

"""simple docstring"""






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case_ : List[str]   =							logging.get_logger(__name__)


snake_case_ : Dict   =							{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all BART models at https://huggingface.co/models?filter=bart
snake_case_ : List[Any]   =							{
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
}

snake_case_ : List[str]   =							{
    """facebook/bart-base""": 1_0_2_4,
    """facebook/bart-large""": 1_0_2_4,
    """facebook/bart-large-mnli""": 1_0_2_4,
    """facebook/bart-large-cnn""": 1_0_2_4,
    """facebook/bart-large-xsum""": 1_0_2_4,
    """yjernite/bart_eli5""": 1_0_2_4,
}



@lru_cache()
def 	lowercase_						(     ):




					'''simple docstring'''






					UpperCAmelCase      : Tuple   =					(
					    list(range(ord("!"						)				,   ord("~"						) + 1						)						) + list(range(ord("¡"						)				,   ord("¬"						) + 1						)						) + list(range(ord("®"						)				,   ord("ÿ"						) + 1						)						)
					)
					UpperCAmelCase      : Optional[Any]   =					bs[:]
					UpperCAmelCase      : Any   =					0
					for b in range(2**8						):
										if b not in bs:
															bs.append(SCREAMING_SNAKE_CASE__						)
															cs.append(2**8 + n						)
															n += 1
					UpperCAmelCase      : Optional[Any]   =					[chr(SCREAMING_SNAKE_CASE__						) for n in cs]
					return dict(zip(SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__						)						)



def 	lowercase_						(     _lowercase							:		int						):




					'''simple docstring'''






					UpperCAmelCase      : List[str]   =					set()
					UpperCAmelCase      : List[Any]   =					word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)						)
										UpperCAmelCase      : List[str]   =					char
					return pairs







class    snake_case__	(			__SCREAMING_SNAKE_CASE  ):
			SCREAMING_SNAKE_CASE__  					=    VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE__  					=    PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE__  					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE__  					=    ['''input_ids''', '''attention_mask''']







			def __init__(      self      :			Optional[int]      ,	lowercase      :			List[str]      ,	lowercase      :			Union[str, Any]      ,	lowercase      :			Any="replace"      ,	lowercase      :			str="<s>"      ,	lowercase      :			Dict="</s>"      ,	lowercase      :			Tuple="</s>"      ,	lowercase      :			str="<s>"      ,	lowercase      :			List[Any]="<unk>"      ,	lowercase      :			Union[str, Any]="<pad>"      ,	lowercase      :			Union[str, Any]="<mask>"      ,	lowercase      :			Optional[Any]=False      ,	**lowercase      :			List[str]      ,	):





								'''simple docstring'''



								UpperCAmelCase      : Dict   =					AddedToken(UpperCamelCase__      ,	lstrip=UpperCamelCase__      ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__      ,	UpperCamelCase__	) else bos_token
								UpperCAmelCase      : Optional[int]   =					AddedToken(UpperCamelCase__      ,	lstrip=UpperCamelCase__      ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__      ,	UpperCamelCase__	) else eos_token
								UpperCAmelCase      : Dict   =					AddedToken(UpperCamelCase__      ,	lstrip=UpperCamelCase__      ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__      ,	UpperCamelCase__	) else sep_token
								UpperCAmelCase      : Dict   =					AddedToken(UpperCamelCase__      ,	lstrip=UpperCamelCase__      ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__      ,	UpperCamelCase__	) else cls_token
								UpperCAmelCase      : int   =					AddedToken(UpperCamelCase__      ,	lstrip=UpperCamelCase__      ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__      ,	UpperCamelCase__	) else unk_token
								UpperCAmelCase      : Optional[Any]   =					AddedToken(UpperCamelCase__      ,	lstrip=UpperCamelCase__      ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__      ,	UpperCamelCase__	) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase      : Union[str, Any]   =					AddedToken(UpperCamelCase__      ,	lstrip=UpperCamelCase__      ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__      ,	UpperCamelCase__	) else mask_token

								super().__init__(
								    errors=UpperCamelCase__      ,	bos_token=UpperCamelCase__      ,	eos_token=UpperCamelCase__      ,	unk_token=UpperCamelCase__      ,	sep_token=UpperCamelCase__      ,	cls_token=UpperCamelCase__      ,	pad_token=UpperCamelCase__      ,	mask_token=UpperCamelCase__      ,	add_prefix_space=UpperCamelCase__      ,	**UpperCamelCase__      ,	)

								with open(UpperCamelCase__      ,	encoding="utf-8"	) as vocab_handle:
													UpperCAmelCase      : Any   =					json.load(UpperCamelCase__	)
								UpperCAmelCase      : str   =					{v: k for k, v in self.encoder.items()}
								UpperCAmelCase      : int   =					errors  # how to handle errors in decoding
								UpperCAmelCase      : Any   =					bytes_to_unicode()
								UpperCAmelCase      : Dict   =					{v: k for k, v in self.byte_encoder.items()}
								with open(UpperCamelCase__      ,	encoding="utf-8"	) as merges_handle:
													UpperCAmelCase      : Tuple   =					merges_handle.read().split("\n"	)[1:-1]
								UpperCAmelCase      : List[str]   =					[tuple(merge.split()	) for merge in bpe_merges]
								UpperCAmelCase      : List[str]   =					dict(zip(UpperCamelCase__      ,	range(len(UpperCamelCase__	)	)	)	)
								UpperCAmelCase      : str   =					{}
								UpperCAmelCase      : Optional[Any]   =					add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								UpperCAmelCase      : List[str]   =					re.compile(r"\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"	)







			@property
			def       __lowerCAmelCase					(      self      :			int	):





								'''simple docstring'''



								return len(self.encoder	)







			def       __lowerCAmelCase					(      self      :			Optional[Any]	):





								'''simple docstring'''



								return dict(self.encoder      ,	**self.added_tokens_encoder	)







			def       __lowerCAmelCase					(      self      :			int      ,	lowercase      :			str	):





								'''simple docstring'''



								if token in self.cache:
													return self.cache[token]
								UpperCAmelCase      : List[Any]   =					tuple(UpperCamelCase__	)
								UpperCAmelCase      : Optional[int]   =					get_pairs(UpperCamelCase__	)

								if not pairs:
													return token

								while True:
													UpperCAmelCase      : Optional[int]   =					min(UpperCamelCase__      ,	key=lambda lowercase	: self.bpe_ranks.get(UpperCamelCase__      ,	float("inf"	)	)	)
													if bigram not in self.bpe_ranks:
																		break
													UpperCAmelCase      : Any   =					bigram
													UpperCAmelCase      : Any   =					[]
													UpperCAmelCase      : List[Any]   =					0
													while i < len(UpperCamelCase__	):
																		try:
																							UpperCAmelCase      : List[str]   =					word.index(UpperCamelCase__      ,	UpperCamelCase__	)
																		except ValueError:
																							new_word.extend(word[i:]	)
																							break
																		else:
																							new_word.extend(word[i:j]	)
																							UpperCAmelCase      : Dict   =					j

																		if word[i] == first and i < len(UpperCamelCase__	) - 1 and word[i + 1] == second:
																							new_word.append(first + second	)
																							i += 2
																		else:
																							new_word.append(word[i]	)
																							i += 1
													UpperCAmelCase      : Tuple   =					tuple(UpperCamelCase__	)
													UpperCAmelCase      : Optional[Any]   =					new_word
													if len(UpperCamelCase__	) == 1:
																		break
													else:
																		UpperCAmelCase      : str   =					get_pairs(UpperCamelCase__	)
								UpperCAmelCase      : Optional[int]   =					''' '''.join(UpperCamelCase__	)
								UpperCAmelCase      : int   =					word
								return word







			def       __lowerCAmelCase					(      self      :			List[Any]      ,	lowercase      :			Tuple	):





								'''simple docstring'''



								UpperCAmelCase      : int   =					[]
								for token in re.findall(self.pat      ,	UpperCamelCase__	):
													UpperCAmelCase      : Any   =					''''''.join(
													    self.byte_encoder[b] for b in token.encode("utf-8"	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
													bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase__	).split(" "	)	)
								return bpe_tokens







			def       __lowerCAmelCase					(      self      :			str      ,	lowercase      :			Optional[int]	):





								'''simple docstring'''



								return self.encoder.get(UpperCamelCase__      ,	self.encoder.get(self.unk_token	)	)







			def       __lowerCAmelCase					(      self      :			List[str]      ,	lowercase      :			List[str]	):





								'''simple docstring'''



								return self.decoder.get(UpperCamelCase__	)







			def       __lowerCAmelCase					(      self      :			int      ,	lowercase      :			Tuple	):





								'''simple docstring'''



								UpperCAmelCase      : Optional[int]   =					''''''.join(UpperCamelCase__	)
								UpperCAmelCase      : Optional[Any]   =					bytearray([self.byte_decoder[c] for c in text]	).decode("utf-8"      ,	errors=self.errors	)
								return text







			def       __lowerCAmelCase					(      self      :			Tuple      ,	lowercase      :			str      ,	lowercase      :			Optional[str] = None	):





								'''simple docstring'''



								if not os.path.isdir(UpperCamelCase__	):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""	)
													return
								UpperCAmelCase      : int   =					os.path.join(
								    UpperCamelCase__      ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
								UpperCAmelCase      : str   =					os.path.join(
								    UpperCamelCase__      ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]	)

								with open(UpperCamelCase__      ,	"w"      ,	encoding="utf-8"	) as f:
													f.write(json.dumps(self.encoder      ,	indent=2      ,	sort_keys=UpperCamelCase__      ,	ensure_ascii=UpperCamelCase__	) + "\n"	)

								UpperCAmelCase      : Union[str, Any]   =					0
								with open(UpperCamelCase__      ,	"w"      ,	encoding="utf-8"	) as writer:
													writer.write("#version: 0.2\n"	)
													for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,	key=lambda lowercase	: kv[1]	):
																		if index != token_index:
																							logger.warning(
																							    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																							    " Please check that the tokenizer is not corrupted!"	)
																							UpperCAmelCase      : Optional[int]   =					token_index
																		writer.write(" ".join(UpperCamelCase__	) + "\n"	)
																		index += 1

								return vocab_file, merge_file







			def       __lowerCAmelCase					(      self      :			List[Any]      ,	lowercase      :			List[int]      ,	lowercase      :			Optional[List[int]] = None	):





								'''simple docstring'''



								if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase      : int   =					[self.cls_token_id]
								UpperCAmelCase      : Union[str, Any]   =					[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep







			def       __lowerCAmelCase					(      self      :			Any      ,	lowercase      :			List[int]      ,	lowercase      :			Optional[List[int]] = None      ,	lowercase      :			bool = False	):





								'''simple docstring'''



								if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=UpperCamelCase__      ,	token_ids_a=UpperCamelCase__      ,	already_has_special_tokens=UpperCamelCase__	)

								if token_ids_a is None:
													return [1] + ([0] * len(UpperCamelCase__	)) + [1]
								return [1] + ([0] * len(UpperCamelCase__	)) + [1, 1] + ([0] * len(UpperCamelCase__	)) + [1]







			def       __lowerCAmelCase					(      self      :			int      ,	lowercase      :			List[int]      ,	lowercase      :			Optional[List[int]] = None	):





								'''simple docstring'''



								UpperCAmelCase      : Optional[Any]   =					[self.sep_token_id]
								UpperCAmelCase      : Dict   =					[self.cls_token_id]

								if token_ids_a is None:
													return len(cls + token_ids_a + sep	) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]







			def       __lowerCAmelCase					(      self      :			List[Any]      ,	lowercase      :			Tuple      ,	lowercase      :			Union[str, Any]=False      ,	**lowercase      :			Dict	):





								'''simple docstring'''



								UpperCAmelCase      : Optional[Any]   =					kwargs.pop("add_prefix_space"      ,	self.add_prefix_space	)
								if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__	) > 0 and not text[0].isspace()):
													UpperCAmelCase      : Optional[Any]   =					''' ''' + text
								return (text, kwargs)




'''simple docstring'''
import gc
import math
import unittest

import torch

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

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


lowercase__						       =      logging.get_logger(__name__)

enable_full_determinism()






class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



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






				"""simple docstring"""







				snake_case  :       Any     			=						4
				snake_case  :       List[Any]     			=						3
				snake_case  :       str     			=						(32, 32)

				snake_case  :       Optional[Any]     			=						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       Tuple     			=						torch.tensor([10]      ).to(UpperCamelCase__      )

				return {"sample": noise, "timestep": time_step}



		@property
		def       lowerCAmelCase				(      self				:			Any      )						->			Union[str, Any]:






				"""simple docstring"""







				return (3, 32, 32)



		@property
		def       lowerCAmelCase				(      self				:			str      )						->			Dict:






				"""simple docstring"""







				return (3, 32, 32)



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






				"""simple docstring"""







				snake_case  :       Any     			=						{
				    '''block_out_channels''': (32, 64),
				    '''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
				    '''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
				    '''attention_head_dim''': 3,
				    '''out_channels''': 3,
				    '''in_channels''': 3,
				    '''layers_per_block''': 2,
				    '''sample_size''': 32,
				}
				snake_case  :       Dict     			=						self.dummy_input
				return init_dict, inputs_dict







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



		@property
		def       lowerCAmelCase				(      self				:			Any      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       str     			=						4
				snake_case  :       Tuple     			=						4
				snake_case  :       str     			=						(32, 32)

				snake_case  :       List[Any]     			=						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       str     			=						torch.tensor([10]      ).to(UpperCamelCase__      )

				return {"sample": noise, "timestep": time_step}



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				return (4, 32, 32)



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






				"""simple docstring"""







				return (4, 32, 32)



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






				"""simple docstring"""







				snake_case  :       List[Any]     			=						{
				    '''sample_size''': 32,
				    '''in_channels''': 4,
				    '''out_channels''': 4,
				    '''layers_per_block''': 2,
				    '''block_out_channels''': (32, 64),
				    '''attention_head_dim''': 32,
				    '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
				    '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
				}
				snake_case  :       str     			=						self.dummy_input
				return init_dict, inputs_dict



		def       lowerCAmelCase				(      self				:			int      )						->			str:






				"""simple docstring"""







				snake_case			,snake_case  :       int     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )

				self.assertIsNotNone(UpperCamelCase__      )
				self.assertEqual(len(loading_info['''missing_keys''']      )  ,	0      )

				model.to(UpperCamelCase__      )
				snake_case  :       List[Any]     			=						model(**self.dummy_input      ).sample

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



		@unittest.skipIf(torch_device != '''cuda'''  ,	'''This test is supposed to run on GPU'''      )
		def       lowerCAmelCase				(      self				:			int      )						->			Dict:






				"""simple docstring"""







				snake_case			,snake_case  :       str     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )
				model.to(UpperCamelCase__      )
				snake_case  :       Any     			=						model(**self.dummy_input      ).sample

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



		@unittest.skipIf(torch_device != '''cuda'''  ,	'''This test is supposed to run on GPU'''      )
		def       lowerCAmelCase				(      self				:			Any      )						->			str:






				"""simple docstring"""







				snake_case			,snake_case  :       Any     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )
				model_accelerate.to(UpperCamelCase__      )
				model_accelerate.eval()

				snake_case  :       Optional[Any]     			=						torch.randn(
				    1  ,	model_accelerate.config.in_channels  ,	model_accelerate.config.sample_size  ,	model_accelerate.config.sample_size  ,	generator=torch.manual_seed(0      )  ,	)
				snake_case  :       int     			=						noise.to(UpperCamelCase__      )
				snake_case  :       List[str]     			=						torch.tensor([10] * noise.shape[0]      ).to(UpperCamelCase__      )

				snake_case  :       Union[str, Any]     			=						model_accelerate(UpperCamelCase__  ,	UpperCamelCase__      )['''sample''']

				# two models don't need to stay in the device at the same time
				del model_accelerate
				torch.cuda.empty_cache()
				gc.collect()

				snake_case			,snake_case  :       List[Any]     			=						UNetaDModel.from_pretrained(
				    '''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__  ,	low_cpu_mem_usage=UpperCamelCase__      )
				model_normal_load.to(UpperCamelCase__      )
				model_normal_load.eval()
				snake_case  :       Union[str, Any]     			=						model_normal_load(UpperCamelCase__  ,	UpperCamelCase__      )['''sample''']

				assert torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-3      )



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






				"""simple docstring"""







				snake_case  :       int     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''      )
				model.eval()
				model.to(UpperCamelCase__      )

				snake_case  :       Any     			=						torch.randn(
				    1  ,	model.config.in_channels  ,	model.config.sample_size  ,	model.config.sample_size  ,	generator=torch.manual_seed(0      )  ,	)
				snake_case  :       Dict     			=						noise.to(UpperCamelCase__      )
				snake_case  :       Any     			=						torch.tensor([10] * noise.shape[0]      ).to(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       Union[str, Any]     			=						model(UpperCamelCase__  ,	UpperCamelCase__      ).sample

				snake_case  :       int     			=						output[0, -1, -3:, -3:].flatten().cpu()
				# fmt: off
				snake_case  :       Tuple     			=						torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-3      )      )







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



		@property
		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			List[Any]=(32, 32)      )						->			Any:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						4
				snake_case  :       Tuple     			=						3

				snake_case  :       List[Any]     			=						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       str     			=						torch.tensor(batch_size * [10]      ).to(dtype=torch.intaa  ,	device=UpperCamelCase__      )

				return {"sample": noise, "timestep": time_step}



		@property
		def       lowerCAmelCase				(      self				:			List[str]      )						->			List[Any]:






				"""simple docstring"""







				return (3, 32, 32)



		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Dict:






				"""simple docstring"""







				return (3, 32, 32)



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






				"""simple docstring"""







				snake_case  :       Dict     			=						{
				    '''block_out_channels''': [32, 64, 64, 64],
				    '''in_channels''': 3,
				    '''layers_per_block''': 1,
				    '''out_channels''': 3,
				    '''time_embedding_type''': '''fourier''',
				    '''norm_eps''': 1e-6,
				    '''mid_block_scale_factor''': math.sqrt(2.0      ),
				    '''norm_num_groups''': None,
				    '''down_block_types''': [
				        '''SkipDownBlock2D''',
				        '''AttnSkipDownBlock2D''',
				        '''SkipDownBlock2D''',
				        '''SkipDownBlock2D''',
				    ],
				    '''up_block_types''': [
				        '''SkipUpBlock2D''',
				        '''SkipUpBlock2D''',
				        '''AttnSkipUpBlock2D''',
				        '''SkipUpBlock2D''',
				    ],
				}
				snake_case  :       Dict     			=						self.dummy_input
				return init_dict, inputs_dict



		@slow
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			int:






				"""simple docstring"""







				snake_case			,snake_case  :       str     			=						UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''  ,	output_loading_info=UpperCamelCase__      )
				self.assertIsNotNone(UpperCamelCase__      )
				self.assertEqual(len(loading_info['''missing_keys''']      )  ,	0      )

				model.to(UpperCamelCase__      )
				snake_case  :       str     			=						self.dummy_input
				snake_case  :       Dict     			=						floats_tensor((4, 3) + (256, 256)      ).to(UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						noise
				snake_case  :       List[Any]     			=						model(**UpperCamelCase__      )

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



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






				"""simple docstring"""







				snake_case  :       int     			=						UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''      )
				model.to(UpperCamelCase__      )

				snake_case  :       Dict     			=						4
				snake_case  :       Optional[int]     			=						3
				snake_case  :       Tuple     			=						(256, 256)

				snake_case  :       Optional[int]     			=						torch.ones((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       List[str]     			=						torch.tensor(batch_size * [1e-4]      ).to(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       List[str]     			=						model(UpperCamelCase__  ,	UpperCamelCase__      ).sample

				snake_case  :       Dict     			=						output[0, -3:, -3:, -1].flatten().cpu()
				# fmt: off
				snake_case  :       Optional[int]     			=						torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-2      )      )



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






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''      )
				model.to(UpperCamelCase__      )

				snake_case  :       Optional[Any]     			=						4
				snake_case  :       List[Any]     			=						3
				snake_case  :       Union[str, Any]     			=						(32, 32)

				snake_case  :       Any     			=						torch.ones((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       Any     			=						torch.tensor(batch_size * [1e-4]      ).to(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       Dict     			=						model(UpperCamelCase__  ,	UpperCamelCase__      ).sample

				snake_case  :       Optional[Any]     			=						output[0, -3:, -3:, -1].flatten().cpu()
				# fmt: off
				snake_case  :       int     			=						torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-2      )      )



		def       lowerCAmelCase				(      self				:			List[str]      )						->			List[Any]:






				"""simple docstring"""







				pass


