"""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        __UpperCAmelCase						:
				A__				:       Optional[Any]  							=	MarianConfig
				A__				:       Optional[Any]  							=	{}
				A__				:       Optional[Any]  							=	'''gelu'''

				def __init__(				self     ,			_lowerCamelCase     ,			_lowerCamelCase=13     ,			_lowerCamelCase=7     ,			_lowerCamelCase=True     ,			_lowerCamelCase=False     ,			_lowerCamelCase=99     ,			_lowerCamelCase=32     ,			_lowerCamelCase=2     ,			_lowerCamelCase=4     ,			_lowerCamelCase=37     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=20     ,			_lowerCamelCase=2     ,			_lowerCamelCase=1     ,			_lowerCamelCase=0     ,			):
										lowerCamelCase__ =parent
										lowerCamelCase__ =batch_size
										lowerCamelCase__ =seq_length
										lowerCamelCase__ =is_training
										lowerCamelCase__ =use_labels
										lowerCamelCase__ =vocab_size
										lowerCamelCase__ =hidden_size
										lowerCamelCase__ =num_hidden_layers
										lowerCamelCase__ =num_attention_heads
										lowerCamelCase__ =intermediate_size

										lowerCamelCase__ =hidden_dropout_prob
										lowerCamelCase__ =attention_probs_dropout_prob
										lowerCamelCase__ =max_position_embeddings
										lowerCamelCase__ =eos_token_id
										lowerCamelCase__ =pad_token_id
										lowerCamelCase__ =bos_token_id

				def 		_a     (				self				):
										lowerCamelCase__ =ids_tensor([self.batch_size, self.seq_length - 1]     ,			self.vocab_size				)
										lowerCamelCase__ =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)     ,			1				)
										lowerCamelCase__ =tf.concat([input_ids, eos_tensor]     ,			axis=1				)

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

										lowerCamelCase__ =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     ,			)
										lowerCamelCase__ =prepare_marian_inputs_dict(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase				)
										return config, inputs_dict






				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										lowerCamelCase__ =TFMarianModel(config=_lowerCamelCase				).get_decoder()
										lowerCamelCase__ =inputs_dict["input_ids"]

										lowerCamelCase__ =input_ids[:1, :]
										lowerCamelCase__ =inputs_dict["attention_mask"][:1, :]
										lowerCamelCase__ =inputs_dict["head_mask"]
										lowerCamelCase__ =1

										# first forward pass
										lowerCamelCase__ =model(_lowerCamelCase     ,			attention_mask=_lowerCamelCase     ,			head_mask=_lowerCamelCase     ,			use_cache=_lowerCamelCase				)

										lowerCamelCase__ ,				lowerCamelCase__ =outputs.to_tuple()

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

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

										lowerCamelCase__ =model(_lowerCamelCase     ,			attention_mask=_lowerCamelCase				)[0]
										lowerCamelCase__ =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
										lowerCamelCase__ =int(ids_tensor((1,)     ,			output_from_past.shape[-1]				)				)
										lowerCamelCase__ =output_from_no_past[:, -3:, random_slice_idx]
										lowerCamelCase__ =output_from_past[:, :, random_slice_idx]

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





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase=None       ,   __lowerCAmelCase=None       ,   __lowerCAmelCase=None       ,   __lowerCAmelCase=None       ,   __lowerCAmelCase=None       ,   )							-> Dict:




						'''simple docstring'''



						if attention_mask is None:
												lowerCamelCase__ =tf.cast(tf.math.not_equal(__lowerCAmelCase       ,   config.pad_token_id    )       ,   tf.inta    )
						if decoder_attention_mask is None:
												lowerCamelCase__ =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:
												lowerCamelCase__ =tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
						if decoder_head_mask is None:
												lowerCamelCase__ =tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
						if cross_attn_head_mask is None:
												lowerCamelCase__ =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        __UpperCAmelCase						(     __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase     ):
				A__				:       Any  							=	(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
				A__				:       List[str]  							=	(TFMarianMTModel,) if is_tf_available() else ()
				A__				:       Optional[Any]  							=	(
				    {
				        '''conversational''': TFMarianMTModel,
				        '''feature-extraction''': TFMarianModel,
				        '''summarization''': TFMarianMTModel,
				        '''text2text-generation''': TFMarianMTModel,
				        '''translation''': TFMarianMTModel,
				    }
				    if is_tf_available()
				    else {}
				)
				A__				:       List[Any]  							=	True
				A__				:       Optional[int]  							=	False
				A__				:       Tuple  							=	False

				def 		_a     (				self				):
										lowerCamelCase__ =TFMarianModelTester(self				)
										lowerCamelCase__ =ConfigTester(self     ,			config_class=_lowerCamelCase				)

				def 		_a     (				self				):
										self.config_tester.run_common_tests()






				def 		_a     (				self				):
										lowerCamelCase__ =self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*_lowerCamelCase				)






@require_tf
class        __UpperCAmelCase						(     unittest.TestCase     ):
				A__				:       List[Any]  							=	1_0_0_0  # show more chars for failing integration tests

				@classmethod
				def 		_a     (				cls				):
										lowerCamelCase__ =F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
										return cls

				@cached_property
				def 		_a     (				self				):
										return AutoTokenizer.from_pretrained(self.model_name				)

				@property
				def 		_a     (				self				):
										return self.tokenizer.eos_token_id

				@cached_property
				def 		_a     (				self				):
										warnings.simplefilter("error"				)
										lowerCamelCase__ =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
										assert isinstance(_lowerCamelCase     ,			_lowerCamelCase				)
										lowerCamelCase__ =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     ,			**_lowerCamelCase				):
										lowerCamelCase__ =self.translate_src_text(**_lowerCamelCase				)
										self.assertListEqual(self.expected_text     ,			_lowerCamelCase				)






				def 		_a     (				self     ,			**_lowerCamelCase				):
										lowerCamelCase__ =self.tokenizer(self.src_text     ,			**_lowerCamelCase     ,			padding=_lowerCamelCase     ,			return_tensors="tf"				)
										lowerCamelCase__ =self.model.generate(
										    model_inputs.input_ids     ,			attention_mask=model_inputs.attention_mask     ,			num_beams=2     ,			max_length=128				)
										lowerCamelCase__ =self.tokenizer.batch_decode(generated_ids.numpy()     ,			skip_special_tokens=_lowerCamelCase				)
										return generated_words






@require_sentencepiece
@require_tokenizers
@require_tf
class        __UpperCAmelCase						(     __lowerCAmelCase     ):
				A__				:       Optional[int]  							=	'''mt'''
				A__				:       List[Any]  							=	'''en'''
				A__				:       List[str]  							=	['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
				A__				:       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				):
										self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class        __UpperCAmelCase						(     __lowerCAmelCase     ):
				A__				:       Dict  							=	'''en'''
				A__				:       List[Any]  							=	'''zh'''
				A__				:       Dict  							=	['''My name is Wolfgang and I live in Berlin''']
				A__				:       List[Any]  							=	['''我叫沃尔夫冈 我住在柏林''']

				@unittest.skip("Skipping until #12647 is resolved."				)
				@slow
				def 		_a     (				self				):
										self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class        __UpperCAmelCase						(     __lowerCAmelCase     ):
				A__				:       Dict  							=	'''en'''
				A__				:       Any  							=	'''ROMANCE'''
				A__				:       Union[str, Any]  							=	[
				    '''>>fr<< Don\'t spend so much time watching TV.''',
				    '''>>pt<< Your message has been sent.''',
				    '''>>es<< He\'s two years older than me.''',
				]
				A__				:       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 		_a     (				self				):
										self._assert_generated_batch_equal_expected()






				@unittest.skip("Skipping until #12647 is resolved."				)
				@slow
				def 		_a     (				self				):
										lowerCamelCase__ =TranslationPipeline(self.model     ,			self.tokenizer     ,			framework="tf"				)
										lowerCamelCase__ =pipeline(self.src_text				)
										self.assertEqual(self.expected_text     ,			[x["translation_text"] for x in output]				)



"""simple docstring"""


from __future__ import annotations





def       lowerCamelCase_			( __lowerCAmelCase    )							-> int | float:




						'''simple docstring'''



						lowerCamelCase__ =sorted(__lowerCAmelCase    )
						lowerCamelCase__ =len(__lowerCAmelCase    )
						lowerCamelCase__ =length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)





def       lowerCamelCase_			( )							-> Tuple:




						'''simple docstring'''



						import doctest

						doctest.testmod()


if __name__ == "__main__":
	main()


