





"""simple docstring"""



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 		__snake_case (		unittest.TestCase					):





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase=13 ,						__lowerCamelCase=7 ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=99 ,						__lowerCamelCase=32 ,						__lowerCamelCase=5 ,						__lowerCamelCase=4 ,						__lowerCamelCase=37 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=512 ,						__lowerCamelCase=16 ,						__lowerCamelCase=2 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=4 ,						):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						int									=					ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size  )

     __A   :						List[Any]									=					None
     if self.use_attention_mask:
         __A   :						Union[str, Any]									=					random_attention_mask([self.batch_size, self.seq_length]  )

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

     __A   :						str									=					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  ):



     '''simple docstring'''




     __A   :						Any									=					self.prepare_config_and_inputs()
     __A			,	__A			,	__A			,	__A   :						Dict									=					config_and_inputs
     __A   :						List[str]									=					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
     return config, inputs_dict






@require_flax
class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					True

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

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Tuple									=					FlaxRoFormerModelTester(self  )







 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     for model_class_name in self.all_model_classes:
         __A   :						int									=					model_class_name.from_pretrained('''junnyu/roformer_chinese_small''' ,						from_pt=__lowerCamelCase  )
         __A   :						str									=					model(np.ones((1, 1)  )  )
         self.assertIsNotNone(__lowerCamelCase  )






@require_flax
class 		__snake_case (		unittest.TestCase					):





 """simple docstring"""


 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''  )
     __A   :						Optional[Any]									=					jnp.array([[0, 1, 2, 3, 4, 5]]  )
     __A   :						str									=					model(__lowerCamelCase  )[0]

     __A   :						int									=					5_0000

     __A   :						int									=					(1, 6, vocab_size)
     self.assertEqual(output.shape ,						__lowerCamelCase  )

     __A   :						List[Any]									=					jnp.array(
         [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]  )

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











"""simple docstring"""



import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a_			=      {
    """E""": 12.70,
    """T""": 9.06,
    """A""": 8.17,
    """O""": 7.51,
    """I""": 6.97,
    """N""": 6.75,
    """S""": 6.33,
    """H""": 6.09,
    """R""": 5.99,
    """D""": 4.25,
    """L""": 4.03,
    """C""": 2.78,
    """U""": 2.76,
    """M""": 2.41,
    """W""": 2.36,
    """F""": 2.23,
    """G""": 2.02,
    """Y""": 1.97,
    """P""": 1.93,
    """B""": 1.29,
    """V""": 0.98,
    """K""": 0.77,
    """J""": 0.15,
    """X""": 0.15,
    """Q""": 0.10,
    """Z""": 0.07,
}
a_			=      """ETAOINSHRDLCUMWFGYPBVKJXQZ"""
a_			=      """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""







def 		__lowercase					(       snake_case_  :  str						)       ->dict[str, int]:



    '''simple docstring'''







    __A   :						Any									=					{letter: 0 for letter in string.ascii_uppercase}
    for letter in message.upper():
        if letter in LETTERS:
            letter_count[letter] += 1

    return letter_count







def 		__lowercase					(       snake_case_  :  tuple						)       ->str:



    '''simple docstring'''







    return x[0]







def 		__lowercase					(       snake_case_  :  str						)       ->str:



    '''simple docstring'''







    __A   :						Union[str, Any]									=					get_letter_count(snake_case_						)
    __A   :						dict[int, list[str]]									=					{
        freq: [] for letter, freq in letter_to_freq.items()
    }
    for letter in LETTERS:
        freq_to_letter[letter_to_freq[letter]].append(snake_case_						)

    __A   :						dict[int, str]									=					{}

    for freq in freq_to_letter:
        freq_to_letter[freq].sort(key=ETAOIN.find    ,reverse=snake_case_						)
        __A   :						Optional[int]									=					''''''.join(freq_to_letter[freq]						)

    __A   :						str									=					list(freq_to_letter_str.items()						)
    freq_pairs.sort(key=snake_case_    ,reverse=snake_case_						)

    __A   :						list[str]									=					[freq_pair[1] for freq_pair in freq_pairs]

    return "".join(snake_case_						)







def 		__lowercase					(       snake_case_  :  str						)       ->int:



    '''simple docstring'''







    __A   :						Any									=					get_frequency_order(snake_case_						)
    __A   :						str									=					0
    for common_letter in ETAOIN[:6]:
        if common_letter in freq_order[:6]:
            match_score += 1

    for uncommon_letter in ETAOIN[-6:]:
        if uncommon_letter in freq_order[-6:]:
            match_score += 1

    return match_score


if __name__ == "__main__":
       import doctest

       doctest.testmod()




