
"""simple docstring"""
from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
    from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor






class   _UpperCAmelCase					(       _snake_case):
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = self.config_class(**self.inputs_dict  )
      self.parent.assertTrue(hasattr(snake_case_    ,     "hidden_sizes"  )  )
      self.parent.assertTrue(hasattr(snake_case_    ,     "neck_hidden_sizes"  )  )
      self.parent.assertTrue(hasattr(snake_case_    ,     "num_attention_heads"  )  )






class   _UpperCAmelCase					:
    def __init__(     self    ,     snake_case_    ,     snake_case_=13    ,     snake_case_=32    ,     snake_case_=2    ,     snake_case_=3    ,     snake_case_=6_40    ,     snake_case_=4    ,     snake_case_="silu"    ,     snake_case_=3    ,     snake_case_=32    ,     snake_case_=0.1    ,     snake_case_=0.1    ,     snake_case_=0.1    ,     snake_case_=0.02    ,     snake_case_=True    ,     snake_case_=True    ,     snake_case_=10    ,     snake_case_=None    ,     ):
      _snake_case  :    List[Any]            = parent
      _snake_case  :    Union[str, Any]            = batch_size
      _snake_case  :    List[Any]            = image_size
      _snake_case  :    List[Any]            = patch_size
      _snake_case  :    Optional[Any]            = num_channels
      _snake_case  :    List[Any]            = last_hidden_size
      _snake_case  :    Tuple            = num_attention_heads
      _snake_case  :    Any            = hidden_act
      _snake_case  :    List[Any]            = conv_kernel_size
      _snake_case  :    int            = output_stride
      _snake_case  :    Dict            = hidden_dropout_prob
      _snake_case  :    Optional[int]            = attention_probs_dropout_prob
      _snake_case  :    List[Any]            = classifier_dropout_prob
      _snake_case  :    List[str]            = use_labels
      _snake_case  :    Dict            = is_training
      _snake_case  :    List[str]            = num_labels
      _snake_case  :    Optional[Any]            = initializer_range
      _snake_case  :    Dict            = scope
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[int]            = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

      _snake_case  :    List[str]            = None
      _snake_case  :    int            = None
      if self.use_labels:
        _snake_case  :    Any            = ids_tensor([self.batch_size]    ,     self.num_labels  )
        _snake_case  :    Dict            = ids_tensor([self.batch_size, self.image_size, self.image_size]    ,     self.num_labels  )

      _snake_case  :    Optional[Any]            = self.get_config()

      return config, pixel_values, labels, pixel_labels
    def        lowerCamelCase__						(     self  ):
      return MobileViTConfig(
          image_size=self.image_size    ,     patch_size=self.patch_size    ,     num_channels=self.num_channels    ,     num_attention_heads=self.num_attention_heads    ,     hidden_act=self.hidden_act    ,     conv_kernel_size=self.conv_kernel_size    ,     output_stride=self.output_stride    ,     hidden_dropout_prob=self.hidden_dropout_prob    ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,     classifier_dropout_prob=self.classifier_dropout_prob    ,     initializer_range=self.initializer_range    ,     )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    str            = TFMobileViTModel(config=snake_case_  )
      _snake_case  :    Optional[int]            = model(snake_case_    ,     training=snake_case_  )
      _snake_case  :    List[Any]            = self.image_size // self.output_stride
      self.parent.assertEqual(
          result.last_hidden_state.shape    ,     (self.batch_size, self.last_hidden_size, expected_height, expected_width)  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    int            = self.num_labels
      _snake_case  :    Optional[Any]            = TFMobileViTForImageClassification(snake_case_  )
      _snake_case  :    str            = model(snake_case_    ,     labels=snake_case_    ,     training=snake_case_  )
      self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_labels)  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    List[str]            = self.num_labels
      _snake_case  :    Any            = TFMobileViTForSemanticSegmentation(snake_case_  )
      _snake_case  :    Optional[Any]            = self.image_size // self.output_stride

      _snake_case  :    Any            = model(snake_case_    ,     training=snake_case_  )
      self.parent.assertEqual(
          result.logits.shape    ,     (self.batch_size, self.num_labels, expected_height, expected_width)  )

      _snake_case  :    List[Any]            = model(snake_case_    ,     labels=snake_case_    ,     training=snake_case_  )
      self.parent.assertEqual(
          result.logits.shape    ,     (self.batch_size, self.num_labels, expected_height, expected_width)  )


    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = self.prepare_config_and_inputs()
      _snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case  :    str            = config_and_inputs
      _snake_case  :    Optional[int]            = {"pixel_values": pixel_values}
      return config, inputs_dict






@require_tf
class   _UpperCAmelCase					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			Tuple	=       (
        (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
        if is_tf_available()
        else ()
    )
    __lowercase				:			int	=       (
        {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
        if is_tf_available()
        else {}
    )

    __lowercase				:			Optional[int]	=       False
    __lowercase				:			Optional[int]	=       False
    __lowercase				:			List[Any]	=       False
    __lowercase				:			int	=       False
    __lowercase				:			Union[str, Any]	=       False
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = TFMobileViTModelTester(self  )
      _snake_case  :    Dict            = TFMobileViTConfigTester(self    ,     config_class=snake_case_    ,     has_text_modality=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      self.config_tester.run_common_tests()
    @unittest.skip(reason="MobileViT does not use inputs_embeds"  )
    def        lowerCamelCase__						(     self  ):
      pass
    @unittest.skip(reason="MobileViT does not support input and output embeddings"  )
    def        lowerCamelCase__						(     self  ):
      pass
    @unittest.skip(reason="MobileViT does not output attentions"  )
    def        lowerCamelCase__						(     self  ):
      pass
    def        lowerCamelCase__						(     self  ):
      _snake_case      ,       _snake_case  :    Optional[int]            = self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        _snake_case  :    List[Any]            = model_class(snake_case_  )
        _snake_case  :    Any            = inspect.signature(model.call  )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        _snake_case  :    int            = [*signature.parameters.keys()]

        _snake_case  :    List[str]            = ["pixel_values"]
        self.assertListEqual(arg_names[:1]    ,     snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case_  )
    def        lowerCamelCase__						(     self  ):
      def check_hidden_states_output(snake_case_    ,     snake_case_    ,     snake_case_  ):
        _snake_case  :    Optional[int]            = model_class(snake_case_  )

        _snake_case  :    Dict            = model(**self._prepare_for_class(snake_case_    ,     snake_case_  )  )

        _snake_case  :    Optional[Any]            = outputs.hidden_states

        _snake_case  :    Any            = 5
        self.assertEqual(len(snake_case_  )    ,     snake_case_  )

        # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
        # with the width and height being successively divided by 2.
        _snake_case  :    Dict            = 2
        for i in range(len(snake_case_  )  ):
          self.assertListEqual(
              list(hidden_states[i].shape[-2:]  )    ,     [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]    ,     )
          divisor *= 2

        self.assertEqual(self.model_tester.output_stride    ,     divisor // 2  )

      _snake_case      ,       _snake_case  :    Optional[int]            = self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        _snake_case  :    Union[str, Any]            = True
        check_hidden_states_output(snake_case_    ,     snake_case_    ,     snake_case_  )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        _snake_case  :    Optional[Any]            = True

        check_hidden_states_output(snake_case_    ,     snake_case_    ,     snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*snake_case_  )
    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices("GPU"  )  ) == 0    ,     reason="TF does not support backprop for grouped convolutions on CPU."    ,     )
    def        lowerCamelCase__						(     self  ):
      super().test_dataset_conversion()
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_=2E-1    ,     snake_case_=2E-1  ):
      self.assertTrue(np.allclose(snake_case_    ,     snake_case_    ,     atol=snake_case_    ,     rtol=snake_case_  )  )
    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices("GPU"  )  ) == 0    ,     reason="TF does not support backprop for grouped convolutions on CPU."    ,     )
    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case      ,       _snake_case  :    Optional[int]            = self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
        if model_class.__name__ != "TFMobileViTModel":
          _snake_case  :    List[str]            = model_class(snake_case_  )
          if getattr(snake_case_    ,     "hf_compute_loss"    ,     snake_case_  ):
            super().test_keras_fit()
    def        lowerCamelCase__						(     self  ):
      _snake_case      ,       _snake_case  :    Optional[int]            = self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
        # set an ignore index to correctly test the masked loss used in
        # `TFMobileViTForSemanticSegmentation`.
        if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
          _snake_case  :    Optional[Any]            = 5

        _snake_case  :    Optional[Any]            = model_class(snake_case_  )
        if getattr(snake_case_    ,     "hf_compute_loss"    ,     snake_case_  ):
          # The number of elements in the loss should be the same as the number of elements in the label
          _snake_case  :    Optional[Any]            = self._prepare_for_class(inputs_dict.copy()    ,     snake_case_    ,     return_labels=snake_case_  )
          _snake_case  :    int            = prepared_for_class[
              sorted(prepared_for_class.keys() - inputs_dict.keys()    ,     reverse=snake_case_  )[0]
          ]
          _snake_case  :    Optional[Any]            = added_label.shape.as_list()[:1]

          # Test that model correctly compute the loss with kwargs
          _snake_case  :    List[str]            = self._prepare_for_class(inputs_dict.copy()    ,     snake_case_    ,     return_labels=snake_case_  )
          _snake_case  :    str            = {"input_ids", "pixel_values", "input_features"}
          _snake_case  :    Optional[int]            = possible_input_names.intersection(set(snake_case_  )  ).pop()
          _snake_case  :    int            = prepared_for_class.pop(snake_case_  )

          _snake_case  :    Union[str, Any]            = model(snake_case_    ,     **snake_case_  )[0]
          self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )

          # Test that model correctly compute the loss when we mask some positions
          _snake_case  :    Optional[Any]            = self._prepare_for_class(inputs_dict.copy()    ,     snake_case_    ,     return_labels=snake_case_  )
          _snake_case  :    Any            = {"input_ids", "pixel_values", "input_features"}
          _snake_case  :    Optional[int]            = possible_input_names.intersection(set(snake_case_  )  ).pop()
          _snake_case  :    List[str]            = prepared_for_class.pop(snake_case_  )
          if "labels" in prepared_for_class:
            _snake_case  :    Optional[int]            = prepared_for_class["labels"].numpy()
            if len(labels.shape  ) > 1 and labels.shape[1] != 1:
              # labels[0] = -100
              _snake_case  :    Optional[int]            = tf.convert_to_tensor(snake_case_  )
              _snake_case  :    Union[str, Any]            = model(snake_case_    ,     **snake_case_  )[0]
              self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )
              self.assertTrue(not np.any(np.isnan(loss.numpy()  )  )  )

                # Test that model correctly compute the loss with a dict
          _snake_case  :    Tuple            = self._prepare_for_class(inputs_dict.copy()    ,     snake_case_    ,     return_labels=snake_case_  )
          _snake_case  :    Tuple            = model(snake_case_  )[0]
          self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )

          # Test that model correctly compute the loss with a tuple
          _snake_case  :    Dict            = self._prepare_for_class(inputs_dict.copy()    ,     snake_case_    ,     return_labels=snake_case_  )

          # Get keys that were added with the _prepare_for_class function
          _snake_case  :    Union[str, Any]            = prepared_for_class.keys() - inputs_dict.keys()
          _snake_case  :    Any            = inspect.signature(model.call  ).parameters
          _snake_case  :    List[Any]            = list(signature.keys()  )

          # Create a dictionary holding the location of the tensors in the tuple
          _snake_case  :    Union[str, Any]            = {0: input_name}
          for label_key in label_keys:
            _snake_case  :    Tuple            = signature_names.index(snake_case_  )
            _snake_case  :    int            = label_key
          _snake_case  :    List[Any]            = sorted(tuple_index_mapping.items()  )
          # Initialize a list with their default values, update the values and convert to a tuple
          _snake_case  :    List[Any]            = []

          for name in signature_names:
            if name != "kwargs":
              list_input.append(signature[name].default  )

          for index, value in sorted_tuple_index_mapping:
            _snake_case  :    int            = prepared_for_class[value]

          _snake_case  :    str            = tuple(snake_case_  )

          # Send to model
          _snake_case  :    Union[str, Any]            = model(tuple_input[:-1]  )[0]

          self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )


    @slow
    def        lowerCamelCase__						(     self  ):
      for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        _snake_case  :    Optional[int]            = TFMobileViTModel.from_pretrained(snake_case_  )
        self.assertIsNotNone(snake_case_  )


def 					a__ ( ):

  """simple docstring"""







  _snake_case  :    Union[str, Any]            = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
  return image






@require_tf
class   _UpperCAmelCase					(       unittest.TestCase):
    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[Any]            = TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"  )

      _snake_case  :    List[Any]            = MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small"  )
      _snake_case  :    List[Any]            = prepare_img()
      _snake_case  :    List[str]            = image_processor(images=snake_case_    ,     return_tensors="tf"  )

      # forward pass
      _snake_case  :    Union[str, Any]            = model(**snake_case_    ,     training=snake_case_  )

      # verify the logits
      _snake_case  :    Optional[Any]            = tf.TensorShape((1, 10_00)  )
      self.assertEqual(outputs.logits.shape    ,     snake_case_  )

      _snake_case  :    List[Any]            = tf.constant([-1.9364, -1.2327, -0.4653]  )

      tf.debugging.assert_near(outputs.logits[0, :3]    ,     snake_case_    ,     atol=1E-4    ,     rtol=1E-04  )


    @slow
    def        lowerCamelCase__						(     self  ):
      # `from_pt` will be removed
      _snake_case  :    List[str]            = TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"  )

      _snake_case  :    int            = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"  )

      _snake_case  :    List[Any]            = prepare_img()
      _snake_case  :    str            = image_processor(images=snake_case_    ,     return_tensors="tf"  )

      # forward pass
      _snake_case  :    Tuple            = model(inputs.pixel_values    ,     training=snake_case_  )
      _snake_case  :    Tuple            = outputs.logits

      # verify the logits
      _snake_case  :    Any            = tf.TensorShape((1, 21, 32, 32)  )
      self.assertEqual(logits.shape    ,     snake_case_  )

      _snake_case  :    str            = tf.constant(
          [
              [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
              [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
              [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
          ]  )

      tf.debugging.assert_near(logits[0, :3, :3, :3]    ,     snake_case_    ,     rtol=1E-4    ,     atol=1E-4  )







import unittest
from unittest import skip

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow
from transformers.trainer_utils import set_seed


if is_torch_available():
       import torch

       from transformers import JukeboxModel, JukeboxPrior, JukeboxTokenizer





@require_torch
class        lowerCamelCase__(   unittest.TestCase):
      UpperCAmelCase__ :      Optional[Any]       						=  (JukeboxModel,) if is_torch_available() else ()
      UpperCAmelCase__ :      Union[str, Any]       						=  'openai/jukebox-1b-lyrics'
      UpperCAmelCase__ :      Optional[int]       						=  {
          'artist': 'Zac Brown Band',
          'genres': 'Country',
          'lyrics': 'I met a traveller from an antique land,\n    Who said "Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ',
      }
      # fmt: off
      UpperCAmelCase__ :      Dict       						=  [
          1864, 1536, 1213, 1870, 1357, 1536, 519, 880, 1323, 789, 1082, 534,
          1000, 1445, 1105, 1130, 967, 515, 1434, 1620, 534, 1495, 283, 1445,
          333, 1307, 539, 1631, 1528, 375, 1434, 673, 627, 710, 778, 1883,
          1405, 1276, 1455, 1228
      ]

      UpperCAmelCase__ :      List[Any]       						=  [
          1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653
      ]

      UpperCAmelCase__ :      str       						=  [
          1125, 1751, 697, 1776, 1141, 1476, 391, 697, 1125, 684, 867, 416,
          844, 1372, 1274, 717, 1274, 844, 1299, 1419, 697, 1370, 317, 1125,
          191, 1440, 1370, 1440, 1370, 282, 1621, 1370, 368, 349, 867, 1872,
          1262, 869, 1728, 747
      ]
      UpperCAmelCase__ :      str       						=  [
          416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416
      ]

      UpperCAmelCase__ :      str       						=  [
          1755, 842, 307, 1843, 1022, 1395, 234, 1554, 806, 739, 1022, 442,
          616, 556, 268, 1499, 933, 457, 1440, 1837, 755, 985, 308, 902,
          293, 1443, 1671, 1141, 1533, 555, 1562, 1061, 287, 417, 1022, 2008,
          1186, 1015, 1777, 268
      ]
      UpperCAmelCase__ :      Tuple       						=  [
          854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842,
          185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
          591, 842, 1353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
          185, 842, 591, 89
      ]

      UpperCAmelCase__ :      List[Any]       						=  [105_8304, 0, 78_6432, 7169, 507, 76, 27, 40, 30, 76]

      UpperCAmelCase__ :      Union[str, Any]       						=  [
          390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002,
          1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002,
          1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907,
          1907, 1788, 596, 1626
      ]
      UpperCAmelCase__ :      List[str]       						=  [
          1236, 1668, 1484, 1920, 1848, 1409, 139, 864, 1828, 1272, 1599, 824,
          1672, 139, 555, 1484, 824, 1920, 555, 596, 1579, 1599, 1231, 1599,
          1637, 1407, 212, 824, 1599, 116, 1433, 824, 258, 1599, 1433, 1895,
          1063, 1433, 1433, 1599
      ]
      UpperCAmelCase__ :      str       						=  [
          1684, 1873, 1119, 1189, 395, 611, 1901, 972, 890, 1337, 1392, 1927,
          96, 972, 672, 780, 1119, 890, 158, 771, 1073, 1927, 353, 1331,
          1269, 1459, 1333, 1645, 812, 1577, 1337, 606, 353, 981, 1466, 619,
          197, 391, 302, 1930
      ]
      UpperCAmelCase__ :      Union[str, Any]       						=  [
          390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002,
          1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002,
          1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907,
          1907, 1788, 596, 1626
      ]
      UpperCAmelCase__ :      Optional[int]       						=  [
          -0.04_92, -0.05_24, -0.05_65, -0.06_40, -0.06_86, -0.06_84, -0.06_77, -0.06_64,
          -0.06_05, -0.04_90, -0.03_30, -0.01_68, -0.00_83, -0.00_75, -0.00_51, 0.00_25,
          0.01_36, 0.02_61, 0.03_86, 0.04_97, 0.05_80, 0.05_99, 0.05_83, 0.06_14,
          0.07_40, 0.08_89, 0.10_23, 0.11_62, 0.12_11, 0.12_12, 0.12_51, 0.13_36,
          0.15_02, 0.16_86, 0.18_83, 0.21_48, 0.23_63, 0.24_58, 0.25_07, 0.25_31
      ]
      UpperCAmelCase__ :      Union[str, Any]       						=  [
          0.02_56, -0.05_44, 0.16_00, -0.00_32, 0.10_66, 0.08_25, -0.00_13, 0.34_40,
          0.02_10, 0.04_12, -0.17_77, -0.08_92, -0.01_64, 0.02_85, -0.06_13, -0.06_17,
          -0.01_37, -0.02_01, -0.01_75, 0.02_15, -0.06_27, 0.05_20, -0.07_30, 0.09_70,
          -0.01_00, 0.04_42, -0.05_86, 0.02_07, -0.00_15, -0.00_82
      ]
      UpperCAmelCase__ :      str       						=  [
          0.04_15, 0.08_77, 0.00_22, -0.00_55, 0.07_51, 0.03_34, 0.03_24, -0.00_68,
          0.00_11, 0.00_17, -0.06_76, 0.06_55, -0.01_43, 0.03_99, 0.03_03, 0.07_43,
          -0.01_68, -0.03_94, -0.11_13, 0.01_24, 0.04_42, 0.02_67, -0.00_03, -0.15_36,
          -0.01_16, -0.18_37, -0.01_80, -0.10_26, -0.07_77, -0.04_56
      ]
      UpperCAmelCase__ :      int       						=  [
          76, 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45, 45, 76, 38, 31, 33,
          45, 76, 41, 32, 76, 45, 46, 41, 40, 31, 78, 76
      ]
      def      lowerCAmelCase__		(							self:					Dict  ):
             __lowerCamelCase  =		JukeboxTokenizer.from_pretrained(self.model_id  )
             __lowerCamelCase  =		tokenizer(**self.metas  )["""input_ids"""]
             return tokens
      @slow
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             __lowerCamelCase  =		JukeboxModel.from_pretrained(self.model_id       ,			min_duration=0  ).eval()
             __lowerCamelCase  =		self.prepare_inputs()

             set_seed(0  )
             __lowerCamelCase  =		[torch.zeros(1       ,			0       ,			dtype=torch.long  ).cpu() for _ in range(3  )]
             __lowerCamelCase  =		model._sample(UpperCamelCase_       ,			UpperCamelCase_       ,			[0]       ,			sample_length=40 * model.priors[0].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             self.assertIn(zs[0][0].detach().cpu().tolist()       ,			[self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]  )

             set_seed(0  )
             __lowerCamelCase  =		model._sample(UpperCamelCase_       ,			UpperCamelCase_       ,			[1]       ,			sample_length=40 * model.priors[1].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             self.assertIn(zs[1][0].detach().cpu().tolist()       ,			[self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]  )

             set_seed(0  )
             __lowerCamelCase  =		model._sample(UpperCamelCase_       ,			UpperCamelCase_       ,			[2]       ,			sample_length=40 * model.priors[2].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             self.assertIn(zs[2][0].detach().cpu().tolist()       ,			[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]  )
      @slow
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             __lowerCamelCase  =		False
             __lowerCamelCase  =		JukeboxModel.from_pretrained(self.model_id       ,			min_duration=0  ).eval()

             __lowerCamelCase  =		self.prepare_inputs()
             set_seed(0  )
             __lowerCamelCase  =		[torch.zeros(1       ,			0       ,			dtype=torch.long  ) for _ in range(3  )]

             __lowerCamelCase  =		model.priors[0]
             __lowerCamelCase  =		0
             __lowerCamelCase  =		top_prior.get_music_tokens_conds(UpperCamelCase_       ,			start=UpperCamelCase_       ,			end=start + top_prior.n_ctx  )
             __lowerCamelCase  =		top_prior.get_metadata(labels[0].clone()       ,			UpperCamelCase_       ,			1_05_83_04       ,			0  )

             self.assertIsNone(UpperCamelCase_  )
             self.assertListEqual(metadata.numpy()[0][:10].tolist()       ,			self.EXPECTED_Y_COND  )

             __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase  =		top_prior.get_cond(UpperCamelCase_       ,			UpperCamelCase_  )
             torch.testing.assert_allclose(
                 audio_conditioning[0][0][:30].detach()       ,			torch.tensor(self.EXPECTED_AUDIO_COND  )       ,			atol=1E-4       ,			rtol=1E-4  )
             torch.testing.assert_allclose(
                 metadata_conditioning[0][0][:30].detach()       ,			torch.tensor(self.EXPECTED_META_COND  )       ,			atol=1E-4       ,			rtol=1E-4  )
             torch.testing.assert_allclose(
                 lyric_tokens[0, :30].detach()       ,			torch.tensor(self.EXPECTED_LYRIC_COND  )       ,			atol=1E-4       ,			rtol=1E-4  )
      @slow
      def      lowerCAmelCase__		(							self:					List[Any]  ):
             __lowerCamelCase  =		False

             __lowerCamelCase  =		JukeboxModel.from_pretrained(self.model_id       ,			min_duration=0  ).eval()
             set_seed(0  )
             __lowerCamelCase  =		torch.rand((1, 51_20, 1)  )
             __lowerCamelCase  =		list(self.prepare_inputs()  )

             __lowerCamelCase  =		[model.vqvae.encode(UpperCamelCase_       ,			start_level=2       ,			bs_chunks=waveform.shape[0]  )[0], None, None]
             __lowerCamelCase  =		model._sample(
                 UpperCamelCase_       ,			UpperCamelCase_       ,			sample_levels=[0]       ,			save_results=UpperCamelCase_       ,			sample_length=40 * model.priors[0].raw_to_tokens  )
             torch.testing.assert_allclose(zs[0][0][:40]       ,			torch.tensor(self.EXPECTED_PRIMED_0  )  )

             __lowerCamelCase  =		torch.cat((zs[0], torch.zeros(1       ,			20_48 - zs[0].shape[-1]  ))       ,			dim=-1  ).long()
             __lowerCamelCase  =		[upper_a, model.vqvae.encode(UpperCamelCase_       ,			start_level=1       ,			bs_chunks=waveform.shape[0]  )[0], None]
             __lowerCamelCase  =		model._sample(
                 UpperCamelCase_       ,			UpperCamelCase_       ,			sample_levels=[1]       ,			save_results=UpperCamelCase_       ,			sample_length=40 * model.priors[1].raw_to_tokens  )
             torch.testing.assert_allclose(zs[1][0][:40]       ,			torch.tensor(self.EXPECTED_PRIMED_1  )  )

             __lowerCamelCase  =		torch.cat((zs[1], torch.zeros(1       ,			20_48 - zs[1].shape[-1]  ))       ,			dim=-1  ).long()
             __lowerCamelCase  =		[upper_a, upper_a, model.vqvae.encode(UpperCamelCase_       ,			start_level=0       ,			bs_chunks=waveform.shape[0]  )[0]]
             __lowerCamelCase  =		model._sample(
                 UpperCamelCase_       ,			UpperCamelCase_       ,			sample_levels=[2]       ,			save_results=UpperCamelCase_       ,			sample_length=40 * model.priors[2].raw_to_tokens  )
             torch.testing.assert_allclose(zs[2][0][:40].cpu()       ,			torch.tensor(self.EXPECTED_PRIMED_2  )  )




      @slow
      def      lowerCAmelCase__		(							self:					List[Any]  ):
             __lowerCamelCase  =		JukeboxModel.from_pretrained(self.model_id       ,			min_duration=0  ).eval()
             set_seed(0  )
             __lowerCamelCase  =		torch.rand((1, 51_20, 1)  )
             with torch.no_grad():
                    __lowerCamelCase  =		model.vqvae.encode(UpperCamelCase_       ,			start_level=2       ,			bs_chunks=x.shape[0]  )
             torch.testing.assert_allclose(zs[0][0]       ,			torch.tensor(self.EXPECTED_VQVAE_ENCODE  )  )

             with torch.no_grad():
                    __lowerCamelCase  =		model.vqvae.decode(UpperCamelCase_       ,			start_level=2       ,			bs_chunks=x.shape[0]  )
             torch.testing.assert_allclose(x[0, :40, 0]       ,			torch.tensor(self.EXPECTED_VQVAE_DECODE  )       ,			atol=1E-4       ,			rtol=1E-4  )





@require_torch
class        lowerCamelCase__(   unittest.TestCase):
      UpperCAmelCase__ :      List[Any]       						=  (JukeboxModel,) if is_torch_available() else ()
      UpperCAmelCase__ :      Optional[int]       						=  'openai/jukebox-5b-lyrics'
      UpperCAmelCase__ :      Optional[Any]       						=  {
          'artist': 'Zac Brown Band',
          'genres': 'Country',
          'lyrics': 'I met a traveller from an antique land,\n    Who said "Two vast and trunkless legs of stone\n    Stand in the desert. . . . Near them, on the sand,\n    Half sunk a shattered visage lies, whose frown,\n    And wrinkled lip, and sneer of cold command,\n    Tell that its sculptor well those passions read\n    Which yet survive, stamped on these lifeless things,\n    The hand that mocked them, and the heart that fed;\n    And on the pedestal, these words appear:\n    My name is Ozymandias, King of Kings;\n    Look on my Works, ye Mighty, and despair!\n    Nothing beside remains. Round the decay\n    Of that colossal Wreck, boundless and bare\n    The lone and level sands stretch far away\n    ',
      }

      # fmt: off
      UpperCAmelCase__ :      Optional[int]       						=  [
          1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          1489, 1489, 1489, 1489, 1150, 1853, 1509, 1150, 1357, 1509, 6, 1272
      ]
      UpperCAmelCase__ :      Union[str, Any]       						=  [
          1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
      ]

      UpperCAmelCase__ :      Optional[int]       						=  [
          1125, 416, 1125, 1125, 1125, 1125, 1125, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
      ]
      UpperCAmelCase__ :      List[str]       						=  [
          416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416
      ]

      UpperCAmelCase__ :      Optional[Any]       						=  [
          1755, 1061, 234, 1755, 1061, 1755, 185, 290, 307, 307, 616, 616,
          616, 616, 616, 616, 307, 290, 417, 1755, 234, 1755, 185, 290,
          290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1755,
          234, 234, 1755, 234, 185, 185, 307, 616, 616, 616, 616, 290,
          1755, 1755, 1755, 234, 234, 1755, 1572, 290, 307, 616, 34, 616
      ]
      UpperCAmelCase__ :      str       						=  [
          854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842, 185,
          417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842,
          1353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591,
          89, 591, 842, 591, 842, 591, 417, 1372, 842, 1372, 842, 34, 842,
          185, 89, 591, 842, 185, 842, 591, 632
      ]

      UpperCAmelCase__ :      List[Any]       						=  [
          1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
      ]
      UpperCAmelCase__ :      Dict       						=  [
          1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
          653, 653, 653, 653, 653, 653, 653, 1853, 1177, 1536, 1228,
          710, 475, 1489, 1229, 1224, 231, 1224, 252, 1434, 653, 475,
          1106, 1877, 1599, 1228, 1600, 1683, 1182, 1853, 475, 1864,
          252, 1229, 1434, 2001
      ]

      UpperCAmelCase__ :      Optional[int]       						=  [
          1125, 1125, 416, 1125, 1125, 416, 1125, 1125, 416, 416, 1125, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
          416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
      ]
      UpperCAmelCase__ :      str       						=  [
          491, 1755, 34, 1613, 1755, 417, 992, 1613, 222, 842, 1353, 1613,
          844, 632, 185, 1613, 844, 632, 185, 1613, 185, 842, 677, 1613,
          185, 114, 1353, 1613, 307, 89, 844, 1613, 307, 1332, 234, 1979,
          307, 89, 1353, 616, 34, 842, 185, 842, 34, 842, 185, 842,
          307, 114, 185, 89, 34, 1268, 185, 89, 34, 842, 185, 89
      ]
      def      lowerCAmelCase__		(							self:					Optional[Any]       ,			UpperCamelCase_:					Optional[int]  ):
             __lowerCamelCase  =		JukeboxTokenizer.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		tokenizer(**self.metas  )["""input_ids"""]
             return tokens
      @slow
      def      lowerCAmelCase__		(							self:					Tuple  ):
             __lowerCamelCase  =		JukeboxModel.from_pretrained(self.model_id       ,			min_duration=0  ).eval()
             __lowerCamelCase  =		self.prepare_inputs(self.model_id  )

             set_seed(0  )
             __lowerCamelCase  =		[torch.zeros(1       ,			0       ,			dtype=torch.long  ).cpu() for _ in range(3  )]
             __lowerCamelCase  =		model._sample(UpperCamelCase_       ,			UpperCamelCase_       ,			[0]       ,			sample_length=60 * model.priors[0].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             self.assertIn(zs[0][0].detach().cpu().tolist()       ,			[self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]  )

             set_seed(0  )
             __lowerCamelCase  =		model._sample(UpperCamelCase_       ,			UpperCamelCase_       ,			[1]       ,			sample_length=60 * model.priors[1].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             self.assertIn(zs[1][0].detach().cpu().tolist()       ,			[self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]  )

             set_seed(0  )
             __lowerCamelCase  =		model._sample(UpperCamelCase_       ,			UpperCamelCase_       ,			[2]       ,			sample_length=60 * model.priors[2].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             self.assertIn(zs[2][0].detach().cpu().tolist()       ,			[self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]  )
      @slow
      @skip("""Not enough GPU memory on CI runners"""  )
      def      lowerCAmelCase__		(							self:					List[Any]  ):
             __lowerCamelCase  =		JukeboxModel.from_pretrained(self.model_id       ,			min_duration=0  ).eval()
             __lowerCamelCase  =		[i.cuda() for i in self.prepare_inputs(self.model_id  )]

             set_seed(0  )
             model.priors[0].cuda()
             __lowerCamelCase  =		[torch.zeros(1       ,			0       ,			dtype=torch.long  ).cuda() for _ in range(3  )]
             __lowerCamelCase  =		model._sample(UpperCamelCase_       ,			UpperCamelCase_       ,			[0]       ,			sample_length=60 * model.priors[0].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             torch.testing.assert_allclose(zs[0][0].cpu()       ,			torch.tensor(self.EXPECTED_GPU_OUTPUTS_2  )  )
             model.priors[0].cpu()

             set_seed(0  )
             model.priors[1].cuda()
             __lowerCamelCase  =		model._sample(UpperCamelCase_       ,			UpperCamelCase_       ,			[1]       ,			sample_length=60 * model.priors[1].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             torch.testing.assert_allclose(zs[1][0].cpu()       ,			torch.tensor(self.EXPECTED_GPU_OUTPUTS_1  )  )
             model.priors[1].cpu()

             set_seed(0  )
             model.priors[2].cuda()
             __lowerCamelCase  =		model._sample(UpperCamelCase_       ,			UpperCamelCase_       ,			[2]       ,			sample_length=60 * model.priors[2].raw_to_tokens       ,			save_results=UpperCamelCase_  )
             torch.testing.assert_allclose(zs[2][0].cpu()       ,			torch.tensor(self.EXPECTED_GPU_OUTPUTS_0  )  )




      @slow
      def      lowerCAmelCase__		(							self:					Dict  ):
             __lowerCamelCase  =		"""ArthurZ/jukebox_prior_0"""
             __lowerCamelCase  =		JukeboxPrior.from_pretrained(UpperCamelCase_       ,			min_duration=0  ).eval().half().to("""cuda"""  )

             __lowerCamelCase  =		self.prepare_inputs(UpperCamelCase_  )[0].cuda()
             __lowerCamelCase  =		model.get_metadata(UpperCamelCase_       ,			0       ,			76_80       ,			0  )
             set_seed(0  )
             __lowerCamelCase  =		model.sample(1       ,			metadata=UpperCamelCase_       ,			sample_tokens=60  )
             self.assertIn(outputs[0].cpu().tolist()       ,			[self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2]  )

