



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__lowerCamelCase     :							List[Any]  							=				logging.get_logger(__name__)

__lowerCamelCase     :							List[Any]  							=				{
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}





class  __magic_name__   (							A__						):
		lowercase      :       Tuple          ='''umt5'''
		lowercase      :       Any          =['''past_key_values''']






		def __init__(						self      :     Dict				,		UpperCamelCase__      :     int=25_01_12				,		UpperCamelCase__      :     Optional[int]=5_12				,		UpperCamelCase__      :     str=64				,		UpperCamelCase__      :     str=10_24				,		UpperCamelCase__      :     int=8				,		UpperCamelCase__      :     int=None				,		UpperCamelCase__      :     List[Any]=6				,		UpperCamelCase__      :     List[Any]=32				,		UpperCamelCase__      :     str=1_28				,		UpperCamelCase__      :     Tuple=0.1				,		UpperCamelCase__      :     Tuple=1e-6				,		UpperCamelCase__      :     int=1.0				,		UpperCamelCase__      :     Optional[int]="gated-gelu"				,		UpperCamelCase__      :     List[str]=True				,		UpperCamelCase__      :     Optional[Any]=True				,		UpperCamelCase__      :     Tuple="T5Tokenizer"				,		UpperCamelCase__      :     List[Any]=True				,		UpperCamelCase__      :     Tuple=0				,		UpperCamelCase__      :     Optional[int]=1				,		UpperCamelCase__      :     List[Any]=0				,		**UpperCamelCase__      :     Union[str, Any]				,		)				->					int:
								'''simple docstring'''







								super().__init__(
								    is_encoder_decoder=UpperCamelCase__				,		tokenizer_class=UpperCamelCase__				,		tie_word_embeddings=UpperCamelCase__				,		pad_token_id=UpperCamelCase__				,		eos_token_id=UpperCamelCase__				,		decoder_start_token_id=UpperCamelCase__				,		**UpperCamelCase__				,		)
								UpperCAmelCase         =     vocab_size
								UpperCAmelCase         =     d_model
								UpperCAmelCase         =     d_kv
								UpperCAmelCase         =     d_ff
								UpperCAmelCase         =     num_layers
								UpperCAmelCase         =     (
								    num_decoder_layers if num_decoder_layers is not None else self.num_layers
								)  # default = symmetry
								UpperCAmelCase         =     num_heads
								UpperCAmelCase         =     relative_attention_num_buckets
								UpperCAmelCase         =     relative_attention_max_distance
								UpperCAmelCase         =     dropout_rate
								UpperCAmelCase         =     layer_norm_epsilon
								UpperCAmelCase         =     initializer_factor
								UpperCAmelCase         =     feed_forward_proj
								UpperCAmelCase         =     use_cache

								UpperCAmelCase         =     self.feed_forward_proj.split("-"      )
								UpperCAmelCase         =     act_info[-1]
								UpperCAmelCase         =     act_info[0] == "gated"

								if len(UpperCamelCase__      ) > 1 and act_info[0] != "gated" or len(UpperCamelCase__      ) > 2:
														raise ValueError(
														    F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
														    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
														    "'gated-gelu' or 'relu'"      )

								if feed_forward_proj == "gated-gelu":
														UpperCAmelCase         =     "gelu_new"






		@property
		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					int:
								'''simple docstring'''







								return self.d_model






		@property
		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					int:
								'''simple docstring'''







								return self.num_heads







		@property
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					int:
								'''simple docstring'''







								return self.num_layers







class  __magic_name__   (							A__						):






		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Mapping[str, Mapping[int, str]]:
								'''simple docstring'''







								UpperCAmelCase         =     {
								    "input_ids": {0: "batch", 1: "encoder_sequence"},
								    "attention_mask": {0: "batch", 1: "encoder_sequence"},
								}
								if self.use_past:
														UpperCAmelCase         =     "past_encoder_sequence + sequence"
														UpperCAmelCase         =     {0: "batch"}
														UpperCAmelCase         =     {0: "batch", 1: "past_decoder_sequence + sequence"}
								else:
														UpperCAmelCase         =     {0: "batch", 1: "decoder_sequence"}
														UpperCAmelCase         =     {0: "batch", 1: "decoder_sequence"}

								if self.use_past:
														self.fill_with_past_key_values_(UpperCamelCase__				,		direction="inputs"      )

								return common_inputs






		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]      )				->					int:
								'''simple docstring'''







								return 13







		@property
		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					float:
								'''simple docstring'''







								return 5e-4








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  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       Optional[Any]          =CpmAntTokenizer
		lowercase      :       Dict          =False






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								super().setUp()

								UpperCAmelCase         =     [
								    "<d>",
								    "</d>",
								    "<s>",
								    "</s>",
								    "</_>",
								    "<unk>",
								    "<pad>",
								    "</n>",
								    "我",
								    "是",
								    "C",
								    "P",
								    "M",
								    "A",
								    "n",
								    "t",
								]
								UpperCAmelCase         =     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 SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"      )
								UpperCAmelCase         =     "今天天气真好！"
								UpperCAmelCase         =     ["今天", "天气", "真", "好", "！"]
								UpperCAmelCase         =     tokenizer.tokenize(UpperCamelCase__      )
								self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )
								UpperCAmelCase         =     "今天天气真好！"
								UpperCAmelCase         =     [tokenizer.bos_token] + tokens

								UpperCAmelCase         =     [6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__      )				,		UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer.decode(UpperCamelCase__      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )



