



from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowercase    	=		logging.get_logger(__name__)



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					['''input_values''', '''padding_mask''']




     def __init__(					self			,    a__ = 1			,    a__ = 2_4_0_0_0			,    a__ = 0.0			,    a__ = None			,    a__ = None			,    **a__			,    ):
         super().__init__(feature_size=a__			,    sampling_rate=a__			,    padding_value=a__			,    **a__)
         A__					=       chunk_length_s
         A__					=       overlap




     @property
     def 		snake_case_		(					self):
         if self.chunk_length_s is None:
             return None
         else:
             return int(self.chunk_length_s * self.sampling_rate)




     @property
     def 		snake_case_		(					self):
         if self.chunk_length_s is None or self.overlap is None:
             return None
         else:
             return max(1			,    int((1.0 - self.overlap) * self.chunk_length))




     def __call__(					self			,    a__			,    a__ = None			,    a__ = False			,    a__ = None			,    a__ = None			,    a__ = None			,    ):
         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     F"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
                     F" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
                     F" {self.sampling_rate} and not {sampling_rate}.")
         else:
             logger.warning(
                 '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                 '''Failing to do so can result in silent errors that might be hard to debug.''')

         if padding and truncation:
             raise ValueError('''Both padding and truncation were set. Make sure you only set one.''')
         elif padding is None:
             # by default let's pad the inputs
             A__					=       True

         A__					=       bool(
             isinstance(a__			,    (list, tuple)) and (isinstance(raw_audio[0]			,    (np.ndarray, tuple, list))))

         if is_batched:
             A__					=       [np.asarray(a__			,    dtype=np.floataa).T for audio in raw_audio]
         elif not is_batched and not isinstance(a__			,    np.ndarray):
             A__					=       np.asarray(a__			,    dtype=np.floataa)
         elif isinstance(a__			,    np.ndarray) and raw_audio.dtype is np.dtype(np.floataa):
             A__					=       raw_audio.astype(np.floataa)

         # always return batch
         if not is_batched:
             A__					=       [np.asarray(a__).T]

         # verify inputs are valid
         for idx, example in enumerate(a__):
             if example.ndim > 2:
                 raise ValueError(F"Expected input shape (channels, length) but got shape {example.shape}")
             if self.feature_size == 1 and example.ndim != 1:
                 raise ValueError(F"Expected mono audio but example has {example.shape[-1]} channels")
             if self.feature_size == 2 and example.shape[-1] != 2:
                 raise ValueError(F"Expected stereo audio but example has {example.shape[-1]} channels")

         A__					=       None
         A__					=       BatchFeature({'''input_values''': raw_audio})
         if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
             if truncation:
                 A__					=       min(array.shape[0] for array in raw_audio)
                 A__					=       int(np.floor(max_length / self.chunk_stride))
                 A__					=       (nb_step - 1) * self.chunk_stride + self.chunk_length
             elif padding:
                 A__					=       max(array.shape[0] for array in raw_audio)
                 A__					=       int(np.ceil(max_length / self.chunk_stride))
                 A__					=       (nb_step - 1) * self.chunk_stride + self.chunk_length
                 A__					=       '''max_length'''
             else:
                 A__					=       input_values

        # normal padding on batch
         if padded_inputs is None:
             A__					=       self.pad(
                 a__			,    max_length=a__			,    truncation=a__			,    padding=a__			,    return_attention_mask=a__			,    )
             if padding:
                 A__					=       padded_inputs.pop('''attention_mask''')

         A__					=       []
         for example in padded_inputs.pop('''input_values'''):
             if self.feature_size == 1:
                 A__					=       example[..., None]
             input_values.append(example.T)

         A__					=       input_values
         if return_tensors is not None:
             A__					=       padded_inputs.convert_to_tensors(a__)

         return padded_inputs





from typing import TYPE_CHECKING

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


_lowercase    	=		{
    "configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
    "processing_layoutlmv2": ["LayoutLMv2Processor"],
    "tokenization_layoutlmv2": ["LayoutLMv2Tokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		["LayoutLMv2TokenizerFast"]

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		["LayoutLMv2FeatureExtractor"]
   _lowercase    	=		["LayoutLMv2ImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		[
       "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMv2ForQuestionAnswering",
       "LayoutLMv2ForSequenceClassification",
       "LayoutLMv2ForTokenClassification",
       "LayoutLMv2Layer",
       "LayoutLMv2Model",
       "LayoutLMv2PreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
   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_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlmva import (
          LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMvaForQuestionAnswering,
          LayoutLMvaForSequenceClassification,
          LayoutLMvaForTokenClassification,
          LayoutLMvaLayer,
          LayoutLMvaModel,
          LayoutLMvaPreTrainedModel,
      )
else:
   import sys

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