
from typing import List, Optional

import numpy as np

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






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  'EncodecFeatureExtractor'
							_snake_case     		=  ('T5Tokenizer', 'T5TokenizerFast')



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Tuple:





													'''simple docstring'''






													super().__init__(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			self.feature_extractor
													__UpperCamelCase       =			False



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=True			)->   Optional[Any]:





													'''simple docstring'''






													return self.tokenizer.get_decoder_prompt_ids(task=SCREAMING_SNAKE_CASE_     ,	language=SCREAMING_SNAKE_CASE_     ,	no_timestamps=SCREAMING_SNAKE_CASE_			)



							def __call__(  self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   int:





													'''simple docstring'''






													if self._in_target_context_manager:
																			return self.current_processor(*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			kwargs.pop('''audio'''     ,	SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			kwargs.pop('''sampling_rate'''     ,	SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			kwargs.pop('''text'''     ,	SCREAMING_SNAKE_CASE_			)
													if len(SCREAMING_SNAKE_CASE_			) > 0:
																			__UpperCamelCase       =			args[0]
																			__UpperCamelCase       =			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       =			self.tokenizer(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)

													if audio is not None:
																			__UpperCamelCase       =			self.feature_extractor(SCREAMING_SNAKE_CASE_     ,	*SCREAMING_SNAKE_CASE_     ,	sampling_rate=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)

													if audio is None:
																			return inputs

													elif text is None:
																			return audio_inputs

													else:
																			__UpperCamelCase       =			audio_inputs['''input_values''']
																			if "padding_mask" in audio_inputs:
																									__UpperCamelCase       =			audio_inputs['''padding_mask''']
																			return inputs



							def     A__		(  self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			kwargs.pop('''audio'''     ,	SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			kwargs.pop('''padding_mask'''     ,	SCREAMING_SNAKE_CASE_			)

													if len(SCREAMING_SNAKE_CASE_			) > 0:
																			__UpperCamelCase       =			args[0]
																			__UpperCamelCase       =			args[1:]

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



							def     A__		(  self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   List[Any]:





													'''simple docstring'''






													return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None			)->   List[np.ndarray]:





													'''simple docstring'''






													__UpperCamelCase       =			to_numpy(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			audio_values.shape

													if padding_mask is None:
																			return list(SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			to_numpy(SCREAMING_SNAKE_CASE_			)

													# 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       =			seq_len - padding_mask.shape[-1]
													__UpperCamelCase       =			1 - self.feature_extractor.padding_value
													__UpperCamelCase       =			np.pad(SCREAMING_SNAKE_CASE_     ,	((0, 0), (0, difference))     ,	'''constant'''     ,	constant_values=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			audio_values.tolist()
													for i in range(SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			np.asarray(audio_values[i]			)[
																			    padding_mask[i][None, :] != self.feature_extractor.padding_value
																			]
																			__UpperCamelCase       =			sliced_audio.reshape(SCREAMING_SNAKE_CASE_     ,	-1			)

													return audio_values






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


lowercase__							:							Tuple									=			logging.get_logger(__name__)

lowercase__							:							int									=			{
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  'table-transformer'
							_snake_case     		=  ['past_key_values']
							_snake_case     		=  {
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=100     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=2048     ,	SCREAMING_SNAKE_CASE_=8     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=2048     ,	SCREAMING_SNAKE_CASE_=8     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_="relu"     ,	SCREAMING_SNAKE_CASE_=256     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=1.0     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_="sine"     ,	SCREAMING_SNAKE_CASE_="resnet50"     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=0.1     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Tuple:





													'''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.'''			)
																									__UpperCamelCase       =			CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']			)
																			elif isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			):
																									__UpperCamelCase       =			backbone_config.get('''model_type'''			)
																									__UpperCamelCase       =			CONFIG_MAPPING[backbone_model_type]
																									__UpperCamelCase       =			config_class.from_dict(SCREAMING_SNAKE_CASE_			)
																			# set timm attributes to None
																			__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			None, None, None

													__UpperCamelCase       =			use_timm_backbone
													__UpperCamelCase       =			backbone_config
													__UpperCamelCase       =			num_channels
													__UpperCamelCase       =			num_queries
													__UpperCamelCase       =			d_model
													__UpperCamelCase       =			encoder_ffn_dim
													__UpperCamelCase       =			encoder_layers
													__UpperCamelCase       =			encoder_attention_heads
													__UpperCamelCase       =			decoder_ffn_dim
													__UpperCamelCase       =			decoder_layers
													__UpperCamelCase       =			decoder_attention_heads
													__UpperCamelCase       =			dropout
													__UpperCamelCase       =			attention_dropout
													__UpperCamelCase       =			activation_dropout
													__UpperCamelCase       =			activation_function
													__UpperCamelCase       =			init_std
													__UpperCamelCase       =			init_xavier_std
													__UpperCamelCase       =			encoder_layerdrop
													__UpperCamelCase       =			decoder_layerdrop
													__UpperCamelCase       =			encoder_layers
													__UpperCamelCase       =			auxiliary_loss
													__UpperCamelCase       =			position_embedding_type
													__UpperCamelCase       =			backbone
													__UpperCamelCase       =			use_pretrained_backbone
													__UpperCamelCase       =			dilation
													# Hungarian matcher
													__UpperCamelCase       =			class_cost
													__UpperCamelCase       =			bbox_cost
													__UpperCamelCase       =			giou_cost
													# Loss coefficients
													__UpperCamelCase       =			mask_loss_coefficient
													__UpperCamelCase       =			dice_loss_coefficient
													__UpperCamelCase       =			bbox_loss_coefficient
													__UpperCamelCase       =			giou_loss_coefficient
													__UpperCamelCase       =			eos_coefficient
													super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return self.encoder_attention_heads



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return self.d_model






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  version.parse('1.11'    )



							@property
							def     A__		(  self			)->   Mapping[str, Mapping[int, str]]:





													'''simple docstring'''






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



							@property
							def     A__		(  self			)->   float:





													'''simple docstring'''






													return 1E-5



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return 12




