



"""simple docstring"""






import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


_lowercase : Optional[int]      						=						logging.get_logger(__name__)

_lowercase : List[str]      						=						{"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}

_lowercase : Union[str, Any]      						=						{
    "vocab_file": {
        "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt",
    },
    "emoji_file": {
        "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json",
    },
}

_lowercase : int      						=						{
    "abeja/gpt-neox-japanese-2.7b": 2_0_4_8,
}






def      snake_case__   (		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    Optional[Any]	):
			"""simple docstring"""
			with open(__lowerCamelCase							,		'''r'''							,		encoding='''utf-8'''	) as f:
						lowerCamelCase__		:		Dict			=json.loads(f.read()	)

			lowerCamelCase__		:		Optional[int]			=collections.OrderedDict()
			lowerCamelCase__		:		str			=collections.OrderedDict()
			lowerCamelCase__		:		Dict			=collections.OrderedDict()
			with open(__lowerCamelCase							,		'''r'''							,		encoding='''utf-8'''	) as f:
						lowerCamelCase__		:		Optional[int]			=f.readlines()
			lowerCamelCase__		:		Any			=[[t.rstrip('''\n'''	)] if (t == ''',''' or ''',''' not in t) else t.rstrip('''\n'''	).split(''','''	) for t in token]
			for idx, b in enumerate(__lowerCamelCase	):
						lowerCamelCase__		:		Optional[Any]			=b
						lowerCamelCase__		:		Dict			=idx
						for wd in b:
									lowerCamelCase__		:		Optional[Any]			=idx

			return vocab, raw_vocab, ids_to_tokens, emoji

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							VOCAB_FILES_NAMES
					_a   			=							PRETRAINED_VOCAB_FILES_MAP
					_a   			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a   			=							['input_ids', 'attention_mask']



					def __init__(       self							:  Union[str, Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Any="<|endoftext|>",  lowerCamelCase							:  Any="<|endoftext|>",  lowerCamelCase							:  str="<|startoftext|>",  lowerCamelCase							:  str="<|endoftext|>",  lowerCamelCase							:  int=False,  **lowerCamelCase							:  List[str],  )->					Any:
								super().__init__(
								    unk_token=lowerCamelCase,  pad_token=lowerCamelCase,  bos_token=lowerCamelCase,  eos_token=lowerCamelCase,  do_clean_text=lowerCamelCase,  **lowerCamelCase,  )
								if not os.path.isfile(lowerCamelCase     ):
											raise ValueError(
											    F'''Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'''
											    ''' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'''     )
								if not os.path.isfile(lowerCamelCase     ):
											raise ValueError(
											    F'''Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'''
											    ''' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'''     )
								lowerCamelCase__		:		Tuple			=do_clean_text
								lowerCamelCase__	,   lowerCamelCase__	,   lowerCamelCase__	,   lowerCamelCase__		:		Any			=load_vocab_and_emoji(lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__		:		Any			=SubWordJapaneseTokenizer(
								    vocab=self.vocab,  ids_to_tokens=self.ids_to_tokens,  emoji=self.emoji     )



					@property
					def 		snake_case       (       self							:  str     )->					str:
								# self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
								return len(self.raw_vocab     )



					def 		snake_case       (       self							:  Tuple     )->					List[Any]:
								return dict(self.raw_vocab,  **self.added_tokens_encoder     )



					def 		snake_case       (       self							:  List[Any],  lowerCamelCase							:  Optional[int]     )->					Union[str, Any]:
								return self.subword_tokenizer.tokenize(lowerCamelCase,  clean=self.do_clean_text     )



					def 		snake_case       (       self							:  List[str],  lowerCamelCase							:  int     )->					Tuple:
								return self.vocab.get(lowerCamelCase,  self.vocab.get(self.unk_token     )     )



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  Any     )->					str:
								return self.subword_tokenizer.convert_id_to_token(lowerCamelCase     )



					def 		snake_case       (       self							:  List[Any],  lowerCamelCase							:  Optional[int]     )->					Optional[int]:
								lowerCamelCase__		:		Optional[Any]			=''''''.join(lowerCamelCase     ).strip()
								return out_string



					def 		snake_case       (       self							:  str,  lowerCamelCase							:  "Conversation"     )->					List[int]:
								lowerCamelCase__		:		Tuple			=[]
								for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(lowerCamelCase,  add_special_tokens=lowerCamelCase     ) + [self.eos_token_id]     )

								if len(lowerCamelCase     ) > self.model_max_length:
											lowerCamelCase__		:		Any			=input_ids[-self.model_max_length :]
								return input_ids




					def 		snake_case       (       self							:  List[str],  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[str] = None     )->					Tuple[str]:
								lowerCamelCase__		:		List[Any]			=0
								if os.path.isdir(lowerCamelCase     ):
											lowerCamelCase__		:		List[str]			=os.path.join(
											    lowerCamelCase,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )
											lowerCamelCase__		:		List[str]			=os.path.join(
											    lowerCamelCase,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''emoji_file''']     )
								else:
											lowerCamelCase__		:		List[Any]			=(
											    (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''vocab_file''']
											)
											lowerCamelCase__		:		Optional[Any]			=(
											    (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''emoji_file''']
											)
								with open(lowerCamelCase,  '''w''',  encoding='''utf-8'''     ) as writer:
											for token_index, token in self.ids_to_tokens.items():
														if index != token_index:
																	logger.warning(
																	    F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
																	    ''' Please check that the vocabulary is not corrupted!'''     )
																	lowerCamelCase__		:		str			=token_index
														writer.write(''','''.join(lowerCamelCase     ) + '''\n'''     )
														index += 1
								with open(lowerCamelCase,  '''w''',  encoding='''utf-8'''     ) as writer:
											json.dump(self.emoji,  lowerCamelCase     )
								return vocab_file, emoji_file





class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  List[Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  List[str]     )->					Tuple:
								lowerCamelCase__		:		Union[str, Any]			=vocab  # same as swe
								lowerCamelCase__		:		List[str]			=ids_to_tokens  # same as bpe
								lowerCamelCase__		:		List[str]			=emoji
								lowerCamelCase__		:		Optional[Any]			=np.max([len(lowerCamelCase     ) for w in self.vocab.keys()]     )
								lowerCamelCase__		:		List[Any]			=re.compile(r'''(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)'''     )
								lowerCamelCase__		:		int			=re.compile(r'''[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*'''     )
								lowerCamelCase__		:		Union[str, Any]			=re.compile(r'''[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}'''     )
								lowerCamelCase__		:		str			=re.compile(
								    r'''([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'''     )
								lowerCamelCase__		:		Any			=re.compile(
								    r'''(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'''     )
								lowerCamelCase__		:		List[str]			=re.compile(
								    r'''((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*'''     )
								lowerCamelCase__		:		Any			='''─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'''
								lowerCamelCase__		:		Optional[Any]			='''▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'''
								lowerCamelCase__		:		Dict			=str.maketrans({k: '''<BLOCK>''' for k in keisen + blocks}     )



					def __len__(       self							:  Dict     )->					List[str]:
								return len(self.ids_to_tokens     )



					def 		snake_case       (       self							:  List[str],  lowerCamelCase							:  Dict     )->					Optional[Any]:
								lowerCamelCase__		:		Any			=self.content_repattera.sub('''<URL>''',  lowerCamelCase     )
								lowerCamelCase__		:		Optional[int]			=self.content_repattera.sub('''<EMAIL>''',  lowerCamelCase     )
								lowerCamelCase__		:		Optional[int]			=self.content_repattera.sub('''<TEL>''',  lowerCamelCase     )
								lowerCamelCase__		:		Optional[int]			=self.content_repattera.sub('''<DATE>''',  lowerCamelCase     )
								lowerCamelCase__		:		str			=self.content_repattera.sub('''<DATE>''',  lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=self.content_repattera.sub('''<PRICE>''',  lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=content.translate(self.content_transa     )
								while "<BLOCK><BLOCK>" in content:
											lowerCamelCase__		:		List[Any]			=content.replace('''<BLOCK><BLOCK>''',  '''<BLOCK>'''     )
								return content



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  List[Any]=False     )->					Any:
								lowerCamelCase__		:		Any			=text.replace(''' ''',  '''<SP>'''     )
								lowerCamelCase__		:		Any			=text.replace('''　''',  '''<SP>'''     )
								lowerCamelCase__		:		List[str]			=text.replace('''\r\n''',  '''<BR>'''     )
								lowerCamelCase__		:		int			=text.replace('''\n''',  '''<BR>'''     )
								lowerCamelCase__		:		Tuple			=text.replace('''\r''',  '''<BR>'''     )
								lowerCamelCase__		:		List[Any]			=text.replace('''\t''',  '''<TAB>'''     )
								lowerCamelCase__		:		int			=text.replace('''—''',  '''ー'''     )
								lowerCamelCase__		:		Optional[Any]			=text.replace('''−''',  '''ー'''     )
								for k, v in self.emoji["emoji"].items():
											if k in text:
														lowerCamelCase__		:		Any			=text.replace(lowerCamelCase,  lowerCamelCase     )
								if clean:
											lowerCamelCase__		:		Optional[int]			=self.clean_text(lowerCamelCase     )

								def check_simbol(lowerCamelCase							:  List[str]     ):
											lowerCamelCase__		:		Any			=x.encode()
											if len(lowerCamelCase     ) == 1 and len(lowerCamelCase     ) == 2:
														lowerCamelCase__		:		Optional[int]			=(int(e[0]     ) << 8) + int(e[1]     )
														if (
														    (c >= 0xc2_a1 and c <= 0xc2_bf)
														    or (c >= 0xc7_80 and c <= 0xc7_83)
														    or (c >= 0xca_b9 and c <= 0xcb_bf)
														    or (c >= 0xcc_80 and c <= 0xcd_a2)
														):
																	return True
											return False

								def checkuae(lowerCamelCase							:  Union[str, Any]     ):
											lowerCamelCase__		:		Optional[Any]			=x.encode()
											if len(lowerCamelCase     ) == 1 and len(lowerCamelCase     ) == 3:
														lowerCamelCase__		:		Any			=(int(e[0]     ) << 16) + (int(e[1]     ) << 8) + int(e[2]     )
														if c >= 0xe2_80_80 and c <= 0xe2_b0_7f:
																	return True
											return False

								lowerCamelCase__		:		Optional[Any]			=0
								lowerCamelCase__		:		Optional[int]			=[]
								while pos < len(lowerCamelCase     ):
											lowerCamelCase__		:		Optional[Any]			=min(len(lowerCamelCase     ),  pos + self.maxlen + 1     ) if text[pos] == '''<''' else pos + 3
											lowerCamelCase__		:		Optional[int]			=[]  # (token_id, token, pos)
											for e in range(lowerCamelCase,  lowerCamelCase,  -1     ):
														lowerCamelCase__		:		Dict			=text[pos:e]
														if wd in self.vocab:
																	if wd[0] == "<" and len(lowerCamelCase     ) > 2:
																				lowerCamelCase__		:		str			=[(self.vocab[wd], wd, e)]
																				break
																	else:
																				candidates.append((self.vocab[wd], wd, e)     )
											if len(lowerCamelCase     ) > 0:
														# the smallest token_id is adopted
														lowerCamelCase__	,   lowerCamelCase__	,   lowerCamelCase__		:		Optional[Any]			=sorted(lowerCamelCase,  key=lambda lowerCamelCase     : x[0]     )[0]
														result.append(lowerCamelCase     )
														lowerCamelCase__		:		int			=e
											else:
														lowerCamelCase__		:		str			=pos + 1
														lowerCamelCase__		:		List[str]			=text[pos:end]
														if check_simbol(lowerCamelCase     ):
																	result.append('''<KIGOU>'''     )
														elif checkuae(lowerCamelCase     ):
																	result.append('''<U2000U2BFF>'''     )
														else:
																	for i in wd.encode('''utf-8'''     ):
																				result.append('''<|byte%d|>''' % i     )
														lowerCamelCase__		:		str			=end
								return result




					def 		snake_case       (       self							:  Any,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  str="\n"     )->					int:
								lowerCamelCase__		:		List[str]			=[]
								lowerCamelCase__		:		List[Any]			=[]
								lowerCamelCase__		:		Dict			=self.ids_to_tokens[index][0]
								if word[:6] == "<|byte" and word[-2:] == "|>":
											byte_tokens.append(int(word[6:-2]     )     )
								else:
											if len(lowerCamelCase     ) > 0:
														words.append(bytearray(lowerCamelCase     ).decode('''utf-8''',  errors='''replace'''     )     )
														lowerCamelCase__		:		List[Any]			=[]
											if word[:7] == "<|emoji" and word[-2:] == "|>":
														words.append(self.emoji['''emoji_inv'''][word]     )
											elif word == "<SP>":
														words.append(''' '''     )
											elif word == "<BR>":
														words.append(lowerCamelCase     )
											elif word == "<TAB>":
														words.append('''\t'''     )
											elif word == "<BLOCK>":
														words.append('''▀'''     )
											elif word == "<KIGOU>":
														words.append('''ǀ'''     )
											elif word == "<U2000U2BFF>":
														words.append('''‖'''     )
											else:
														words.append(lowerCamelCase     )
								if len(lowerCamelCase     ) > 0:
											words.append(bytearray(lowerCamelCase     ).decode('''utf-8''',  errors='''replace'''     )     )
								lowerCamelCase__		:		Optional[Any]			=''''''.join(lowerCamelCase     )
								return text









"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_		,   unittest.TestCase   ):


					'''simple docstring'''





					_a   			=							DDIMPipeline
					_a   			=							UNCONDITIONAL_IMAGE_GENERATION_PARAMS
					_a   			=							PipelineTesterMixin.required_optional_params - {
					    'num_images_per_prompt',
					    'latents',
					    'callback',
					    'callback_steps',
					}
					_a   			=							UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
					_a   			=							False



					def 		snake_case       (       self							:  str     )->					Optional[Any]:
								torch.manual_seed(0     )
								lowerCamelCase__		:		Union[str, Any]			=UNetaDModel(
								    block_out_channels=(32, 64),  layers_per_block=2,  sample_size=32,  in_channels=3,  out_channels=3,  down_block_types=('''DownBlock2D''', '''AttnDownBlock2D'''),  up_block_types=('''AttnUpBlock2D''', '''UpBlock2D'''),  )
								lowerCamelCase__		:		Optional[Any]			=DDIMScheduler()
								lowerCamelCase__		:		List[Any]			={'''unet''': unet, '''scheduler''': scheduler}
								return components



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  Optional[Any]=0     )->					Optional[int]:
								if str(lowerCamelCase     ).startswith('''mps'''     ):
											lowerCamelCase__		:		Dict			=torch.manual_seed(lowerCamelCase     )
								else:
											lowerCamelCase__		:		Optional[int]			=torch.Generator(device=lowerCamelCase     ).manual_seed(lowerCamelCase     )
								lowerCamelCase__		:		Tuple			={
								    '''batch_size''': 1,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''output_type''': '''numpy''',
								}
								return inputs



					def 		snake_case       (       self							:  Dict     )->					str:
								lowerCamelCase__		:		Optional[Any]			='''cpu'''

								lowerCamelCase__		:		int			=self.get_dummy_components()
								lowerCamelCase__		:		Optional[int]			=self.pipeline_class(**lowerCamelCase     )
								pipe.to(lowerCamelCase     )
								pipe.set_progress_bar_config(disable=lowerCamelCase     )

								lowerCamelCase__		:		List[str]			=self.get_dummy_inputs(lowerCamelCase     )
								lowerCamelCase__		:		Any			=pipe(**lowerCamelCase     ).images
								lowerCamelCase__		:		Optional[Any]			=image[0, -3:, -3:, -1]

								self.assertEqual(image.shape,  (1, 32, 32, 3)     )
								lowerCamelCase__		:		Tuple			=np.array(
								    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]     )
								lowerCamelCase__		:		str			=np.abs(image_slice.flatten() - expected_slice     ).max()
								self.assertLessEqual(lowerCamelCase,  1E-3     )



					def 		snake_case       (       self							:  Union[str, Any]     )->					List[Any]:
								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3     )



					def 		snake_case       (       self							:  Union[str, Any]     )->					int:
								super().test_save_load_local(expected_max_difference=3E-3     )



					def 		snake_case       (       self							:  List[Any]     )->					List[Any]:
								super().test_save_load_optional_components(expected_max_difference=3E-3     )




					def 		snake_case       (       self							:  Optional[Any]     )->					Optional[int]:
								super().test_inference_batch_single_identical(expected_max_diff=3E-3     )





@slow
@require_torch_gpu
class        __SCREAMING_SNAKE_CASE	(      unittest.TestCase   ):


					'''simple docstring'''





					def 		snake_case       (       self							:  Optional[Any]     )->					List[str]:
								lowerCamelCase__		:		Optional[Any]			='''google/ddpm-cifar10-32'''

								lowerCamelCase__		:		Union[str, Any]			=UNetaDModel.from_pretrained(lowerCamelCase     )
								lowerCamelCase__		:		Optional[int]			=DDIMScheduler()

								lowerCamelCase__		:		int			=DDIMPipeline(unet=lowerCamelCase,  scheduler=lowerCamelCase     )
								ddim.to(lowerCamelCase     )
								ddim.set_progress_bar_config(disable=lowerCamelCase     )

								lowerCamelCase__		:		Tuple			=torch.manual_seed(0     )
								lowerCamelCase__		:		int			=ddim(generator=lowerCamelCase,  eta=0.0,  output_type='''numpy'''     ).images

								lowerCamelCase__		:		Tuple			=image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								lowerCamelCase__		:		Any			=np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453]     )

								assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2




					def 		snake_case       (       self							:  Optional[int]     )->					Any:
								lowerCamelCase__		:		str			='''google/ddpm-ema-bedroom-256'''

								lowerCamelCase__		:		Optional[int]			=UNetaDModel.from_pretrained(lowerCamelCase     )
								lowerCamelCase__		:		Any			=DDIMScheduler.from_pretrained(lowerCamelCase     )

								lowerCamelCase__		:		Optional[Any]			=DDIMPipeline(unet=lowerCamelCase,  scheduler=lowerCamelCase     )
								ddpm.to(lowerCamelCase     )
								ddpm.set_progress_bar_config(disable=lowerCamelCase     )

								lowerCamelCase__		:		List[str]			=torch.manual_seed(0     )
								lowerCamelCase__		:		Optional[Any]			=ddpm(generator=lowerCamelCase,  output_type='''numpy'''     ).images

								lowerCamelCase__		:		Any			=image[0, -3:, -3:, -1]

								assert image.shape == (1, 256, 256, 3)
								lowerCamelCase__		:		Any			=np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069]     )

								assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2




