import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
     from transformers.models.roberta.modeling_flax_roberta import (
         FlaxRobertaForCausalLM,
         FlaxRobertaForMaskedLM,
         FlaxRobertaForMultipleChoice,
         FlaxRobertaForQuestionAnswering,
         FlaxRobertaForSequenceClassification,
         FlaxRobertaForTokenClassification,
         FlaxRobertaModel,
     )



class     _A   (    unittest.TestCase							):






  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_=512   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=4   ,				)		->      List[Any]:





       '''simple docstring'''





       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 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       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__										=      RobertaConfig(
           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=SCREAMING_SNAKE_CASE_   ,				initializer_range=self.initializer_range   ,				)

       return config, input_ids, token_type_ids, attention_mask






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       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






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__										=      config_and_inputs

       UpperCamelCase__										=      True
       UpperCamelCase__										=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
       UpperCamelCase__										=      ids_tensor([self.batch_size, self.seq_length]   ,				vocab_size=2	)

       return (
           config,
           input_ids,
           token_type_ids,
           encoder_hidden_states,
           encoder_attention_mask,
       )



@require_flax
class     _A   (    __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	int						=True

  SCREAMING_SNAKE_CASE_	:	Optional[Any]						=(
      (
          FlaxRobertaModel,
          FlaxRobertaForCausalLM,
          FlaxRobertaForMaskedLM,
          FlaxRobertaForSequenceClassification,
          FlaxRobertaForTokenClassification,
          FlaxRobertaForMultipleChoice,
          FlaxRobertaForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      FlaxRobertaModelTester(self	)






  @slow
  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       for model_class_name in self.all_model_classes:
            UpperCamelCase__										=      model_class_name.from_pretrained('''roberta-base'''   ,				from_pt=SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      model(np.ones((1, 1)	)	)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)




from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def  __UpperCamelCase    (						A							):
     UpperCamelCase__						,       UpperCamelCase__										=      analyze_text(A							)
     UpperCamelCase__										=      list(''' ''' + ascii_lowercase							)
     # what is our total sum of probabilities.
     UpperCamelCase__										=      sum(single_char_strings.values()							)

     # one length string
     UpperCamelCase__										=      0
     # for each alpha we go in our dict and if it is in it we calculate entropy
     for ch in my_alphas:
          if ch in single_char_strings:
               UpperCamelCase__										=      single_char_strings[ch]
               UpperCamelCase__										=      my_str / all_sum
               my_fir_sum += prob * math.loga(A							)  # entropy formula.

    # print entropy
     print(f"{round(-1 * my_fir_sum							):.1f}"							)

     # two len string
     UpperCamelCase__										=      sum(two_char_strings.values()							)
     UpperCamelCase__										=      0
     # for each alpha (two in size) calculate entropy.
     for cha in my_alphas:
          for cha in my_alphas:
               UpperCamelCase__										=      cha + cha
               if sequence in two_char_strings:
                    UpperCamelCase__										=      two_char_strings[sequence]
                    UpperCamelCase__										=      int(A							) / all_sum
                    my_sec_sum += prob * math.loga(A							)

    # print second entropy
     print(f"{round(-1 * my_sec_sum							):.1f}"							)

     # print the difference between them
     print(f"{round((-1 * my_sec_sum) - (-1 * my_fir_sum)							):.1f}"							)







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      Counter()  # type: ignore
     UpperCamelCase__										=      Counter()  # type: ignore
     single_char_strings[text[-1]] += 1

     # first case when we have space at start.
     two_char_strings[" " + text[0]] += 1
     for i in range(0 ,							len(A							) - 1							):
          single_char_strings[text[i]] += 1
          two_char_strings[text[i : i + 2]] += 1
     return single_char_strings, two_char_strings







def  __UpperCamelCase    (						):
     import doctest

     doctest.testmod()
     # text = (
     #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
     #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
     #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
     #     "beloved evident through no service elderly is. Blind there if every no so "
     #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
     #     "of message cottage windows do besides against uncivil.  Delightful "
     #     "unreserved impossible few estimating men favourable see entreaties. She "
     #     "propriety immediate was improving. He or entrance humoured likewise "
     #     "moderate. Much nor game son say feel. Fat make met can must form into "
     #     "gate. Me we offending prevailed discovery. "
     # )

     # calculate_prob(text)


if __name__ == "__main__":
     main()



