






import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
a		:       Optional[Any]			     = logging.get_logger()
@dataclass
class     a    :



      """simple docstring"""


      a  : nn.Module
      a  : List[nn.Module]							       =	field(default_factory=lowercase__   )
      a  : list							       =	field(default_factory=lowercase__   )
      def 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			List[str]		,      __lowercase					:			Tensor		,      __lowercase					:			Tensor					)							->      Optional[Any]:
        __UpperCAmelCase							:	Union[str, Any]           =  len(list(m.modules()					)					) == 1 or isinstance(__lowercase		,      nn.Convad					) or isinstance(__lowercase		,      nn.BatchNormad					)
        if has_not_submodules:
          self.traced.append(__lowercase					)
      def __call__(      self					:			Dict		,      __lowercase					:			Tensor					)							->      Tuple:
        for m in self.module.modules():
          self.handles.append(m.register_forward_hook(self._forward_hook					)					)
        self.module(__lowercase					)
        [x.remove() for x in self.handles]
        return self




      @property
      def 	UpperCAmelCase      (      self					:			str					)							->      int:
        # check the len of the state_dict keys to see if we have learnable params
        return list(filter(lambda __lowercase					: len(list(x.state_dict().keys()					)					) > 0		,      self.traced					)					)


@dataclass
class     a    :



      """simple docstring"""


      a  : nn.Module
      a  : nn.Module
      a  : int							       =	1
      a  : List							       =	field(default_factory=lowercase__   )
      a  : List							       =	field(default_factory=lowercase__   )
      a  : bool							       =	True
      def __call__(      self					:			Any		,      __lowercase					:			Tensor					)							->      Any:
        __UpperCAmelCase							:	List[Any]           =  Tracker(self.dest					)(__lowercase					).parametrized
        __UpperCAmelCase							:	Optional[Any]           =  Tracker(self.src					)(__lowercase					).parametrized

        __UpperCAmelCase							:	Any           =  list(filter(lambda __lowercase					: type(__lowercase					) not in self.src_skip		,      __lowercase					)					)
        __UpperCAmelCase							:	Tuple           =  list(filter(lambda __lowercase					: type(__lowercase					) not in self.dest_skip		,      __lowercase					)					)

        if len(__lowercase					) != len(__lowercase					) and self.raise_if_mismatch:
          raise Exception(
              f"""Numbers of operations are different. Source module has {len(__lowercase					)} operations while"""
              f""" destination module has {len(__lowercase					)}."""					)

        for dest_m, src_m in zip(__lowercase		,      __lowercase					):
          dest_m.load_state_dict(src_m.state_dict()					)
          if self.verbose == 1:
            print(f"""Transfered from={src_m} to={dest_m}"""					)


class     a    ( nn.Module   ):



      """simple docstring"""


      def __init__(      self					:			Any		,      __lowercase					:			nn.Module					)							->      Dict:
        super().__init__()

        __UpperCAmelCase							:	List[Tuple[str, nn.Module]]           =  []
        # - get the stem
        feature_blocks.append(("""conv1""", model.stem)					)
        # - get all the feature blocks
        for k, v in model.trunk_output.named_children():
          assert k.startswith("""block"""					), f"""Unexpected layer name {k}"""
          __UpperCAmelCase							:	str           =  len(__lowercase					) + 1
          feature_blocks.append((f"""res{block_index}""", v)					)

        __UpperCAmelCase							:	Any           =  nn.ModuleDict(__lowercase					)




      def 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			Tensor					)							->      Tuple:
        return get_trunk_forward_outputs(
            __lowercase		,      out_feat_keys=__lowercase		,      feature_blocks=self._feature_blocks		,      )


class     a    ( lowercase__   ):



      """simple docstring"""


      def 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			str					)							->      str:
        __UpperCAmelCase							:	Dict           =  x.split("""-"""					)
        return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]					)




      def __getitem__(      self					:			Dict		,      __lowercase					:			str					)							->      Callable[[], Tuple[nn.Module, Dict]]:
        # default to timm!
        if x not in self:
          __UpperCAmelCase							:	Optional[Any]           =  self.convert_name_to_timm(__lowercase					)
          __UpperCAmelCase							:	str           =  partial(lambda: (timm.create_model(__lowercase		,      pretrained=__lowercase					).eval(), None)					)

        else:
          __UpperCAmelCase							:	List[Any]           =  super().__getitem__(__lowercase					)

        return val


class     a    ( lowercase__   ):



      """simple docstring"""


      def __getitem__(      self					:			str		,      __lowercase					:			str					)							->      Callable[[], nn.Module]:
        if "seer" in x and "in1k" not in x:
          __UpperCAmelCase							:	int           =  RegNetModel
        else:
          __UpperCAmelCase							:	Union[str, Any]           =  RegNetForImageClassification
        return val






def 				lowerCamelCase__      (    __lowerCamelCase			:     Optional[Any]  ,		__lowerCamelCase			:     Optional[Any]  ,		__lowerCamelCase			:     List[Tuple[str, str]]		):
  for from_key, to_key in keys:
    __UpperCAmelCase							:	str           =  from_state_dict[from_key].clone()
    print(f"""Copied key={from_key} to={to_key}"""		)
  return to_state_dict






def 				lowerCamelCase__      (    __lowerCamelCase			:     str  ,		__lowerCamelCase			:     Callable[[], nn.Module]  ,		__lowerCamelCase			:     Callable[[], nn.Module]  ,		__lowerCamelCase			:     RegNetConfig  ,		__lowerCamelCase			:     Path  ,		__lowerCamelCase			:     bool = True  ,		):
  print(f"""Converting {name}..."""		)
  with torch.no_grad():
    __UpperCAmelCase      , __UpperCAmelCase							:	Optional[Any]           =  from_model_func()
    __UpperCAmelCase							:	Optional[Any]           =  our_model_func(__lowerCamelCase		).eval()
    __UpperCAmelCase							:	List[Any]           =  ModuleTransfer(src=__lowerCamelCase  ,		dest=__lowerCamelCase  ,		raise_if_mismatch=__lowerCamelCase		)
    __UpperCAmelCase							:	List[Any]           =  torch.randn((1, 3, 224, 224)		)
    module_transfer(__lowerCamelCase		)

  if from_state_dict is not None:
    __UpperCAmelCase							:	Dict           =  []
    # for seer - in1k finetuned we have to manually copy the head
    if "seer" in name and "in1k" in name:
      __UpperCAmelCase							:	Tuple           =  [("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
    __UpperCAmelCase							:	Optional[int]           =  manually_copy_vissl_head(__lowerCamelCase  ,		our_model.state_dict()  ,		__lowerCamelCase		)
    our_model.load_state_dict(__lowerCamelCase		)

  __UpperCAmelCase							:	Optional[Any]           =  our_model(__lowerCamelCase  ,		output_hidden_states=__lowerCamelCase		)
  __UpperCAmelCase							:	Dict           =  (
      our_outputs.logits if isinstance(__lowerCamelCase  ,		__lowerCamelCase		) else our_outputs.last_hidden_state
  )

  __UpperCAmelCase							:	Tuple           =  from_model(__lowerCamelCase		)
  __UpperCAmelCase							:	List[Any]           =  from_output[-1] if type(__lowerCamelCase		) is list else from_output

  # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
  if "seer" in name and "in1k" in name:
    __UpperCAmelCase							:	List[str]           =  our_outputs.hidden_states[-1]

  assert torch.allclose(__lowerCamelCase  ,		__lowerCamelCase		), "The model logits don't match the original one."

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / name  ,		commit_message="""Add model"""  ,		use_temp_dir=__lowerCamelCase  ,		)

    __UpperCAmelCase							:	int           =  224 if """seer""" not in name else 384
    # we can use the convnext one
    __UpperCAmelCase							:	Any           =  AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""  ,		size=__lowerCamelCase		)
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / name  ,		commit_message="""Add image processor"""  ,		use_temp_dir=__lowerCamelCase  ,		)

    print(f"""Pushed {name}"""		)






def 				lowerCamelCase__      (    __lowerCamelCase			:     Path  ,		__lowerCamelCase			:     str = None  ,		__lowerCamelCase			:     bool = True		):
  __UpperCAmelCase							:	str           =  """imagenet-1k-id2label.json"""
  __UpperCAmelCase							:	Union[str, Any]           =  1000
  __UpperCAmelCase							:	int           =  (1, num_labels)

  __UpperCAmelCase							:	Tuple           =  """huggingface/label-files"""
  __UpperCAmelCase							:	int           =  num_labels
  __UpperCAmelCase							:	str           =  json.load(open(cached_download(hf_hub_url(__lowerCamelCase  ,		__lowerCamelCase  ,		repo_type="""dataset"""		)		)  ,		"""r"""		)		)
  __UpperCAmelCase							:	Dict           =  {int(__lowerCamelCase		): v for k, v in idalabel.items()}

  __UpperCAmelCase							:	Optional[Any]           =  idalabel
  __UpperCAmelCase							:	Dict           =  {v: k for k, v in idalabel.items()}

  __UpperCAmelCase							:	Dict           =  partial(__lowerCamelCase  ,		num_labels=__lowerCamelCase  ,		idalabel=__lowerCamelCase  ,		labelaid=__lowerCamelCase		)

  __UpperCAmelCase							:	int           =  {
      """regnet-x-002""": ImageNetPreTrainedConfig(
          depths=[1, 1, 4, 7]  ,		hidden_sizes=[24, 56, 152, 368]  ,		groups_width=8  ,		layer_type="""x"""		),
      """regnet-x-004""": ImageNetPreTrainedConfig(
          depths=[1, 2, 7, 12]  ,		hidden_sizes=[32, 64, 160, 384]  ,		groups_width=16  ,		layer_type="""x"""		),
      """regnet-x-006""": ImageNetPreTrainedConfig(
          depths=[1, 3, 5, 7]  ,		hidden_sizes=[48, 96, 240, 528]  ,		groups_width=24  ,		layer_type="""x"""		),
      """regnet-x-008""": ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 5]  ,		hidden_sizes=[64, 128, 288, 672]  ,		groups_width=16  ,		layer_type="""x"""		),
      """regnet-x-016""": ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 2]  ,		hidden_sizes=[72, 168, 408, 912]  ,		groups_width=24  ,		layer_type="""x"""		),
      """regnet-x-032""": ImageNetPreTrainedConfig(
          depths=[2, 6, 15, 2]  ,		hidden_sizes=[96, 192, 432, 1008]  ,		groups_width=48  ,		layer_type="""x"""		),
      """regnet-x-040""": ImageNetPreTrainedConfig(
          depths=[2, 5, 14, 2]  ,		hidden_sizes=[80, 240, 560, 1360]  ,		groups_width=40  ,		layer_type="""x"""		),
      """regnet-x-064""": ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 1]  ,		hidden_sizes=[168, 392, 784, 1624]  ,		groups_width=56  ,		layer_type="""x"""		),
      """regnet-x-080""": ImageNetPreTrainedConfig(
          depths=[2, 5, 15, 1]  ,		hidden_sizes=[80, 240, 720, 1920]  ,		groups_width=120  ,		layer_type="""x"""		),
      """regnet-x-120""": ImageNetPreTrainedConfig(
          depths=[2, 5, 11, 1]  ,		hidden_sizes=[224, 448, 896, 2240]  ,		groups_width=112  ,		layer_type="""x"""		),
      """regnet-x-160""": ImageNetPreTrainedConfig(
          depths=[2, 6, 13, 1]  ,		hidden_sizes=[256, 512, 896, 2048]  ,		groups_width=128  ,		layer_type="""x"""		),
      """regnet-x-320""": ImageNetPreTrainedConfig(
          depths=[2, 7, 13, 1]  ,		hidden_sizes=[336, 672, 1344, 2520]  ,		groups_width=168  ,		layer_type="""x"""		),
      # y variant
      """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]  ,		hidden_sizes=[24, 56, 152, 368]  ,		groups_width=8		),
      """regnet-y-004""": ImageNetPreTrainedConfig(
          depths=[1, 3, 6, 6]  ,		hidden_sizes=[48, 104, 208, 440]  ,		groups_width=8		),
      """regnet-y-006""": ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 4]  ,		hidden_sizes=[48, 112, 256, 608]  ,		groups_width=16		),
      """regnet-y-008""": ImageNetPreTrainedConfig(
          depths=[1, 3, 8, 2]  ,		hidden_sizes=[64, 128, 320, 768]  ,		groups_width=16		),
      """regnet-y-016""": ImageNetPreTrainedConfig(
          depths=[2, 6, 17, 2]  ,		hidden_sizes=[48, 120, 336, 888]  ,		groups_width=24		),
      """regnet-y-032""": ImageNetPreTrainedConfig(
          depths=[2, 5, 13, 1]  ,		hidden_sizes=[72, 216, 576, 1512]  ,		groups_width=24		),
      """regnet-y-040""": ImageNetPreTrainedConfig(
          depths=[2, 6, 12, 2]  ,		hidden_sizes=[128, 192, 512, 1088]  ,		groups_width=64		),
      """regnet-y-064""": ImageNetPreTrainedConfig(
          depths=[2, 7, 14, 2]  ,		hidden_sizes=[144, 288, 576, 1296]  ,		groups_width=72		),
      """regnet-y-080""": ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 1]  ,		hidden_sizes=[168, 448, 896, 2016]  ,		groups_width=56		),
      """regnet-y-120""": ImageNetPreTrainedConfig(
          depths=[2, 5, 11, 1]  ,		hidden_sizes=[224, 448, 896, 2240]  ,		groups_width=112		),
      """regnet-y-160""": ImageNetPreTrainedConfig(
          depths=[2, 4, 11, 1]  ,		hidden_sizes=[224, 448, 1232, 3024]  ,		groups_width=112		),
      """regnet-y-320""": ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]  ,		hidden_sizes=[232, 696, 1392, 3712]  ,		groups_width=232		),
      # models created by SEER -> https://arxiv.org/abs/2202.08360
      """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]  ,		hidden_sizes=[232, 696, 1392, 3712]  ,		groups_width=232		),
      """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]  ,		hidden_sizes=[328, 984, 1968, 4920]  ,		groups_width=328		),
      """regnet-y-1280-seer""": RegNetConfig(
          depths=[2, 7, 17, 1]  ,		hidden_sizes=[528, 1056, 2904, 7392]  ,		groups_width=264		),
      """regnet-y-2560-seer""": RegNetConfig(
          depths=[3, 7, 16, 1]  ,		hidden_sizes=[640, 1696, 2544, 5088]  ,		groups_width=640		),
      """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]  ,		hidden_sizes=[2020, 4040, 11110, 28280]  ,		groups_width=1010		),
      # finetuned on imagenet
      """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]  ,		hidden_sizes=[232, 696, 1392, 3712]  ,		groups_width=232		),
      """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]  ,		hidden_sizes=[328, 984, 1968, 4920]  ,		groups_width=328		),
      """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]  ,		hidden_sizes=[528, 1056, 2904, 7392]  ,		groups_width=264		),
      """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[3, 7, 16, 1]  ,		hidden_sizes=[640, 1696, 2544, 5088]  ,		groups_width=640		),
      """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]  ,		hidden_sizes=[2020, 4040, 11110, 28280]  ,		groups_width=1010		),
  }

  __UpperCAmelCase							:	List[str]           =  NameToOurModelFuncMap()
  __UpperCAmelCase							:	Optional[Any]           =  NameToFromModelFuncMap()
  # add seer weights logic

  def load_using_classy_vision(__lowerCamelCase			:     str  ,		__lowerCamelCase			:     Callable[[], nn.Module]		) -> Tuple[nn.Module, Dict]:
    __UpperCAmelCase							:	int           =  torch.hub.load_state_dict_from_url(__lowerCamelCase  ,		model_dir=str(__lowerCamelCase		)  ,		map_location="""cpu"""		)
    __UpperCAmelCase							:	List[str]           =  model_func()
    # check if we have a head, if yes add it
    __UpperCAmelCase							:	str           =  files["""classy_state_dict"""]["""base_model"""]["""model"""]
    __UpperCAmelCase							:	int           =  model_state_dict["""trunk"""]
    model.load_state_dict(__lowerCamelCase		)
    return model.eval(), model_state_dict["heads"]

  # pretrained
  __UpperCAmelCase							:	Optional[int]           =  partial(
      __lowerCamelCase  ,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""  ,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)  ,		)

  __UpperCAmelCase							:	Dict           =  partial(
      __lowerCamelCase  ,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""  ,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)  ,		)

  __UpperCAmelCase							:	List[str]           =  partial(
      __lowerCamelCase  ,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"""  ,		lambda: FakeRegNetVisslWrapper(RegNetYaaagf()		)  ,		)

  __UpperCAmelCase							:	Optional[int]           =  partial(
      __lowerCamelCase  ,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""  ,		lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=27  ,		group_width=1010  ,		w_a=1744  ,		w_a=6_2_0.8_3  ,		w_m=2.5_2		)		)		)  ,		)

  # IN1K finetuned
  __UpperCAmelCase							:	int           =  partial(
      __lowerCamelCase  ,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""  ,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)  ,		)

  __UpperCAmelCase							:	str           =  partial(
      __lowerCamelCase  ,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""  ,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)  ,		)

  __UpperCAmelCase							:	Any           =  partial(
      __lowerCamelCase  ,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""  ,		lambda: FakeRegNetVisslWrapper(RegNetYaaagf()		)  ,		)

  __UpperCAmelCase							:	List[str]           =  partial(
      __lowerCamelCase  ,		"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""  ,		lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=27  ,		group_width=1010  ,		w_a=1744  ,		w_a=6_2_0.8_3  ,		w_m=2.5_2		)		)		)  ,		)

  if model_name:
    convert_weight_and_push(
        __lowerCamelCase  ,		names_to_from_model_map[model_name]  ,		names_to_ours_model_map[model_name]  ,		names_to_config[model_name]  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(
          __lowerCamelCase  ,		names_to_from_model_map[model_name]  ,		names_to_ours_model_map[model_name]  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		)
  return config, expected_shape


if __name__ == "__main__":
       a		:       Optional[int]			     = argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default=None,
           type=str,
           help=(
               "The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
               " currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
           ),
       )
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default=None,
           type=Path,
           required=True,
           help="Path to the output PyTorch model directory.",
       )
       parser.add_argument(
           "--push_to_hub",
           default=True,
           type=bool,
           required=False,
           help="If True, push model and image processor to the hub.",
       )

       a		:       List[Any]			     = parser.parse_args()

       a		:       Path			     = args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)










import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : List[str]							       =	'MCTCTFeatureExtractor'
      a  : str							       =	'AutoTokenizer'
      def __init__(      self					:			Tuple		,      __lowercase					:			int		,      __lowercase					:			Dict					)							->      Any:
        super().__init__(__lowercase		,      __lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  self.feature_extractor
        __UpperCAmelCase							:	Optional[int]           =  False
      def __call__(      self					:			int		,      *__lowercase					:			Tuple		,      **__lowercase					:			Optional[int]					)							->      Union[str, Any]:
        # 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."""					)
          __UpperCAmelCase							:	Dict           =  kwargs.pop("""raw_speech"""					)
        else:
          __UpperCAmelCase							:	Dict           =  kwargs.pop("""audio"""		,      __lowercase					)
        __UpperCAmelCase							:	List[str]           =  kwargs.pop("""sampling_rate"""		,      __lowercase					)
        __UpperCAmelCase							:	Tuple           =  kwargs.pop("""text"""		,      __lowercase					)
        if len(__lowercase					) > 0:
          __UpperCAmelCase							:	Tuple           =  args[0]
          __UpperCAmelCase							:	str           =  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:
          __UpperCAmelCase							:	Tuple           =  self.feature_extractor(__lowercase		,      *__lowercase		,      sampling_rate=__lowercase		,      **__lowercase					)
        if text is not None:
          __UpperCAmelCase							:	str           =  self.tokenizer(__lowercase		,      **__lowercase					)

        if text is None:
          return inputs
        elif audio is None:
          return encodings
        else:
          __UpperCAmelCase							:	Dict           =  encodings["""input_ids"""]
          return inputs
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      *__lowercase					:			List[Any]		,      **__lowercase					:			int					)							->      List[Any]:
        return self.tokenizer.batch_decode(*__lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			Optional[int]		,      *__lowercase					:			Optional[Any]		,      **__lowercase					:			List[str]					)							->      int:
        # For backward compatibility
        if self._in_target_context_manager:
          return self.current_processor.pad(*__lowercase		,      **__lowercase					)

        __UpperCAmelCase							:	Optional[int]           =  kwargs.pop("""input_features"""		,      __lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  kwargs.pop("""labels"""		,      __lowercase					)
        if len(__lowercase					) > 0:
          __UpperCAmelCase							:	Union[str, Any]           =  args[0]
          __UpperCAmelCase							:	str           =  args[1:]

        if input_features is not None:
          __UpperCAmelCase							:	Any           =  self.feature_extractor.pad(__lowercase		,      *__lowercase		,      **__lowercase					)
        if labels is not None:
          __UpperCAmelCase							:	Union[str, Any]           =  self.tokenizer.pad(__lowercase		,      **__lowercase					)

        if labels is None:
          return input_features
        elif input_features is None:
          return labels
        else:
          __UpperCAmelCase							:	Any           =  labels["""input_ids"""]
          return input_features
      def 	UpperCAmelCase      (      self					:			Any		,      *__lowercase					:			Union[str, Any]		,      **__lowercase					:			Dict					)							->      List[Any]:
        return self.tokenizer.decode(*__lowercase		,      **__lowercase					)




      @contextmanager
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      Tuple:
        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."""					)
        __UpperCAmelCase							:	Any           =  True
        __UpperCAmelCase							:	Optional[int]           =  self.tokenizer
        yield
        __UpperCAmelCase							:	List[Any]           =  self.feature_extractor
        __UpperCAmelCase							:	int           =  False


