


import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase								=	get_tests_dir("""fixtures/spiece.model""")






@require_sentencepiece
@require_tokenizers
class   _lowerCamelCase (					UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    AlbertTokenizer
       snake_case      =    AlbertTokenizerFast
       snake_case      =    True
       snake_case      =    True
       snake_case      =    True



       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         super().setUp()

         # We have a SentencePiece fixture for testing
         A_      :				List[str]		     =					AlbertTokenizer(_SCREAMING_SNAKE_CASE		)
         tokenizer.save_pretrained(self.tmpdirname		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				Dict		     =					'''this is a test'''
         A_      :				Optional[Any]		     =					'''this is a test'''
         return input_text, output_text



       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_      :				Optional[int]		     =					'''<pad>'''
         A_      :				Tuple		     =					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        _snake_case       (      self		)->Any:




         '''simple docstring'''





         A_      :				int		     =					list(self.get_tokenizer().get_vocab().keys()		)

         self.assertEqual(vocab_keys[0]				,       '''<pad>'''		)
         self.assertEqual(vocab_keys[1]				,       '''<unk>'''		)
         self.assertEqual(vocab_keys[-1]				,       '''▁eloquent'''		)
         self.assertEqual(len(_SCREAMING_SNAKE_CASE		)				,       3_0000		)



       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         self.assertEqual(self.get_tokenizer().vocab_size				,       3_0000		)



       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         if not self.test_rust_tokenizer:
           return

         A_      :				Union[str, Any]		     =					self.get_tokenizer()
         A_      :				str		     =					self.get_rust_tokenizer()

         A_      :				Tuple		     =					'''I was born in 92000, and this is falsé.'''

         A_      :				List[str]		     =					tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[int]		     =					rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)
         self.assertListEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         A_      :				List[str]		     =					tokenizer.encode(_SCREAMING_SNAKE_CASE				,       add_special_tokens=_SCREAMING_SNAKE_CASE		)
         A_      :				List[str]		     =					rust_tokenizer.encode(_SCREAMING_SNAKE_CASE				,       add_special_tokens=_SCREAMING_SNAKE_CASE		)
         self.assertListEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         A_      :				List[str]		     =					self.get_rust_tokenizer()
         A_      :				int		     =					tokenizer.encode(_SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					rust_tokenizer.encode(_SCREAMING_SNAKE_CASE		)
         self.assertListEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				str		     =					AlbertTokenizer(_SCREAMING_SNAKE_CASE				,       keep_accents=_SCREAMING_SNAKE_CASE		)

         A_      :				Dict		     =					tokenizer.tokenize('''This is a test'''		)
         self.assertListEqual(_SCREAMING_SNAKE_CASE				,       ['''▁this''', '''▁is''', '''▁a''', '''▁test''']		)

         self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)				,       [48, 25, 21, 1289]		)

         A_      :				Tuple		     =					tokenizer.tokenize('''I was born in 92000, and this is falsé.'''		)
         self.assertListEqual(
             _SCREAMING_SNAKE_CASE				,       ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']		)
         A_      :				int		     =					tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)
         self.assertListEqual(_SCREAMING_SNAKE_CASE				,       [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]		)

         A_      :				List[Any]		     =					tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE		)
         self.assertListEqual(
             _SCREAMING_SNAKE_CASE				,       ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']				,       )



       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         A_      :				Tuple		     =					AlbertTokenizer(_SCREAMING_SNAKE_CASE		)

         A_      :				Any		     =					tokenizer.encode('''sequence builders'''		)
         A_      :				Tuple		     =					tokenizer.encode('''multi-sequence build'''		)

         A_      :				Union[str, Any]		     =					tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]







       @slow
       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         A_      :				str		     =					{'''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, 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], [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]], '''input_ids''': [[2, 2_1970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 1_2051, 18, 17, 7103, 2153, 673, 8, 3515, 1_8684, 8, 4461, 6, 1927, 297, 8, 1_2060, 2607, 18, 13, 5, 4461, 15, 1_0538, 38, 8, 135, 15, 822, 58, 15, 993, 1_0363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 1_0641, 6, 29, 84, 2512, 2430, 782, 1_8684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 1_1712, 15, 7103, 2153, 673, 17, 2_4883, 9990, 9, 3], [2, 1_1502, 25, 1006, 20, 782, 8, 1_1809, 855, 1732, 1_9393, 1_8667, 37, 367, 2_1018, 69, 1854, 34, 1_1860, 1_9124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 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], [2, 14, 2231, 886, 2385, 1_7659, 84, 14, 1_6792, 1952, 9, 3, 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]], '''token_type_ids''': [[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, 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, 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, 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='''albert-base-v2'''				,       revision='''6b6560eaf5ff2e250b00c50f380c5389a9c2d82e'''				,       )








from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "glpn"



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=[2, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[8, 4, 2, 1]				,       _SCREAMING_SNAKE_CASE=[32, 64, 160, 256]				,       _SCREAMING_SNAKE_CASE=[7, 3, 3, 3]				,       _SCREAMING_SNAKE_CASE=[4, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[1, 2, 5, 8]				,       _SCREAMING_SNAKE_CASE=[4, 4, 4, 4]				,       _SCREAMING_SNAKE_CASE="gelu"				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=10				,       _SCREAMING_SNAKE_CASE=-1				,       **_SCREAMING_SNAKE_CASE				,       )->Any:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					num_channels
         A_      :				Union[str, Any]		     =					num_encoder_blocks
         A_      :				int		     =					depths
         A_      :				Dict		     =					sr_ratios
         A_      :				Any		     =					hidden_sizes
         A_      :				int		     =					patch_sizes
         A_      :				Optional[int]		     =					strides
         A_      :				str		     =					mlp_ratios
         A_      :				List[str]		     =					num_attention_heads
         A_      :				str		     =					hidden_act
         A_      :				int		     =					hidden_dropout_prob
         A_      :				List[Any]		     =					attention_probs_dropout_prob
         A_      :				Optional[Any]		     =					initializer_range
         A_      :				Tuple		     =					drop_path_rate
         A_      :				Optional[int]		     =					layer_norm_eps
         A_      :				List[str]		     =					decoder_hidden_size
         A_      :				List[Any]		     =					max_depth
         A_      :				List[Any]		     =					head_in_index




