





from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_lowerCamelCase       =	logging.get_logger(__name__)




class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    ["input_values", "attention_mask"]


							def __init__(							self			:Optional[Any]							, __A			:Optional[Any]=1							, __A			:Any=1_6000							, __A			:str=128							, __A			:Any=1024							, __A			:str=0.0							, __A			:Union[str, Any]=True							, __A			:List[Any]=-4.2_6_7_7_3_9_3							, __A			:Dict=4.5_6_8_9_9_7_4							, __A			:Union[str, Any]=False							, **__A			:List[Any]							, )      ->      Union[str, Any]:



									"""simple docstring"""



									super().__init__(feature_size=__A							, sampling_rate=__A							, padding_value=__A							, **__A						)
									SCREAMING_SNAKE_CASE__								=					num_mel_bins
									SCREAMING_SNAKE_CASE__								=					max_length
									SCREAMING_SNAKE_CASE__								=					do_normalize
									SCREAMING_SNAKE_CASE__								=					mean
									SCREAMING_SNAKE_CASE__								=					std
									SCREAMING_SNAKE_CASE__								=					return_attention_mask


							def    _snake_case     (							self			:Optional[int]							, __A			:np.ndarray							, __A			:int							, )      ->      np.ndarray:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					torch.from_numpy(__A						).unsqueeze(0						)
									SCREAMING_SNAKE_CASE__								=					ta_kaldi.fbank(
									    __A							, htk_compat=__A							, sample_frequency=self.sampling_rate							, use_energy=__A							, window_type="""hanning"""							, num_mel_bins=self.num_mel_bins							, dither=0.0							, frame_shift=10							, )

									SCREAMING_SNAKE_CASE__								=					fbank.shape[0]
									SCREAMING_SNAKE_CASE__								=					max_length - n_frames

									# pad or truncate, depending on difference
									if difference > 0:
											SCREAMING_SNAKE_CASE__								=					torch.nn.ZeroPadad((0, 0, 0, difference)						)
											SCREAMING_SNAKE_CASE__								=					pad_module(__A						)
									elif difference < 0:
											SCREAMING_SNAKE_CASE__								=					fbank[0:max_length, :]

									SCREAMING_SNAKE_CASE__								=					fbank.numpy()

									return fbank


							def    _snake_case     (							self			:Any							, __A			:np.ndarray						)      ->      np.ndarray:



									"""simple docstring"""



									return (input_values - (self.mean)) / (self.std * 2)


							def __call__(							self			:int							, __A			:Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							, __A			:Optional[int] = None							, __A			:Optional[Union[str, TensorType]] = None							, **__A			:List[Any]							, )      ->      BatchFeature:



									"""simple docstring"""



									if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
													raise ValueError(
													    f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
													    f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
													    f''' {self.sampling_rate} and not {sampling_rate}.'''						)
									else:
											logger.warning(
											    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
											    """Failing to do so can result in silent errors that might be hard to debug."""						)

									SCREAMING_SNAKE_CASE__								=					isinstance(__A							, np.ndarray						) and len(raw_speech.shape						) > 1
									if is_batched_numpy and len(raw_speech.shape						) > 2:
											raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''						)
									SCREAMING_SNAKE_CASE__								=					is_batched_numpy or (
									    isinstance(__A							, (list, tuple)						) and (isinstance(raw_speech[0]							, (np.ndarray, tuple, list)						))
									)

									if is_batched:
											SCREAMING_SNAKE_CASE__								=					[np.asarray(__A							, dtype=np.floataa						) for speech in raw_speech]
									elif not is_batched and not isinstance(__A							, np.ndarray						):
											SCREAMING_SNAKE_CASE__								=					np.asarray(__A							, dtype=np.floataa						)
									elif isinstance(__A							, np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
											SCREAMING_SNAKE_CASE__								=					raw_speech.astype(np.floataa						)

									# always return batch
									if not is_batched:
											SCREAMING_SNAKE_CASE__								=					[raw_speech]

									# extract fbank features and pad/truncate to max_length
									SCREAMING_SNAKE_CASE__								=					[self._extract_fbank_features(__A							, max_length=self.max_length						) for waveform in raw_speech]

									# convert into BatchFeature
									SCREAMING_SNAKE_CASE__								=					BatchFeature({"""input_values""": features}						)

									# make sure list is in array format
									SCREAMING_SNAKE_CASE__								=					padded_inputs.get("""input_values"""						)
									if isinstance(input_values[0]							, __A						):
											SCREAMING_SNAKE_CASE__								=					[np.asarray(__A							, dtype=np.floataa						) for feature in input_values]

									# normalization
									if self.do_normalize:
											SCREAMING_SNAKE_CASE__								=					[self.normalize(__A						) for feature in input_values]

									if return_tensors is not None:
											SCREAMING_SNAKE_CASE__								=					padded_inputs.convert_to_tensors(__A						)

									return padded_inputs






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, 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 MarianTokenizer
	from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							List[str]    ,      UpperCamelCase__:							Union[str, Any]    ,      UpperCamelCase__:							Union[str, Any]=None    ,      UpperCamelCase__:							Tuple=None    ,      UpperCamelCase__:							Tuple=None    ,      UpperCamelCase__:							int=None    ,      UpperCamelCase__:							Union[str, Any]=None    ,      UpperCamelCase__:							List[str]=None    ,      ):
		if attention_mask is None:
				SCREAMING_SNAKE_CASE__								=					np.where(input_ids != config.pad_token_id    ,      1    ,      0			)
		if decoder_attention_mask is None:
				SCREAMING_SNAKE_CASE__								=					np.where(decoder_input_ids != config.pad_token_id    ,      1    ,      0			)
		if head_mask is None:
				SCREAMING_SNAKE_CASE__								=					np.ones((config.encoder_layers, config.encoder_attention_heads)			)
		if decoder_head_mask is None:
				SCREAMING_SNAKE_CASE__								=					np.ones((config.decoder_layers, config.decoder_attention_heads)			)
		if cross_attn_head_mask is None:
				SCREAMING_SNAKE_CASE__								=					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": attention_mask,
		}




class      UpperCamelCase_				:


							def __init__(							self			:Optional[Any]							, __A			:Union[str, Any]							, __A			:int=13							, __A			:Dict=7							, __A			:Tuple=True							, __A			:Optional[Any]=False							, __A			:Optional[int]=99							, __A			:int=16							, __A			:Optional[Any]=2							, __A			:List[Any]=4							, __A			:List[Any]=4							, __A			:Optional[Any]="gelu"							, __A			:List[str]=0.1							, __A			:List[Any]=0.1							, __A			:str=32							, __A			:Dict=2							, __A			:Optional[Any]=1							, __A			:List[Any]=0							, __A			:List[str]=0.0_2							, )      ->      Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					parent
									SCREAMING_SNAKE_CASE__								=					batch_size
									SCREAMING_SNAKE_CASE__								=					seq_length
									SCREAMING_SNAKE_CASE__								=					is_training
									SCREAMING_SNAKE_CASE__								=					use_labels
									SCREAMING_SNAKE_CASE__								=					vocab_size
									SCREAMING_SNAKE_CASE__								=					hidden_size
									SCREAMING_SNAKE_CASE__								=					num_hidden_layers
									SCREAMING_SNAKE_CASE__								=					num_attention_heads
									SCREAMING_SNAKE_CASE__								=					intermediate_size
									SCREAMING_SNAKE_CASE__								=					hidden_act
									SCREAMING_SNAKE_CASE__								=					hidden_dropout_prob
									SCREAMING_SNAKE_CASE__								=					attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__								=					max_position_embeddings
									SCREAMING_SNAKE_CASE__								=					eos_token_id
									SCREAMING_SNAKE_CASE__								=					pad_token_id
									SCREAMING_SNAKE_CASE__								=					bos_token_id
									SCREAMING_SNAKE_CASE__								=					initializer_range


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



									"""simple docstring"""



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

									SCREAMING_SNAKE_CASE__								=					shift_tokens_right(__A							, 1							, 2						)

									SCREAMING_SNAKE_CASE__								=					MarianConfig(
									    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							, initializer_range=self.initializer_range							, use_cache=__A							, )
									SCREAMING_SNAKE_CASE__								=					prepare_marian_inputs_dict(__A							, __A							, __A						)
									return config, inputs_dict


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					self.prepare_config_and_inputs()
									return config, inputs_dict


							def    _snake_case     (							self			:Any							, __A			:Any							, __A			:List[Any]							, __A			:Optional[int]						)      ->      Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					20
									SCREAMING_SNAKE_CASE__								=					model_class_name(__A						)

									SCREAMING_SNAKE_CASE__								=					model.encode(inputs_dict["""input_ids"""]						)

									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					(
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

									SCREAMING_SNAKE_CASE__								=					model.init_cache(decoder_input_ids.shape[0]							, __A							, __A						)
									SCREAMING_SNAKE_CASE__								=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							, dtype="""i4"""						)

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

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

									SCREAMING_SNAKE_CASE__								=					model.decode(__A							, __A						)

									SCREAMING_SNAKE_CASE__								=					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    _snake_case     (							self			:Optional[int]							, __A			:Optional[int]							, __A			:List[str]							, __A			:int						)      ->      str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					20
									SCREAMING_SNAKE_CASE__								=					model_class_name(__A						)

									SCREAMING_SNAKE_CASE__								=					model.encode(inputs_dict["""input_ids"""]						)

									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					(
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

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

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

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

									SCREAMING_SNAKE_CASE__								=					model.decode(__A							, __A							, decoder_attention_mask=__A						)

									SCREAMING_SNAKE_CASE__								=					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_				(   UpperCamelCase__ ,     unittest.TestCase ,     UpperCamelCase__			):
							lowerCamelCase_							=    True
							lowerCamelCase_							=    (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
							lowerCamelCase_							=    (FlaxMarianMTModel,) if is_flax_available() else ()


							def    _snake_case     (							self			:Any						)      ->      str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					FlaxMarianModelTester(self						)


							def    _snake_case     (							self			:Any						)      ->      Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					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    _snake_case     (							self			:Union[str, Any]						)      ->      List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					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    _snake_case     (							self			:List[Any]						)      ->      Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__						):
													SCREAMING_SNAKE_CASE__								=					self._prepare_for_class(__A							, __A						)
													SCREAMING_SNAKE_CASE__								=					model_class(__A						)

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

													with self.subTest("""JIT Enabled"""						):
															SCREAMING_SNAKE_CASE__								=					encode_jitted(**__A						).to_tuple()

													with self.subTest("""JIT Disabled"""						):
															with jax.disable_jit():
																	SCREAMING_SNAKE_CASE__								=					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    _snake_case     (							self			:Tuple						)      ->      Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__						):
													SCREAMING_SNAKE_CASE__								=					model_class(__A						)
													SCREAMING_SNAKE_CASE__								=					model.encode(inputs_dict["""input_ids"""]							, inputs_dict["""attention_mask"""]						)

													SCREAMING_SNAKE_CASE__								=					{
													    """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			:Union[str, Any]							, __A			:Optional[Any]							, __A			:List[Any]						):
															return model.decode(
															    decoder_input_ids=__A							, decoder_attention_mask=__A							, encoder_outputs=__A							, )

													with self.subTest("""JIT Enabled"""						):
															SCREAMING_SNAKE_CASE__								=					decode_jitted(**__A						).to_tuple()

													with self.subTest("""JIT Disabled"""						):
															with jax.disable_jit():
																	SCREAMING_SNAKE_CASE__								=					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    _snake_case     (							self			:Optional[Any]						)      ->      str:



									"""simple docstring"""



									for model_class_name in self.all_model_classes:
											SCREAMING_SNAKE_CASE__								=					model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""						)
											# FlaxMarianForSequenceClassification expects eos token in input_ids
											SCREAMING_SNAKE_CASE__								=					np.ones((1, 1)						) * model.config.eos_token_id
											SCREAMING_SNAKE_CASE__								=					model(__A						)
											self.assertIsNotNone(__A						)




@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_				(   unittest.TestCase			):
							lowerCamelCase_							=    None
							lowerCamelCase_							=    None


							@classmethod
							def    _snake_case     (							cls			:int						)      ->      None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
									return cls


							@cached_property
							def    _snake_case     (							self			:Any						)      ->      int:



									"""simple docstring"""



									return MarianTokenizer.from_pretrained(self.model_name						)


							@property
							def    _snake_case     (							self			:Optional[int]						)      ->      int:



									"""simple docstring"""



									return self.tokenizer.eos_token_id


							@cached_property
							def    _snake_case     (							self			:Optional[int]						)      ->      str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					FlaxMarianMTModel.from_pretrained(self.model_name						)
									self.assertEqual(model.config.decoder_start_token_id							, model.config.pad_token_id						)
									return model


							def    _snake_case     (							self			:Optional[int]							, **__A			:int						)      ->      Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.translate_src_text(**__A						)
									self.assertListEqual(self.expected_text							, __A						)


							def    _snake_case     (							self			:Union[str, Any]							, **__A			:List[Any]						)      ->      Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.tokenizer(self.src_text							, padding=__A							, return_tensors="""np"""							, **__A						)
									SCREAMING_SNAKE_CASE__								=					self.model.generate(
									    model_inputs.input_ids							, attention_mask=model_inputs.attention_mask							, num_beams=2							, max_length=128							, ).sequences
									SCREAMING_SNAKE_CASE__								=					self.tokenizer.batch_decode(__A							, skip_special_tokens=__A						)
									return generated_words




@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    "en"
							lowerCamelCase_							=    "fr"
							lowerCamelCase_							=    [
							    "I am a small frog.",
							    "Now I can forget the 100 words of german that I know.",
							]
							lowerCamelCase_							=    [
							    "Je suis une petite grenouille.",
							    "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
							]


							@slow
							def    _snake_case     (							self			:str						)      ->      Optional[Any]:



									"""simple docstring"""



									self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    "fr"
							lowerCamelCase_							=    "en"
							lowerCamelCase_							=    [
							    "Donnez moi le micro.",
							    "Tom et Mary étaient assis à une table.",  # Accents
							]
							lowerCamelCase_							=    [
							    "Give me the microphone.",
							    "Tom and Mary were sitting at a table.",
							]


							@slow
							def    _snake_case     (							self			:Any						)      ->      Tuple:



									"""simple docstring"""



									self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    "mt"
							lowerCamelCase_							=    "en"
							lowerCamelCase_							=    ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
							lowerCamelCase_							=    ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]


							@slow
							def    _snake_case     (							self			:List[str]						)      ->      int:



									"""simple docstring"""



									self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    "en"
							lowerCamelCase_							=    "de"
							lowerCamelCase_							=    [
							    "I am a small frog.",
							    "Now I can forget the 100 words of german that I know.",
							    "Tom asked his teacher for advice.",
							    "That's how I would do it.",
							    "Tom really admired Mary's courage.",
							    "Turn around and close your eyes.",
							]
							lowerCamelCase_							=    [
							    "Ich bin ein kleiner Frosch.",
							    "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
							    "Tom bat seinen Lehrer um Rat.",
							    "So würde ich das machen.",
							    "Tom bewunderte Marias Mut wirklich.",
							    "Drehen Sie sich um und schließen Sie die Augen.",
							]


							@slow
							def    _snake_case     (							self			:List[Any]						)      ->      List[str]:



									"""simple docstring"""



									self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    "en"
							lowerCamelCase_							=    "zh"
							lowerCamelCase_							=    ["My name is Wolfgang and I live in Berlin"]
							lowerCamelCase_							=    ["我叫沃尔夫冈 我住在柏林"]


							@slow
							def    _snake_case     (							self			:List[Any]						)      ->      int:



									"""simple docstring"""



									self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    "ru"
							lowerCamelCase_							=    "fr"
							lowerCamelCase_							=    ["Он показал мне рукопись своей новой пьесы."]
							lowerCamelCase_							=    ["Il m'a montré le manuscrit de sa nouvelle pièce."]


							@slow
							def    _snake_case     (							self			:List[Any]						)      ->      List[str]:



									"""simple docstring"""



									self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    "en"
							lowerCamelCase_							=    "ROMANCE"
							lowerCamelCase_							=    [
							    ">>fr<< Don't spend so much time watching TV.",
							    ">>pt<< Your message has been sent.",
							    ">>es<< He's two years older than me.",
							]
							lowerCamelCase_							=    [
							    "Ne passez pas autant de temps à regarder la télé.",
							    "A sua mensagem foi enviada.",
							    "Es dos años más viejo que yo.",
							]


							@slow
							def    _snake_case     (							self			:str						)      ->      int:



									"""simple docstring"""



									self._assert_generated_batch_equal_expected()