"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel

@require_tf
class 	UpperCamelCase    :
				UpperCAmelCase       :   List[str]					=	MBartConfig
				UpperCAmelCase       :   str					=	{}
				UpperCAmelCase       :   Any					=	"""gelu"""



				def __init__(self       :   Optional[int]			,						_A       :   int			,						_A       :   str=13			,						_A       :   int=7			,						_A       :   List[str]=True			,						_A       :   Union[str, Any]=False			,						_A       :   List[Any]=99			,						_A       :   Optional[Any]=32			,						_A       :   Optional[int]=2			,						_A       :   Optional[Any]=4			,						_A       :   List[str]=37			,						_A       :   Optional[Any]=0.1			,						_A       :   List[str]=0.1			,						_A       :   Tuple=20			,						_A       :   List[Any]=2			,						_A       :   List[Any]=1			,						_A       :   Union[str, Any]=0			,						)					->			str:
					__snake_case    :							Tuple   	=				parent
					__snake_case    :							Optional[int]   	=				batch_size
					__snake_case    :							Optional[Any]   	=				seq_length
					__snake_case    :							List[str]   	=				is_training
					__snake_case    :							int   	=				use_labels
					__snake_case    :							List[str]   	=				vocab_size
					__snake_case    :							Dict   	=				hidden_size
					__snake_case    :							Optional[int]   	=				num_hidden_layers
					__snake_case    :							List[Any]   	=				num_attention_heads
					__snake_case    :							Dict   	=				intermediate_size
					__snake_case    :							Tuple   	=				hidden_dropout_prob
					__snake_case    :							int   	=				attention_probs_dropout_prob
					__snake_case    :							Any   	=				max_position_embeddings
					__snake_case    :							Optional[int]   	=				eos_token_id
					__snake_case    :							Optional[Any]   	=				pad_token_id
					__snake_case    :							int   	=				bos_token_id



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

					__snake_case    :							int   	=				ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size)

					__snake_case    :							str   	=				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			,						**self.config_updates			,						)
					__snake_case    :							List[str]   	=				prepare_mbart_inputs_dict(_A			,						_A			,						_A)
					return config, inputs_dict






				def       _lowercase  (self       :   Tuple			,						_A       :   str			,						_A       :   Any)					->			int:
					__snake_case    :							Optional[int]   	=				TFMBartModel(config=_A).get_decoder()
					__snake_case    :							Dict   	=				inputs_dict['input_ids']

					__snake_case    :							int   	=				input_ids[:1, :]
					__snake_case    :							Dict   	=				inputs_dict['attention_mask'][:1, :]
					__snake_case    :							int   	=				inputs_dict['head_mask']
					__snake_case    :							List[str]   	=				1

					# first forward pass
					__snake_case    :							int   	=				model(_A			,						attention_mask=_A			,						head_mask=_A			,						use_cache=_A)

					__snake_case	,       __snake_case    :							Optional[int]   	=				outputs.to_tuple()
					__snake_case    :							int   	=				past_key_values[1]







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       List[Any]    ,							UpperCAmelCase_					:       Any    ,							UpperCAmelCase_					:       List[Any]    ,							UpperCAmelCase_					:       int=None    ,							UpperCAmelCase_					:       Union[str, Any]=None    ,							UpperCAmelCase_					:       Optional[int]=None    ,							UpperCAmelCase_					:       Any=None    ,							UpperCAmelCase_					:       Optional[int]=None    ,							)							->    Dict:

	'''simple docstring'''





	if attention_mask is None:
		__snake_case    :							Optional[int]   	=				tf.cast(tf.math.not_equal(UpperCAmelCase_    ,							config.pad_token_id						)    ,							tf.inta						)
	if decoder_attention_mask is None:
		__snake_case    :							Optional[Any]   	=				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:
		__snake_case    :							Optional[Any]   	=				tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
	if decoder_head_mask is None:
		__snake_case    :							Dict   	=				tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
	if cross_attn_head_mask is None:
		__snake_case    :							Optional[int]   	=				tf.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,
	    "head_mask": head_mask,
	    "decoder_head_mask": decoder_head_mask,
	    "cross_attn_head_mask": cross_attn_head_mask,
	}

@require_tf
class 	UpperCamelCase    (					lowercase   ,			lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   Optional[int]					=	(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
				UpperCAmelCase       :   Any					=	(TFMBartForConditionalGeneration,) if is_tf_available() else ()
				UpperCAmelCase       :   Tuple					=	(
				    {
				        """conversational""": TFMBartForConditionalGeneration,
				        """feature-extraction""": TFMBartModel,
				        """summarization""": TFMBartForConditionalGeneration,
				        """text2text-generation""": TFMBartForConditionalGeneration,
				        """translation""": TFMBartForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				UpperCAmelCase       :   Any					=	True
				UpperCAmelCase       :   Optional[Any]					=	False
				UpperCAmelCase       :   Optional[int]					=	False



				def       _lowercase  (self       :   Optional[int]			,						_A       :   Union[str, Any]			,						_A       :   int			,						_A       :   List[str]			,						_A       :   Optional[int]			,						_A       :   Optional[int])					->			Any:
					if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
						# Exception encountered when calling layer '...'
						return True

					return False



				def       _lowercase  (self       :   str)					->			List[Any]:
					__snake_case    :							Tuple   	=				TFMBartModelTester(self)
					__snake_case    :							Union[str, Any]   	=				ConfigTester(self			,						config_class=_A)



				def       _lowercase  (self       :   int)					->			Dict:
					self.config_tester.run_common_tests()






				def       _lowercase  (self       :   Dict)					->			Any:
					__snake_case    :							Union[str, Any]   	=				self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*_A)







@require_sentencepiece
@require_tokenizers
@require_tf
class 	UpperCamelCase    (					unittest.TestCase		):
				UpperCAmelCase       :   List[str]					=	[
				    """ UN Chief Says There Is No Military Solution in Syria""",
				]
				UpperCAmelCase       :   Dict					=	[
				    """Şeful ONU declară că nu există o soluţie militară în Siria""",
				]
				UpperCAmelCase       :   List[str]					=	"""facebook/mbart-large-en-ro"""



				@cached_property
				def       _lowercase  (self       :   List[Any])					->			List[Any]:
					return AutoTokenizer.from_pretrained(self.model_name)



				@cached_property
				def       _lowercase  (self       :   Tuple)					->			Optional[int]:
					__snake_case    :							Optional[Any]   	=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
					return model



				def       _lowercase  (self       :   int			,						**_A       :   int)					->			List[Any]:
					__snake_case    :							str   	=				self.translate_src_text(**_A)
					self.assertListEqual(self.expected_text			,						_A)



				def       _lowercase  (self       :   List[Any]			,						**_A       :   Dict)					->			Optional[int]:
					__snake_case    :							Union[str, Any]   	=				self.tokenizer(self.src_text			,						**_A			,						return_tensors='tf')
					__snake_case    :							Optional[Any]   	=				self.model.generate(
					    model_inputs.input_ids			,						attention_mask=model_inputs.attention_mask			,						num_beams=2)
					__snake_case    :							Tuple   	=				self.tokenizer.batch_decode(_A			,						skip_special_tokens=_A)
					return generated_words






				@slow
				def       _lowercase  (self       :   Optional[Any])					->			Optional[int]:
					self._assert_generated_batch_equal_expected()






"""simple docstring"""







import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class 	UpperCamelCase    (					lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   List[Any]					=	MvpTokenizer
				UpperCAmelCase       :   int					=	MvpTokenizerFast
				UpperCAmelCase       :   Optional[Any]					=	True
				UpperCAmelCase       :   Tuple					=	filter_roberta_detectors



				def       _lowercase  (self       :   Any)					->			str:
					super().setUp()
					__snake_case    :							Optional[Any]   	=				[
					    'l',
					    'o',
					    'w',
					    'e',
					    'r',
					    's',
					    't',
					    'i',
					    'd',
					    'n',
					    '\u0120',
					    '\u0120l',
					    '\u0120n',
					    '\u0120lo',
					    '\u0120low',
					    'er',
					    '\u0120lowest',
					    '\u0120newer',
					    '\u0120wider',
					    '<unk>',
					]
					__snake_case    :							List[Any]   	=				dict(zip(_A			,						range(len(_A))))
					__snake_case    :							Optional[Any]   	=				['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
					__snake_case    :							Optional[Any]   	=				{'unk_token': '<unk>'}

					__snake_case    :							int   	=				os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['vocab_file'])
					__snake_case    :							str   	=				os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['merges_file'])
					with open(self.vocab_file			,						'w'			,						encoding='utf-8') as fp:
						fp.write(json.dumps(_A) + '\n')
					with open(self.merges_file			,						'w'			,						encoding='utf-8') as fp:
						fp.write('\n'.join(_A))



				def       _lowercase  (self       :   List[str]			,						**_A       :   Any)					->			str:
					kwargs.update(self.special_tokens_map)
					return self.tokenizer_class.from_pretrained(self.tmpdirname			,						**_A)



				def       _lowercase  (self       :   Optional[Any]			,						**_A       :   Tuple)					->			Any:
					kwargs.update(self.special_tokens_map)
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,						**_A)



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   str)					->			Optional[Any]:
					return "lower newer", "lower newer"



				@cached_property
				def       _lowercase  (self       :   Tuple)					->			int:
					return MvpTokenizer.from_pretrained('RUCAIBox/mvp')



				@cached_property
				def       _lowercase  (self       :   int)					->			Tuple:
					return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp')



				@require_torch
				def       _lowercase  (self       :   Optional[int])					->			List[Any]:
					__snake_case    :							Optional[int]   	=				['A long paragraph for summarization.', 'Another paragraph for summarization.']
					__snake_case    :							Any   	=				[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							str   	=				tokenizer(_A			,						max_length=len(_A)			,						padding=_A			,						return_tensors='pt')
						self.assertIsInstance(_A			,						_A)

						self.assertEqual((2, 9)			,						batch.input_ids.shape)
						self.assertEqual((2, 9)			,						batch.attention_mask.shape)
						__snake_case    :							List[str]   	=				batch.input_ids.tolist()[0]
						self.assertListEqual(_A			,						_A)
						# Test that special tokens are reset



				@require_torch
				def       _lowercase  (self       :   List[str])					->			Any:
					__snake_case    :							List[Any]   	=				['A long paragraph for summarization.', 'Another paragraph for summarization.']
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Tuple   	=				tokenizer(_A			,						padding=_A			,						return_tensors='pt')
						# check if input_ids are returned and no labels
						self.assertIn('input_ids'			,						_A)
						self.assertIn('attention_mask'			,						_A)
						self.assertNotIn('labels'			,						_A)
						self.assertNotIn('decoder_attention_mask'			,						_A)



				@require_torch
				def       _lowercase  (self       :   List[Any])					->			Tuple:
					__snake_case    :							List[Any]   	=				[
					    'Summary of the text.',
					    'Another summary.',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Any   	=				tokenizer(text_target=_A			,						max_length=32			,						padding='max_length'			,						return_tensors='pt')
						self.assertEqual(32			,						targets['input_ids'].shape[1])



				@require_torch
				def       _lowercase  (self       :   Tuple)					->			Dict:
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Dict   	=				tokenizer(
						    ['I am a small frog' * 10_24, 'I am a small frog']			,						padding=_A			,						truncation=_A			,						return_tensors='pt')
						self.assertIsInstance(_A			,						_A)
						self.assertEqual(batch.input_ids.shape			,						(2, 10_24))



				@require_torch
				def       _lowercase  (self       :   Optional[int])					->			Dict:
					__snake_case    :							List[Any]   	=				['A long paragraph for summarization.']
					__snake_case    :							str   	=				[
					    'Summary of the text.',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						__snake_case    :							Any   	=				tokenizer(_A			,						text_target=_A			,						return_tensors='pt')
						__snake_case    :							str   	=				inputs['input_ids']
						__snake_case    :							Optional[Any]   	=				inputs['labels']
						self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
						self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
						self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
						self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())



				def       _lowercase  (self       :   str)					->			Tuple:
					pass






				def       _lowercase  (self       :   str)					->			Any:
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
							__snake_case    :							Any   	=				self.rust_tokenizer_class.from_pretrained(_A			,						**_A)
							__snake_case    :							List[Any]   	=				self.tokenizer_class.from_pretrained(_A			,						**_A)
							__snake_case    :							List[Any]   	=				'A, <mask> AllenNLP sentence.'
							__snake_case    :							List[Any]   	=				tokenizer_r.encode_plus(_A			,						add_special_tokens=_A			,						return_token_type_ids=_A)
							__snake_case    :							int   	=				tokenizer_p.encode_plus(_A			,						add_special_tokens=_A			,						return_token_type_ids=_A)

							# token_type_ids should put 0 everywhere
							self.assertEqual(sum(tokens_r['token_type_ids'])			,						sum(tokens_p['token_type_ids']))

							# attention_mask should put 1 everywhere, so sum over length should be 1
							self.assertEqual(
							    sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask'])			,						sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask'])			,						)

							__snake_case    :							Tuple   	=				tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
							__snake_case    :							Union[str, Any]   	=				tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])

							# Rust correctly handles the space before the mask while python doesnt
							self.assertSequenceEqual(tokens_p['input_ids']			,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])
							self.assertSequenceEqual(tokens_r['input_ids']			,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])

							self.assertSequenceEqual(
							    _A			,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
							self.assertSequenceEqual(
							    _A			,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])





