
import unittest

import numpy as np

from transformers import RoFormerConfig, 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.numpy as jnp

    from transformers.models.roformer.modeling_flax_roformer import (
        FlaxRoFormerForMaskedLM,
        FlaxRoFormerForMultipleChoice,
        FlaxRoFormerForQuestionAnswering,
        FlaxRoFormerForSequenceClassification,
        FlaxRoFormerForTokenClassification,
        FlaxRoFormerModel,
    )




class 	__A(				unittest.TestCase ):

  """simple docstring"""







  def __init__(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_=13       ,			SCREAMING_SNAKE_CASE_=7       ,			SCREAMING_SNAKE_CASE_=True       ,			SCREAMING_SNAKE_CASE_=True       ,			SCREAMING_SNAKE_CASE_=True       ,			SCREAMING_SNAKE_CASE_=True       ,			SCREAMING_SNAKE_CASE_=99       ,			SCREAMING_SNAKE_CASE_=32       ,			SCREAMING_SNAKE_CASE_=5       ,			SCREAMING_SNAKE_CASE_=4       ,			SCREAMING_SNAKE_CASE_=37       ,			SCREAMING_SNAKE_CASE_="gelu"       ,			SCREAMING_SNAKE_CASE_=0.1       ,			SCREAMING_SNAKE_CASE_=0.1       ,			SCREAMING_SNAKE_CASE_=5_12       ,			SCREAMING_SNAKE_CASE_=16       ,			SCREAMING_SNAKE_CASE_=2       ,			SCREAMING_SNAKE_CASE_=0.02       ,			SCREAMING_SNAKE_CASE_=4       ,			):
       UpperCamelCase__				=				parent
       UpperCamelCase__				=				batch_size
       UpperCamelCase__				=				seq_length
       UpperCamelCase__				=				is_training
       UpperCamelCase__				=				use_attention_mask
       UpperCamelCase__				=				use_token_type_ids
       UpperCamelCase__				=				use_labels
       UpperCamelCase__				=				vocab_size
       UpperCamelCase__				=				hidden_size
       UpperCamelCase__				=				num_hidden_layers
       UpperCamelCase__				=				num_attention_heads
       UpperCamelCase__				=				intermediate_size
       UpperCamelCase__				=				hidden_act
       UpperCamelCase__				=				hidden_dropout_prob
       UpperCamelCase__				=				attention_probs_dropout_prob
       UpperCamelCase__				=				max_position_embeddings
       UpperCamelCase__				=				type_vocab_size
       UpperCamelCase__				=				type_sequence_label_size
       UpperCamelCase__				=				initializer_range
       UpperCamelCase__				=				num_choices







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size						)

       UpperCamelCase__				=				None
       if self.use_attention_mask:
            UpperCamelCase__				=				random_attention_mask([self.batch_size, self.seq_length]						)

       UpperCamelCase__				=				None
       if self.use_token_type_ids:
            UpperCamelCase__				=				ids_tensor([self.batch_size, self.seq_length]       ,			self.type_vocab_size						)

       UpperCamelCase__				=				RoFormerConfig(
           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       ,			)

       return config, input_ids, token_type_ids, attention_mask







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				self.prepare_config_and_inputs()
       UpperCamelCase__		,       UpperCamelCase__		,       UpperCamelCase__		,       UpperCamelCase__				=				config_and_inputs
       UpperCamelCase__				=				{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
       return config, inputs_dict






@require_flax
class 	__A(				_lowerCAmelCase				,      unittest.TestCase ):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__			    =			True

  SCREAMING_SNAKE_CASE__			    =			(
      (
          FlaxRoFormerModel,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )







  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				FlaxRoFormerModelTester(self						)







  @slow
  def        UpperCAmelCase_					(self						):
       for model_class_name in self.all_model_classes:
            UpperCamelCase__				=				model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""       ,			from_pt=_lowerCamelCase						)
            UpperCamelCase__				=				model(np.ones((1, 1)						)						)
            self.assertIsNotNone(_lowerCamelCase						)






@require_flax
class 	__A(				unittest.TestCase ):

  """simple docstring"""







  @slow
  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""						)
       UpperCamelCase__				=				jnp.array([[0, 1, 2, 3, 4, 5]]						)
       UpperCamelCase__				=				model(_lowerCamelCase						)[0]

       UpperCamelCase__				=				5_00_00

       UpperCamelCase__				=				(1, 6, vocab_size)
       self.assertEqual(output.shape       ,			_lowerCamelCase						)

       UpperCamelCase__				=				jnp.array(
           [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]						)

       self.assertTrue(jnp.allclose(output[:, :3, :3]       ,			_lowerCamelCase       ,			atol=1E-4						)						)













"""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


lowercase_				     =     get_tests_dir('fixtures/test_sentencepiece_no_bos.model')


@require_sentencepiece
@require_tokenizers
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	Optional[Any]						 = PegasusTokenizer
   A_						:	int						 = PegasusTokenizerFast
   A_						:	Optional[Any]						 = True
   A_						:	Union[str, Any]						 = True


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     super().setUp()

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


   @cached_property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained('''google/pegasus-large'''  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int,       **_lowerCamelCase :     List[Any]  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained(self.tmpdirname,       **_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     Dict  ):

     '''simple docstring'''
     return ("This is a test", "This is a test")


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= '''</s>'''
     __A									= 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 :     int  ):

     '''simple docstring'''
     __A									= 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(_lowerCamelCase  ),       11_03  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     self.assertEqual(self.get_tokenizer().vocab_size,       11_03  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= self.tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= (
         '''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>'''
     )
     __A									= rust_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     __A									= py_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

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


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= 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
     __A									= '''To ensure a smooth flow of bank resolutions.'''
     __A									= [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
     __A									= tokenizer([raw_input_str],       return_tensors=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )
     assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]  ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= ['''This is going to be way too long.''' * 1_50, '''short example''']
     __A									= ['''not super long but more than 5 tokens''', '''tiny''']
     __A									= self._large_tokenizer(_lowerCamelCase,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )
     __A									= self._large_tokenizer(
         text_target=_lowerCamelCase,       max_length=5,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       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(_lowerCamelCase  ) == 2  # input_ids, attention_mask.






   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     # fmt: off
     __A									= {'''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=_lowerCamelCase,       model_name='''google/bigbird-pegasus-large-arxiv''',       revision='''ba85d0851d708441f91440d509690f1ab6353415''',       )





@require_sentencepiece
@require_tokenizers
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	str						 = PegasusTokenizer
   A_						:	Union[str, Any]						 = PegasusTokenizerFast
   A_						:	Any						 = True
   A_						:	str						 = True


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     super().setUp()

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


   @cached_property
   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       **_lowerCamelCase :     Dict  ):

     '''simple docstring'''
     return PegasusTokenizer.from_pretrained(self.tmpdirname,       **_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     List[str]  ):

     '''simple docstring'''
     return ("This is a test", "This is a test")


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= self.tokenizer_class.from_pretrained(self.tmpdirname  )
     __A									= (
         '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
         ''' <pad> <pad> <pad>'''
     )
     __A									= rust_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     __A									= py_tokenizer([raw_input_str],       return_tensors=_lowerCamelCase,       add_special_tokens=_lowerCamelCase  ).input_ids[0]
     self.assertListEqual(_lowerCamelCase,       _lowerCamelCase  )


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= ['''This is going to be way too long.''' * 10_00, '''short example''']
     __A									= ['''not super long but more than 5 tokens''', '''tiny''']
     __A									= self._large_tokenizer(_lowerCamelCase,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       return_tensors='''pt'''  )
     __A									= self._large_tokenizer(
         text_target=_lowerCamelCase,       max_length=5,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       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(_lowerCamelCase  ) == 2  # input_ids, attention_mask.






   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= (
         '''This is an example string that is used to test the original TF implementation against the HF'''
         ''' implementation'''
     )

     __A									= self._large_tokenizer(_lowerCamelCase  ).input_ids

     self.assertListEqual(
         _lowerCamelCase,       [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],       )




