
from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, 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, TFMBartForConditionalGeneration, TFMBartModel




@require_tf
class        _SCREAMING_SNAKE_CASE	:
	lowerCAmelCase__   =					MBartConfig
	lowerCAmelCase__   =					{}
	lowerCAmelCase__   =					'gelu'



	def __init__(       self		,    lowercase		,    lowercase=13		,    lowercase=7		,    lowercase=True		,    lowercase=False		,    lowercase=99		,    lowercase=32		,    lowercase=2		,    lowercase=4		,    lowercase=37		,    lowercase=0.1		,    lowercase=0.1		,    lowercase=20		,    lowercase=2		,    lowercase=1		,    lowercase=0		,    )							->			Any:
					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        SCREAMING_SNAKE_CASE_(       self					)							->			List[Any]:
					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_mbart_inputs_dict(lowercase		,    lowercase		,    lowercase					)
					return config, inputs_dict



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase					)							->			Optional[int]:
					lowerCamelCase_         =     TFMBartModel(config=lowercase					).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(lowercase		,    attention_mask=lowercase		,    head_mask=lowercase		,    use_cache=lowercase					)

					lowerCamelCase_      ,       lowerCamelCase_         =     outputs.to_tuple()
					lowerCamelCase_         =     past_key_values[1]





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__=None      ,					lowerCamelCase__=None      ,					lowerCamelCase__=None      ,					lowerCamelCase__=None      ,					lowerCamelCase__=None      ,					):
				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        _SCREAMING_SNAKE_CASE	( snake_case_     ,			snake_case_     ,			unittest.TestCase		):
	lowerCAmelCase__   =					(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
	lowerCAmelCase__   =					(TFMBartForConditionalGeneration,) if is_tf_available() else ()
	lowerCAmelCase__   =					(
	    {
	        'conversational': TFMBartForConditionalGeneration,
	        'feature-extraction': TFMBartModel,
	        'summarization': TFMBartForConditionalGeneration,
	        'text2text-generation': TFMBartForConditionalGeneration,
	        'translation': TFMBartForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	lowerCAmelCase__   =					True
	lowerCAmelCase__   =					False
	lowerCAmelCase__   =					False



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase					)							->			Any:
					if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
									# Exception encountered when calling layer '...'
									return True

					return False



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					lowerCamelCase_         =     TFMBartModelTester(self					)
					lowerCamelCase_         =     ConfigTester(self		,    config_class=lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					self.config_tester.run_common_tests()



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[Any]:
					lowerCamelCase_         =     self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*lowercase					)




@require_sentencepiece
@require_tokenizers
@require_tf
class        _SCREAMING_SNAKE_CASE	( unittest.TestCase		):
	lowerCAmelCase__   =					[
	    ' UN Chief Says There Is No Military Solution in Syria',
	]
	lowerCAmelCase__   =					[
	    'Şeful ONU declară că nu există o soluţie militară în Siria',
	]
	lowerCAmelCase__   =					'facebook/mbart-large-en-ro'



	@cached_property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					return AutoTokenizer.from_pretrained(self.model_name					)



	@cached_property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Tuple:
					lowerCamelCase_         =     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
					return model



	def        SCREAMING_SNAKE_CASE_(       self		,    **lowercase					)							->			str:
					lowerCamelCase_         =     self.translate_src_text(**lowercase					)
					self.assertListEqual(self.expected_text		,    lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self		,    **lowercase					)							->			Dict:
					lowerCamelCase_         =     self.tokenizer(self.src_text		,    **lowercase		,    return_tensors="tf"					)
					lowerCamelCase_         =     self.model.generate(
					    model_inputs.input_ids		,    attention_mask=model_inputs.attention_mask		,    num_beams=2					)
					lowerCamelCase_         =     self.tokenizer.batch_decode(lowercase		,    skip_special_tokens=lowercase					)
					return generated_words



	@slow
	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[Any]:
					self._assert_generated_batch_equal_expected()




from __future__ import annotations

import math





def      lowerCamelCase_					(							lowerCamelCase__       ):

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5      ,					int(math.sqrt(lowerCamelCase__       ) + 1       )      ,					6       ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True


__A							=[num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]





def      lowerCamelCase_					(							lowerCamelCase__       ):
				if not isinstance(lowerCamelCase__      ,					lowerCamelCase__       ):
								raise ValueError("n must be an integer"       )
				if n <= 0:
								raise ValueError("n must be >= 0"       )

				lowerCamelCase_         =     []
				for num in range(len(lowerCamelCase__       )       ):
								lowerCamelCase_         =     0
								while 2 * i * i <= odd_composites[num]:
												lowerCamelCase_         =     odd_composites[num] - 2 * i * i
												if is_prime(lowerCamelCase__       ):
																break
												i += 1
								else:
												list_nums.append(odd_composites[num]       )
												if len(lowerCamelCase__       ) == n:
																return list_nums

				return []





def      lowerCamelCase_					(							):
				return compute_nums(1       )[0]


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


