




"""simple docstring"""

import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a							      =     '''▁'''

a							      =     get_tests_dir('''fixtures/test_sentencepiece.model''')





@require_sentencepiece
class 							lowercase_			(	__lowerCAmelCase     ,      unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Any     =      BertGenerationTokenizer
 UpperCAmelCase       :	str     =      False
 UpperCAmelCase       :	Optional[int]     =      True

 def      lowerCAmelCase_				(					self			:			Tuple						):
       super().setUp()

       _A     =  BertGenerationTokenizer(_UpperCAmelCase       ,				keep_accents=_UpperCAmelCase						)
       tokenizer.save_pretrained(self.tmpdirname						)

 def      lowerCAmelCase_				(					self			:			Tuple						):
       _A     =  '<s>'
       _A     =  1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase						)       ,				_UpperCAmelCase						)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase						)       ,				_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			int						):
       _A     =  list(self.get_tokenizer().get_vocab().keys()						)

       self.assertEqual(vocab_keys[0]       ,				'<unk>'						)
       self.assertEqual(vocab_keys[1]       ,				'<s>'						)
       self.assertEqual(vocab_keys[-1]       ,				'<pad>'						)
       self.assertEqual(len(_UpperCAmelCase						)       ,				1_002						)

 def      lowerCAmelCase_				(					self			:			Dict						):
       self.assertEqual(self.get_tokenizer().vocab_size       ,				1_000						)

 def      lowerCAmelCase_				(					self			:			List[str]						):
       _A     =  BertGenerationTokenizer(_UpperCAmelCase       ,				keep_accents=_UpperCAmelCase						)

       _A     =  tokenizer.tokenize('This is a test'						)
       self.assertListEqual(_UpperCAmelCase       ,				['▁This', '▁is', '▁a', '▁t', 'est']						)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)       ,				[285, 46, 10, 170, 382]       ,				)

       _A     =  tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
       self.assertListEqual(
           _UpperCAmelCase       ,				[
               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',
               '.',
           ]       ,				)
       _A     =  tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)
       self.assertListEqual(
           _UpperCAmelCase       ,				[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]       ,				)

       _A     =  tokenizer.convert_ids_to_tokens(_UpperCAmelCase						)
       self.assertListEqual(
           _UpperCAmelCase       ,				[
               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      lowerCAmelCase_				(					self			:			Optional[Any]						):
       return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'						)

 @slow
 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       _A     =  'Hello World!'
       _A     =  [18_536, 2_260, 101]

       self.assertListEqual(_UpperCAmelCase       ,				self.big_tokenizer.encode(_UpperCAmelCase						)						)

 @slow
 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       _A     =  (
           'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
           ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
       )
       _A     =  [
           871,
           419,
           358,
           946,
           991,
           2_521,
           452,
           358,
           1_357,
           387,
           7_751,
           3_536,
           112,
           985,
           456,
           126,
           865,
           938,
           5_400,
           5_734,
           458,
           1_368,
           467,
           786,
           2_462,
           5_246,
           1_159,
           633,
           865,
           4_519,
           457,
           582,
           852,
           2_557,
           427,
           916,
           508,
           405,
           34_324,
           497,
           391,
           408,
           11_342,
           1_244,
           385,
           100,
           938,
           985,
           456,
           574,
           362,
           12_597,
           3_200,
           3_129,
           1_172,
       ]

       self.assertListEqual(_UpperCAmelCase       ,				self.big_tokenizer.encode(_UpperCAmelCase						)						)

 @require_torch
 @slow
 def      lowerCAmelCase_				(					self			:			Tuple						):
       import torch

       from transformers import BertGenerationConfig, BertGenerationEncoder

       # Build sequence
       _A     =  list(self.big_tokenizer.get_vocab().keys()						)[:10]
       _A     =  ' '.join(_UpperCAmelCase						)
       _A     =  self.big_tokenizer.encode_plus(_UpperCAmelCase       ,				return_tensors='pt'       ,				return_token_type_ids=_UpperCAmelCase						)
       _A     =  self.big_tokenizer.batch_encode_plus(
           [sequence + ' ' + sequence]       ,				return_tensors='pt'       ,				return_token_type_ids=_UpperCAmelCase						)

       _A     =  BertGenerationConfig()
       _A     =  BertGenerationEncoder(_UpperCAmelCase						)

       assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

       with torch.no_grad():
             model(**_UpperCAmelCase						)
             model(**_UpperCAmelCase						)



 @slow
 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       # fmt: off
       _A     =  {'input_ids': [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=_UpperCAmelCase       ,				model_name='google/bert_for_seq_generation_L-24_bbc_encoder'       ,				revision='c817d1fd1be2ffa69431227a1fe320544943d4db'       ,				)





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase		   =			logging.get_logger(__name__)

lowerCamelCase		   =			{
    'microsoft/focalnet-tiny': 'https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json',
}




class A    (  UpperCamelCase_       ,				UpperCamelCase_	):
      UpperCamelCase__				:    str ='focalnet'






      def __init__(   self		:	Optional[int]     ,  lowercase_		:	List[Any]=224     ,  lowercase_		:	str=4     ,  lowercase_		:	Any=3     ,  lowercase_		:	Dict=96     ,  lowercase_		:	List[str]=False     ,  lowercase_		:	Any=[192, 384, 768, 768]     ,  lowercase_		:	Dict=[2, 2, 6, 2]     ,  lowercase_		:	Optional[int]=[2, 2, 2, 2]     ,  lowercase_		:	List[Any]=[3, 3, 3, 3]     ,  lowercase_		:	Any="gelu"     ,  lowercase_		:	str=4.0     ,  lowercase_		:	List[Any]=0.0     ,  lowercase_		:	Tuple=0.1     ,  lowercase_		:	Optional[Any]=False     ,  lowercase_		:	Optional[int]=1E-4     ,  lowercase_		:	Dict=False     ,  lowercase_		:	Optional[Any]=False     ,  lowercase_		:	Dict=False     ,  lowercase_		:	List[Any]=0.02     ,  lowercase_		:	Any=1E-5     ,  lowercase_		:	Any=32     ,  lowercase_		:	List[str]=None     ,  lowercase_		:	int=None     ,  **lowercase_		:	Optional[Any]     ,  )   ->		Optional[Any]:
             """simple docstring"""




             super().__init__(**lowercase_					)

             _lowerCamelCase			:						Union[str, Any]            =image_size
             _lowerCamelCase			:						str            =patch_size
             _lowerCamelCase			:						str            =num_channels
             _lowerCamelCase			:						Tuple            =embed_dim
             _lowerCamelCase			:						Dict            =use_conv_embed
             _lowerCamelCase			:						int            =hidden_sizes
             _lowerCamelCase			:						str            =depths
             _lowerCamelCase			:						int            =focal_levels
             _lowerCamelCase			:						List[Any]            =focal_windows
             _lowerCamelCase			:						List[Any]            =hidden_act
             _lowerCamelCase			:						int            =mlp_ratio
             _lowerCamelCase			:						Any            =hidden_dropout_prob
             _lowerCamelCase			:						Optional[Any]            =drop_path_rate
             _lowerCamelCase			:						int            =use_layerscale
             _lowerCamelCase			:						str            =layerscale_value
             _lowerCamelCase			:						Dict            =use_post_layernorm
             _lowerCamelCase			:						str            =use_post_layernorm_in_modulation
             _lowerCamelCase			:						List[Any]            =normalize_modulator
             _lowerCamelCase			:						Optional[Any]            =initializer_range
             _lowerCamelCase			:						List[str]            =layer_norm_eps
             _lowerCamelCase			:						Optional[int]            =encoder_stride
             _lowerCamelCase			:						List[str]            =['stem'] + [F'''stage{idx}''' for idx in range(1     ,  len(self.depths					) + 1					)]
             _lowerCamelCase      ,	_lowerCamelCase			:						Union[str, Any]            =get_aligned_output_features_output_indices(
                 out_features=lowercase_     ,  out_indices=lowercase_     ,  stage_names=self.stage_names					)


