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


_A						      =				logging.get_logger(__name__)

_A						      =				{
    "microsoft/trocr-base-handwritten": (
        "https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							int     =	"trocr"
     UpperCAmelCase__       :							Dict     =	["past_key_values"]
     UpperCAmelCase__       :							str     =	{
         "num_attention_heads": "decoder_attention_heads",
         "hidden_size": "d_model",
         "num_hidden_layers": "decoder_layers",
     }





     def __init__(self   :					Any       ,					_A   :					Any=5_0_2_6_5       ,					_A   :					Any=1_0_2_4       ,					_A   :					List[str]=1_2       ,					_A   :					Dict=1_6       ,					_A   :					Optional[int]=4_0_9_6       ,					_A   :					Dict="gelu"       ,					_A   :					List[Any]=5_1_2       ,					_A   :					Optional[Any]=0.1       ,					_A   :					List[str]=0.0       ,					_A   :					Union[str, Any]=0.0       ,					_A   :					Optional[Any]=2       ,					_A   :					str=0.02       ,					_A   :					Dict=0.0       ,					_A   :					Optional[Any]=True       ,					_A   :					Optional[Any]=False       ,					_A   :					Union[str, Any]=True       ,					_A   :					List[Any]=True       ,					_A   :					str=1       ,					_A   :					Any=0       ,					_A   :					List[str]=2       ,					**_A   :					Optional[Any]       ,					)		->		Any:
         snake_case				  =      vocab_size
         snake_case				  =      d_model
         snake_case				  =      decoder_layers
         snake_case				  =      decoder_attention_heads
         snake_case				  =      decoder_ffn_dim
         snake_case				  =      activation_function
         snake_case				  =      max_position_embeddings
         snake_case				  =      dropout
         snake_case				  =      attention_dropout
         snake_case				  =      activation_dropout
         snake_case				  =      init_std
         snake_case				  =      decoder_layerdrop
         snake_case				  =      use_cache
         snake_case				  =      scale_embedding
         snake_case				  =      use_learned_position_embeddings
         snake_case				  =      layernorm_embedding

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



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	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							Any     =	CpmAntTokenizer
     UpperCAmelCase__       :							Optional[Any]     =	False





     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Dict:
         super().setUp()

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

         snake_case				  =      [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(_A     )       ,					_A     )

         snake_case				  =      tokenizer.decode(_A     )
         self.assertEqual(_A       ,					_A     )

