
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    ,  __magic_name__    ,  __magic_name__=1_3    ,  __magic_name__=7    ,  __magic_name__=True    ,  __magic_name__=True    ,  __magic_name__=True    ,  __magic_name__=True    ,  __magic_name__=9_9    ,  __magic_name__=3_2    ,  __magic_name__=5    ,  __magic_name__=4    ,  __magic_name__=3_7    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_2    ,  __magic_name__=1_6    ,  __magic_name__=2    ,  __magic_name__=0.02    ,  __magic_name__=4    ,  ):
       lowerCamelCase		:	Any							=      parent
       lowerCamelCase		:	Optional[Any]							=      batch_size
       lowerCamelCase		:	str							=      seq_length
       lowerCamelCase		:	str							=      is_training
       lowerCamelCase		:	str							=      use_attention_mask
       lowerCamelCase		:	Any							=      use_token_type_ids
       lowerCamelCase		:	Dict							=      use_labels
       lowerCamelCase		:	str							=      vocab_size
       lowerCamelCase		:	Any							=      hidden_size
       lowerCamelCase		:	Union[str, Any]							=      num_hidden_layers
       lowerCamelCase		:	Optional[Any]							=      num_attention_heads
       lowerCamelCase		:	Tuple							=      intermediate_size
       lowerCamelCase		:	Any							=      hidden_act
       lowerCamelCase		:	List[Any]							=      hidden_dropout_prob
       lowerCamelCase		:	Any							=      attention_probs_dropout_prob
       lowerCamelCase		:	int							=      max_position_embeddings
       lowerCamelCase		:	str							=      type_vocab_size
       lowerCamelCase		:	Dict							=      type_sequence_label_size
       lowerCamelCase		:	Tuple							=      initializer_range
       lowerCamelCase		:	str							=      num_choices
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[Any]							=      ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size      )

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

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

       lowerCamelCase		:	int							=      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=__magic_name__    ,  initializer_range=self.initializer_range    ,  )

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






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      self.prepare_config_and_inputs()
       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	int							=      config_and_inputs

       lowerCamelCase		:	Tuple							=      True
       lowerCamelCase		:	Any							=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
       lowerCamelCase		:	str							=      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__		(	__SCREAMING_SNAKE_CASE  ,   unittest.TestCase):
      _UpperCAmelCase				:    int           =			True

      _UpperCAmelCase				:    Any           =			(
          (
              FlaxRobertaModel,
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[Any]							=      FlaxRobertaModelTester(self      )






      @slow
      def 						UpperCamelCase__							(      self      ):
       for model_class_name in self.all_model_classes:
        lowerCamelCase		:	Tuple							=      model_class_name.from_pretrained("""roberta-base"""    ,  from_pt=__magic_name__      )
        lowerCamelCase		:	Optional[Any]							=      model(np.ones((1, 1)      )      )
        self.assertIsNotNone(__magic_name__      )






from __future__ import annotations

import collections
import pprint
from pathlib import Path





def     _a					(						lowerCamelCase							):
 return "".join(sorted(lowerCamelCase							)							)





def     _a					(						lowerCamelCase							):
 return word_by_signature[signature(lowerCamelCase							)]


_lowerCamelCase					   =Path(__file__).parent.joinpath("""words.txt""").read_text(encoding="""utf-8""")
_lowerCamelCase					   =sorted({word.strip().lower() for word in data.splitlines()})

_lowerCamelCase					   =collections.defaultdict(list)
for word in word_list:
      word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
      _lowerCamelCase					   ={word: anagram(word) for word in word_list if len(anagram(word)) > 1}

      with open("""anagrams.txt""", """w""") as file:
            file.write("""all_anagrams = \n """)
            file.write(pprint.pformat(all_anagrams))




