
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


lowercase  :					Union[str, Any]						    =							get_tests_dir("""fixtures/test_sentencepiece.model""")


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

lowercase  :					Optional[Any]						    =							250004
lowercase  :					Optional[int]						    =							250020

@require_sentencepiece
@require_tokenizers
class __snake_case		(      lowerCAmelCase					,	unittest.TestCase     ):
    _a		:						Optional[int]=				MBartTokenizer
    _a		:						str=				MBartTokenizerFast
    _a		:						Dict=				True
    _a		:						int=				True
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           super().setUp()

           # We have a SentencePiece fixture for testing
           lowercase							:     str       	=      MBartTokenizer(snake_case		,keep_accents=snake_case      )
           tokenizer.save_pretrained(self.tmpdirname      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      MBartTokenizer(snake_case		,keep_accents=snake_case      )

           lowercase							:     Any       	=      tokenizer.tokenize("""This is a test"""      )
           self.assertListEqual(snake_case		,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]      )

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(snake_case      )		,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]		,)

           lowercase							:     str       	=      tokenizer.tokenize("""I was born in 92000, and this is falsé."""      )
           self.assertListEqual(
               snake_case		,[
                   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							:     Optional[Any]       	=      tokenizer.convert_tokens_to_ids(snake_case      )
           self.assertListEqual(
               snake_case		,[
                   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							:     str       	=      tokenizer.convert_ids_to_tokens(snake_case      )
           self.assertListEqual(
               snake_case		,[
                   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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''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							:     Union[str, Any]       	=      (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							:     Tuple       	=      self.rust_tokenizer_class.from_pretrained(snake_case		,**snake_case      )
                         lowercase							:     Optional[Any]       	=      self.tokenizer_class.from_pretrained(snake_case		,**snake_case      )

                         lowercase							:     Optional[int]       	=      tempfile.mkdtemp()

                         lowercase							:     Dict       	=      tokenizer_r.save_pretrained(snake_case      )
                         lowercase							:     Tuple       	=      tokenizer_p.save_pretrained(snake_case      )

                         # 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							:     Optional[Any]       	=      tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f      )
                         self.assertSequenceEqual(snake_case		,snake_case      )

                         # Checks everything loads correctly in the same way
                         lowercase							:     Any       	=      tokenizer_r.from_pretrained(snake_case      )
                         lowercase							:     str       	=      tokenizer_p.from_pretrained(snake_case      )

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

                         shutil.rmtree(snake_case      )

                         # Save tokenizer rust, legacy_format=True
                         lowercase							:     int       	=      tempfile.mkdtemp()

                         lowercase							:     Dict       	=      tokenizer_r.save_pretrained(snake_case		,legacy_format=snake_case      )
                         lowercase							:     str       	=      tokenizer_p.save_pretrained(snake_case      )

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

                         # Checks everything loads correctly in the same way
                         lowercase							:     Optional[int]       	=      tokenizer_r.from_pretrained(snake_case      )
                         lowercase							:     Tuple       	=      tokenizer_p.from_pretrained(snake_case      )

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

                         shutil.rmtree(snake_case      )

                         # Save tokenizer rust, legacy_format=False
                         lowercase							:     List[Any]       	=      tempfile.mkdtemp()

                         lowercase							:     List[Any]       	=      tokenizer_r.save_pretrained(snake_case		,legacy_format=snake_case      )
                         lowercase							:     Union[str, Any]       	=      tokenizer_p.save_pretrained(snake_case      )

                         # 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							:     Tuple       	=      tokenizer_r.from_pretrained(snake_case      )
                         lowercase							:     Optional[int]       	=      tokenizer_p.from_pretrained(snake_case      )

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

                         shutil.rmtree(snake_case      )






@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case		(      unittest.TestCase     ):
    _a		:						Any=				"facebook/mbart-large-en-ro"
    _a		:						Union[str, 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.",
    ]
    _a		:						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.",
    ]
    _a		:						Optional[Any]=				[8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2, EN_CODE]
    @classmethod
    def       _SCREAMING_SNAKE_CASE       (  cls      ):



           '''simple docstring'''







           lowercase							:     MBartTokenizer       	=      MBartTokenizer.from_pretrained(
               cls.checkpoint_name		,src_lang="""en_XX"""		,tgt_lang="""ro_RO"""      )
           lowercase							:     Optional[int]       	=      1
           return cls
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]		,250001      )
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]		,250004      )
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]		,250020      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
           self.assertListEqual(self.expected_src_tokens		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self.assertIn(snake_case		,self.tokenizer.all_special_ids      )
           lowercase							:     int       	=      [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
           lowercase							:     Dict       	=      self.tokenizer.decode(snake_case		,skip_special_tokens=snake_case      )
           lowercase							:     str       	=      self.tokenizer.decode(generated_ids[1:]		,skip_special_tokens=snake_case      )
           self.assertEqual(snake_case		,snake_case      )
           self.assertNotIn(self.tokenizer.eos_token		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      ["""this is gunna be a long sentence """ * 20]
           assert isinstance(src_text[0]		,snake_case      )
           lowercase							:     Tuple       	=      10
           lowercase							:     Dict       	=      self.tokenizer(snake_case		,max_length=snake_case		,truncation=snake_case      ).input_ids[0]
           self.assertEqual(ids[-2]		,2      )
           self.assertEqual(ids[-1]		,snake_case      )
           self.assertEqual(len(snake_case      )		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]      )		,[250026, 250001]      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      tempfile.mkdtemp()
           lowercase							:     Dict       	=      self.tokenizer.fairseq_tokens_to_ids
           self.tokenizer.save_pretrained(snake_case      )
           lowercase							:     List[str]       	=      MBartTokenizer.from_pretrained(snake_case      )
           self.assertDictEqual(new_tok.fairseq_tokens_to_ids		,snake_case      )
    @require_torch
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.tokenizer(self.src_text		,text_target=self.tgt_text		,padding=snake_case		,return_tensors="""pt"""      )
           lowercase							:     List[str]       	=      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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.tokenizer(
               self.src_text		,text_target=self.tgt_text		,padding=snake_case		,truncation=snake_case		,max_length=len(self.expected_src_tokens      )		,return_tensors="""pt"""		,)

           lowercase							:     Any       	=      shift_tokens_right(batch["""labels"""]		,self.tokenizer.pad_token_id      )

           self.assertIsInstance(snake_case		,snake_case      )

           self.assertEqual((2, 14)		,batch.input_ids.shape      )
           self.assertEqual((2, 14)		,batch.attention_mask.shape      )
           lowercase							:     int       	=      batch.input_ids.tolist()[0]
           self.assertListEqual(self.expected_src_tokens		,snake_case      )
           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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.tokenizer(self.src_text		,padding=snake_case		,truncation=snake_case		,max_length=3		,return_tensors="""pt"""      )
           lowercase							:     Optional[int]       	=      self.tokenizer(
               text_target=self.tgt_text		,padding=snake_case		,truncation=snake_case		,max_length=10		,return_tensors="""pt"""      )
           lowercase							:     Dict       	=      targets["""input_ids"""]
           lowercase							:     int       	=      shift_tokens_right(snake_case		,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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      self.tokenizer._build_translation_inputs(
               """A test"""		,return_tensors="""pt"""		,src_lang="""en_XX"""		,tgt_lang="""ar_AR"""      )

           self.assertEqual(
               nested_simplify(snake_case      )		,{
                   # A, test, EOS, en_XX
                   """input_ids""": [[62, 3034, 2, 250004]],
                   """attention_mask""": [[1, 1, 1, 1]],
                   # ar_AR
                   """forced_bos_token_id""": 250001,
               }		,)



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor







@require_vision
class  lowercase				(       unittest.TestCase			):




				def     a							(					self       ):
											snake_case_							=    tempfile.mkdtemp()

											snake_case_							=    [
											    '[UNK]',
											    '[CLS]',
											    '[SEP]',
											    '[PAD]',
											    '[MASK]',
											    '的',
											    '价',
											    '格',
											    '是',
											    '15',
											    '便',
											    'alex',
											    '##andra',
											    '，',
											    '。',
											    '-',
											    't',
											    'shirt',
											]
											snake_case_							=    os.path.join(self.tmpdirname      ,   VOCAB_FILES_NAMES['vocab_file']       )
											with open(self.vocab_file      ,   'w'      ,   encoding='utf-8'       ) as vocab_writer:
																		vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )

											snake_case_							=    {
											    'do_resize': True,
											    'size': {'height': 224, 'width': 224},
											    'do_center_crop': True,
											    'crop_size': {'height': 18, 'width': 18},
											    'do_normalize': True,
											    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
											    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
											    'do_convert_rgb': True,
											}
											snake_case_							=    os.path.join(self.tmpdirname      ,   snake_case       )
											with open(self.image_processor_file      ,   'w'      ,   encoding='utf-8'       ) as fp:
																		json.dump(snake_case      ,   snake_case       )




				def     a							(					self      ,   **snake_case       ):
											return BertTokenizer.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self      ,   **snake_case       ):
											return BertTokenizerFast.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self      ,   **snake_case       ):
											return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self       ):
											shutil.rmtree(self.tmpdirname       )




				def     a							(					self       ):
											snake_case_							=    [np.random.randint(255      ,   size=(3, 30, 400)      ,   dtype=np.uinta       )]

											snake_case_							=    [Image.fromarray(np.moveaxis(snake_case      ,   0      ,   -1       )       ) for x in image_inputs]

											return image_inputs




				def     a							(					self       ):
											snake_case_							=    self.get_tokenizer()
											snake_case_							=    self.get_rust_tokenizer()
											snake_case_							=    self.get_image_processor()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )
											processor_slow.save_pretrained(self.tmpdirname       )
											snake_case_							=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname      ,   use_fast=snake_case       )

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )
											processor_fast.save_pretrained(self.tmpdirname       )
											snake_case_							=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname       )

											self.assertEqual(processor_slow.tokenizer.get_vocab()      ,   tokenizer_slow.get_vocab()       )
											self.assertEqual(processor_fast.tokenizer.get_vocab()      ,   tokenizer_fast.get_vocab()       )
											self.assertEqual(tokenizer_slow.get_vocab()      ,   tokenizer_fast.get_vocab()       )
											self.assertIsInstance(processor_slow.tokenizer      ,   snake_case       )
											self.assertIsInstance(processor_fast.tokenizer      ,   snake_case       )

											self.assertEqual(processor_slow.image_processor.to_json_string()      ,   image_processor.to_json_string()       )
											self.assertEqual(processor_fast.image_processor.to_json_string()      ,   image_processor.to_json_string()       )
											self.assertIsInstance(processor_slow.image_processor      ,   snake_case       )
											self.assertIsInstance(processor_fast.image_processor      ,   snake_case       )




				def     a							(					self       ):
											snake_case_							=    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()      ,   image_processor=self.get_image_processor()       )
											processor.save_pretrained(self.tmpdirname       )

											snake_case_							=    self.get_tokenizer(cls_token='(CLS)'      ,   sep_token='(SEP)'       )
											snake_case_							=    self.get_image_processor(do_normalize=snake_case       )

											snake_case_							=    ChineseCLIPProcessor.from_pretrained(
											    self.tmpdirname      ,   cls_token='(CLS)'      ,   sep_token='(SEP)'      ,   do_normalize=snake_case       )

											self.assertEqual(processor.tokenizer.get_vocab()      ,   tokenizer_add_kwargs.get_vocab()       )
											self.assertIsInstance(processor.tokenizer      ,   snake_case       )

											self.assertEqual(processor.image_processor.to_json_string()      ,   image_processor_add_kwargs.to_json_string()       )
											self.assertIsInstance(processor.image_processor      ,   snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    self.prepare_image_inputs()

											snake_case_							=    image_processor(snake_case      ,   return_tensors='np'       )
											snake_case_							=    processor(images=snake_case      ,   return_tensors='np'       )

											for key in input_feat_extract.keys():
																		self.assertAlmostEqual(input_feat_extract[key].sum()      ,   input_processor[key].sum()      ,   delta=1e-2       )




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    'Alexandra，T-shirt的价格是15便士。'

											snake_case_							=    processor(text=snake_case       )

											snake_case_							=    tokenizer(snake_case       )

											for key in encoded_tok.keys():
																		self.assertListEqual(encoded_tok[key]      ,   encoded_processor[key]       )




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    'Alexandra，T-shirt的价格是15便士。'
											snake_case_							=    self.prepare_image_inputs()

											snake_case_							=    processor(text=snake_case      ,   images=snake_case       )

											self.assertListEqual(list(inputs.keys()       )      ,   ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']       )

											# test if it raises when no input is passed
											with pytest.raises(snake_case       ):
																		processor()




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											snake_case_							=    processor.batch_decode(snake_case       )
											snake_case_							=    tokenizer.batch_decode(snake_case       )

											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    'Alexandra，T-shirt的价格是15便士。'
											snake_case_							=    self.prepare_image_inputs()

											snake_case_							=    processor(text=snake_case      ,   images=snake_case       )

											self.assertListEqual(list(inputs.keys()       )      ,   processor.model_input_names       )




