


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     a		(_lowerCAmelCase					,    unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  CpmAntTokenizer
							__UpperCAmelCase   :							List[Any]					  =  False

							def    __snake_case (							self	:  Union[str, Any]    )     ->   int:
														super().setUp()

														__snake_case		:      Any										=       [
														    "<d>",
														    "</d>",
														    "<s>",
														    "</s>",
														    "</_>",
														    "<unk>",
														    "<pad>",
														    "</n>",
														    "我",
														    "是",
														    "C",
														    "P",
														    "M",
														    "A",
														    "n",
														    "t",
														]
														__snake_case		:      List[str]										=       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    __snake_case (							self	:  Any    )     ->   Optional[Any]:
														__snake_case		:      List[Any]										=       CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"    )
														__snake_case		:      List[str]										=       "今天天气真好！"
														__snake_case		:      Dict										=       ["今天", "天气", "真", "好", "！"]
														__snake_case		:      Any										=       tokenizer.tokenize(lowerCamelCase    )
														self.assertListEqual(lowerCamelCase   ,     lowerCamelCase    )
														__snake_case		:      int										=       "今天天气真好！"
														__snake_case		:      Tuple										=       [tokenizer.bos_token] + tokens

														__snake_case		:      Optional[Any]										=       [6, 9802, 14962, 2082, 831, 244]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase    )   ,     lowerCamelCase    )

														__snake_case		:      Any										=       tokenizer.decode(lowerCamelCase    )
														self.assertEqual(lowerCamelCase   ,     lowerCamelCase    )





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


_snake_case					:		Any      =    logging.get_logger(__name__)

_snake_case					:		Dict      =    {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  "speech_to_text_2"
							__UpperCAmelCase   :							Dict					  =  ["past_key_values"]
							__UpperCAmelCase   :							str					  =  {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}

							def __init__(							self	:  str   ,     lowerCamelCase	:  List[Any]=10000   ,     lowerCamelCase	:  Tuple=6   ,     lowerCamelCase	:  Optional[Any]=2048   ,     lowerCamelCase	:  Optional[int]=4   ,     lowerCamelCase	:  Optional[int]=0.0   ,     lowerCamelCase	:  str=True   ,     lowerCamelCase	:  str="relu"   ,     lowerCamelCase	:  Tuple=256   ,     lowerCamelCase	:  Union[str, Any]=0.1   ,     lowerCamelCase	:  Optional[int]=0.0   ,     lowerCamelCase	:  Any=0.0   ,     lowerCamelCase	:  Optional[int]=0.02   ,     lowerCamelCase	:  Dict=2   ,     lowerCamelCase	:  List[str]=True   ,     lowerCamelCase	:  str=1   ,     lowerCamelCase	:  Any=0   ,     lowerCamelCase	:  List[Any]=2   ,     lowerCamelCase	:  List[Any]=1024   ,     **lowerCamelCase	:  List[str]   ,     )     ->   Any:
														__snake_case		:      Tuple										=       vocab_size
														__snake_case		:      int										=       d_model
														__snake_case		:      List[str]										=       decoder_ffn_dim
														__snake_case		:      Union[str, Any]										=       decoder_layers
														__snake_case		:      Dict										=       decoder_attention_heads
														__snake_case		:      Any										=       dropout
														__snake_case		:      List[str]										=       attention_dropout
														__snake_case		:      Optional[Any]										=       activation_dropout
														__snake_case		:      Union[str, Any]										=       activation_function
														__snake_case		:      Union[str, Any]										=       init_std
														__snake_case		:      Union[str, Any]										=       decoder_layerdrop
														__snake_case		:      Optional[int]										=       use_cache
														__snake_case		:      Optional[int]										=       decoder_layers
														__snake_case		:      str										=       scale_embedding  # scale factor will be sqrt(d_model) if True
														__snake_case		:      Dict										=       max_target_positions

														super().__init__(
														    pad_token_id=lowerCamelCase   ,     bos_token_id=lowerCamelCase   ,     eos_token_id=lowerCamelCase   ,     decoder_start_token_id=lowerCamelCase   ,     **lowerCamelCase   ,     )

