






"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFLEDForConditionalGeneration, TFLEDModel

@require_tf
class 						A_							:
				UpperCAmelCase__        =				LEDConfig
				UpperCAmelCase__        =				{}
				UpperCAmelCase__        =				'''gelu'''







				def __init__(							self							:						Optional[Any]						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						List[Any]=1_3						,    __lowerCamelCase							:						str=7						,    __lowerCamelCase							:						Any=True						,    __lowerCamelCase							:						int=False						,    __lowerCamelCase							:						List[Any]=9_9						,    __lowerCamelCase							:						Any=3_2						,    __lowerCamelCase							:						Optional[Any]=2						,    __lowerCamelCase							:						Dict=4						,    __lowerCamelCase							:						int=3_7						,    __lowerCamelCase							:						Tuple=0.1						,    __lowerCamelCase							:						Union[str, Any]=0.1						,    __lowerCamelCase							:						Optional[Any]=2_0						,    __lowerCamelCase							:						List[str]=2						,    __lowerCamelCase							:						str=1						,    __lowerCamelCase							:						List[Any]=0						,    __lowerCamelCase							:						Dict=4						,    )				->					Optional[Any]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     seq_length
										__magic_name__            =     is_training
										__magic_name__            =     use_labels
										__magic_name__            =     vocab_size
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     intermediate_size

										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     max_position_embeddings
										__magic_name__            =     eos_token_id
										__magic_name__            =     pad_token_id
										__magic_name__            =     bos_token_id
										__magic_name__            =     attention_window

										# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
										# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
										# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
										# because its local attention only attends to `self.attention_window` and one before and one after
										__magic_name__            =     self.attention_window + 2

										# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
										# the `test_attention_outputs` and `test_hidden_states_output` tests
										__magic_name__            =     (
										    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
										)







				def     _snake_case					(							self							:						Dict	)				->					Optional[Any]:
										__magic_name__            =     ids_tensor([self.batch_size, self.seq_length - 1]						,    self.vocab_size	)
										__magic_name__            =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)						,    1	)
										__magic_name__            =     tf.concat([input_ids, eos_tensor]						,    axis=1	)

										__magic_name__            =     ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size	)

										__magic_name__            =     self.config_cls(
										    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_ids=[2]						,    bos_token_id=self.bos_token_id						,    pad_token_id=self.pad_token_id						,    decoder_start_token_id=self.pad_token_id						,    attention_window=self.attention_window						,    **self.config_updates						,    )
										__magic_name__            =     prepare_led_inputs_dict(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     tf.concat(
										    [tf.zeros_like(__lowerCamelCase	)[:, :-1], tf.ones_like(__lowerCamelCase	)[:, -1:]]						,    axis=-1						,    )
										__magic_name__            =     global_attention_mask
										return config, inputs_dict







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Any	)				->					Union[str, Any]:
										__magic_name__            =     TFLEDModel(config=__lowerCamelCase	).get_decoder()
										__magic_name__            =     inputs_dict["input_ids"]

										__magic_name__            =     input_ids[:1, :]
										__magic_name__            =     inputs_dict["attention_mask"][:1, :]
										__magic_name__            =     1

										# first forward pass
										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase						,    use_cache=__lowerCamelCase	)

										__magic_name__      ,    __magic_name__            =     outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										__magic_name__            =     ids_tensor((self.batch_size, 3)						,    config.vocab_size	)
										__magic_name__            =     tf.cast(ids_tensor((self.batch_size, 3)						,    2	)						,    tf.inta	)

										# append to next input_ids and
										__magic_name__            =     tf.concat([input_ids, next_tokens]						,    axis=-1	)
										__magic_name__            =     tf.concat([attention_mask, next_attn_mask]						,    axis=-1	)

										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase	)[0]
										__magic_name__            =     model(__lowerCamelCase						,    attention_mask=__lowerCamelCase						,    past_key_values=__lowerCamelCase	)[0]

										self.parent.assertEqual(next_tokens.shape[1]						,    output_from_past.shape[1]	)

										# select random slice
										__magic_name__            =     int(ids_tensor((1,)						,    output_from_past.shape[-1]	)	)
										__magic_name__            =     output_from_no_past[:, -3:, random_slice_idx]
										__magic_name__            =     output_from_past[:, :, random_slice_idx]

										# test that outputs are equal for slice
										tf.debugging.assert_near(__lowerCamelCase						,    __lowerCamelCase						,    rtol=1e-3	)






def _lowerCAmelCase	(  __lowerCamelCase:str ,						__lowerCamelCase:str ,						__lowerCamelCase:List[Any] ,						__lowerCamelCase:Any=None ,						__lowerCamelCase:Dict=None ,						__lowerCamelCase:List[Any]=None ,						__lowerCamelCase:Union[str, Any]=None ,						):

						'''simple docstring'''






						if attention_mask is None:
												__magic_name__            =     tf.cast(tf.math.not_equal(__lowerCamelCase ,						config.pad_token_id   ) ,						tf.inta   )
						if decoder_attention_mask is None:
												__magic_name__            =     tf.concat(
												    [
												        tf.ones(decoder_input_ids[:, :1].shape ,						dtype=tf.inta   ),
												        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,						config.pad_token_id   ) ,						tf.inta   ),
												    ] ,						axis=-1 ,						)
						if head_mask is None:
												__magic_name__            =     tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
						if decoder_head_mask is None:
												__magic_name__            =     tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
						return {
						    "input_ids": input_ids,
						    "attention_mask": attention_mask,
						    "decoder_input_ids": decoder_input_ids,
						    "decoder_attention_mask": decoder_attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						}

@require_tf
class 						A_							(      snake_case_					,							snake_case_					,							unittest.TestCase  ):
				UpperCAmelCase__        =				(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
				UpperCAmelCase__        =				(TFLEDForConditionalGeneration,) if is_tf_available() else ()
				UpperCAmelCase__        =				(
				    {
				        '''conversational''': TFLEDForConditionalGeneration,
				        '''feature-extraction''': TFLEDModel,
				        '''summarization''': TFLEDForConditionalGeneration,
				        '''text2text-generation''': TFLEDForConditionalGeneration,
				        '''translation''': TFLEDForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				UpperCAmelCase__        =				True
				UpperCAmelCase__        =				False
				UpperCAmelCase__        =				False
				UpperCAmelCase__        =				False







				def     _snake_case					(							self							:						int	)				->					Optional[int]:
										__magic_name__            =     TFLEDModelTester(self	)
										__magic_name__            =     ConfigTester(self						,    config_class=__lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[Any]	)				->					Dict:
										self.config_tester.run_common_tests()







				def     _snake_case					(							self							:						Optional[int]	)				->					List[Any]:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase	)







				def     _snake_case					(							self							:						List[str]	)				->					str:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()
										__magic_name__            =     tf.zeros_like(inputs_dict["attention_mask"]	)
										__magic_name__            =     2
										__magic_name__            =     tf.where(
										    tf.range(self.model_tester.seq_length	)[None, :] < num_global_attn_indices						,    1						,    inputs_dict["global_attention_mask"]						,    )

										__magic_name__            =     True
										__magic_name__            =     self.model_tester.seq_length
										__magic_name__            =     self.model_tester.encoder_seq_length

										def check_decoder_attentions_output(__lowerCamelCase							:						int	):
																__magic_name__            =     outputs.decoder_attentions
																self.assertEqual(len(__lowerCamelCase	)						,    self.model_tester.num_hidden_layers	)
																self.assertListEqual(
																    list(decoder_attentions[0].shape[-3:]	)						,    [self.model_tester.num_attention_heads, seq_length, seq_length]						,    )

										def check_encoder_attentions_output(__lowerCamelCase							:						Any	):
																__magic_name__            =     [t.numpy() for t in outputs.encoder_attentions]
																__magic_name__            =     [t.numpy() for t in outputs.encoder_global_attentions]
																self.assertEqual(len(__lowerCamelCase	)						,    self.model_tester.num_hidden_layers	)
																self.assertEqual(len(__lowerCamelCase	)						,    self.model_tester.num_hidden_layers	)
																self.assertListEqual(
																    list(attentions[0].shape[-3:]	)						,    [self.model_tester.num_attention_heads, seq_length, seq_length]						,    )
																self.assertListEqual(
																    list(global_attentions[0].shape[-3:]	)						,    [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]						,    )

										for model_class in self.all_model_classes:
																__magic_name__            =     True
																__magic_name__            =     False
																__magic_name__            =     False
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     model(self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)	)
																__magic_name__            =     len(__lowerCamelCase	)
																self.assertEqual(config.output_hidden_states						,    __lowerCamelCase	)
																check_encoder_attentions_output(__lowerCamelCase	)

																if self.is_encoder_decoder:
																						__magic_name__            =     model_class(__lowerCamelCase	)
																						__magic_name__            =     model(self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)	)
																						self.assertEqual(config.output_hidden_states						,    __lowerCamelCase	)
																						check_decoder_attentions_output(__lowerCamelCase	)

																# Check that output attentions can also be changed via the config
																del inputs_dict["output_attentions"]
																__magic_name__            =     True
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     model(self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)	)
																self.assertEqual(config.output_hidden_states						,    __lowerCamelCase	)
																check_encoder_attentions_output(__lowerCamelCase	)

																# Check attention is always last and order is fine
																__magic_name__            =     True
																__magic_name__            =     True
																__magic_name__            =     model_class(__lowerCamelCase	)
																__magic_name__            =     model(self._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase	)	)

																self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)						,    len(__lowerCamelCase	)	)
																self.assertEqual(model.config.output_hidden_states						,    __lowerCamelCase	)
																check_encoder_attentions_output(__lowerCamelCase	)







				@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."	)
				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[str]:
										pass







				def     _snake_case					(							self							:						int	)				->					str:
										# TODO: Head-masking not yet implement
										pass






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






						return tf.constant(__lowerCamelCase ,						dtype=tf.intaa   )


lowercase    			=       1e-4


@slow
@require_tf
class 						A_							(      unittest.TestCase  ):







				def     _snake_case					(							self							:						Optional[Any]	)				->					List[str]:
										__magic_name__            =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	).led

										# change to intended input here
										__magic_name__            =     _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     prepare_led_inputs_dict(model.config						,    __lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     model(**__lowerCamelCase	)[0]
										__magic_name__            =     (1, 1_0_2_4, 7_6_8)
										self.assertEqual(output.shape						,    __lowerCamelCase	)
										# change to expected output here
										__magic_name__            =     tf.convert_to_tensor(
										    [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]						,    )
										tf.debugging.assert_near(output[:, :3, :3]						,    __lowerCamelCase						,    atol=1e-3	)







				def     _snake_case					(							self							:						Any	)				->					Dict:
										__magic_name__            =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"	)

										# change to intended input here
										__magic_name__            =     _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]	)
										__magic_name__            =     prepare_led_inputs_dict(model.config						,    __lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     model(**__lowerCamelCase	)[0]
										__magic_name__            =     (1, 1_0_2_4, model.config.vocab_size)
										self.assertEqual(output.shape						,    __lowerCamelCase	)
										# change to expected output here
										__magic_name__            =     tf.convert_to_tensor(
										    [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]						,    )
										tf.debugging.assert_near(output[:, :3, :3]						,    __lowerCamelCase						,    atol=1e-3						,    rtol=1e-3	)











"""simple docstring"""

import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowercase    			=       get_tests_dir('''fixtures/test_sentencepiece.model''')

if is_torch_available():
			from transformers.models.mbart.modeling_mbart import shift_tokens_right

lowercase    			=       250004
lowercase    			=       250020

@require_sentencepiece
@require_tokenizers
class 						A_							(      snake_case_					,							unittest.TestCase  ):
				UpperCAmelCase__        =				MBartaaTokenizer
				UpperCAmelCase__        =				MBartaaTokenizerFast
				UpperCAmelCase__        =				True
				UpperCAmelCase__        =				True







				def     _snake_case					(							self							:						Any	)				->					Dict:
										super().setUp()

										# We have a SentencePiece fixture for testing
										__magic_name__            =     MBartaaTokenizer(__lowerCamelCase						,    src_lang="en_XX"						,    tgt_lang="ro_RO"						,    keep_accents=__lowerCamelCase	)
										tokenizer.save_pretrained(self.tmpdirname	)







				def     _snake_case					(							self							:						Dict	)				->					List[Any]:
										__magic_name__            =     "<s>"
										__magic_name__            =     0

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase	)						,    __lowerCamelCase	)
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase	)						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						int	)				->					Union[str, Any]:
										__magic_name__            =     list(self.get_tokenizer().get_vocab().keys()	)

										self.assertEqual(vocab_keys[0]						,    "<s>"	)
										self.assertEqual(vocab_keys[1]						,    "<pad>"	)
										self.assertEqual(vocab_keys[-1]						,    "<mask>"	)
										self.assertEqual(len(__lowerCamelCase	)						,    1_0_5_4	)







				def     _snake_case					(							self							:						Any	)				->					Dict:
										self.assertEqual(self.get_tokenizer().vocab_size						,    1_0_5_4	)







				def     _snake_case					(							self							:						str	)				->					Optional[Any]:
										__magic_name__            =     MBartaaTokenizer(__lowerCamelCase						,    src_lang="en_XX"						,    tgt_lang="ro_RO"						,    keep_accents=__lowerCamelCase	)

										__magic_name__            =     tokenizer.tokenize("This is a test"	)
										self.assertListEqual(__lowerCamelCase						,    ["▁This", "▁is", "▁a", "▁t", "est"]	)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__lowerCamelCase	)						,    [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]						,    )

										__magic_name__            =     tokenizer.tokenize("I was born in 92000, and this is falsé."	)
										self.assertListEqual(
										    __lowerCamelCase						,    [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."]						,    )
										__magic_name__            =     tokenizer.convert_tokens_to_ids(__lowerCamelCase	)
										self.assertListEqual(
										    __lowerCamelCase						,    [
										        value + tokenizer.fairseq_offset
										        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
										    ]						,    )

										__magic_name__            =     tokenizer.convert_ids_to_tokens(__lowerCamelCase	)
										self.assertListEqual(
										    __lowerCamelCase						,    [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", "."]						,    )







				@slow
				def     _snake_case					(							self							:						List[str]	)				->					List[str]:
										# fmt: off
										__magic_name__            =     {"input_ids": [[2_5_0_0_0_4, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [2_5_0_0_0_4, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_5_0_0_0_4, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=__lowerCamelCase						,    model_name="facebook/mbart-large-50"						,    revision="d3913889c59cd5c9e456b269c376325eabad57e2"						,    )







				def     _snake_case					(							self							:						int	)				->					Any:
										if not self.test_slow_tokenizer:
																# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																return

										__magic_name__            =     (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
																						__magic_name__            =     self.rust_tokenizer_class.from_pretrained(__lowerCamelCase						,    **__lowerCamelCase	)
																						__magic_name__            =     self.tokenizer_class.from_pretrained(__lowerCamelCase						,    **__lowerCamelCase	)

																						__magic_name__            =     tempfile.mkdtemp()

																						__magic_name__            =     tokenizer_r.save_pretrained(__lowerCamelCase	)
																						__magic_name__            =     tokenizer_p.save_pretrained(__lowerCamelCase	)

																						# Checks it save with the same files + the tokenizer.json file for the fast one
																						self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files	)	)
																						__magic_name__            =     tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f	)
																						self.assertSequenceEqual(__lowerCamelCase						,    __lowerCamelCase	)

																						# Checks everything loads correctly in the same way
																						__magic_name__            =     tokenizer_r.from_pretrained(__lowerCamelCase	)
																						__magic_name__            =     tokenizer_p.from_pretrained(__lowerCamelCase	)

																						# Check special tokens are set accordingly on Rust and Python
																						for key in tokenizer_pp.special_tokens_map:
																												self.assertTrue(hasattr(__lowerCamelCase						,    __lowerCamelCase	)	)
																												# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																												# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																						shutil.rmtree(__lowerCamelCase	)

																						# Save tokenizer rust, legacy_format=True
																						__magic_name__            =     tempfile.mkdtemp()

																						__magic_name__            =     tokenizer_r.save_pretrained(__lowerCamelCase						,    legacy_format=__lowerCamelCase	)
																						__magic_name__            =     tokenizer_p.save_pretrained(__lowerCamelCase	)

																						# Checks it save with the same files
																						self.assertSequenceEqual(__lowerCamelCase						,    __lowerCamelCase	)

																						# Checks everything loads correctly in the same way
																						__magic_name__            =     tokenizer_r.from_pretrained(__lowerCamelCase	)
																						__magic_name__            =     tokenizer_p.from_pretrained(__lowerCamelCase	)

																						# Check special tokens are set accordingly on Rust and Python
																						for key in tokenizer_pp.special_tokens_map:
																												self.assertTrue(hasattr(__lowerCamelCase						,    __lowerCamelCase	)	)

																						shutil.rmtree(__lowerCamelCase	)

																						# Save tokenizer rust, legacy_format=False
																						__magic_name__            =     tempfile.mkdtemp()

																						__magic_name__            =     tokenizer_r.save_pretrained(__lowerCamelCase						,    legacy_format=__lowerCamelCase	)
																						__magic_name__            =     tokenizer_p.save_pretrained(__lowerCamelCase	)

																						# Checks it saved the tokenizer.json file
																						self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files	)	)

																						# Checks everything loads correctly in the same way
																						__magic_name__            =     tokenizer_r.from_pretrained(__lowerCamelCase	)
																						__magic_name__            =     tokenizer_p.from_pretrained(__lowerCamelCase	)

																						# Check special tokens are set accordingly on Rust and Python
																						for key in tokenizer_pp.special_tokens_map:
																												self.assertTrue(hasattr(__lowerCamelCase						,    __lowerCamelCase	)	)

																						shutil.rmtree(__lowerCamelCase	)





@require_torch
@require_sentencepiece
@require_tokenizers
class 						A_							(      unittest.TestCase  ):
				UpperCAmelCase__        =				'''facebook/mbart-large-50-one-to-many-mmt'''
				UpperCAmelCase__        =				[
				    ''' UN Chief Says There Is No Military Solution in Syria''',
				    ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
				]
				UpperCAmelCase__        =				[
				    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
				    '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
				    ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
				    ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
				]
				UpperCAmelCase__        =				[EN_CODE, 8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2]







				@classmethod
				def     _snake_case					(							cls							:						Any	)				->					Dict:
										__magic_name__            =     MBartaaTokenizer.from_pretrained(
										    cls.checkpoint_name						,    src_lang="en_XX"						,    tgt_lang="ro_RO"	)
										__magic_name__            =     1
										return cls







				def     _snake_case					(							self							:						Any	)				->					Dict:
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]						,    2_5_0_0_0_1	)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]						,    2_5_0_0_0_4	)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]						,    2_5_0_0_2_0	)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"]						,    2_5_0_0_3_8	)







				def     _snake_case					(							self							:						Any	)				->					str:
										__magic_name__            =     self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
										self.assertListEqual(self.expected_src_tokens						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Any	)				->					List[str]:
										self.assertIn(__lowerCamelCase						,    self.tokenizer.all_special_ids	)
										__magic_name__            =     [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
										__magic_name__            =     self.tokenizer.decode(__lowerCamelCase						,    skip_special_tokens=__lowerCamelCase	)
										__magic_name__            =     self.tokenizer.decode(generated_ids[1:]						,    skip_special_tokens=__lowerCamelCase	)
										self.assertEqual(__lowerCamelCase						,    __lowerCamelCase	)
										self.assertNotIn(self.tokenizer.eos_token						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						int	)				->					List[str]:
										__magic_name__            =     ["this is gunna be a long sentence " * 2_0]
										assert isinstance(src_text[0]						,    __lowerCamelCase	)
										__magic_name__            =     1_0
										__magic_name__            =     self.tokenizer(__lowerCamelCase						,    max_length=__lowerCamelCase						,    truncation=__lowerCamelCase	).input_ids[0]
										self.assertEqual(ids[0]						,    __lowerCamelCase	)
										self.assertEqual(ids[-1]						,    2	)
										self.assertEqual(len(__lowerCamelCase	)						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						str	)				->					Tuple:
										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]	)						,    [2_5_0_0_5_3, 2_5_0_0_0_1]	)







				def     _snake_case					(							self							:						Any	)				->					str:
										__magic_name__            =     tempfile.mkdtemp()
										__magic_name__            =     self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(__lowerCamelCase	)
										__magic_name__            =     MBartaaTokenizer.from_pretrained(__lowerCamelCase	)
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,    __lowerCamelCase	)







				@require_torch
				def     _snake_case					(							self							:						int	)				->					Dict:
										__magic_name__            =     self.tokenizer(self.src_text						,    text_target=self.tgt_text						,    padding=__lowerCamelCase						,    return_tensors="pt"	)
										__magic_name__            =     shift_tokens_right(batch["labels"]						,    self.tokenizer.pad_token_id	)

										# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
										assert batch.input_ids[1][0] == EN_CODE
										assert batch.input_ids[1][-1] == 2
										assert batch.labels[1][0] == RO_CODE
										assert batch.labels[1][-1] == 2
										assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]







				@require_torch
				def     _snake_case					(							self							:						Dict	)				->					Tuple:
										__magic_name__            =     self.tokenizer(
										    self.src_text						,    text_target=self.tgt_text						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=len(self.expected_src_tokens	)						,    return_tensors="pt"						,    )
										__magic_name__            =     shift_tokens_right(batch["labels"]						,    self.tokenizer.pad_token_id	)

										self.assertIsInstance(__lowerCamelCase						,    __lowerCamelCase	)

										self.assertEqual((2, 1_4)						,    batch.input_ids.shape	)
										self.assertEqual((2, 1_4)						,    batch.attention_mask.shape	)
										__magic_name__            =     batch.input_ids.tolist()[0]
										self.assertListEqual(self.expected_src_tokens						,    __lowerCamelCase	)
										self.assertEqual(2						,    batch.decoder_input_ids[0, 0]	)  # decoder_start_token_id
										# Test that special tokens are reset
										self.assertEqual(self.tokenizer.prefix_tokens						,    [EN_CODE]	)
										self.assertEqual(self.tokenizer.suffix_tokens						,    [self.tokenizer.eos_token_id]	)







				def     _snake_case					(							self							:						Optional[int]	)				->					Dict:
										__magic_name__            =     self.tokenizer(self.src_text						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=3						,    return_tensors="pt"	)
										__magic_name__            =     self.tokenizer(
										    text_target=self.tgt_text						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=1_0						,    return_tensors="pt"	)
										__magic_name__            =     targets["input_ids"]
										__magic_name__            =     shift_tokens_right(__lowerCamelCase						,    self.tokenizer.pad_token_id	)

										self.assertEqual(batch.input_ids.shape[1]						,    3	)
										self.assertEqual(batch.decoder_input_ids.shape[1]						,    1_0	)







				@require_torch
				def     _snake_case					(							self							:						Optional[int]	)				->					Tuple:
										__magic_name__            =     self.tokenizer._build_translation_inputs(
										    "A test"						,    return_tensors="pt"						,    src_lang="en_XX"						,    tgt_lang="ar_AR"	)

										self.assertEqual(
										    nested_simplify(__lowerCamelCase	)						,    {
										        # en_XX, A, test, EOS
										        "input_ids": [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
										        "attention_mask": [[1, 1, 1, 1]],
										        # ar_AR
										        "forced_bos_token_id": 2_5_0_0_0_1,
										    }						,    )



