

'''simple docstring'''


from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
						import tensorflow as tf

						from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel



@require_tf
class        lowercase    :
							_lowerCamelCase		:   Union[str, Any]= MarianConfig
							_lowerCamelCase		:   Optional[Any]= {}
							_lowerCamelCase		:   Any= 'gelu'


							def __init__(      self     ,      _snake_case     ,      _snake_case=13     ,      _snake_case=7     ,      _snake_case=True     ,      _snake_case=False     ,      _snake_case=99     ,      _snake_case=32     ,      _snake_case=2     ,      _snake_case=4     ,      _snake_case=37     ,      _snake_case=0.1     ,      _snake_case=0.1     ,      _snake_case=20     ,      _snake_case=2     ,      _snake_case=1     ,      _snake_case=0     ,      )				->					Any:
								UpperCAmelCase_		:   Tuple													=       parent
								UpperCAmelCase_		:   Optional[int]													=       batch_size
								UpperCAmelCase_		:   Tuple													=       seq_length
								UpperCAmelCase_		:   Optional[Any]													=       is_training
								UpperCAmelCase_		:   Optional[int]													=       use_labels
								UpperCAmelCase_		:   Any													=       vocab_size
								UpperCAmelCase_		:   List[Any]													=       hidden_size
								UpperCAmelCase_		:   List[Any]													=       num_hidden_layers
								UpperCAmelCase_		:   Any													=       num_attention_heads
								UpperCAmelCase_		:   List[Any]													=       intermediate_size

								UpperCAmelCase_		:   Optional[Any]													=       hidden_dropout_prob
								UpperCAmelCase_		:   Dict													=       attention_probs_dropout_prob
								UpperCAmelCase_		:   Optional[int]													=       max_position_embeddings
								UpperCAmelCase_		:   int													=       eos_token_id
								UpperCAmelCase_		:   Any													=       pad_token_id
								UpperCAmelCase_		:   Union[str, Any]													=       bos_token_id


							def     _snake_case				(      self)				->					List[Any]:
								UpperCAmelCase_		:   int													=       ids_tensor([self.batch_size, self.seq_length - 1]     ,      self.vocab_size)
								UpperCAmelCase_		:   int													=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)     ,      1)
								UpperCAmelCase_		:   Optional[Any]													=       tf.concat([input_ids, eos_tensor]     ,      axis=1)

								UpperCAmelCase_		:   Any													=       ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size)

								UpperCAmelCase_		:   List[str]													=       self.config_cls(
								    vocab_size=self.vocab_size     ,      d_model=self.hidden_size     ,      encoder_layers=self.num_hidden_layers     ,      decoder_layers=self.num_hidden_layers     ,      encoder_attention_heads=self.num_attention_heads     ,      decoder_attention_heads=self.num_attention_heads     ,      encoder_ffn_dim=self.intermediate_size     ,      decoder_ffn_dim=self.intermediate_size     ,      dropout=self.hidden_dropout_prob     ,      attention_dropout=self.attention_probs_dropout_prob     ,      max_position_embeddings=self.max_position_embeddings     ,      eos_token_ids=[2]     ,      bos_token_id=self.bos_token_id     ,      pad_token_id=self.pad_token_id     ,      decoder_start_token_id=self.pad_token_id     ,      **self.config_updates     ,      )
								UpperCAmelCase_		:   str													=       prepare_marian_inputs_dict(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
								return config, inputs_dict





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					Union[str, Any]:
								UpperCAmelCase_		:   Union[str, Any]													=       TFMarianModel(config=lowerCamelCase_).get_decoder()
								UpperCAmelCase_		:   int													=       inputs_dict['input_ids']

								UpperCAmelCase_		:   Tuple													=       input_ids[:1, :]
								UpperCAmelCase_		:   Any													=       inputs_dict['attention_mask'][:1, :]
								UpperCAmelCase_		:   Any													=       inputs_dict['head_mask']
								UpperCAmelCase_		:   str													=       1

								# first forward pass
								UpperCAmelCase_		:   Tuple													=       model(lowerCamelCase_     ,      attention_mask=lowerCamelCase_     ,      head_mask=lowerCamelCase_     ,      use_cache=lowerCamelCase_)

								UpperCAmelCase_		:   Any													=       outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								UpperCAmelCase_		:   List[Any]													=       ids_tensor((self.batch_size, 3)     ,      config.vocab_size)
								UpperCAmelCase_		:   Dict													=       tf.cast(ids_tensor((self.batch_size, 3)     ,      2)     ,      tf.inta)

								# append to next input_ids and
								UpperCAmelCase_		:   int													=       tf.concat([input_ids, next_tokens]     ,      axis=-1)
								UpperCAmelCase_		:   Union[str, Any]													=       tf.concat([attention_mask, next_attn_mask]     ,      axis=-1)

								UpperCAmelCase_		:   Union[str, Any]													=       model(lowerCamelCase_     ,      attention_mask=lowerCamelCase_)[0]
								UpperCAmelCase_		:   Optional[int]													=       model(lowerCamelCase_     ,      attention_mask=lowerCamelCase_     ,      past_key_values=lowerCamelCase_)[0]

								self.parent.assertEqual(next_tokens.shape[1]     ,      output_from_past.shape[1])

								# select random slice
								UpperCAmelCase_		:   Any													=       int(ids_tensor((1,)     ,      output_from_past.shape[-1]))
								UpperCAmelCase_		:   List[Any]													=       output_from_no_past[:, -3:, random_slice_idx]
								UpperCAmelCase_		:   List[Any]													=       output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(lowerCamelCase_     ,      lowerCamelCase_     ,      rtol=1e-3)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase=None ,)     ->     Union[str, Any]:
	if attention_mask is None:
		UpperCAmelCase_		:   Any													=       tf.cast(tf.math.not_equal(_lowerCamelCase ,config.pad_token_id	) ,tf.inta	)
	if decoder_attention_mask is None:
		UpperCAmelCase_		:   Tuple													=       tf.concat(
		    [
		        tf.ones(decoder_input_ids[:, :1].shape ,dtype=tf.inta	),
		        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,config.pad_token_id	) ,tf.inta	),
		    ] ,axis=-1 ,)
	if head_mask is None:
		UpperCAmelCase_		:   List[str]													=       tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
	if decoder_head_mask is None:
		UpperCAmelCase_		:   Optional[int]													=       tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
	if cross_attn_head_mask is None:
		UpperCAmelCase_		:   List[Any]													=       tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": decoder_attention_mask,
	    "head_mask": head_mask,
	    "decoder_head_mask": decoder_head_mask,
	    "cross_attn_head_mask": cross_attn_head_mask,
	}



@require_tf
class        lowercase    (	snake_case_,       snake_case_,       unittest.TestCase			):
							_lowerCamelCase		:   List[Any]= (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
							_lowerCamelCase		:   Any= (TFMarianMTModel,) if is_tf_available() else ()
							_lowerCamelCase		:   List[str]= (
							    {
							        'conversational': TFMarianMTModel,
							        'feature-extraction': TFMarianModel,
							        'summarization': TFMarianMTModel,
							        'text2text-generation': TFMarianMTModel,
							        'translation': TFMarianMTModel,
							    }
							    if is_tf_available()
							    else {}
							)
							_lowerCamelCase		:   str= True
							_lowerCamelCase		:   List[str]= False
							_lowerCamelCase		:   Optional[int]= False


							def     _snake_case				(      self)				->					int:
								UpperCAmelCase_		:   Dict													=       TFMarianModelTester(self)
								UpperCAmelCase_		:   List[str]													=       ConfigTester(self     ,      config_class=lowerCamelCase_)


							def     _snake_case				(      self)				->					Optional[Any]:
								self.config_tester.run_common_tests()





							def     _snake_case				(      self)				->					int:
								UpperCAmelCase_		:   Optional[int]													=       self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase_)



@require_tf
class        lowercase    (	unittest.TestCase			):
							_lowerCamelCase		:   List[str]= 1000  # show more chars for failing integration tests


							@classmethod
							def     _snake_case				(      cls)				->					Tuple:
								UpperCAmelCase_		:   Optional[Any]													=       F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
								return cls


							@cached_property
							def     _snake_case				(      self)				->					Optional[int]:
								return AutoTokenizer.from_pretrained(self.model_name)


							@property
							def     _snake_case				(      self)				->					List[str]:
								return self.tokenizer.eos_token_id


							@cached_property
							def     _snake_case				(      self)				->					Optional[int]:
								warnings.simplefilter('error')
								UpperCAmelCase_		:   TFMarianMTModel													=       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
								assert isinstance(lowerCamelCase_     ,      lowerCamelCase_)
								UpperCAmelCase_		:   Optional[int]													=       model.config
								self.assertListEqual(c.bad_words_ids     ,      [[c.pad_token_id]])
								self.assertEqual(c.max_length     ,      512)
								self.assertEqual(c.decoder_start_token_id     ,      c.pad_token_id)
								return model


							def     _snake_case				(      self     ,      **_snake_case)				->					Optional[Any]:
								UpperCAmelCase_		:   int													=       self.translate_src_text(**lowerCamelCase_)
								self.assertListEqual(self.expected_text     ,      lowerCamelCase_)





							def     _snake_case				(      self     ,      **_snake_case)				->					Optional[Any]:
								UpperCAmelCase_		:   List[str]													=       self.tokenizer(self.src_text     ,      **lowerCamelCase_     ,      padding=lowerCamelCase_     ,      return_tensors='tf')
								UpperCAmelCase_		:   List[Any]													=       self.model.generate(
								    model_inputs.input_ids     ,      attention_mask=model_inputs.attention_mask     ,      num_beams=2     ,      max_length=128)
								UpperCAmelCase_		:   Tuple													=       self.tokenizer.batch_decode(generated_ids.numpy()     ,      skip_special_tokens=lowerCamelCase_)
								return generated_words



@require_sentencepiece
@require_tokenizers
@require_tf
class        lowercase    (	snake_case_			):
							_lowerCamelCase		:   Dict= 'mt'
							_lowerCamelCase		:   int= 'en'
							_lowerCamelCase		:   List[str]= ['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
							_lowerCamelCase		:   Dict= ['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']


							@unittest.skip('Skipping until #12647 is resolved.')
							@slow
							def     _snake_case				(      self)				->					Optional[int]:
								self._assert_generated_batch_equal_expected()



@require_sentencepiece
@require_tokenizers
@require_tf
class        lowercase    (	snake_case_			):
							_lowerCamelCase		:   Dict= 'en'
							_lowerCamelCase		:   Any= 'zh'
							_lowerCamelCase		:   Optional[int]= ['My name is Wolfgang and I live in Berlin']
							_lowerCamelCase		:   Dict= ['我叫沃尔夫冈 我住在柏林']


							@unittest.skip('Skipping until #12647 is resolved.')
							@slow
							def     _snake_case				(      self)				->					Any:
								self._assert_generated_batch_equal_expected()



@require_sentencepiece
@require_tokenizers
@require_tf
class        lowercase    (	snake_case_			):
							_lowerCamelCase		:   int= 'en'
							_lowerCamelCase		:   str= 'ROMANCE'
							_lowerCamelCase		:   Optional[Any]= [
							    '>>fr<< Don\'t spend so much time watching TV.',
							    '>>pt<< Your message has been sent.',
							    '>>es<< He\'s two years older than me.',
							]
							_lowerCamelCase		:   List[Any]= [
							    'Ne passez pas autant de temps à regarder la télé.',
							    'A sua mensagem foi enviada.',
							    'Es dos años más viejo que yo.',
							]


							@unittest.skip('Skipping until #12647 is resolved.')
							@slow
							def     _snake_case				(      self)				->					List[Any]:
								self._assert_generated_batch_equal_expected()





							@unittest.skip('Skipping until #12647 is resolved.')
							@slow
							def     _snake_case				(      self)				->					Optional[Any]:
								UpperCAmelCase_		:   Optional[Any]													=       TranslationPipeline(self.model     ,      self.tokenizer     ,      framework='tf')
								UpperCAmelCase_		:   Optional[Any]													=       pipeline(self.src_text)
								self.assertEqual(self.expected_text     ,      [x['translation_text'] for x in output])














"""simple docstring"""
from itertools import combinations, product






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							):
 if xa == ya == 0 or xa == ya == 0:
  return False
 lowerCamelCase__	:     Tuple								=		xa * xa + ya * ya
 lowerCamelCase__	:     Union[str, Any]								=		xa * xa + ya * ya
 lowerCamelCase__	:     Optional[Any]								=		(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
 return (
     a_square + b_square == c_square
     or a_square + c_square == b_square
     or b_square + c_square == a_square
 )






def 			lowerCamelCase_    (		_lowerCamelCase = 50							):
 return sum(
     1
     for pta, pta in combinations(product(range(limit + 1							)     ,  repeat=2							)     ,  2							)
     if is_right(*_lowerCamelCase     ,  *_lowerCamelCase							)							)


if __name__ == "__main__":
      print(f"{solution() = }")

