





from typing import List, Optional

import numpy as np

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





class      lowercase__    (							__UpperCAmelCase  ):
  __UpperCamelCase	     =  """EncodecFeatureExtractor"""
  __UpperCamelCase	     =  ("""T5Tokenizer""", """T5TokenizerFast""")







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







  def 	UpperCAmelCase__		(	self    ,						_lowercase=None    ,						_lowercase=None    ,						_lowercase=True						):
       return self.tokenizer.get_decoder_prompt_ids(task=UpperCAmelCase_    ,						language=UpperCAmelCase_    ,						no_timestamps=UpperCAmelCase_						)







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

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

       if audio is not None:
            lowerCAmelCase_					:       Optional[int]    =		self.feature_extractor(UpperCAmelCase_    ,						*UpperCAmelCase_    ,						sampling_rate=UpperCAmelCase_    ,						**UpperCAmelCase_						)

       if audio is None:
            return inputs

       elif text is None:
            return audio_inputs

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







  def 	UpperCAmelCase__		(	self    ,						*_lowercase    ,						**_lowercase						):
       lowerCAmelCase_					:       Union[str, Any]    =		kwargs.pop("""audio"""    ,						UpperCAmelCase_						)
       lowerCAmelCase_					:       Optional[Any]    =		kwargs.pop("""padding_mask"""    ,						UpperCAmelCase_						)

       if len(UpperCAmelCase_						) > 0:
            lowerCAmelCase_					:       Tuple    =		args[0]
            lowerCAmelCase_					:       List[str]    =		args[1:]

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







  def 	UpperCAmelCase__		(	self    ,						*_lowercase    ,						**_lowercase						):
       return self.tokenizer.decode(*UpperCAmelCase_    ,						**UpperCAmelCase_						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       lowerCAmelCase_					:       Optional[Any]    =		to_numpy(UpperCAmelCase_						)
       lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       Optional[Any]    =		audio_values.shape

       if padding_mask is None:
            return list(UpperCAmelCase_						)

       lowerCAmelCase_					:       List[Any]    =		to_numpy(UpperCAmelCase_						)

       # 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_					:       Tuple    =		seq_len - padding_mask.shape[-1]
       lowerCAmelCase_					:       Tuple    =		1 - self.feature_extractor.padding_value
       lowerCAmelCase_					:       Tuple    =		np.pad(UpperCAmelCase_    ,						((0, 0), (0, difference))    ,						"""constant"""    ,						constant_values=UpperCAmelCase_						)

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

       return audio_values







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_					:   Tuple 							=						{
    """configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Tuple 							=						["""VivitImageProcessor"""]


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Any 							=						[
          """VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """VivitModel""",
          """VivitPreTrainedModel""",
          """VivitForVideoClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_vivit import VivitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_vivit import (
                VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                VivitForVideoClassification,
                VivitModel,
                VivitPreTrainedModel,
            )


else:
      import sys

      UpperCAmelCase_					:   Dict 							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
