



"""simple docstring"""







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:   str       			=							"""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      (   __UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase=None					,							__UpperCAmelCase=None					,							__UpperCAmelCase=None					,							__UpperCAmelCase=None					,							__UpperCAmelCase=None					,							__UpperCAmelCase=None					,							):
		if attention_mask is None:
				_lowercase							:      Optional[Any]			=					np.where(input_ids != config.pad_token_id					,							1					,							0	)
		if decoder_attention_mask is None:
				_lowercase							:      int			=					np.where(decoder_input_ids != config.pad_token_id					,							1					,							0	)
		if head_mask is None:
				_lowercase							:      Any			=					np.ones((config.encoder_layers, config.encoder_attention_heads)	)
		if decoder_head_mask is None:
				_lowercase							:      Dict			=					np.ones((config.decoder_layers, config.decoder_attention_heads)	)
		if cross_attn_head_mask is None:
				_lowercase							:      List[str]			=					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    UpperCamelCase     (			unittest.TestCase    ):




	"""simple docstring"""






	def __init__(			self							,UpperCAmelCase_							,UpperCAmelCase_=13							,UpperCAmelCase_=7							,UpperCAmelCase_=True							,UpperCAmelCase_=False							,UpperCAmelCase_=99							,UpperCAmelCase_=16							,UpperCAmelCase_=2							,UpperCAmelCase_=4							,UpperCAmelCase_=4							,UpperCAmelCase_="gelu"							,UpperCAmelCase_=0.1							,UpperCAmelCase_=0.1							,UpperCAmelCase_=32							,UpperCAmelCase_=2							,UpperCAmelCase_=1							,UpperCAmelCase_=0							,UpperCAmelCase_=2							,UpperCAmelCase_=0.02							,):
			_lowercase							:      Optional[int]			=					parent
			_lowercase							:      Optional[int]			=					batch_size
			_lowercase							:      int			=					seq_length
			_lowercase							:      Optional[Any]			=					is_training
			_lowercase							:      Union[str, Any]			=					use_labels
			_lowercase							:      Optional[Any]			=					vocab_size
			_lowercase							:      Optional[Any]			=					hidden_size
			_lowercase							:      Union[str, Any]			=					num_hidden_layers
			_lowercase							:      List[Any]			=					num_attention_heads
			_lowercase							:      Tuple			=					intermediate_size
			_lowercase							:      int			=					hidden_act
			_lowercase							:      Any			=					hidden_dropout_prob
			_lowercase							:      List[Any]			=					attention_probs_dropout_prob
			_lowercase							:      Dict			=					max_position_embeddings
			_lowercase							:      Optional[Any]			=					eos_token_id
			_lowercase							:      Optional[int]			=					pad_token_id
			_lowercase							:      List[Any]			=					bos_token_id
			_lowercase							:      Optional[Any]			=					decoder_start_token_id
			_lowercase							:      List[Any]			=					initializer_range






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Optional[int]			=					np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,self.vocab_size					)							,3							,self.vocab_size					)
			_lowercase							:      Optional[int]			=					np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,dtype=np.intaa					))							,-1					)

			_lowercase							:      int			=					shift_tokens_right(UpperCAmelCase_							,1					)

			_lowercase							:      str			=					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=UpperCAmelCase_							,)
			_lowercase							:      Dict			=					prepare_mbart_inputs_dict(UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					)
			return config, inputs_dict






	def 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase							:      List[str]			=					self.prepare_config_and_inputs()
			return config, inputs_dict






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					):
			_lowercase							:      Optional[int]			=					20
			_lowercase							:      Optional[Any]			=					model_class_name(UpperCAmelCase_					)

			_lowercase							:      Any			=					model.encode(inputs_dict["""input_ids"""]					)

			_lowercase			,   _lowercase							:      List[str]			=					(
			    inputs_dict["""decoder_input_ids"""],
			    inputs_dict["""decoder_attention_mask"""],
			)

			_lowercase							:      int			=					model.init_cache(decoder_input_ids.shape[0]							,UpperCAmelCase_							,UpperCAmelCase_					)
			_lowercase							:      Tuple			=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,dtype="""i4"""					)

			_lowercase							:      Optional[int]			=					jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]							,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,)
			_lowercase							:      Optional[int]			=					model.decode(
			    decoder_input_ids[:, :-1]							,UpperCAmelCase_							,decoder_attention_mask=UpperCAmelCase_							,past_key_values=UpperCAmelCase_							,decoder_position_ids=UpperCAmelCase_							,)

			_lowercase							:      Dict			=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,dtype="""i4"""					)
			_lowercase							:      str			=					model.decode(
			    decoder_input_ids[:, -1:]							,UpperCAmelCase_							,decoder_attention_mask=UpperCAmelCase_							,past_key_values=outputs_cache.past_key_values							,decoder_position_ids=UpperCAmelCase_							,)

			_lowercase							:      List[str]			=					model.decode(UpperCAmelCase_							,UpperCAmelCase_					)

			_lowercase							:      Any			=					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 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					):
			_lowercase							:      int			=					20
			_lowercase							:      List[str]			=					model_class_name(UpperCAmelCase_					)

			_lowercase							:      Tuple			=					model.encode(inputs_dict["""input_ids"""]					)

			_lowercase			,   _lowercase							:      Any			=					(
			    inputs_dict["""decoder_input_ids"""],
			    inputs_dict["""decoder_attention_mask"""],
			)

			_lowercase							:      Optional[int]			=					jnp.concatenate(
			    [
			        decoder_attention_mask,
			        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
			    ]							,axis=-1							,)

			_lowercase							:      Tuple			=					model.init_cache(decoder_input_ids.shape[0]							,UpperCAmelCase_							,UpperCAmelCase_					)
			_lowercase							:      Union[str, Any]			=					jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]							,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,)

			_lowercase							:      int			=					model.decode(
			    decoder_input_ids[:, :-1]							,UpperCAmelCase_							,decoder_attention_mask=UpperCAmelCase_							,past_key_values=UpperCAmelCase_							,decoder_position_ids=UpperCAmelCase_							,)
			_lowercase							:      List[Any]			=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,dtype="""i4"""					)
			_lowercase							:      Any			=					model.decode(
			    decoder_input_ids[:, -1:]							,UpperCAmelCase_							,past_key_values=outputs_cache.past_key_values							,decoder_attention_mask=UpperCAmelCase_							,decoder_position_ids=UpperCAmelCase_							,)

			_lowercase							:      Optional[Any]			=					model.decode(UpperCAmelCase_							,UpperCAmelCase_							,decoder_attention_mask=UpperCAmelCase_					)

			_lowercase							:      Union[str, Any]			=					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    UpperCamelCase     (			unittest.TestCase    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    List[Any]		    =   9_9






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      List[str]			=					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							,)

			_lowercase							:      Union[str, Any]			=					input_ids.shape[0]
			_lowercase							:      List[Any]			=					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 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase			,   _lowercase							:      Optional[int]			=					self._get_config_and_data()
			_lowercase							:      Tuple			=					FlaxMBartForSequenceClassification(UpperCAmelCase_					)
			_lowercase							:      List[Any]			=					model(input_ids=UpperCAmelCase_							,decoder_input_ids=UpperCAmelCase_					)
			_lowercase							:      Any			=					(batch_size, config.num_labels)
			self.assertEqual(outputs["""logits"""].shape							,UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase			,   _lowercase							:      Any			=					self._get_config_and_data()
			_lowercase							:      int			=					FlaxMBartForQuestionAnswering(UpperCAmelCase_					)
			_lowercase							:      int			=					model(input_ids=UpperCAmelCase_					)

			self.assertEqual(outputs["""start_logits"""].shape							,input_ids.shape					)
			self.assertEqual(outputs["""end_logits"""].shape							,input_ids.shape					)






	def 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase			,   _lowercase							:      Optional[int]			=					self._get_config_and_data()
			_lowercase							:      Any			=					FlaxMBartForConditionalGeneration(UpperCAmelCase_					)
			_lowercase							:      List[str]			=					lm_model(input_ids=UpperCAmelCase_					)
			_lowercase							:      Optional[Any]			=					(batch_size, input_ids.shape[1], config.vocab_size)
			self.assertEqual(outputs["""logits"""].shape							,UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      str			=					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							,)
			_lowercase							:      List[Any]			=					FlaxMBartForConditionalGeneration(UpperCAmelCase_					)
			_lowercase							:      str			=					np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]							,dtype=np.intaa					)
			_lowercase							:      Optional[int]			=					np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]							,dtype=np.intaa					)
			_lowercase							:      List[str]			=					lm_model(input_ids=UpperCAmelCase_							,decoder_input_ids=UpperCAmelCase_					)
			_lowercase							:      Optional[int]			=					(*summary.shape, config.vocab_size)
			self.assertEqual(outputs["""logits"""].shape							,UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Optional[int]			=					np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]							,dtype=np.intaa					)
			_lowercase							:      Optional[Any]			=					shift_tokens_right(UpperCAmelCase_							,1					)
			_lowercase							:      Optional[int]			=					np.equal(UpperCAmelCase_							,1					).astype(np.floataa					).sum()
			_lowercase							:      List[Any]			=					np.equal(UpperCAmelCase_							,1					).astype(np.floataa					).sum()
			self.assertEqual(shifted.shape							,input_ids.shape					)
			self.assertEqual(UpperCAmelCase_							,n_pad_before - 1					)
			self.assertTrue(np.equal(shifted[:, 0]							,2					).all()					)







@require_flax
class    UpperCamelCase     (			snake_case ,					unittest.TestCase ,					snake_case    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    List[Any]		    =   True
	SCREAMING_SNAKE_CASE_				:    List[str]		    =   (
	    (
	        FlaxMBartModel,
	        FlaxMBartForConditionalGeneration,
	        FlaxMBartForSequenceClassification,
	        FlaxMBartForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE_				:    Dict		    =   (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      List[Any]			=					FlaxMBartModelTester(self					)






	def 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase							:      Dict			=					self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward(UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase							:      Dict			=					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(UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase							:      Any			=					self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__					):
							_lowercase							:      Optional[int]			=					self._prepare_for_class(UpperCAmelCase_							,UpperCAmelCase_					)
							_lowercase							:      Optional[int]			=					model_class(UpperCAmelCase_					)

							@jax.jit
							def encode_jitted(UpperCAmelCase_							,UpperCAmelCase_=None							,**UpperCAmelCase_					):
									return model.encode(input_ids=UpperCAmelCase_							,attention_mask=UpperCAmelCase_					)

							with self.subTest("""JIT Enabled"""					):
									_lowercase							:      Any			=					encode_jitted(**UpperCAmelCase_					).to_tuple()

							with self.subTest("""JIT Disabled"""					):
									with jax.disable_jit():
											_lowercase							:      Tuple			=					encode_jitted(**UpperCAmelCase_					).to_tuple()

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






	def 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase							:      str			=					self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__					):
							_lowercase							:      Dict			=					model_class(UpperCAmelCase_					)
							_lowercase							:      int			=					model.encode(inputs_dict["""input_ids"""]							,inputs_dict["""attention_mask"""]					)

							_lowercase							:      List[Any]			=					{
							    """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(UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					):
									return model.decode(
									    decoder_input_ids=UpperCAmelCase_							,decoder_attention_mask=UpperCAmelCase_							,encoder_outputs=UpperCAmelCase_							,)

							with self.subTest("""JIT Enabled"""					):
									_lowercase							:      Dict			=					decode_jitted(**UpperCAmelCase_					).to_tuple()

							with self.subTest("""JIT Disabled"""					):
									with jax.disable_jit():
											_lowercase							:      Optional[int]			=					decode_jitted(**UpperCAmelCase_					).to_tuple()

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






	@slow
	def 					lowerCamelCase__ (			self					):
			for model_class_name in self.all_model_classes:
					_lowercase							:      Dict			=					model_class_name.from_pretrained("""facebook/mbart-large-cc25"""							,from_pt=UpperCAmelCase_					)
					# FlaxMBartForSequenceClassification expects eos token in input_ids
					_lowercase							:      Optional[int]			=					np.ones((1, 1)					) * model.config.eos_token_id
					_lowercase							:      Dict			=					model(UpperCAmelCase_					)
					self.assertIsNotNone(UpperCAmelCase_					)







@require_flax
@require_sentencepiece
@require_tokenizers
class    UpperCamelCase     (			unittest.TestCase    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    Tuple		    =   [
	    " UN Chief Says There Is No Military Solution in Syria",
	]
	SCREAMING_SNAKE_CASE_				:    Optional[Any]		    =   [
	    "Şeful ONU declară că nu există o soluţie militară în Siria",
	]
	SCREAMING_SNAKE_CASE_				:    Any		    =   "facebook/mbart-large-en-ro"






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






	@cached_property
	def 					lowerCamelCase__ (			self					):
			_lowercase							:      Union[str, Any]			=					FlaxMBartForConditionalGeneration.from_pretrained(self.model_name							,from_pt=UpperCAmelCase_					)
			return model






	def 					lowerCamelCase__ (			self							,**UpperCAmelCase_					):
			_lowercase							:      Optional[int]			=					self.translate_src_text(**UpperCAmelCase_					)
			self.assertListEqual(self.expected_text							,UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self							,**UpperCAmelCase_					):
			_lowercase							:      str			=					self.tokenizer(self.src_text							,**UpperCAmelCase_							,return_tensors="""np"""					)
			_lowercase							:      Optional[Any]			=					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=UpperCAmelCase_							,num_beams=2							,).sequences
			_lowercase							:      Any			=					self.tokenizer.batch_decode(UpperCAmelCase_							,skip_special_tokens=UpperCAmelCase_					)
			return generated_words






	@slow
	def 					lowerCamelCase__ (			self					):
			self._assert_generated_batch_equal_expected()








"""simple docstring"""







from itertools import combinations_with_replacement


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase = 100	):
		_lowercase							:      list[int]			=					[*list(range(1					,							21	)	), 25]
		_lowercase							:      list[int]			=					[2 * x for x in range(1					,							21	)] + [50]
		_lowercase							:      list[int]			=					[3 * x for x in range(1					,							21	)]
		_lowercase							:      list[int]			=					singles + doubles + triples + [0]

		_lowercase							:      int			=					0
		_lowercase							:      int
		_lowercase							:      int
		_lowercase							:      int
		_lowercase							:      int

		for double in doubles:
				for throwa, throwa in combinations_with_replacement(__UpperCAmelCase					,							2	):
						_lowercase							:      Union[str, Any]			=					double + throwa + throwa
						if checkout_total < limit:
								num_checkouts += 1

		return num_checkouts


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



