"""simple docstring"""





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  _snake_case     (					a__				, unittest.TestCase		):
						snake_case__			  =			DebertaVaTokenizer
						snake_case__			  =			DebertaVaTokenizerFast
						snake_case__			  =			True
						snake_case__			  =			True






						def       lowerCamelCase__     (       self      :			Any    ):
													super().setUp()

													# We have a SentencePiece fixture for testing
													__lowerCamelCase					:		List[str]		   =     DebertaVaTokenizer(UpperCAmelCase							,					unk_token="<unk>"    )
													tokenizer.save_pretrained(self.tmpdirname    )






						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			Union[str, Any]    ):
													__lowerCamelCase					:		Dict		   =     "this is a test"
													__lowerCamelCase					:		Dict		   =     "this is a test"
													return input_text, output_text






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Optional[int]		   =     "<pad>"
													__lowerCamelCase					:		Union[str, Any]		   =     0

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






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		List[str]		   =     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       lowerCamelCase__     (       self      :			Optional[Any]    ):
													self.assertEqual(self.get_tokenizer().vocab_size							,					30000    )






						def       lowerCamelCase__     (       self      :			str    ):
													# fmt: off
													__lowerCamelCase					:		str		   =     " \tHeLLo!how  \n Are yoU?  "
													__lowerCamelCase					:		Any		   =     ["▁hello", "!", "how", "▁are", "▁you", "?"]
													# fmt: on

													__lowerCamelCase					:		List[Any]		   =     DebertaVaTokenizer(UpperCAmelCase							,					do_lower_case=UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )    )

													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase					:		Tuple		   =     DebertaVaTokenizerFast(UpperCAmelCase							,					do_lower_case=UpperCAmelCase    )
													__lowerCamelCase					:		str		   =     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       lowerCamelCase__     (       self      :			int    ):
													pass






						@unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one."    )
						def       lowerCamelCase__     (       self      :			int    ):
													pass






						def       lowerCamelCase__     (       self      :			List[Any]    ):
													# fmt: off
													__lowerCamelCase					:		Union[str, Any]		   =     "I was born in 92000, and this is falsé."
													__lowerCamelCase					:		Tuple		   =     ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
													# fmt: on

													__lowerCamelCase					:		Optional[Any]		   =     DebertaVaTokenizer(UpperCAmelCase							,					split_by_punct=UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )    )

													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase					:		Dict		   =     DebertaVaTokenizerFast(UpperCAmelCase							,					split_by_punct=UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )    )

													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[Any]    ):
													# fmt: off
													__lowerCamelCase					:		str		   =     "I was born in 92000, and this is falsé."
													__lowerCamelCase					:		int		   =     ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
													# fmt: on

													__lowerCamelCase					:		List[str]		   =     DebertaVaTokenizer(UpperCAmelCase							,					do_lower_case=UpperCAmelCase							,					split_by_punct=UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )    )
													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase					:		List[Any]		   =     DebertaVaTokenizerFast(UpperCAmelCase							,					do_lower_case=UpperCAmelCase							,					split_by_punct=UpperCAmelCase    )
													__lowerCamelCase					:		int		   =     rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )    )
													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Any    ):
													# fmt: off
													__lowerCamelCase					:		Union[str, Any]		   =     "I was born in 92000, and this is falsé."
													__lowerCamelCase					:		str		   =     ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
													# fmt: on

													__lowerCamelCase					:		Union[str, Any]		   =     DebertaVaTokenizer(UpperCAmelCase							,					do_lower_case=UpperCAmelCase							,					split_by_punct=UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )    )

													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase					:		Union[str, Any]		   =     DebertaVaTokenizerFast(UpperCAmelCase							,					do_lower_case=UpperCAmelCase							,					split_by_punct=UpperCAmelCase    )
													__lowerCamelCase					:		Union[str, Any]		   =     rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )    )

													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[Any]    ):
													# fmt: off
													__lowerCamelCase					:		Optional[Any]		   =     "I was born in 92000, and this is falsé."
													__lowerCamelCase					:		Union[str, Any]		   =     ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
													# fmt: on

													__lowerCamelCase					:		List[Any]		   =     DebertaVaTokenizer(UpperCAmelCase							,					do_lower_case=UpperCAmelCase							,					split_by_punct=UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )    )

													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )

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

													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													# fmt: off
													__lowerCamelCase					:		int		   =     " \tHeLLo!how  \n Are yoU?  "
													__lowerCamelCase					:		Optional[int]		   =     ["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"]
													# fmt: on

													__lowerCamelCase					:		Optional[Any]		   =     DebertaVaTokenizer(UpperCAmelCase							,					do_lower_case=UpperCAmelCase							,					split_by_punct=UpperCAmelCase    )
													__lowerCamelCase					:		int		   =     tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )    )

													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase					:		List[str]		   =     DebertaVaTokenizerFast(UpperCAmelCase							,					do_lower_case=UpperCAmelCase							,					split_by_punct=UpperCAmelCase    )
													__lowerCamelCase					:		Tuple		   =     rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )    )

													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		List[str]		   =     self.get_tokenizer()
													__lowerCamelCase					:		int		   =     self.get_rust_tokenizer()

													__lowerCamelCase					:		Optional[Any]		   =     "I was born in 92000, and this is falsé."

													__lowerCamelCase					:		Any		   =     tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )    )
													__lowerCamelCase					:		Optional[int]		   =     rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )    )
													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase					:		Dict		   =     tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )
													__lowerCamelCase					:		int		   =     rust_tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )
													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase					:		Dict		   =     self.get_rust_tokenizer()
													__lowerCamelCase					:		Optional[Any]		   =     tokenizer.encode(UpperCAmelCase    )
													__lowerCamelCase					:		Union[str, Any]		   =     rust_tokenizer.encode(UpperCAmelCase    )
													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     "This is a test"
													__lowerCamelCase					:		Optional[int]		   =     [13, 1, 4398, 25, 21, 1289]
													__lowerCamelCase					:		Union[str, Any]		   =     ["▁", "T", "his", "▁is", "▁a", "▁test"]
													__lowerCamelCase					:		List[Any]		   =     ["▁", "<unk>", "his", "▁is", "▁a", "▁test"]

													__lowerCamelCase					:		Optional[Any]		   =     DebertaVaTokenizer(UpperCAmelCase							,					keep_accents=UpperCAmelCase    )
													__lowerCamelCase					:		List[str]		   =     DebertaVaTokenizerFast(UpperCAmelCase							,					keep_accents=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[int]		   =     tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )
													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     tokenizer.tokenize(UpperCAmelCase    )
													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     tokenizer.convert_ids_to_tokens(UpperCAmelCase    )
													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase					:		int		   =     rust_tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )
													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )
													__lowerCamelCase					:		Tuple		   =     rust_tokenizer.tokenize(UpperCAmelCase    )
													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )
													__lowerCamelCase					:		Tuple		   =     rust_tokenizer.convert_ids_to_tokens(UpperCAmelCase    )
													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )

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

													__lowerCamelCase					:		str		   =     tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )
													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )
													__lowerCamelCase					:		List[str]		   =     tokenizer.tokenize(UpperCAmelCase    )
													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     tokenizer.convert_ids_to_tokens(UpperCAmelCase    )
													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase					:		Optional[int]		   =     rust_tokenizer.encode(UpperCAmelCase							,					add_special_tokens=UpperCAmelCase    )
													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     rust_tokenizer.tokenize(UpperCAmelCase    )
													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )
													__lowerCamelCase					:		str		   =     rust_tokenizer.convert_ids_to_tokens(UpperCAmelCase    )
													self.assertListEqual(UpperCAmelCase							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Dict		   =     DebertaVaTokenizer(UpperCAmelCase    )

													__lowerCamelCase					:		int		   =     tokenizer.encode("sequence builders"    )
													__lowerCamelCase					:		str		   =     tokenizer.encode("multi-sequence build"    )

													__lowerCamelCase					:		Tuple		   =     tokenizer.build_inputs_with_special_tokens(UpperCAmelCase    )
													__lowerCamelCase					:		int		   =     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       lowerCamelCase__     (       self      :			Optional[Any]    ):
													# fmt: off
													__lowerCamelCase					:		Tuple		   =     {"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"							,					)
"""simple docstring"""





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  _snake_case     (					a__		):






						def __init__(       self      :			str							,					UpperCAmelCase      :			TransformeraDModel							,					UpperCAmelCase      :			AutoencoderKL							,					UpperCAmelCase      :			KarrasDiffusionSchedulers							,					UpperCAmelCase      :			Optional[Dict[int, str]] = None							,					):
													super().__init__()
													self.register_modules(transformer=UpperCAmelCase							,					vae=UpperCAmelCase							,					scheduler=UpperCAmelCase    )

													# create a imagenet -> id dictionary for easier use
													__lowerCamelCase					:		Optional[Any]		   =     {}
													if idalabel is not None:
																				for key, value in idalabel.items():
																											for label in value.split(","    ):
																																		__lowerCamelCase					:		List[str]		   =     int(UpperCAmelCase    )
																				__lowerCamelCase					:		Union[str, Any]		   =     dict(sorted(self.labels.items()    )    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Union[str, List[str]]    ):

													if not isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																				__lowerCamelCase					:		List[str]		   =     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      :			int							,					UpperCAmelCase      :			List[int]							,					UpperCAmelCase      :			float = 4.0							,					UpperCAmelCase      :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					UpperCAmelCase      :			int = 50							,					UpperCAmelCase      :			Optional[str] = "pil"							,					UpperCAmelCase      :			bool = True							,					):
													__lowerCamelCase					:		int		   =     len(UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     self.transformer.config.sample_size
													__lowerCamelCase					:		Dict		   =     self.transformer.config.in_channels

													__lowerCamelCase					:		Any		   =     randn_tensor(
													    shape=(batch_size, latent_channels, latent_size, latent_size)							,					generator=UpperCAmelCase							,					device=self.device							,					dtype=self.transformer.dtype							,					)
													__lowerCamelCase					:		Optional[Any]		   =     torch.cat([latents] * 2    ) if guidance_scale > 1 else latents

													__lowerCamelCase					:		Optional[int]		   =     torch.tensor(UpperCAmelCase							,					device=self.device    ).reshape(-1    )
													__lowerCamelCase					:		Optional[int]		   =     torch.tensor([1000] * batch_size							,					device=self.device    )
													__lowerCamelCase					:		Any		   =     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					:		str		   =     latent_model_input[: len(UpperCAmelCase    ) // 2]
																											__lowerCamelCase					:		Optional[Any]		   =     torch.cat([half, half]							,					dim=0    )
																				__lowerCamelCase					:		Dict		   =     self.scheduler.scale_model_input(UpperCAmelCase							,					UpperCAmelCase    )

																				__lowerCamelCase					:		Dict		   =     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					:		List[str]		   =     latent_model_input.device.type == "mps"
																											if isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																																		__lowerCamelCase					:		List[Any]		   =     torch.floataa if is_mps else torch.floataa
																											else:
																																		__lowerCamelCase					:		Optional[Any]		   =     torch.intaa if is_mps else torch.intaa
																											__lowerCamelCase					:		Union[str, Any]		   =     torch.tensor([timesteps]							,					dtype=UpperCAmelCase							,					device=latent_model_input.device    )
																				elif len(timesteps.shape    ) == 0:
																											__lowerCamelCase					:		Union[str, Any]		   =     timesteps[None].to(latent_model_input.device    )
																				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																				__lowerCamelCase					:		List[Any]		   =     timesteps.expand(latent_model_input.shape[0]    )
																				# predict noise model_output
																				__lowerCamelCase					:		Union[str, Any]		   =     self.transformer(
																				    UpperCAmelCase							,					timestep=UpperCAmelCase							,					class_labels=UpperCAmelCase    ).sample

																				# perform guidance
																				if guidance_scale > 1:
																											__lowerCamelCase   ,  __lowerCamelCase					:		str		   =     noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																											__lowerCamelCase   ,  __lowerCamelCase					:		Union[str, Any]		   =     torch.split(UpperCAmelCase							,					len(UpperCAmelCase    ) // 2							,					dim=0    )

																											__lowerCamelCase					:		List[str]		   =     uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																											__lowerCamelCase					:		Union[str, Any]		   =     torch.cat([half_eps, half_eps]							,					dim=0    )

																											__lowerCamelCase					:		Optional[Any]		   =     torch.cat([eps, rest]							,					dim=1    )

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

																				# compute previous image: x_t -> x_t-1
																				__lowerCamelCase					:		Optional[Any]		   =     self.scheduler.step(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    ).prev_sample

													if guidance_scale > 1:
																				__lowerCamelCase   ,  __lowerCamelCase					:		List[str]		   =     latent_model_input.chunk(2							,					dim=0    )
													else:
																				__lowerCamelCase					:		Optional[Any]		   =     latent_model_input

													__lowerCamelCase					:		Tuple		   =     1 / self.vae.config.scaling_factor * latents
													__lowerCamelCase					:		Any		   =     self.vae.decode(UpperCAmelCase    ).sample

													__lowerCamelCase					:		Tuple		   =     (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					:		Optional[int]		   =     samples.cpu().permute(0							,					2							,					3							,					1    ).float().numpy()

													if output_type == "pil":
																				__lowerCamelCase					:		Dict		   =     self.numpy_to_pil(UpperCAmelCase    )

													if not return_dict:
																				return (samples,)

													return ImagePipelineOutput(images=UpperCAmelCase    )