


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'Speech2TextFeatureExtractor'
    __magic_name__        =		'Speech2TextTokenizer'
    def __init__(	self      , __snake_case      , __snake_case   ):
         super().__init__(__snake_case      , __snake_case   )
         snake_case					=    self.feature_extractor
         snake_case					=    False
    def __call__(	self      , *__snake_case      , **__snake_case   ):
         # For backward compatibility
         if self._in_target_context_manager:
              return self.current_processor(*__snake_case      , **__snake_case   )

         if "raw_speech" in kwargs:
              warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''   )
              snake_case					=    kwargs.pop('''raw_speech'''   )
         else:
              snake_case					=    kwargs.pop('''audio'''      , __snake_case   )
         snake_case					=    kwargs.pop('''sampling_rate'''      , __snake_case   )
         snake_case					=    kwargs.pop('''text'''      , __snake_case   )
         if len(__snake_case   ) > 0:
              snake_case					=    args[0]
              snake_case					=    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 audio is not None:
              snake_case					=    self.feature_extractor(__snake_case      , *__snake_case      , sampling_rate=__snake_case      , **__snake_case   )
         if text is not None:
              snake_case					=    self.tokenizer(__snake_case      , **__snake_case   )

         if text is None:
              return inputs
         elif audio is None:
              return encodings
         else:
              snake_case					=    encodings['''input_ids''']
              return inputs
    def 		a_      (	self      , *__snake_case      , **__snake_case   ):
         return self.tokenizer.batch_decode(*__snake_case      , **__snake_case   )
    def 		a_      (	self      , *__snake_case      , **__snake_case   ):
         return self.tokenizer.decode(*__snake_case      , **__snake_case   )




    @contextmanager
    def 		a_      (	self   ):
         warnings.warn(
             '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
             '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
             '''your audio inputs, or in a separate call.'''   )
         snake_case					=    True
         snake_case					=    self.tokenizer
         yield
         snake_case					=    self.feature_extractor
         snake_case					=    False








import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


_SCREAMING_SNAKE_CASE      :			Dict      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Any      				=   {
    "facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'detr'
    __magic_name__        =		['past_key_values']
    __magic_name__        =		{
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }
    def __init__(	self      , __snake_case=True      , __snake_case=None      , __snake_case=3      , __snake_case=1_0_0      , __snake_case=6      , __snake_case=2_0_4_8      , __snake_case=8      , __snake_case=6      , __snake_case=2_0_4_8      , __snake_case=8      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=True      , __snake_case="relu"      , __snake_case=2_5_6      , __snake_case=0.1      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=0.02      , __snake_case=1.0      , __snake_case=False      , __snake_case="sine"      , __snake_case="resnet50"      , __snake_case=True      , __snake_case=False      , __snake_case=1      , __snake_case=5      , __snake_case=2      , __snake_case=1      , __snake_case=1      , __snake_case=5      , __snake_case=2      , __snake_case=0.1      , **__snake_case      , ):
         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.'''   )
                   snake_case					=    CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']   )
              elif isinstance(__snake_case      , __snake_case   ):
                   snake_case					=    backbone_config.get('''model_type'''   )
                   snake_case					=    CONFIG_MAPPING[backbone_model_type]
                   snake_case					=    config_class.from_dict(__snake_case   )
              # set timm attributes to None
              snake_case			,  snake_case			,  snake_case					=    None, None, None

         snake_case					=    use_timm_backbone
         snake_case					=    backbone_config
         snake_case					=    num_channels
         snake_case					=    num_queries
         snake_case					=    d_model
         snake_case					=    encoder_ffn_dim
         snake_case					=    encoder_layers
         snake_case					=    encoder_attention_heads
         snake_case					=    decoder_ffn_dim
         snake_case					=    decoder_layers
         snake_case					=    decoder_attention_heads
         snake_case					=    dropout
         snake_case					=    attention_dropout
         snake_case					=    activation_dropout
         snake_case					=    activation_function
         snake_case					=    init_std
         snake_case					=    init_xavier_std
         snake_case					=    encoder_layerdrop
         snake_case					=    decoder_layerdrop
         snake_case					=    encoder_layers
         snake_case					=    auxiliary_loss
         snake_case					=    position_embedding_type
         snake_case					=    backbone
         snake_case					=    use_pretrained_backbone
         snake_case					=    dilation
         # Hungarian matcher
         snake_case					=    class_cost
         snake_case					=    bbox_cost
         snake_case					=    giou_cost
         # Loss coefficients
         snake_case					=    mask_loss_coefficient
         snake_case					=    dice_loss_coefficient
         snake_case					=    bbox_loss_coefficient
         snake_case					=    giou_loss_coefficient
         snake_case					=    eos_coefficient
         super().__init__(is_encoder_decoder=__snake_case      , **__snake_case   )
    @property
    def 		a_      (	self   ):
         return self.encoder_attention_heads
    @property
    def 		a_      (	self   ):
         return self.d_model
    @classmethod
    def 		a_      (	cls      , __snake_case      , **__snake_case   ):
         return cls(backbone_config=__snake_case      , **__snake_case   )




    def 		a_      (	self   ):
         snake_case					=    copy.deepcopy(self.__dict__   )
         if output["backbone_config"] is not None:
              snake_case					=    self.backbone_config.to_dict()
         snake_case					=    self.__class__.model_type
         return output






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		version.parse('1.11'     )
    @property
    def 		a_      (	self   ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ('''pixel_mask''', {0: '''batch'''}),
             ]   )
    @property
    def 		a_      (	self   ):
         return 1E-5




    @property
    def 		a_      (	self   ):
         return 1_2




