


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

import regex as re

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


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

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

__UpperCAmelCase			:			Tuple			      =					{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

__UpperCAmelCase			:			Tuple			      =					{
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
}

__UpperCAmelCase			:			Optional[int]			      =					{
    "gpt2": 1024,
    "gpt2-medium": 1024,
    "gpt2-large": 1024,
    "gpt2-xl": 1024,
    "distilgpt2": 1024,
}


@lru_cache()
def   a						(				):






       """simple docstring"""





       UpperCamelCase     :						List[Any] 						=		(
           list(range(ord('''!'''							)				,      ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)				,      ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)				,      ord('''ÿ'''							) + 1							)							)
       )
       UpperCamelCase     :						int 						=		bs[:]
       UpperCamelCase     :						List[str] 						=		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[int] 						=		[chr(SCREAMING_SNAKE_CASE_							) for n in cs]
       return dict(zip(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)							)


def   a						(				SCREAMING_SNAKE_CASE_   :		List[str]							):






       """simple docstring"""





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



class  UpperCAmelCase_						(				_a):
       '''simple docstring'''
       __UpperCamelCase   :  Any				=       VOCAB_FILES_NAMES
       __UpperCamelCase   :  Optional[Any]				=       PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase   :  int				=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase   :  str				=       ["input_ids", "attention_mask"]






       def __init__(  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE="replace"			,       __SCREAMING_SNAKE_CASE="<|endoftext|>"			,       __SCREAMING_SNAKE_CASE="<|endoftext|>"			,       __SCREAMING_SNAKE_CASE="<|endoftext|>"			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=False			,       __SCREAMING_SNAKE_CASE=False			,       **__SCREAMING_SNAKE_CASE			,       ):




              """simple docstring"""




              UpperCamelCase     :						Optional[Any] 						=		AddedToken(__SCREAMING_SNAKE_CASE			,       lstrip=__SCREAMING_SNAKE_CASE			,       rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ) else bos_token
              UpperCamelCase     :						Union[str, Any] 						=		AddedToken(__SCREAMING_SNAKE_CASE			,       lstrip=__SCREAMING_SNAKE_CASE			,       rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ) else eos_token
              UpperCamelCase     :						List[Any] 						=		AddedToken(__SCREAMING_SNAKE_CASE			,       lstrip=__SCREAMING_SNAKE_CASE			,       rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ) else unk_token
              UpperCamelCase     :						Union[str, Any] 						=		AddedToken(__SCREAMING_SNAKE_CASE			,       lstrip=__SCREAMING_SNAKE_CASE			,       rstrip=__SCREAMING_SNAKE_CASE    ) if isinstance(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ) else pad_token
              super().__init__(
                  errors=__SCREAMING_SNAKE_CASE			,       unk_token=__SCREAMING_SNAKE_CASE			,       bos_token=__SCREAMING_SNAKE_CASE			,       eos_token=__SCREAMING_SNAKE_CASE			,       pad_token=__SCREAMING_SNAKE_CASE			,       add_prefix_space=__SCREAMING_SNAKE_CASE			,       add_bos_token=__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE			,       )
              UpperCamelCase     :						List[str] 						=		add_bos_token

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

              # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
              UpperCamelCase     :						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 			_lowercase (  self    ):




              """simple docstring"""




              return len(self.encoder    )






       def 			_lowercase (  self    ):




              """simple docstring"""




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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              if token in self.cache:
                     return self.cache[token]
              UpperCamelCase     :						int 						=		tuple(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						int 						=		get_pairs(__SCREAMING_SNAKE_CASE    )

              if not pairs:
                     return token

              while True:
                     UpperCamelCase     :						List[Any] 						=		min(__SCREAMING_SNAKE_CASE			,       key=lambda __SCREAMING_SNAKE_CASE    : self.bpe_ranks.get(__SCREAMING_SNAKE_CASE			,       float('''inf'''    )    )    )
                     if bigram not in self.bpe_ranks:
                            break
                     UpperCamelCase						,			UpperCamelCase     :						List[Any] 						=		bigram
                     UpperCamelCase     :						str 						=		[]
                     UpperCamelCase     :						str 						=		0
                     while i < len(__SCREAMING_SNAKE_CASE    ):
                            try:
                                   UpperCamelCase     :						Any 						=		word.index(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
                            except ValueError:
                                   new_word.extend(word[i:]    )
                                   break
                            else:
                                   new_word.extend(word[i:j]    )
                                   UpperCamelCase     :						List[str] 						=		j

                            if word[i] == first and i < len(__SCREAMING_SNAKE_CASE    ) - 1 and word[i + 1] == second:
                                   new_word.append(first + second    )
                                   i += 2
                            else:
                                   new_word.append(word[i]    )
                                   i += 1
                     UpperCamelCase     :						List[str] 						=		tuple(__SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						Union[str, Any] 						=		new_word
                     if len(__SCREAMING_SNAKE_CASE    ) == 1:
                            break
                     else:
                            UpperCamelCase     :						str 						=		get_pairs(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						List[Any] 						=		''' '''.join(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						List[str] 						=		word
              return word






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=None    ):




              """simple docstring"""




              if self.add_bos_token:
                     UpperCamelCase     :						List[Any] 						=		[self.bos_token_id]
              else:
                     UpperCamelCase     :						Any 						=		[]

              UpperCamelCase     :						int 						=		bos_token_ids + token_ids_a

              if token_ids_a is None:
                     return output

              return output + bos_token_ids + token_ids_a






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE = None			,       __SCREAMING_SNAKE_CASE = False    ):




              """simple docstring"""




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

              if not self.add_bos_token:
                     return super().get_special_tokens_mask(
                         token_ids_a=__SCREAMING_SNAKE_CASE			,       token_ids_a=__SCREAMING_SNAKE_CASE			,       already_has_special_tokens=__SCREAMING_SNAKE_CASE    )

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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		[]
              for token in re.findall(self.pat			,       __SCREAMING_SNAKE_CASE    ):
                     UpperCamelCase     :						str 						=		''''''.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(__SCREAMING_SNAKE_CASE    ).split(''' '''    )    )
              return bpe_tokens






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              return self.decoder.get(__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		''''''.join(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Union[str, Any] 						=		bytearray([self.byte_decoder[c] for c in text]    ).decode('''utf-8'''			,       errors=self.errors    )
              return text






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE = None    ):




              """simple docstring"""




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

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

              UpperCamelCase     :						Union[str, Any] 						=		0
              with open(__SCREAMING_SNAKE_CASE			,       '''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 __SCREAMING_SNAKE_CASE    : 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     :						str 						=		token_index
                            writer.write(''' '''.join(__SCREAMING_SNAKE_CASE    ) + '''\n'''    )
                            index += 1

              return vocab_file, merge_file






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=False			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						List[Any] 						=		kwargs.pop('''add_prefix_space'''			,       self.add_prefix_space    )
              if is_split_into_words or add_prefix_space:
                     UpperCamelCase     :						str 						=		''' ''' + text
              return (text, kwargs)






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[int] 						=		[]
              for is_user, text in conversation.iter_texts():
                     input_ids.extend(self.encode(__SCREAMING_SNAKE_CASE			,       add_special_tokens=__SCREAMING_SNAKE_CASE    ) + [self.eos_token_id]    )
              if len(__SCREAMING_SNAKE_CASE    ) > self.model_max_length:
                     UpperCamelCase     :						int 						=		input_ids[-self.model_max_length :]
              return input_ids





import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()



class  UpperCAmelCase_						(				unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase   :  Tuple				=       "hf-internal-testing/tiny-stable-diffusion-pipe"
       __UpperCamelCase   :  Optional[Any]				=       1
       __UpperCamelCase   :  str				=       77
       __UpperCamelCase   :  Tuple				=       32
       __UpperCamelCase   :  List[str]				=       4
       __UpperCamelCase   :  Optional[int]				=       64
       __UpperCamelCase   :  Union[str, Any]				=       torch.manual_seed(0)






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE=0.9_999    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[int] 						=		UNetaDConditionModel.from_pretrained(self.model_id			,       subfolder='''unet'''    )
              UpperCamelCase     :						Any 						=		unet.to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Dict 						=		EMAModel(unet.parameters()			,       decay=__SCREAMING_SNAKE_CASE			,       model_cls=__SCREAMING_SNAKE_CASE			,       model_config=unet.config    )
              return unet, ema_unet






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Tuple 						=		torch.randn(
                  self.batch_size			,       self.num_in_channels			,       self.latent_height			,       self.latent_width			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						Optional[int] 						=		torch.randint(0			,       1_000			,       size=(self.batch_size,)			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						List[Any] 						=		torch.randn(
                  self.batch_size			,       self.prompt_length			,       self.text_encoder_hidden_dim			,       generator=self.generator    ).to(__SCREAMING_SNAKE_CASE    )
              return noisy_latents, timesteps, encoder_hidden_states






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		{}
              for k, param in unet.state_dict().items():
                     UpperCamelCase     :						int 						=		torch.randn_like(__SCREAMING_SNAKE_CASE    ) + (param * torch.randn_like(__SCREAMING_SNAKE_CASE    ))
                     updated_state_dict.update({k: updated_param}    )
              unet.load_state_dict(__SCREAMING_SNAKE_CASE    )
              return unet






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Optional[Any] 						=		self.get_models()
              # Take the first (hypothetical) EMA step.
              ema_unet.step(unet.parameters()    )
              assert ema_unet.optimization_step == 1

              # Take two more.
              for _ in range(2    ):
                     ema_unet.step(unet.parameters()    )
              assert ema_unet.optimization_step == 3






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						List[Any] 						=		self.get_models()
              # Since the `unet` is not being updated (i.e., backprop'd)
              # there won't be any difference between the `params` of `unet`
              # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
              ema_unet.step(unet.parameters()    )
              UpperCamelCase     :						int 						=		list(unet.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              # The above holds true even if we call `ema.step()` multiple times since
              # `unet` params are still not being updated.
              for _ in range(4    ):
                     ema_unet.step(unet.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Any 						=		self.get_models()
              # Here we simulate the parameter updates for `unet`. Since there might
              # be some parameters which are initialized to zero we take extra care to
              # initialize their values to something non-zero before the multiplication.
              UpperCamelCase     :						Union[str, Any] 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )

              # Take the EMA step.
              ema_unet.step(unet_pseudo_updated_step_one.parameters()    )

              # Now the EMA'd parameters won't be equal to the original model parameters.
              UpperCamelCase     :						Any 						=		list(unet_pseudo_updated_step_one.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert ~torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )

              # Ensure this is the case when we take multiple EMA steps.
              for _ in range(4    ):
                     ema_unet.step(unet.parameters()    )
              for s_param, param in zip(ema_unet.shadow_params			,       __SCREAMING_SNAKE_CASE    ):
                     assert ~torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Optional[int] 						=		self.get_models()

              # First backprop + EMA
              UpperCamelCase     :						Dict 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_one.parameters()    )
              UpperCamelCase     :						List[str] 						=		ema_unet.shadow_params

              # Second backprop + EMA
              UpperCamelCase     :						str 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_two.parameters()    )
              UpperCamelCase     :						List[Any] 						=		ema_unet.shadow_params

              for step_one, step_two in zip(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):
                     assert ~torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						int 						=		self.get_models(decay=0.0    )
              UpperCamelCase     :						Optional[int] 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_one.parameters()    )
              UpperCamelCase     :						Union[str, Any] 						=		ema_unet.shadow_params

              UpperCamelCase     :						Any 						=		self.simulate_backprop(__SCREAMING_SNAKE_CASE    )
              ema_unet.step(unet_step_two.parameters()    )
              UpperCamelCase     :						int 						=		ema_unet.shadow_params

              for step_one, step_two in zip(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):
                     assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       @skip_mps
       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						str 						=		self.get_models()
              UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase     :						Union[str, Any] 						=		self.get_dummy_inputs()

              with tempfile.TemporaryDirectory() as tmpdir:
                     ema_unet.save_pretrained(__SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						Optional[int] 						=		UNetaDConditionModel.from_pretrained(__SCREAMING_SNAKE_CASE			,       model_cls=__SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						List[str] 						=		loaded_unet.to(unet.device    )

              # Since no EMA step has been performed the outputs should match.
              UpperCamelCase     :						Optional[Any] 						=		unet(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ).sample
              UpperCamelCase     :						List[str] 						=		loaded_unet(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ).sample

              assert torch.allclose(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       atol=1e-4    )

