
'''simple docstring'''

import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    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


__snake_case:  Dict								=			get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
    from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__snake_case:  int								=			25_60_47
__snake_case:  Union[str, Any]								=			25_61_45


@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase ( __A	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					NllbTokenizer
     a_    	=					NllbTokenizerFast
     a_    	=					True
     a_    	=					True
     a_    	=					{}


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       super().setUp()

       # We have a SentencePiece fixture for testing
       a_      :	Union[str, Any]      =							NllbTokenizer(UpperCamelCase__							,		keep_accents=UpperCamelCase__ )
       tokenizer.save_pretrained(self.tmpdirname )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							NllbTokenizer(UpperCamelCase__							,		keep_accents=UpperCamelCase__ )

       a_      :	str      =							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_85, 46, 10, 1_70, 3_82]]							,		)

       a_      :	List[Any]      =							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""",
               """.""",
           ]							,		)
       a_      :	Union[str, Any]      =							tokenizer.convert_tokens_to_ids(UpperCamelCase__ )
       self.assertListEqual(
           UpperCamelCase__							,		[
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
           ]							,		)

       a_      :	Optional[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>""",
               """.""",
           ]							,		)


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
           a_      :	str      =							self.rust_tokenizer_class.from_pretrained(UpperCamelCase__							,		**UpperCamelCase__ )
           a_      :	List[Any]      =							self.tokenizer_class.from_pretrained(UpperCamelCase__							,		**UpperCamelCase__ )

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

           a_      :	Optional[Any]      =							tokenizer_r.save_pretrained(UpperCamelCase__ )
           a_      :	Optional[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 ) )
           a_      :	Tuple      =							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
           a_      :	List[Any]      =							tokenizer_r.from_pretrained(UpperCamelCase__ )
           a_      :	Tuple      =							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=True
           a_      :	int      =							tempfile.mkdtemp()

           a_      :	Optional[int]      =							tokenizer_r.save_pretrained(UpperCamelCase__							,		legacy_format=UpperCamelCase__ )
           a_      :	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
           a_      :	List[Any]      =							tokenizer_r.from_pretrained(UpperCamelCase__ )
           a_      :	Tuple      =							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
           a_      :	Dict      =							tempfile.mkdtemp()

           a_      :	Dict      =							tokenizer_r.save_pretrained(UpperCamelCase__							,		legacy_format=UpperCamelCase__ )
           a_      :	str      =							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
           a_      :	str      =							tokenizer_r.from_pretrained(UpperCamelCase__ )
           a_      :	Tuple      =							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
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       if not self.test_seqaseq:
         return

       a_      :	Union[str, Any]      =							self.get_tokenizers()
       for tokenizer in tokenizers:
         with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
           # Longer text that will definitely require truncation.
           a_      :	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.',
           ]
           a_      :	int      =							[
               '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.',
           ]
           try:
             a_      :	Union[str, Any]      =							tokenizer.prepare_seqaseq_batch(
                 src_texts=UpperCamelCase__							,		tgt_texts=UpperCamelCase__							,		max_length=3							,		max_target_length=10							,		return_tensors="""pt"""							,		src_lang="""eng_Latn"""							,		tgt_lang="""ron_Latn"""							,		)
           except NotImplementedError:
             return
           self.assertEqual(batch.input_ids.shape[1]							,		3 )
           self.assertEqual(batch.labels.shape[1]							,		10 )
           # max_target_length will default to max_length if not specified
           a_      :	Optional[int]      =							tokenizer.prepare_seqaseq_batch(
               UpperCamelCase__							,		tgt_texts=UpperCamelCase__							,		max_length=3							,		return_tensors="""pt""" )
           self.assertEqual(batch.input_ids.shape[1]							,		3 )
           self.assertEqual(batch.labels.shape[1]							,		3 )

           a_      :	Any      =							tokenizer.prepare_seqaseq_batch(
               src_texts=UpperCamelCase__							,		max_length=3							,		max_target_length=10							,		return_tensors="""pt""" )
           self.assertEqual(batch_encoder_only.input_ids.shape[1]							,		3 )
           self.assertEqual(batch_encoder_only.attention_mask.shape[1]							,		3 )
           self.assertNotIn("""decoder_input_ids"""							,		UpperCamelCase__ )


     @unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece.""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
           a_      :	str      =							[AddedToken("""<special>"""							,		lstrip=UpperCamelCase__ )]

           a_      :	Tuple      =							self.rust_tokenizer_class.from_pretrained(
               UpperCamelCase__							,		additional_special_tokens=UpperCamelCase__							,		**UpperCamelCase__ )
           a_      :	Dict      =							tokenizer_r.encode("""Hey this is a <special> token""" )

           a_      :	int      =							tokenizer_r.encode("""<special>"""							,		add_special_tokens=UpperCamelCase__ )[0]

           self.assertTrue(special_token_id in r_output )

           if self.test_slow_tokenizer:
             a_      :	Optional[int]      =							self.rust_tokenizer_class.from_pretrained(
                 UpperCamelCase__							,		additional_special_tokens=UpperCamelCase__							,		**UpperCamelCase__							,		)
             a_      :	int      =							self.tokenizer_class.from_pretrained(
                 UpperCamelCase__							,		additional_special_tokens=UpperCamelCase__							,		**UpperCamelCase__ )

             a_      :	Dict      =							tokenizer_p.encode("""Hey this is a <special> token""" )

             a_      :	str      =							tokenizer_cr.encode("""Hey this is a <special> token""" )

             self.assertEqual(UpperCamelCase__							,		UpperCamelCase__ )
             self.assertEqual(UpperCamelCase__							,		UpperCamelCase__ )
             self.assertTrue(special_token_id in p_output )
             self.assertTrue(special_token_id in cr_output )







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

     """simple docstring"""

     a_    	=					"""facebook/nllb-200-distilled-600M"""
     a_    	=					[
         """ 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_    	=					[
         """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_    	=					[
         256_047,
         16_297,
         134_408,
         8_165,
         248_066,
         14_734,
         950,
         1_135,
         105_721,
         3_573,
         83,
         27_352,
         108,
         49_486,
         2,
     ]


     @classmethod
     def 							_lowerCAmelCase  (      cls ):

       '''simple docstring'''

       a_      :	NllbTokenizer      =							NllbTokenizer.from_pretrained(
           cls.checkpoint_name							,		src_lang="""eng_Latn"""							,		tgt_lang="""ron_Latn""" )
       a_      :	List[str]      =							1
       return cls


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""]							,		25_60_01 )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""]							,		25_60_02 )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""]							,		25_60_57 )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
       self.assertListEqual(self.expected_src_tokens							,		UpperCamelCase__ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       self.assertIn(UpperCamelCase__							,		self.tokenizer.all_special_ids )
       # fmt: off
       a_      :	str      =							[RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
       # fmt: on

       a_      :	Dict      =							self.tokenizer.decode(UpperCamelCase__							,		skip_special_tokens=UpperCamelCase__ )
       a_      :	str      =							self.tokenizer.decode(generated_ids[1:]							,		skip_special_tokens=UpperCamelCase__ )
       self.assertEqual(UpperCamelCase__							,		UpperCamelCase__ )
       self.assertNotIn(self.tokenizer.eos_token							,		UpperCamelCase__ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							['this is gunna be a long sentence ' * 20]
       assert isinstance(src_text[0]							,		UpperCamelCase__ )
       a_      :	Optional[int]      =							10
       a_      :	Dict      =							self.tokenizer(UpperCamelCase__							,		max_length=UpperCamelCase__							,		truncation=UpperCamelCase__ ).input_ids[0]
       self.assertEqual(ids[-1]							,		2 )
       self.assertEqual(ids[0]							,		UpperCamelCase__ )
       self.assertEqual(len(UpperCamelCase__ )							,		UpperCamelCase__ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""] )							,		[25_62_03, 3] )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							tempfile.mkdtemp()
       a_      :	List[Any]      =							self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(UpperCamelCase__ )
       a_      :	Optional[Any]      =							NllbTokenizer.from_pretrained(UpperCamelCase__ )
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,		UpperCamelCase__ )


     @require_torch
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.tokenizer(
           self.src_text							,		text_target=self.tgt_text							,		padding=UpperCamelCase__							,		truncation=UpperCamelCase__							,		max_length=len(self.expected_src_tokens )							,		return_tensors="""pt"""							,		)
       a_      :	int      =							shift_tokens_right(
           batch["""labels"""]							,		self.tokenizer.pad_token_id							,		self.tokenizer.lang_code_to_id["""ron_Latn"""] )

       self.assertIsInstance(UpperCamelCase__							,		UpperCamelCase__ )

       self.assertEqual((2, 15)							,		batch.input_ids.shape )
       self.assertEqual((2, 15)							,		batch.attention_mask.shape )
       a_      :	int      =							batch.input_ids.tolist()[0]
       self.assertListEqual(self.expected_src_tokens							,		UpperCamelCase__ )
       self.assertEqual(UpperCamelCase__							,		batch.decoder_input_ids[0, 0] )  # EOS
       # 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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.tokenizer(self.src_text							,		padding=UpperCamelCase__							,		truncation=UpperCamelCase__							,		max_length=3							,		return_tensors="""pt""" )
       a_      :	Dict      =							self.tokenizer(
           text_target=self.tgt_text							,		padding=UpperCamelCase__							,		truncation=UpperCamelCase__							,		max_length=10							,		return_tensors="""pt""" )
       a_      :	Union[str, Any]      =							targets['input_ids']
       a_      :	int      =							shift_tokens_right(
           UpperCamelCase__							,		self.tokenizer.pad_token_id							,		decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]							,		)

       self.assertEqual(batch.input_ids.shape[1]							,		3 )
       self.assertEqual(batch.decoder_input_ids.shape[1]							,		10 )


     @require_torch
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.tokenizer._build_translation_inputs(
           """A test"""							,		return_tensors="""pt"""							,		src_lang="""eng_Latn"""							,		tgt_lang="""fra_Latn""" )

       self.assertEqual(
           nested_simplify(UpperCamelCase__ )							,		{
               # A, test, EOS, en_XX
               """input_ids""": [[25_60_47, 70, 73_56, 2]],
               """attention_mask""": [[1, 1, 1, 1]],
               # ar_AR
               """forced_bos_token_id""": 25_60_57,
           }							,		)






     @require_torch
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							True
       a_      :	Any      =							self.tokenizer(
           """UN Chief says there is no military solution in Syria"""							,		src_lang="""eng_Latn"""							,		tgt_lang="""fra_Latn""" )
       self.assertEqual(
           inputs.input_ids							,		[1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47] )

       a_      :	Any      =							False
       a_      :	Any      =							self.tokenizer(
           """UN Chief says there is no military solution in Syria"""							,		src_lang="""eng_Latn"""							,		tgt_lang="""fra_Latn""" )
       self.assertEqual(
           inputs.input_ids							,		[25_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2] )

'''simple docstring'''

import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()


class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					PriorTransformer
     a_    	=					"hidden_states"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							4
       a_      :	List[str]      =							8
       a_      :	List[str]      =							7

       a_      :	Tuple      =							floats_tensor((batch_size, embedding_dim) ).to(lowerCAmelCase_ )

       a_      :	List[Any]      =							floats_tensor((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	List[Any]      =							floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase_ )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=0 ):

       '''simple docstring'''

       torch.manual_seed(lowerCAmelCase_ )
       a_      :	List[Any]      =							4
       a_      :	Union[str, Any]      =							8
       a_      :	Optional[int]      =							7

       a_      :	List[Any]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )

       a_      :	Optional[Any]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase_ )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return (4, 8)


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return (4, 8)


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							{
           """num_attention_heads""": 2,
           """attention_head_dim""": 4,
           """num_layers""": 2,
           """embedding_dim""": 8,
           """num_embeddings""": 7,
           """additional_embeddings""": 4,
       }
       a_      :	Optional[int]      =							self.dummy_input
       return init_dict, inputs_dict


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      , a_      :	Optional[Any]      =							PriorTransformer.from_pretrained(
           """hf-internal-testing/prior-dummy"""							,		output_loading_info=lowerCAmelCase_ )
       self.assertIsNotNone(lowerCAmelCase_ )
       self.assertEqual(len(loading_info["""missing_keys"""] )							,		0 )

       model.to(lowerCAmelCase_ )
       a_      :	List[Any]      =							model(**self.dummy_input )[0]

       assert hidden_states is not None, "Make sure output is not None"


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      , a_      :	Optional[int]      =							self.prepare_init_args_and_inputs_for_common()

       a_      :	str      =							self.model_class(**lowerCAmelCase_ )
       a_      :	Optional[Any]      =							inspect.signature(model.forward )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       a_      :	str      =							[*signature.parameters.keys()]

       a_      :	str      =							["""hidden_states""", """timestep"""]
       self.assertListEqual(arg_names[:2]							,		lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy""" )
       a_      :	Optional[Any]      =							model.to(lowerCAmelCase_ )

       if hasattr(lowerCAmelCase_							,		"""set_default_attn_processor""" ):
         model.set_default_attn_processor()

       a_      :	Tuple      =							self.get_dummy_seed_input()

       with torch.no_grad():
         a_      :	List[str]      =							model(**lowerCAmelCase_ )[0]

       a_      :	Optional[Any]      =							output[0, :5].flatten().cpu()
       print(lowerCAmelCase_ )

       # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
       # the expected output slices are not the same for CPU and GPU.
       a_      :	List[str]      =							torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239] )
       self.assertTrue(torch_all_close(lowerCAmelCase_							,		lowerCAmelCase_							,		rtol=1E-2 ) )







@slow
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=1							,		lowerCAmelCase_=7_68							,		lowerCAmelCase_=77							,		lowerCAmelCase_=0 ):

       '''simple docstring'''

       torch.manual_seed(lowerCAmelCase_ )
       a_      :	str      =							batch_size
       a_      :	Dict      =							embedding_dim
       a_      :	Tuple      =							num_embeddings

       a_      :	Optional[Any]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )

       a_      :	List[str]      =							torch.randn((batch_size, embedding_dim) ).to(lowerCAmelCase_ )
       a_      :	List[Any]      =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowerCAmelCase_ )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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






     @parameterized.expand(
         [
             # fmt: off
             [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
             [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
             # fmt: on
         ] )
     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Any      =							PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""							,		subfolder="""prior""" )
       model.to(lowerCAmelCase_ )
       a_      :	Optional[int]      =							self.get_dummy_seed_input(seed=lowerCAmelCase_ )

       with torch.no_grad():
         a_      :	str      =							model(**lowerCAmelCase_ )[0]

       assert list(sample.shape ) == [1, 7_68]

       a_      :	int      =							sample[0, :8].flatten().cpu()
       print(lowerCAmelCase_ )
       a_      :	str      =							torch.tensor(lowerCAmelCase_ )

       assert torch_all_close(lowerCAmelCase_							,		lowerCAmelCase_							,		atol=1E-3 )


