

'''simple docstring'''





import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    GPTaTokenizer
				lowerCamelCase_		       =    GPTaTokenizerFast
				lowerCamelCase_		       =    True
				lowerCamelCase_		       =    {'add_prefix_space': True}
				lowerCamelCase_		       =    False







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										super().setUp()

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

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







				def  lowerCamelCase_	(       self     :		str		,					**UpperCAmelCase__     :		Dict   ):
										'''simple docstring'''
										kwargs.update(self.special_tokens_map   )
										return GPTaTokenizer.from_pretrained(self.tmpdirname		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		str		,					**UpperCAmelCase__     :		List[Any]   ):
										'''simple docstring'''
										kwargs.update(self.special_tokens_map   )
										return GPTaTokenizerFast.from_pretrained(self.tmpdirname		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Any		,					UpperCAmelCase__     :		Tuple   ):
										'''simple docstring'''
										lowercase :							Dict    		='''lower newer'''
										lowercase :							Any    		='''lower newer'''
										return input_text, output_text







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							Any    		=GPTaTokenizer(self.vocab_file		,					self.merges_file		,					**self.special_tokens_map   )
										lowercase :							Tuple    		='''lower newer'''
										lowercase :							List[str]    		=['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
										lowercase :							Tuple    		=tokenizer.tokenize(UpperCAmelCase__		,					add_prefix_space=UpperCAmelCase__   )
										self.assertListEqual(UpperCAmelCase__		,					UpperCAmelCase__   )

										lowercase :							Tuple    		=tokens + [tokenizer.unk_token]
										lowercase :							Any    		=[14, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__   )		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										if not self.test_rust_tokenizer:
																return

										lowercase :							Optional[int]    		=self.get_tokenizer()
										lowercase :							List[Any]    		=self.get_rust_tokenizer(add_prefix_space=UpperCAmelCase__   )

										lowercase :							Optional[Any]    		='''lower newer'''

										# Testing tokenization
										lowercase :							Any    		=tokenizer.tokenize(UpperCAmelCase__		,					add_prefix_space=UpperCAmelCase__   )
										lowercase :							Optional[Any]    		=rust_tokenizer.tokenize(UpperCAmelCase__   )
										self.assertListEqual(UpperCAmelCase__		,					UpperCAmelCase__   )

										# Testing conversion to ids without special tokens
										lowercase :							Optional[Any]    		=tokenizer.encode(UpperCAmelCase__		,					add_special_tokens=UpperCAmelCase__		,					add_prefix_space=UpperCAmelCase__   )
										lowercase :							List[str]    		=rust_tokenizer.encode(UpperCAmelCase__		,					add_special_tokens=UpperCAmelCase__   )
										self.assertListEqual(UpperCAmelCase__		,					UpperCAmelCase__   )

										# Testing conversion to ids with special tokens
										lowercase :							int    		=self.get_rust_tokenizer(add_prefix_space=UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=tokenizer.encode(UpperCAmelCase__		,					add_prefix_space=UpperCAmelCase__   )
										lowercase :							Any    		=rust_tokenizer.encode(UpperCAmelCase__   )
										self.assertListEqual(UpperCAmelCase__		,					UpperCAmelCase__   )

										# Testing the unknown token
										lowercase :							Any    		=tokens + [rust_tokenizer.unk_token]
										lowercase :							int    		=[14, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(UpperCAmelCase__   )		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Tuple		,					*UpperCAmelCase__     :		List[str]		,					**UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										# It's very difficult to mix/test pretokenization with byte-level
										# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
										pass







				def  lowerCamelCase_	(       self     :		Any		,					UpperCAmelCase__     :		List[Any]=15   ):
										'''simple docstring'''
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
																						lowercase :							Optional[int]    		=self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__		,					**UpperCAmelCase__   )

																						# Simple input
																						lowercase :							Union[str, Any]    		='''This is a simple input'''
																						lowercase :							Dict    		=['''This is a simple input 1''', '''This is a simple input 2''']
																						lowercase :							Dict    		=('''This is a simple input''', '''This is a pair''')
																						lowercase :							Optional[Any]    		=[
																						    ('''This is a simple input 1''', '''This is a simple input 2'''),
																						    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																						]

																						# Simple input tests
																						self.assertRaises(UpperCAmelCase__		,					tokenizer_r.encode		,					UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='''max_length'''   )

																						# Simple input
																						self.assertRaises(UpperCAmelCase__		,					tokenizer_r.encode_plus		,					UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='''max_length'''   )

																						# Simple input
																						self.assertRaises(
																						    UpperCAmelCase__		,					tokenizer_r.batch_encode_plus		,					UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='''max_length'''		,					)

																						# Pair input
																						self.assertRaises(UpperCAmelCase__		,					tokenizer_r.encode		,					UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='''max_length'''   )

																						# Pair input
																						self.assertRaises(UpperCAmelCase__		,					tokenizer_r.encode_plus		,					UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='''max_length'''   )

																						# Pair input
																						self.assertRaises(
																						    UpperCAmelCase__		,					tokenizer_r.batch_encode_plus		,					UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='''max_length'''		,					)







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										lowercase :							List[str]    		=GPTaTokenizer.from_pretrained(self.tmpdirname		,					pad_token='''<pad>'''   )

										# Simple input
										lowercase :							Dict    		='''This is a simple input'''
										lowercase :							Optional[int]    		=['''This is a simple input looooooooong''', '''This is a simple input''']
										lowercase :							int    		=('''This is a simple input''', '''This is a pair''')
										lowercase :							Optional[int]    		=[
										    ('''This is a simple input loooooong''', '''This is a simple input'''),
										    ('''This is a simple pair loooooong''', '''This is a simple pair'''),
										]

										lowercase :							str    		=tokenizer.pad_token_id

										lowercase :							int    		=tokenizer(UpperCAmelCase__		,					padding='''max_length'''		,					max_length=30		,					return_tensors='''np'''   )
										lowercase :							List[Any]    		=tokenizer(UpperCAmelCase__		,					padding=UpperCAmelCase__		,					truncate=UpperCAmelCase__		,					return_tensors='''np'''   )
										lowercase :							Optional[int]    		=tokenizer(*UpperCAmelCase__		,					padding='''max_length'''		,					max_length=60		,					return_tensors='''np'''   )
										lowercase :							Union[str, Any]    		=tokenizer(UpperCAmelCase__		,					padding=UpperCAmelCase__		,					truncate=UpperCAmelCase__		,					return_tensors='''np'''   )

										# s
										# test single string max_length padding
										self.assertEqual(out_s['''input_ids'''].shape[-1]		,					30   )
										self.assertTrue(pad_token_id in out_s['''input_ids''']   )
										self.assertTrue(0 in out_s['''attention_mask''']   )

										# s2
										# test automatic padding
										self.assertEqual(out_sa['''input_ids'''].shape[-1]		,					33   )
										# long slice doesn't have padding
										self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]   )
										self.assertFalse(0 in out_sa['''attention_mask'''][0]   )
										# short slice does have padding
										self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]   )
										self.assertTrue(0 in out_sa['''attention_mask'''][1]   )

										# p
										# test single pair max_length padding
										self.assertEqual(out_p['''input_ids'''].shape[-1]		,					60   )
										self.assertTrue(pad_token_id in out_p['''input_ids''']   )
										self.assertTrue(0 in out_p['''attention_mask''']   )

										# p2
										# test automatic padding pair
										self.assertEqual(out_pa['''input_ids'''].shape[-1]		,					52   )
										# long slice pair doesn't have padding
										self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]   )
										self.assertFalse(0 in out_pa['''attention_mask'''][0]   )
										# short slice pair does have padding
										self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]   )
										self.assertTrue(0 in out_pa['''attention_mask'''][1]   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		='''$$$'''
										lowercase :							List[Any]    		=GPTaTokenizer.from_pretrained(self.tmpdirname		,					bos_token=UpperCAmelCase__		,					add_bos_token=UpperCAmelCase__   )

										lowercase :							List[Any]    		='''This is a simple input'''
										lowercase :							Optional[Any]    		=['''This is a simple input 1''', '''This is a simple input 2''']

										lowercase :							Union[str, Any]    		=tokenizer.bos_token_id

										lowercase :							Optional[Any]    		=tokenizer(UpperCAmelCase__   )
										lowercase :							List[str]    		=tokenizer(UpperCAmelCase__   )

										self.assertEqual(out_s.input_ids[0]		,					UpperCAmelCase__   )
										self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids   )   )

										lowercase :							str    		=tokenizer.decode(out_s.input_ids   )
										lowercase :							List[str]    		=tokenizer.batch_decode(out_sa.input_ids   )

										self.assertEqual(decode_s.split()[0]		,					UpperCAmelCase__   )
										self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa   )   )







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										pass







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										# TODO: change to self.get_tokenizers() when the fast version is implemented
										lowercase :							List[str]    		=[self.get_tokenizer(do_lower_case=UpperCAmelCase__		,					add_bos_token=UpperCAmelCase__   )]
										for tokenizer in tokenizers:
																with self.subTest(F'''{tokenizer.__class__.__name__}'''   ):
																						lowercase :							int    		='''Encode this.'''
																						lowercase :							List[str]    		='''This one too please.'''
																						lowercase :							Dict    		=tokenizer.encode(UpperCAmelCase__		,					add_special_tokens=UpperCAmelCase__   )
																						encoded_sequence += tokenizer.encode(UpperCAmelCase__		,					add_special_tokens=UpperCAmelCase__   )
																						lowercase :							Tuple    		=tokenizer.encode_plus(
																						    UpperCAmelCase__		,					UpperCAmelCase__		,					add_special_tokens=UpperCAmelCase__		,					return_special_tokens_mask=UpperCAmelCase__		,					)
																						lowercase :							Dict    		=encoded_sequence_dict['''input_ids''']
																						lowercase :							str    		=encoded_sequence_dict['''special_tokens_mask''']
																						self.assertEqual(len(UpperCAmelCase__   )		,					len(UpperCAmelCase__   )   )

																						lowercase :							str    		=[
																						    (x if not special_tokens_mask[i] else None) for i, x in enumerate(UpperCAmelCase__   )
																						]
																						lowercase :							Any    		=[x for x in filtered_sequence if x is not None]
																						self.assertEqual(UpperCAmelCase__		,					UpperCAmelCase__   )





@require_tokenizers
class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										# More context:
										# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
										# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
										# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

										lowercase :							List[Any]    		=AutoTokenizer.from_pretrained('''facebook/opt-350m'''		,					from_slow=UpperCAmelCase__   )
										lowercase :							str    		='''A photo of a cat'''

										lowercase :							Optional[int]    		=tokenizer.encode(
										    UpperCAmelCase__		,					)
										self.assertEqual(UpperCAmelCase__		,					[2, 250, 1345, 9, 10, 4758]   )
										tokenizer.save_pretrained('''test_opt'''   )

										lowercase :							int    		=AutoTokenizer.from_pretrained('''./test_opt'''   )
										lowercase :							Optional[Any]    		=tokenizer.encode(
										    UpperCAmelCase__		,					)
										self.assertEqual(UpperCAmelCase__		,					[2, 250, 1345, 9, 10, 4758]   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							int    		=AutoTokenizer.from_pretrained('''facebook/opt-350m'''		,					use_slow=UpperCAmelCase__   )
										lowercase :							Any    		='''A photo of a cat'''

										lowercase :							List[str]    		=tokenizer.encode(
										    UpperCAmelCase__		,					)
										# Same as above
										self.assertEqual(UpperCAmelCase__		,					[2, 250, 1345, 9, 10, 4758]   )







				@unittest.skip('''This test is failing because of a bug in the fast tokenizer'''   )
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=AutoTokenizer.from_pretrained('''facebook/opt-350m'''		,					from_slow=UpperCAmelCase__   )

										lowercase :							List[str]    		='''bos'''
										lowercase :							List[Any]    		=tokenizer.get_vocab()['''bos''']

										lowercase :							Optional[int]    		='''A photo of a cat'''
										lowercase :							Tuple    		=tokenizer.encode(
										    UpperCAmelCase__		,					)
										# We changed the bos token
										self.assertEqual(UpperCAmelCase__		,					[31957, 250, 1345, 9, 10, 4758]   )
										tokenizer.save_pretrained('''./tok'''   )
										lowercase :							Tuple    		=AutoTokenizer.from_pretrained('''./tok'''   )
										self.assertTrue(tokenizer.is_fast   )
										lowercase :							Optional[int]    		=tokenizer.encode(
										    UpperCAmelCase__		,					)
										self.assertEqual(UpperCAmelCase__		,					[31957, 250, 1345, 9, 10, 4758]   )












'''simple docstring'''


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_		(		__magic_name__    ,__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Optional[Any]								=       AutoencoderKL
 __lowerCamelCase    :				List[Any]								=       "sample"
 __lowerCamelCase    :				Tuple								=       1e-2




 @property
 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   4
     _lowerCAmelCase						      =   3
     _lowerCAmelCase						      =   (32, 32)

     _lowerCAmelCase						      =   floats_tensor((batch_size, num_channels) + sizes		).to(_lowerCAmelCase		)

     return {"sample": image}




 @property
 def 				_snake_case (						self		)     ->				Any:
     return (3, 32, 32)




 @property
 def 				_snake_case (						self		)     ->				List[Any]:
     return (3, 32, 32)




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   {
         "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						      =   self.dummy_input
     return init_dict, inputs_dict




 def 				_snake_case (						self		)     ->				Optional[int]:
     pass




 def 				_snake_case (						self		)     ->				Any:
     pass




 @unittest.skipIf(torch_device == "mps"      ,  "Gradient checkpointing skipped on MPS"		)
 def 				_snake_case (						self		)     ->				str:
     # enable deterministic behavior for gradient checkpointing
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.prepare_init_args_and_inputs_for_common()
     _lowerCAmelCase						      =   self.model_class(**_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)

     assert not model.is_gradient_checkpointing and model.training

     _lowerCAmelCase						      =   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						      =   torch.randn_like(_lowerCAmelCase		)
     _lowerCAmelCase						      =   (out - labels).mean()
     loss.backward()

     # re-instantiate the model now enabling gradient checkpointing
     _lowerCAmelCase						      =   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						      =   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						      =   (out_a - labels).mean()
     loss_a.backward()

     # compare the output and parameters gradients
     self.assertTrue((loss - loss_a).abs() < 1E-5		)
     _lowerCAmelCase						      =   dict(model.named_parameters()		)
     _lowerCAmelCase						      =   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 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   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						      =   model(**self.dummy_input		)

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





 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"		)
     _lowerCAmelCase						      =   model.to(_lowerCAmelCase		)
     model.eval()

     if torch_device == "mps":
         _lowerCAmelCase						      =   torch.manual_seed(0		)
     else:
         _lowerCAmelCase						      =   torch.Generator(device=_lowerCAmelCase		).manual_seed(0		)

     _lowerCAmelCase						      =   torch.randn(
         1      ,  model.config.in_channels      ,  model.config.sample_size      ,  model.config.sample_size      ,  generator=torch.manual_seed(0		)      ,  )
     _lowerCAmelCase						      =   image.to(_lowerCAmelCase		)
     with torch.no_grad():
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  sample_posterior=_lowerCAmelCase      ,  generator=_lowerCAmelCase		).sample

     _lowerCAmelCase						      =   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						      =   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						      =   torch.tensor(
             [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]		)
     else:
         _lowerCAmelCase						      =   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		):




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Union[str, Any]:
     return f'''gaussian_noise_s={seed}_shape={'_'.join([str(_lowerCAmelCase		) for s in shape]		)}.npy'''




 def 				_snake_case (						self		)     ->				List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




 def 				_snake_case (						self      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase=(4, 3, 512, 512)      ,  _lowerCAmelCase=False		)     ->				Tuple:
     _lowerCAmelCase						      =   torch.floataa if fpaa else torch.floataa
     _lowerCAmelCase						      =   torch.from_numpy(load_hf_numpy(self.get_file_format(_lowerCAmelCase      ,  _lowerCAmelCase		)		)		).to(_lowerCAmelCase		).to(_lowerCAmelCase		)
     return image




 def 				_snake_case (						self      ,  _lowerCAmelCase="CompVis/stable-diffusion-v1-4"      ,  _lowerCAmelCase=False		)     ->				Tuple:
     _lowerCAmelCase						      =   "fp16" if fpaa else None
     _lowerCAmelCase						      =   torch.floataa if fpaa else torch.floataa

     _lowerCAmelCase						      =   AutoencoderKL.from_pretrained(
         _lowerCAmelCase      ,  subfolder="vae"      ,  torch_dtype=_lowerCAmelCase      ,  revision=_lowerCAmelCase      ,  )
     model.to(_lowerCAmelCase		).eval()

     return model




 def 				_snake_case (						self      ,  _lowerCAmelCase=0		)     ->				str:
     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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Dict:
     _lowerCAmelCase						      =   self.get_sd_vae_model()
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_generator(_lowerCAmelCase		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  generator=_lowerCAmelCase      ,  sample_posterior=_lowerCAmelCase		).sample

     assert sample.shape == image.shape

     _lowerCAmelCase						      =   sample[-1, -2:, -2:, :2].flatten().float().cpu()
     _lowerCAmelCase						      =   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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.get_sd_vae_model(fpaa=_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase      ,  fpaa=_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_generator(_lowerCAmelCase		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  generator=_lowerCAmelCase      ,  sample_posterior=_lowerCAmelCase		).sample

     assert sample.shape == image.shape

     _lowerCAmelCase						      =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
     _lowerCAmelCase						      =   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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Tuple:
     _lowerCAmelCase						      =   self.get_sd_vae_model()
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model(_lowerCAmelCase		).sample

     assert sample.shape == image.shape

     _lowerCAmelCase						      =   sample[-1, -2:, -2:, :2].flatten().float().cpu()
     _lowerCAmelCase						      =   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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.get_sd_vae_model()
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase      ,  shape=(3, 4, 64, 64)		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model.decode(_lowerCAmelCase		).sample

     assert list(sample.shape		) == [3, 3, 512, 512]

     _lowerCAmelCase						      =   sample[-1, -2:, :2, -2:].flatten().cpu()
     _lowerCAmelCase						      =   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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				str:
     _lowerCAmelCase						      =   self.get_sd_vae_model(fpaa=_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase      ,  shape=(3, 4, 64, 64)      ,  fpaa=_lowerCAmelCase		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model.decode(_lowerCAmelCase		).sample

     assert list(sample.shape		) == [3, 3, 512, 512]

     _lowerCAmelCase						      =   sample[-1, -2:, :2, -2:].flatten().float().cpu()
     _lowerCAmelCase						      =   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 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				List[str]:
     _lowerCAmelCase						      =   self.get_sd_vae_model(fpaa=_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase      ,  shape=(3, 4, 64, 64)      ,  fpaa=_lowerCAmelCase		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model.decode(_lowerCAmelCase		).sample

     model.enable_xformers_memory_efficient_attention()
     with torch.no_grad():
         _lowerCAmelCase						      =   model.decode(_lowerCAmelCase		).sample

     assert list(sample.shape		) == [3, 3, 512, 512]

     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 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Any:
     _lowerCAmelCase						      =   self.get_sd_vae_model()
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase      ,  shape=(3, 4, 64, 64)		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model.decode(_lowerCAmelCase		).sample

     model.enable_xformers_memory_efficient_attention()
     with torch.no_grad():
         _lowerCAmelCase						      =   model.decode(_lowerCAmelCase		).sample

     assert list(sample.shape		) == [3, 3, 512, 512]

     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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				int:
     _lowerCAmelCase						      =   self.get_sd_vae_model()
     _lowerCAmelCase						      =   self.get_sd_image(_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.get_generator(_lowerCAmelCase		)

     with torch.no_grad():
         _lowerCAmelCase						      =   model.encode(_lowerCAmelCase		).latent_dist
         _lowerCAmelCase						      =   dist.sample(generator=_lowerCAmelCase		)

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

     _lowerCAmelCase						      =   sample[0, -1, -3:, -3:].flatten().cpu()
     _lowerCAmelCase						      =   torch.tensor(_lowerCAmelCase		)

     _lowerCAmelCase						      =   3E-3 if torch_device != "mps" else 1E-2
     assert torch_all_close(_lowerCAmelCase      ,  _lowerCAmelCase      ,  atol=_lowerCAmelCase		)


