

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


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

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

__UpperCAmelCase    				=     2_5_0_0_0_4
__UpperCAmelCase    				=     2_5_0_0_2_0





@require_sentencepiece
@require_tokenizers
class A__    ( A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					str      =						MBartaaTokenizer
 _lowercase     :					List[str]      =						MBartaaTokenizerFast
 _lowercase     :					Optional[Any]      =						True
 _lowercase     :					str      =						True

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      super().setUp()

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

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    "<s>"
      _lowerCAmelCase   :					int        =    0

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

 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					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(__UpperCamelCase       )     ,						1_0_5_4       )

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      self.assertEqual(self.get_tokenizer().vocab_size     ,						1_0_5_4       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    MBartaaTokenizer(__UpperCamelCase     ,						src_lang="en_XX"     ,						tgt_lang="ro_RO"     ,						keep_accents=__UpperCamelCase       )

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

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

      _lowerCAmelCase   :					Optional[int]        =    tokenizer.tokenize("I was born in 92000, and this is falsé."       )
      self.assertListEqual(
          __UpperCamelCase     ,						[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", "."]     ,						)
      _lowerCAmelCase   :					str        =    tokenizer.convert_tokens_to_ids(__UpperCamelCase       )
      self.assertListEqual(
          __UpperCamelCase     ,						[
              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]
          ]     ,						)

      _lowerCAmelCase   :					Any        =    tokenizer.convert_ids_to_tokens(__UpperCamelCase       )
      self.assertListEqual(
          __UpperCamelCase     ,						[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    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, 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=__UpperCamelCase     ,						model_name="facebook/mbart-large-50"     ,						revision="d3913889c59cd5c9e456b269c376325eabad57e2"     ,						)


 def    __magic_name__       (		self :	Dict       ):






      '''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

      _lowerCAmelCase   :					str        =    (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})'''       ):
                _lowerCAmelCase   :					List[Any]        =    self.rust_tokenizer_class.from_pretrained(__UpperCamelCase     ,						**__UpperCamelCase       )
                _lowerCAmelCase   :					List[Any]        =    self.tokenizer_class.from_pretrained(__UpperCamelCase     ,						**__UpperCamelCase       )

                _lowerCAmelCase   :					List[str]        =    tempfile.mkdtemp()

                _lowerCAmelCase   :					Dict        =    tokenizer_r.save_pretrained(__UpperCamelCase       )
                _lowerCAmelCase   :					List[Any]        =    tokenizer_p.save_pretrained(__UpperCamelCase       )

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

                # Checks everything loads correctly in the same way
                _lowerCAmelCase   :					List[Any]        =    tokenizer_r.from_pretrained(__UpperCamelCase       )
                _lowerCAmelCase   :					str        =    tokenizer_p.from_pretrained(__UpperCamelCase       )

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

                shutil.rmtree(__UpperCamelCase       )

                # Save tokenizer rust, legacy_format=True
                _lowerCAmelCase   :					Dict        =    tempfile.mkdtemp()

                _lowerCAmelCase   :					Optional[Any]        =    tokenizer_r.save_pretrained(__UpperCamelCase     ,						legacy_format=__UpperCamelCase       )
                _lowerCAmelCase   :					List[str]        =    tokenizer_p.save_pretrained(__UpperCamelCase       )

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

                # Checks everything loads correctly in the same way
                _lowerCAmelCase   :					Dict        =    tokenizer_r.from_pretrained(__UpperCamelCase       )
                _lowerCAmelCase   :					Any        =    tokenizer_p.from_pretrained(__UpperCamelCase       )

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

                shutil.rmtree(__UpperCamelCase       )

                # Save tokenizer rust, legacy_format=False
                _lowerCAmelCase   :					Optional[int]        =    tempfile.mkdtemp()

                _lowerCAmelCase   :					Tuple        =    tokenizer_r.save_pretrained(__UpperCamelCase     ,						legacy_format=__UpperCamelCase       )
                _lowerCAmelCase   :					List[Any]        =    tokenizer_p.save_pretrained(__UpperCamelCase       )

                # 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
                _lowerCAmelCase   :					Optional[Any]        =    tokenizer_r.from_pretrained(__UpperCamelCase       )
                _lowerCAmelCase   :					Union[str, Any]        =    tokenizer_p.from_pretrained(__UpperCamelCase       )

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

                shutil.rmtree(__UpperCamelCase       )





@require_torch
@require_sentencepiece
@require_tokenizers
class A__    ( unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					str      =						'''facebook/mbart-large-50-one-to-many-mmt'''
 _lowercase     :					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.''',
 ]
 _lowercase     :					List[Any]      =						[
     '''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.''',
 ]
 _lowercase     :					Any      =						[EN_CODE, 8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2]

 @classmethod
 def    __magic_name__       (		cls :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    MBartaaTokenizer.from_pretrained(
          cls.checkpoint_name     ,						src_lang="en_XX"     ,						tgt_lang="ro_RO"       )
      _lowerCAmelCase   :					Union[str, Any]        =    1
      return cls

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      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    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      self.assertIn(__UpperCamelCase     ,						self.tokenizer.all_special_ids       )
      _lowerCAmelCase   :					Union[str, 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]
      _lowerCAmelCase   :					Any        =    self.tokenizer.decode(__UpperCamelCase     ,						skip_special_tokens=__UpperCamelCase       )
      _lowerCAmelCase   :					int        =    self.tokenizer.decode(generated_ids[1:]     ,						skip_special_tokens=__UpperCamelCase       )
      self.assertEqual(__UpperCamelCase     ,						__UpperCamelCase       )
      self.assertNotIn(self.tokenizer.eos_token     ,						__UpperCamelCase       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    ["this is gunna be a long sentence " * 2_0]
      assert isinstance(src_text[0]     ,						__UpperCamelCase       )
      _lowerCAmelCase   :					Optional[int]        =    1_0
      _lowerCAmelCase   :					Dict        =    self.tokenizer(__UpperCamelCase     ,						max_length=__UpperCamelCase     ,						truncation=__UpperCamelCase       ).input_ids[0]
      self.assertEqual(ids[0]     ,						__UpperCamelCase       )
      self.assertEqual(ids[-1]     ,						2       )
      self.assertEqual(len(__UpperCamelCase       )     ,						__UpperCamelCase       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]       )     ,						[2_5_0_0_5_3, 2_5_0_0_0_1]       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    tempfile.mkdtemp()
      _lowerCAmelCase   :					Any        =    self.tokenizer.fairseq_tokens_to_ids
      self.tokenizer.save_pretrained(__UpperCamelCase       )
      _lowerCAmelCase   :					str        =    MBartaaTokenizer.from_pretrained(__UpperCamelCase       )
      self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,						__UpperCamelCase       )

 @require_torch
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    self.tokenizer(self.src_text     ,						text_target=self.tgt_text     ,						padding=__UpperCamelCase     ,						return_tensors="pt"       )
      _lowerCAmelCase   :					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    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.tokenizer(
          self.src_text     ,						text_target=self.tgt_text     ,						padding=__UpperCamelCase     ,						truncation=__UpperCamelCase     ,						max_length=len(self.expected_src_tokens       )     ,						return_tensors="pt"     ,						)
      _lowerCAmelCase   :					Dict        =    shift_tokens_right(batch["labels"]     ,						self.tokenizer.pad_token_id       )

      self.assertIsInstance(__UpperCamelCase     ,						__UpperCamelCase       )

      self.assertEqual((2, 1_4)     ,						batch.input_ids.shape       )
      self.assertEqual((2, 1_4)     ,						batch.attention_mask.shape       )
      _lowerCAmelCase   :					Optional[int]        =    batch.input_ids.tolist()[0]
      self.assertListEqual(self.expected_src_tokens     ,						__UpperCamelCase       )
      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    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.tokenizer(self.src_text     ,						padding=__UpperCamelCase     ,						truncation=__UpperCamelCase     ,						max_length=3     ,						return_tensors="pt"       )
      _lowerCAmelCase   :					str        =    self.tokenizer(
          text_target=self.tgt_text     ,						padding=__UpperCamelCase     ,						truncation=__UpperCamelCase     ,						max_length=1_0     ,						return_tensors="pt"       )
      _lowerCAmelCase   :					Tuple        =    targets["input_ids"]
      _lowerCAmelCase   :					Dict        =    shift_tokens_right(__UpperCamelCase     ,						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    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




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

      self.assertEqual(
          nested_simplify(__UpperCamelCase       )     ,						{
              # 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 gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class A__    ( A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					List[str]      =						KandinskyVaaControlnetImgaImgPipeline
 _lowercase     :					Tuple      =						['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
 _lowercase     :					str      =						['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
 _lowercase     :					Optional[Any]      =						[
     '''generator''',
     '''height''',
     '''width''',
     '''strength''',
     '''guidance_scale''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 _lowercase     :					Tuple      =						False

 @property
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      return 3_2

 @property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      return 3_2

 @property
 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      return self.time_input_dim

 @property
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      return self.time_input_dim * 4

 @property
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      return 1_0_0

 @property
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      torch.manual_seed(0       )

      _lowerCAmelCase   :					Tuple        =    {
          "in_channels": 8,
          # Out channels is double in channels because predicts mean and variance
          "out_channels": 8,
          "addition_embed_type": "image_hint",
          "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
          "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
          "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
          "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
          "layers_per_block": 1,
          "encoder_hid_dim": self.text_embedder_hidden_size,
          "encoder_hid_dim_type": "image_proj",
          "cross_attention_dim": self.cross_attention_dim,
          "attention_head_dim": 4,
          "resnet_time_scale_shift": "scale_shift",
          "class_embed_type": None,
      }

      _lowerCAmelCase   :					Optional[int]        =    UNetaDConditionModel(**A_       )
      return model

 @property
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      return {
          "block_out_channels": [3_2, 3_2, 6_4, 6_4],
          "down_block_types": [
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "DownEncoderBlock2D",
              "AttnDownEncoderBlock2D",
          ],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 1_2,
          "out_channels": 3,
          "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
          "vq_embed_dim": 4,
      }

 @property
 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      torch.manual_seed(0       )
      _lowerCAmelCase   :					List[str]        =    VQModel(**self.dummy_movq_kwargs       )
      return model

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    self.dummy_unet
      _lowerCAmelCase   :					List[Any]        =    self.dummy_movq

      _lowerCAmelCase   :					Tuple        =    {
          "num_train_timesteps": 1_0_0_0,
          "beta_schedule": "linear",
          "beta_start": 0.00085,
          "beta_end": 0.012,
          "clip_sample": False,
          "set_alpha_to_one": False,
          "steps_offset": 0,
          "prediction_type": "epsilon",
          "thresholding": False,
      }

      _lowerCAmelCase   :					int        =    DDIMScheduler(**A_       )

      _lowerCAmelCase   :					Tuple        =    {
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }

      return components

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	Union[str, Any]     ,						A_ :	Optional[int]=0       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    floats_tensor((1, self.text_embedder_hidden_size)     ,						rng=random.Random(A_       )       ).to(A_       )
      _lowerCAmelCase   :					str        =    floats_tensor((1, self.text_embedder_hidden_size)     ,						rng=random.Random(seed + 1       )       ).to(
          A_       )
      # create init_image
      _lowerCAmelCase   :					Dict        =    floats_tensor((1, 3, 6_4, 6_4)     ,						rng=random.Random(A_       )       ).to(A_       )
      _lowerCAmelCase   :					Dict        =    image.cpu().permute(0     ,						2     ,						3     ,						1       )[0]
      _lowerCAmelCase   :					Tuple        =    Image.fromarray(np.uinta(A_       )       ).convert("RGB"       ).resize((2_5_6, 2_5_6)       )
      # create hint
      _lowerCAmelCase   :					Union[str, Any]        =    floats_tensor((1, 3, 6_4, 6_4)     ,						rng=random.Random(A_       )       ).to(A_       )

      if str(A_       ).startswith("mps"       ):
           _lowerCAmelCase   :					Tuple        =    torch.manual_seed(A_       )
      else:
           _lowerCAmelCase   :					Any        =    torch.Generator(device=A_       ).manual_seed(A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    {
          "image": init_image,
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "hint": hint,
          "generator": generator,
          "height": 6_4,
          "width": 6_4,
          "num_inference_steps": 1_0,
          "guidance_scale": 7.0,
          "strength": 0.2,
          "output_type": "np",
      }
      return inputs


 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    "cpu"

      _lowerCAmelCase   :					Tuple        =    self.get_dummy_components()

      _lowerCAmelCase   :					Optional[int]        =    self.pipeline_class(**A_       )
      _lowerCAmelCase   :					Optional[Any]        =    pipe.to(A_       )

      pipe.set_progress_bar_config(disable=A_       )

      _lowerCAmelCase   :					List[str]        =    pipe(**self.get_dummy_inputs(A_       )       )
      _lowerCAmelCase   :					List[Any]        =    output.images

      _lowerCAmelCase   :					str        =    pipe(
          **self.get_dummy_inputs(A_       )     ,						return_dict=A_     ,						)[0]

      _lowerCAmelCase   :					int        =    image[0, -3:, -3:, -1]
      _lowerCAmelCase   :					List[str]        =    image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 6_4, 6_4, 3)

      _lowerCAmelCase   :					List[Any]        =    np.array(
          [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]       )
      assert (
          np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
      ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2
      ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





@slow
@require_torch_gpu
class A__    ( unittest.TestCase ):



 """simple docstring"""







 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"       )

      _lowerCAmelCase   :					str        =    load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"       )
      _lowerCAmelCase   :					Union[str, Any]        =    init_image.resize((5_1_2, 5_1_2)       )

      _lowerCAmelCase   :					Dict        =    load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/hint_image_cat.png"       )
      _lowerCAmelCase   :					Tuple        =    torch.from_numpy(np.array(A_       )       ).float() / 255.0
      _lowerCAmelCase   :					Optional[int]        =    hint.permute(2     ,						0     ,						1       ).unsqueeze(0       )

      _lowerCAmelCase   :					List[str]        =    "A robot, 4k photo"

      _lowerCAmelCase   :					Dict        =    KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-prior"     ,						torch_dtype=torch.floataa       )
      pipe_prior.to(A_       )

      _lowerCAmelCase   :					str        =    KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-controlnet-depth"     ,						torch_dtype=torch.floataa       )
      _lowerCAmelCase   :					Optional[int]        =    pipeline.to(A_       )

      pipeline.set_progress_bar_config(disable=A_       )

      _lowerCAmelCase   :					Dict        =    torch.Generator(device="cpu"       ).manual_seed(0       )

      _lowerCAmelCase      ,			_lowerCAmelCase   :					Tuple        =    pipe_prior(
          A_     ,						image=A_     ,						strength=0.85     ,						generator=A_     ,						negative_prompt=""     ,						).to_tuple()

      _lowerCAmelCase   :					List[Any]        =    pipeline(
          image=A_     ,						image_embeds=A_     ,						negative_image_embeds=A_     ,						hint=A_     ,						generator=A_     ,						num_inference_steps=1_0_0     ,						height=5_1_2     ,						width=5_1_2     ,						strength=0.5     ,						output_type="np"     ,						)

      _lowerCAmelCase   :					str        =    output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 3)

      assert_mean_pixel_difference(A_     ,						A_       )






