

"""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


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

enable_full_determinism()







class       snake_case__  ( UpperCAmelCase__					,  UpperCAmelCase__					,  unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						UNetaDModel
			SCREAMING_SNAKE_CASE__     				=						"sample"







			@property
			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				4
								UpperCAmelCase			:   Any      =				3
								UpperCAmelCase			:   Tuple      =				(32, 32)

								UpperCAmelCase			:   Dict      =				floats_tensor((batch_size, num_channels) + sizes  ).to(lowerCamelCase__  )
								UpperCAmelCase			:   Union[str, Any]      =				torch.tensor([10]  ).to(lowerCamelCase__  )

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







			@property
			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								return (3, 32, 32)







			@property
			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								return (3, 32, 32)







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   str      =				{
								    "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,
								}
								UpperCAmelCase			:   List[str]      =				self.dummy_input
								return init_dict, inputs_dict







class       snake_case__  ( UpperCAmelCase__					,  UpperCAmelCase__					,  unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						UNetaDModel
			SCREAMING_SNAKE_CASE__     				=						"sample"







			@property
			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				4
								UpperCAmelCase			:   Optional[int]      =				4
								UpperCAmelCase			:   Any      =				(32, 32)

								UpperCAmelCase			:   Dict      =				floats_tensor((batch_size, num_channels) + sizes  ).to(lowerCamelCase__  )
								UpperCAmelCase			:   Optional[Any]      =				torch.tensor([10]  ).to(lowerCamelCase__  )

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







			@property
			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								return (4, 32, 32)







			@property
			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								return (4, 32, 32)







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				{
								    "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"),
								}
								UpperCAmelCase			:   List[Any]      =				self.dummy_input
								return init_dict, inputs_dict







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"			,  output_loading_info=lowerCamelCase__  )

								self.assertIsNotNone(lowerCamelCase__  )
								self.assertEqual(len(loading_info["missing_keys"]  )			,  0  )

								model.to(lowerCamelCase__  )
								UpperCAmelCase			:   Optional[int]      =				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  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"			,  output_loading_info=lowerCamelCase__  )
								model.to(lowerCamelCase__  )
								UpperCAmelCase			:   str      =				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			: List[str]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"			,  output_loading_info=lowerCamelCase__  )
								model_accelerate.to(lowerCamelCase__  )
								model_accelerate.eval()

								UpperCAmelCase			:   List[Any]      =				torch.randn(
								    1			,  model_accelerate.config.in_channels			,  model_accelerate.config.sample_size			,  model_accelerate.config.sample_size			,  generator=torch.manual_seed(0  )			,  )
								UpperCAmelCase			:   Tuple      =				noise.to(lowerCamelCase__  )
								UpperCAmelCase			:   Union[str, Any]      =				torch.tensor([10] * noise.shape[0]  ).to(lowerCamelCase__  )

								UpperCAmelCase			:   Any      =				model_accelerate(lowerCamelCase__			,  lowerCamelCase__  )["sample"]

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

								UpperCAmelCase			:   int      =				UNetaDModel.from_pretrained(
								    "fusing/unet-ldm-dummy-update"			,  output_loading_info=lowerCamelCase__			,  low_cpu_mem_usage=lowerCamelCase__  )
								model_normal_load.to(lowerCamelCase__  )
								model_normal_load.eval()
								UpperCAmelCase			:   List[str]      =				model_normal_load(lowerCamelCase__			,  lowerCamelCase__  )["sample"]

								assert torch_all_close(lowerCamelCase__			,  lowerCamelCase__			,  rtol=1E-3  )







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"  )
								model.eval()
								model.to(lowerCamelCase__  )

								UpperCAmelCase			:   List[str]      =				torch.randn(
								    1			,  model.config.in_channels			,  model.config.sample_size			,  model.config.sample_size			,  generator=torch.manual_seed(0  )			,  )
								UpperCAmelCase			:   List[Any]      =				noise.to(lowerCamelCase__  )
								UpperCAmelCase			:   Union[str, Any]      =				torch.tensor([10] * noise.shape[0]  ).to(lowerCamelCase__  )

								with torch.no_grad():
													UpperCAmelCase			:   Any      =				model(lowerCamelCase__			,  lowerCamelCase__  ).sample

								UpperCAmelCase			:   List[str]      =				output[0, -1, -3:, -3:].flatten().cpu()
								# fmt: off
								UpperCAmelCase			:   Tuple      =				torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00]  )
								# fmt: on

								self.assertTrue(torch_all_close(lowerCamelCase__			,  lowerCamelCase__			,  rtol=1E-3  )  )







class       snake_case__  ( UpperCAmelCase__					,  UpperCAmelCase__					,  unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						UNetaDModel
			SCREAMING_SNAKE_CASE__     				=						"sample"







			@property
			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: Dict=(32, 32)  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				4
								UpperCAmelCase			:   Union[str, Any]      =				3

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

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







			@property
			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								return (3, 32, 32)







			@property
			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								return (3, 32, 32)







			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				{
								    "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",
								    ],
								}
								UpperCAmelCase			:   Optional[int]      =				self.dummy_input
								return init_dict, inputs_dict







			@slow
			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Any      =				UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"			,  output_loading_info=lowerCamelCase__  )
								self.assertIsNotNone(lowerCamelCase__  )
								self.assertEqual(len(loading_info["missing_keys"]  )			,  0  )

								model.to(lowerCamelCase__  )
								UpperCAmelCase			:   Optional[Any]      =				self.dummy_input
								UpperCAmelCase			:   Union[str, Any]      =				floats_tensor((4, 3) + (2_56, 2_56)  ).to(lowerCamelCase__  )
								UpperCAmelCase			:   int      =				noise
								UpperCAmelCase			:   Dict      =				model(**lowerCamelCase__  )

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







			@slow
			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"  )
								model.to(lowerCamelCase__  )

								UpperCAmelCase			:   List[str]      =				4
								UpperCAmelCase			:   int      =				3
								UpperCAmelCase			:   Optional[int]      =				(2_56, 2_56)

								UpperCAmelCase			:   Optional[int]      =				torch.ones((batch_size, num_channels) + sizes  ).to(lowerCamelCase__  )
								UpperCAmelCase			:   int      =				torch.tensor(batch_size * [1E-4]  ).to(lowerCamelCase__  )

								with torch.no_grad():
													UpperCAmelCase			:   Tuple      =				model(lowerCamelCase__			,  lowerCamelCase__  ).sample

								UpperCAmelCase			:   List[Any]      =				output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								UpperCAmelCase			:   Union[str, Any]      =				torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08]  )
								# fmt: on

								self.assertTrue(torch_all_close(lowerCamelCase__			,  lowerCamelCase__			,  rtol=1E-2  )  )







			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update"  )
								model.to(lowerCamelCase__  )

								UpperCAmelCase			:   Tuple      =				4
								UpperCAmelCase			:   List[Any]      =				3
								UpperCAmelCase			:   Any      =				(32, 32)

								UpperCAmelCase			:   Optional[int]      =				torch.ones((batch_size, num_channels) + sizes  ).to(lowerCamelCase__  )
								UpperCAmelCase			:   int      =				torch.tensor(batch_size * [1E-4]  ).to(lowerCamelCase__  )

								with torch.no_grad():
													UpperCAmelCase			:   Any      =				model(lowerCamelCase__			,  lowerCamelCase__  ).sample

								UpperCAmelCase			:   Optional[Any]      =				output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								UpperCAmelCase			:   Dict      =				torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6]  )
								# fmt: on

								self.assertTrue(torch_all_close(lowerCamelCase__			,  lowerCamelCase__			,  rtol=1E-2  )  )







			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								pass





"""simple docstring"""






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


snake_case_   :   Union[str, Any]        =       logging.get_logger(__name__)

snake_case_   :   Tuple        =       {"""vocab_file""": """sentencepiece.bpe.model"""}

snake_case_   :   List[str]        =       {
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

snake_case_   :   List[Any]        =       {
    """camembert-base""": 5_1_2,
}

snake_case_   :   Any        =       """▁"""







class       snake_case__  ( lowerCAmelCase_     ):
			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			: int			,  lowercase			: Union[str, Any]			,  lowercase			: str="<s>"			,  lowercase			: str="</s>"			,  lowercase			: Optional[int]="</s>"			,  lowercase			: Dict="<s>"			,  lowercase			: Optional[Any]="<unk>"			,  lowercase			: List[Any]="<pad>"			,  lowercase			: Any="<mask>"			,  lowercase			: Optional[int]=["<s>NOTUSED", "</s>NOTUSED"]			,  lowercase			: Optional[Dict[str, Any]] = None			,  **lowercase			: Dict			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				AddedToken(lowercase			,  lstrip=lowercase			,  rstrip=lowercase  ) if isinstance(lowercase			,  lowercase  ) else mask_token

								UpperCAmelCase			:   int      =				{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=lowercase			,  eos_token=lowercase			,  unk_token=lowercase			,  sep_token=lowercase			,  cls_token=lowercase			,  pad_token=lowercase			,  mask_token=lowercase			,  additional_special_tokens=lowercase			,  sp_model_kwargs=self.sp_model_kwargs			,  **lowercase			,  )
								UpperCAmelCase			:   Tuple      =				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(str(lowercase  )  )
								UpperCAmelCase			:   str      =				vocab_file
								# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
								# sentencepiece vocabulary (this is the case for <s> and </s>
								UpperCAmelCase			:   Union[str, Any]      =				{"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
								UpperCAmelCase			:   Union[str, Any]      =				len(self.fairseq_tokens_to_ids  )
								UpperCAmelCase			:   Dict      =				len(self.sp_model  ) + len(self.fairseq_tokens_to_ids  )
								UpperCAmelCase			:   List[Any]      =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}







			def 			__lowerCAmelCase				(			self			: Union[str, 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			:   Dict      =				[self.cls_token_id]
								UpperCAmelCase			:   List[str]      =				[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep







			def 			__lowerCAmelCase				(			self			: Dict			,  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=lowercase			,  token_ids_a=lowercase			,  already_has_special_tokens=lowercase  )

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







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





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				[self.sep_token_id]
								UpperCAmelCase			:   List[str]      =				[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]







			@property
			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								return len(self.fairseq_tokens_to_ids  ) + len(self.sp_model  )







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				{self.convert_ids_to_tokens(lowercase  ): i for i in range(self.vocab_size  )}
								vocab.update(self.added_tokens_encoder  )
								return vocab







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





								'''simple docstring'''



								return self.sp_model.encode(lowercase			,  out_type=lowercase  )







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





								'''simple docstring'''



								if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
								elif self.sp_model.PieceToId(lowercase  ) == 0:
													# Convert sentence piece unk token to fairseq unk token index
													return self.unk_token_id
								return self.fairseq_offset + self.sp_model.PieceToId(lowercase  )







			def 			__lowerCAmelCase				(			self			: Any			,  lowercase			: Dict  ):





								'''simple docstring'''



								if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset  )







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





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				[]
								UpperCAmelCase			:   Tuple      =				""
								UpperCAmelCase			:   Union[str, Any]      =				False
								for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																		if not prev_is_special:
																							out_string += " "
																		out_string += self.sp_model.decode(lowercase  ) + token
																		UpperCAmelCase			:   Optional[int]      =				True
																		UpperCAmelCase			:   str      =				[]
													else:
																		current_sub_tokens.append(lowercase  )
																		UpperCAmelCase			:   Any      =				False
								out_string += self.sp_model.decode(lowercase  )
								return out_string.strip()







			def __getstate__(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				self.__dict__.copy()
								UpperCAmelCase			:   Dict      =				None
								return state







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





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				d

								# for backward compatibility
								if not hasattr(self			,  "sp_model_kwargs"  ):
													UpperCAmelCase			:   Any      =				{}

								UpperCAmelCase			:   List[Any]      =				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(self.vocab_file  )







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





								'''simple docstring'''



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

								if os.path.abspath(self.vocab_file  ) != os.path.abspath(lowercase  ) and os.path.isfile(self.vocab_file  ):
													copyfile(self.vocab_file			,  lowercase  )
								elif not os.path.isfile(self.vocab_file  ):
													with open(lowercase			,  "wb"  ) as fi:
																		UpperCAmelCase			:   List[Any]      =				self.sp_model.serialized_model_proto()
																		fi.write(lowercase  )

								return (out_vocab_file,)


