from typing import List, Optional

import numpy as np

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

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Tuple          =    'EncodecFeatureExtractor'
    SCREAMING_SNAKE_CASE:				int          =    ('T5Tokenizer', 'T5TokenizerFast')




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       super().__init__(lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_:							Optional[int]       =	self.feature_extractor
       lowerCAmelCase_:							Optional[Any]       =	False




    def      _a    (    self       ,						lowerCamelCase__=None       ,						lowerCamelCase__=None       ,						lowerCamelCase__=True ):
       return self.tokenizer.get_decoder_prompt_ids(task=lowerCamelCase__       ,						language=lowerCamelCase__       ,						no_timestamps=lowerCamelCase__ )




    def __call__(    self       ,						*lowerCamelCase__       ,						**lowerCamelCase__ ):
       # For backward compatibility
       if self._in_target_context_manager:
          return self.current_processor(*lowerCamelCase__       ,						**lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	kwargs.pop("audio"       ,						lowerCamelCase__ )
       lowerCAmelCase_:							str       =	kwargs.pop("sampling_rate"       ,						lowerCamelCase__ )
       lowerCAmelCase_:							List[str]       =	kwargs.pop("text"       ,						lowerCamelCase__ )
       if len(lowerCamelCase__ ) > 0:
          lowerCAmelCase_:							Any       =	args[0]
          lowerCAmelCase_:							Optional[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:
          lowerCAmelCase_:							List[Any]       =	self.tokenizer(lowerCamelCase__       ,						**lowerCamelCase__ )

       if audio is not None:
          lowerCAmelCase_:							Tuple       =	self.feature_extractor(lowerCamelCase__       ,						*lowerCamelCase__       ,						sampling_rate=lowerCamelCase__       ,						**lowerCamelCase__ )

       if audio is None:
          return inputs

       elif text is None:
          return audio_inputs

       else:
          lowerCAmelCase_:							List[str]       =	audio_inputs["input_values"]
          if "padding_mask" in audio_inputs:
             lowerCAmelCase_:							Union[str, Any]       =	audio_inputs["padding_mask"]
          return inputs




    def      _a    (    self       ,						*lowerCamelCase__       ,						**lowerCamelCase__ ):
       lowerCAmelCase_:							List[str]       =	kwargs.pop("audio"       ,						lowerCamelCase__ )
       lowerCAmelCase_:							str       =	kwargs.pop("padding_mask"       ,						lowerCamelCase__ )

       if len(lowerCamelCase__ ) > 0:
          lowerCAmelCase_:							Any       =	args[0]
          lowerCAmelCase_:							List[str]       =	args[1:]

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




    def      _a    (    self       ,						*lowerCamelCase__       ,						**lowerCamelCase__ ):
       return self.tokenizer.decode(*lowerCamelCase__       ,						**lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None ):
       lowerCAmelCase_:							Union[str, Any]       =	to_numpy(lowerCamelCase__ )
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Tuple       =	audio_values.shape

       if padding_mask is None:
          return list(lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	to_numpy(lowerCamelCase__ )

       # 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)
       lowerCAmelCase_:							Any       =	seq_len - padding_mask.shape[-1]
       lowerCAmelCase_:							List[Any]       =	1 - self.feature_extractor.padding_value
       lowerCAmelCase_:							List[str]       =	np.pad(lowerCamelCase__       ,						((0, 0), (0, difference))       ,						"constant"       ,						constant_values=lowerCamelCase__ )

       lowerCAmelCase_:							Dict       =	audio_values.tolist()
       for i in range(lowerCamelCase__ ):
          lowerCAmelCase_:							Tuple       =	np.asarray(audio_values[i] )[
              padding_mask[i][None, :] != self.feature_extractor.padding_value
          ]
          lowerCAmelCase_:							Optional[Any]       =	sliced_audio.reshape(lowerCamelCase__       ,						-1 )

       return audio_values
from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


a  :      Optional[int]            =    logging.get_logger(__name__)

a  :      Optional[Any]            =    {
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Dict          =    'efficientnet'




    def __init__(    self       ,						lowerCamelCase__ = 3       ,						lowerCamelCase__ = 600       ,						lowerCamelCase__ = 2.0       ,						lowerCamelCase__ = 3.1       ,						lowerCamelCase__ = 8       ,						lowerCamelCase__ = [3, 3, 5, 3, 5, 5, 3]       ,						lowerCamelCase__ = [32, 16, 24, 40, 80, 112, 192]       ,						lowerCamelCase__ = [16, 24, 40, 80, 112, 192, 320]       ,						lowerCamelCase__ = []       ,						lowerCamelCase__ = [1, 2, 2, 2, 1, 2, 1]       ,						lowerCamelCase__ = [1, 2, 2, 3, 3, 4, 1]       ,						lowerCamelCase__ = [1, 6, 6, 6, 6, 6, 6]       ,						lowerCamelCase__ = 0.2_5       ,						lowerCamelCase__ = "swish"       ,						lowerCamelCase__ = 2_560       ,						lowerCamelCase__ = "mean"       ,						lowerCamelCase__ = 0.0_2       ,						lowerCamelCase__ = 0.0_0_1       ,						lowerCamelCase__ = 0.9_9       ,						lowerCamelCase__ = 0.5       ,						lowerCamelCase__ = 0.2       ,						**lowerCamelCase__       ,						):
       super().__init__(**lowerCamelCase__ )

       lowerCAmelCase_:							str       =	num_channels
       lowerCAmelCase_:							str       =	image_size
       lowerCAmelCase_:							int       =	width_coefficient
       lowerCAmelCase_:							Union[str, Any]       =	depth_coefficient
       lowerCAmelCase_:							int       =	depth_divisor
       lowerCAmelCase_:							List[str]       =	kernel_sizes
       lowerCAmelCase_:							Tuple       =	in_channels
       lowerCAmelCase_:							List[str]       =	out_channels
       lowerCAmelCase_:							List[str]       =	depthwise_padding
       lowerCAmelCase_:							Optional[int]       =	strides
       lowerCAmelCase_:							List[str]       =	num_block_repeats
       lowerCAmelCase_:							Any       =	expand_ratios
       lowerCAmelCase_:							List[Any]       =	squeeze_expansion_ratio
       lowerCAmelCase_:							Optional[int]       =	hidden_act
       lowerCAmelCase_:							Optional[int]       =	hidden_dim
       lowerCAmelCase_:							Dict       =	pooling_type
       lowerCAmelCase_:							Optional[Any]       =	initializer_range
       lowerCAmelCase_:							int       =	batch_norm_eps
       lowerCAmelCase_:							List[str]       =	batch_norm_momentum
       lowerCAmelCase_:							List[Any]       =	dropout_rate
       lowerCAmelCase_:							Union[str, Any]       =	drop_connect_rate
       lowerCAmelCase_:							Tuple       =	sum(lowerCamelCase__ ) * 4





class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    version.parse('1.11'			)




    @property
    def      _a    (    self ):
       return OrderedDict(
           [
               ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
           ] )




    @property
    def      _a    (    self ):
       return 1E-5