



import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class  lowerCamelCase__    (							lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=LongformerTokenizer
		snake_case_			=True
		snake_case_			=LongformerTokenizerFast
		snake_case_			=True



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

						"""simple docstring"""






						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						lowerCAmelCase__  :       str				=  [
						    '''l''',
						    '''o''',
						    '''w''',
						    '''e''',
						    '''r''',
						    '''s''',
						    '''t''',
						    '''i''',
						    '''d''',
						    '''n''',
						    '''\u0120''',
						    '''\u0120l''',
						    '''\u0120n''',
						    '''\u0120lo''',
						    '''\u0120low''',
						    '''er''',
						    '''\u0120lowest''',
						    '''\u0120newer''',
						    '''\u0120wider''',
						    '''<unk>''',
						]
						lowerCAmelCase__  :       Optional[Any]				=  dict(zip(__lowerCamelCase	,range(len(__lowerCamelCase				)				)				)				)
						lowerCAmelCase__  :       Optional[Any]				=  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
						lowerCAmelCase__  :       Optional[int]				=  {'''unk_token''': '''<unk>'''}

						lowerCAmelCase__  :       Optional[Any]				=  os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['''vocab_file''']				)
						lowerCAmelCase__  :       Dict				=  os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['''merges_file''']				)
						with open(self.vocab_file	,'''w'''	,encoding='''utf-8'''				) as fp:
										fp.write(json.dumps(__lowerCamelCase				) + '''\n'''				)
						with open(self.merges_file	,'''w'''	,encoding='''utf-8'''				) as fp:
										fp.write('''\n'''.join(__lowerCamelCase				)				)



		def 	lowerCAmelCase__   (self	,**__lowerCamelCase				)  ->    Optional[Any]:

						"""simple docstring"""






						kwargs.update(self.special_tokens_map				)
						return self.tokenizer_class.from_pretrained(self.tmpdirname	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,**__lowerCamelCase				)  ->    Optional[Any]:

						"""simple docstring"""






						kwargs.update(self.special_tokens_map				)
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  '''lower newer'''
						lowerCAmelCase__  :       Tuple				=  '''lower newer'''
						return input_text, output_text



		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  self.tokenizer_class(self.vocab_file	,self.merges_file	,**self.special_tokens_map				)
						lowerCAmelCase__  :       Union[str, Any]				=  '''lower newer'''
						lowerCAmelCase__  :       Optional[int]				=  ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
						lowerCAmelCase__  :       Optional[int]				=  tokenizer.tokenize(__lowerCamelCase				)  # , add_prefix_space=True)
						self.assertListEqual(__lowerCamelCase	,__lowerCamelCase				)

						lowerCAmelCase__  :       str				=  tokens + [tokenizer.unk_token]
						lowerCAmelCase__  :       Any				=  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase				)	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  self.get_tokenizer()

						self.assertListEqual(tokenizer.encode('''Hello world!'''	,add_special_tokens=__lowerCamelCase				)	,[0, 3_14_14, 2_32, 3_28, 2]				)
						self.assertListEqual(
						    tokenizer.encode('''Hello world! cécé herlolip 418'''	,add_special_tokens=__lowerCamelCase				)	,[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]	,)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''				)

						lowerCAmelCase__  :       List[Any]				=  tokenizer.encode('''sequence builders'''	,add_special_tokens=__lowerCamelCase				)
						lowerCAmelCase__  :       int				=  tokenizer.encode('''multi-sequence build'''	,add_special_tokens=__lowerCamelCase				)

						lowerCAmelCase__  :       Optional[Any]				=  tokenizer.encode(
						    '''sequence builders'''	,add_special_tokens=__lowerCamelCase	,add_prefix_space=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  tokenizer.encode(
						    '''sequence builders'''	,'''multi-sequence build'''	,add_special_tokens=__lowerCamelCase	,add_prefix_space=__lowerCamelCase				)

						lowerCAmelCase__  :       Any				=  tokenizer.build_inputs_with_special_tokens(__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  tokenizer.build_inputs_with_special_tokens(__lowerCamelCase	,__lowerCamelCase				)

						assert encoded_sentence == encoded_text_from_decode
						assert encoded_pair == encoded_pair_from_decode



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

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  self.get_tokenizer()

						lowerCAmelCase__  :       List[str]				=  '''Encode this sequence.'''
						lowerCAmelCase__  :       Dict				=  tokenizer.byte_encoder[''' '''.encode('''utf-8'''				)[0]]

						# Testing encoder arguments
						lowerCAmelCase__  :       Dict				=  tokenizer.encode(__lowerCamelCase	,add_special_tokens=__lowerCamelCase	,add_prefix_space=__lowerCamelCase				)
						lowerCAmelCase__  :       Any				=  tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
						self.assertNotEqual(__lowerCamelCase	,__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  tokenizer.encode(__lowerCamelCase	,add_special_tokens=__lowerCamelCase	,add_prefix_space=__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
						self.assertEqual(__lowerCamelCase	,__lowerCamelCase				)

						tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}				)
						lowerCAmelCase__  :       str				=  tokenizer.encode(__lowerCamelCase	,add_special_tokens=__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  tokenizer.convert_ids_to_tokens(encoded[1]				)[0]
						self.assertNotEqual(__lowerCamelCase	,__lowerCamelCase				)

						# Testing spaces after special tokens
						lowerCAmelCase__  :       Any				=  '''<mask>'''
						tokenizer.add_special_tokens(
						    {'''mask_token''': AddedToken(__lowerCamelCase	,lstrip=__lowerCamelCase	,rstrip=__lowerCamelCase				)}				)  # mask token has a left space
						lowerCAmelCase__  :       Any				=  tokenizer.convert_tokens_to_ids(__lowerCamelCase				)

						lowerCAmelCase__  :       Optional[Any]				=  '''Encode <mask> sequence'''
						lowerCAmelCase__  :       Dict				=  '''Encode <mask>sequence'''

						lowerCAmelCase__  :       List[Any]				=  tokenizer.encode(__lowerCamelCase				)
						lowerCAmelCase__  :       int				=  encoded.index(__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
						self.assertEqual(__lowerCamelCase	,__lowerCamelCase				)

						lowerCAmelCase__  :       List[str]				=  tokenizer.encode(__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  encoded.index(__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
						self.assertNotEqual(__lowerCamelCase	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						pass



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

						"""simple docstring"""






						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
														lowerCAmelCase__  :       int				=  self.rust_tokenizer_class.from_pretrained(__lowerCamelCase	,**__lowerCamelCase				)
														lowerCAmelCase__  :       Union[str, Any]				=  self.tokenizer_class.from_pretrained(__lowerCamelCase	,**__lowerCamelCase				)
														lowerCAmelCase__  :       Any				=  '''A, <mask> AllenNLP sentence.'''
														lowerCAmelCase__  :       str				=  tokenizer_r.encode_plus(__lowerCamelCase	,add_special_tokens=__lowerCamelCase	,return_token_type_ids=__lowerCamelCase				)
														lowerCAmelCase__  :       str				=  tokenizer_p.encode_plus(__lowerCamelCase	,add_special_tokens=__lowerCamelCase	,return_token_type_ids=__lowerCamelCase				)

														# token_type_ids should put 0 everywhere
														self.assertEqual(sum(tokens_r['''token_type_ids''']				)	,sum(tokens_p['''token_type_ids''']				)				)

														# attention_mask should put 1 everywhere, so sum over length should be 1
														self.assertEqual(
														    sum(tokens_r['''attention_mask''']				) / len(tokens_r['''attention_mask''']				)	,sum(tokens_p['''attention_mask''']				) / len(tokens_p['''attention_mask''']				)	,)

														lowerCAmelCase__  :       List[str]				=  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']				)
														lowerCAmelCase__  :       Union[str, Any]				=  tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']				)

														# Rust correctly handles the space before the mask while python doesnt
														self.assertSequenceEqual(tokens_p['''input_ids''']	,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]				)
														self.assertSequenceEqual(tokens_r['''input_ids''']	,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]				)

														self.assertSequenceEqual(
														    __lowerCamelCase	,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)
														self.assertSequenceEqual(
														    __lowerCamelCase	,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						for trim_offsets, add_prefix_space in itertools.product([True, False]	,repeat=2				):
										lowerCAmelCase__  :       str				=  self.rust_tokenizer_class.from_pretrained(
										    self.tmpdirname	,use_fast=__lowerCamelCase	,add_prefix_space=__lowerCamelCase	,trim_offsets=__lowerCamelCase				)

										lowerCAmelCase__  :       List[str]				=  json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()				)
										lowerCAmelCase__  :       Optional[Any]				=  json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()				)

										self.assertEqual(pre_tokenizer_state['''add_prefix_space''']	,__lowerCamelCase				)

										self.assertEqual(post_processor_state['''add_prefix_space''']	,__lowerCamelCase				)
										self.assertEqual(post_processor_state['''trim_offsets''']	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
														lowerCAmelCase__  :       str				=  '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
														lowerCAmelCase__  :       List[str]				=  f"""{text_of_1_token} {text_of_1_token}"""

														lowerCAmelCase__  :       List[str]				=  self.rust_tokenizer_class.from_pretrained(
														    __lowerCamelCase	,use_fast=__lowerCamelCase	,add_prefix_space=__lowerCamelCase	,trim_offsets=__lowerCamelCase				)
														lowerCAmelCase__  :       Dict				=  tokenizer_r(__lowerCamelCase	,return_offsets_mapping=__lowerCamelCase	,add_special_tokens=__lowerCamelCase				)
														self.assertEqual(encoding.offset_mapping[0]	,(0, len(__lowerCamelCase				))				)
														self.assertEqual(
														    encoding.offset_mapping[1]	,(len(__lowerCamelCase				) + 1, len(__lowerCamelCase				) + 1 + len(__lowerCamelCase				))	,)

														lowerCAmelCase__  :       Any				=  self.rust_tokenizer_class.from_pretrained(
														    __lowerCamelCase	,use_fast=__lowerCamelCase	,add_prefix_space=__lowerCamelCase	,trim_offsets=__lowerCamelCase				)
														lowerCAmelCase__  :       Any				=  tokenizer_r(__lowerCamelCase	,return_offsets_mapping=__lowerCamelCase	,add_special_tokens=__lowerCamelCase				)
														self.assertEqual(encoding.offset_mapping[0]	,(0, len(__lowerCamelCase				))				)
														self.assertEqual(
														    encoding.offset_mapping[1]	,(len(__lowerCamelCase				) + 1, len(__lowerCamelCase				) + 1 + len(__lowerCamelCase				))	,)

														lowerCAmelCase__  :       int				=  self.rust_tokenizer_class.from_pretrained(
														    __lowerCamelCase	,use_fast=__lowerCamelCase	,add_prefix_space=__lowerCamelCase	,trim_offsets=__lowerCamelCase				)
														lowerCAmelCase__  :       Union[str, Any]				=  tokenizer_r(__lowerCamelCase	,return_offsets_mapping=__lowerCamelCase	,add_special_tokens=__lowerCamelCase				)
														self.assertEqual(encoding.offset_mapping[0]	,(0, len(__lowerCamelCase				))				)
														self.assertEqual(
														    encoding.offset_mapping[1]	,(len(__lowerCamelCase				), len(__lowerCamelCase				) + 1 + len(__lowerCamelCase				))	,)

														lowerCAmelCase__  :       Optional[int]				=  self.rust_tokenizer_class.from_pretrained(
														    __lowerCamelCase	,use_fast=__lowerCamelCase	,add_prefix_space=__lowerCamelCase	,trim_offsets=__lowerCamelCase				)
														lowerCAmelCase__  :       Optional[Any]				=  tokenizer_r(__lowerCamelCase	,return_offsets_mapping=__lowerCamelCase	,add_special_tokens=__lowerCamelCase				)
														self.assertEqual(encoding.offset_mapping[0]	,(0, len(__lowerCamelCase				))				)
														self.assertEqual(
														    encoding.offset_mapping[1]	,(len(__lowerCamelCase				), len(__lowerCamelCase				) + 1 + len(__lowerCamelCase				))	,)

														lowerCAmelCase__  :       Optional[int]				=  f""" {text}"""

														# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
														#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
														# )
														# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
														# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
														# self.assertEqual(
														#     encoding.offset_mapping[1],
														#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
														# )

														lowerCAmelCase__  :       Tuple				=  self.rust_tokenizer_class.from_pretrained(
														    __lowerCamelCase	,use_fast=__lowerCamelCase	,add_prefix_space=__lowerCamelCase	,trim_offsets=__lowerCamelCase				)
														lowerCAmelCase__  :       Optional[Any]				=  tokenizer_r(__lowerCamelCase	,return_offsets_mapping=__lowerCamelCase	,add_special_tokens=__lowerCamelCase				)
														self.assertEqual(encoding.offset_mapping[0]	,(1, 1 + len(__lowerCamelCase				))				)
														self.assertEqual(
														    encoding.offset_mapping[1]	,(1 + len(__lowerCamelCase				) + 1, 1 + len(__lowerCamelCase				) + 1 + len(__lowerCamelCase				))	,)

														lowerCAmelCase__  :       Dict				=  self.rust_tokenizer_class.from_pretrained(
														    __lowerCamelCase	,use_fast=__lowerCamelCase	,add_prefix_space=__lowerCamelCase	,trim_offsets=__lowerCamelCase				)
														lowerCAmelCase__  :       int				=  tokenizer_r(__lowerCamelCase	,return_offsets_mapping=__lowerCamelCase	,add_special_tokens=__lowerCamelCase				)
														self.assertEqual(encoding.offset_mapping[0]	,(0, 1 + len(__lowerCamelCase				))				)
														self.assertEqual(
														    encoding.offset_mapping[1]	,(1 + len(__lowerCamelCase				), 1 + len(__lowerCamelCase				) + 1 + len(__lowerCamelCase				))	,)

														lowerCAmelCase__  :       Any				=  self.rust_tokenizer_class.from_pretrained(
														    __lowerCamelCase	,use_fast=__lowerCamelCase	,add_prefix_space=__lowerCamelCase	,trim_offsets=__lowerCamelCase				)
														lowerCAmelCase__  :       List[str]				=  tokenizer_r(__lowerCamelCase	,return_offsets_mapping=__lowerCamelCase	,add_special_tokens=__lowerCamelCase				)
														self.assertEqual(encoding.offset_mapping[0]	,(0, 1 + len(__lowerCamelCase				))				)
														self.assertEqual(
														    encoding.offset_mapping[1]	,(1 + len(__lowerCamelCase				), 1 + len(__lowerCamelCase				) + 1 + len(__lowerCamelCase				))	,)






import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()



class  lowerCamelCase__    (							lowerCamelCase__   ,       lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=AutoencoderKL
		snake_case_			="""sample"""
		snake_case_			=1e-2



		@property
		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






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

						lowerCAmelCase__  :       Tuple				=  floats_tensor((batch_size, num_channels) + sizes				).to(__lowerCamelCase				)

						return {"sample": image}



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

						"""simple docstring"""






						return (3, 32, 32)



		@property
		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						return (3, 32, 32)



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  {
						    '''block_out_channels''': [32, 64],
						    '''in_channels''': 3,
						    '''out_channels''': 3,
						    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
						    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
						    '''latent_channels''': 4,
						}
						lowerCAmelCase__  :       str				=  self.dummy_input
						return init_dict, inputs_dict



		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						pass



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						pass



		@unittest.skipIf(torch_device == '''mps'''	,'''Gradient checkpointing skipped on MPS'''				)
		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__  :       int				=  self.prepare_init_args_and_inputs_for_common()
						lowerCAmelCase__  :       Tuple				=  self.model_class(**__lowerCamelCase				)
						model.to(__lowerCamelCase				)

						assert not model.is_gradient_checkpointing and model.training

						lowerCAmelCase__  :       Tuple				=  model(**__lowerCamelCase				).sample
						# run the backwards pass on the model. For backwards pass, for simplicity purpose,
						# we won't calculate the loss and rather backprop on out.sum()
						model.zero_grad()

						lowerCAmelCase__  :       Optional[Any]				=  torch.randn_like(__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  (out - labels).mean()
						loss.backward()

						# re-instantiate the model now enabling gradient checkpointing
						lowerCAmelCase__  :       str				=  self.model_class(**__lowerCamelCase				)
						# clone model
						model_a.load_state_dict(model.state_dict()				)
						model_a.to(__lowerCamelCase				)
						model_a.enable_gradient_checkpointing()

						assert model_a.is_gradient_checkpointing and model_a.training

						lowerCAmelCase__  :       Dict				=  model_a(**__lowerCamelCase				).sample
						# run the backwards pass on the model. For backwards pass, for simplicity purpose,
						# we won't calculate the loss and rather backprop on out.sum()
						model_a.zero_grad()
						lowerCAmelCase__  :       Any				=  (out_a - labels).mean()
						loss_a.backward()

						# compare the output and parameters gradients
						self.assertTrue((loss - loss_a).abs() < 1e-5				)
						lowerCAmelCase__  :       List[str]				=  dict(model.named_parameters()				)
						lowerCAmelCase__  :       Optional[Any]				=  dict(model_a.named_parameters()				)
						for name, param in named_params.items():
										self.assertTrue(torch_all_close(param.grad.data	,named_params_a[name].grad.data	,atol=5e-5				)				)



		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__  :       str				=  AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''	,output_loading_info=__lowerCamelCase				)
						self.assertIsNotNone(__lowerCamelCase				)
						self.assertEqual(len(loading_info['''missing_keys''']				)	,0				)

						model.to(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  model(**self.dummy_input				)

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



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

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''				)
						lowerCAmelCase__  :       Tuple				=  model.to(__lowerCamelCase				)
						model.eval()

						if torch_device == "mps":
										lowerCAmelCase__  :       Union[str, Any]				=  torch.manual_seed(0				)
						else:
										lowerCAmelCase__  :       Any				=  torch.Generator(device=__lowerCamelCase				).manual_seed(0				)

						lowerCAmelCase__  :       Optional[int]				=  torch.randn(
						    1	,model.config.in_channels	,model.config.sample_size	,model.config.sample_size	,generator=torch.manual_seed(0				)	,)
						lowerCAmelCase__  :       Dict				=  image.to(__lowerCamelCase				)
						with torch.no_grad():
										lowerCAmelCase__  :       List[Any]				=  model(__lowerCamelCase	,sample_posterior=__lowerCamelCase	,generator=__lowerCamelCase				).sample

						lowerCAmelCase__  :       Optional[Any]				=  output[0, -1, -3:, -3:].flatten().cpu()

						# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
						# the expected output slices are not the same for CPU and GPU.
						if torch_device == "mps":
										lowerCAmelCase__  :       List[str]				=  torch.tensor(
										    [
										        -4.0078e-01,
										        -3.8323e-04,
										        -1.2681e-01,
										        -1.1462e-01,
										        2.0095e-01,
										        1.0893e-01,
										        -8.8247e-02,
										        -3.0361e-01,
										        -9.8644e-03,
										    ]				)
						elif torch_device == "cpu":
										lowerCAmelCase__  :       str				=  torch.tensor(
										    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]				)
						else:
										lowerCAmelCase__  :       Any				=  torch.tensor(
										    [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]				)

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



@slow
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    List[Any]:

						"""simple docstring"""






						return f"""gaussian_noise_s={seed}_shape={'_'.join([str(__lowerCamelCase				) for s in shape]				)}.npy"""



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



		def 	lowerCAmelCase__   (self	,__lowerCamelCase=0	,__lowerCamelCase=(4, 3, 5_12, 5_12)	,__lowerCamelCase=False				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  torch.floataa if fpaa else torch.floataa
						lowerCAmelCase__  :       Dict				=  torch.from_numpy(load_hf_numpy(self.get_file_format(__lowerCamelCase	,__lowerCamelCase				)				)				).to(__lowerCamelCase				).to(__lowerCamelCase				)
						return image



		def 	lowerCAmelCase__   (self	,__lowerCamelCase="CompVis/stable-diffusion-v1-4"	,__lowerCamelCase=False				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  '''fp16''' if fpaa else None
						lowerCAmelCase__  :       Optional[Any]				=  torch.floataa if fpaa else torch.floataa

						lowerCAmelCase__  :       List[Any]				=  AutoencoderKL.from_pretrained(
						    __lowerCamelCase	,subfolder='''vae'''	,torch_dtype=__lowerCamelCase	,revision=__lowerCamelCase	,)
						model.to(__lowerCamelCase				).eval()

						return model



		def 	lowerCAmelCase__   (self	,__lowerCamelCase=0				)  ->    str:

						"""simple docstring"""






						if torch_device == "mps":
										return torch.manual_seed(__lowerCamelCase				)
						return torch.Generator(device=__lowerCamelCase				).manual_seed(__lowerCamelCase				)



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
		        [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
		        # fmt: on
		    ]				)
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  self.get_sd_vae_model()
						lowerCAmelCase__  :       Any				=  self.get_sd_image(__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  self.get_generator(__lowerCamelCase				)

						with torch.no_grad():
										lowerCAmelCase__  :       Dict				=  model(__lowerCamelCase	,generator=__lowerCamelCase	,sample_posterior=__lowerCamelCase				).sample

						assert sample.shape == image.shape

						lowerCAmelCase__  :       Optional[Any]				=  sample[-1, -2:, -2:, :2].flatten().float().cpu()
						lowerCAmelCase__  :       Any				=  torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice				)

						assert torch_all_close(__lowerCamelCase	,__lowerCamelCase	,atol=3e-3				)



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
		        [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
		        # fmt: on
		    ]				)
		@require_torch_gpu
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  self.get_sd_vae_model(fpaa=__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  self.get_sd_image(__lowerCamelCase	,fpaa=__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  self.get_generator(__lowerCamelCase				)

						with torch.no_grad():
										lowerCAmelCase__  :       Optional[Any]				=  model(__lowerCamelCase	,generator=__lowerCamelCase	,sample_posterior=__lowerCamelCase				).sample

						assert sample.shape == image.shape

						lowerCAmelCase__  :       Optional[Any]				=  sample[-1, -2:, :2, -2:].flatten().float().cpu()
						lowerCAmelCase__  :       Any				=  torch.tensor(__lowerCamelCase				)

						assert torch_all_close(__lowerCamelCase	,__lowerCamelCase	,atol=1e-2				)



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
		        [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
		        # fmt: on
		    ]				)
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  self.get_sd_vae_model()
						lowerCAmelCase__  :       Dict				=  self.get_sd_image(__lowerCamelCase				)

						with torch.no_grad():
										lowerCAmelCase__  :       Tuple				=  model(__lowerCamelCase				).sample

						assert sample.shape == image.shape

						lowerCAmelCase__  :       Dict				=  sample[-1, -2:, -2:, :2].flatten().float().cpu()
						lowerCAmelCase__  :       List[Any]				=  torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice				)

						assert torch_all_close(__lowerCamelCase	,__lowerCamelCase	,atol=3e-3				)



		@parameterized.expand(
		    [
		        # fmt: off
		        [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
		        [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
		        # fmt: on
		    ]				)
		@require_torch_gpu
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  self.get_sd_vae_model()
						lowerCAmelCase__  :       Union[str, Any]				=  self.get_sd_image(__lowerCamelCase	,shape=(3, 4, 64, 64)				)

						with torch.no_grad():
										lowerCAmelCase__  :       str				=  model.decode(__lowerCamelCase				).sample

						assert list(sample.shape				) == [3, 3, 5_12, 5_12]

						lowerCAmelCase__  :       Dict				=  sample[-1, -2:, :2, -2:].flatten().cpu()
						lowerCAmelCase__  :       Any				=  torch.tensor(__lowerCamelCase				)

						assert torch_all_close(__lowerCamelCase	,__lowerCamelCase	,atol=1e-3				)



		@parameterized.expand(
		    [
		        # fmt: off
		        [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
		        [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
		        # fmt: on
		    ]				)
		@require_torch_gpu
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  self.get_sd_vae_model(fpaa=__lowerCamelCase				)
						lowerCAmelCase__  :       Dict				=  self.get_sd_image(__lowerCamelCase	,shape=(3, 4, 64, 64)	,fpaa=__lowerCamelCase				)

						with torch.no_grad():
										lowerCAmelCase__  :       Optional[Any]				=  model.decode(__lowerCamelCase				).sample

						assert list(sample.shape				) == [3, 3, 5_12, 5_12]

						lowerCAmelCase__  :       Dict				=  sample[-1, -2:, :2, -2:].flatten().float().cpu()
						lowerCAmelCase__  :       Union[str, Any]				=  torch.tensor(__lowerCamelCase				)

						assert torch_all_close(__lowerCamelCase	,__lowerCamelCase	,atol=5e-3				)



		@parameterized.expand([(13,), (16,), (27,)]				)
		@require_torch_gpu
		@unittest.skipIf(not is_xformers_available()	,reason='''xformers is not required when using PyTorch 2.0.'''				)
		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  self.get_sd_vae_model(fpaa=__lowerCamelCase				)
						lowerCAmelCase__  :       str				=  self.get_sd_image(__lowerCamelCase	,shape=(3, 4, 64, 64)	,fpaa=__lowerCamelCase				)

						with torch.no_grad():
										lowerCAmelCase__  :       Any				=  model.decode(__lowerCamelCase				).sample

						model.enable_xformers_memory_efficient_attention()
						with torch.no_grad():
										lowerCAmelCase__  :       Optional[Any]				=  model.decode(__lowerCamelCase				).sample

						assert list(sample.shape				) == [3, 3, 5_12, 5_12]

						assert torch_all_close(__lowerCamelCase	,__lowerCamelCase	,atol=1e-1				)



		@parameterized.expand([(13,), (16,), (37,)]				)
		@require_torch_gpu
		@unittest.skipIf(not is_xformers_available()	,reason='''xformers is not required when using PyTorch 2.0.'''				)
		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  self.get_sd_vae_model()
						lowerCAmelCase__  :       Dict				=  self.get_sd_image(__lowerCamelCase	,shape=(3, 4, 64, 64)				)

						with torch.no_grad():
										lowerCAmelCase__  :       Tuple				=  model.decode(__lowerCamelCase				).sample

						model.enable_xformers_memory_efficient_attention()
						with torch.no_grad():
										lowerCAmelCase__  :       Optional[Any]				=  model.decode(__lowerCamelCase				).sample

						assert list(sample.shape				) == [3, 3, 5_12, 5_12]

						assert torch_all_close(__lowerCamelCase	,__lowerCamelCase	,atol=1e-2				)



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
		        [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
		        # fmt: on
		    ]				)
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  self.get_sd_vae_model()
						lowerCAmelCase__  :       Tuple				=  self.get_sd_image(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  self.get_generator(__lowerCamelCase				)

						with torch.no_grad():
										lowerCAmelCase__  :       List[Any]				=  model.encode(__lowerCamelCase				).latent_dist
										lowerCAmelCase__  :       int				=  dist.sample(generator=__lowerCamelCase				)

						assert list(sample.shape				) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

						lowerCAmelCase__  :       Optional[int]				=  sample[0, -1, -3:, -3:].flatten().cpu()
						lowerCAmelCase__  :       int				=  torch.tensor(__lowerCamelCase				)

						lowerCAmelCase__  :       Any				=  3e-3 if torch_device != '''mps''' else 1e-2
						assert torch_all_close(__lowerCamelCase	,__lowerCamelCase	,atol=__lowerCamelCase				)

