'''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   :	Optional[int] 							=   "▁"

_a   :	Optional[int] 							=   get_tests_dir("fixtures/test_sentencepiece.model")






@require_sentencepiece
class 		__A       (__magic_name__ ,					unittest.TestCase   ):
       snake_case       :List[Any]				    =					BertGenerationTokenizer
       snake_case       :Tuple				    =					False
       snake_case       :List[str]				    =					True






       def 			_snake_case						(      self      ):
              super().setUp()

              __UpperCAmelCase  :				Any       				=		BertGenerationTokenizer(UpperCamelCase_				,     keep_accents=UpperCamelCase_      )
              tokenizer.save_pretrained(self.tmpdirname      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				int       				=		"<s>"
              __UpperCAmelCase  :				Optional[int]       				=		1

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






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		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_      )				,     10_02      )






       def 			_snake_case						(      self      ):
              self.assertEqual(self.get_tokenizer().vocab_size				,     10_00      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				str       				=		BertGenerationTokenizer(UpperCamelCase_				,     keep_accents=UpperCamelCase_      )

              __UpperCAmelCase  :				Tuple       				=		tokenizer.tokenize("This is a test"      )
              self.assertListEqual(UpperCamelCase_				,     ["▁This", "▁is", "▁a", "▁t", "est"]      )

              self.assertListEqual(
                  tokenizer.convert_tokens_to_ids(UpperCamelCase_      )				,     [2_85, 46, 10, 1_70, 3_82]				,     )

              __UpperCAmelCase  :				Any       				=		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",
                      ".",
                  ]				,     )
              __UpperCAmelCase  :				Optional[Any]       				=		tokenizer.convert_tokens_to_ids(UpperCamelCase_      )
              self.assertListEqual(
                  UpperCamelCase_				,     [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]				,     )

              __UpperCAmelCase  :				Any       				=		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 			_snake_case						(      self      ):
              return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"      )






       @slow
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Dict       				=		"Hello World!"
              __UpperCAmelCase  :				Optional[Any]       				=		[1_85_36, 22_60, 1_01]

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






       @slow
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				str       				=		(
                  "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"
              )
              __UpperCAmelCase  :				int       				=		[
                  8_71,
                  4_19,
                  3_58,
                  9_46,
                  9_91,
                  25_21,
                  4_52,
                  3_58,
                  13_57,
                  3_87,
                  77_51,
                  35_36,
                  1_12,
                  9_85,
                  4_56,
                  1_26,
                  8_65,
                  9_38,
                  54_00,
                  57_34,
                  4_58,
                  13_68,
                  4_67,
                  7_86,
                  24_62,
                  52_46,
                  11_59,
                  6_33,
                  8_65,
                  45_19,
                  4_57,
                  5_82,
                  8_52,
                  25_57,
                  4_27,
                  9_16,
                  5_08,
                  4_05,
                  3_43_24,
                  4_97,
                  3_91,
                  4_08,
                  1_13_42,
                  12_44,
                  3_85,
                  1_00,
                  9_38,
                  9_85,
                  4_56,
                  5_74,
                  3_62,
                  1_25_97,
                  32_00,
                  31_29,
                  11_72,
              ]

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






       @require_torch
       @slow
       def 			_snake_case						(      self      ):
              import torch

              from transformers import BertGenerationConfig, BertGenerationEncoder

              # Build sequence
              __UpperCAmelCase  :				Union[str, Any]       				=		list(self.big_tokenizer.get_vocab().keys()      )[:10]
              __UpperCAmelCase  :				str       				=		" ".join(UpperCamelCase_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		self.big_tokenizer.encode_plus(UpperCamelCase_				,     return_tensors="pt"				,     return_token_type_ids=UpperCamelCase_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		self.big_tokenizer.batch_encode_plus(
                  [sequence + " " + sequence]				,     return_tensors="pt"				,     return_token_type_ids=UpperCamelCase_      )

              __UpperCAmelCase  :				Optional[Any]       				=		BertGenerationConfig()
              __UpperCAmelCase  :				Tuple       				=		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 			_snake_case						(      self      ):
              # fmt: off
              __UpperCAmelCase  :				List[Any]       				=		{"input_ids": [[3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14], [4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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"				,     )







'''simple docstring'''




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


_a   :	Any 							=   logging.get_logger(__name__)

_a   :	List[Any] 							=   {
    "microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}






class 		__A       (__magic_name__   ):
       snake_case       :Any				    =					"cvt"






       def __init__(      self				,     UpperCamelCase_=3				,     UpperCamelCase_=[7, 3, 3]				,     UpperCamelCase_=[4, 2, 2]				,     UpperCamelCase_=[2, 1, 1]				,     UpperCamelCase_=[64, 1_92, 3_84]				,     UpperCamelCase_=[1, 3, 6]				,     UpperCamelCase_=[1, 2, 10]				,     UpperCamelCase_=[4.0, 4.0, 4.0]				,     UpperCamelCase_=[0.0, 0.0, 0.0]				,     UpperCamelCase_=[0.0, 0.0, 0.0]				,     UpperCamelCase_=[0.0, 0.0, 0.1]				,     UpperCamelCase_=[True, True, True]				,     UpperCamelCase_=[False, False, True]				,     UpperCamelCase_=["dw_bn", "dw_bn", "dw_bn"]				,     UpperCamelCase_=[3, 3, 3]				,     UpperCamelCase_=[1, 1, 1]				,     UpperCamelCase_=[2, 2, 2]				,     UpperCamelCase_=[1, 1, 1]				,     UpperCamelCase_=[1, 1, 1]				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=1E-12				,     **UpperCamelCase_				,     ):
              super().__init__(**UpperCamelCase_      )
              __UpperCAmelCase  :				Optional[int]       				=		num_channels
              __UpperCAmelCase  :				Optional[Any]       				=		patch_sizes
              __UpperCAmelCase  :				List[str]       				=		patch_stride
              __UpperCAmelCase  :				Tuple       				=		patch_padding
              __UpperCAmelCase  :				int       				=		embed_dim
              __UpperCAmelCase  :				str       				=		num_heads
              __UpperCAmelCase  :				Any       				=		depth
              __UpperCAmelCase  :				List[str]       				=		mlp_ratio
              __UpperCAmelCase  :				List[str]       				=		attention_drop_rate
              __UpperCAmelCase  :				Dict       				=		drop_rate
              __UpperCAmelCase  :				Dict       				=		drop_path_rate
              __UpperCAmelCase  :				str       				=		qkv_bias
              __UpperCAmelCase  :				Optional[int]       				=		cls_token
              __UpperCAmelCase  :				Optional[Any]       				=		qkv_projection_method
              __UpperCAmelCase  :				Tuple       				=		kernel_qkv
              __UpperCAmelCase  :				Optional[Any]       				=		padding_kv
              __UpperCAmelCase  :				Optional[int]       				=		stride_kv
              __UpperCAmelCase  :				Any       				=		padding_q
              __UpperCAmelCase  :				List[Any]       				=		stride_q
              __UpperCAmelCase  :				Union[str, Any]       				=		initializer_range
              __UpperCAmelCase  :				Any       				=		layer_norm_eps






