


'''simple docstring'''







import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a__  :					str        =       get_tests_dir('fixtures/test_sentencepiece_no_bos.model')
@require_sentencepiece
@require_tokenizers
class 					lowercase_     (    _snake_case  ,						unittest.TestCase   ):
    __UpperCAmelCase   			=	PegasusTokenizer
    __UpperCAmelCase   			=	PegasusTokenizerFast
    __UpperCAmelCase   			=	True
    __UpperCAmelCase   			=	True


    def    __a							(     self    ):
          super().setUp()

          # We have a SentencePiece fixture for testing
          UpperCamelCase__										=				PegasusTokenizer(__snake_case    )
          tokenizer.save_pretrained(self.tmpdirname    )


    @cached_property
    def    __a							(     self    ):
          return PegasusTokenizer.from_pretrained("google/pegasus-large"    )


    def    __a							(     self  ,					**a    ):
          return PegasusTokenizer.from_pretrained(self.tmpdirname  ,					**__snake_case    )


    def    __a							(     self  ,					a    ):
          return ("This is a test", "This is a test")


    def    __a							(     self    ):
          UpperCamelCase__										=				"""</s>"""
          UpperCamelCase__										=				1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case    )  ,					__snake_case    )
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case    )  ,					__snake_case    )


    def    __a							(     self    ):
          UpperCamelCase__										=				list(self.get_tokenizer().get_vocab().keys()    )

          self.assertEqual(vocab_keys[0]  ,					"<pad>"    )
          self.assertEqual(vocab_keys[1]  ,					"</s>"    )
          self.assertEqual(vocab_keys[-1]  ,					"v"    )
          self.assertEqual(len(__snake_case    )  ,					11_03    )


    def    __a							(     self    ):
          self.assertEqual(self.get_tokenizer().vocab_size  ,					11_03    )


    def    __a							(     self    ):
          UpperCamelCase__										=				self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
          UpperCamelCase__										=				self.tokenizer_class.from_pretrained(self.tmpdirname    )
          UpperCamelCase__										=				(
              """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
              """ </s> <pad> <pad> <pad>"""
          )
          UpperCamelCase__										=				rust_tokenizer([raw_input_str]  ,					return_tensors=__snake_case  ,					add_special_tokens=__snake_case    ).input_ids[0]
          UpperCamelCase__										=				py_tokenizer([raw_input_str]  ,					return_tensors=__snake_case  ,					add_special_tokens=__snake_case    ).input_ids[0]
          self.assertListEqual(__snake_case  ,					__snake_case    )


    def    __a							(     self    ):
          UpperCamelCase__										=				self._large_tokenizer
          # <mask_1> masks whole sentence while <mask_2> masks single word
          UpperCamelCase__										=				"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
          UpperCamelCase__										=				[2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
          UpperCamelCase__										=				tokenizer([raw_input_str]  ,					return_tensors=__snake_case    ).input_ids[0]
          self.assertListEqual(__snake_case  ,					__snake_case    )


    def    __a							(     self    ):
          UpperCamelCase__										=				self._large_tokenizer
          # The tracebacks for the following asserts are **better** without messages or self.assertEqual
          assert tokenizer.vocab_size == 9_61_03
          assert tokenizer.pad_token_id == 0
          assert tokenizer.eos_token_id == 1
          assert tokenizer.offset == 1_03
          assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
          assert tokenizer.unk_token == "<unk>"
          assert tokenizer.model_max_length == 10_24
          UpperCamelCase__										=				"""To ensure a smooth flow of bank resolutions."""
          UpperCamelCase__										=				[4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
          UpperCamelCase__										=				tokenizer([raw_input_str]  ,					return_tensors=__snake_case    ).input_ids[0]
          self.assertListEqual(__snake_case  ,					__snake_case    )
          assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]    ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]


    @require_torch
    def    __a							(     self    ):
          UpperCamelCase__										=				["""This is going to be way too long.""" * 1_50, """short example"""]
          UpperCamelCase__										=				["""not super long but more than 5 tokens""", """tiny"""]
          UpperCamelCase__										=				self._large_tokenizer(__snake_case  ,					padding=__snake_case  ,					truncation=__snake_case  ,					return_tensors="pt"    )
          UpperCamelCase__										=				self._large_tokenizer(
              text_target=__snake_case  ,					max_length=5  ,					padding=__snake_case  ,					truncation=__snake_case  ,					return_tensors="pt"    )

          assert batch.input_ids.shape == (2, 10_24)
          assert batch.attention_mask.shape == (2, 10_24)
          assert targets["input_ids"].shape == (2, 5)
          assert len(__snake_case    ) == 2  # input_ids, attention_mask.


    @slow
    def    __a							(     self    ):
          # fmt: off
          UpperCamelCase__										=				{"""input_ids""": [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=__snake_case  ,					model_name="google/bigbird-pegasus-large-arxiv"  ,					revision="ba85d0851d708441f91440d509690f1ab6353415"  ,					)

@require_sentencepiece
@require_tokenizers
class 					lowercase_     (    _snake_case  ,						unittest.TestCase   ):
    __UpperCAmelCase   			=	PegasusTokenizer
    __UpperCAmelCase   			=	PegasusTokenizerFast
    __UpperCAmelCase   			=	True
    __UpperCAmelCase   			=	True


    def    __a							(     self    ):
          super().setUp()

          # We have a SentencePiece fixture for testing
          UpperCamelCase__										=				PegasusTokenizer(__snake_case  ,					offset=0  ,					mask_token_sent=__snake_case  ,					mask_token="[MASK]"    )
          tokenizer.save_pretrained(self.tmpdirname    )


    @cached_property
    def    __a							(     self    ):
          return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"    )


    def    __a							(     self  ,					**a    ):
          return PegasusTokenizer.from_pretrained(self.tmpdirname  ,					**__snake_case    )


    def    __a							(     self  ,					a    ):
          return ("This is a test", "This is a test")


    def    __a							(     self    ):
          UpperCamelCase__										=				self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
          UpperCamelCase__										=				self.tokenizer_class.from_pretrained(self.tmpdirname    )
          UpperCamelCase__										=				(
              """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
              """ <pad> <pad> <pad>"""
          )
          UpperCamelCase__										=				rust_tokenizer([raw_input_str]  ,					return_tensors=__snake_case  ,					add_special_tokens=__snake_case    ).input_ids[0]
          UpperCamelCase__										=				py_tokenizer([raw_input_str]  ,					return_tensors=__snake_case  ,					add_special_tokens=__snake_case    ).input_ids[0]
          self.assertListEqual(__snake_case  ,					__snake_case    )


    @require_torch
    def    __a							(     self    ):
          UpperCamelCase__										=				["""This is going to be way too long.""" * 10_00, """short example"""]
          UpperCamelCase__										=				["""not super long but more than 5 tokens""", """tiny"""]
          UpperCamelCase__										=				self._large_tokenizer(__snake_case  ,					padding=__snake_case  ,					truncation=__snake_case  ,					return_tensors="pt"    )
          UpperCamelCase__										=				self._large_tokenizer(
              text_target=__snake_case  ,					max_length=5  ,					padding=__snake_case  ,					truncation=__snake_case  ,					return_tensors="pt"    )

          assert batch.input_ids.shape == (2, 40_96)
          assert batch.attention_mask.shape == (2, 40_96)
          assert targets["input_ids"].shape == (2, 5)
          assert len(__snake_case    ) == 2  # input_ids, attention_mask.


    def    __a							(     self    ):
          UpperCamelCase__										=				(
              """This is an example string that is used to test the original TF implementation against the HF"""
              """ implementation"""
          )

          UpperCamelCase__										=				self._large_tokenizer(__snake_case    ).input_ids

          self.assertListEqual(
              __snake_case  ,					[1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]  ,					)









'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_		:   Union[str, Any]							   =		logging.get_logger(__name__)

UpperCAmelCase_		:   Optional[int]							   =		{
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      str      	=  """big_bird"""






							def __init__(    self    ,					__snake_case=5_0358    ,					__snake_case=768    ,					__snake_case=12    ,					__snake_case=12    ,					__snake_case=3072    ,					__snake_case="gelu_new"    ,					__snake_case=0.1    ,					__snake_case=0.1    ,					__snake_case=4096    ,					__snake_case=2    ,					__snake_case=0.02    ,					__snake_case=1e-12    ,					__snake_case=True    ,					__snake_case=0    ,					__snake_case=1    ,					__snake_case=2    ,					__snake_case=66    ,					__snake_case="block_sparse"    ,					__snake_case=True    ,					__snake_case=False    ,					__snake_case=64    ,					__snake_case=3    ,					__snake_case=None    ,					**__snake_case    ,					):
											super().__init__(
											    pad_token_id=__snake_case    ,					bos_token_id=__snake_case    ,					eos_token_id=__snake_case    ,					sep_token_id=__snake_case    ,					**__snake_case    ,					)

											_SCREAMING_SNAKE_CASE :       str     		=      vocab_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      max_position_embeddings
											_SCREAMING_SNAKE_CASE :       List[str]     		=      hidden_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      num_hidden_layers
											_SCREAMING_SNAKE_CASE :       Any     		=      num_attention_heads
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      intermediate_size
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      hidden_act
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      hidden_dropout_prob
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE :       Tuple     		=      initializer_range
											_SCREAMING_SNAKE_CASE :       Any     		=      type_vocab_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      layer_norm_eps
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      use_cache

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      rescale_embeddings
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      attention_type
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      use_bias
											_SCREAMING_SNAKE_CASE :       int     		=      block_size
											_SCREAMING_SNAKE_CASE :       Any     		=      num_random_blocks
											_SCREAMING_SNAKE_CASE :       List[str]     		=      classifier_dropout





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							@property
							def UpperCAmelCase_	(    self  ):
											if self.task == "multiple-choice":
															_SCREAMING_SNAKE_CASE :       int     		=      {0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      {0: """batch""", 1: """sequence"""}
											return OrderedDict(
											    [
											        ("""input_ids""", dynamic_axis),
											        ("""attention_mask""", dynamic_axis),
											    ]  )




