


"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
       from ...onnx.config import PatchingSpec
       from ...tokenization_utils_base import PreTrainedTokenizerBase


lowerCamelCase_       :       Optional[Any]      						=  logging.get_logger(__name__)

lowerCamelCase_       :       Optional[int]      						=  {
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  "longformer"


 def __init__(		self			,   __A = 512			,   __A = 2			,   __A = 1			,   __A = 0			,   __A = 2			,   __A = 3_0522			,   __A = 768			,   __A = 12			,   __A = 12			,   __A = 3072			,   __A = "gelu"			,   __A = 0.1			,   __A = 0.1			,   __A = 512			,   __A = 2			,   __A = 0.02			,   __A = 1E-1_2			,   __A = False			,   **__A			,   ) ->					Dict:
     super().__init__(pad_token_id=__A			,   **__A						)

     a					=attention_window
     a					=sep_token_id
     a					=bos_token_id
     a					=eos_token_id
     a					=vocab_size
     a					=hidden_size
     a					=num_hidden_layers
     a					=num_attention_heads
     a					=hidden_act
     a					=intermediate_size
     a					=hidden_dropout_prob
     a					=attention_probs_dropout_prob
     a					=max_position_embeddings
     a					=type_vocab_size
     a					=initializer_range
     a					=layer_norm_eps
     a					=onnx_export






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 def __init__(		self			,   __A			,   __A = "default"			,   __A = None						) ->					Dict:
     super().__init__(__A			,   __A			,   __A						)
     a					=True


 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
         a					={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         a					={0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
             ('''global_attention_mask''', dynamic_axis),
         ]						)


 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Mapping[str, Mapping[int, str]]:
     a					=super().outputs
     if self.task == "default":
         a					={0: '''batch'''}
     return outputs


 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					float:
     return 1E-4


 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     # needs to be >= 14 to support tril operator
     return max(super().default_onnx_opset			,   14						)





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = -1			,   __A = -1			,   __A = False			,   __A = None			,   ) ->					Mapping[str, Any]:
     a					=super().generate_dummy_inputs(
         preprocessor=__A			,   batch_size=__A			,   seq_length=__A			,   is_pair=__A			,   framework=__A						)
     import torch

     # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
     # makes the export fail randomly
     a					=torch.zeros_like(inputs['''input_ids''']						)
     # make every second token global
     a					=1

     return inputs



"""simple docstring"""







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_rjieba
@require_tokenizers
class 				__A    (	_SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  RoFormerTokenizer
 __lowerCAmelCase			 =  RoFormerTokenizerFast
 __lowerCAmelCase			 =  True
 __lowerCAmelCase			 =  True


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     super().setUp()


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					Optional[int]:
     return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					List[Any]:
     return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					='''永和服装饰品有限公司,今天天气非常好'''
     a					='''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
     return input_text, output_text


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a					=self.get_tokenizer()
     a		,				a					=self.get_chinese_input_output_texts()
     a					=tokenizer.tokenize(__A						)

     self.assertListEqual(__A			,   output_text.split()						)

     a					=tokens + [tokenizer.unk_token]
     a					=[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A						)			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=self.get_rust_tokenizer()
     a		,				a					=self.get_chinese_input_output_texts()
     a					=tokenizer.tokenize(__A						)
     self.assertListEqual(__A			,   output_text.split()						)
     a					=tokens + [tokenizer.unk_token]
     a					=[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A						)			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     pass


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     pass





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     pass