



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 							a_  :
						UpperCamelCase__      :						List[str]				       =MarianConfig
						UpperCamelCase__      :						Optional[int]				       ={}
						UpperCamelCase__      :						Any				       ="gelu"

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

												UpperCAmelCase_     							=     hidden_dropout_prob
												UpperCAmelCase_     							=     attention_probs_dropout_prob
												UpperCAmelCase_     							=     max_position_embeddings
												UpperCAmelCase_     							=     eos_token_id
												UpperCAmelCase_     							=     pad_token_id
												UpperCAmelCase_     							=     bos_token_id

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

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

												UpperCAmelCase_     							=     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_     							=     prepare_marian_inputs_dict(_lowercase   ,						_lowercase   ,						_lowercase)
												return config, inputs_dict




						def 	__a       (    self	:Union[str, Any]   ,						_lowercase	:str   ,						_lowercase	:Any)		->					List[str]:
												UpperCAmelCase_     							=     TFMarianModel(config=_lowercase).get_decoder()
												UpperCAmelCase_     							=     inputs_dict['''input_ids''']

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

												# first forward pass
												UpperCAmelCase_     							=     model(_lowercase   ,						attention_mask=_lowercase   ,						head_mask=_lowercase   ,						use_cache=_lowercase)

												UpperCAmelCase_							,      UpperCAmelCase_     							=     outputs.to_tuple()

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

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

												UpperCAmelCase_     							=     model(_lowercase   ,						attention_mask=_lowercase)[0]
												UpperCAmelCase_     							=     model(_lowercase   ,						attention_mask=_lowercase   ,						past_key_values=_lowercase)[0]

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

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

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







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase=None							,					__UpperCAmelCase=None							,					__UpperCAmelCase=None							,					__UpperCAmelCase=None							,					__UpperCAmelCase=None							,					)       ->       Union[str, Any]:



						'''simple docstring'''






						if attention_mask is None:
												UpperCAmelCase_     							=     tf.cast(tf.math.not_equal(__UpperCAmelCase							,					config.pad_token_id	)							,					tf.inta	)
						if decoder_attention_mask is None:
												UpperCAmelCase_     							=     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_     							=     tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
						if decoder_head_mask is None:
												UpperCAmelCase_     							=     tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
						if cross_attn_head_mask is None:
												UpperCAmelCase_     							=     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 							a_  (		_snake_case					, _snake_case					, unittest.TestCase				):
						UpperCamelCase__      :						int				       =(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
						UpperCamelCase__      :						Union[str, Any]				       =(TFMarianMTModel,) if is_tf_available() else ()
						UpperCamelCase__      :						List[Any]				       =(
						    {
						        "conversational": TFMarianMTModel,
						        "feature-extraction": TFMarianModel,
						        "summarization": TFMarianMTModel,
						        "text2text-generation": TFMarianMTModel,
						        "translation": TFMarianMTModel,
						    }
						    if is_tf_available()
						    else {}
						)
						UpperCamelCase__      :						List[Any]				       =True
						UpperCamelCase__      :						Union[str, Any]				       =False
						UpperCamelCase__      :						str				       =False

						def 	__a       (    self	:Union[str, Any])		->					List[Any]:
												UpperCAmelCase_     							=     TFMarianModelTester(self)
												UpperCAmelCase_     							=     ConfigTester(self   ,						config_class=_lowercase)

						def 	__a       (    self	:List[str])		->					Optional[Any]:
												self.config_tester.run_common_tests()




						def 	__a       (    self	:Optional[Any])		->					Optional[int]:
												UpperCAmelCase_     							=     self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*_lowercase)







@require_tf
class 							a_  (		unittest.TestCase				):
						UpperCamelCase__      :						str				       =10_00  # show more chars for failing integration tests

						@classmethod
						def 	__a       (    cls	:Optional[Any])		->					None:
												UpperCAmelCase_     							=     f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
												return cls

						@cached_property
						def 	__a       (    self	:Tuple)		->					MarianTokenizer:
												return AutoTokenizer.from_pretrained(self.model_name)

						@property
						def 	__a       (    self	:Optional[int])		->					int:
												return self.tokenizer.eos_token_id

						@cached_property
						def 	__a       (    self	:Dict)		->					str:
												warnings.simplefilter('''error''')
												UpperCAmelCase_     							=     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
												assert isinstance(_lowercase   ,						_lowercase)
												UpperCAmelCase_     							=     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 	__a       (    self	:Dict   ,						**_lowercase	:str)		->					Union[str, Any]:
												UpperCAmelCase_     							=     self.translate_src_text(**_lowercase)
												self.assertListEqual(self.expected_text   ,						_lowercase)




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







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

						@unittest.skip('''Skipping until #12647 is resolved.''')
						@slow
						def 	__a       (    self	:Dict)		->					Union[str, Any]:
												self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 							a_  (		_snake_case				):
						UpperCamelCase__      :						int				       ="en"
						UpperCamelCase__      :						Union[str, Any]				       ="zh"
						UpperCamelCase__      :						List[Any]				       =["My name is Wolfgang and I live in Berlin"]
						UpperCamelCase__      :						Any				       =["我叫沃尔夫冈 我住在柏林"]

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







@require_sentencepiece
@require_tokenizers
@require_tf
class 							a_  (		_snake_case				):
						UpperCamelCase__      :						int				       ="en"
						UpperCamelCase__      :						List[str]				       ="ROMANCE"
						UpperCamelCase__      :						Optional[int]				       =[
						    ">>fr<< Don't spend so much time watching TV.",
						    ">>pt<< Your message has been sent.",
						    ">>es<< He's two years older than me.",
						]
						UpperCamelCase__      :						Tuple				       =[
						    "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 	__a       (    self	:Optional[int])		->					Optional[int]:
												self._assert_generated_batch_equal_expected()




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










from __future__ import annotations

from pathlib import Path







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase	)       ->       int:



						'''simple docstring'''






						return pointa[0] * pointa[1] - pointa[1] * pointa[0]







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       bool:



						'''simple docstring'''






						UpperCAmelCase_     							=     (xa, ya)
						UpperCAmelCase_     							=     (xa - xa, ya - ya)
						UpperCAmelCase_     							=     (xa - xa, ya - ya)
						UpperCAmelCase_     							=     -vector_product(__UpperCAmelCase							,					__UpperCAmelCase	) / vector_product(
						    __UpperCAmelCase							,					__UpperCAmelCase	)
						UpperCAmelCase_     							=     +vector_product(__UpperCAmelCase							,					__UpperCAmelCase	) / vector_product(
						    __UpperCAmelCase							,					__UpperCAmelCase	)

						return a > 0 and b > 0 and a + b < 1







def 				A			(		__UpperCAmelCase = "p102_triangles.txt"	)       ->       int:



						'''simple docstring'''






						UpperCAmelCase_     							=     Path(__UpperCAmelCase	).parent.joinpath(__UpperCAmelCase	).read_text(encoding='''utf-8'''	)

						UpperCAmelCase_     							=     []
						for line in data.strip().split('''\n'''	):
												triangles.append([int(__UpperCAmelCase	) for number in line.split(''','''	)]	)

						UpperCAmelCase_     							=     0
						UpperCAmelCase_     							=     42

						for triangle in triangles:
												ret += contains_origin(*__UpperCAmelCase	)

						return ret


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





