]
           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    snake_case__      (   self ,		*lowerCamelCase__ ,		**lowerCamelCase__					)   -> Dict:

           """simple docstring"""



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

       def    snake_case__      (   self ,		*lowerCamelCase__ ,		**lowerCamelCase__					)   -> str:

           """simple docstring"""



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

       @property
       def    snake_case__      (   self					)   -> int:

           """simple docstring"""



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

       @property
       def    snake_case__      (   self					)   -> Dict:

           """simple docstring"""



           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    snake_case__      (   self					)   -> Any:

           """simple docstring"""



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










'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm
def     snake_case_  (       a__		:     int   ,a__		:     bool = False      ):



    """simple docstring"""




    if device_specific:
        seed += AcceleratorState().process_index
    random.seed(a__      )
    np.random.seed(a__      )
    torch.manual_seed(a__      )
    if is_xpu_available():
        torch.xpu.manual_seed_all(a__      )
    elif is_npu_available():
        torch.npu.manual_seed_all(a__      )
    else:
        torch.cuda.manual_seed_all(a__      )
    # ^^ safe to call this function even if cuda is not available
    if is_tpu_available():
        xm.set_rng_state(a__      )
def     snake_case_  (       a__		:     Optional[RNGType] = None   ,a__		:     Optional[torch.Generator] = None      ):



    """simple docstring"""




    if rng_type == RNGType.TORCH:
        __lowercase          =						torch.get_rng_state()
    elif rng_type == RNGType.CUDA:
        __lowercase          =						torch.cuda.get_rng_state()
    elif rng_type == RNGType.XLA:
        assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
        __lowercase          =						torch.tensor(xm.get_rng_state()      )
    elif rng_type == RNGType.NPU:
        assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
        __lowercase          =						torch.npu.get_rng_state()
    elif rng_type == RNGType.XPU:
        assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
        __lowercase          =						torch.xpu.get_rng_state()
    elif rng_type == RNGType.GENERATOR:
        assert generator is not None, "Need a generator to synchronize its seed."
        __lowercase          =						generator.get_state()

    # Broadcast the rng state from device 0 to other devices
    __lowercase          =						AcceleratorState()
    if state.distributed_type == DistributedType.TPU:
        __lowercase          =						rng_state.to(xm.xla_device()      )
        xm.collective_broadcast([rng_state]      )
        xm.mark_step()
        __lowercase          =						rng_state.cpu()
    elif (
        state.distributed_type in CUDA_DISTRIBUTED_TYPES
        or state.distributed_type == DistributedType.MULTI_NPU
        or state.distributed_type == DistributedType.MULTI_XPU
    ):
        __lowercase          =						rng_state.to(state.device      )
        torch.distributed.broadcast(a__   ,0      )
        __lowercase          =						rng_state.cpu()
    elif state.distributed_type == DistributedType.MULTI_CPU:
        torch.distributed.broadcast(a__   ,0      )

    # Set the broadcast rng state
    if rng_type == RNGType.TORCH:
        torch.set_rng_state(a__      )
    elif rng_type == RNGType.CUDA:
        torch.cuda.set_rng_state(a__      )
    elif rng_type == RNGType.NPU:
        torch.npu.set_rng_state(a__      )
    elif rng_type == RNGType.XPU:
        torch.xpu.set_rng_state(a__      )
    elif rng_type == RNGType.XLA:
        xm.set_rng_state(rng_state.item()      )
    elif rng_type == RNGType.GENERATOR:
        generator.set_state(a__      )



def     snake_case_  (       a__		:     List[Union[str, RNGType]]   ,a__		:     Optional[torch.Generator] = None      ):



    """simple docstring"""




    for rng_type in rng_types:
        synchronize_rng_state(RNGType(a__      )   ,generator=a__      )




