






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


a_		:    Optional[Any]   							=     logging.get_logger(__name__)

a_		:    Tuple   							=     {
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}

class 		_snake_case	(	A__       ):
			_lowercase		:     Optional[int]        =				'''dpt'''






			def __init__(							self		,       a=768		,       a=12		,       a=12		,       a=3072		,       a="gelu"		,       a=0.0		,       a=0.0		,       a=0.02		,       a=1E-12		,       a=384		,       a=16		,       a=3		,       a=False		,       a=True		,       a=[2, 5, 8, 11]		,       a="project"		,       a=[4, 2, 1, 0.5]		,       a=[96, 192, 384, 768]		,       a=256		,       a=-1		,       a=False		,       a=True		,       a=0.4		,       a=255		,       a=0.1		,       a=[1, 1024, 24, 24]		,       a=[0, 1]		,       a=None		,       **a		,       )   ->							List[str]:
						super().__init__(**a)

						SCREAMING_SNAKE_CASE							=			hidden_size
						SCREAMING_SNAKE_CASE							=			is_hybrid

						if self.is_hybrid:
									if backbone_config is None:
												logger.info('Initializing the config with a `BiT` backbone.')
												SCREAMING_SNAKE_CASE							=			{
												    'global_padding': 'same',
												    'layer_type': 'bottleneck',
												    'depths': [3, 4, 9],
												    'out_features': ['stage1', 'stage2', 'stage3'],
												    'embedding_dynamic_padding': True,
												}
												SCREAMING_SNAKE_CASE							=			BitConfig(**a)
									elif isinstance(a		,       a):
												logger.info('Initializing the config with a `BiT` backbone.')
												SCREAMING_SNAKE_CASE							=			BitConfig(**a)
									elif isinstance(a		,       a):
												SCREAMING_SNAKE_CASE							=			backbone_config
									else:
												raise ValueError(
												    f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.''')

									SCREAMING_SNAKE_CASE							=			backbone_featmap_shape
									SCREAMING_SNAKE_CASE							=			neck_ignore_stages

									if readout_type != "project":
												raise ValueError('Readout type must be \'project\' when using `DPT-hybrid` mode.')
						else:
									SCREAMING_SNAKE_CASE							=			None
									SCREAMING_SNAKE_CASE							=			None
									SCREAMING_SNAKE_CASE							=			[]

						SCREAMING_SNAKE_CASE							=			num_hidden_layers
						SCREAMING_SNAKE_CASE							=			num_attention_heads
						SCREAMING_SNAKE_CASE							=			intermediate_size
						SCREAMING_SNAKE_CASE							=			hidden_act
						SCREAMING_SNAKE_CASE							=			hidden_dropout_prob
						SCREAMING_SNAKE_CASE							=			attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE							=			initializer_range
						SCREAMING_SNAKE_CASE							=			layer_norm_eps
						SCREAMING_SNAKE_CASE							=			image_size
						SCREAMING_SNAKE_CASE							=			patch_size
						SCREAMING_SNAKE_CASE							=			num_channels
						SCREAMING_SNAKE_CASE							=			qkv_bias
						SCREAMING_SNAKE_CASE							=			backbone_out_indices
						if readout_type not in ["ignore", "add", "project"]:
									raise ValueError('Readout_type must be one of [\'ignore\', \'add\', \'project\']')
						SCREAMING_SNAKE_CASE							=			readout_type
						SCREAMING_SNAKE_CASE							=			reassemble_factors
						SCREAMING_SNAKE_CASE							=			neck_hidden_sizes
						SCREAMING_SNAKE_CASE							=			fusion_hidden_size
						SCREAMING_SNAKE_CASE							=			head_in_index
						SCREAMING_SNAKE_CASE							=			use_batch_norm_in_fusion_residual
						# auxiliary head attributes (semantic segmentation)
						SCREAMING_SNAKE_CASE							=			use_auxiliary_head
						SCREAMING_SNAKE_CASE							=			auxiliary_loss_weight
						SCREAMING_SNAKE_CASE							=			semantic_loss_ignore_index
						SCREAMING_SNAKE_CASE							=			semantic_classifier_dropout







			def  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						SCREAMING_SNAKE_CASE							=			copy.deepcopy(self.__dict__)

						if output["backbone_config"] is not None:
									SCREAMING_SNAKE_CASE							=			self.backbone_config.to_dict()

						SCREAMING_SNAKE_CASE							=			self.__class__.model_type
						return output







'''simple docstring'''



import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase					:			int       =	get_tests_dir('fixtures/test_sentencepiece.model')







@require_sentencepiece
class 		lowerCAmelCase__  ( a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = XLMProphetNetTokenizer
 lowerCAmelCase__       = False
 lowerCAmelCase__       = True
 def  UpperCAmelCase__						(						self   :			str     )    ->					Optional[int]:


      """simple docstring"""




      super().setUp()

      # We have a SentencePiece fixture for testing
      __SCREAMING_SNAKE_CASE      = XLMProphetNetTokenizer(__SCREAMING_SNAKE_CASE   ,				keep_accents=__SCREAMING_SNAKE_CASE     )
      tokenizer.save_pretrained(self.tmpdirname     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """[PAD]"""
      __SCREAMING_SNAKE_CASE      = 0

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE     )   ,				__SCREAMING_SNAKE_CASE     )
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE     )   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = list(self.get_tokenizer().get_vocab().keys()     )

      self.assertEqual(vocab_keys[0]   ,				"""[PAD]"""     )
      self.assertEqual(vocab_keys[1]   ,				"""[CLS]"""     )
      self.assertEqual(vocab_keys[-1]   ,				"""j"""     )
      self.assertEqual(len(__SCREAMING_SNAKE_CASE     )   ,				1_012     )
 def  UpperCAmelCase__						(						self   :			Any     )    ->					List[str]:


      """simple docstring"""




      self.assertEqual(self.get_tokenizer().vocab_size   ,				1_012     )
 def  UpperCAmelCase__						(						self   :			int     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = XLMProphetNetTokenizer(__SCREAMING_SNAKE_CASE   ,				keep_accents=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = tokenizer.tokenize("""This is a test"""     )
      self.assertListEqual(__SCREAMING_SNAKE_CASE   ,				["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]     )

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE     )   ,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]   ,				)

      __SCREAMING_SNAKE_CASE      = tokenizer.tokenize("""I was born in 92000, and this is falsé."""     )
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE   ,				[
              SPIECE_UNDERLINE + """I""",
              SPIECE_UNDERLINE + """was""",
              SPIECE_UNDERLINE + """b""",
              """or""",
              """n""",
              SPIECE_UNDERLINE + """in""",
              SPIECE_UNDERLINE + """""",
              """9""",
              """2""",
              """0""",
              """0""",
              """0""",
              """,""",
              SPIECE_UNDERLINE + """and""",
              SPIECE_UNDERLINE + """this""",
              SPIECE_UNDERLINE + """is""",
              SPIECE_UNDERLINE + """f""",
              """al""",
              """s""",
              """é""",
              """.""",
          ]   ,				)
      __SCREAMING_SNAKE_CASE      = tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE     )
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE   ,				[
              value + tokenizer.fairseq_offset
              for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
          ]   ,				)

      __SCREAMING_SNAKE_CASE      = tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE     )
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE   ,				[
              SPIECE_UNDERLINE + """I""",
              SPIECE_UNDERLINE + """was""",
              SPIECE_UNDERLINE + """b""",
              """or""",
              """n""",
              SPIECE_UNDERLINE + """in""",
              SPIECE_UNDERLINE + """""",
              """[UNK]""",
              """2""",
              """0""",
              """0""",
              """0""",
              """,""",
              SPIECE_UNDERLINE + """and""",
              SPIECE_UNDERLINE + """this""",
              SPIECE_UNDERLINE + """is""",
              SPIECE_UNDERLINE + """f""",
              """al""",
              """s""",
              """[UNK]""",
              """.""",
          ]   ,				)
 @cached_property
 def  UpperCAmelCase__						(						self   :			str     )    ->					str:


      """simple docstring"""




      return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""     )
 @slow
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """Hello World!"""
      __SCREAMING_SNAKE_CASE      = [35_389, 6_672, 49, 2]
      self.assertListEqual(__SCREAMING_SNAKE_CASE   ,				self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE     )     )


 @slow
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = {"""input_ids""": [[11_073, 82_783, 18, 26, 82_783, 549, 51_540, 248, 17_209, 1_301, 217, 20, 215_186, 1_325, 147, 17_209, 1_301, 217, 20, 56_370, 53, 122_020, 20, 16_477, 27, 87_355, 4_548, 20, 4_728, 78_392, 17, 159_969, 18, 26, 24_491, 629, 15, 538, 22_704, 5_439, 15, 2_788, 24_491, 9_885, 15, 43_534, 605, 15, 814, 18_403, 33_200, 29, 15, 43_534, 24_458, 12_410, 111, 24_966, 83_669, 9_637, 144_068, 26, 850, 22_346, 27, 147, 24_966, 83_669, 83_490, 26, 39_113, 735, 27, 689, 656, 2_800, 1_339, 4_600, 53, 122_020, 115_785, 34, 816, 1_339, 46_887, 18, 147, 53_905, 1_951, 42_238, 41_170, 17_732, 834, 436, 15, 27_523, 98_733, 217, 147, 5_542, 4_981, 930, 17_347, 16, 2], [20_091, 629, 94, 82_786, 58, 490, 20, 1_528, 84, 53_905, 344, 80_592, 110_128, 18_822, 5_267, 1_306, 62, 152_537, 308, 7_997, 401, 124_427, 549, 35_442, 225, 109, 15_055, 25_748, 147, 7_119, 43_712, 34, 767, 135_366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63_784, 119_466, 17, 147_808, 88_214, 18, 656, 81, 32, 3_296, 10_280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=__SCREAMING_SNAKE_CASE   ,				model_name="""microsoft/xprophetnet-large-wiki100-cased"""   ,				revision="""1acad1643ddd54a44df6a1b797ada8373685d90e"""   ,				)






