
from ...processing_utils import ProcessorMixin




class 			UpperCAmelCase_     (					UpperCamelCase_	):


  '''simple docstring'''







  a__       			=						"""SpeechT5FeatureExtractor"""
  a__       			=						"""SpeechT5Tokenizer"""
  def __init__(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					Optional[int]       )     ->      Dict:







    """simple docstring"""






    super().__init__(a__     ,				a__       )
  def __call__(					self     :					Tuple     ,				*UpperCamelCase__     :					Any     ,				**UpperCamelCase__     :					int       )     ->      Optional[int]:







    """simple docstring"""






    __magic_name__								=			kwargs.pop("""audio"""     ,				a__       )
    __magic_name__								=			kwargs.pop("""text"""     ,				a__       )
    __magic_name__								=			kwargs.pop("""text_target"""     ,				a__       )
    __magic_name__								=			kwargs.pop("""audio_target"""     ,				a__       )
    __magic_name__								=			kwargs.pop("""sampling_rate"""     ,				a__       )

    if audio is not None and text is not None:
      raise ValueError(
          """Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"""       )
    if audio_target is not None and text_target is not None:
      raise ValueError(
          """Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"""       )
    if audio is None and audio_target is None and text is None and text_target is None:
      raise ValueError(
          """You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."""       )

    if audio is not None:
      __magic_name__								=			self.feature_extractor(a__     ,				*a__     ,				sampling_rate=a__     ,				**a__       )
    elif text is not None:
      __magic_name__								=			self.tokenizer(a__     ,				**a__       )
    else:
      __magic_name__								=			None

    if audio_target is not None:
      __magic_name__								=			self.feature_extractor(audio_target=a__     ,				*a__     ,				sampling_rate=a__     ,				**a__       )
      __magic_name__								=			targets["input_values"]
    elif text_target is not None:
      __magic_name__								=			self.tokenizer(a__     ,				**a__       )
      __magic_name__								=			targets["input_ids"]
    else:
      __magic_name__								=			None

    if inputs is None:
      return targets

    if targets is not None:
      __magic_name__								=			labels

      __magic_name__								=			targets.get("""attention_mask"""       )
      if decoder_attention_mask is not None:
        __magic_name__								=			decoder_attention_mask

    return inputs
  def 				_lowercase				(					self     :					Dict     ,				*UpperCamelCase__     :					int     ,				**UpperCamelCase__     :					Any       )     ->      str:







    """simple docstring"""






    __magic_name__								=			kwargs.pop("""input_values"""     ,				a__       )
    __magic_name__								=			kwargs.pop("""input_ids"""     ,				a__       )
    __magic_name__								=			kwargs.pop("""labels"""     ,				a__       )

    if input_values is not None and input_ids is not None:
      raise ValueError("""Cannot process both `input_values` and `input_ids` inputs."""       )
    if input_values is None and input_ids is None and labels is None:
      raise ValueError(
          """You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."""       )

    if input_values is not None:
      __magic_name__								=			self.feature_extractor.pad(a__     ,				*a__     ,				**a__       )
    elif input_ids is not None:
      __magic_name__								=			self.tokenizer.pad(a__     ,				**a__       )
    else:
      __magic_name__								=			None

    if labels is not None:
      if "input_ids" in labels or (isinstance(a__     ,				a__       ) and "input_ids" in labels[0]):
        __magic_name__								=			self.tokenizer.pad(a__     ,				**a__       )
        __magic_name__								=			targets["input_ids"]
      else:
        __magic_name__								=			self.feature_extractor.feature_size
        __magic_name__								=			self.feature_extractor.num_mel_bins
        __magic_name__								=			self.feature_extractor.pad(a__     ,				*a__     ,				**a__       )
        __magic_name__								=			feature_size_hack
        __magic_name__								=			targets["input_values"]
    else:
      __magic_name__								=			None

    if inputs is None:
      return targets

    if targets is not None:
      __magic_name__								=			labels

      __magic_name__								=			targets.get("""attention_mask"""       )
      if decoder_attention_mask is not None:
        __magic_name__								=			decoder_attention_mask

    return inputs
  def 				_lowercase				(					self     :					Dict     ,				*UpperCamelCase__     :					int     ,				**UpperCamelCase__     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    return self.tokenizer.batch_decode(*a__     ,				**a__       )





  def 				_lowercase				(					self     :					Any     ,				*UpperCamelCase__     :					Any     ,				**UpperCamelCase__     :					Optional[int]       )     ->      str:







    """simple docstring"""






    return self.tokenizer.decode(*a__     ,				**a__       )



'''simple docstring'''

import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image
else:




     class    lowerCAmelCase       :


      @staticmethod
      def    _A	(    *a__  : str			,							**a__  : List[str]    ):
       '''simple docstring'''

       pass


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	hashlib.mda(image.tobytes()			)
 return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    lowerCAmelCase       (     unittest.TestCase  ):
 A_				:				List[Any]	=			MODEL_FOR_DEPTH_ESTIMATION_MAPPING


 def    _A	(    self  : List[Any]			,							a__  : int			,							a__  : Dict			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	DepthEstimationPipeline(model=a__			,							image_processor=a__    )
  return depth_estimator, [
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
  ]


 def    _A	(    self  : int			,							a__  : Optional[int]			,							a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
  self.assertEqual({"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )}			,							a__    )
  import datasets

  lowerCAmelCase__     :   List[str]     		=	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"			,							"image"			,							split="test"    )
  lowerCAmelCase__     :   List[str]     		=	depth_estimator(
      [
          Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    ),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
          # RGBA
          dataset[0]["file"],
          # LA
          dataset[1]["file"],
          # L
          dataset[2]["file"],
      ]    )
  self.assertEqual(
      [
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
      ]			,							a__			,							)


 @require_tf
 @unittest.skip("Depth estimation is not implemented in TF"    )
 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  pass


 @slow
 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	"Intel/dpt-large"
  lowerCAmelCase__     :   Dict     		=	pipeline("depth-estimation"			,							model=a__    )
  lowerCAmelCase__     :   Any     		=	depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"    )
  lowerCAmelCase__     :   Any     		=	hashimage(outputs["depth"]    )

  # This seems flaky.
  # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
  self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()    )			,							29.304    )
  self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()    )			,							2.662    )


 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"    )





