


import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin




@require_jieba
class        lowerCamelCase      (     _UpperCamelCase       ,	unittest.TestCase   ):
	_lowerCAmelCase    :		int        =	CpmAntTokenizer
	_lowerCAmelCase    :		Union[str, Any]        =	False


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

							__UpperCAmelCase :       Any  						=  [
							    '''<d>''',
							    '''</d>''',
							    '''<s>''',
							    '''</s>''',
							    '''</_>''',
							    '''<unk>''',
							    '''<pad>''',
							    '''</n>''',
							    '''我''',
							    '''是''',
							    '''C''',
							    '''P''',
							    '''M''',
							    '''A''',
							    '''n''',
							    '''t''',
							]
							__UpperCAmelCase :       List[Any]  						=  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]))


	@tooslow
	def    A(						self):
							__UpperCAmelCase :       Tuple  						=  CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b''')
							__UpperCAmelCase :       List[Any]  						=  '''今天天气真好！'''
							__UpperCAmelCase :       Union[str, Any]  						=  ['''今天''', '''天气''', '''真''', '''好''', '''！''']
							__UpperCAmelCase :       str  						=  tokenizer.tokenize(lowercase__)
							self.assertListEqual(lowercase__							,    lowercase__)
							__UpperCAmelCase :       Optional[Any]  						=  '''今天天气真好！'''
							__UpperCAmelCase :       Optional[int]  						=  [tokenizer.bos_token] + tokens

							__UpperCAmelCase :       Tuple  						=  [6, 9_8_0_2, 1_4_9_6_2, 2_0_8_2, 8_3_1, 2_4_4]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__)							,    lowercase__)

							__UpperCAmelCase :       List[Any]  						=  tokenizer.decode(lowercase__)
							self.assertEqual(lowercase__							,    lowercase__)





import functools
import operator

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


lowerCAmelCase							=						logging.get_logger(__name__)

lowerCAmelCase							=						{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}




class        lowerCamelCase      (     _UpperCamelCase   ):
	_lowerCAmelCase    :		List[str]        =	'''sew-d'''


	def __init__(						self							,    lowercase__=3_2							,    lowercase__=7_6_8							,    lowercase__=1_2							,    lowercase__=1_2							,    lowercase__=3_0_7_2							,    lowercase__=2							,    lowercase__=5_1_2							,    lowercase__=2_5_6							,    lowercase__=True							,    lowercase__=True							,    lowercase__=("p2c", "c2p")							,    lowercase__="layer_norm"							,    lowercase__="gelu_python"							,    lowercase__=0.1							,    lowercase__=0.1							,    lowercase__=0.1							,    lowercase__=0.0							,    lowercase__=0.1							,    lowercase__=0.0_2							,    lowercase__=1e-7							,    lowercase__=1e-5							,    lowercase__="group"							,    lowercase__="gelu"							,    lowercase__=(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)							,    lowercase__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)							,    lowercase__=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)							,    lowercase__=False							,    lowercase__=1_2_8							,    lowercase__=1_6							,    lowercase__=True							,    lowercase__=0.0_5							,    lowercase__=1_0							,    lowercase__=2							,    lowercase__=0.0							,    lowercase__=1_0							,    lowercase__=0							,    lowercase__="mean"							,    lowercase__=False							,    lowercase__=False							,    lowercase__=2_5_6							,    lowercase__=0							,    lowercase__=1							,    lowercase__=2							,    **lowercase__							,    ):
							super().__init__(**lowercase__							,    pad_token_id=lowercase__							,    bos_token_id=lowercase__							,    eos_token_id=lowercase__)
							__UpperCAmelCase :       Dict  						=  hidden_size
							__UpperCAmelCase :       int  						=  feat_extract_norm
							__UpperCAmelCase :       List[str]  						=  feat_extract_activation
							__UpperCAmelCase :       str  						=  list(lowercase__)
							__UpperCAmelCase :       Optional[int]  						=  list(lowercase__)
							__UpperCAmelCase :       Tuple  						=  list(lowercase__)
							__UpperCAmelCase :       Tuple  						=  conv_bias
							__UpperCAmelCase :       int  						=  num_conv_pos_embeddings
							__UpperCAmelCase :       int  						=  num_conv_pos_embedding_groups
							__UpperCAmelCase :       Any  						=  len(self.conv_dim)
							__UpperCAmelCase :       str  						=  num_hidden_layers
							__UpperCAmelCase :       Optional[Any]  						=  intermediate_size
							__UpperCAmelCase :       Union[str, Any]  						=  squeeze_factor
							__UpperCAmelCase :       Union[str, Any]  						=  max_position_embeddings
							__UpperCAmelCase :       List[str]  						=  position_buckets
							__UpperCAmelCase :       Tuple  						=  share_att_key
							__UpperCAmelCase :       int  						=  relative_attention
							__UpperCAmelCase :       str  						=  norm_rel_ebd
							__UpperCAmelCase :       Dict  						=  list(lowercase__)
							__UpperCAmelCase :       int  						=  hidden_act
							__UpperCAmelCase :       int  						=  num_attention_heads
							__UpperCAmelCase :       Optional[int]  						=  hidden_dropout
							__UpperCAmelCase :       int  						=  attention_dropout
							__UpperCAmelCase :       Optional[int]  						=  activation_dropout
							__UpperCAmelCase :       Optional[Any]  						=  feat_proj_dropout
							__UpperCAmelCase :       Optional[Any]  						=  final_dropout
							__UpperCAmelCase :       Optional[int]  						=  layer_norm_eps
							__UpperCAmelCase :       str  						=  feature_layer_norm_eps
							__UpperCAmelCase :       Optional[int]  						=  initializer_range
							__UpperCAmelCase :       Tuple  						=  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
							__UpperCAmelCase :       Optional[int]  						=  apply_spec_augment
							__UpperCAmelCase :       List[str]  						=  mask_time_prob
							__UpperCAmelCase :       Union[str, Any]  						=  mask_time_length
							__UpperCAmelCase :       Optional[int]  						=  mask_time_min_masks
							__UpperCAmelCase :       Optional[int]  						=  mask_feature_prob
							__UpperCAmelCase :       List[str]  						=  mask_feature_length
							__UpperCAmelCase :       List[Any]  						=  mask_feature_min_masks

							# ctc loss
							__UpperCAmelCase :       int  						=  ctc_loss_reduction
							__UpperCAmelCase :       Union[str, Any]  						=  ctc_zero_infinity

							# sequence classification
							__UpperCAmelCase :       List[str]  						=  use_weighted_layer_sum
							__UpperCAmelCase :       Tuple  						=  classifier_proj_size


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

