





"""simple docstring"""


from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[Any]	     =					"EncodecFeatureExtractor"
  SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					("T5Tokenizer", "T5TokenizerFast")

  def __init__(		self   :       int					,   __a   :       Union[str, Any]					,   __a   :       Tuple	)		->				Union[str, Any]:
        super().__init__(__a					,   __a	)
        _UpperCamelCase      :	str						=       self.feature_extractor
        _UpperCamelCase      :	int						=       False

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       Any=None					,   __a   :       Any=None					,   __a   :       Any=True	)		->				Dict:
        return self.tokenizer.get_decoder_prompt_ids(task=__a					,   language=__a					,   no_timestamps=__a	)

  def __call__(		self   :       Optional[Any]					,   *__a   :       Union[str, Any]					,   **__a   :       str	)		->				Any:
        # For backward compatibility
        if self._in_target_context_manager:
              return self.current_processor(*__a					,   **__a	)

        _UpperCamelCase      :	Optional[int]						=       kwargs.pop("audio"					,   __a	)
        _UpperCamelCase      :	str						=       kwargs.pop("sampling_rate"					,   __a	)
        _UpperCamelCase      :	str						=       kwargs.pop("text"					,   __a	)
        if len(__a	) > 0:
              _UpperCamelCase      :	Dict						=       args[0]
              _UpperCamelCase      :	Union[str, Any]						=       args[1:]

        if audio is None and text is None:
              raise ValueError("You need to specify either an `audio` or `text` input to process."	)

        if text is not None:
              _UpperCamelCase      :	List[str]						=       self.tokenizer(__a					,   **__a	)

        if audio is not None:
              _UpperCamelCase      :	Optional[Any]						=       self.feature_extractor(__a					,   *__a					,   sampling_rate=__a					,   **__a	)

        if audio is None:
              return inputs

        elif text is None:
              return audio_inputs

        else:
              _UpperCamelCase      :	Dict						=       audio_inputs["input_values"]
              if "padding_mask" in audio_inputs:
                    _UpperCamelCase      :	Optional[Any]						=       audio_inputs["padding_mask"]
              return inputs

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]					,   *__a   :       List[str]					,   **__a   :       Any	)		->				Optional[Any]:
        _UpperCamelCase      :	Dict						=       kwargs.pop("audio"					,   __a	)
        _UpperCamelCase      :	List[Any]						=       kwargs.pop("padding_mask"					,   __a	)

        if len(__a	) > 0:
              _UpperCamelCase      :	List[str]						=       args[0]
              _UpperCamelCase      :	Optional[int]						=       args[1:]

        if audio_values is not None:
              return self._decode_audio(__a					,   padding_mask=__a	)
        else:
              return self.tokenizer.batch_decode(*__a					,   **__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   *__a   :       List[Any]					,   **__a   :       List[str]	)		->				Union[str, Any]:
        return self.tokenizer.decode(*__a					,   **__a	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       Union[str, Any]					,   __a   :       Optional = None	)		->				List[np.ndarray]:
        _UpperCamelCase      :	str						=       to_numpy(__a	)
        _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	Optional[Any]						=       audio_values.shape

        if padding_mask is None:
              return list(__a	)

        _UpperCamelCase      :	Optional[int]						=       to_numpy(__a	)

        # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
        # token (so that the generated audio values are **not** treated as padded tokens)
        _UpperCamelCase      :	List[str]						=       seq_len - padding_mask.shape[-1]
        _UpperCamelCase      :	List[str]						=       1 - self.feature_extractor.padding_value
        _UpperCamelCase      :	Optional[Any]						=       np.pad(__a					,   ((0, 0), (0, difference))					,   "constant"					,   constant_values=__a	)

        _UpperCamelCase      :	Union[str, Any]						=       audio_values.tolist()
        for i in range(__a	):
              _UpperCamelCase      :	Optional[int]						=       np.asarray(audio_values[i]	)[
                  padding_mask[i][None, :] != self.feature_extractor.padding_value
              ]
              _UpperCamelCase      :	Any						=       sliced_audio.reshape(__a					,   -1	)

        return audio_values







"""simple docstring"""


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


lowerCamelCase__				  =   logging.get_logger(__name__)

lowerCamelCase__				  =   {
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple	     =					"yolos"

  def __init__(		self   :       Dict					,   __a   :       Optional[Any]=768					,   __a   :       List[Any]=12					,   __a   :       Any=12					,   __a   :       List[Any]=3072					,   __a   :       Optional[int]="gelu"					,   __a   :       Dict=0.0					,   __a   :       Optional[Any]=0.0					,   __a   :       Any=0.02					,   __a   :       Optional[int]=1e-1_2					,   __a   :       List[Any]=[512, 864]					,   __a   :       List[str]=16					,   __a   :       str=3					,   __a   :       Optional[Any]=True					,   __a   :       Optional[Any]=100					,   __a   :       List[str]=True					,   __a   :       Any=False					,   __a   :       List[str]=1					,   __a   :       str=5					,   __a   :       Optional[Any]=2					,   __a   :       Tuple=5					,   __a   :       Any=2					,   __a   :       Union[str, Any]=0.1					,   **__a   :       List[str]					,   )		->				List[str]:
        super().__init__(**__a	)

        _UpperCamelCase      :	Dict						=       hidden_size
        _UpperCamelCase      :	Any						=       num_hidden_layers
        _UpperCamelCase      :	str						=       num_attention_heads
        _UpperCamelCase      :	Dict						=       intermediate_size
        _UpperCamelCase      :	List[str]						=       hidden_act
        _UpperCamelCase      :	List[str]						=       hidden_dropout_prob
        _UpperCamelCase      :	str						=       attention_probs_dropout_prob
        _UpperCamelCase      :	Tuple						=       initializer_range
        _UpperCamelCase      :	List[str]						=       layer_norm_eps
        _UpperCamelCase      :	Tuple						=       image_size
        _UpperCamelCase      :	Tuple						=       patch_size
        _UpperCamelCase      :	Dict						=       num_channels
        _UpperCamelCase      :	Any						=       qkv_bias
        _UpperCamelCase      :	str						=       num_detection_tokens
        _UpperCamelCase      :	str						=       use_mid_position_embeddings
        _UpperCamelCase      :	List[str]						=       auxiliary_loss
        # Hungarian matcher
        _UpperCamelCase      :	List[Any]						=       class_cost
        _UpperCamelCase      :	int						=       bbox_cost
        _UpperCamelCase      :	Optional[int]						=       giou_cost
        # Loss coefficients
        _UpperCamelCase      :	List[Any]						=       bbox_loss_coefficient
        _UpperCamelCase      :	str						=       giou_loss_coefficient
        _UpperCamelCase      :	Dict						=       eos_coefficient





class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[str]	     =					version.parse("1.11"   )

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]	)

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				float:
        return 1e-4


  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				int:
        return 12
