





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase :					int 							=logging.get_logger(__name__)

lowerCAmelCase :					Optional[int] 							={
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}
class 				a_       (    _lowerCAmelCase							):
     __A								=      "table-transformer"
     __A								=      ["past_key_values"]
     __A								=      {
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }





     def __init__(			self		: Any     ,				lowercase		: Dict=True     ,				lowercase		: List[str]=None     ,				lowercase		: int=3     ,				lowercase		: str=100     ,				lowercase		: str=6     ,				lowercase		: str=2_048     ,				lowercase		: Dict=8     ,				lowercase		: Optional[int]=6     ,				lowercase		: Tuple=2_048     ,				lowercase		: Dict=8     ,				lowercase		: Optional[Any]=0.0     ,				lowercase		: str=0.0     ,				lowercase		: str=True     ,				lowercase		: Any="relu"     ,				lowercase		: int=256     ,				lowercase		: Dict=0.1     ,				lowercase		: Any=0.0     ,				lowercase		: List[str]=0.0     ,				lowercase		: Tuple=0.02     ,				lowercase		: Optional[int]=1.0     ,				lowercase		: List[Any]=False     ,				lowercase		: Union[str, Any]="sine"     ,				lowercase		: str="resnet50"     ,				lowercase		: Tuple=True     ,				lowercase		: Union[str, Any]=False     ,				lowercase		: int=1     ,				lowercase		: Optional[int]=5     ,				lowercase		: Any=2     ,				lowercase		: int=1     ,				lowercase		: int=1     ,				lowercase		: int=5     ,				lowercase		: List[Any]=2     ,				lowercase		: Dict=0.1     ,				**lowercase		: Union[str, Any]     ,				):





        """simple docstring"""




        if backbone_config is not None and use_timm_backbone:
           raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."						)

        if not use_timm_backbone:
           if backbone_config is None:
              logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."						)
              lowercase_      :str         =			CONFIG_MAPPING["resnet"](out_features=["stage4"]						)
           elif isinstance(_a     ,				_a						):
              lowercase_      :str         =			backbone_config.get("model_type"						)
              lowercase_      :str         =			CONFIG_MAPPING[backbone_model_type]
              lowercase_      :str         =			config_class.from_dict(_a						)
           # set timm attributes to None
           lowercase_      :Optional[Any]         =			None, None, None

        lowercase_      :List[str]         =			use_timm_backbone
        lowercase_      :Optional[int]         =			backbone_config
        lowercase_      :Tuple         =			num_channels
        lowercase_      :int         =			num_queries
        lowercase_      :Optional[Any]         =			d_model
        lowercase_      :Tuple         =			encoder_ffn_dim
        lowercase_      :str         =			encoder_layers
        lowercase_      :Union[str, Any]         =			encoder_attention_heads
        lowercase_      :Optional[int]         =			decoder_ffn_dim
        lowercase_      :Union[str, Any]         =			decoder_layers
        lowercase_      :str         =			decoder_attention_heads
        lowercase_      :Dict         =			dropout
        lowercase_      :List[Any]         =			attention_dropout
        lowercase_      :Union[str, Any]         =			activation_dropout
        lowercase_      :Any         =			activation_function
        lowercase_      :str         =			init_std
        lowercase_      :Optional[Any]         =			init_xavier_std
        lowercase_      :List[str]         =			encoder_layerdrop
        lowercase_      :Optional[int]         =			decoder_layerdrop
        lowercase_      :Union[str, Any]         =			encoder_layers
        lowercase_      :Union[str, Any]         =			auxiliary_loss
        lowercase_      :Tuple         =			position_embedding_type
        lowercase_      :Dict         =			backbone
        lowercase_      :Any         =			use_pretrained_backbone
        lowercase_      :Optional[Any]         =			dilation
        # Hungarian matcher
        lowercase_      :List[str]         =			class_cost
        lowercase_      :Any         =			bbox_cost
        lowercase_      :int         =			giou_cost
        # Loss coefficients
        lowercase_      :Union[str, Any]         =			mask_loss_coefficient
        lowercase_      :List[str]         =			dice_loss_coefficient
        lowercase_      :Tuple         =			bbox_loss_coefficient
        lowercase_      :List[str]         =			giou_loss_coefficient
        lowercase_      :List[str]         =			eos_coefficient
        super().__init__(is_encoder_decoder=_a     ,				**_a						)





     @property
     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        return self.encoder_attention_heads







     @property
     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        return self.d_model







class 				a_       (    _lowerCAmelCase							):
     __A								=      version.parse("1.11"							)





     @property
     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                ("pixel_mask", {0: "batch"}),
            ]						)





     @property
     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        return 1e-5







     @property
     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        return 12









'''simple docstring'''


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCAmelCase :					Optional[int] 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):
     __A								=      ["input_features"]





     def __init__(			self		: Any     ,				lowercase		: Tuple=80     ,				lowercase		: Optional[int]=16_000     ,				lowercase		: Optional[Any]=160     ,				lowercase		: Optional[int]=30     ,				lowercase		: List[Any]=400     ,				lowercase		: Dict=0.0     ,				lowercase		: Tuple=False     ,				**lowercase		: Optional[int]     ,				):





        """simple docstring"""




        super().__init__(
            feature_size=lowercase     ,				sampling_rate=lowercase     ,				padding_value=lowercase     ,				return_attention_mask=lowercase     ,				**lowercase     ,				)
        lowercase_      :Optional[int]         =			n_fft
        lowercase_      :List[Any]         =			hop_length
        lowercase_      :Tuple         =			chunk_length
        lowercase_      :List[str]         =			chunk_length * sampling_rate
        lowercase_      :Optional[Any]         =			self.n_samples // hop_length
        lowercase_      :Any         =			sampling_rate
        lowercase_      :List[Any]         =			mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2     ,				num_mel_filters=lowercase     ,				min_frequency=0.0     ,				max_frequency=80_00.0     ,				sampling_rate=lowercase     ,				norm="slaney"     ,				mel_scale="slaney"     ,				)





     def 							lowercase__				(			self		: str     ,				lowercase		: np.array						):





        """simple docstring"""




        lowercase_      :Any         =			spectrogram(
            lowercase     ,				window_function(self.n_fft     ,				"hann"						)     ,				frame_length=self.n_fft     ,				hop_length=self.hop_length     ,				power=2.0     ,				mel_filters=self.mel_filters     ,				log_mel="log10"     ,				)
        lowercase_      :Any         =			log_spec[:, :-1]
        lowercase_      :List[Any]         =			np.maximum(lowercase     ,				log_spec.max() - 8.0						)
        lowercase_      :Dict         =			(log_spec + 4.0) / 4.0
        return log_spec





     @staticmethod
     # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
     def 							lowercase__				(			lowercase		: List[np.ndarray]     ,				lowercase		: List[np.ndarray]     ,				lowercase		: float = 0.0						):





        """simple docstring"""




        if attention_mask is not None:
           lowercase_      :Optional[int]         =			np.array(lowercase     ,				np.intaa						)
           lowercase_      :Any         =			[]

           for vector, length in zip(lowercase     ,				attention_mask.sum(-1						)						):
              lowercase_      :Dict         =			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7						)
              if length < normed_slice.shape[0]:
                 lowercase_      :List[Any]         =			padding_value

              normed_input_values.append(lowercase						)
        else:
           lowercase_      :List[Any]         =			[(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

        return normed_input_values





     def __call__(			self		: Tuple     ,				lowercase		: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,				lowercase		: bool = True     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[Union[str, TensorType]] = None     ,				lowercase		: Optional[bool] = None     ,				lowercase		: Optional[str] = "max_length"     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[bool] = None     ,				**lowercase		: Union[str, Any]     ,				):





        """simple docstring"""




        if sampling_rate is not None:
           if sampling_rate != self.sampling_rate:
              raise ValueError(
                  F'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
                  F' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
                  F' was sampled with {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."						)

        lowercase_      :List[str]         =			isinstance(lowercase     ,				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}'						)
        lowercase_      :Optional[Any]         =			is_batched_numpy or (
            isinstance(lowercase     ,				(list, tuple)						) and (isinstance(raw_speech[0]     ,				(np.ndarray, tuple, list)						))
        )

        if is_batched:
           lowercase_      :Any         =			[np.asarray([speech]     ,				dtype=np.floataa						).T for speech in raw_speech]
        elif not is_batched and not isinstance(lowercase     ,				np.ndarray						):
           lowercase_      :List[Any]         =			np.asarray(lowercase     ,				dtype=np.floataa						)
        elif isinstance(lowercase     ,				np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
           lowercase_      :Union[str, Any]         =			raw_speech.astype(np.floataa						)

        # always return batch
        if not is_batched:
           lowercase_      :Optional[int]         =			[np.asarray([raw_speech]						).T]

        lowercase_      :int         =			BatchFeature({"input_features": raw_speech}						)

        # convert into correct format for padding

        lowercase_      :Tuple         =			self.pad(
            lowercase     ,				padding=lowercase     ,				max_length=max_length if max_length else self.n_samples     ,				truncation=lowercase     ,				pad_to_multiple_of=lowercase     ,				return_attention_mask=return_attention_mask or do_normalize     ,				)

        # zero-mean and unit-variance normalization
        if do_normalize:
           lowercase_      :Union[str, Any]         =			self.zero_mean_unit_var_norm(
               padded_inputs["input_features"]     ,				attention_mask=padded_inputs["attention_mask"]     ,				padding_value=self.padding_value     ,				)
           lowercase_      :List[Any]         =			np.stack(padded_inputs["input_features"]     ,				axis=0						)

        # make sure list is in array format
        lowercase_      :Union[str, Any]         =			padded_inputs.get("input_features"						).transpose(2     ,				0     ,				1						)

        lowercase_      :List[str]         =			[self._np_extract_fbank_features(lowercase						) for waveform in input_features[0]]

        if isinstance(input_features[0]     ,				lowercase						):
           lowercase_      :Tuple         =			[np.asarray(lowercase     ,				dtype=np.floataa						) for feature in input_features]
        else:
           lowercase_      :Union[str, Any]         =			input_features

        if return_attention_mask:
           # rescale from sample (48000) to feature (3000)
           lowercase_      :Dict         =			padded_inputs["attention_mask"][:, :: self.hop_length]

        if return_tensors is not None:
           lowercase_      :Tuple         =			padded_inputs.convert_to_tensors(lowercase						)

        return padded_inputs







     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			copy.deepcopy(self.__dict__						)
        lowercase_      :List[str]         =			self.__class__.__name__
        if "mel_filters" in output:
           del output["mel_filters"]
        return output


