






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


a_							=    get_tests_dir('''fixtures/spiece.model''')


@require_sentencepiece
@require_tokenizers
class      lowercase__     (			_UpperCAmelCase,					unittest.TestCase	):
 a_         =DebertaVaTokenizer
 a_         =DebertaVaTokenizerFast
 a_         =True
 a_         =True

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







       '''simple docstring'''





       super().setUp()

       # We have a SentencePiece fixture for testing
       lowerCAmelCase__       =						DebertaVaTokenizer(UpperCamelCase_		,    unk_token="<unk>"       )
       tokenizer.save_pretrained(self.tmpdirname       )

 def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Optional[int]:







       '''simple docstring'''





       lowerCAmelCase__       =						"this is a test"
       lowerCAmelCase__       =						"this is a test"
       return input_text, output_text

 def 		UpperCAmelCase  (							self       )->					List[str]:







       '''simple docstring'''





       lowerCAmelCase__       =						"<pad>"
       lowerCAmelCase__       =						0

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

 def 		UpperCAmelCase  (							self       )->					Tuple:







       '''simple docstring'''





       lowerCAmelCase__       =						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(UpperCamelCase_       )		,    30001       )

 def 		UpperCAmelCase  (							self       )->					str:







       '''simple docstring'''





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

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







       '''simple docstring'''





       lowerCAmelCase__       =						" \tHeLLo!how  \n Are yoU?  "
       lowerCAmelCase__       =						["▁hello", "!", "how", "▁are", "▁you", "?"]
       # fmt: on

       lowerCAmelCase__       =						DebertaVaTokenizer(UpperCamelCase_		,    do_lower_case=UpperCamelCase_       )
       lowerCAmelCase__       =						tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )       )

       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

       lowerCAmelCase__       =						DebertaVaTokenizerFast(UpperCamelCase_		,    do_lower_case=UpperCamelCase_       )
       lowerCAmelCase__       =						rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )       )

       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

 @unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one."       )
 def 		UpperCAmelCase  (							self       )->					Optional[Any]:







       '''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       )->					Any:







       '''simple docstring'''





       pass

 def 		UpperCAmelCase  (							self       )->					int:







       '''simple docstring'''





       lowerCAmelCase__       =						"I was born in 92000, and this is falsé."
       lowerCAmelCase__       =						["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
       # fmt: on

       lowerCAmelCase__       =						DebertaVaTokenizer(UpperCamelCase_		,    split_by_punct=UpperCamelCase_       )
       lowerCAmelCase__       =						tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )       )

       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

       lowerCAmelCase__       =						DebertaVaTokenizerFast(UpperCamelCase_		,    split_by_punct=UpperCamelCase_       )
       lowerCAmelCase__       =						rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )       )

       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

 def 		UpperCAmelCase  (							self       )->					str:







       '''simple docstring'''





       lowerCAmelCase__       =						"I was born in 92000, and this is falsé."
       lowerCAmelCase__       =						["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
       # fmt: on

       lowerCAmelCase__       =						DebertaVaTokenizer(UpperCamelCase_		,    do_lower_case=UpperCamelCase_		,    split_by_punct=UpperCamelCase_       )
       lowerCAmelCase__       =						tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )       )
       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

       lowerCAmelCase__       =						DebertaVaTokenizerFast(UpperCamelCase_		,    do_lower_case=UpperCamelCase_		,    split_by_punct=UpperCamelCase_       )
       lowerCAmelCase__       =						rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )       )
       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

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







       '''simple docstring'''





       lowerCAmelCase__       =						"I was born in 92000, and this is falsé."
       lowerCAmelCase__       =						["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
       # fmt: on

       lowerCAmelCase__       =						DebertaVaTokenizer(UpperCamelCase_		,    do_lower_case=UpperCamelCase_		,    split_by_punct=UpperCamelCase_       )
       lowerCAmelCase__       =						tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )       )

       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

       lowerCAmelCase__       =						DebertaVaTokenizerFast(UpperCamelCase_		,    do_lower_case=UpperCamelCase_		,    split_by_punct=UpperCamelCase_       )
       lowerCAmelCase__       =						rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )       )

       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

 def 		UpperCAmelCase  (							self       )->					Tuple:







       '''simple docstring'''





       lowerCAmelCase__       =						"I was born in 92000, and this is falsé."
       lowerCAmelCase__       =						["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
       # fmt: on

       lowerCAmelCase__       =						DebertaVaTokenizer(UpperCamelCase_		,    do_lower_case=UpperCamelCase_		,    split_by_punct=UpperCamelCase_       )
       lowerCAmelCase__       =						tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )       )

       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

       lowerCAmelCase__       =						DebertaVaTokenizerFast(UpperCamelCase_		,    do_lower_case=UpperCamelCase_		,    split_by_punct=UpperCamelCase_       )
       lowerCAmelCase__       =						rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )       )

       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

 def 		UpperCAmelCase  (							self       )->					Dict:







       '''simple docstring'''





       lowerCAmelCase__       =						" \tHeLLo!how  \n Are yoU?  "
       lowerCAmelCase__       =						["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"]
       # fmt: on

       lowerCAmelCase__       =						DebertaVaTokenizer(UpperCamelCase_		,    do_lower_case=UpperCamelCase_		,    split_by_punct=UpperCamelCase_       )
       lowerCAmelCase__       =						tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )       )

       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

       lowerCAmelCase__       =						DebertaVaTokenizerFast(UpperCamelCase_		,    do_lower_case=UpperCamelCase_		,    split_by_punct=UpperCamelCase_       )
       lowerCAmelCase__       =						rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )       )

       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

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







       '''simple docstring'''





       lowerCAmelCase__       =						self.get_tokenizer()
       lowerCAmelCase__       =						self.get_rust_tokenizer()

       lowerCAmelCase__       =						"I was born in 92000, and this is falsé."

       lowerCAmelCase__       =						tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )       )
       lowerCAmelCase__       =						rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )       )
       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

       lowerCAmelCase__       =						tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )
       lowerCAmelCase__       =						rust_tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )
       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

       lowerCAmelCase__       =						self.get_rust_tokenizer()
       lowerCAmelCase__       =						tokenizer.encode(UpperCamelCase_       )
       lowerCAmelCase__       =						rust_tokenizer.encode(UpperCamelCase_       )
       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

 def 		UpperCAmelCase  (							self       )->					Dict:







       '''simple docstring'''





       lowerCAmelCase__       =						"This is a test"
       lowerCAmelCase__       =						[13, 1, 4398, 25, 21, 1289]
       lowerCAmelCase__       =						["▁", "T", "his", "▁is", "▁a", "▁test"]
       lowerCAmelCase__       =						["▁", "<unk>", "his", "▁is", "▁a", "▁test"]

       lowerCAmelCase__       =						DebertaVaTokenizer(UpperCamelCase_		,    keep_accents=UpperCamelCase_       )
       lowerCAmelCase__       =						DebertaVaTokenizerFast(UpperCamelCase_		,    keep_accents=UpperCamelCase_       )

       lowerCAmelCase__       =						tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )
       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )
       lowerCAmelCase__       =						tokenizer.tokenize(UpperCamelCase_       )
       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )
       lowerCAmelCase__       =						tokenizer.convert_ids_to_tokens(UpperCamelCase_       )
       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

       lowerCAmelCase__       =						rust_tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )
       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )
       lowerCAmelCase__       =						rust_tokenizer.tokenize(UpperCamelCase_       )
       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )
       lowerCAmelCase__       =						rust_tokenizer.convert_ids_to_tokens(UpperCamelCase_       )
       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

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

       lowerCAmelCase__       =						tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )
       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )
       lowerCAmelCase__       =						tokenizer.tokenize(UpperCamelCase_       )
       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )
       lowerCAmelCase__       =						tokenizer.convert_ids_to_tokens(UpperCamelCase_       )
       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

       lowerCAmelCase__       =						rust_tokenizer.encode(UpperCamelCase_		,    add_special_tokens=UpperCamelCase_       )
       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )
       lowerCAmelCase__       =						rust_tokenizer.tokenize(UpperCamelCase_       )
       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )
       lowerCAmelCase__       =						rust_tokenizer.convert_ids_to_tokens(UpperCamelCase_       )
       self.assertListEqual(UpperCamelCase_		,    UpperCamelCase_       )

 def 		UpperCAmelCase  (							self       )->					Dict:







       '''simple docstring'''





       lowerCAmelCase__       =						DebertaVaTokenizer(UpperCamelCase_       )

       lowerCAmelCase__       =						tokenizer.encode("sequence builders"       )
       lowerCAmelCase__       =						tokenizer.encode("multi-sequence build"       )

       lowerCAmelCase__       =						tokenizer.build_inputs_with_special_tokens(UpperCamelCase_       )
       lowerCAmelCase__       =						tokenizer.build_inputs_with_special_tokens(UpperCamelCase_		,    UpperCamelCase_       )

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


 @slow
 def 		UpperCAmelCase  (							self       )->					Dict:







       '''simple docstring'''





       lowerCAmelCase__       =						{"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=UpperCamelCase_		,    model_name="microsoft/deberta-v2-xlarge"		,    revision="ad6e42c1532ddf3a15c39246b63f5559d558b670"		,    )














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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class      lowercase__     (			_UpperCAmelCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    )->					List[str]:







							'''simple docstring'''





							super().__init__()
							self.register_modules(transformer=__UpperCAmelCase		,    vae=__UpperCAmelCase		,    scheduler=__UpperCAmelCase       )

							# create a imagenet -> id dictionary for easier use
							lowerCAmelCase__       =						{}
							if idalabel is not None:
													for key, value in idalabel.items():
																			for label in value.split(","       ):
																									lowerCAmelCase__       =						int(__UpperCAmelCase       )
													lowerCAmelCase__       =						dict(sorted(self.labels.items()       )       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					List[int]:







							'''simple docstring'''





							if not isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						list(__UpperCAmelCase       )

							for l in label:
													if l not in self.labels:
																			raise ValueError(
																			    F"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."       )

							return [self.labels[l] for l in label]


	@torch.no_grad()
	def __call__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase = 4.0		,    __UpperCAmelCase = None		,    __UpperCAmelCase = 50		,    __UpperCAmelCase = "pil"		,    __UpperCAmelCase = True		,    )->					Union[ImagePipelineOutput, Tuple]:







							'''simple docstring'''





							lowerCAmelCase__       =						len(__UpperCAmelCase       )
							lowerCAmelCase__       =						self.transformer.config.sample_size
							lowerCAmelCase__       =						self.transformer.config.in_channels

							lowerCAmelCase__       =						randn_tensor(
							    shape=(batch_size, latent_channels, latent_size, latent_size)		,    generator=__UpperCAmelCase		,    device=self.device		,    dtype=self.transformer.dtype		,    )
							lowerCAmelCase__       =						torch.cat([latents] * 2       ) if guidance_scale > 1 else latents

							lowerCAmelCase__       =						torch.tensor(__UpperCAmelCase		,    device=self.device       ).reshape(-1       )
							lowerCAmelCase__       =						torch.tensor([1000] * batch_size		,    device=self.device       )
							lowerCAmelCase__       =						torch.cat([class_labels, class_null]		,    0       ) if guidance_scale > 1 else class_labels

							# set step values
							self.scheduler.set_timesteps(__UpperCAmelCase       )

							for t in self.progress_bar(self.scheduler.timesteps       ):
													if guidance_scale > 1:
																			lowerCAmelCase__       =						latent_model_input[: len(__UpperCAmelCase       ) // 2]
																			lowerCAmelCase__       =						torch.cat([half, half]		,    dim=0       )
													lowerCAmelCase__       =						self.scheduler.scale_model_input(__UpperCAmelCase		,    __UpperCAmelCase       )

													lowerCAmelCase__       =						t
													if not torch.is_tensor(__UpperCAmelCase       ):
																			# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																			# This would be a good case for the `match` statement (Python 3.10+)
																			lowerCAmelCase__       =						latent_model_input.device.type == "mps"
																			if isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
																									lowerCAmelCase__       =						torch.floataa if is_mps else torch.floataa
																			else:
																									lowerCAmelCase__       =						torch.intaa if is_mps else torch.intaa
																			lowerCAmelCase__       =						torch.tensor([timesteps]		,    dtype=__UpperCAmelCase		,    device=latent_model_input.device       )
													elif len(timesteps.shape       ) == 0:
																			lowerCAmelCase__       =						timesteps[None].to(latent_model_input.device       )
													# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
													lowerCAmelCase__       =						timesteps.expand(latent_model_input.shape[0]       )
													# predict noise model_output
													lowerCAmelCase__       =						self.transformer(
													    __UpperCAmelCase		,    timestep=__UpperCAmelCase		,    class_labels=__UpperCAmelCase       ).sample

													# perform guidance
													if guidance_scale > 1:
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						torch.split(__UpperCAmelCase		,    len(__UpperCAmelCase       ) // 2		,    dim=0       )

																			lowerCAmelCase__       =						uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																			lowerCAmelCase__       =						torch.cat([half_eps, half_eps]		,    dim=0       )

																			lowerCAmelCase__       =						torch.cat([eps, rest]		,    dim=1       )

													# learned sigma
													if self.transformer.config.out_channels // 2 == latent_channels:
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						torch.split(__UpperCAmelCase		,    __UpperCAmelCase		,    dim=1       )
													else:
																			lowerCAmelCase__       =						noise_pred

													# compute previous image: x_t -> x_t-1
													lowerCAmelCase__       =						self.scheduler.step(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       ).prev_sample

							if guidance_scale > 1:
													lowerCAmelCase__      ,							lowerCAmelCase__       =						latent_model_input.chunk(2		,    dim=0       )
							else:
													lowerCAmelCase__       =						latent_model_input

							lowerCAmelCase__       =						1 / self.vae.config.scaling_factor * latents
							lowerCAmelCase__       =						self.vae.decode(__UpperCAmelCase       ).sample

							lowerCAmelCase__       =						(samples / 2 + 0.5).clamp(0		,    1       )

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							lowerCAmelCase__       =						samples.cpu().permute(0		,    2		,    3		,    1       ).float().numpy()

							if output_type == "pil":
													lowerCAmelCase__       =						self.numpy_to_pil(__UpperCAmelCase       )

							if not return_dict:
													return (samples,)

							return ImagePipelineOutput(images=__UpperCAmelCase       )






