

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


_UpperCAmelCase	:     Any		       = logging.get_logger(__name__)

_UpperCAmelCase	:     Optional[int]		       = {
    """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 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				int     =    '''cvt'''




				def __init__(      self   ,     snake_case=3   ,     snake_case=[7, 3, 3]   ,     snake_case=[4, 2, 2]   ,     snake_case=[2, 1, 1]   ,     snake_case=[64, 192, 384]   ,     snake_case=[1, 3, 6]   ,     snake_case=[1, 2, 10]   ,     snake_case=[4.0, 4.0, 4.0]   ,     snake_case=[0.0, 0.0, 0.0]   ,     snake_case=[0.0, 0.0, 0.0]   ,     snake_case=[0.0, 0.0, 0.1]   ,     snake_case=[True, True, True]   ,     snake_case=[False, False, True]   ,     snake_case=["dw_bn", "dw_bn", "dw_bn"]   ,     snake_case=[3, 3, 3]   ,     snake_case=[1, 1, 1]   ,     snake_case=[2, 2, 2]   ,     snake_case=[1, 1, 1]   ,     snake_case=[1, 1, 1]   ,     snake_case=0.02   ,     snake_case=1e-1_2   ,     **snake_case   ,     ):
											super().__init__(**snake_case )
											snake_case_           =					num_channels
											snake_case_           =					patch_sizes
											snake_case_           =					patch_stride
											snake_case_           =					patch_padding
											snake_case_           =					embed_dim
											snake_case_           =					num_heads
											snake_case_           =					depth
											snake_case_           =					mlp_ratio
											snake_case_           =					attention_drop_rate
											snake_case_           =					drop_rate
											snake_case_           =					drop_path_rate
											snake_case_           =					qkv_bias
											snake_case_           =					cls_token
											snake_case_           =					qkv_projection_method
											snake_case_           =					kernel_qkv
											snake_case_           =					padding_kv
											snake_case_           =					stride_kv
											snake_case_           =					padding_q
											snake_case_           =					stride_q
											snake_case_           =					initializer_range
											snake_case_           =					layer_norm_eps







import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				int     =    PhobertTokenizer
				__SCREAMING_SNAKE_CASE      :				int     =    False




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

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											snake_case_           =					['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
											snake_case_           =					dict(zip(snake_case   ,     range(len(snake_case ) ) ) )
											snake_case_           =					['#version: 0.2', 'l à</w>']
											snake_case_           =					{'unk_token': '<unk>'}

											snake_case_           =					os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES['vocab_file'] )
											snake_case_           =					os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES['merges_file'] )

											with open(self.vocab_file   ,     'w'   ,     encoding='utf-8' ) as fp:
																		for token in vocab_tokens:
																									fp.write(F'''{token} {vocab_tokens[token]}\n''' )
											with open(self.merges_file   ,     'w'   ,     encoding='utf-8' ) as fp:
																		fp.write('\n'.join(snake_case ) )




				def 					a							(      self   ,     **snake_case ):
											kwargs.update(self.special_tokens_map )
											return PhobertTokenizer.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     snake_case ):
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
											return input_text, output_text




				def 					a							(      self ):
											snake_case_           =					PhobertTokenizer(self.vocab_file   ,     self.merges_file   ,     **self.special_tokens_map )
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
											snake_case_           =					tokenizer.tokenize(snake_case )
											print(snake_case )
											self.assertListEqual(snake_case   ,     snake_case )

											snake_case_           =					tokens + [tokenizer.unk_token]

											snake_case_           =					[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case )   ,     snake_case )




