

"""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
							__lowerCAmelCase				      =   '''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 		A_	(   __UpperCamelCase     :				List[Any]       ,	__UpperCamelCase     :				Tuple       ,	__UpperCamelCase     :				Tuple=None       ,	__UpperCamelCase     :				str=None       ,	__UpperCamelCase     :				List[Any]=None       ,	__UpperCamelCase     :				List[Any]=None       ,	__UpperCamelCase     :				List[Any]=None       ,	__UpperCamelCase     :				List[str]=None       ,	):
		if attention_mask is None:
				lowercase										=							np.where(input_ids != config.pad_token_id       ,	1       ,	0				)
		if decoder_attention_mask is None:
				lowercase										=							np.where(decoder_input_ids != config.pad_token_id       ,	1       ,	0				)
		if head_mask is None:
				lowercase										=							np.ones((config.encoder_layers, config.encoder_attention_heads)				)
		if decoder_head_mask is None:
				lowercase										=							np.ones((config.decoder_layers, config.decoder_attention_heads)				)
		if cross_attn_head_mask is None:
				lowercase										=							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       :    List[Any] ,		a       :    Union[str, Any] ,		a       :    Optional[int]=13 ,		a       :    Tuple=7 ,		a       :    int=True ,		a       :    List[Any]=False ,		a       :    int=99 ,		a       :    List[Any]=16 ,		a       :    str=2 ,		a       :    Optional[Any]=4 ,		a       :    Optional[Any]=4 ,		a       :    Any="gelu" ,		a       :    Tuple=0.1 ,		a       :    Dict=0.1 ,		a       :    int=32 ,		a       :    List[Any]=2 ,		a       :    Any=1 ,		a       :    Tuple=0 ,		a       :    str=2 ,		a       :    int=0.02 ,		)   ->    Tuple:

									"""simple docstring"""
									lowercase										=							parent
									lowercase										=							batch_size
									lowercase										=							seq_length
									lowercase										=							is_training
									lowercase										=							use_labels
									lowercase										=							vocab_size
									lowercase										=							hidden_size
									lowercase										=							num_hidden_layers
									lowercase										=							num_attention_heads
									lowercase										=							intermediate_size
									lowercase										=							hidden_act
									lowercase										=							hidden_dropout_prob
									lowercase										=							attention_probs_dropout_prob
									lowercase										=							max_position_embeddings
									lowercase										=							eos_token_id
									lowercase										=							pad_token_id
									lowercase										=							bos_token_id
									lowercase										=							decoder_start_token_id
									lowercase										=							initializer_range

							def 					_lowerCAmelCase  ( self       :    str    )   ->    int:

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

									lowercase										=							shift_tokens_right(a ,		1    )

									lowercase										=							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=a ,		)
									lowercase										=							prepare_mbart_inputs_dict(a ,		a ,		a    )
									return config, inputs_dict

							def 					_lowerCAmelCase  ( self       :    Any    )   ->    Optional[Any]:

									"""simple docstring"""
									lowercase					,  lowercase										=							self.prepare_config_and_inputs()
									return config, inputs_dict

							def 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    Optional[int] ,		a       :    str ,		a       :    str    )   ->    Union[str, Any]:

									"""simple docstring"""
									lowercase										=							20
									lowercase										=							model_class_name(a    )

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

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

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

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

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

									lowercase										=							model.decode(a ,		a    )

									lowercase										=							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       :    Optional[Any] ,		a       :    List[Any] ,		a       :    Dict ,		a       :    int    )   ->    List[Any]:

									"""simple docstring"""
									lowercase										=							20
									lowercase										=							model_class_name(a    )

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

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

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

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

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

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

									lowercase										=							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       ):
							__lowerCAmelCase					:    List[str]     =  99

							def 					_lowerCAmelCase  ( self       :    Optional[Any]    )   ->    List[Any]:

									"""simple docstring"""
									lowercase										=							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										=							input_ids.shape[0]
									lowercase										=							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       :    Optional[Any]    )   ->    Optional[int]:

									"""simple docstring"""
									lowercase					,  lowercase					,  lowercase										=							self._get_config_and_data()
									lowercase										=							FlaxMBartForSequenceClassification(a    )
									lowercase										=							model(input_ids=a ,		decoder_input_ids=a    )
									lowercase										=							(batch_size, config.num_labels)
									self.assertEqual(outputs['''logits'''].shape ,		a    )

							def 					_lowerCAmelCase  ( self       :    Optional[Any]    )   ->    str:

									"""simple docstring"""
									lowercase					,  lowercase					,  lowercase										=							self._get_config_and_data()
									lowercase										=							FlaxMBartForQuestionAnswering(a    )
									lowercase										=							model(input_ids=a    )

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

							def 					_lowerCAmelCase  ( self       :    Optional[Any]    )   ->    str:

									"""simple docstring"""
									lowercase					,  lowercase					,  lowercase										=							self._get_config_and_data()
									lowercase										=							FlaxMBartForConditionalGeneration(a    )
									lowercase										=							lm_model(input_ids=a    )
									lowercase										=							(batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs['''logits'''].shape ,		a    )

							def 					_lowerCAmelCase  ( self       :    Optional[int]    )   ->    int:

									"""simple docstring"""
									lowercase										=							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										=							FlaxMBartForConditionalGeneration(a    )
									lowercase										=							np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] ,		dtype=np.intaa    )
									lowercase										=							np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] ,		dtype=np.intaa    )
									lowercase										=							lm_model(input_ids=a ,		decoder_input_ids=a    )
									lowercase										=							(*summary.shape, config.vocab_size)
									self.assertEqual(outputs['''logits'''].shape ,		a    )

							def 					_lowerCAmelCase  ( self       :    Dict    )   ->    Optional[int]:

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




@require_flax
class 	_lowerCAmelCase    (      __snake_case       ,       unittest.TestCase       ,       __snake_case       ):
							__lowerCAmelCase					:    List[str]     =  True
							__lowerCAmelCase					:    List[Any]     =  (
							    (
							        FlaxMBartModel,
							        FlaxMBartForConditionalGeneration,
							        FlaxMBartForSequenceClassification,
							        FlaxMBartForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)
							__lowerCAmelCase					:    Optional[int]     =  (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()

							def 					_lowerCAmelCase  ( self       :    List[str]    )   ->    List[Any]:

									"""simple docstring"""
									lowercase										=							FlaxMBartModelTester(self    )

							def 					_lowerCAmelCase  ( self       :    int    )   ->    Optional[Any]:

									"""simple docstring"""
									lowercase					,  lowercase										=							self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(a ,		a ,		a    )

							def 					_lowerCAmelCase  ( self       :    List[str]    )   ->    Optional[Any]:

									"""simple docstring"""
									lowercase					,  lowercase										=							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(a ,		a ,		a    )

							def 					_lowerCAmelCase  ( self       :    str    )   ->    Union[str, Any]:

									"""simple docstring"""
									lowercase					,  lowercase										=							self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

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

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

							def 					_lowerCAmelCase  ( self       :    Optional[Any]    )   ->    List[str]:

									"""simple docstring"""
									lowercase					,  lowercase										=							self.model_tester.prepare_config_and_inputs_for_common()

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

													lowercase										=							{
													    '''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(a       :    Tuple ,		a       :    List[str] ,		a       :    int    ):
															return model.decode(
															    decoder_input_ids=a ,		decoder_attention_mask=a ,		encoder_outputs=a ,		)

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

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

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

							@slow
							def 					_lowerCAmelCase  ( self       :    Tuple    )   ->    Dict:

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




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

							@cached_property
							def 					_lowerCAmelCase  ( self       :    str    )   ->    Optional[Any]:

									"""simple docstring"""
									return AutoTokenizer.from_pretrained(self.model_name    )

							@cached_property
							def 					_lowerCAmelCase  ( self       :    List[str]    )   ->    str:

									"""simple docstring"""
									lowercase										=							FlaxMBartForConditionalGeneration.from_pretrained(self.model_name ,		from_pt=a    )
									return model

							def 					_lowerCAmelCase  ( self       :    List[Any] ,		**a       :    int    )   ->    Optional[Any]:

									"""simple docstring"""
									lowercase										=							self.translate_src_text(**a    )
									self.assertListEqual(self.expected_text ,		a    )

							def 					_lowerCAmelCase  ( self       :    List[Any] ,		**a       :    int    )   ->    Dict:

									"""simple docstring"""
									lowercase										=							self.tokenizer(self.src_text ,		**a ,		return_tensors='''np'''    )
									lowercase										=							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=a ,		num_beams=2 ,		).sequences
									lowercase										=							self.tokenizer.batch_decode(a ,		skip_special_tokens=a    )
									return generated_words

							@slow
							def 					_lowerCAmelCase  ( self       :    List[str]    )   ->    Union[str, Any]:

									"""simple docstring"""
									self._assert_generated_batch_equal_expected()


"""simple docstring"""






from typing import Callable
def 		A_	(   __UpperCamelCase     :				Callable				):

		def wrapper(__UpperCamelCase     :				Optional[Any]				):
				lowercase										=							function_with_docstring.__doc__
				return target_function

		return wrapper