






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				UpperCamelCase__						:      Optional[Any]								=					"""platform"""

				import jax
				import jax.numpy as jnp

				from transformers import AutoTokenizer
				from transformers.models.mbart.modeling_flax_mbart import (
				    FlaxMBartForConditionalGeneration,
				    FlaxMBartForQuestionAnswering,
				    FlaxMBartForSequenceClassification,
				    FlaxMBartModel,
				    shift_tokens_right,
				)





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_=None,  snake_case_=None,  snake_case_=None,  snake_case_=None,  snake_case_=None,  snake_case_=None,  )   ->  Dict:


	"""simple docstring"""



	if attention_mask is None:
		a		=			np.where(input_ids != config.pad_token_id,  1,  0  )
	if decoder_attention_mask is None:
		a		=			np.where(decoder_input_ids != config.pad_token_id,  1,  0  )
	if head_mask is None:
		a		=			np.ones((config.encoder_layers, config.encoder_attention_heads)  )
	if decoder_head_mask is None:
		a		=			np.ones((config.decoder_layers, config.decoder_attention_heads)  )
	if cross_attn_head_mask is None:
		a		=			np.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,
	}







class 				lowerCamelCase_     (	unittest.TestCase     ):
							def __init__(						self		:					Optional[int]							,__lowerCamelCase		:					Union[str, Any]							,__lowerCamelCase		:					Any=13							,__lowerCamelCase		:					Optional[int]=7							,__lowerCamelCase		:					Dict=True							,__lowerCamelCase		:					List[Any]=False							,__lowerCamelCase		:					Optional[Any]=99							,__lowerCamelCase		:					Union[str, Any]=16							,__lowerCamelCase		:					int=2							,__lowerCamelCase		:					str=4							,__lowerCamelCase		:					List[str]=4							,__lowerCamelCase		:					Optional[Any]="gelu"							,__lowerCamelCase		:					Optional[int]=0.1							,__lowerCamelCase		:					Tuple=0.1							,__lowerCamelCase		:					Union[str, Any]=32							,__lowerCamelCase		:					Union[str, Any]=2							,__lowerCamelCase		:					List[Any]=1							,__lowerCamelCase		:					Optional[int]=0							,__lowerCamelCase		:					Dict=2							,__lowerCamelCase		:					List[str]=0.02							,):





								'''simple docstring'''



								a		=			parent
								a		=			batch_size
								a		=			seq_length
								a		=			is_training
								a		=			use_labels
								a		=			vocab_size
								a		=			hidden_size
								a		=			num_hidden_layers
								a		=			num_attention_heads
								a		=			intermediate_size
								a		=			hidden_act
								a		=			hidden_dropout_prob
								a		=			attention_probs_dropout_prob
								a		=			max_position_embeddings
								a		=			eos_token_id
								a		=			pad_token_id
								a		=			bos_token_id
								a		=			decoder_start_token_id
								a		=			initializer_range
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict					):





								'''simple docstring'''



								a		=			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,self.vocab_size					)							,3							,self.vocab_size					)
								a		=			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,dtype=np.intaa					))							,-1					)

								a		=			shift_tokens_right(__lowerCamelCase							,1					)

								a		=			MBartConfig(
								    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_id=self.eos_token_id							,bos_token_id=self.bos_token_id							,pad_token_id=self.pad_token_id							,decoder_start_token_id=self.decoder_start_token_id							,initializer_range=self.initializer_range							,use_cache=__lowerCamelCase							,)
								a		=			prepare_mbart_inputs_dict(__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)
								return config, inputs_dict
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]					):





								'''simple docstring'''



								a	, a		=			self.prepare_config_and_inputs()
								return config, inputs_dict
							def  SCREAMING_SNAKE_CASE_ (						self		:					int							,__lowerCamelCase		:					Tuple							,__lowerCamelCase		:					str							,__lowerCamelCase		:					str					):





								'''simple docstring'''



								a		=			20
								a		=			model_class_name(__lowerCamelCase					)

								a		=			model.encode(inputs_dict['''input_ids''']					)

								a	, a		=			(
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

								a		=			model.init_cache(decoder_input_ids.shape[0]							,__lowerCamelCase							,__lowerCamelCase					)
								a		=			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,dtype='''i4'''					)

								a		=			jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]							,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,)
								a		=			model.decode(
								    decoder_input_ids[:, :-1]							,__lowerCamelCase							,decoder_attention_mask=__lowerCamelCase							,past_key_values=__lowerCamelCase							,decoder_position_ids=__lowerCamelCase							,)

								a		=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,dtype='''i4'''					)
								a		=			model.decode(
								    decoder_input_ids[:, -1:]							,__lowerCamelCase							,decoder_attention_mask=__lowerCamelCase							,past_key_values=outputs_cache.past_key_values							,decoder_position_ids=__lowerCamelCase							,)

								a		=			model.decode(__lowerCamelCase							,__lowerCamelCase					)

								a		=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
								self.parent.assertTrue(diff < 1e-3							,msg=F"""Max diff is {diff}"""					)




							def  SCREAMING_SNAKE_CASE_ (						self		:					List[str]							,__lowerCamelCase		:					List[Any]							,__lowerCamelCase		:					List[str]							,__lowerCamelCase		:					List[str]					):





								'''simple docstring'''



								a		=			20
								a		=			model_class_name(__lowerCamelCase					)

								a		=			model.encode(inputs_dict['''input_ids''']					)

								a	, a		=			(
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

								a		=			jnp.concatenate(
								    [
								        decoder_attention_mask,
								        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
								    ]							,axis=-1							,)

								a		=			model.init_cache(decoder_input_ids.shape[0]							,__lowerCamelCase							,__lowerCamelCase					)
								a		=			jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]							,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,)

								a		=			model.decode(
								    decoder_input_ids[:, :-1]							,__lowerCamelCase							,decoder_attention_mask=__lowerCamelCase							,past_key_values=__lowerCamelCase							,decoder_position_ids=__lowerCamelCase							,)
								a		=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,dtype='''i4'''					)
								a		=			model.decode(
								    decoder_input_ids[:, -1:]							,__lowerCamelCase							,past_key_values=outputs_cache.past_key_values							,decoder_attention_mask=__lowerCamelCase							,decoder_position_ids=__lowerCamelCase							,)

								a		=			model.decode(__lowerCamelCase							,__lowerCamelCase							,decoder_attention_mask=__lowerCamelCase					)

								a		=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
								self.parent.assertTrue(diff < 1e-3							,msg=F"""Max diff is {diff}"""					)







@require_flax
class 				lowerCamelCase_     (	unittest.TestCase     ):
							SCREAMING_SNAKE_CASE_						     =			99
							def  SCREAMING_SNAKE_CASE_ (						self		:					int					):





								'''simple docstring'''



								a		=			np.array(
								    [
								        [71, 82, 18, 33, 46, 91, 2],
								        [68, 34, 26, 58, 30, 82, 2],
								        [5, 97, 17, 39, 94, 40, 2],
								        [76, 83, 94, 25, 70, 78, 2],
								        [87, 59, 41, 35, 48, 66, 2],
								        [55, 13, 16, 58, 5, 2, 1],  # note padding
								        [64, 27, 31, 51, 12, 75, 2],
								        [52, 64, 86, 17, 83, 39, 2],
								        [48, 61, 9, 24, 71, 82, 2],
								        [26, 1, 60, 48, 22, 13, 2],
								        [21, 5, 62, 28, 14, 76, 2],
								        [45, 98, 37, 86, 59, 48, 2],
								        [70, 70, 50, 9, 28, 0, 2],
								    ]							,dtype=np.intaa							,)

								a		=			input_ids.shape[0]
								a		=			MBartConfig(
								    vocab_size=self.vocab_size							,d_model=24							,encoder_layers=2							,decoder_layers=2							,encoder_attention_heads=2							,decoder_attention_heads=2							,encoder_ffn_dim=32							,decoder_ffn_dim=32							,max_position_embeddings=48							,eos_token_id=2							,pad_token_id=1							,bos_token_id=0							,)
								return config, input_ids, batch_size
							def  SCREAMING_SNAKE_CASE_ (						self		:					str					):





								'''simple docstring'''



								a	, a	, a		=			self._get_config_and_data()
								a		=			FlaxMBartForSequenceClassification(__lowerCamelCase					)
								a		=			model(input_ids=__lowerCamelCase							,decoder_input_ids=__lowerCamelCase					)
								a		=			(batch_size, config.num_labels)
								self.assertEqual(outputs['''logits'''].shape							,__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[Any]					):





								'''simple docstring'''



								a	, a	, a		=			self._get_config_and_data()
								a		=			FlaxMBartForQuestionAnswering(__lowerCamelCase					)
								a		=			model(input_ids=__lowerCamelCase					)

								self.assertEqual(outputs['''start_logits'''].shape							,input_ids.shape					)
								self.assertEqual(outputs['''end_logits'''].shape							,input_ids.shape					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								a	, a	, a		=			self._get_config_and_data()
								a		=			FlaxMBartForConditionalGeneration(__lowerCamelCase					)
								a		=			lm_model(input_ids=__lowerCamelCase					)
								a		=			(batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape							,__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[int]					):





								'''simple docstring'''



								a		=			MBartConfig(
								    vocab_size=self.vocab_size							,d_model=14							,encoder_layers=2							,decoder_layers=2							,encoder_attention_heads=2							,decoder_attention_heads=2							,encoder_ffn_dim=8							,decoder_ffn_dim=8							,max_position_embeddings=48							,)
								a		=			FlaxMBartForConditionalGeneration(__lowerCamelCase					)
								a		=			np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]							,dtype=np.intaa					)
								a		=			np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]							,dtype=np.intaa					)
								a		=			lm_model(input_ids=__lowerCamelCase							,decoder_input_ids=__lowerCamelCase					)
								a		=			(*summary.shape, config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape							,__lowerCamelCase					)




							def  SCREAMING_SNAKE_CASE_ (						self		:					List[str]					):





								'''simple docstring'''



								a		=			np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]							,dtype=np.intaa					)
								a		=			shift_tokens_right(__lowerCamelCase							,1					)
								a		=			np.equal(__lowerCamelCase							,1					).astype(np.floataa					).sum()
								a		=			np.equal(__lowerCamelCase							,1					).astype(np.floataa					).sum()
								self.assertEqual(shifted.shape							,input_ids.shape					)
								self.assertEqual(__lowerCamelCase							,n_pad_before - 1					)
								self.assertTrue(np.equal(shifted[:, 0]							,2					).all()					)







@require_flax
class 				lowerCamelCase_     (	a_ ,						unittest.TestCase ,						a_     ):
							SCREAMING_SNAKE_CASE_						     =			True
							SCREAMING_SNAKE_CASE_						     =			(
							    (
							        FlaxMBartModel,
							        FlaxMBartForConditionalGeneration,
							        FlaxMBartForSequenceClassification,
							        FlaxMBartForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)
							SCREAMING_SNAKE_CASE_						     =			(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								a		=			FlaxMBartModelTester(self					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[int]					):





								'''simple docstring'''



								a	, a		=			self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					int					):





								'''simple docstring'''



								a	, a		=			self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					str					):





								'''simple docstring'''



								a	, a		=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__					):
										a		=			self._prepare_for_class(__lowerCamelCase							,__lowerCamelCase					)
										a		=			model_class(__lowerCamelCase					)

										@jax.jit
										def encode_jitted(__lowerCamelCase		:					Any							,__lowerCamelCase		:					Union[str, Any]=None							,**__lowerCamelCase		:					List[Any]					):
											return model.encode(input_ids=__lowerCamelCase							,attention_mask=__lowerCamelCase					)

										with self.subTest('''JIT Enabled'''					):
											a		=			encode_jitted(**__lowerCamelCase					).to_tuple()

										with self.subTest('''JIT Disabled'''					):
											with jax.disable_jit():
												a		=			encode_jitted(**__lowerCamelCase					).to_tuple()

										self.assertEqual(len(__lowerCamelCase					)							,len(__lowerCamelCase					)					)
										for jitted_output, output in zip(__lowerCamelCase							,__lowerCamelCase					):
											self.assertEqual(jitted_output.shape							,output.shape					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]					):





								'''simple docstring'''



								a	, a		=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__					):
										a		=			model_class(__lowerCamelCase					)
										a		=			model.encode(inputs_dict['''input_ids''']							,inputs_dict['''attention_mask''']					)

										a		=			{
										    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
										    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
										    '''encoder_outputs''': encoder_outputs,
										}

										@jax.jit
										def decode_jitted(__lowerCamelCase		:					str							,__lowerCamelCase		:					Tuple							,__lowerCamelCase		:					Any					):
											return model.decode(
											    decoder_input_ids=__lowerCamelCase							,decoder_attention_mask=__lowerCamelCase							,encoder_outputs=__lowerCamelCase							,)

										with self.subTest('''JIT Enabled'''					):
											a		=			decode_jitted(**__lowerCamelCase					).to_tuple()

										with self.subTest('''JIT Disabled'''					):
											with jax.disable_jit():
												a		=			decode_jitted(**__lowerCamelCase					).to_tuple()

										self.assertEqual(len(__lowerCamelCase					)							,len(__lowerCamelCase					)					)
										for jitted_output, output in zip(__lowerCamelCase							,__lowerCamelCase					):
											self.assertEqual(jitted_output.shape							,output.shape					)




							@slow
							def  SCREAMING_SNAKE_CASE_ (						self		:					str					):





								'''simple docstring'''



								for model_class_name in self.all_model_classes:
									a		=			model_class_name.from_pretrained('''facebook/mbart-large-cc25'''							,from_pt=__lowerCamelCase					)
									# FlaxMBartForSequenceClassification expects eos token in input_ids
									a		=			np.ones((1, 1)					) * model.config.eos_token_id
									a		=			model(__lowerCamelCase					)
									self.assertIsNotNone(__lowerCamelCase					)







@require_flax
@require_sentencepiece
@require_tokenizers
class 				lowerCamelCase_     (	unittest.TestCase     ):
							SCREAMING_SNAKE_CASE_						     =			[
							    ' UN Chief Says There Is No Military Solution in Syria',
							]
							SCREAMING_SNAKE_CASE_						     =			[
							    'Şeful ONU declară că nu există o soluţie militară în Siria',
							]
							SCREAMING_SNAKE_CASE_						     =			'facebook/mbart-large-en-ro'
							@cached_property
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[str]					):





								'''simple docstring'''



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





								'''simple docstring'''



								a		=			FlaxMBartForConditionalGeneration.from_pretrained(self.model_name							,from_pt=__lowerCamelCase					)
								return model
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[Any]							,**__lowerCamelCase		:					List[Any]					):





								'''simple docstring'''



								a		=			self.translate_src_text(**__lowerCamelCase					)
								self.assertListEqual(self.expected_text							,__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple							,**__lowerCamelCase		:					Tuple					):





								'''simple docstring'''



								a		=			self.tokenizer(self.src_text							,**__lowerCamelCase							,return_tensors='''np'''					)
								a		=			self.model.generate(
								    model_inputs.input_ids							,attention_mask=model_inputs.attention_mask							,decoder_start_token_id=self.tokenizer.lang_code_to_id['''ro_RO''']							,early_stopping=__lowerCamelCase							,num_beams=2							,).sequences
								a		=			self.tokenizer.batch_decode(__lowerCamelCase							,skip_special_tokens=__lowerCamelCase					)
								return generated_words




							@slow
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								self._assert_generated_batch_equal_expected()













def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  int:


	"""simple docstring"""



	return n * (2 * n - 1)





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  bool:


	"""simple docstring"""



	a		=			(1 + 2_4 * n) ** 0.5
	return ((1 + root) / 6) % 1 == 0





def   SCREAMING_SNAKE_CASE__						(   snake_case_ = 1_4_4  )   ->  int:


	"""simple docstring"""



	a		=			start
	a		=			hexagonal_num(snake_case_  )
	while not is_pentagonal(snake_case_  ):
		n += 1
		a		=			hexagonal_num(snake_case_  )
	return num


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





