



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase	       =	logging.get_logger(__name__)

__UpperCAmelCase	       =	{
    '''facebook/data2vec-vision-base-ft''': (
        '''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
    ),
}



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : Optional[Any]       					=  "data2vec-vision"
       def __init__(      self,      SCREAMING_SNAKE_CASE_=768,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=3072,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=0.0,      SCREAMING_SNAKE_CASE_=0.0,      SCREAMING_SNAKE_CASE_=0.02,      SCREAMING_SNAKE_CASE_=1e-12,      SCREAMING_SNAKE_CASE_=224,      SCREAMING_SNAKE_CASE_=16,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=[3, 5, 7, 11],      SCREAMING_SNAKE_CASE_=[1, 2, 3, 6],      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=0.4,      SCREAMING_SNAKE_CASE_=256,      SCREAMING_SNAKE_CASE_=1,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=255,      **SCREAMING_SNAKE_CASE_,      )					->		Optional[int]:
          super().__init__(**SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  List[Any]						     =     hidden_size
          UpperCamelCase   :  Optional[Any]						     =     num_hidden_layers
          UpperCamelCase   :  List[Any]						     =     num_attention_heads
          UpperCamelCase   :  Tuple						     =     intermediate_size
          UpperCamelCase   :  Union[str, Any]						     =     hidden_act
          UpperCamelCase   :  Optional[int]						     =     hidden_dropout_prob
          UpperCamelCase   :  Dict						     =     attention_probs_dropout_prob
          UpperCamelCase   :  List[Any]						     =     initializer_range
          UpperCamelCase   :  str						     =     layer_norm_eps

          UpperCamelCase   :  Optional[Any]						     =     image_size
          UpperCamelCase   :  Optional[int]						     =     patch_size
          UpperCamelCase   :  Optional[Any]						     =     num_channels
          UpperCamelCase   :  Tuple						     =     use_mask_token
          UpperCamelCase   :  int						     =     use_absolute_position_embeddings
          UpperCamelCase   :  Optional[Any]						     =     use_relative_position_bias
          UpperCamelCase   :  Optional[Any]						     =     use_shared_relative_position_bias
          UpperCamelCase   :  int						     =     layer_scale_init_value
          UpperCamelCase   :  Tuple						     =     drop_path_rate
          UpperCamelCase   :  Union[str, Any]						     =     use_mean_pooling
          # decode head attributes (semantic segmentation)
          UpperCamelCase   :  Union[str, Any]						     =     out_indices
          UpperCamelCase   :  List[Any]						     =     pool_scales
          # auxiliary head attributes (semantic segmentation)
          UpperCamelCase   :  Optional[int]						     =     use_auxiliary_head
          UpperCamelCase   :  List[Any]						     =     auxiliary_loss_weight
          UpperCamelCase   :  List[str]						     =     auxiliary_channels
          UpperCamelCase   :  Union[str, Any]						     =     auxiliary_num_convs
          UpperCamelCase   :  Optional[Any]						     =     auxiliary_concat_input
          UpperCamelCase   :  Dict						     =     semantic_loss_ignore_index






class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : Optional[int]       					=  version.parse("1.11"							)
       @property
       def  snake_case_	(      self	)					->		Mapping[str, Mapping[int, str]]:
          return OrderedDict(
              [
                  ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
              ]	)






       @property
       def  snake_case_	(      self	)					->		float:
          return 1e-4








import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch



class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : Union[str, Any]       					=  JukeboxTokenizer
       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        ",
       }
       @require_torch
       def  snake_case_	(      self	)					->		Optional[Any]:
          import torch

          UpperCamelCase   :  Tuple						     =     JukeboxTokenizer.from_pretrained('openai/jukebox-1b-lyrics'	)
          UpperCamelCase   :  List[str]						     =     tokenizer(**self.metas	)['input_ids']
          # fmt: off
          UpperCamelCase   :  Dict						     =     [
              torch.tensor([[
                  0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
                  76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
                  44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
                  47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
                  76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
                  30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 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, 76, 76, 76, 76, 76, 76, 76,
                  19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
                  76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
                  76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
                  64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
                  30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
                  27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
                  34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
                  27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
                  41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
                  76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
                  44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
                  76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
                  32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
                  40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                  20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
                  45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
                  31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
                  45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
                  76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
                  34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
                  31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
                  40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
                  38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
                  78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
                  76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
                  41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
                  27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
                  46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
                  76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
                  41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
                  46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
                  41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
                  78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
                  40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
                  27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
                  76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
                  76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
                  41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
                  76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
                  27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
                  78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
                  34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
                  44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
                  40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
                  78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
                  46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
                  38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
                  40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
                  27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
                  20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
                  76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
                  76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
                  76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
                  76, 76]]	),
              torch.tensor([[0, 0, 0, 1069, 11]]	),
              torch.tensor([[0, 0, 0, 1069, 11]]	),
          ]
          # fmt: on
          self.assertTrue(torch.allclose(tokens[0],      EXPECTED_OUTPUT[0]	)	)
          self.assertTrue(torch.allclose(tokens[1],      EXPECTED_OUTPUT[1]	)	)
          self.assertTrue(torch.allclose(tokens[2],      EXPECTED_OUTPUT[2]	)	)






       @require_torch
       def  snake_case_	(      self	)					->		Optional[Any]:
          import torch

          UpperCamelCase   :  str						     =     JukeboxTokenizer.from_pretrained('openai/jukebox-5b-lyrics'	)
          UpperCamelCase   :  Dict						     =     tokenizer(**self.metas	)['input_ids']
          # fmt: off
          UpperCamelCase   :  Optional[int]						     =     [
              torch.tensor([[
                  0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
                  31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
                  31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
                  40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
                  79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
                  77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
                  27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
                  37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
                  32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
                  77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
                  77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
                  77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
                  46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
                  77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
                  77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
                  77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
                  77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
                  64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
                  40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
                  40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
                  38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
                  31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
                  41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
                  77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
                  46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
                  41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
                  31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
                  31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                  23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
                  44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
                  31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
                  38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
                  40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
                  77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
                  27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
                  31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
                  34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
                  31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
                  1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
                  31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
                  45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
                  31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
                  77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
                  15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
                  11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
                  45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
                  41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
                  44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
                  46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
                  27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
                  77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
                  35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
                  77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
                  31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
                  77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
                  41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
                  77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
                  40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
                  77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
                  77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
                  27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
                  77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
                  77, 77, 77, 77, 77, 77]]	),
              torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]	),
              torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]	),
          ]
          # fmt: on
          self.assertTrue(torch.allclose(tokens[0],      EXPECTED_OUTPUT[0]	)	)
          self.assertTrue(torch.allclose(tokens[1],      EXPECTED_OUTPUT[1]	)	)
          self.assertTrue(torch.allclose(tokens[2],      EXPECTED_OUTPUT[2]	)	)



