



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


_lowerCamelCase						    =  get_tests_dir("""fixtures/test_sentencepiece.model""")

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

_lowerCamelCase						    =  250004
_lowerCamelCase						    =  250020

@require_sentencepiece
@require_tokenizers
class       _SCREAMING_SNAKE_CASE	(UpperCamelCase__			,    unittest.TestCase ):
     lowerCAmelCase     :   Union[str, Any]					=			MBartaaTokenizer
     lowerCAmelCase     :   Union[str, Any]					=			MBartaaTokenizerFast
     lowerCAmelCase     :   Dict					=			True
     lowerCAmelCase     :   List[Any]					=			True





     def 	__snake_case    (       self :							str				)->Any:
      super().setUp()

      # We have a SentencePiece fixture for testing
      __SCREAMING_SNAKE_CASE       :    List[str]       =	MBartaaTokenizer(__A     ,  src_lang="en_XX"     ,  tgt_lang="ro_RO"     ,  keep_accents=__A				)
      tokenizer.save_pretrained(self.tmpdirname				)





     def 	__snake_case    (       self :							Optional[int]				)->Any:
      __SCREAMING_SNAKE_CASE       :    str       =	"<s>"
      __SCREAMING_SNAKE_CASE       :    Dict       =	0

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





     def 	__snake_case    (       self :							Union[str, Any]				)->int:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	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(__A				)     ,  1_0_5_4				)





     def 	__snake_case    (       self :							Optional[Any]				)->int:
      self.assertEqual(self.get_tokenizer().vocab_size     ,  1_0_5_4				)





     def 	__snake_case    (       self :							int				)->Any:
      __SCREAMING_SNAKE_CASE       :    Dict       =	MBartaaTokenizer(__A     ,  src_lang="en_XX"     ,  tgt_lang="ro_RO"     ,  keep_accents=__A				)

      __SCREAMING_SNAKE_CASE       :    int       =	tokenizer.tokenize("This is a test"				)
      self.assertListEqual(__A     ,  ["▁This", "▁is", "▁a", "▁t", "est"]				)

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

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	tokenizer.tokenize("I was born in 92000, and this is falsé."				)
      self.assertListEqual(
          __A     ,  [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", "."]     ,  )
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	tokenizer.convert_tokens_to_ids(__A				)
      self.assertListEqual(
          __A     ,  [
              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]
          ]     ,  )

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	tokenizer.convert_ids_to_tokens(__A				)
      self.assertListEqual(
          __A     ,  [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 :							Union[str, Any]				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    Any       =	{"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=__A     ,  model_name="facebook/mbart-large-50"     ,  revision="d3913889c59cd5c9e456b269c376325eabad57e2"     ,  )






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



      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

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	(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})"""				):
        __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.rust_tokenizer_class.from_pretrained(__A     ,  **__A				)
        __SCREAMING_SNAKE_CASE       :    Dict       =	self.tokenizer_class.from_pretrained(__A     ,  **__A				)

        __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	tempfile.mkdtemp()

        __SCREAMING_SNAKE_CASE       :    List[Any]       =	tokenizer_r.save_pretrained(__A				)
        __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	tokenizer_p.save_pretrained(__A				)

        # 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				)				)
        __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f				)
        self.assertSequenceEqual(__A     ,  __A				)

        # Checks everything loads correctly in the same way
        __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	tokenizer_r.from_pretrained(__A				)
        __SCREAMING_SNAKE_CASE       :    int       =	tokenizer_p.from_pretrained(__A				)

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

        shutil.rmtree(__A				)

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

        __SCREAMING_SNAKE_CASE       :    Optional[int]       =	tokenizer_r.save_pretrained(__A     ,  legacy_format=__A				)
        __SCREAMING_SNAKE_CASE       :    Optional[int]       =	tokenizer_p.save_pretrained(__A				)

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

        # Checks everything loads correctly in the same way
        __SCREAMING_SNAKE_CASE       :    List[str]       =	tokenizer_r.from_pretrained(__A				)
        __SCREAMING_SNAKE_CASE       :    Tuple       =	tokenizer_p.from_pretrained(__A				)

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

        shutil.rmtree(__A				)

        # Save tokenizer rust, legacy_format=False
        __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	tempfile.mkdtemp()

        __SCREAMING_SNAKE_CASE       :    int       =	tokenizer_r.save_pretrained(__A     ,  legacy_format=__A				)
        __SCREAMING_SNAKE_CASE       :    int       =	tokenizer_p.save_pretrained(__A				)

        # 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
        __SCREAMING_SNAKE_CASE       :    str       =	tokenizer_r.from_pretrained(__A				)
        __SCREAMING_SNAKE_CASE       :    Dict       =	tokenizer_p.from_pretrained(__A				)

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

        shutil.rmtree(__A				)






@require_torch
@require_sentencepiece
@require_tokenizers
class       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):
     lowerCAmelCase     :   List[Any]					=			"""facebook/mbart-large-50-one-to-many-mmt"""
     lowerCAmelCase     :   List[str]					=			[
         """ 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.""",
     ]
     lowerCAmelCase     :   Tuple					=			[
         """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.""",
     ]
     lowerCAmelCase     :   Optional[int]					=			[EN_CODE, 8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2]





     @classmethod
     def 	__snake_case    (       cls :							Tuple				)->Any:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	MBartaaTokenizer.from_pretrained(
          cls.checkpoint_name     ,  src_lang="en_XX"     ,  tgt_lang="ro_RO"				)
      __SCREAMING_SNAKE_CASE       :    int       =	1
      return cls





     def 	__snake_case    (       self :							Any				)->str:
      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 :							Dict				)->str:
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
      self.assertListEqual(self.expected_src_tokens     ,  __A				)





     def 	__snake_case    (       self :							int				)->Tuple:
      self.assertIn(__A     ,  self.tokenizer.all_special_ids				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	[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]
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.tokenizer.decode(__A     ,  skip_special_tokens=__A				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	self.tokenizer.decode(generated_ids[1:]     ,  skip_special_tokens=__A				)
      self.assertEqual(__A     ,  __A				)
      self.assertNotIn(self.tokenizer.eos_token     ,  __A				)





     def 	__snake_case    (       self :							Tuple				)->Optional[Any]:
      __SCREAMING_SNAKE_CASE       :    str       =	["this is gunna be a long sentence " * 2_0]
      assert isinstance(src_text[0]     ,  __A				)
      __SCREAMING_SNAKE_CASE       :    str       =	1_0
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.tokenizer(__A     ,  max_length=__A     ,  truncation=__A				).input_ids[0]
      self.assertEqual(ids[0]     ,  __A				)
      self.assertEqual(ids[-1]     ,  2				)
      self.assertEqual(len(__A				)     ,  __A				)





     def 	__snake_case    (       self :							List[Any]				)->List[str]:
      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 :							Optional[Any]				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	tempfile.mkdtemp()
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.tokenizer.fairseq_tokens_to_ids
      self.tokenizer.save_pretrained(__A				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	MBartaaTokenizer.from_pretrained(__A				)
      self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,  __A				)





     @require_torch
     def 	__snake_case    (       self :							str				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.tokenizer(self.src_text     ,  text_target=self.tgt_text     ,  padding=__A     ,  return_tensors="pt"				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	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 :							Optional[int]				)->Any:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.tokenizer(
          self.src_text     ,  text_target=self.tgt_text     ,  padding=__A     ,  truncation=__A     ,  max_length=len(self.expected_src_tokens				)     ,  return_tensors="pt"     ,  )
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	shift_tokens_right(batch["labels"]     ,  self.tokenizer.pad_token_id				)

      self.assertIsInstance(__A     ,  __A				)

      self.assertEqual((2, 1_4)     ,  batch.input_ids.shape				)
      self.assertEqual((2, 1_4)     ,  batch.attention_mask.shape				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	batch.input_ids.tolist()[0]
      self.assertListEqual(self.expected_src_tokens     ,  __A				)
      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 :							Dict				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    int       =	self.tokenizer(self.src_text     ,  padding=__A     ,  truncation=__A     ,  max_length=3     ,  return_tensors="pt"				)
      __SCREAMING_SNAKE_CASE       :    int       =	self.tokenizer(
          text_target=self.tgt_text     ,  padding=__A     ,  truncation=__A     ,  max_length=1_0     ,  return_tensors="pt"				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	targets["input_ids"]
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	shift_tokens_right(__A     ,  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 :							Dict				)->int:
      __SCREAMING_SNAKE_CASE       :    Any       =	self.tokenizer._build_translation_inputs(
          "A test"     ,  return_tensors="pt"     ,  src_lang="en_XX"     ,  tgt_lang="ar_AR"				)

      self.assertEqual(
          nested_simplify(__A				)     ,  {
              # 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,
          }     ,  )











import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import BridgeTowerImageProcessor

class       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     def __init__(       self :							Union[str, Any]     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Dict[str, int] = None     ,  UpperCamelCase :							int = 3_2     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Union[int, float] = 1 / 2_5_5     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Optional[Union[float, List[float]]] = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]     ,  UpperCamelCase :							Optional[Union[float, List[float]]] = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]     ,  UpperCamelCase :							bool = True     ,  UpperCamelCase :							Dict=7     ,  UpperCamelCase :							Dict=3_0     ,  UpperCamelCase :							Tuple=4_0_0     ,  UpperCamelCase :							Union[str, Any]=3     ,  )->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	parent
      __SCREAMING_SNAKE_CASE       :    str       =	do_resize
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	size if size is not None else {"shortest_edge": 2_8_8}
      __SCREAMING_SNAKE_CASE       :    List[str]       =	size_divisor
      __SCREAMING_SNAKE_CASE       :    str       =	do_rescale
      __SCREAMING_SNAKE_CASE       :    int       =	rescale_factor
      __SCREAMING_SNAKE_CASE       :    Tuple       =	do_normalize
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	do_center_crop
      __SCREAMING_SNAKE_CASE       :    List[str]       =	image_mean
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image_std
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	do_pad
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	batch_size
      __SCREAMING_SNAKE_CASE       :    str       =	num_channels
      __SCREAMING_SNAKE_CASE       :    List[str]       =	min_resolution
      __SCREAMING_SNAKE_CASE       :    Tuple       =	max_resolution





     def 	__snake_case    (       self :							Optional[Any]				)->List[str]:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }






     def 	__snake_case    (       self :							Optional[Any]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Dict=False				)->Optional[Any]:
      if not batched:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.size["shortest_edge"]
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	image_inputs[0]
       if isinstance(UpperCamelCase     ,  Image.Image				):
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	image.size
       else:
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	image.shape[1], image.shape[2]
       __SCREAMING_SNAKE_CASE       :    Tuple       =	size / min(UpperCamelCase     ,  UpperCamelCase				)
       if h < w:
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    int       =	size, scale * w
       else:
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[str]       =	scale * h, size

       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	int((1_3_3_3 / 8_0_0) * size				)
       if max(UpperCamelCase     ,  UpperCamelCase				) > max_size:
        __SCREAMING_SNAKE_CASE       :    Any       =	max_size / max(UpperCamelCase     ,  UpperCamelCase				)
        __SCREAMING_SNAKE_CASE       :    List[Any]       =	newh * scale
        __SCREAMING_SNAKE_CASE       :    Optional[int]       =	neww * scale

       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	int(newh + 0.5				), int(neww + 0.5				)
       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	(
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

      else:
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[]
       for image in image_inputs:
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.get_expected_values([image]				)
        expected_values.append((expected_height, expected_width)				)
       __SCREAMING_SNAKE_CASE       :    int       =	max(UpperCamelCase     ,  key=lambda UpperCamelCase				: item[0]				)[0]
       __SCREAMING_SNAKE_CASE       :    List[str]       =	max(UpperCamelCase     ,  key=lambda UpperCamelCase				: item[1]				)[1]

      return expected_height, expected_width






@require_torch
@require_vision
class       _SCREAMING_SNAKE_CASE	(UpperCamelCase			,    unittest.TestCase ):
     lowerCAmelCase					=			BridgeTowerImageProcessor if is_vision_available() else None





     def 	__snake_case    (       self :							str				)->int:
      __SCREAMING_SNAKE_CASE       :    str       =	BridgeTowerImageProcessingTester(self				)





     @property
     def 	__snake_case    (       self :							str				)->Any:
      return self.image_processor_tester.prepare_image_processor_dict()





     def 	__snake_case    (       self :							Optional[int]				)->Dict:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.image_processing_class(**self.image_processor_dict				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "image_mean"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "image_std"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "do_normalize"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "do_resize"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "size"				)				)
      self.assertTrue(hasattr(UpperCamelCase     ,  "size_divisor"				)				)





     def 	__snake_case    (       self :							Optional[int]				)->int:
      pass





     def 	__snake_case    (       self :							str				)->Union[str, Any]:
      # Initialize image processor
      __SCREAMING_SNAKE_CASE       :    Any       =	self.image_processing_class(**self.image_processor_dict				)
      # create random PIL images
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase				)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase     ,  Image.Image				)

      # Test not batched input
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image_processing(image_inputs[0]     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Tuple       =	self.image_processor_tester.get_expected_values(UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,  )

      # Test batched
      __SCREAMING_SNAKE_CASE       :    Any       =	image_processing(UpperCamelCase     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.image_processor_tester.get_expected_values(UpperCamelCase     ,  batched=UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,  )





     def 	__snake_case    (       self :							List[str]				)->Optional[int]:
      # Initialize image processor
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.image_processing_class(**self.image_processor_dict				)
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase     ,  numpify=UpperCamelCase				)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase     ,  np.ndarray				)

      # Test not batched input
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image_processing(image_inputs[0]     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.image_processor_tester.get_expected_values(UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,  )

      # Test batched
      __SCREAMING_SNAKE_CASE       :    str       =	image_processing(UpperCamelCase     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	self.image_processor_tester.get_expected_values(UpperCamelCase     ,  batched=UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,  )






     def 	__snake_case    (       self :							Optional[Any]				)->Union[str, Any]:
      # Initialize image processor
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.image_processing_class(**self.image_processor_dict				)
      # create random PyTorch tensors
      __SCREAMING_SNAKE_CASE       :    List[str]       =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase     ,  torchify=UpperCamelCase				)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase     ,  torch.Tensor				)

      # Test not batched input
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	image_processing(image_inputs[0]     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Dict       =	self.image_processor_tester.get_expected_values(UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,  )

      # Test batched
      __SCREAMING_SNAKE_CASE       :    str       =	image_processing(UpperCamelCase     ,  return_tensors="pt"				).pixel_values

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Any       =	self.image_processor_tester.get_expected_values(UpperCamelCase     ,  batched=UpperCamelCase				)
      self.assertEqual(
          encoded_images.shape     ,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,  )






