






'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TFDebertaVaForMaskedLM,
         TFDebertaVaForQuestionAnswering,
         TFDebertaVaForSequenceClassification,
         TFDebertaVaForTokenClassification,
         TFDebertaVaModel,
     )






class     a_						:

  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=2      ,		_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=0.0_2      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE="None"      ,		_SCREAMING_SNAKE_CASE=3      ,		_SCREAMING_SNAKE_CASE=4      ,		_SCREAMING_SNAKE_CASE=None      ,		)  ->				int:



        """simple docstring"""






        UpperCamelCase      = parent
        UpperCamelCase      = batch_size
        UpperCamelCase      = seq_length
        UpperCamelCase      = is_training
        UpperCamelCase      = use_input_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_labels
        UpperCamelCase      = num_choices
        UpperCamelCase      = relative_attention
        UpperCamelCase      = position_biased_input
        UpperCamelCase      = pos_att_type
        UpperCamelCase      = scope

  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = ids_tensor([self.batch_size, self.seq_length]      ,		self.vocab_size				)

        UpperCamelCase      = None
        if self.use_input_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      = None
        UpperCamelCase      = None
        UpperCamelCase      = None
        if self.use_labels:
              UpperCamelCase      = ids_tensor([self.batch_size]      ,		self.type_sequence_label_size				)
              UpperCamelCase      = ids_tensor([self.batch_size, self.seq_length]      ,		self.num_labels				)

        UpperCamelCase      = DebertaVaConfig(
            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      ,		relative_attention=self.relative_attention      ,		position_biased_input=self.position_biased_input      ,		initializer_range=self.initializer_range      ,		return_dict=_SCREAMING_SNAKE_CASE      ,		)

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = TFDebertaVaModel(config=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

        UpperCamelCase      = [input_ids, input_mask]
        UpperCamelCase      = model(_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = model(_SCREAMING_SNAKE_CASE				)

        self.parent.assertEqual(result.last_hidden_state.shape      ,		(self.batch_size, self.seq_length, self.hidden_size)				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = TFDebertaVaForMaskedLM(config=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = {
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }
        UpperCamelCase      = model(_SCREAMING_SNAKE_CASE				)
        self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.seq_length, self.vocab_size)				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Any:



        """simple docstring"""






        UpperCamelCase      = self.num_labels
        UpperCamelCase      = TFDebertaVaForSequenceClassification(config=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = {
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }

        UpperCamelCase      = model(_SCREAMING_SNAKE_CASE				)
        self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.num_labels)				)

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



        """simple docstring"""






        UpperCamelCase      = self.num_labels
        UpperCamelCase      = TFDebertaVaForTokenClassification(config=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = {
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }
        UpperCamelCase      = model(_SCREAMING_SNAKE_CASE				)
        self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.seq_length, self.num_labels)				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = TFDebertaVaForQuestionAnswering(config=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = {
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }

        UpperCamelCase      = model(_SCREAMING_SNAKE_CASE				)
        self.parent.assertEqual(result.start_logits.shape      ,		(self.batch_size, self.seq_length)				)
        self.parent.assertEqual(result.end_logits.shape      ,		(self.batch_size, self.seq_length)				)


  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = self.prepare_config_and_inputs()
        (
            (
            UpperCamelCase
        )  ,(
            UpperCamelCase
        )  ,(
            UpperCamelCase
        )  ,(
            UpperCamelCase
        )  ,(
            UpperCamelCase
        )  ,(
            UpperCamelCase
        )  ,(
            UpperCamelCase
        )  ,
        )      = config_and_inputs
        UpperCamelCase      = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
        return config, inputs_dict






@require_tf
class     a_						(      lowerCamelCase     ,	lowerCamelCase     ,	unittest.TestCase    ):
  lowercase							=				(
      (
          TFDebertaVaModel,
          TFDebertaVaForMaskedLM,
          TFDebertaVaForQuestionAnswering,
          TFDebertaVaForSequenceClassification,
          TFDebertaVaForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  lowercase							=				(
      {
          """feature-extraction""": TFDebertaVaModel,
          """fill-mask""": TFDebertaVaForMaskedLM,
          """question-answering""": TFDebertaVaForQuestionAnswering,
          """text-classification""": TFDebertaVaForSequenceClassification,
          """token-classification""": TFDebertaVaForTokenClassification,
          """zero-shot""": TFDebertaVaForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )

  lowercase							=				False
  lowercase							=				False

  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = TFDebertaVaModelTester(self				)
        UpperCamelCase      = ConfigTester(self      ,		config_class=_SCREAMING_SNAKE_CASE      ,		hidden_size=37				)

  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        self.config_tester.run_common_tests()

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        UpperCamelCase      = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE				)

  def   A__						(				self				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        UpperCamelCase      = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        UpperCamelCase      = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE				)


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



        """simple docstring"""






        UpperCamelCase      = TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""				)
        self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)






@require_tf
class     a_						(      unittest.TestCase    ):

  @unittest.skip(reason="""Model not available yet"""				)
  def   A__						(				self				)  ->				int:



        """simple docstring"""






        pass


  @slow
  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""				)
        UpperCamelCase      = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				)
        UpperCamelCase      = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
        UpperCamelCase      = model(_SCREAMING_SNAKE_CASE      ,		attention_mask=_SCREAMING_SNAKE_CASE				)[0]

        UpperCamelCase      = tf.constant(
            [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]				)
        tf.debugging.assert_near(output[:, 1:4, 1:4]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-4				)












'''simple docstring'''

from __future__ import annotations

import collections
import pprint
from pathlib import Path







def    lowercase__			(				__UpperCamelCase   )->			str:
      return "".join(sorted(__UpperCamelCase   )   )







def    lowercase__			(				__UpperCamelCase   )->			list[str]:
      return word_by_signature[signature(__UpperCamelCase   )]


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

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

if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       {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))
