
'''simple docstring'''





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


A							     = logging.get_logger(__name__)



class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			['pixel_values']

  def __init__(self	:			Tuple						,			lowerCAmelCase_	:			bool = True						,			lowerCAmelCase_	:			Dict[str, int] = None						,			lowerCAmelCase_	:			bool = True						,			lowerCAmelCase_	:			Dict[str, int] = None						,			lowerCAmelCase_	:			PILImageResampling = PILImageResampling.BICUBIC						,			lowerCAmelCase_	:			bool = True						,			lowerCAmelCase_	:			Union[int, float] = 1 / 2_55						,			lowerCAmelCase_	:			bool = True						,			lowerCAmelCase_	:			Optional[Union[float, List[float]]] = None						,			lowerCAmelCase_	:			Optional[Union[float, List[float]]] = None						,			**lowerCAmelCase_	:			Optional[Any]						,			) ->							None:
        super().__init__(**lowerCAmelCase_     )
        snake_case_    							=				crop_size if crop_size is not None else {"height": 2_56, "width": 2_56}
        snake_case_    							=				get_size_dict(lowerCAmelCase_						,			param_name="crop_size"     )
        snake_case_    							=				size if size is not None else {"height": 2_24, "width": 2_24}
        snake_case_    							=				get_size_dict(lowerCAmelCase_     )

        snake_case_    							=				do_center_crop
        snake_case_    							=				crop_size
        snake_case_    							=				do_resize
        snake_case_    							=				size
        snake_case_    							=				resample
        snake_case_    							=				do_rescale
        snake_case_    							=				rescale_factor
        snake_case_    							=				do_normalize
        snake_case_    							=				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        snake_case_    							=				image_std if image_std is not None else IMAGENET_DEFAULT_STD

  def 		_lowerCamelCase						(self	:			int						,			lowerCAmelCase_	:			np.ndarray						,			lowerCAmelCase_	:			Dict[str, int]						,			lowerCAmelCase_	:			Optional[int] = None						,			lowerCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None						,			**lowerCAmelCase_	:			List[Any]						,			) ->							np.ndarray:
        snake_case_    							=				self.size if size is None else size
        snake_case_    							=				get_size_dict(lowerCAmelCase_     )
        snake_case_    							=				get_size_dict(lowerCAmelCase_						,			param_name="crop_size"     )

        snake_case_							,						snake_case_    							=				get_image_size(lowerCAmelCase_     )
        snake_case_    							=				min(lowerCAmelCase_						,			lowerCAmelCase_     )
        snake_case_    							=				(size["height"] / crop_size["height"]) * min_dim
        snake_case_    							=				(size["width"] / crop_size["width"]) * min_dim
        return center_crop(lowerCAmelCase_						,			size=(cropped_height, cropped_width)						,			data_format=lowerCAmelCase_						,			**lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			np.ndarray						,			lowerCAmelCase_	:			Dict[str, int]						,			lowerCAmelCase_	:			PILImageResampling = PIL.Image.BICUBIC						,			lowerCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None						,			**lowerCAmelCase_	:			Any						,			) ->							np.ndarray:
        snake_case_    							=				get_size_dict(lowerCAmelCase_     )
        if "height" not in size or "width" not in size:
              raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''     )
        return resize(
            lowerCAmelCase_						,			size=(size["height"], size["width"])						,			resample=lowerCAmelCase_						,			data_format=lowerCAmelCase_						,			**lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			lowerCAmelCase_	:			np.ndarray						,			lowerCAmelCase_	:			Union[int, float]						,			lowerCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None						,			**lowerCAmelCase_	:			Optional[int]						,			) ->							Optional[int]:
        return rescale(lowerCAmelCase_						,			scale=lowerCAmelCase_						,			data_format=lowerCAmelCase_						,			**lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Any						,			lowerCAmelCase_	:			np.ndarray						,			lowerCAmelCase_	:			Union[float, List[float]]						,			lowerCAmelCase_	:			Union[float, List[float]]						,			lowerCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None						,			**lowerCAmelCase_	:			Optional[int]						,			) ->							np.ndarray:
        return normalize(lowerCAmelCase_						,			mean=lowerCAmelCase_						,			std=lowerCAmelCase_						,			data_format=lowerCAmelCase_						,			**lowerCAmelCase_     )


  def 		_lowerCamelCase						(self	:			Tuple						,			lowerCAmelCase_	:			ImageInput						,			lowerCAmelCase_	:			Optional[bool] = None						,			lowerCAmelCase_	:			Optional[Dict[str, int]] = None						,			lowerCAmelCase_	:			Optional[bool] = None						,			lowerCAmelCase_	:			Optional[Dict[str, int]] = None						,			lowerCAmelCase_	:			PILImageResampling = None						,			lowerCAmelCase_	:			Optional[bool] = None						,			lowerCAmelCase_	:			Optional[float] = None						,			lowerCAmelCase_	:			Optional[bool] = None						,			lowerCAmelCase_	:			Optional[Union[float, List[float]]] = None						,			lowerCAmelCase_	:			Optional[Union[float, List[float]]] = None						,			lowerCAmelCase_	:			Optional[Union[str, TensorType]] = None						,			lowerCAmelCase_	:			ChannelDimension = ChannelDimension.FIRST						,			**lowerCAmelCase_	:			Optional[int]						,			) ->							PIL.Image.Image:
        snake_case_    							=				do_center_crop if do_center_crop is not None else self.do_center_crop
        snake_case_    							=				crop_size if crop_size is not None else self.crop_size
        snake_case_    							=				get_size_dict(lowerCAmelCase_						,			param_name="crop_size"     )
        snake_case_    							=				do_resize if do_resize is not None else self.do_resize
        snake_case_    							=				size if size is not None else self.size
        snake_case_    							=				get_size_dict(lowerCAmelCase_     )
        snake_case_    							=				resample if resample is not None else self.resample
        snake_case_    							=				do_rescale if do_rescale is not None else self.do_rescale
        snake_case_    							=				rescale_factor if rescale_factor is not None else self.rescale_factor
        snake_case_    							=				do_normalize if do_normalize is not None else self.do_normalize
        snake_case_    							=				image_mean if image_mean is not None else self.image_mean
        snake_case_    							=				image_std if image_std is not None else self.image_std

        snake_case_    							=				make_list_of_images(lowerCAmelCase_     )

        if not valid_images(lowerCAmelCase_     ):
              raise ValueError(
                  "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                  "torch.Tensor, tf.Tensor or jax.ndarray."     )

        if do_center_crop and crop_size is None:
              raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided."     )

        if do_resize and size is None:
              raise ValueError("Size must be specified if do_resize is True."     )

        if do_rescale and rescale_factor is None:
              raise ValueError("Rescale factor must be specified if do_rescale is True."     )

        if do_normalize and (image_mean is None or image_std is None):
              raise ValueError("Image mean and image standard deviation must be specified if do_normalize is True."     )

        # All transformations expect numpy arrays.
        snake_case_    							=				[to_numpy_array(lowerCAmelCase_     ) for image in images]

        if do_center_crop:
              snake_case_    							=				[self.center_crop(lowerCAmelCase_						,			lowerCAmelCase_						,			size=lowerCAmelCase_     ) for image in images]

        if do_resize:
              snake_case_    							=				[self.resize(image=lowerCAmelCase_						,			size=lowerCAmelCase_						,			resample=lowerCAmelCase_     ) for image in images]

        if do_rescale:
              snake_case_    							=				[self.rescale(image=lowerCAmelCase_						,			scale=lowerCAmelCase_     ) for image in images]

        if do_normalize:
              snake_case_    							=				[self.normalize(image=lowerCAmelCase_						,			mean=lowerCAmelCase_						,			std=lowerCAmelCase_     ) for image in images]

        snake_case_    							=				[to_channel_dimension_format(lowerCAmelCase_						,			lowerCAmelCase_     ) for image in images]

        snake_case_    							=				{"pixel_values": images}
        return BatchFeature(data=lowerCAmelCase_						,			tensor_type=lowerCAmelCase_     )


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
     snake_case__						      =			'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    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase=None			,    __UpperCAmelCase=None			,    __UpperCAmelCase=None			,    __UpperCAmelCase=None			,    __UpperCAmelCase=None			,    __UpperCAmelCase=None			,    )    ->     str:



      '''simple docstring'''

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

     '''simple docstring'''

     def __init__(       self					,					A_					,					A_=13					,					A_=7					,					A_=True					,					A_=False					,					A_=99					,					A_=16					,					A_=2					,					A_=4					,					A_=4					,					A_="gelu"					,					A_=0.1					,					A_=0.1					,					A_=32					,					A_=2					,					A_=1					,					A_=0					,					A_=0.02					,					)			->					Union[str, Any]:






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



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






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

           _lowerCamelCase           =					shift_tokens_right(A_					,					1					,					2    )

           _lowerCamelCase           =					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_					,					)
           _lowerCamelCase           =					prepare_marian_inputs_dict(A_					,					A_					,					A_    )
           return config, inputs_dict



     def 		UpperCamelCase_    (       self    )			->					List[Any]:






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



     def 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_    )			->					Tuple:






           """simple docstring"""
           _lowerCamelCase           =					20
           _lowerCamelCase           =					model_class_name(A_    )

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

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

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

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

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

           _lowerCamelCase           =					model.decode(A_					,					A_    )

           _lowerCamelCase           =					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 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					20
           _lowerCamelCase           =					model_class_name(A_    )

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

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

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

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

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

           _lowerCamelCase           =					model.decode(A_					,					A_					,					decoder_attention_mask=A_    )

           _lowerCamelCase           =					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					(			__lowercase ,	unittest.TestCase ,	__lowercase			):

     '''simple docstring'''

     A_					    =	True
     A_					    =	(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
     A_					    =	(FlaxMarianMTModel,) if is_flax_available() else ()



     def 		UpperCamelCase_    (       self    )			->					Tuple:






           """simple docstring"""
           _lowerCamelCase           =					FlaxMarianModelTester(self    )



     def 		UpperCamelCase_    (       self    )			->					Tuple:






           """simple docstring"""
           _lowerCamelCase     ,       _lowerCamelCase           =					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 		UpperCamelCase_    (       self    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase     ,       _lowerCamelCase           =					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 		UpperCamelCase_    (       self    )			->					List[str]:






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

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__    ):
                       _lowerCamelCase           =					self._prepare_for_class(A_					,					A_    )
                       _lowerCamelCase           =					model_class(A_    )

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

                       with self.subTest('''JIT Enabled'''    ):
                             _lowerCamelCase           =					encode_jitted(**A_    ).to_tuple()

                       with self.subTest('''JIT Disabled'''    ):
                             with jax.disable_jit():
                                   _lowerCamelCase           =					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 		UpperCamelCase_    (       self    )			->					str:






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

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

                       _lowerCamelCase           =					{
                           '''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_					,					A_					,					A_    ):
                             return model.decode(
                                 decoder_input_ids=A_					,					decoder_attention_mask=A_					,					encoder_outputs=A_					,					)

                       with self.subTest('''JIT Enabled'''    ):
                             _lowerCamelCase           =					decode_jitted(**A_    ).to_tuple()

                       with self.subTest('''JIT Disabled'''    ):
                             with jax.disable_jit():
                                   _lowerCamelCase           =					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 		UpperCamelCase_    (       self    )			->					int:






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





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

     '''simple docstring'''

     A_					    =	None
     A_					    =	None



     @classmethod
     def 		UpperCamelCase_    (       cls    )			->					None:






           """simple docstring"""
           _lowerCamelCase           =					F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
           return cls



     @cached_property
     def 		UpperCamelCase_    (       self    )			->					Dict:






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



     @property
     def 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           return self.tokenizer.eos_token_id



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






           """simple docstring"""
           _lowerCamelCase           =					FlaxMarianMTModel.from_pretrained(self.model_name    )
           self.assertEqual(model.config.decoder_start_token_id					,					model.config.pad_token_id    )
           return model



     def 		UpperCamelCase_    (       self					,					**A_    )			->					List[str]:






           """simple docstring"""
           _lowerCamelCase           =					self.translate_src_text(**A_    )
           self.assertListEqual(self.expected_text					,					A_    )





     def 		UpperCamelCase_    (       self					,					**A_    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					self.tokenizer(self.src_text					,					padding=A_					,					return_tensors='''np'''					,					**A_    )
           _lowerCamelCase           =					self.model.generate(
               model_inputs.input_ids					,					attention_mask=model_inputs.attention_mask					,					num_beams=2					,					max_length=1_28					,					).sequences
           _lowerCamelCase           =					self.tokenizer.batch_decode(A_					,					skip_special_tokens=A_    )
           return generated_words





@require_flax
@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     A_					    =	'en'
     A_					    =	'fr'
     A_					    =	[
         'I am a small frog.',
         'Now I can forget the 100 words of german that I know.',
     ]
     A_					    =	[
         'Je suis une petite grenouille.',
         'Maintenant, je peux oublier les 100 mots d\'allemand que je connais.',
     ]



     @slow
     def 		UpperCamelCase_    (       self    )			->					int:






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





@require_flax
@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     A_					    =	'fr'
     A_					    =	'en'
     A_					    =	[
         'Donnez moi le micro.',
         'Tom et Mary étaient assis à une table.',  # Accents
     ]
     A_					    =	[
         'Give me the microphone.',
         'Tom and Mary were sitting at a table.',
     ]



     @slow
     def 		UpperCamelCase_    (       self    )			->					Optional[int]:






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





@require_flax
@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     A_					    =	'mt'
     A_					    =	'en'
     A_					    =	['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
     A_					    =	['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']



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






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





@require_flax
@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     A_					    =	'en'
     A_					    =	'de'
     A_					    =	[
         '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.',
     ]
     A_					    =	[
         '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 		UpperCamelCase_    (       self    )			->					Union[str, Any]:






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





@require_flax
@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     A_					    =	'en'
     A_					    =	'zh'
     A_					    =	['My name is Wolfgang and I live in Berlin']
     A_					    =	['我叫沃尔夫冈 我住在柏林']



     @slow
     def 		UpperCamelCase_    (       self    )			->					str:






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





@require_flax
@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     A_					    =	'ru'
     A_					    =	'fr'
     A_					    =	['Он показал мне рукопись своей новой пьесы.']
     A_					    =	['Il m\'a montré le manuscrit de sa nouvelle pièce.']



     @slow
     def 		UpperCamelCase_    (       self    )			->					Any:






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





@require_flax
@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     A_					    =	'en'
     A_					    =	'ROMANCE'
     A_					    =	[
         '>>fr<< Don\'t spend so much time watching TV.',
         '>>pt<< Your message has been sent.',
         '>>es<< He\'s two years older than me.',
     ]
     A_					    =	[
         'Ne passez pas autant de temps à regarder la télé.',
         'A sua mensagem foi enviada.',
         'Es dos años más viejo que yo.',
     ]



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






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