





'''simple docstring'''






import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


A :			Any							   =      get_logger(__name__)





def    lowercase_   (		lowercase__    ) ->List[Any]:
						return TestCommand(
						    args.dataset					,	args.name					,	args.cache_dir					,	args.data_dir					,	args.all_configs					,	args.save_info or args.save_infos					,	args.ignore_verifications					,	args.force_redownload					,	args.clear_cache					,	)







class       lowerCamelCase       (    __UpperCAmelCase	):
					_SCREAMING_SNAKE_CASE				 =     False  # to tell pytest it's not a test class


					@staticmethod
					def  SCREAMING_SNAKE_CASE_		(       __snake_case						:	ArgumentParser							):





											'''simple docstring'''





											_snake_case: Any	       =		parser.add_parser('test'							,  help='Test dataset implementation.'							)
											test_parser.add_argument('--name'							,  type=__snake_case							,  default=__snake_case							,  help='Dataset processing name'							)
											test_parser.add_argument(
											    '--cache_dir'							,  type=__snake_case							,  default=__snake_case							,  help='Cache directory where the datasets are stored.'							,  )
											test_parser.add_argument(
											    '--data_dir'							,  type=__snake_case							,  default=__snake_case							,  help='Can be used to specify a manual directory to get the files from.'							,  )
											test_parser.add_argument('--all_configs'							,  action='store_true'							,  help='Test all dataset configurations'							)
											test_parser.add_argument(
											    '--save_info'							,  action='store_true'							,  help='Save the dataset infos in the dataset card (README.md)'							)
											test_parser.add_argument(
											    '--ignore_verifications'							,  action='store_true'							,  help='Run the test without checksums and splits checks.'							,  )
											test_parser.add_argument('--force_redownload'							,  action='store_true'							,  help='Force dataset redownload'							)
											test_parser.add_argument(
											    '--clear_cache'							,  action='store_true'							,  help='Remove downloaded files and cached datasets after each config test'							,  )
											# aliases
											test_parser.add_argument('--save_infos'							,  action='store_true'							,  help='alias to save_info'							)
											test_parser.add_argument('dataset'							,  type=__snake_case							,  help='Name of the dataset to download'							)
											test_parser.set_defaults(func=_test_command_factory							)


					def __init__(       self						:	str							,  __snake_case						:	str							,  __snake_case						:	str							,  __snake_case						:	str							,  __snake_case						:	str							,  __snake_case						:	bool							,  __snake_case						:	bool							,  __snake_case						:	bool							,  __snake_case						:	bool							,  __snake_case						:	bool							,  ):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		dataset
											_snake_case: Any	       =		name
											_snake_case: Tuple	       =		cache_dir
											_snake_case: Dict	       =		data_dir
											_snake_case: Optional[int]	       =		all_configs
											_snake_case: Union[str, Any]	       =		save_infos
											_snake_case: Dict	       =		ignore_verifications
											_snake_case: int	       =		force_redownload
											_snake_case: Dict	       =		clear_cache
											if clear_cache and not cache_dir:
																	print(
																	    'When --clear_cache is used, specifying a cache directory is mandatory.\n'
																	    'The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'
																	    'Please provide a --cache_dir that will be used to test the dataset script.'							)
																	exit(1							)
											if save_infos:
																	_snake_case: Tuple	       =		True




					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





											fl_logger().setLevel(__snake_case							)
											if self._name is not None and self._all_configs:
																	print('Both parameters `config` and `all_configs` can\'t be used at once.'							)
																	exit(1							)
											_snake_case       ,    _snake_case: Any	       =		self._dataset, self._name
											_snake_case: List[Any]	       =		dataset_module_factory(__snake_case							)
											_snake_case: Dict	       =		import_main_class(module.module_path							)
											_snake_case: Any	       =		len(builder_cls.BUILDER_CONFIGS							) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

											def get_builders() -> Generator[DatasetBuilder, None, None]:
																	if self._all_configs and builder_cls.BUILDER_CONFIGS:
																							for i, config in enumerate(builder_cls.BUILDER_CONFIGS							):
																													if "config_name" in module.builder_kwargs:
																																			yield builder_cls(
																																			    cache_dir=self._cache_dir							,  data_dir=self._data_dir							,  **module.builder_kwargs							,  )
																													else:
																																			yield builder_cls(
																																			    config_name=config.name							,  cache_dir=self._cache_dir							,  data_dir=self._data_dir							,  **module.builder_kwargs							,  )
																	else:
																							if "config_name" in module.builder_kwargs:
																													yield builder_cls(cache_dir=self._cache_dir							,  data_dir=self._data_dir							,  **module.builder_kwargs							)
																							else:
																													yield builder_cls(
																													    config_name=__snake_case							,  cache_dir=self._cache_dir							,  data_dir=self._data_dir							,  **module.builder_kwargs							,  )

											for j, builder in enumerate(get_builders()							):
																	print(f'''Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})'''							)
																	_snake_case: Optional[Any]	       =		os.path.exists(
																	    os.path.join(builder.get_imported_module_dir()							,  datasets.config.DATASETDICT_INFOS_FILENAME							)							)  # record checksums only if we need to update a (deprecated) dataset_infos.json
																	builder.download_and_prepare(
																	    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
																	    if not self._force_redownload
																	    else DownloadMode.FORCE_REDOWNLOAD							,  verification_mode=VerificationMode.NO_CHECKS
																	    if self._ignore_verifications
																	    else VerificationMode.ALL_CHECKS							,  try_from_hf_gcs=__snake_case							,  )
																	builder.as_dataset()
																	if self._save_infos:
																							builder._save_infos()

																	# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
																	# The dataset_infos are saved in the YAML part of the README.md

																	# Let's move it to the original directory of the dataset script, to allow the user to
																	# upload them on S3 at the same time afterwards.
																	if self._save_infos:
																							_snake_case: Optional[int]	       =		os.path.join(builder_cls.get_imported_module_dir()							,  'README.md'							)
																							_snake_case: Optional[int]	       =		Path(__snake_case							).name + '.py'
																							_snake_case: List[str]	       =		os.path.join(__snake_case							,  __snake_case							)
																							if os.path.isfile(__snake_case							):
																													_snake_case: Union[str, Any]	       =		os.path.dirname(__snake_case							)
																							elif os.path.isfile(__snake_case							):
																													_snake_case: Optional[Any]	       =		path
																							elif os.path.isdir(__snake_case							):  # for local directories containing only data files
																													_snake_case: Optional[int]	       =		path
																							else:  # in case of a remote dataset
																													_snake_case: List[Any]	       =		None
																													print(f'''Dataset card saved at {dataset_readme_path}'''							)

																							# Move dataset_info back to the user
																							if dataset_dir is not None:
																													_snake_case: Optional[int]	       =		os.path.join(__snake_case							,  'README.md'							)
																													copyfile(__snake_case							,  __snake_case							)
																													print(f'''Dataset card saved at {user_dataset_readme_path}'''							)

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
																	if self._clear_cache:
																							if os.path.isdir(builder._cache_dir							):
																													logger.warning(f'''Clearing cache at {builder._cache_dir}'''							)
																													rmtree(builder._cache_dir							)
																							_snake_case: Union[str, Any]	       =		os.path.join(self._cache_dir							,  datasets.config.DOWNLOADED_DATASETS_DIR							)
																							if os.path.isdir(__snake_case							):
																													logger.warning(f'''Clearing cache at {download_dir}'''							)
																													rmtree(__snake_case							)

											print('Test successful.'							)












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





											'''simple docstring'''





											_snake_case: Dict	       =		parent
											_snake_case: List[Any]	       =		batch_size
											_snake_case: Union[str, Any]	       =		seq_length
											_snake_case: List[str]	       =		is_training
											_snake_case: Optional[int]	       =		use_labels
											_snake_case: List[str]	       =		vocab_size
											_snake_case: Union[str, Any]	       =		hidden_size
											_snake_case: Optional[Any]	       =		num_hidden_layers
											_snake_case: int	       =		num_attention_heads
											_snake_case: Tuple	       =		intermediate_size
											_snake_case: Optional[int]	       =		hidden_act
											_snake_case: Optional[int]	       =		hidden_dropout_prob
											_snake_case: Union[str, Any]	       =		attention_probs_dropout_prob
											_snake_case: Tuple	       =		max_position_embeddings
											_snake_case: List[Any]	       =		eos_token_id
											_snake_case: int	       =		pad_token_id
											_snake_case: Optional[Any]	       =		bos_token_id
											_snake_case: Any	       =		decoder_start_token_id
											_snake_case: Dict	       =		initializer_range


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





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

											_snake_case: List[Any]	       =		shift_tokens_right(__snake_case							,  1							)

											_snake_case: Dict	       =		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=__snake_case							,  )
											_snake_case: Optional[Any]	       =		prepare_mbart_inputs_dict(__snake_case							,  __snake_case							,  __snake_case							)
											return config, inputs_dict


					def  SCREAMING_SNAKE_CASE_		(       self						:	Any							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: int	       =		self.prepare_config_and_inputs()
											return config, inputs_dict


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							,  __snake_case						:	str							,  __snake_case						:	List[Any]							,  __snake_case						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: List[str]	       =		20
											_snake_case: Optional[Any]	       =		model_class_name(__snake_case							)

											_snake_case: List[Any]	       =		model.encode(inputs_dict['input_ids']							)

											_snake_case       ,    _snake_case: str	       =		(
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

											_snake_case: str	       =		model.init_cache(decoder_input_ids.shape[0]							,  __snake_case							,  __snake_case							)
											_snake_case: Tuple	       =		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,  dtype='i4'							)

											_snake_case: 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)							,  )
											_snake_case: Dict	       =		model.decode(
											    decoder_input_ids[:, :-1]							,  __snake_case							,  decoder_attention_mask=__snake_case							,  past_key_values=__snake_case							,  decoder_position_ids=__snake_case							,  )

											_snake_case: Optional[int]	       =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,  dtype='i4'							)
											_snake_case: List[str]	       =		model.decode(
											    decoder_input_ids[:, -1:]							,  __snake_case							,  decoder_attention_mask=__snake_case							,  past_key_values=outputs_cache.past_key_values							,  decoder_position_ids=__snake_case							,  )

											_snake_case: Any	       =		model.decode(__snake_case							,  __snake_case							)

											_snake_case: List[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  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  __snake_case						:	int							,  __snake_case						:	int							,  __snake_case						:	int							):





											'''simple docstring'''





											_snake_case: Dict	       =		20
											_snake_case: Any	       =		model_class_name(__snake_case							)

											_snake_case: Dict	       =		model.encode(inputs_dict['input_ids']							)

											_snake_case       ,    _snake_case: Tuple	       =		(
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

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

											_snake_case: str	       =		model.init_cache(decoder_input_ids.shape[0]							,  __snake_case							,  __snake_case							)
											_snake_case: Dict	       =		jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]							,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,  )

											_snake_case: int	       =		model.decode(
											    decoder_input_ids[:, :-1]							,  __snake_case							,  decoder_attention_mask=__snake_case							,  past_key_values=__snake_case							,  decoder_position_ids=__snake_case							,  )
											_snake_case: Any	       =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,  dtype='i4'							)
											_snake_case: str	       =		model.decode(
											    decoder_input_ids[:, -1:]							,  __snake_case							,  past_key_values=outputs_cache.past_key_values							,  decoder_attention_mask=__snake_case							,  decoder_position_ids=__snake_case							,  )

											_snake_case: Any	       =		model.decode(__snake_case							,  __snake_case							,  decoder_attention_mask=__snake_case							)

											_snake_case: Optional[int]	       =		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						:	List[str]							):





											'''simple docstring'''





											_snake_case: int	       =		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							,  )

											_snake_case: Any	       =		input_ids.shape[0]
											_snake_case: str	       =		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						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case       ,    _snake_case       ,    _snake_case: Tuple	       =		self._get_config_and_data()
											_snake_case: Any	       =		FlaxMBartForSequenceClassification(__snake_case							)
											_snake_case: List[Any]	       =		model(input_ids=__snake_case							,  decoder_input_ids=__snake_case							)
											_snake_case: List[str]	       =		(batch_size, config.num_labels)
											self.assertEqual(outputs['logits'].shape							,  __snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





											_snake_case       ,    _snake_case       ,    _snake_case: Union[str, Any]	       =		self._get_config_and_data()
											_snake_case: List[str]	       =		FlaxMBartForQuestionAnswering(__snake_case							)
											_snake_case: Union[str, Any]	       =		model(input_ids=__snake_case							)

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


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case       ,    _snake_case       ,    _snake_case: Any	       =		self._get_config_and_data()
											_snake_case: Optional[int]	       =		FlaxMBartForConditionalGeneration(__snake_case							)
											_snake_case: List[str]	       =		lm_model(input_ids=__snake_case							)
											_snake_case: List[Any]	       =		(batch_size, input_ids.shape[1], config.vocab_size)
											self.assertEqual(outputs['logits'].shape							,  __snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





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




					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





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







@require_flax
class       lowerCamelCase       (    __UpperCAmelCase  ,				unittest.TestCase  ,				__UpperCAmelCase	):
					_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						:	Tuple							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		FlaxMBartModelTester(self							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: int	       =		self.model_tester.prepare_config_and_inputs()
											for model_class in self.all_model_classes:
																	self.model_tester.check_use_cache_forward(__snake_case							,  __snake_case							,  __snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: Tuple	       =		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(__snake_case							,  __snake_case							,  __snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: Any	       =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__							):
																							_snake_case: Dict	       =		self._prepare_for_class(__snake_case							,  __snake_case							)
																							_snake_case: List[Any]	       =		model_class(__snake_case							)

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

																							with self.subTest('JIT Enabled'							):
																													_snake_case: List[str]	       =		encode_jitted(**__snake_case							).to_tuple()

																							with self.subTest('JIT Disabled'							):
																													with jax.disable_jit():
																																			_snake_case: List[Any]	       =		encode_jitted(**__snake_case							).to_tuple()

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


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							):





											'''simple docstring'''





											_snake_case       ,    _snake_case: Optional[int]	       =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__							):
																							_snake_case: Tuple	       =		model_class(__snake_case							)
																							_snake_case: Dict	       =		model.encode(inputs_dict['input_ids']							,  inputs_dict['attention_mask']							)

																							_snake_case: Dict	       =		{
																							    '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(__snake_case						:	Dict							,  __snake_case						:	Dict							,  __snake_case						:	Union[str, Any]							):
																													return model.decode(
																													    decoder_input_ids=__snake_case							,  decoder_attention_mask=__snake_case							,  encoder_outputs=__snake_case							,  )

																							with self.subTest('JIT Enabled'							):
																													_snake_case: str	       =		decode_jitted(**__snake_case							).to_tuple()

																							with self.subTest('JIT Disabled'							):
																													with jax.disable_jit():
																																			_snake_case: Dict	       =		decode_jitted(**__snake_case							).to_tuple()

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




					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





											for model_class_name in self.all_model_classes:
																	_snake_case: List[str]	       =		model_class_name.from_pretrained('facebook/mbart-large-cc25'							,  from_pt=__snake_case							)
																	# FlaxMBartForSequenceClassification expects eos token in input_ids
																	_snake_case: List[str]	       =		np.ones((1, 1)							) * model.config.eos_token_id
																	_snake_case: Optional[Any]	       =		model(__snake_case							)
																	self.assertIsNotNone(__snake_case							)







@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						:	Optional[int]							):





											'''simple docstring'''





											return AutoTokenizer.from_pretrained(self.model_name							)


					@cached_property
					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											_snake_case: Dict	       =		FlaxMBartForConditionalGeneration.from_pretrained(self.model_name							,  from_pt=__snake_case							)
											return model


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							,  **__snake_case						:	int							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		self.translate_src_text(**__snake_case							)
											self.assertListEqual(self.expected_text							,  __snake_case							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							,  **__snake_case						:	Tuple							):





											'''simple docstring'''





											_snake_case: int	       =		self.tokenizer(self.src_text							,  **__snake_case							,  return_tensors='np'							)
											_snake_case: 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=__snake_case							,  num_beams=2							,  ).sequences
											_snake_case: Dict	       =		self.tokenizer.batch_decode(__snake_case							,  skip_special_tokens=__snake_case							)
											return generated_words




					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





											self._assert_generated_batch_equal_expected()





