



import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class 				_snake_case			:




				@property
				def 							A__      (						self					:						str						):
							return self.get_dummy_input()




				@property
				def 							A__      (						self					:						str						):
							if self.block_type == "down":
										return (4, 32, 16, 16)
							elif self.block_type == "mid":
										return (4, 32, 32, 32)
							elif self.block_type == "up":
										return (4, 32, 64, 64)

							raise ValueError(F'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''						)




				def 							A__      (						self					:						Optional[int],					__lowercase					:						Dict=True,					__lowercase					:						List[Any]=False,					__lowercase					:						Optional[int]=False,					__lowercase					:						str=False,					):
							lowercase__					  =				4
							lowercase__					  =				32
							lowercase__					  =				(32, 32)

							lowercase__					  =				torch.manual_seed(0						)
							lowercase__					  =				torch.device(lowerCAmelCase__						)
							lowercase__					  =				(batch_size, num_channels) + sizes
							lowercase__					  =				randn_tensor(lowerCAmelCase__,					generator=lowerCAmelCase__,					device=lowerCAmelCase__						)
							lowercase__					  =				{"hidden_states": hidden_states}

							if include_temb:
										lowercase__					  =				128
										lowercase__					  =				randn_tensor((batch_size, temb_channels),					generator=lowerCAmelCase__,					device=lowerCAmelCase__						)

							if include_res_hidden_states_tuple:
										lowercase__					  =				torch.manual_seed(1						)
										lowercase__					  =				(randn_tensor(lowerCAmelCase__,					generator=lowerCAmelCase__,					device=lowerCAmelCase__						),)

							if include_encoder_hidden_states:
										lowercase__					  =				floats_tensor((batch_size, 32, 32)						).to(lowerCAmelCase__						)

							if include_skip_sample:
										lowercase__					  =				randn_tensor(((batch_size, 3) + sizes),					generator=lowerCAmelCase__,					device=lowerCAmelCase__						)

							return dummy_input




				def 							A__      (						self					:						str						):
							lowercase__					  =				{
							    "in_channels": 32,
							    "out_channels": 32,
							    "temb_channels": 128,
							}
							if self.block_type == "up":
										lowercase__					  =				32

							if self.block_type == "mid":
										init_dict.pop("out_channels"						)

							lowercase__					  =				self.dummy_input
							return init_dict, inputs_dict




				def 							A__      (						self					:						Any,					__lowercase					:						str						):
							lowercase__					  =				self.prepare_init_args_and_inputs_for_common()
							lowercase__					  =				self.block_class(**lowerCAmelCase__						)
							unet_block.to(lowerCAmelCase__						)
							unet_block.eval()

							with torch.no_grad():
										lowercase__					  =				unet_block(**lowerCAmelCase__						)

							if isinstance(lowerCAmelCase__,					lowerCAmelCase__						):
										lowercase__					  =				output[0]

							self.assertEqual(output.shape,					self.output_shape						)

							lowercase__					  =				output[0, -1, -3:, -3:]
							lowercase__					  =				torch.tensor(lowerCAmelCase__						).to(lowerCAmelCase__						)
							assert torch_all_close(output_slice.flatten(),					lowerCAmelCase__,					atol=5e-3						)




				@unittest.skipIf(torch_device == "mps",					"Training is not supported in mps"						)
				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				self.prepare_init_args_and_inputs_for_common()
							lowercase__					  =				self.block_class(**lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.train()
							lowercase__					  =				model(**lowerCAmelCase__						)

							if isinstance(lowerCAmelCase__,					lowerCAmelCase__						):
										lowercase__					  =				output[0]

							lowercase__					  =				torch.device(lowerCAmelCase__						)
							lowercase__					  =				randn_tensor(output.shape,					device=lowerCAmelCase__						)
							lowercase__					  =				torch.nn.functional.mse_loss(lowerCAmelCase__,					lowerCAmelCase__						)
							loss.backward()









import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class 				_snake_case			(					lowercase__):




				def __init__(						self					:						Optional[Any],					__lowercase					:						str = "▁",					__lowercase					:						bool = True,					__lowercase					:						Union[str, AddedToken] = "<unk>",					__lowercase					:						Union[str, AddedToken] = "</s>",					__lowercase					:						Union[str, AddedToken] = "<pad>",					):
							lowercase__					  =				{
							    "pad": {"id": 0, "token": pad_token},
							    "eos": {"id": 1, "token": eos_token},
							    "unk": {"id": 2, "token": unk_token},
							}

							lowercase__					  =				[None] * len(self.special_tokens						)
							for token_dict in self.special_tokens.values():
										lowercase__					  =				token_dict["token"]

							lowercase__					  =				Tokenizer(Unigram()						)

							lowercase__					  =				normalizers.Sequence(
							    [
							        normalizers.Nmt(),
							        normalizers.NFKC(),
							        normalizers.Replace(Regex(" {2,}"						),					" "						),
							        normalizers.Lowercase(),
							    ]						)
							lowercase__					  =				pre_tokenizers.Sequence(
							    [
							        pre_tokenizers.Metaspace(replacement=__lowercase,					add_prefix_space=__lowercase						),
							        pre_tokenizers.Digits(individual_digits=__lowercase						),
							        pre_tokenizers.Punctuation(),
							    ]						)
							lowercase__					  =				decoders.Metaspace(replacement=__lowercase,					add_prefix_space=__lowercase						)

							lowercase__					  =				TemplateProcessing(
							    single=F'''$A {self.special_tokens["eos"]["token"]}''',					special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])],					)

							lowercase__					  =				{
							    "model": "SentencePieceUnigram",
							    "replacement": replacement,
							    "add_prefix_space": add_prefix_space,
							}

							super().__init__(__lowercase,					__lowercase						)




				def 							A__      (						self					:						Union[str, Any],					__lowercase					:						Union[str, List[str]],					__lowercase					:						int = 8000,					__lowercase					:						bool = True,					):
							lowercase__					  =				trainers.UnigramTrainer(
							    vocab_size=__lowercase,					special_tokens=self.special_tokens_list,					show_progress=__lowercase,					)

							if isinstance(__lowercase,					__lowercase						):
										lowercase__					  =				[files]
							self._tokenizer.train(__lowercase,					trainer=__lowercase						)

							self.add_unk_id()




				def 							A__      (						self					:						List[Any],					__lowercase					:						Union[Iterator[str], Iterator[Iterator[str]]],					__lowercase					:						int = 8000,					__lowercase					:						bool = True,					):
							lowercase__					  =				trainers.UnigramTrainer(
							    vocab_size=__lowercase,					special_tokens=self.special_tokens_list,					show_progress=__lowercase,					)

							self._tokenizer.train_from_iterator(__lowercase,					trainer=__lowercase						)

							self.add_unk_id()




				def 							A__      (						self					:						str						):
							lowercase__					  =				json.loads(self._tokenizer.to_str()						)

							lowercase__					  =				self.special_tokens["unk"]["id"]

							lowercase__					  =				Tokenizer.from_str(json.dumps(__lowercase						)						)




