





import unittest

from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE      	=		get_tests_dir("""fixtures/spiece.model""")
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			List[str]       =       DebertaVaTokenizer
						__magic_name__:			Optional[Any]       =       DebertaVaTokenizerFast
						__magic_name__:			str       =       True
						__magic_name__:			Union[str, Any]       =       True






						def UpperCAmelCase_				(      self      :      int	)							->    Any:







							"""simple docstring"""


							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_		:			Union[str, Any]											=	DebertaVaTokenizer(_A		,   unk_token='<unk>'	)
							tokenizer.save_pretrained(self.tmpdirname	)






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      List[str]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	'this is a test'
							snake_case_		:			Union[str, Any]											=	'this is a test'
							return input_text, output_text






						def UpperCAmelCase_				(      self      :      Tuple	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			int											=	'<pad>'
							snake_case_		:			str											=	0

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






						def UpperCAmelCase_				(      self      :      Dict	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	list(self.get_tokenizer().get_vocab().keys()	)
							self.assertEqual(vocab_keys[0]		,   '<pad>'	)
							self.assertEqual(vocab_keys[1]		,   '<unk>'	)
							self.assertEqual(vocab_keys[-1]		,   '[PAD]'	)
							self.assertEqual(len(_A	)		,   30001	)






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    int:







							"""simple docstring"""


							self.assertEqual(self.get_tokenizer().vocab_size		,   30000	)






						def UpperCAmelCase_				(      self      :      List[str]	)							->    int:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	' \tHeLLo!how  \n Are yoU?  '
							snake_case_		:			int											=	['▁hello', '!', 'how', '▁are', '▁you', '?']
							# fmt: on

							snake_case_		:			List[str]											=	DebertaVaTokenizer(_A		,   do_lower_case=_A	)
							snake_case_		:			int											=	tokenizer.convert_ids_to_tokens(tokenizer.encode(_A		,   add_special_tokens=_A	)	)

							self.assertListEqual(_A		,   _A	)

							snake_case_		:			str											=	DebertaVaTokenizerFast(_A		,   do_lower_case=_A	)
							snake_case_		:			str											=	rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A		,   add_special_tokens=_A	)	)

							self.assertListEqual(_A		,   _A	)






						@unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.'	)
						def UpperCAmelCase_				(      self      :      Any	)							->    Tuple:







							"""simple docstring"""


							pass






						@unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.'	)
						def UpperCAmelCase_				(      self      :      str	)							->    int:







							"""simple docstring"""


							pass






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	'I was born in 92000, and this is falsé.'
							snake_case_		:			List[str]											=	['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
							# fmt: on

							snake_case_		:			Dict											=	DebertaVaTokenizer(_A		,   split_by_punct=_A	)
							snake_case_		:			str											=	tokenizer.convert_ids_to_tokens(tokenizer.encode(_A		,   add_special_tokens=_A	)	)

							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Union[str, Any]											=	DebertaVaTokenizerFast(_A		,   split_by_punct=_A	)
							snake_case_		:			Tuple											=	rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A		,   add_special_tokens=_A	)	)

							self.assertListEqual(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      Tuple	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Any											=	'I was born in 92000, and this is falsé.'
							snake_case_		:			Optional[int]											=	['▁i', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
							# fmt: on

							snake_case_		:			Optional[int]											=	DebertaVaTokenizer(_A		,   do_lower_case=_A		,   split_by_punct=_A	)
							snake_case_		:			List[str]											=	tokenizer.convert_ids_to_tokens(tokenizer.encode(_A		,   add_special_tokens=_A	)	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Optional[Any]											=	DebertaVaTokenizerFast(_A		,   do_lower_case=_A		,   split_by_punct=_A	)
							snake_case_		:			Union[str, Any]											=	rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A		,   add_special_tokens=_A	)	)
							self.assertListEqual(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      Tuple	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	'I was born in 92000, and this is falsé.'
							snake_case_		:			Dict											=	['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
							# fmt: on

							snake_case_		:			Union[str, Any]											=	DebertaVaTokenizer(_A		,   do_lower_case=_A		,   split_by_punct=_A	)
							snake_case_		:			Optional[Any]											=	tokenizer.convert_ids_to_tokens(tokenizer.encode(_A		,   add_special_tokens=_A	)	)

							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Optional[int]											=	DebertaVaTokenizerFast(_A		,   do_lower_case=_A		,   split_by_punct=_A	)
							snake_case_		:			str											=	rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A		,   add_special_tokens=_A	)	)

							self.assertListEqual(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Any:







							"""simple docstring"""


							snake_case_		:			str											=	'I was born in 92000, and this is falsé.'
							snake_case_		:			Union[str, Any]											=	['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
							# fmt: on

							snake_case_		:			Dict											=	DebertaVaTokenizer(_A		,   do_lower_case=_A		,   split_by_punct=_A	)
							snake_case_		:			Any											=	tokenizer.convert_ids_to_tokens(tokenizer.encode(_A		,   add_special_tokens=_A	)	)

							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Union[str, Any]											=	DebertaVaTokenizerFast(_A		,   do_lower_case=_A		,   split_by_punct=_A	)
							snake_case_		:			List[str]											=	rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A		,   add_special_tokens=_A	)	)

							self.assertListEqual(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	' \tHeLLo!how  \n Are yoU?  '
							snake_case_		:			Optional[Any]											=	['▁', '<unk>', 'e', '<unk>', 'o', '!', 'how', '▁', '<unk>', 're', '▁yo', '<unk>', '?']
							# fmt: on

							snake_case_		:			List[Any]											=	DebertaVaTokenizer(_A		,   do_lower_case=_A		,   split_by_punct=_A	)
							snake_case_		:			Union[str, Any]											=	tokenizer.convert_ids_to_tokens(tokenizer.encode(_A		,   add_special_tokens=_A	)	)

							self.assertListEqual(_A		,   _A	)

							snake_case_		:			int											=	DebertaVaTokenizerFast(_A		,   do_lower_case=_A		,   split_by_punct=_A	)
							snake_case_		:			Optional[Any]											=	rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A		,   add_special_tokens=_A	)	)

							self.assertListEqual(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      Any	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	self.get_tokenizer()
							snake_case_		:			Optional[int]											=	self.get_rust_tokenizer()

							snake_case_		:			Tuple											=	'I was born in 92000, and this is falsé.'

							snake_case_		:			Dict											=	tokenizer.convert_ids_to_tokens(tokenizer.encode(_A		,   add_special_tokens=_A	)	)
							snake_case_		:			Union[str, Any]											=	rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_A		,   add_special_tokens=_A	)	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Dict											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							snake_case_		:			Any											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			int											=	self.get_rust_tokenizer()
							snake_case_		:			Optional[int]											=	tokenizer.encode(_A	)
							snake_case_		:			int											=	rust_tokenizer.encode(_A	)
							self.assertListEqual(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	'This is a test'
							snake_case_		:			Tuple											=	[13, 1, 4398, 25, 21, 1289]
							snake_case_		:			Union[str, Any]											=	['▁', 'T', 'his', '▁is', '▁a', '▁test']
							snake_case_		:			Optional[int]											=	['▁', '<unk>', 'his', '▁is', '▁a', '▁test']

							snake_case_		:			List[Any]											=	DebertaVaTokenizer(_A		,   keep_accents=_A	)
							snake_case_		:			int											=	DebertaVaTokenizerFast(_A		,   keep_accents=_A	)

							snake_case_		:			int											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)
							snake_case_		:			Optional[int]											=	tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)
							snake_case_		:			Optional[Any]											=	tokenizer.convert_ids_to_tokens(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Optional[Any]											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)
							snake_case_		:			List[str]											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)
							snake_case_		:			str											=	rust_tokenizer.convert_ids_to_tokens(_A	)
							self.assertListEqual(_A		,   _A	)

							# fmt: off
							snake_case_		:			Tuple											=	'I was born in 92000, and this is falsé.'
							snake_case_		:			Optional[Any]											=	[13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
							snake_case_		:			List[Any]											=	['▁', 'I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.', ]
							snake_case_		:			int											=	['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
							# fmt: on

							snake_case_		:			Optional[Any]											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)
							snake_case_		:			int											=	tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)
							snake_case_		:			Tuple											=	tokenizer.convert_ids_to_tokens(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			List[Any]											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)
							snake_case_		:			List[str]											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)
							snake_case_		:			Dict											=	rust_tokenizer.convert_ids_to_tokens(_A	)
							self.assertListEqual(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	DebertaVaTokenizer(_A	)

							snake_case_		:			Union[str, Any]											=	tokenizer.encode('sequence builders'	)
							snake_case_		:			Optional[Any]											=	tokenizer.encode('multi-sequence build'	)

							snake_case_		:			Optional[Any]											=	tokenizer.build_inputs_with_special_tokens(_A	)
							snake_case_		:			Tuple											=	tokenizer.build_inputs_with_special_tokens(_A		,   _A	)

							self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]		,   _A	)
							self.assertEqual(
							    [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id]		,   _A		,   )






						@slow
						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			str											=	{'input_ids': [[1, 39867, 36, 19390, 486, 27, 35052, 81436, 18, 60685, 1225, 7, 35052, 81436, 18, 9367, 16899, 18, 15937, 53, 594, 773, 18, 16287, 30465, 36, 15937, 6, 41139, 38, 36979, 60763, 191, 6, 34132, 99, 6, 50538, 390, 43230, 6, 34132, 2779, 20850, 14, 699, 1072, 1194, 36, 382, 10901, 53, 7, 699, 1072, 2084, 36, 20422, 630, 53, 19, 105, 3049, 1896, 1053, 16899, 1506, 11, 37978, 4243, 7, 1237, 31869, 200, 16566, 654, 6, 35052, 81436, 7, 55630, 13593, 4, 2], [1, 26, 15011, 13, 667, 8, 1053, 18, 23611, 1237, 72356, 12820, 34, 104134, 1209, 35, 13313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 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, 5, 1232, 2864, 15785, 14951, 105, 5, 8581, 1250, 4, 2, 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]], 'token_type_ids': [[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, 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, 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]], '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, 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, 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='microsoft/deberta-v2-xlarge'		,   revision='ad6e42c1532ddf3a15c39246b63f5559d558b670'		,   )








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 SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			Dict       =       PriorTransformer
						__magic_name__:			str       =       "hidden_states"






						@property
						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Any											=	4
							snake_case_		:			int											=	8
							snake_case_		:			Dict											=	7

							snake_case_		:			Union[str, Any]											=	floats_tensor((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			int											=	floats_tensor((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			str											=	floats_tensor((batch_size, num_embeddings, embedding_dim)	).to(_A	)

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






						def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      List[Any]=0	)							->    str:







							"""simple docstring"""


							torch.manual_seed(_A	)
							snake_case_		:			List[Any]											=	4
							snake_case_		:			str											=	8
							snake_case_		:			Any											=	7

							snake_case_		:			List[Any]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			Optional[int]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			List[Any]											=	torch.randn((batch_size, num_embeddings, embedding_dim)	).to(_A	)

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






						@property
						def UpperCAmelCase_				(      self      :      str	)							->    Optional[Any]:







							"""simple docstring"""


							return (4, 8)






						@property
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[Any]:







							"""simple docstring"""


							return (4, 8)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	{
							    'num_attention_heads': 2,
							    'attention_head_dim': 4,
							    'num_layers': 2,
							    'embedding_dim': 8,
							    'num_embeddings': 7,
							    'additional_embeddings': 4,
							}
							snake_case_		:			Tuple											=	self.dummy_input
							return init_dict, inputs_dict






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Dict:







							"""simple docstring"""


							snake_case_						,snake_case_		:			str											=	PriorTransformer.from_pretrained(
							    'hf-internal-testing/prior-dummy'		,   output_loading_info=_A	)
							self.assertIsNotNone(_A	)
							self.assertEqual(len(loading_info['missing_keys']	)		,   0	)

							model.to(_A	)
							snake_case_		:			Optional[Any]											=	model(**self.dummy_input	)[0]

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






						def UpperCAmelCase_				(      self      :      Tuple	)							->    List[Any]:







							"""simple docstring"""


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

							snake_case_		:			Tuple											=	self.model_class(**_A	)
							snake_case_		:			Any											=	inspect.signature(model.forward	)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							snake_case_		:			List[str]											=	[*signature.parameters.keys()]

							snake_case_		:			int											=	['hidden_states', 'timestep']
							self.assertListEqual(arg_names[:2]		,   _A	)






						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			str											=	PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'	)
							snake_case_		:			str											=	model.to(_A	)

							if hasattr(_A		,   'set_default_attn_processor'	):
								model.set_default_attn_processor()

							snake_case_		:			Optional[int]											=	self.get_dummy_seed_input()

							with torch.no_grad():
								snake_case_		:			Any											=	model(**_A	)[0]

							snake_case_		:			Any											=	output[0, :5].flatten().cpu()
							print(_A	)

							# 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.
							snake_case_		:			str											=	torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]	)
							self.assertTrue(torch_all_close(_A		,   _A		,   rtol=1E-2	)	)
@slow
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      int=1		,   _A      :      int=768		,   _A      :      str=77		,   _A      :      List[str]=0	)							->    Tuple:







							"""simple docstring"""


							torch.manual_seed(_A	)
							snake_case_		:			Dict											=	batch_size
							snake_case_		:			Any											=	embedding_dim
							snake_case_		:			int											=	num_embeddings

							snake_case_		:			Dict											=	torch.randn((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			List[Any]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			Optional[int]											=	torch.randn((batch_size, num_embeddings, embedding_dim)	).to(_A	)

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






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Optional[int]:







							"""simple docstring"""


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






						@parameterized.expand(
						    [
						        # fmt: off
						        [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
						        [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
						        # fmt: on
						    ]	)
						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      List[Any]		,   _A      :      List[str]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			str											=	PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'		,   subfolder='prior'	)
							model.to(_A	)
							snake_case_		:			Optional[Any]											=	self.get_dummy_seed_input(seed=_A	)

							with torch.no_grad():
								snake_case_		:			str											=	model(**_A	)[0]

							assert list(sample.shape	) == [1, 768]

							snake_case_		:			Optional[Any]											=	sample[0, :8].flatten().cpu()
							print(_A	)
							snake_case_		:			int											=	torch.tensor(_A	)

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

