


'''simple docstring'''






import functools
import operator

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


__A	:	Dict     =     logging.get_logger(__name__)

__A	:	int     =     {
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Tuple   		=  'sew'





							def __init__(  self		:Optional[Any]  ,_UpperCamelCase		:int=3_2  ,_UpperCamelCase		:Tuple=7_6_8  ,_UpperCamelCase		:Any=1_2  ,_UpperCamelCase		:Union[str, Any]=1_2  ,_UpperCamelCase		:Union[str, Any]=3_0_7_2  ,_UpperCamelCase		:Optional[Any]=2  ,_UpperCamelCase		:List[str]="gelu"  ,_UpperCamelCase		:Tuple=0.1  ,_UpperCamelCase		:List[Any]=0.1  ,_UpperCamelCase		:Union[str, Any]=0.1  ,_UpperCamelCase		:List[Any]=0.0  ,_UpperCamelCase		:Optional[int]=0.1  ,_UpperCamelCase		:Union[str, Any]=0.1  ,_UpperCamelCase		:Any=0.02  ,_UpperCamelCase		:str=1E-5  ,_UpperCamelCase		:Optional[int]="group"  ,_UpperCamelCase		:List[str]="gelu"  ,_UpperCamelCase		:List[str]=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)  ,_UpperCamelCase		:Optional[int]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)  ,_UpperCamelCase		:Optional[int]=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)  ,_UpperCamelCase		:List[str]=False  ,_UpperCamelCase		:Dict=1_2_8  ,_UpperCamelCase		:Optional[int]=1_6  ,_UpperCamelCase		:Union[str, Any]=True  ,_UpperCamelCase		:int=0.05  ,_UpperCamelCase		:str=1_0  ,_UpperCamelCase		:Optional[int]=2  ,_UpperCamelCase		:Any=0.0  ,_UpperCamelCase		:Union[str, Any]=1_0  ,_UpperCamelCase		:Dict=0  ,_UpperCamelCase		:Optional[int]="mean"  ,_UpperCamelCase		:Any=False  ,_UpperCamelCase		:Optional[int]=False  ,_UpperCamelCase		:Union[str, Any]=2_5_6  ,_UpperCamelCase		:Dict=0  ,_UpperCamelCase		:Optional[Any]=1  ,_UpperCamelCase		:int=2  ,**_UpperCamelCase		:Dict  ,):
									super().__init__(**_UpperCamelCase  ,pad_token_id=_UpperCamelCase  ,bos_token_id=_UpperCamelCase  ,eos_token_id=_UpperCamelCase			)
									snake_case_		:							Optional[int]			       =						hidden_size
									snake_case_		:							Any			       =						feat_extract_norm
									snake_case_		:							Any			       =						feat_extract_activation
									snake_case_		:							Dict			       =						list(_UpperCamelCase			)
									snake_case_		:							Tuple			       =						list(_UpperCamelCase			)
									snake_case_		:							str			       =						list(_UpperCamelCase			)
									snake_case_		:							Dict			       =						conv_bias
									snake_case_		:							Tuple			       =						num_conv_pos_embeddings
									snake_case_		:							List[str]			       =						num_conv_pos_embedding_groups
									snake_case_		:							Any			       =						len(self.conv_dim			)
									snake_case_		:							Optional[Any]			       =						num_hidden_layers
									snake_case_		:							str			       =						intermediate_size
									snake_case_		:							Tuple			       =						squeeze_factor
									snake_case_		:							List[Any]			       =						hidden_act
									snake_case_		:							List[str]			       =						num_attention_heads
									snake_case_		:							Union[str, Any]			       =						hidden_dropout
									snake_case_		:							List[str]			       =						attention_dropout
									snake_case_		:							List[str]			       =						activation_dropout
									snake_case_		:							Optional[int]			       =						feat_proj_dropout
									snake_case_		:							Union[str, Any]			       =						final_dropout
									snake_case_		:							Tuple			       =						layerdrop
									snake_case_		:							List[Any]			       =						layer_norm_eps
									snake_case_		:							List[Any]			       =						initializer_range
									snake_case_		:							str			       =						vocab_size

									if (
									    (len(self.conv_stride			) != self.num_feat_extract_layers)
									    or (len(self.conv_kernel			) != self.num_feat_extract_layers)
									    or (len(self.conv_dim			) != self.num_feat_extract_layers)
									):
											raise ValueError(
											    """Configuration for convolutional layers is incorrect."""
											    """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
											    F'''but is `len(config.conv_dim) = {len(self.conv_dim			)}`, `len(config.conv_stride)'''
											    F'''= {len(self.conv_stride			)}`, `len(config.conv_kernel) = {len(self.conv_kernel			)}`.'''			)

									# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
									snake_case_		:							Any			       =						apply_spec_augment
									snake_case_		:							Any			       =						mask_time_prob
									snake_case_		:							List[str]			       =						mask_time_length
									snake_case_		:							List[Any]			       =						mask_time_min_masks
									snake_case_		:							int			       =						mask_feature_prob
									snake_case_		:							Union[str, Any]			       =						mask_feature_length
									snake_case_		:							Union[str, Any]			       =						mask_feature_min_masks

									# ctc loss
									snake_case_		:							Tuple			       =						ctc_loss_reduction
									snake_case_		:							str			       =						ctc_zero_infinity

									# sequence classification
									snake_case_		:							Dict			       =						use_weighted_layer_sum
									snake_case_		:							Any			       =						classifier_proj_size





							@property
							def 		a__   (  self		:Tuple			):
									return functools.reduce(operator.mul  ,self.conv_stride  ,1			)



'''simple docstring'''






import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class 						__UpperCamelCase	(						lowercase__						,  unittest.TestCase							):
							lowercase			:		Dict   		=  TransfoXLTokenizer
							lowercase			:		Optional[Any]   		=  False
							lowercase			:		Dict   		=  False





							def 		a__   (  self		:Union[str, Any]			):
									super().setUp()

									snake_case_		:							Optional[int]			       =						[
									    """<unk>""",
									    """[CLS]""",
									    """[SEP]""",
									    """want""",
									    """unwanted""",
									    """wa""",
									    """un""",
									    """running""",
									    """,""",
									    """low""",
									    """l""",
									]
									snake_case_		:							Optional[int]			       =						os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES["""vocab_file"""]			)
									with open(self.vocab_file  ,"""w"""  ,encoding="""utf-8"""			) as vocab_writer:
											vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]			)			)





							def 		a__   (  self		:List[Any]  ,**_UpperCamelCase		:Optional[Any]			):
									snake_case_		:							Tuple			       =						True
									return TransfoXLTokenizer.from_pretrained(self.tmpdirname  ,**_UpperCamelCase			)





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:Union[str, Any]			):
									snake_case_		:							Any			       =						"""<unk> UNwanted , running"""
									snake_case_		:							Optional[int]			       =						"""<unk> unwanted, running"""
									return input_text, output_text





							def 		a__   (  self		:Dict			):
									snake_case_		:							Dict			       =						TransfoXLTokenizer(vocab_file=self.vocab_file  ,lower_case=_UpperCamelCase			)

									snake_case_		:							Dict			       =						tokenizer.tokenize("""<unk> UNwanted , running"""			)
									self.assertListEqual(_UpperCamelCase  ,["""<unk>""", """unwanted""", """,""", """running"""]			)

									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase			)  ,[0, 4, 8, 7]			)





							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							Dict			       =						TransfoXLTokenizer(lower_case=_UpperCamelCase			)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """			)  ,["""hello""", """!""", """how""", """are""", """you""", """?"""]			)





							def 		a__   (  self		:Any			):
									snake_case_		:							List[Any]			       =						TransfoXLTokenizer(lower_case=_UpperCamelCase			)

									self.assertListEqual(
									    tokenizer.tokenize(""" \tHeLLo ! how  \n Are yoU ?  """			)  ,["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]			)





							def 		a__   (  self		:List[str]			):
									snake_case_		:							str			       =						TransfoXLTokenizer(lower_case=_UpperCamelCase			)
									snake_case_		:							List[str]			       =						"""Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
									snake_case_		:							Optional[int]			       =						[
									    """Hello""",
									    """(""",
									    """bracket""",
									    """)""",
									    """and""",
									    """side""",
									    """@-@""",
									    """scrolled""",
									    """[""",
									    """and""",
									    """]""",
									    """Henry""",
									    """'s""",
									    """$""",
									    """5""",
									    """@,@""",
									    """000""",
									    """with""",
									    """3""",
									    """@.@""",
									    """34""",
									    """m""",
									    """.""",
									    """What""",
									    """'s""",
									    """up""",
									    """!""",
									    """?""",
									]

									self.assertListEqual(tokenizer.tokenize(_UpperCamelCase			)  ,_UpperCamelCase			)

									self.assertEqual(tokenizer.convert_tokens_to_string(_UpperCamelCase			)  ,_UpperCamelCase			)





							def 		a__   (  self		:Dict			):
									snake_case_		:							Union[str, Any]			       =						self.get_tokenizer()
									snake_case_		:							Dict			       =						len(_UpperCamelCase			)

									tokenizer.add_tokens(["""new1""", """new2"""]			)
									tokenizer.move_added_token("""new1"""  ,1			)

									# Check that moved token is not copied (duplicate)
									self.assertEqual(len(_UpperCamelCase			)  ,original_len + 2			)
									# Check that token is moved to specified id
									self.assertEqual(tokenizer.encode("""new1"""			)  ,[1]			)
									self.assertEqual(tokenizer.decode([1]			)  ,"""new1"""			)