
import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = 'MCTCTFeatureExtractor'
	lowerCAmelCase__         = 'AutoTokenizer'



	def __init__(    self						,  lowercase						,  lowercase )				->       str:
					super().__init__(lowercase						,  lowercase )
					lowerCamelCase_						=			self.feature_extractor
					lowerCamelCase_						=			False



	def __call__(    self						,  *lowercase						,  **lowercase )				->       Dict:
					# For backward compatibility
					if self._in_target_context_manager:
									return self.current_processor(*lowercase						,  **lowercase )

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

					if text is None:
									return inputs
					elif audio is None:
									return encodings
					else:
									lowerCamelCase_						=			encodings["input_ids"]
									return inputs



	def     SCREAMING_SNAKE_CASE_(    self						,  *lowercase						,  **lowercase )				->       List[Any]:
					return self.tokenizer.batch_decode(*lowercase						,  **lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  *lowercase						,  **lowercase )				->       List[str]:
					# For backward compatibility
					if self._in_target_context_manager:
									return self.current_processor.pad(*lowercase						,  **lowercase )

					lowerCamelCase_						=			kwargs.pop("input_features"						,  lowercase )
					lowerCamelCase_						=			kwargs.pop("labels"						,  lowercase )
					if len(lowercase ) > 0:
									lowerCamelCase_						=			args[0]
									lowerCamelCase_						=			args[1:]

					if input_features is not None:
									lowerCamelCase_						=			self.feature_extractor.pad(lowercase						,  *lowercase						,  **lowercase )
					if labels is not None:
									lowerCamelCase_						=			self.tokenizer.pad(lowercase						,  **lowercase )

					if labels is None:
									return input_features
					elif input_features is None:
									return labels
					else:
									lowerCamelCase_						=			labels["input_ids"]
									return input_features



	def     SCREAMING_SNAKE_CASE_(    self						,  *lowercase						,  **lowercase )				->       int:
					return self.tokenizer.decode(*lowercase						,  **lowercase )



	@contextmanager
	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					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." )
					lowerCamelCase_						=			True
					lowerCamelCase_						=			self.tokenizer
					yield
					lowerCamelCase_						=			self.feature_extractor
					lowerCamelCase_						=			False









"""simple docstring"""





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer

@dataclass
class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      42







class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase					):





      @register_to_config
      def __init__(       self      ,						lowercase = 3      ,						lowercase = 3      ,						lowercase = ("DownEncoderBlock2D",)      ,						lowercase = ("UpDecoderBlock2D",)      ,						lowercase = (64,)      ,						lowercase = 1      ,						lowercase = "silu"      ,						lowercase = 3      ,						lowercase = 32      ,						lowercase = 256      ,						lowercase = 32      ,						lowercase = None      ,						lowercase = 0.18_215      ,						lowercase = "group"      ,						)		->     Union[str, Any]:
            super().__init__()

            # pass init params to Encoder
            lowerCAmelCase  						=       Encoder(
                in_channels=lowercase      ,						out_channels=lowercase      ,						down_block_types=lowercase      ,						block_out_channels=lowercase      ,						layers_per_block=lowercase      ,						act_fn=lowercase      ,						norm_num_groups=lowercase      ,						double_z=lowercase      ,						)

            lowerCAmelCase  						=       vq_embed_dim if vq_embed_dim is not None else latent_channels

            lowerCAmelCase  						=       nn.Convad(lowercase      ,						lowercase      ,						1 )
            lowerCAmelCase  						=       VectorQuantizer(lowercase      ,						lowercase      ,						beta=0.25      ,						remap=lowercase      ,						sane_index_shape=lowercase )
            lowerCAmelCase  						=       nn.Convad(lowercase      ,						lowercase      ,						1 )

            # pass init params to Decoder
            lowerCAmelCase  						=       Decoder(
                in_channels=lowercase      ,						out_channels=lowercase      ,						up_block_types=lowercase      ,						block_out_channels=lowercase      ,						layers_per_block=lowercase      ,						act_fn=lowercase      ,						norm_num_groups=lowercase      ,						norm_type=lowercase      ,						)





      @apply_forward_hook
      def     _snake_case			(       self      ,						lowercase      ,						lowercase = True )		->     VQEncoderOutput:
            lowerCAmelCase  						=       self.encoder(lowercase )
            lowerCAmelCase  						=       self.quant_conv(lowercase )

            if not return_dict:
                  return (h,)

            return VQEncoderOutput(latents=lowercase )





      @apply_forward_hook
      def     _snake_case			(       self      ,						lowercase      ,						lowercase = False      ,						lowercase = True )		->     Union[DecoderOutput, torch.FloatTensor]:
            # also go through quantization layer
            if not force_not_quantize:
                  lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       self.quantize(lowercase )
            else:
                  lowerCAmelCase  						=       h
            lowerCAmelCase  						=       self.post_quant_conv(lowercase )
            lowerCAmelCase  						=       self.decoder(lowercase      ,						quant if self.config.norm_type == """spatial""" else None )

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = True )		->     Union[DecoderOutput, torch.FloatTensor]:
            lowerCAmelCase  						=       sample
            lowerCAmelCase  						=       self.encode(lowercase ).latents
            lowerCAmelCase  						=       self.decode(lowercase ).sample

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=lowercase )






