class 			UpperCamelCase					:

     '''simple docstring'''

     def __init__(       self					,					A_    )			->					Any:






           """simple docstring"""
           _lowerCamelCase           =					val
           # Number of nodes in left subtree
           _lowerCamelCase           =					0
           _lowerCamelCase           =					None
           _lowerCamelCase           =					None
           _lowerCamelCase           =					None





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






           """simple docstring"""
           assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

           if self.val < other.val:
                 _lowerCamelCase           =					self.right
                 _lowerCamelCase           =					None
                 if self.right:
                       _lowerCamelCase           =					other
                 _lowerCamelCase           =					other
                 _lowerCamelCase           =					self.left_tree_size * 2 + 1
                 return self
           else:
                 _lowerCamelCase           =					other.right
                 _lowerCamelCase           =					None
                 if other.right:
                       _lowerCamelCase           =					self
                 _lowerCamelCase           =					self
                 _lowerCamelCase           =					other.left_tree_size * 2 + 1
                 return other





class 			UpperCamelCase					:

     '''simple docstring'''

     def __init__(       self					,					A_=None					,					A_=None					,					A_=0    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					heap_size
           _lowerCamelCase           =					bottom_root
           _lowerCamelCase           =					min_node



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






           """simple docstring"""

           # Empty heaps corner cases
           if other.size == 0:
                 return None
           if self.size == 0:
                 _lowerCamelCase           =					other.size
                 _lowerCamelCase           =					other.bottom_root
                 _lowerCamelCase           =					other.min_node
                 return None
           # Update size
           _lowerCamelCase           =					self.size + other.size

           # Update min.node
           if self.min_node.val > other.min_node.val:
                 _lowerCamelCase           =					other.min_node
           # Merge

           # Order roots by left_subtree_size
           _lowerCamelCase           =					[]
           _lowerCamelCase     ,       _lowerCamelCase           =					self.bottom_root, other.bottom_root
           while i or j:
                 if i and ((not j) or i.left_tree_size < j.left_tree_size):
                       combined_roots_list.append((i, True)    )
                       _lowerCamelCase           =					i.parent
                 else:
                       combined_roots_list.append((j, False)    )
                       _lowerCamelCase           =					j.parent
        # Insert links between them
           for i in range(len(A_    ) - 1    ):
                 if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
                       _lowerCamelCase           =					combined_roots_list[i + 1][0]
                       _lowerCamelCase           =					combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
           _lowerCamelCase           =					combined_roots_list[0][0]
           while i.parent:
                 if (
                     (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
                 ) or (
                     i.left_tree_size == i.parent.left_tree_size
                     and i.left_tree_size != i.parent.parent.left_tree_size
                 ):
                       # Neighbouring Nodes
                       _lowerCamelCase           =					i.left
                       _lowerCamelCase           =					i.parent.parent

                       # Merging trees
                       _lowerCamelCase           =					i.merge_trees(i.parent    )

                       # Updating links
                       _lowerCamelCase           =					previous_node
                       _lowerCamelCase           =					next_node
                       if previous_node:
                             _lowerCamelCase           =					i
                       if next_node:
                             _lowerCamelCase           =					i
                 else:
                       _lowerCamelCase           =					i.parent
        # Updating self.bottom_root
           while i.left:
                 _lowerCamelCase           =					i.left
           _lowerCamelCase           =					i

           # Update other
           _lowerCamelCase           =					self.size
           _lowerCamelCase           =					self.bottom_root
           _lowerCamelCase           =					self.min_node

           # Return the merged heap
           return self



     def 		UpperCamelCase_    (       self					,					A_    )			->					str:






           """simple docstring"""
           if self.size == 0:
                 _lowerCamelCase           =					Node(A_    )
                 _lowerCamelCase           =					1
                 _lowerCamelCase           =					self.bottom_root
           else:
                 # Create new node
                 _lowerCamelCase           =					Node(A_    )

                 # Update size
                 self.size += 1

                 # update min_node
                 if val < self.min_node.val:
                       _lowerCamelCase           =					new_node
                 # Put new_node as a bottom_root in heap
                 _lowerCamelCase           =					new_node
                 _lowerCamelCase           =					self.bottom_root
                 _lowerCamelCase           =					new_node

                 # Consecutively merge roots with same left_tree_size
                 while (
                     self.bottom_root.parent
                     and self.bottom_root.left_tree_size
                     == self.bottom_root.parent.left_tree_size
                 ):
                       # Next node
                       _lowerCamelCase           =					self.bottom_root.parent.parent

                       # Merge
                       _lowerCamelCase           =					self.bottom_root.merge_trees(self.bottom_root.parent    )

                       # Update Links
                       _lowerCamelCase           =					next_node
                       _lowerCamelCase           =					None
                       if next_node:
                             _lowerCamelCase           =					self.bottom_root



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






           """simple docstring"""
           return self.min_node.val



     def 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           return self.size == 0



     def 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					self.min_node.val

           # Last element in heap corner case
           if self.size == 1:
                 # Update size
                 _lowerCamelCase           =					0

                 # Update bottom root
                 _lowerCamelCase           =					None

                 # Update min_node
                 _lowerCamelCase           =					None

                 return min_value
           # No right subtree corner case
           # The structure of the tree implies that this should be the bottom root
           # and there is at least one other root
           if self.min_node.right is None:
                 # Update size
                 self.size -= 1

                 # Update bottom root
                 _lowerCamelCase           =					self.bottom_root.parent
                 _lowerCamelCase           =					None

                 # Update min_node
                 _lowerCamelCase           =					self.bottom_root
                 _lowerCamelCase           =					self.bottom_root.parent
                 while i:
                       if i.val < self.min_node.val:
                             _lowerCamelCase           =					i
                       _lowerCamelCase           =					i.parent
                 return min_value
           # General case
           # Find the BinomialHeap of the right subtree of min_node
           _lowerCamelCase           =					self.min_node.right
           _lowerCamelCase           =					None
           _lowerCamelCase           =					bottom_of_new
           _lowerCamelCase           =					1

           # Size, min_node and bottom_root
           while bottom_of_new.left:
                 _lowerCamelCase           =					size_of_new * 2 + 1
                 _lowerCamelCase           =					bottom_of_new.left
                 if bottom_of_new.val < min_of_new.val:
                       _lowerCamelCase           =					bottom_of_new
        # Corner case of single root on top left path
           if (not self.min_node.left) and (not self.min_node.parent):
                 _lowerCamelCase           =					size_of_new
                 _lowerCamelCase           =					bottom_of_new
                 _lowerCamelCase           =					min_of_new
                 # print("Single root, multiple nodes case")
                 return min_value
           # Remaining cases
           # Construct heap of right subtree
           _lowerCamelCase           =					BinomialHeap(
               bottom_root=A_					,					min_node=A_					,					heap_size=A_    )

           # Update size
           _lowerCamelCase           =					self.size - 1 - size_of_new

           # Neighbour nodes
           _lowerCamelCase           =					self.min_node.left
           _lowerCamelCase           =					self.min_node.parent

           # Initialize new bottom_root and min_node
           _lowerCamelCase           =					previous_node or next_node
           _lowerCamelCase           =					next_node

           # Update links of previous_node and search below for new min_node and
           # bottom_root
           if previous_node:
                 _lowerCamelCase           =					next_node

                 # Update bottom_root and search for min_node below
                 _lowerCamelCase           =					previous_node
                 _lowerCamelCase           =					previous_node
                 while self.bottom_root.left:
                       _lowerCamelCase           =					self.bottom_root.left
                       if self.bottom_root.val < self.min_node.val:
                             _lowerCamelCase           =					self.bottom_root
           if next_node:
                 _lowerCamelCase           =					previous_node

                 # Search for new min_node above min_node
                 _lowerCamelCase           =					next_node
                 while i:
                       if i.val < self.min_node.val:
                             _lowerCamelCase           =					i
                       _lowerCamelCase           =					i.parent
        # Merge heaps
           self.merge_heaps(A_    )

           return min_value



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






           """simple docstring"""
           _lowerCamelCase           =					self.bottom_root
           while top_root.parent:
                 _lowerCamelCase           =					top_root.parent
           # preorder
           _lowerCamelCase           =					[]
           self.__traversal(A_					,					A_    )
           return heap_pre_order



     def 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_=0    )			->					str:






           """simple docstring"""
           if curr_node:
                 preorder.append((curr_node.val, level)    )
                 self.__traversal(curr_node.left					,					A_					,					level + 1    )
                 self.__traversal(curr_node.right					,					A_					,					level + 1    )
           else:
                 preorder.append(('''#''', level)    )





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






           """simple docstring"""
           if self.is_empty():
                 return ""
           _lowerCamelCase           =					self.pre_order()

           return "\n".join(('''-''' * level + str(A_    )) for value, level in preorder_heap    )


# Unit Tests
if __name__ == "__main__":
     import doctest

     doctest.testmod()
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()