

import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCAmelCase								=			get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Optional[Any]										=XLMRobertaTokenizer
  lowerCamelCase   :      Union[str, Any]										=XLMRobertaTokenizerFast
  lowerCamelCase   :      int										=True
  lowerCamelCase   :      List[Any]										=True







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Optional[int]:






     """simple docstring"""







     super().setUp()

     # We have a SentencePiece fixture for testing
     __lowerCAmelCase  :      Optional[Any]    =						XLMRobertaTokenizer(lowerCAmelCase	,							keep_accents=lowerCAmelCase   )
     tokenizer.save_pretrained(self.tmpdirname   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						"""<pad>"""
     __lowerCAmelCase  :      Optional[int]    =						1

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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						list(self.get_tokenizer().get_vocab().keys()   )

     self.assertEqual(vocab_keys[0]	,							"""<s>"""   )
     self.assertEqual(vocab_keys[1]	,							"""<pad>"""   )
     self.assertEqual(vocab_keys[-1]	,							"""<mask>"""   )
     self.assertEqual(len(lowerCAmelCase   )	,							10_02   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     List[Any]:






     """simple docstring"""







     self.assertEqual(self.get_tokenizer().vocab_size	,							10_02   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						XLMRobertaTokenizer(lowerCAmelCase	,							keep_accents=lowerCAmelCase   )

     __lowerCAmelCase  :      List[str]    =						tokenizer.tokenize("""This is a test"""   )
     self.assertListEqual(lowerCAmelCase	,							["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]   )

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(lowerCAmelCase   )	,							[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]	,							)

     __lowerCAmelCase  :      List[str]    =						tokenizer.tokenize("""I was born in 92000, and this is falsé."""   )
     self.assertListEqual(
         lowerCAmelCase	,							[
             SPIECE_UNDERLINE + """I""",
             SPIECE_UNDERLINE + """was""",
             SPIECE_UNDERLINE + """b""",
             """or""",
             """n""",
             SPIECE_UNDERLINE + """in""",
             SPIECE_UNDERLINE + """""",
             """9""",
             """2""",
             """0""",
             """0""",
             """0""",
             """,""",
             SPIECE_UNDERLINE + """and""",
             SPIECE_UNDERLINE + """this""",
             SPIECE_UNDERLINE + """is""",
             SPIECE_UNDERLINE + """f""",
             """al""",
             """s""",
             """é""",
             """.""",
         ]	,							)
     __lowerCAmelCase  :      int    =						tokenizer.convert_tokens_to_ids(lowerCAmelCase   )
     self.assertListEqual(
         lowerCAmelCase	,							[
             value + tokenizer.fairseq_offset
             for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
         ]	,							)

     __lowerCAmelCase  :      int    =						tokenizer.convert_ids_to_tokens(lowerCAmelCase   )
     self.assertListEqual(
         lowerCAmelCase	,							[
             SPIECE_UNDERLINE + """I""",
             SPIECE_UNDERLINE + """was""",
             SPIECE_UNDERLINE + """b""",
             """or""",
             """n""",
             SPIECE_UNDERLINE + """in""",
             SPIECE_UNDERLINE + """""",
             """<unk>""",
             """2""",
             """0""",
             """0""",
             """0""",
             """,""",
             SPIECE_UNDERLINE + """and""",
             SPIECE_UNDERLINE + """this""",
             SPIECE_UNDERLINE + """is""",
             SPIECE_UNDERLINE + """f""",
             """al""",
             """s""",
             """<unk>""",
             """.""",
         ]	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Optional[int]:






     """simple docstring"""







     if not self.test_slow_tokenizer:
        # as we don't have a slow version, we can't compare the outputs between slow and fast versions
        return

     __lowerCAmelCase  :      Dict    =						(self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
           __lowerCAmelCase  :      List[Any]    =						self.rust_tokenizer_class.from_pretrained(lowerCAmelCase	,							**lowerCAmelCase   )
           __lowerCAmelCase  :      str    =						self.tokenizer_class.from_pretrained(lowerCAmelCase	,							**lowerCAmelCase   )

           __lowerCAmelCase  :      int    =						tempfile.mkdtemp()

           __lowerCAmelCase  :      List[Any]    =						tokenizer_r.save_pretrained(lowerCAmelCase   )
           __lowerCAmelCase  :      int    =						tokenizer_p.save_pretrained(lowerCAmelCase   )

           # Checks it save with the same files + the tokenizer.json file for the fast one
           self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files   )   )
           __lowerCAmelCase  :      Tuple    =						tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f   )
           self.assertSequenceEqual(lowerCAmelCase	,							lowerCAmelCase   )

           # Checks everything loads correctly in the same way
           __lowerCAmelCase  :      List[Any]    =						tokenizer_r.from_pretrained(lowerCAmelCase   )
           __lowerCAmelCase  :      str    =						tokenizer_p.from_pretrained(lowerCAmelCase   )

           # Check special tokens are set accordingly on Rust and Python
           for key in tokenizer_pp.special_tokens_map:
              self.assertTrue(hasattr(lowerCAmelCase	,							lowerCAmelCase   )   )
              # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
              # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

           shutil.rmtree(lowerCAmelCase   )

           # Save tokenizer rust, legacy_format=True
           __lowerCAmelCase  :      List[Any]    =						tempfile.mkdtemp()

           __lowerCAmelCase  :      str    =						tokenizer_r.save_pretrained(lowerCAmelCase	,							legacy_format=lowerCAmelCase   )
           __lowerCAmelCase  :      int    =						tokenizer_p.save_pretrained(lowerCAmelCase   )

           # Checks it save with the same files
           self.assertSequenceEqual(lowerCAmelCase	,							lowerCAmelCase   )

           # Checks everything loads correctly in the same way
           __lowerCAmelCase  :      int    =						tokenizer_r.from_pretrained(lowerCAmelCase   )
           __lowerCAmelCase  :      Optional[int]    =						tokenizer_p.from_pretrained(lowerCAmelCase   )

           # Check special tokens are set accordingly on Rust and Python
           for key in tokenizer_pp.special_tokens_map:
              self.assertTrue(hasattr(lowerCAmelCase	,							lowerCAmelCase   )   )

           shutil.rmtree(lowerCAmelCase   )

           # Save tokenizer rust, legacy_format=False
           __lowerCAmelCase  :      List[str]    =						tempfile.mkdtemp()

           __lowerCAmelCase  :      Any    =						tokenizer_r.save_pretrained(lowerCAmelCase	,							legacy_format=lowerCAmelCase   )
           __lowerCAmelCase  :      Optional[Any]    =						tokenizer_p.save_pretrained(lowerCAmelCase   )

           # Checks it saved the tokenizer.json file
           self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files   )   )

           # Checks everything loads correctly in the same way
           __lowerCAmelCase  :      Tuple    =						tokenizer_r.from_pretrained(lowerCAmelCase   )
           __lowerCAmelCase  :      Any    =						tokenizer_p.from_pretrained(lowerCAmelCase   )

           # Check special tokens are set accordingly on Rust and Python
           for key in tokenizer_pp.special_tokens_map:
              self.assertTrue(hasattr(lowerCAmelCase	,							lowerCAmelCase   )   )

           shutil.rmtree(lowerCAmelCase   )







  @cached_property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Optional[int]:






     """simple docstring"""







     return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Dict:






     """simple docstring"""







     with tempfile.NamedTemporaryFile() as f:
        shutil.copyfile(lowerCAmelCase	,							f.name   )
        __lowerCAmelCase  :      str    =						XLMRobertaTokenizer(f.name	,							keep_accents=lowerCAmelCase   )
        __lowerCAmelCase  :      Tuple    =						pickle.dumps(lowerCAmelCase   )
     pickle.loads(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Optional[int]:






     """simple docstring"""







     if not self.test_rust_tokenizer:
        return

     __lowerCAmelCase  :      List[Any]    =						self.get_tokenizer()
     __lowerCAmelCase  :      List[str]    =						self.get_rust_tokenizer()

     __lowerCAmelCase  :      Optional[Any]    =						"""I was born in 92000, and this is falsé."""

     __lowerCAmelCase  :      Any    =						tokenizer.tokenize(lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						rust_tokenizer.tokenize(lowerCAmelCase   )
     self.assertListEqual(lowerCAmelCase	,							lowerCAmelCase   )

     __lowerCAmelCase  :      Any    =						tokenizer.encode(lowerCAmelCase	,							add_special_tokens=lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						rust_tokenizer.encode(lowerCAmelCase	,							add_special_tokens=lowerCAmelCase   )
     self.assertListEqual(lowerCAmelCase	,							lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[Any]    =						self.get_rust_tokenizer()
     __lowerCAmelCase  :      Any    =						tokenizer.encode(lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[int]    =						rust_tokenizer.encode(lowerCAmelCase   )
     self.assertListEqual(lowerCAmelCase	,							lowerCAmelCase   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						"""Hello World!"""
     __lowerCAmelCase  :      int    =						[0, 3_53_78, 66_61, 38, 2]
     # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
     # xlmr.eval()
     # xlmr.encode(symbols)

     self.assertListEqual(lowerCAmelCase	,							self.big_tokenizer.encode(lowerCAmelCase   )   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						(
         """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
         """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
     )
     __lowerCAmelCase  :      int    =						[
         0,
         32_93,
         83,
         10,
         45_52,
         49_89,
         79_86,
         6_78,
         10,
         59_15,
         1_11,
         17_94_59,
         12_48_50,
         4,
         60_44,
         2_37,
         12,
         6,
         5,
         6,
         4,
         67_80,
         7_05,
         15,
         13_88,
         44,
         3_78,
         1_01_14,
         7_11,
         1_52,
         20,
         6,
         5,
         2_23_76,
         6_42,
         12_21,
         1_51_90,
         3_41_53,
         4_50,
         56_08,
         9_59,
         11_19,
         5_77_02,
         1_36,
         1_86,
         47,
         10_98,
         2_93_67,
         47,
         # 4426, # What fairseq tokenizes from "<unk>": "_<"
         # 3678, # What fairseq tokenizes from "<unk>": "unk"
         # 2740, # What fairseq tokenizes from "<unk>": ">"
         3,  # What we tokenize from "<unk>": "<unk>"
         6,  # Residue from the tokenization: an extra sentencepiece underline
         4,
         60_44,
         2_37,
         62_84,
         5_09_01,
         5_28,
         31,
         90,
         34,
         9_27,
         2,
     ]
     # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
     # xlmr.eval()
     # xlmr.encode(symbols)

     self.assertListEqual(lowerCAmelCase	,							self.big_tokenizer.encode(lowerCAmelCase   )   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						{"""input_ids""": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 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, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 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]], """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, 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, 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, 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, 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=lowerCAmelCase	,							model_name="""xlm-roberta-base"""	,							revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""	,							)





import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   import jax

   from transformers.models.big_bird.modeling_flax_big_bird import (
       FlaxBigBirdForCausalLM,
       FlaxBigBirdForMaskedLM,
       FlaxBigBirdForMultipleChoice,
       FlaxBigBirdForPreTraining,
       FlaxBigBirdForQuestionAnswering,
       FlaxBigBirdForSequenceClassification,
       FlaxBigBirdForTokenClassification,
       FlaxBigBirdModel,
   )



class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def __init__(      self			:   Dict	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   str=2	,							lowerCAmelCase			:   Optional[Any]=56	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   Optional[int]=True	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   Union[str, Any]=True	,							lowerCAmelCase			:   Tuple=99	,							lowerCAmelCase			:   Optional[Any]=32	,							lowerCAmelCase			:   List[str]=2	,							lowerCAmelCase			:   int=2	,							lowerCAmelCase			:   str=7	,							lowerCAmelCase			:   List[Any]="gelu_new"	,							lowerCAmelCase			:   Optional[int]=0.1	,							lowerCAmelCase			:   Optional[Any]=0.1	,							lowerCAmelCase			:   Optional[Any]=5_12	,							lowerCAmelCase			:   Dict=16	,							lowerCAmelCase			:   int=2	,							lowerCAmelCase			:   Optional[Any]=0.02	,							lowerCAmelCase			:   Tuple=4	,							lowerCAmelCase			:   Union[str, Any]="block_sparse"	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   Optional[int]=False	,							lowerCAmelCase			:   Optional[Any]=2	,							lowerCAmelCase			:   List[Any]=3	,							)							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						parent
     __lowerCAmelCase  :      Union[str, Any]    =						batch_size
     __lowerCAmelCase  :      List[Any]    =						seq_length
     __lowerCAmelCase  :      int    =						is_training
     __lowerCAmelCase  :      Union[str, Any]    =						use_attention_mask
     __lowerCAmelCase  :      Tuple    =						use_token_type_ids
     __lowerCAmelCase  :      Union[str, Any]    =						use_labels
     __lowerCAmelCase  :      List[str]    =						vocab_size
     __lowerCAmelCase  :      int    =						hidden_size
     __lowerCAmelCase  :      Tuple    =						num_hidden_layers
     __lowerCAmelCase  :      List[str]    =						num_attention_heads
     __lowerCAmelCase  :      Optional[Any]    =						intermediate_size
     __lowerCAmelCase  :      List[Any]    =						hidden_act
     __lowerCAmelCase  :      Optional[Any]    =						hidden_dropout_prob
     __lowerCAmelCase  :      List[str]    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      Optional[int]    =						max_position_embeddings
     __lowerCAmelCase  :      str    =						type_vocab_size
     __lowerCAmelCase  :      List[Any]    =						type_sequence_label_size
     __lowerCAmelCase  :      Tuple    =						initializer_range
     __lowerCAmelCase  :      str    =						num_choices

     __lowerCAmelCase  :      Any    =						rescale_embeddings
     __lowerCAmelCase  :      str    =						attention_type
     __lowerCAmelCase  :      List[Any]    =						use_bias
     __lowerCAmelCase  :      List[str]    =						block_size
     __lowerCAmelCase  :      Union[str, Any]    =						num_random_blocks







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size   )

     __lowerCAmelCase  :      Optional[Any]    =						None
     if self.use_attention_mask:
        __lowerCAmelCase  :      Optional[Any]    =						random_attention_mask([self.batch_size, self.seq_length]   )

     __lowerCAmelCase  :      Any    =						None
     if self.use_token_type_ids:
        __lowerCAmelCase  :      Optional[int]    =						ids_tensor([self.batch_size, self.seq_length]	,							self.type_vocab_size   )

     __lowerCAmelCase  :      List[Any]    =						BigBirdConfig(
         vocab_size=self.vocab_size	,							hidden_size=self.hidden_size	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							intermediate_size=self.intermediate_size	,							hidden_act=self.hidden_act	,							hidden_dropout_prob=self.hidden_dropout_prob	,							attention_probs_dropout_prob=self.attention_probs_dropout_prob	,							max_position_embeddings=self.max_position_embeddings	,							type_vocab_size=self.type_vocab_size	,							is_decoder=lowerCAmelCase	,							initializer_range=self.initializer_range	,							attention_type=self.attention_type	,							block_size=self.block_size	,							num_random_blocks=self.num_random_blocks	,							use_bias=self.use_bias	,							rescale_embeddings=self.rescale_embeddings	,							)

     return config, input_ids, token_type_ids, attention_mask







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.prepare_config_and_inputs()
     __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      Any    =						config_and_inputs
     __lowerCAmelCase  :      Union[str, Any]    =						{
         """input_ids""": input_ids,
         """token_type_ids""": token_type_ids,
         """attention_mask""": attention_mask,
     }
     return config, inputs_dict






@require_flax
class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      List[Any]										=(
      (
          FlaxBigBirdForCausalLM,
          FlaxBigBirdModel,
          FlaxBigBirdForPreTraining,
          FlaxBigBirdForMaskedLM,
          FlaxBigBirdForMultipleChoice,
          FlaxBigBirdForQuestionAnswering,
          FlaxBigBirdForSequenceClassification,
          FlaxBigBirdForTokenClassification,
      )
      if is_flax_available()
      else ()
  )

  lowerCamelCase   :      List[str]										=False
  lowerCamelCase   :      Union[str, Any]										=False







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						FlaxBigBirdModelTester(self   )







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Optional[Any]:






     """simple docstring"""







     super().test_from_pretrained_save_pretrained()







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[str]:






     """simple docstring"""







     super().test_from_pretrained_with_no_automatic_init()







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Optional[int]:






     """simple docstring"""







     super().test_no_automatic_init()







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     super().test_hidden_states_output()







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     for model_class_name in self.all_model_classes:
        __lowerCAmelCase  :      List[str]    =						model_class_name.from_pretrained("""google/bigbird-roberta-base"""   )
        self.assertIsNotNone(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[str]:






     """simple docstring"""







     if self.test_attn_probs:
        super().test_attention_outputs()







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Tuple    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__   ):
           __lowerCAmelCase  :      List[str]    =						self._prepare_for_class(lowerCAmelCase	,							lowerCAmelCase   )
           __lowerCAmelCase  :      Dict    =						model_class(lowerCAmelCase   )

           @jax.jit
           def model_jitted(lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Optional[Any]=None	,							**lowerCAmelCase			:   Union[str, Any]   ):
              return model(input_ids=lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							**lowerCAmelCase   )

           with self.subTest("""JIT Enabled"""   ):
              __lowerCAmelCase  :      str    =						model_jitted(**lowerCAmelCase   ).to_tuple()

           with self.subTest("""JIT Disabled"""   ):
              with jax.disable_jit():
                 __lowerCAmelCase  :      List[Any]    =						model_jitted(**lowerCAmelCase   ).to_tuple()

           self.assertEqual(len(lowerCAmelCase   )	,							len(lowerCAmelCase   )   )
           for jitted_output, output in zip(lowerCAmelCase	,							lowerCAmelCase   ):
              self.assertEqual(jitted_output.shape	,							output.shape   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]=1e-5	,							lowerCAmelCase			:   Union[str, Any]="outputs"	,							lowerCAmelCase			:   Union[str, Any]=None   )							->     Optional[int]:






     """simple docstring"""







     if name.startswith("""outputs.attentions"""   ):
        return
     else:
        super().check_pt_flax_outputs(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )


