






import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCamelCase   :   str      =    get_tests_dir("""fixtures/test_sentencepiece.model""")


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

__UpperCamelCase   :   List[Any]      =    250004
__UpperCamelCase   :   Optional[int]      =    250020


@require_sentencepiece
@require_tokenizers
class 							__UpperCamelCase     (  _lowerCAmelCase ,      unittest.TestCase ):
	__snake_case						:Optional[int] =							MBartTokenizer
	__snake_case						:int =							MBartTokenizerFast
	__snake_case						:Union[str, Any] =							True
	__snake_case						:List[str] =							True







	def 			_a (	self   :				Tuple      )		->			Tuple:
		"""simple docstring"""







		super().setUp()

		# We have a SentencePiece fixture for testing
		__lowercase   							=	MBartTokenizer(_lowerCAmelCase							,					keep_accents=_lowerCAmelCase      )
		tokenizer.save_pretrained(self.tmpdirname      )







	def 			_a (	self   :				Tuple      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	MBartTokenizer(_lowerCAmelCase							,					keep_accents=_lowerCAmelCase      )

		__lowercase   							=	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 [285, 46, 10, 170, 382]]							,					)

		__lowercase   							=	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""",
		        """.""",
		    ]							,					)
		__lowercase   							=	tokenizer.convert_tokens_to_ids(_lowerCAmelCase      )
		self.assertListEqual(
		    _lowerCAmelCase							,					[
		        value + tokenizer.fairseq_offset
		        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
		        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
		    ]							,					)

		__lowercase   							=	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>""",
		        """.""",
		    ]							,					)







	def 			_a (	self   :				Any      )		->			Union[str, Any]:
		"""simple docstring"""







		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

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

				__lowercase   							=	tempfile.mkdtemp()

				__lowercase   							=	tokenizer_r.save_pretrained(_lowerCAmelCase      )
				__lowercase   							=	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      )      )
				__lowercase   							=	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
				__lowercase   							=	tokenizer_r.from_pretrained(_lowerCAmelCase      )
				__lowercase   							=	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
				__lowercase   							=	tempfile.mkdtemp()

				__lowercase   							=	tokenizer_r.save_pretrained(_lowerCAmelCase							,					legacy_format=_lowerCAmelCase      )
				__lowercase   							=	tokenizer_p.save_pretrained(_lowerCAmelCase      )

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

				# Checks everything loads correctly in the same way
				__lowercase   							=	tokenizer_r.from_pretrained(_lowerCAmelCase      )
				__lowercase   							=	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
				__lowercase   							=	tempfile.mkdtemp()

				__lowercase   							=	tokenizer_r.save_pretrained(_lowerCAmelCase							,					legacy_format=_lowerCAmelCase      )
				__lowercase   							=	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
				__lowercase   							=	tokenizer_r.from_pretrained(_lowerCAmelCase      )
				__lowercase   							=	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 							__UpperCamelCase     (  unittest.TestCase ):
	__snake_case						:Tuple =							'facebook/mbart-large-en-ro'
	__snake_case						:List[Any] =							[
	    ' 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.',
	]
	__snake_case						:str =							[
	    '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.',
	]
	__snake_case						:Tuple =							[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, EN_CODE]







	@classmethod
	def 			_a (	cls   :				Tuple      )		->			Optional[Any]:
		"""simple docstring"""







		__lowercase   							=	MBartTokenizer.from_pretrained(
		    cls.checkpoint_name							,					src_lang="""en_XX"""							,					tgt_lang="""ro_RO"""      )
		__lowercase   							=	1
		return cls







	def 			_a (	self   :				Any      )		->			str:
		"""simple docstring"""







		self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]							,					25_0001      )
		self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]							,					25_0004      )
		self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]							,					25_0020      )







	def 			_a (	self   :				List[Any]      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
		self.assertListEqual(self.expected_src_tokens							,					_lowerCAmelCase      )







	def 			_a (	self   :				List[str]      )		->			int:
		"""simple docstring"""







		self.assertIn(_lowerCAmelCase							,					self.tokenizer.all_special_ids      )
		__lowercase   							=	[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
		__lowercase   							=	self.tokenizer.decode(_lowerCAmelCase							,					skip_special_tokens=_lowerCAmelCase      )
		__lowercase   							=	self.tokenizer.decode(generated_ids[1:]							,					skip_special_tokens=_lowerCAmelCase      )
		self.assertEqual(_lowerCAmelCase							,					_lowerCAmelCase      )
		self.assertNotIn(self.tokenizer.eos_token							,					_lowerCAmelCase      )







	def 			_a (	self   :				Union[str, Any]      )		->			List[str]:
		"""simple docstring"""







		__lowercase   							=	["""this is gunna be a long sentence """ * 20]
		assert isinstance(src_text[0]							,					_lowerCAmelCase      )
		__lowercase   							=	10
		__lowercase   							=	self.tokenizer(_lowerCAmelCase							,					max_length=_lowerCAmelCase							,					truncation=_lowerCAmelCase      ).input_ids[0]
		self.assertEqual(ids[-2]							,					2      )
		self.assertEqual(ids[-1]							,					_lowerCAmelCase      )
		self.assertEqual(len(_lowerCAmelCase      )							,					_lowerCAmelCase      )







	def 			_a (	self   :				Dict      )		->			str:
		"""simple docstring"""







		self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]      )							,					[25_0026, 25_0001]      )







	def 			_a (	self   :				Optional[int]      )		->			str:
		"""simple docstring"""







		__lowercase   							=	tempfile.mkdtemp()
		__lowercase   							=	self.tokenizer.fairseq_tokens_to_ids
		self.tokenizer.save_pretrained(_lowerCAmelCase      )
		__lowercase   							=	MBartTokenizer.from_pretrained(_lowerCAmelCase      )
		self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,					_lowerCAmelCase      )







	@require_torch
	def 			_a (	self   :				List[Any]      )		->			Dict:
		"""simple docstring"""







		__lowercase   							=	self.tokenizer(self.src_text							,					text_target=self.tgt_text							,					padding=_lowerCAmelCase							,					return_tensors="""pt"""      )
		__lowercase   							=	shift_tokens_right(batch["""labels"""]							,					self.tokenizer.pad_token_id      )

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







	@require_torch
	def 			_a (	self   :				Tuple      )		->			Optional[int]:
		"""simple docstring"""







		__lowercase   							=	self.tokenizer(
		    self.src_text							,					text_target=self.tgt_text							,					padding=_lowerCAmelCase							,					truncation=_lowerCAmelCase							,					max_length=len(self.expected_src_tokens      )							,					return_tensors="""pt"""							,					)

		__lowercase   							=	shift_tokens_right(batch["""labels"""]							,					self.tokenizer.pad_token_id      )

		self.assertIsInstance(_lowerCAmelCase							,					_lowerCAmelCase      )

		self.assertEqual((2, 14)							,					batch.input_ids.shape      )
		self.assertEqual((2, 14)							,					batch.attention_mask.shape      )
		__lowercase   							=	batch.input_ids.tolist()[0]
		self.assertListEqual(self.expected_src_tokens							,					_lowerCAmelCase      )
		self.assertEqual(2							,					batch.decoder_input_ids[0, -1]      )  # EOS
		# Test that special tokens are reset
		self.assertEqual(self.tokenizer.prefix_tokens							,					[]      )
		self.assertEqual(self.tokenizer.suffix_tokens							,					[self.tokenizer.eos_token_id, EN_CODE]      )







	def 			_a (	self   :				Optional[Any]      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	self.tokenizer(self.src_text							,					padding=_lowerCAmelCase							,					truncation=_lowerCAmelCase							,					max_length=3							,					return_tensors="""pt"""      )
		__lowercase   							=	self.tokenizer(
		    text_target=self.tgt_text							,					padding=_lowerCAmelCase							,					truncation=_lowerCAmelCase							,					max_length=10							,					return_tensors="""pt"""      )
		__lowercase   							=	targets["""input_ids"""]
		__lowercase   							=	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]							,					10      )







	@require_torch
	def 			_a (	self   :				Dict      )		->			int:
		"""simple docstring"""







		__lowercase   							=	self.tokenizer._build_translation_inputs(
		    """A test"""							,					return_tensors="""pt"""							,					src_lang="""en_XX"""							,					tgt_lang="""ar_AR"""      )

		self.assertEqual(
		    nested_simplify(_lowerCAmelCase      )							,					{
		        # A, test, EOS, en_XX
		        """input_ids""": [[62, 3034, 2, 25_0004]],
		        """attention_mask""": [[1, 1, 1, 1]],
		        # ar_AR
		        """forced_bos_token_id""": 25_0001,
		    }							,					)








# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						"""philschmid/bart-large-cnn-samsum"""
 UpperCAmelCase__						=						(
     """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
     """and returns a summary of the text."""
 )
 UpperCAmelCase__						=						"""summarizer"""
 UpperCAmelCase__						=						AutoTokenizer
 UpperCAmelCase__						=						AutoModelForSeqaSeqLM

 UpperCAmelCase__						=						["""text"""]
 UpperCAmelCase__						=						["""text"""]
 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : List[Any]      )     ->      List[str]:
     return self.pre_processor(UpperCAmelCase     ,	return_tensors='pt'     ,	truncation=UpperCAmelCase      )
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : Dict      )     ->      str:
     return self.model.generate(**UpperCAmelCase      )[0]







 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : List[Any]      )     ->      Union[str, Any]:
     return self.pre_processor.decode(UpperCAmelCase     ,	skip_special_tokens=UpperCAmelCase     ,	clean_up_tokenization_spaces=UpperCAmelCase      )





