





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__A							   =							{
    "configuration_layoutlmv3": [
        "LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LayoutLMv3Config",
        "LayoutLMv3OnnxConfig",
    ],
    "processing_layoutlmv3": ["LayoutLMv3Processor"],
    "tokenization_layoutlmv3": ["LayoutLMv3Tokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A							   =							["LayoutLMv3TokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A							   =							[
         "LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST",
         "LayoutLMv3ForQuestionAnswering",
         "LayoutLMv3ForSequenceClassification",
         "LayoutLMv3ForTokenClassification",
         "LayoutLMv3Model",
         "LayoutLMv3PreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A							   =							[
         "TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFLayoutLMv3ForQuestionAnswering",
         "TFLayoutLMv3ForSequenceClassification",
         "TFLayoutLMv3ForTokenClassification",
         "TFLayoutLMv3Model",
         "TFLayoutLMv3PreTrainedModel",
     ]

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A							   =							["LayoutLMv3FeatureExtractor"]
     __A							   =							["LayoutLMv3ImageProcessor"]


if TYPE_CHECKING:
     from .configuration_layoutlmva import (
         LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
         LayoutLMvaConfig,
         LayoutLMvaOnnxConfig,
     )
     from .processing_layoutlmva import LayoutLMvaProcessor
     from .tokenization_layoutlmva import LayoutLMvaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_layoutlmva import (
              LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
              LayoutLMvaForQuestionAnswering,
              LayoutLMvaForSequenceClassification,
              LayoutLMvaForTokenClassification,
              LayoutLMvaModel,
              LayoutLMvaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_layoutlmva import (
              TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFLayoutLMvaForQuestionAnswering,
              TFLayoutLMvaForSequenceClassification,
              TFLayoutLMvaForTokenClassification,
              TFLayoutLMvaModel,
              TFLayoutLMvaPreTrainedModel,
          )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
          from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
     import sys

     __A							   =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





from __future__ import annotations
class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self			,							snake_case_    ) ->      None:







								'''simple docstring'''

								__lowercase			       = order

								# a_{0} ... a_{k}
								__lowercase			       = [1.0] + [0.0] * order
								# b_{0} ... b_{k}
								__lowercase			       = [1.0] + [0.0] * order

								# x[n-1] ... x[n-k]
								__lowercase			       = [0.0] * self.order
								# y[n-1] ... y[n-k]
								__lowercase			       = [0.0] * self.order






					def A   (					self			,							snake_case_			,							snake_case_    ) ->      None:







								'''simple docstring'''

								if len(snake_case_    ) < self.order:
											__lowercase			       = [1.0, *a_coeffs]

								if len(snake_case_    ) != self.order + 1:
											__lowercase			       = (
											    F'Expected a_coeffs to have {self.order + 1} elements '
											    F'for {self.order}-order filter, got {len(snake_case_    )}'
											)
											raise ValueError(snake_case_    )

								if len(snake_case_    ) != self.order + 1:
											__lowercase			       = (
											    F'Expected b_coeffs to have {self.order + 1} elements '
											    F'for {self.order}-order filter, got {len(snake_case_    )}'
											)
											raise ValueError(snake_case_    )

								__lowercase			       = a_coeffs
								__lowercase			       = b_coeffs






					def A   (					self			,							snake_case_    ) ->      float:







								'''simple docstring'''

								__lowercase			       = 0.0

								# Start at index 1 and do index 0 at the end.
								for i in range(1			,							self.order + 1    ):
											result += (
											    self.b_coeffs[i] * self.input_history[i - 1]
											    - self.a_coeffs[i] * self.output_history[i - 1]
											)

								__lowercase			       = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

								__lowercase			       = self.input_history[:-1]
								__lowercase			       = self.output_history[:-1]

								__lowercase			       = sample
								__lowercase			       = result

								return result


