



'''simple docstring'''






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors



@require_tokenizers
class 							lowercase       (  _lowerCamelCase					,					unittest.TestCase				):


 """simple docstring"""


 UpperCAmelCase							=  MvpTokenizer
 UpperCAmelCase							=  MvpTokenizerFast
 UpperCAmelCase							=  True
 UpperCAmelCase							=  filter_roberta_detectors







 def _snake_case (							self							)		->   Dict:
        super().setUp()
        _UpperCAmelCase  :						Optional[int]			     = [
            """l""",
            """o""",
            """w""",
            """e""",
            """r""",
            """s""",
            """t""",
            """i""",
            """d""",
            """n""",
            """\u0120""",
            """\u0120l""",
            """\u0120n""",
            """\u0120lo""",
            """\u0120low""",
            """er""",
            """\u0120lowest""",
            """\u0120newer""",
            """\u0120wider""",
            """<unk>""",
        ]
        _UpperCAmelCase  :						Tuple			     = dict(zip(a_  ,range(len(a_							)							)							)							)
        _UpperCAmelCase  :						Union[str, Any]			     = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
        _UpperCAmelCase  :						int			     = {"""unk_token""": """<unk>"""}

        _UpperCAmelCase  :						List[Any]			     = os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES["""vocab_file"""]							)
        _UpperCAmelCase  :						int			     = os.path.join(self.tmpdirname  ,VOCAB_FILES_NAMES["""merges_file"""]							)
        with open(self.vocab_file  ,"""w"""  ,encoding="""utf-8"""							) as fp:
               fp.write(json.dumps(a_							) + """\n"""							)
        with open(self.merges_file  ,"""w"""  ,encoding="""utf-8"""							) as fp:
               fp.write("""\n""".join(a_							)							)







 def _snake_case (							self  ,**a_							)		->   Optional[Any]:
        kwargs.update(self.special_tokens_map							)
        return self.tokenizer_class.from_pretrained(self.tmpdirname  ,**a_							)







 def _snake_case (							self  ,**a_							)		->   str:
        kwargs.update(self.special_tokens_map							)
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,**a_							)







 def _snake_case (							self  ,a_							)		->   List[Any]:
        return "lower newer", "lower newer"







 @cached_property
 def _snake_case (							self							)		->   Tuple:
        return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""							)







 @cached_property
 def _snake_case (							self							)		->   str:
        return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""							)







 @require_torch
 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase  :						Tuple			     = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
        _UpperCAmelCase  :						Optional[Any]			     = [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               _UpperCAmelCase  :						List[Any]			     = tokenizer(a_  ,max_length=len(a_							)  ,padding=a_  ,return_tensors="""pt"""							)
               self.assertIsInstance(a_  ,a_							)

               self.assertEqual((2, 9)  ,batch.input_ids.shape							)
               self.assertEqual((2, 9)  ,batch.attention_mask.shape							)
               _UpperCAmelCase  :						Any			     = batch.input_ids.tolist()[0]
               self.assertListEqual(a_  ,a_							)
               # Test that special tokens are reset







 @require_torch
 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase  :						Optional[Any]			     = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               _UpperCAmelCase  :						Union[str, Any]			     = tokenizer(a_  ,padding=a_  ,return_tensors="""pt"""							)
               # check if input_ids are returned and no labels
               self.assertIn("""input_ids"""  ,a_							)
               self.assertIn("""attention_mask"""  ,a_							)
               self.assertNotIn("""labels"""  ,a_							)
               self.assertNotIn("""decoder_attention_mask"""  ,a_							)







 @require_torch
 def _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase  :						int			     = [
            """Summary of the text.""",
            """Another summary.""",
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               _UpperCAmelCase  :						List[Any]			     = tokenizer(text_target=a_  ,max_length=32  ,padding="""max_length"""  ,return_tensors="""pt"""							)
               self.assertEqual(32  ,targets["""input_ids"""].shape[1]							)







 @require_torch
 def _snake_case (							self							)		->   List[Any]:
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               _UpperCAmelCase  :						Optional[Any]			     = tokenizer(
                   ["""I am a small frog""" * 1_024, """I am a small frog"""]  ,padding=a_  ,truncation=a_  ,return_tensors="""pt"""							)
               self.assertIsInstance(a_  ,a_							)
               self.assertEqual(batch.input_ids.shape  ,(2, 1_024)							)







 @require_torch
 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						List[str]			     = ["""A long paragraph for summarization."""]
        _UpperCAmelCase  :						str			     = [
            """Summary of the text.""",
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               _UpperCAmelCase  :						List[str]			     = tokenizer(a_  ,text_target=a_  ,return_tensors="""pt"""							)
               _UpperCAmelCase  :						str			     = inputs["""input_ids"""]
               _UpperCAmelCase  :						Optional[int]			     = inputs["""labels"""]
               self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()							)
               self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()							)
               self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()							)
               self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()							)







 def _snake_case (							self							)		->   Any:
        pass







 def _snake_case (							self							)		->   List[str]:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
                      _UpperCAmelCase  :						Optional[Any]			     = self.rust_tokenizer_class.from_pretrained(a_  ,**a_							)
                      _UpperCAmelCase  :						Dict			     = self.tokenizer_class.from_pretrained(a_  ,**a_							)
                      _UpperCAmelCase  :						Dict			     = """A, <mask> AllenNLP sentence."""
                      _UpperCAmelCase  :						str			     = tokenizer_r.encode_plus(a_  ,add_special_tokens=a_  ,return_token_type_ids=a_							)
                      _UpperCAmelCase  :						List[str]			     = tokenizer_p.encode_plus(a_  ,add_special_tokens=a_  ,return_token_type_ids=a_							)

                      # token_type_ids should put 0 everywhere
                      self.assertEqual(sum(tokens_r["""token_type_ids"""]							)  ,sum(tokens_p["""token_type_ids"""]							)							)

                      # attention_mask should put 1 everywhere, so sum over length should be 1
                      self.assertEqual(
                          sum(tokens_r["""attention_mask"""]							) / len(tokens_r["""attention_mask"""]							)  ,sum(tokens_p["""attention_mask"""]							) / len(tokens_p["""attention_mask"""]							)  ,)

                      _UpperCAmelCase  :						int			     = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]							)
                      _UpperCAmelCase  :						int			     = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]							)

                      # Rust correctly handles the space before the mask while python doesnt
                      self.assertSequenceEqual(tokens_p["""input_ids"""]  ,[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]							)
                      self.assertSequenceEqual(tokens_r["""input_ids"""]  ,[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]							)

                      self.assertSequenceEqual(
                          a_  ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)
                      self.assertSequenceEqual(
                          a_  ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)








'''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


A_							: Any						 =	logging.get_logger(__name__)

A_							: 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 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  """longformer"""







 def __init__(							self  ,a_ = 512  ,a_ = 2  ,a_ = 1  ,a_ = 0  ,a_ = 2  ,a_ = 30_522  ,a_ = 768  ,a_ = 12  ,a_ = 12  ,a_ = 3_072  ,a_ = "gelu"  ,a_ = 0.1  ,a_ = 0.1  ,a_ = 512  ,a_ = 2  ,a_ = 0.02  ,a_ = 1E-1_2  ,a_ = False  ,**a_  ,)		->   List[Any]:
        super().__init__(pad_token_id=a_  ,**a_							)

        _UpperCAmelCase  :						List[Any]			     = attention_window
        _UpperCAmelCase  :						Any			     = sep_token_id
        _UpperCAmelCase  :						Dict			     = bos_token_id
        _UpperCAmelCase  :						Tuple			     = eos_token_id
        _UpperCAmelCase  :						Tuple			     = vocab_size
        _UpperCAmelCase  :						Optional[Any]			     = hidden_size
        _UpperCAmelCase  :						Optional[int]			     = num_hidden_layers
        _UpperCAmelCase  :						Union[str, Any]			     = num_attention_heads
        _UpperCAmelCase  :						Optional[int]			     = hidden_act
        _UpperCAmelCase  :						str			     = intermediate_size
        _UpperCAmelCase  :						List[Any]			     = hidden_dropout_prob
        _UpperCAmelCase  :						Union[str, Any]			     = attention_probs_dropout_prob
        _UpperCAmelCase  :						List[str]			     = max_position_embeddings
        _UpperCAmelCase  :						Optional[int]			     = type_vocab_size
        _UpperCAmelCase  :						Any			     = initializer_range
        _UpperCAmelCase  :						Optional[int]			     = layer_norm_eps
        _UpperCAmelCase  :						Union[str, Any]			     = onnx_export







class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_ = "default"  ,a_ = None							)		->   int:
        super().__init__(a_  ,a_  ,a_							)
        _UpperCAmelCase  :						Tuple			     = True







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







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







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







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







 def _snake_case (							self  ,a_  ,a_ = -1  ,a_ = -1  ,a_ = False  ,a_ = None  ,)		->   Mapping[str, Any]:
        _UpperCAmelCase  :						List[str]			     = 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
        _UpperCAmelCase  :						int			     = torch.zeros_like(inputs["""input_ids"""]							)
        # make every second token global
        _UpperCAmelCase  :						List[str]			     = 1

        return inputs



