


from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

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


lowercase_           =	logging.get_logger(__name__)


class        A_					(       a__    ):




      '''simple docstring'''


      __snake_case					       =   ["""input_values""", """attention_mask"""]




      def __init__(				self:       int						,      a:       Tuple=1						,      a:       str=1_6000						,      a:       int=128						,      a:       int=1024						,      a:       str=0.0						,      a:       List[str]=True						,      a:       Union[str, Any]=-4.2_6_7_7_3_9_3						,      a:       Dict=4.5_6_8_9_9_7_4						,      a:       Tuple=False						,      **a:       List[Any]						,      ):
       super().__init__(feature_size=_snake_case						,      sampling_rate=_snake_case						,      padding_value=_snake_case						,      **_snake_case				)
       __lowerCamelCase		:							str							=						num_mel_bins
       __lowerCamelCase		:							Tuple							=						max_length
       __lowerCamelCase		:							str							=						do_normalize
       __lowerCamelCase		:							Tuple							=						mean
       __lowerCamelCase		:							Optional[Any]							=						std
       __lowerCamelCase		:							Union[str, Any]							=						return_attention_mask




      def      _snake_case	(				self:       int						,      a:       str						,      a:       Optional[Any]						,      ):
       __lowerCamelCase		:							str							=						torch.from_numpy(_snake_case				).unsqueeze(0				)
       __lowerCamelCase		:							Union[str, Any]							=						ta_kaldi.fbank(
           _snake_case						,      htk_compat=_snake_case						,      sample_frequency=self.sampling_rate						,      use_energy=_snake_case						,      window_type='hanning'						,      num_mel_bins=self.num_mel_bins						,      dither=0.0						,      frame_shift=10						,      )

       __lowerCamelCase		:							Union[str, Any]							=						fbank.shape[0]
       __lowerCamelCase		:							str							=						max_length - n_frames

       # pad or truncate, depending on difference
       if difference > 0:
        __lowerCamelCase		:							List[str]							=						torch.nn.ZeroPadad((0, 0, 0, difference)				)
        __lowerCamelCase		:							List[Any]							=						pad_module(_snake_case				)
       elif difference < 0:
        __lowerCamelCase		:							Optional[Any]							=						fbank[0:max_length, :]

       __lowerCamelCase		:							List[Any]							=						fbank.numpy()

       return fbank




      def      _snake_case	(				self:       Dict						,      a:       Dict				):
       return (input_values - (self.mean)) / (self.std * 2)




      def __call__(				self:       Union[str, Any]						,      a:       str						,      a:       Dict = None						,      a:       int = None						,      **a:       str						,      ):






       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 `raw_speech` 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.'				)

       __lowerCamelCase		:							Dict							=						isinstance(_snake_case						,      np.ndarray				) and len(raw_speech.shape				) > 1
       if is_batched_numpy and len(raw_speech.shape				) > 2:
        raise ValueError(F'Only mono-channel audio is supported for input to {self}'				)
       __lowerCamelCase		:							Any							=						is_batched_numpy or (
           isinstance(_snake_case						,      (list, tuple)				) and (isinstance(raw_speech[0]						,      (np.ndarray, tuple, list)				))
       )

       if is_batched:
        __lowerCamelCase		:							List[Any]							=						[np.asarray(_snake_case						,      dtype=np.floataa				) for speech in raw_speech]
       elif not is_batched and not isinstance(_snake_case						,      np.ndarray				):
        __lowerCamelCase		:							List[Any]							=						np.asarray(_snake_case						,      dtype=np.floataa				)
       elif isinstance(_snake_case						,      np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
        __lowerCamelCase		:							Dict							=						raw_speech.astype(np.floataa				)

       # always return batch
       if not is_batched:
        __lowerCamelCase		:							Optional[int]							=						[raw_speech]

       # extract fbank features and pad/truncate to max_length
       __lowerCamelCase		:							List[Any]							=						[self._extract_fbank_features(_snake_case						,      max_length=self.max_length				) for waveform in raw_speech]

       # convert into BatchFeature
       __lowerCamelCase		:							Union[str, Any]							=						BatchFeature({'input_values': features}				)

       # make sure list is in array format
       __lowerCamelCase		:							List[Any]							=						padded_inputs.get('input_values'				)
       if isinstance(input_values[0]						,      _snake_case				):
        __lowerCamelCase		:							str							=						[np.asarray(_snake_case						,      dtype=np.floataa				) for feature in input_values]

       # normalization
       if self.do_normalize:
        __lowerCamelCase		:							List[str]							=						[self.normalize(_snake_case				) for feature in input_values]

       if return_tensors is not None:
        __lowerCamelCase		:							Optional[Any]							=						padded_inputs.convert_to_tensors(_snake_case				)

       return padded_inputs








"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


__UpperCamelCase			:		List[Any]		    =   logging.get_logger(__name__)

__UpperCamelCase			:		Union[str, Any]		    =   {
    '''layoutlmv2-base-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json''',
    '''layoutlmv2-large-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json''',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
       import detectrona







class 	a				(					a__   ):
 snake_case__    	=    '''layoutlmv2'''



 def __init__(			self							,    _snake_case=3_05_22							,    _snake_case=7_68							,    _snake_case=12							,    _snake_case=12							,    _snake_case=30_72							,    _snake_case="gelu"							,    _snake_case=0.1							,    _snake_case=0.1							,    _snake_case=5_12							,    _snake_case=2							,    _snake_case=0.02							,    _snake_case=1E-12							,    _snake_case=0							,    _snake_case=10_24							,    _snake_case=1_28							,    _snake_case=32							,    _snake_case=True							,    _snake_case=2_56							,    _snake_case=64							,    _snake_case=True							,    _snake_case=[7, 7, 2_56]							,    _snake_case=1_28							,    _snake_case=1_28							,    _snake_case=True							,    _snake_case=True							,    _snake_case=False							,    _snake_case=None							,    **_snake_case							,    ):







  """simple docstring"""






  super().__init__(
      vocab_size=_snake_case							,    hidden_size=_snake_case							,    num_hidden_layers=_snake_case							,    num_attention_heads=_snake_case							,    intermediate_size=_snake_case							,    hidden_act=_snake_case							,    hidden_dropout_prob=_snake_case							,    attention_probs_dropout_prob=_snake_case							,    max_position_embeddings=_snake_case							,    type_vocab_size=_snake_case							,    initializer_range=_snake_case							,    layer_norm_eps=_snake_case							,    pad_token_id=_snake_case							,    **_snake_case							,    )
  lowerCAmelCase  							=      max_ad_position_embeddings
  lowerCAmelCase  							=      max_rel_pos
  lowerCAmelCase  							=      rel_pos_bins
  lowerCAmelCase  							=      fast_qkv
  lowerCAmelCase  							=      max_rel_ad_pos
  lowerCAmelCase  							=      rel_ad_pos_bins
  lowerCAmelCase  							=      convert_sync_batchnorm
  lowerCAmelCase  							=      image_feature_pool_shape
  lowerCAmelCase  							=      coordinate_size
  lowerCAmelCase  							=      shape_size
  lowerCAmelCase  							=      has_relative_attention_bias
  lowerCAmelCase  							=      has_spatial_attention_bias
  lowerCAmelCase  							=      has_visual_segment_embedding
  lowerCAmelCase  							=      (
      detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
  )



 @classmethod
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  return {
      "MODEL.MASK_ON": True,
      "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
      "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
      "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
      "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
      "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
      "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 20_00,
      "MODEL.RPN.PRE_NMS_TOPK_TEST": 10_00,
      "MODEL.RPN.POST_NMS_TOPK_TRAIN": 10_00,
      "MODEL.POST_NMS_TOPK_TEST": 10_00,
      "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
      "MODEL.ROI_HEADS.NUM_CLASSES": 5,
      "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
      "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
      "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
      "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
      "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
      "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
      "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
      "MODEL.RESNETS.DEPTH": 1_01,
      "MODEL.RESNETS.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
      "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
      "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
      "MODEL.RESNETS.NUM_GROUPS": 32,
      "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
      "MODEL.RESNETS.STRIDE_IN_1X1": False,
  }



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      detectrona.config.get_cfg()
  for k, v in self.detectrona_config_args.items():
   lowerCAmelCase  							=      k.split('.'			)
   lowerCAmelCase  							=      detectrona_config
   for attribute in attributes[:-1]:
    lowerCAmelCase  							=      getattr(_snake_case							,    _snake_case			)
   setattr(_snake_case							,    attributes[-1]							,    _snake_case			)

  return detectrona_config





