



'''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      __magic_name__						:







							def __init__(							self :	List[str]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	int=13     ,_UpperCAmelCase :	Dict=7     ,_UpperCAmelCase :	Tuple=True     ,_UpperCAmelCase :	Optional[Any]=True     ,_UpperCAmelCase :	List[str]=True     ,_UpperCAmelCase :	Any=True     ,_UpperCAmelCase :	Optional[Any]=99     ,_UpperCAmelCase :	List[str]=32     ,_UpperCAmelCase :	Any=2     ,_UpperCAmelCase :	Optional[Any]=4     ,_UpperCAmelCase :	Any=37     ,_UpperCAmelCase :	Tuple="gelu"     ,_UpperCAmelCase :	Tuple=0.1     ,_UpperCAmelCase :	Any=0.1     ,_UpperCAmelCase :	List[Any]=512     ,_UpperCAmelCase :	List[str]=16     ,_UpperCAmelCase :	int=2     ,_UpperCAmelCase :	List[str]=0.02     ,_UpperCAmelCase :	Any=False     ,_UpperCAmelCase :	Optional[Any]=True     ,_UpperCAmelCase :	Union[str, Any]="None"     ,_UpperCAmelCase :	Union[str, Any]=3     ,_UpperCAmelCase :	Optional[Any]=4     ,_UpperCAmelCase :	Optional[Any]=None     ,):
									_a					:				List[Any]				=							parent
									_a					:				Tuple				=							batch_size
									_a					:				int				=							seq_length
									_a					:				Union[str, Any]				=							is_training
									_a					:				Dict				=							use_input_mask
									_a					:				int				=							use_token_type_ids
									_a					:				List[Any]				=							use_labels
									_a					:				Tuple				=							vocab_size
									_a					:				Any				=							hidden_size
									_a					:				Optional[Any]				=							num_hidden_layers
									_a					:				int				=							num_attention_heads
									_a					:				int				=							intermediate_size
									_a					:				int				=							hidden_act
									_a					:				Any				=							hidden_dropout_prob
									_a					:				Optional[int]				=							attention_probs_dropout_prob
									_a					:				Tuple				=							max_position_embeddings
									_a					:				Optional[int]				=							type_vocab_size
									_a					:				Dict				=							type_sequence_label_size
									_a					:				Tuple				=							initializer_range
									_a					:				Optional[Any]				=							num_labels
									_a					:				Dict				=							num_choices
									_a					:				List[str]				=							relative_attention
									_a					:				str				=							position_biased_input
									_a					:				Dict				=							pos_att_type
									_a					:				Optional[Any]				=							scope







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				List[Any]				=							ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size		)

									_a					:				Optional[Any]				=							None
									if self.use_input_mask:
											_a					:				Any				=							random_attention_mask([self.batch_size, self.seq_length]		)

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

									_a					:				List[str]				=							None
									_a					:				Tuple				=							None
									_a					:				List[str]				=							None
									if self.use_labels:
											_a					:				List[str]				=							ids_tensor([self.batch_size]     ,self.type_sequence_label_size		)
											_a					:				Union[str, Any]				=							ids_tensor([self.batch_size, self.seq_length]     ,self.num_labels		)

									_a					:				List[Any]				=							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=_UpperCAmelCase     ,)

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







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	str		):
									_a					:				List[str]				=							TFDebertaVaModel(config=_UpperCAmelCase		)
									_a					:				Any				=							{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

									_a					:				Any				=							[input_ids, input_mask]
									_a					:				Dict				=							model(_UpperCAmelCase		)

									_a					:				str				=							model(_UpperCAmelCase		)

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







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	Optional[int]		):
									_a					:				Optional[Any]				=							TFDebertaVaForMaskedLM(config=_UpperCAmelCase		)
									_a					:				List[Any]				=							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									_a					:				Optional[int]				=							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.vocab_size)		)







							def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	List[Any]		):
									_a					:				List[Any]				=							self.num_labels
									_a					:				int				=							TFDebertaVaForSequenceClassification(config=_UpperCAmelCase		)
									_a					:				Tuple				=							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									_a					:				Dict				=							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels)		)







							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	Any     ,_UpperCAmelCase :	List[str]		):
									_a					:				str				=							self.num_labels
									_a					:				Any				=							TFDebertaVaForTokenClassification(config=_UpperCAmelCase		)
									_a					:				List[Any]				=							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									_a					:				List[Any]				=							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.num_labels)		)







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Optional[int]     ,_UpperCAmelCase :	Optional[Any]		):
									_a					:				List[str]				=							TFDebertaVaForQuestionAnswering(config=_UpperCAmelCase		)
									_a					:				str				=							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									_a					:				Optional[Any]				=							model(_UpperCAmelCase		)
									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 							__lowercase      (							self :	Optional[Any]		):
									_a					:				Dict				=							self.prepare_config_and_inputs()
									(
									    (
									    _a
									)	, (
									    _a
									)	, (
									    _a
									)	, (
									    _a
									)	, (
									    _a
									)	, (
									    _a
									)	, (
									    _a
									)	, 
									)					:				int				=							config_and_inputs
									_a					:				Optional[int]				=							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
									return config, inputs_dict

@require_tf
class      __magic_name__						(   _UpperCamelCase      ,						_UpperCamelCase      ,						unittest.TestCase       ):
							lowerCAmelCase	:   str           = (
							    (
							        TFDebertaVaModel,
							        TFDebertaVaForMaskedLM,
							        TFDebertaVaForQuestionAnswering,
							        TFDebertaVaForSequenceClassification,
							        TFDebertaVaForTokenClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							lowerCAmelCase	:   str           = (
							    {
							        'feature-extraction': TFDebertaVaModel,
							        'fill-mask': TFDebertaVaForMaskedLM,
							        'question-answering': TFDebertaVaForQuestionAnswering,
							        'text-classification': TFDebertaVaForSequenceClassification,
							        'token-classification': TFDebertaVaForTokenClassification,
							        'zero-shot': TFDebertaVaForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)

							lowerCAmelCase	:   Union[str, Any]           = False
							lowerCAmelCase	:   Optional[Any]           = False







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				Dict				=							TFDebertaVaModelTester(self		)
									_a					:				Optional[int]				=							ConfigTester(self     ,config_class=_UpperCAmelCase     ,hidden_size=37		)







							def 							__lowercase      (							self :	Dict		):
									self.config_tester.run_common_tests()







							def 							__lowercase      (							self :	int		):
									_a					:				Tuple				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				Optional[int]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	Dict		):
									_a					:				Tuple				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[int]		):
									_a					:				Union[str, Any]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	Tuple		):
									_a					:				Any				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase		)







							@slow
							def 							__lowercase      (							self :	Optional[Any]		):
									_a					:				List[str]				=							TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'		)
									self.assertIsNotNone(_UpperCAmelCase		)

@require_tf
class      __magic_name__						(   unittest.TestCase       ):







							@unittest.skip(reason='Model not available yet'		)
							def 							__lowercase      (							self :	Union[str, Any]		):
									pass







							@slow
							def 							__lowercase      (							self :	str		):
									_a					:				Optional[Any]				=							TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'		)
									_a					:				List[Any]				=							tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		)
									_a					:				Any				=							tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
									_a					:				Tuple				=							model(_UpperCAmelCase     ,attention_mask=_UpperCAmelCase		)[0]

									_a					:				Dict				=							tf.constant(
									    [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]		)
									tf.debugging.assert_near(output[:, 1:4, 1:4]     ,_UpperCAmelCase     ,atol=1E-4		)









'''simple docstring'''

from __future__ import annotations

import collections
import pprint
from pathlib import Path



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				str:
		return "".join(sorted(lowerCAmelCase_  )  )



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				list[str]:
		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))




