



'''simple docstring'''
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					_UpperCamelCase											=			'platform'

					import jax
					import jax.numpy as jnp

					from transformers import AutoTokenizer
					from transformers.models.mbart.modeling_flax_mbart import (
					    FlaxMBartForConditionalGeneration,
					    FlaxMBartForQuestionAnswering,
					    FlaxMBartForSequenceClassification,
					    FlaxMBartModel,
					    shift_tokens_right,
					)


def   lowercase_	(   lowerCAmelCase__	:  List[str]			,							lowerCAmelCase__	:  Optional[Any]			,							lowerCAmelCase__	:  Union[str, Any]=None			,							lowerCAmelCase__	:  str=None			,							lowerCAmelCase__	:  Any=None			,							lowerCAmelCase__	:  List[Any]=None			,							lowerCAmelCase__	:  Optional[Any]=None			,							lowerCAmelCase__	:  List[Any]=None			,							):

			"""simple docstring"""





			if attention_mask is None:
						__UpperCAmelCase   :      int										=     np.where(input_ids != config.pad_token_id			,							1			,							0 )
			if decoder_attention_mask is None:
						__UpperCAmelCase   :      str										=     np.where(decoder_input_ids != config.pad_token_id			,							1			,							0 )
			if head_mask is None:
						__UpperCAmelCase   :      Dict										=     np.ones((config.encoder_layers, config.encoder_attention_heads) )
			if decoder_head_mask is None:
						__UpperCAmelCase   :      int										=     np.ones((config.decoder_layers, config.decoder_attention_heads) )
			if cross_attn_head_mask is None:
						__UpperCAmelCase   :      Optional[int]										=     np.ones((config.decoder_layers, config.decoder_attention_heads) )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			}



class _A  (    unittest.TestCase    ):





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

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





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

					'''simple docstring'''
					__UpperCAmelCase   :      Union[str, Any]										=     np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,       self.vocab_size				) ,       3 ,       self.vocab_size				)
					__UpperCAmelCase   :      List[str]										=     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,       dtype=np.intaa				)) ,       -1				)

					__UpperCAmelCase   :      int										=     shift_tokens_right(__UpperCAmelCase ,       1				)

					__UpperCAmelCase   :      Dict										=     MBartConfig(
					    vocab_size=self.vocab_size ,       d_model=self.hidden_size ,       encoder_layers=self.num_hidden_layers ,       decoder_layers=self.num_hidden_layers ,       encoder_attention_heads=self.num_attention_heads ,       decoder_attention_heads=self.num_attention_heads ,       encoder_ffn_dim=self.intermediate_size ,       decoder_ffn_dim=self.intermediate_size ,       dropout=self.hidden_dropout_prob ,       attention_dropout=self.attention_probs_dropout_prob ,       max_position_embeddings=self.max_position_embeddings ,       eos_token_id=self.eos_token_id ,       bos_token_id=self.bos_token_id ,       pad_token_id=self.pad_token_id ,       decoder_start_token_id=self.decoder_start_token_id ,       initializer_range=self.initializer_range ,       use_cache=__UpperCAmelCase ,       )
					__UpperCAmelCase   :      Any										=     prepare_mbart_inputs_dict(__UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)
					return config, inputs_dict





		def 							__A       (     self				)       ->     Any:

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase   :      Dict										=     self.prepare_config_and_inputs()
					return config, inputs_dict





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     20
					__UpperCAmelCase   :      Optional[int]										=     model_class_name(__UpperCAmelCase				)

					__UpperCAmelCase   :      List[Any]										=     model.encode(inputs_dict["""input_ids"""]				)

					__UpperCAmelCase				,  __UpperCAmelCase   :      str										=     (
					    inputs_dict["""decoder_input_ids"""],
					    inputs_dict["""decoder_attention_mask"""],
					)

					__UpperCAmelCase   :      Optional[Any]										=     model.init_cache(decoder_input_ids.shape[0] ,       __UpperCAmelCase ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      Any										=     jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,       dtype="""i4"""				)

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

					__UpperCAmelCase   :      List[str]										=     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,       dtype="""i4"""				)
					__UpperCAmelCase   :      Optional[Any]										=     model.decode(
					    decoder_input_ids[:, -1:] ,       __UpperCAmelCase ,       decoder_attention_mask=__UpperCAmelCase ,       past_key_values=outputs_cache.past_key_values ,       decoder_position_ids=__UpperCAmelCase ,       )

					__UpperCAmelCase   :      Optional[int]										=     model.decode(__UpperCAmelCase ,       __UpperCAmelCase				)

					__UpperCAmelCase   :      Optional[int]										=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
					self.parent.assertTrue(diff < 1E-3 ,       msg=f'Max diff is {diff}'				)





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     20
					__UpperCAmelCase   :      Optional[int]										=     model_class_name(__UpperCAmelCase				)

					__UpperCAmelCase   :      List[Any]										=     model.encode(inputs_dict["""input_ids"""]				)

					__UpperCAmelCase				,  __UpperCAmelCase   :      Any										=     (
					    inputs_dict["""decoder_input_ids"""],
					    inputs_dict["""decoder_attention_mask"""],
					)

					__UpperCAmelCase   :      List[str]										=     jnp.concatenate(
					    [
					        decoder_attention_mask,
					        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
					    ] ,       axis=-1 ,       )

					__UpperCAmelCase   :      Union[str, Any]										=     model.init_cache(decoder_input_ids.shape[0] ,       __UpperCAmelCase ,       __UpperCAmelCase				)
					__UpperCAmelCase   :      Union[str, Any]										=     jnp.broadcast_to(
					    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :] ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,       )

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

					__UpperCAmelCase   :      int										=     model.decode(__UpperCAmelCase ,       __UpperCAmelCase ,       decoder_attention_mask=__UpperCAmelCase				)

					__UpperCAmelCase   :      int										=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
					self.parent.assertTrue(diff < 1E-3 ,       msg=f'Max diff is {diff}'				)



@require_flax
class _A  (    unittest.TestCase    ):
		_SCREAMING_SNAKE_CASE					:    List[Any]						      =			99





		def 							__A       (     self				)       ->     Tuple:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     np.array(
					    [
					        [71, 82, 18, 33, 46, 91, 2],
					        [68, 34, 26, 58, 30, 82, 2],
					        [5, 97, 17, 39, 94, 40, 2],
					        [76, 83, 94, 25, 70, 78, 2],
					        [87, 59, 41, 35, 48, 66, 2],
					        [55, 13, 16, 58, 5, 2, 1],  # note padding
					        [64, 27, 31, 51, 12, 75, 2],
					        [52, 64, 86, 17, 83, 39, 2],
					        [48, 61, 9, 24, 71, 82, 2],
					        [26, 1, 60, 48, 22, 13, 2],
					        [21, 5, 62, 28, 14, 76, 2],
					        [45, 98, 37, 86, 59, 48, 2],
					        [70, 70, 50, 9, 28, 0, 2],
					    ] ,       dtype=np.intaa ,       )

					__UpperCAmelCase   :      List[str]										=     input_ids.shape[0]
					__UpperCAmelCase   :      Dict										=     MBartConfig(
					    vocab_size=self.vocab_size ,       d_model=24 ,       encoder_layers=2 ,       decoder_layers=2 ,       encoder_attention_heads=2 ,       decoder_attention_heads=2 ,       encoder_ffn_dim=32 ,       decoder_ffn_dim=32 ,       max_position_embeddings=48 ,       eos_token_id=2 ,       pad_token_id=1 ,       bos_token_id=0 ,       )
					return config, input_ids, batch_size





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

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      str										=     self._get_config_and_data()
					__UpperCAmelCase   :      Optional[int]										=     FlaxMBartForSequenceClassification(__UpperCAmelCase				)
					__UpperCAmelCase   :      str										=     model(input_ids=__UpperCAmelCase ,       decoder_input_ids=__UpperCAmelCase				)
					__UpperCAmelCase   :      Dict										=     (batch_size, config.num_labels)
					self.assertEqual(outputs["""logits"""].shape ,       __UpperCAmelCase				)





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

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      Union[str, Any]										=     self._get_config_and_data()
					__UpperCAmelCase   :      int										=     FlaxMBartForQuestionAnswering(__UpperCAmelCase				)
					__UpperCAmelCase   :      Any										=     model(input_ids=__UpperCAmelCase				)

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





		def 							__A       (     self				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      Any										=     self._get_config_and_data()
					__UpperCAmelCase   :      Dict										=     FlaxMBartForConditionalGeneration(__UpperCAmelCase				)
					__UpperCAmelCase   :      Tuple										=     lm_model(input_ids=__UpperCAmelCase				)
					__UpperCAmelCase   :      str										=     (batch_size, input_ids.shape[1], config.vocab_size)
					self.assertEqual(outputs["""logits"""].shape ,       __UpperCAmelCase				)





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

					'''simple docstring'''
					__UpperCAmelCase   :      List[Any]										=     MBartConfig(
					    vocab_size=self.vocab_size ,       d_model=14 ,       encoder_layers=2 ,       decoder_layers=2 ,       encoder_attention_heads=2 ,       decoder_attention_heads=2 ,       encoder_ffn_dim=8 ,       decoder_ffn_dim=8 ,       max_position_embeddings=48 ,       )
					__UpperCAmelCase   :      Optional[int]										=     FlaxMBartForConditionalGeneration(__UpperCAmelCase				)
					__UpperCAmelCase   :      Any										=     np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] ,       dtype=np.intaa				)
					__UpperCAmelCase   :      Union[str, Any]										=     np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] ,       dtype=np.intaa				)
					__UpperCAmelCase   :      Optional[Any]										=     lm_model(input_ids=__UpperCAmelCase ,       decoder_input_ids=__UpperCAmelCase				)
					__UpperCAmelCase   :      List[str]										=     (*summary.shape, config.vocab_size)
					self.assertEqual(outputs["""logits"""].shape ,       __UpperCAmelCase				)





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

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



@require_flax
class _A  (    UpperCAmelCase_	,				unittest.TestCase	,				UpperCAmelCase_    ):
		_SCREAMING_SNAKE_CASE					:    List[Any]						      =			True
		_SCREAMING_SNAKE_CASE					:    Any						      =			(
		    (
		        FlaxMBartModel,
		        FlaxMBartForConditionalGeneration,
		        FlaxMBartForSequenceClassification,
		        FlaxMBartForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)
		_SCREAMING_SNAKE_CASE					:    Union[str, Any]						      =			(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()





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

					'''simple docstring'''
					__UpperCAmelCase   :      Dict										=     FlaxMBartModelTester(self				)





		def 							__A       (     self				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase   :      Union[str, Any]										=     self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
								self.model_tester.check_use_cache_forward(__UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)





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

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase   :      Union[str, Any]										=     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(__UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)





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

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase   :      Tuple										=     self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__				):
											__UpperCAmelCase   :      int										=     self._prepare_for_class(__UpperCAmelCase ,       __UpperCAmelCase				)
											__UpperCAmelCase   :      Dict										=     model_class(__UpperCAmelCase				)

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

											with self.subTest("""JIT Enabled"""				):
														__UpperCAmelCase   :      Dict										=     encode_jitted(**__UpperCAmelCase				).to_tuple()

											with self.subTest("""JIT Disabled"""				):
														with jax.disable_jit():
																	__UpperCAmelCase   :      List[str]										=     encode_jitted(**__UpperCAmelCase				).to_tuple()

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





		def 							__A       (     self				)       ->     int:

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase   :      Optional[int]										=     self.model_tester.prepare_config_and_inputs_for_common()

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

											__UpperCAmelCase   :      List[Any]										=     {
											    """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(__UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				):
														return model.decode(
														    decoder_input_ids=__UpperCAmelCase ,       decoder_attention_mask=__UpperCAmelCase ,       encoder_outputs=__UpperCAmelCase ,       )

											with self.subTest("""JIT Enabled"""				):
														__UpperCAmelCase   :      str										=     decode_jitted(**__UpperCAmelCase				).to_tuple()

											with self.subTest("""JIT Disabled"""				):
														with jax.disable_jit():
																	__UpperCAmelCase   :      Any										=     decode_jitted(**__UpperCAmelCase				).to_tuple()

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





		@slow
		def 							__A       (     self				)       ->     str:

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



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





		@cached_property
		def 							__A       (     self				)       ->     int:

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





		@cached_property
		def 							__A       (     self				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      Any										=     FlaxMBartForConditionalGeneration.from_pretrained(self.model_name ,       from_pt=__UpperCAmelCase				)
					return model





		def 							__A       (     self ,       **__UpperCAmelCase				)       ->     str:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     self.translate_src_text(**__UpperCAmelCase				)
					self.assertListEqual(self.expected_text ,       __UpperCAmelCase				)





		def 							__A       (     self ,       **__UpperCAmelCase				)       ->     Any:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     self.tokenizer(self.src_text ,       **__UpperCAmelCase ,       return_tensors="""np"""				)
					__UpperCAmelCase   :      Optional[Any]										=     self.model.generate(
					    model_inputs.input_ids ,       attention_mask=model_inputs.attention_mask ,       decoder_start_token_id=self.tokenizer.lang_code_to_id["""ro_RO"""] ,       early_stopping=__UpperCAmelCase ,       num_beams=2 ,       ).sequences
					__UpperCAmelCase   :      str										=     self.tokenizer.batch_decode(__UpperCAmelCase ,       skip_special_tokens=__UpperCAmelCase				)
					return generated_words





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

					'''simple docstring'''
					self._assert_generated_batch_equal_expected()









'''simple docstring'''
from __future__ import annotations

from typing import Any



class _A  :





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     None:  # noqa: A002

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     item
					__UpperCAmelCase   :      str										=     next



class _A  :





		def __init__(     self				)       ->     None:

					'''simple docstring'''
					__UpperCAmelCase   :      Node | None										=     None
					__UpperCAmelCase   :      Optional[int]										=     0





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     None:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     Node(__UpperCAmelCase ,       self.head				)
					self.size += 1





		def 							__A       (     self				)       ->     Any:

					'''simple docstring'''
					# Switched 'self.is_empty()' to 'self.head is None'
					# because mypy was considering the possibility that 'self.head'
					# can be None in below else part and giving error
					if self.head is None:
								return None
					else:
								__UpperCAmelCase   :      str										=     self.head.item
								__UpperCAmelCase   :      Tuple										=     self.head.next
								self.size -= 1
								return item





		def 							__A       (     self				)       ->     bool:

					'''simple docstring'''
					return self.head is None





		def __str__(     self				)       ->     str:

					'''simple docstring'''
					if self.is_empty():
								return ""
					else:
								__UpperCAmelCase   :      Union[str, Any]										=     self.head
								__UpperCAmelCase   :      List[Any]										=     """"""
								__UpperCAmelCase   :      list[str]										=     []
								while iterate:
											item_list.append(str(iterate.item				)				)
											__UpperCAmelCase   :      Any										=     iterate.next

								__UpperCAmelCase   :      Dict										=     """ --> """.join(__UpperCAmelCase				)

								return item_str





		def __len__(     self				)       ->     int:

					'''simple docstring'''
					return self.size




