code: 
  
  
  
  import tempfile
  import unittest
  
  import transformers
  from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
  from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow
  
  from ...generation.test_flax_utils import FlaxGenerationTesterMixin
  from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  
  
  if is_flax_available():
  	import jax
  	import jax.numpy as jnp
  	import numpy as np
  
  	from transformers.modeling_flax_pytorch_utils import (
  	    convert_pytorch_state_dict_to_flax,
  	    load_flax_weights_in_pytorch_model,
  	)
  	from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel
  
  
  if is_torch_available():
  	import torch
  
  @require_flax
  class      UpperCAmelCase_						:
  
  						'''simple docstring'''
  
  
  
  
  						def __init__(					self   ,							_A   ,							_A=14   ,							_A=7   ,							_A=True   ,							_A=True   ,							_A=True   ,							_A=99   ,							_A=32   ,							_A=5   ,							_A=4   ,							_A=37   ,							_A="gelu"   ,							_A=0.1   ,							_A=0.1   ,							_A=512   ,							_A=0.0_2   ,							_A=None   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      parent
  													__SCREAMING_SNAKE_CASE								=      batch_size
  													__SCREAMING_SNAKE_CASE								=      seq_length
  													__SCREAMING_SNAKE_CASE								=      is_training
  													__SCREAMING_SNAKE_CASE								=      use_input_mask
  													__SCREAMING_SNAKE_CASE								=      use_labels
  													__SCREAMING_SNAKE_CASE								=      vocab_size
  													__SCREAMING_SNAKE_CASE								=      d_model
  													__SCREAMING_SNAKE_CASE								=      num_hidden_layers
  													__SCREAMING_SNAKE_CASE								=      num_attention_heads
  													__SCREAMING_SNAKE_CASE								=      ffn_dim
  													__SCREAMING_SNAKE_CASE								=      activation_function
  													__SCREAMING_SNAKE_CASE								=      activation_dropout
  													__SCREAMING_SNAKE_CASE								=      attention_dropout
  													__SCREAMING_SNAKE_CASE								=      max_position_embeddings
  													__SCREAMING_SNAKE_CASE								=      initializer_range
  													__SCREAMING_SNAKE_CASE								=      None
  													__SCREAMING_SNAKE_CASE								=      0
  													__SCREAMING_SNAKE_CASE								=      2
  													__SCREAMING_SNAKE_CASE								=      1
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      np.clip(ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size	)   ,							3   ,							self.vocab_size	)
  
  													__SCREAMING_SNAKE_CASE								=      None
  													if self.use_input_mask:
  																				__SCREAMING_SNAKE_CASE								=      random_attention_mask([self.batch_size, self.seq_length]	)
  
  													__SCREAMING_SNAKE_CASE								=      XGLMConfig(
  													    vocab_size=self.vocab_size   ,							d_model=self.hidden_size   ,							num_layers=self.num_hidden_layers   ,							attention_heads=self.num_attention_heads   ,							ffn_dim=self.ffn_dim   ,							activation_function=self.activation_function   ,							activation_dropout=self.activation_dropout   ,							attention_dropout=self.attention_dropout   ,							max_position_embeddings=self.max_position_embeddings   ,							initializer_range=self.initializer_range   ,							use_cache=__A   ,							bos_token_id=self.bos_token_id   ,							eos_token_id=self.eos_token_id   ,							pad_token_id=self.pad_token_id   ,							)
  
  													return (config, input_ids, input_mask)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.prepare_config_and_inputs()
  													__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      config_and_inputs
  													__SCREAMING_SNAKE_CASE								=      {'input_ids': input_ids, 'attention_mask': attention_mask}
  													return config, inputs_dict
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      self.prepare_config_and_inputs()
  
  													__SCREAMING_SNAKE_CASE								=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
  													__SCREAMING_SNAKE_CASE								=      ids_tensor([self.batch_size, self.seq_length]   ,							vocab_size=2	)
  
  													return (
  													    config,
  													    input_ids,
  													    attention_mask,
  													    encoder_hidden_states,
  													    encoder_attention_mask,
  													)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A   ,							_A   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      20
  													__SCREAMING_SNAKE_CASE								=      model_class_name(__A	)
  
  													__SCREAMING_SNAKE_CASE								=      model.init_cache(input_ids.shape[0]   ,							__A	)
  													__SCREAMING_SNAKE_CASE								=      jnp.ones((input_ids.shape[0], max_decoder_length)   ,							dtype='i4'	)
  
  													__SCREAMING_SNAKE_CASE								=      jnp.broadcast_to(
  													    jnp.arange(input_ids.shape[-1] - 1	)[None, :]   ,							(input_ids.shape[0], input_ids.shape[-1] - 1)	)
  													__SCREAMING_SNAKE_CASE								=      model(
  													    input_ids[:, :-1]   ,							attention_mask=__A   ,							past_key_values=__A   ,							position_ids=__A   ,							)
  
  													__SCREAMING_SNAKE_CASE								=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,							dtype='i4'	)
  													__SCREAMING_SNAKE_CASE								=      model(
  													    input_ids[:, -1:]   ,							attention_mask=__A   ,							past_key_values=outputs_cache.past_key_values   ,							position_ids=__A   ,							)
  
  													__SCREAMING_SNAKE_CASE								=      model(__A	)
  
  													__SCREAMING_SNAKE_CASE								=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
  													self.parent.assertTrue(diff < 1e-3   ,							msg=f"""Max diff is {diff}"""	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A   ,							_A   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      20
  													__SCREAMING_SNAKE_CASE								=      model_class_name(__A	)
  
  													__SCREAMING_SNAKE_CASE								=      jnp.concatenate(
  													    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])	)]   ,							axis=-1   ,							)
  
  													__SCREAMING_SNAKE_CASE								=      model.init_cache(input_ids.shape[0]   ,							__A	)
  													__SCREAMING_SNAKE_CASE								=      jnp.broadcast_to(
  													    jnp.arange(input_ids.shape[-1] - 1	)[None, :]   ,							(input_ids.shape[0], input_ids.shape[-1] - 1)	)
  
  													__SCREAMING_SNAKE_CASE								=      model(
  													    input_ids[:, :-1]   ,							attention_mask=__A   ,							past_key_values=__A   ,							position_ids=__A   ,							)
  													__SCREAMING_SNAKE_CASE								=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,							dtype='i4'	)
  													__SCREAMING_SNAKE_CASE								=      model(
  													    input_ids[:, -1:]   ,							past_key_values=outputs_cache.past_key_values   ,							attention_mask=__A   ,							position_ids=__A   ,							)
  
  													__SCREAMING_SNAKE_CASE								=      model(__A   ,							attention_mask=__A	)
  													__SCREAMING_SNAKE_CASE								=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
  													self.parent.assertTrue(diff < 1e-3   ,							msg=f"""Max diff is {diff}"""	)
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_flax
  class      UpperCAmelCase_						(     _SCREAMING_SNAKE_CASE					,					_SCREAMING_SNAKE_CASE					,					unittest.TestCase ):
  
  						'''simple docstring'''
  
  
  
  
  						UpperCamelCase__    :				List[str]       						=						(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
  						UpperCamelCase__    :				Union[str, Any]       						=						(FlaxXGLMForCausalLM,) if is_flax_available() else ()
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      FlaxXGLMModelTester(self	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													for model_class_name in self.all_model_classes:
  																				__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      self.model_tester.prepare_config_and_inputs()
  																				self.model_tester.check_use_cache_forward(__A   ,							__A   ,							__A   ,							__A	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													for model_class_name in self.all_model_classes:
  																				__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      self.model_tester.prepare_config_and_inputs()
  																				self.model_tester.check_use_cache_forward_with_attn_mask(
  																				    __A   ,							__A   ,							__A   ,							__A	)
  
  
  
  
  
  
  						@slow
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      XGLMTokenizer.from_pretrained('XGLM'   ,							padding_side='left'	)
  													__SCREAMING_SNAKE_CASE								=      tokenizer(['Hello this is a long string', 'Hey']   ,							return_tensors='np'   ,							padding=__A   ,							truncation=__A	)
  
  													__SCREAMING_SNAKE_CASE								=      FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M'	)
  													__SCREAMING_SNAKE_CASE								=      1
  													__SCREAMING_SNAKE_CASE								=      False
  
  													__SCREAMING_SNAKE_CASE								=      jax.jit(model.generate	)
  
  													__SCREAMING_SNAKE_CASE								=      jit_generate(inputs['input_ids']   ,							attention_mask=inputs['attention_mask']	).sequences
  
  													__SCREAMING_SNAKE_CASE								=      tokenizer.batch_decode(__A   ,							skip_special_tokens=__A	)
  
  													__SCREAMING_SNAKE_CASE								=      [
  													    'Hello this is a long string of questions, but I\'m not sure if I\'m',
  													    'Hey, I\'m a newbie to the forum and I\'',
  													]
  
  													self.assertListEqual(__A   ,							__A	)
  
  
  
  
  
  
  						@is_pt_flax_cross_test
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      self.model_tester.prepare_config_and_inputs_for_common()
  
  													for model_class in self.all_model_classes:
  																				with self.subTest(model_class.__name__	):
  																											# prepare inputs
  																											__SCREAMING_SNAKE_CASE								=      self._prepare_for_class(__A   ,							__A	)
  																											__SCREAMING_SNAKE_CASE								=      {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}
  
  																											# load corresponding PyTorch class
  																											__SCREAMING_SNAKE_CASE								=      model_class.__name__[4:]  # Skip the "Flax" at the beginning
  																											__SCREAMING_SNAKE_CASE								=      getattr(__A   ,							__A	)
  
  																											__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      pt_inputs['input_ids'].shape
  																											__SCREAMING_SNAKE_CASE								=      np.random.randint(0   ,							seq_length - 1   ,							size=(batch_size,)	)
  																											for batch_idx, start_index in enumerate(__A	):
  																																		__SCREAMING_SNAKE_CASE								=      0
  																																		__SCREAMING_SNAKE_CASE								=      1
  																																		__SCREAMING_SNAKE_CASE								=      0
  																																		__SCREAMING_SNAKE_CASE								=      1
  																											__SCREAMING_SNAKE_CASE								=      pt_model_class(__A	).eval()
  																											# Flax models don't use the `use_cache` option and cache is not returned as a default.
  																											# So we disable `use_cache` here for PyTorch model.
  																											__SCREAMING_SNAKE_CASE								=      False
  																											__SCREAMING_SNAKE_CASE								=      model_class(__A   ,							dtype=jnp.floataa	)
  																											__SCREAMING_SNAKE_CASE								=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,							__A	)
  																											__SCREAMING_SNAKE_CASE								=      fx_state
  
  																											with torch.no_grad():
  																																		__SCREAMING_SNAKE_CASE								=      pt_model(**__A	).to_tuple()
  
  																											__SCREAMING_SNAKE_CASE								=      fx_model(**__A	).to_tuple()
  																											self.assertEqual(len(__A	)   ,							len(__A	)   ,							'Output lengths differ between Flax and PyTorch'	)
  																											for fx_output, pt_output in zip(__A   ,							__A	):
  																																		self.assert_almost_equals(fx_output[:, -1]   ,							pt_output[:, -1].numpy()   ,							4e-2	)
  
  																											with tempfile.TemporaryDirectory() as tmpdirname:
  																																		pt_model.save_pretrained(__A	)
  																																		__SCREAMING_SNAKE_CASE								=      model_class.from_pretrained(__A   ,							from_pt=__A	)
  
  																											__SCREAMING_SNAKE_CASE								=      fx_model_loaded(**__A	).to_tuple()
  																											self.assertEqual(
  																											    len(__A	)   ,							len(__A	)   ,							'Output lengths differ between Flax and PyTorch'	)
  																											for fx_output_loaded, pt_output in zip(__A   ,							__A	):
  																																		self.assert_almost_equals(fx_output_loaded[:, -1]   ,							pt_output[:, -1].numpy()   ,							4e-2	)
  
  
  
  
  
  
  						@is_pt_flax_cross_test
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      self.model_tester.prepare_config_and_inputs_for_common()
  													for model_class in self.all_model_classes:
  																				with self.subTest(model_class.__name__	):
  																											# prepare inputs
  																											__SCREAMING_SNAKE_CASE								=      self._prepare_for_class(__A   ,							__A	)
  																											__SCREAMING_SNAKE_CASE								=      {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}
  
  																											# load corresponding PyTorch class
  																											__SCREAMING_SNAKE_CASE								=      model_class.__name__[4:]  # Skip the "Flax" at the beginning
  																											__SCREAMING_SNAKE_CASE								=      getattr(__A   ,							__A	)
  
  																											__SCREAMING_SNAKE_CASE								=      pt_model_class(__A	).eval()
  																											__SCREAMING_SNAKE_CASE								=      False
  																											__SCREAMING_SNAKE_CASE								=      model_class(__A   ,							dtype=jnp.floataa	)
  
  																											__SCREAMING_SNAKE_CASE								=      load_flax_weights_in_pytorch_model(__A   ,							fx_model.params	)
  																											__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      pt_inputs['input_ids'].shape
  																											__SCREAMING_SNAKE_CASE								=      np.random.randint(0   ,							seq_length - 1   ,							size=(batch_size,)	)
  																											for batch_idx, start_index in enumerate(__A	):
  																																		__SCREAMING_SNAKE_CASE								=      0
  																																		__SCREAMING_SNAKE_CASE								=      1
  																																		__SCREAMING_SNAKE_CASE								=      0
  																																		__SCREAMING_SNAKE_CASE								=      1
  
  																											# make sure weights are tied in PyTorch
  																											pt_model.tie_weights()
  
  																											with torch.no_grad():
  																																		__SCREAMING_SNAKE_CASE								=      pt_model(**__A	).to_tuple()
  
  																											__SCREAMING_SNAKE_CASE								=      fx_model(**__A	).to_tuple()
  																											self.assertEqual(len(__A	)   ,							len(__A	)   ,							'Output lengths differ between Flax and PyTorch'	)
  																											for fx_output, pt_output in zip(__A   ,							__A	):
  																																		self.assert_almost_equals(fx_output[:, -1]   ,							pt_output[:, -1].numpy()   ,							4e-2	)
  
  																											with tempfile.TemporaryDirectory() as tmpdirname:
  																																		fx_model.save_pretrained(__A	)
  																																		__SCREAMING_SNAKE_CASE								=      pt_model_class.from_pretrained(__A   ,							from_flax=__A	)
  
  																											with torch.no_grad():
  																																		__SCREAMING_SNAKE_CASE								=      pt_model_loaded(**__A	).to_tuple()
  
  																											self.assertEqual(
  																											    len(__A	)   ,							len(__A	)   ,							'Output lengths differ between Flax and PyTorch'	)
  																											for fx_output, pt_output in zip(__A   ,							__A	):
  																																		self.assert_almost_equals(fx_output[:, -1]   ,							pt_output[:, -1].numpy()   ,							4e-2	)
  
  
  
  
  
  
  						@slow
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													for model_class_name in self.all_model_classes:
  																				__SCREAMING_SNAKE_CASE								=      model_class_name.from_pretrained('facebook/xglm-564M'	)
  																				__SCREAMING_SNAKE_CASE								=      model(np.ones((1, 1)	)	)
  																				self.assertIsNotNone(__A	)
  
  
  
  
  
  
  
style_context: 
  
  
  
  import gc
  import random
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
  from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
  from diffusers.utils import floats_tensor, nightly, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  
  enable_full_determinism()
  
  class      UpperCAmelCase_						(     unittest.TestCase ):
  
  						'''simple docstring'''
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													super().tearDown()
  													gc.collect()
  													torch.cuda.empty_cache()
  
  
  
  
  
  
  						@property
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      1
  													__SCREAMING_SNAKE_CASE								=      3
  													__SCREAMING_SNAKE_CASE								=      (32, 32)
  
  													__SCREAMING_SNAKE_CASE								=      floats_tensor((batch_size, num_channels) + sizes   ,							rng=random.Random(0	)	).to(_A	)
  													return image
  
  
  
  
  
  
  						@property
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													torch.manual_seed(0	)
  													__SCREAMING_SNAKE_CASE								=      UNetaDConditionModel(
  													    block_out_channels=(32, 64)   ,							layers_per_block=2   ,							sample_size=32   ,							in_channels=4   ,							out_channels=4   ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,							cross_attention_dim=32   ,							)
  													return model
  
  
  
  
  
  
  						@property
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													torch.manual_seed(0	)
  													__SCREAMING_SNAKE_CASE								=      AutoencoderKL(
  													    block_out_channels=[32, 64]   ,							in_channels=3   ,							out_channels=3   ,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,							latent_channels=4   ,							)
  													return model
  
  
  
  
  
  
  						@property
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													torch.manual_seed(0	)
  													__SCREAMING_SNAKE_CASE								=      CLIPTextConfig(
  													    bos_token_id=0   ,							eos_token_id=2   ,							hidden_size=32   ,							intermediate_size=37   ,							layer_norm_eps=1e-05   ,							num_attention_heads=4   ,							num_hidden_layers=5   ,							pad_token_id=1   ,							vocab_size=1_000   ,							)
  													return CLIPTextModel(_A	)
  
  
  
  
  
  
  						@property
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													def extract(*_A   ,							**_A	):
  
  																				class      UpperCAmelCase_						:
  
  																										'''simple docstring'''
  
  
  
  
  																										def __init__(					self	):
  
  
  
  
  
  
  
  																																	'''simple docstring'''
  
  																																	__SCREAMING_SNAKE_CASE								=      torch.ones([0]	)
  
  
  
  
  
  
  																										def  _A			(					self   ,							_A	):
  
  
  
  
  
  
  
  																																	'''simple docstring'''
  
  																																	self.pixel_values.to(_A	)
  																																	return self
  
  
  
  
  
  
  
  																				return Out()
  
  													return extract
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      'cpu'  # ensure determinism for the device-dependent torch.Generator
  													__SCREAMING_SNAKE_CASE								=      self.dummy_cond_unet
  													__SCREAMING_SNAKE_CASE								=      DDIMScheduler(
  													    beta_start=0.0_0_0_8_5   ,							beta_end=0.0_1_2   ,							beta_schedule='scaled_linear'   ,							clip_sample=_A   ,							set_alpha_to_one=_A   ,							)
  
  													__SCREAMING_SNAKE_CASE								=      self.dummy_vae
  													__SCREAMING_SNAKE_CASE								=      self.dummy_text_encoder
  													__SCREAMING_SNAKE_CASE								=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)
  
  													# make sure here that pndm scheduler skips prk
  													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline(
  													    unet=_A   ,							scheduler=_A   ,							vae=_A   ,							text_encoder=_A   ,							tokenizer=_A   ,							safety_checker=_A   ,							feature_extractor=self.dummy_extractor   ,							)
  													__SCREAMING_SNAKE_CASE								=      sd_pipe.to(_A	)
  													sd_pipe.set_progress_bar_config(disable=_A	)
  
  													__SCREAMING_SNAKE_CASE								=      'A painting of a squirrel eating a burger'
  
  													__SCREAMING_SNAKE_CASE								=      torch.Generator(device=_A	).manual_seed(0	)
  													__SCREAMING_SNAKE_CASE								=      sd_pipe([prompt]   ,							generator=_A   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='np'	)
  													__SCREAMING_SNAKE_CASE								=      output.images
  
  													__SCREAMING_SNAKE_CASE								=      torch.Generator(device=_A	).manual_seed(0	)
  													__SCREAMING_SNAKE_CASE								=      sd_pipe(
  													    [prompt]   ,							generator=_A   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='np'   ,							return_dict=_A   ,							)[0]
  
  													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
  													__SCREAMING_SNAKE_CASE								=      image_from_tuple[0, -3:, -3:, -1]
  
  													assert image.shape == (1, 64, 64, 3)
  													__SCREAMING_SNAKE_CASE								=      np.array([0.5_7_5_3, 0.6_1_1_4, 0.5_0_0_1, 0.5_0_3_4, 0.5_4_7_0, 0.4_7_2_9, 0.4_9_7_1, 0.4_8_6_7, 0.4_8_6_7]	)
  
  													assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
  													assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      'cpu'  # ensure determinism for the device-dependent torch.Generator
  													__SCREAMING_SNAKE_CASE								=      self.dummy_cond_unet
  													__SCREAMING_SNAKE_CASE								=      PNDMScheduler(skip_prk_steps=_A	)
  													__SCREAMING_SNAKE_CASE								=      self.dummy_vae
  													__SCREAMING_SNAKE_CASE								=      self.dummy_text_encoder
  													__SCREAMING_SNAKE_CASE								=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)
  
  													# make sure here that pndm scheduler skips prk
  													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline(
  													    unet=_A   ,							scheduler=_A   ,							vae=_A   ,							text_encoder=_A   ,							tokenizer=_A   ,							safety_checker=_A   ,							feature_extractor=self.dummy_extractor   ,							)
  													__SCREAMING_SNAKE_CASE								=      sd_pipe.to(_A	)
  													sd_pipe.set_progress_bar_config(disable=_A	)
  
  													__SCREAMING_SNAKE_CASE								=      'A painting of a squirrel eating a burger'
  													__SCREAMING_SNAKE_CASE								=      torch.Generator(device=_A	).manual_seed(0	)
  													__SCREAMING_SNAKE_CASE								=      sd_pipe([prompt]   ,							generator=_A   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='np'	)
  
  													__SCREAMING_SNAKE_CASE								=      output.images
  
  													__SCREAMING_SNAKE_CASE								=      torch.Generator(device=_A	).manual_seed(0	)
  													__SCREAMING_SNAKE_CASE								=      sd_pipe(
  													    [prompt]   ,							generator=_A   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='np'   ,							return_dict=_A   ,							)[0]
  
  													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
  													__SCREAMING_SNAKE_CASE								=      image_from_tuple[0, -3:, -3:, -1]
  
  													assert image.shape == (1, 64, 64, 3)
  													__SCREAMING_SNAKE_CASE								=      np.array([0.5_1_2_2, 0.5_7_1_2, 0.4_8_2_5, 0.5_0_5_3, 0.5_6_4_6, 0.4_7_6_9, 0.5_1_7_9, 0.4_8_9_4, 0.4_9_9_4]	)
  
  													assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
  													assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained(
  													    'hf-internal-testing/tiny-stable-diffusion-lms-pipe'   ,							safety_checker=_A	)
  													assert isinstance(_A   ,							_A	)
  													assert isinstance(pipe.scheduler   ,							_A	)
  													assert pipe.safety_checker is None
  
  													__SCREAMING_SNAKE_CASE								=      pipe('example prompt'   ,							num_inference_steps=2	).images[0]
  													assert image is not None
  
  													# check that there's no error when saving a pipeline with one of the models being None
  													with tempfile.TemporaryDirectory() as tmpdirname:
  																				pipe.save_pretrained(_A	)
  																				__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained(_A	)
  
  													# sanity check that the pipeline still works
  													assert pipe.safety_checker is None
  													__SCREAMING_SNAKE_CASE								=      pipe('example prompt'   ,							num_inference_steps=2	).images[0]
  													assert image is not None
  
  
  
  
  
  
  						@unittest.skipIf(torch_device != 'cuda'   ,							'This test requires a GPU'	)
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.dummy_cond_unet
  													__SCREAMING_SNAKE_CASE								=      PNDMScheduler(skip_prk_steps=_A	)
  													__SCREAMING_SNAKE_CASE								=      self.dummy_vae
  													__SCREAMING_SNAKE_CASE								=      self.dummy_text_encoder
  													__SCREAMING_SNAKE_CASE								=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)
  
  													# put models in fp16
  													__SCREAMING_SNAKE_CASE								=      unet.half()
  													__SCREAMING_SNAKE_CASE								=      vae.half()
  													__SCREAMING_SNAKE_CASE								=      bert.half()
  
  													# make sure here that pndm scheduler skips prk
  													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline(
  													    unet=_A   ,							scheduler=_A   ,							vae=_A   ,							text_encoder=_A   ,							tokenizer=_A   ,							safety_checker=_A   ,							feature_extractor=self.dummy_extractor   ,							)
  													__SCREAMING_SNAKE_CASE								=      sd_pipe.to(_A	)
  													sd_pipe.set_progress_bar_config(disable=_A	)
  
  													__SCREAMING_SNAKE_CASE								=      'A painting of a squirrel eating a burger'
  													__SCREAMING_SNAKE_CASE								=      sd_pipe([prompt]   ,							num_inference_steps=2   ,							output_type='np'	).images
  
  													assert image.shape == (1, 64, 64, 3)
  
  
  
  
  
  
  
  @nightly
  @require_torch_gpu
  class      UpperCAmelCase_						(     unittest.TestCase ):
  
  						'''simple docstring'''
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													super().tearDown()
  													gc.collect()
  													torch.cuda.empty_cache()
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      'cuda'
  													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'	)
  													__SCREAMING_SNAKE_CASE								=      pipe.to(_A	)
  													pipe.set_progress_bar_config(disable=_A	)
  
  													__SCREAMING_SNAKE_CASE								=      'a photo of a cat'
  													__SCREAMING_SNAKE_CASE								=      {
  													    'editing_prompt': ['sunglasses'],
  													    'reverse_editing_direction': [False],
  													    'edit_warmup_steps': 10,
  													    'edit_guidance_scale': 6,
  													    'edit_threshold': 0.9_5,
  													    'edit_momentum_scale': 0.5,
  													    'edit_mom_beta': 0.6,
  													}
  
  													__SCREAMING_SNAKE_CASE								=      3
  													__SCREAMING_SNAKE_CASE								=      7
  
  													# no sega enabled
  													__SCREAMING_SNAKE_CASE								=      torch.Generator(_A	)
  													generator.manual_seed(_A	)
  													__SCREAMING_SNAKE_CASE								=      pipe(
  													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							)
  
  													__SCREAMING_SNAKE_CASE								=      output.images
  													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
  													__SCREAMING_SNAKE_CASE								=      [
  													    0.3_4_6_7_3_1_1_3,
  													    0.3_8_4_9_2_7_3_3,
  													    0.3_7_5_9_7_3_5_2,
  													    0.3_4_0_8_6_3_3_5,
  													    0.3_5_6_5_0_7_4_8,
  													    0.3_5_5_7_9_2_0_5,
  													    0.3_3_8_4_7_6_3,
  													    0.3_4_3_4_0_2_3_6,
  													    0.3_5_7_3_2_7_1,
  													]
  
  													assert image.shape == (1, 512, 512, 3)
  
  													assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
  
  													# with sega enabled
  													# generator = torch.manual_seed(seed)
  													generator.manual_seed(_A	)
  													__SCREAMING_SNAKE_CASE								=      pipe(
  													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							**_A   ,							)
  
  													__SCREAMING_SNAKE_CASE								=      output.images
  													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
  													__SCREAMING_SNAKE_CASE								=      [
  													    0.4_1_8_8_7_8_2_6,
  													    0.3_7_7_2_8_7_6_6,
  													    0.3_0_1_3_8_2_7_2,
  													    0.4_1_4_1_6_3_3_5,
  													    0.4_1_6_6_4_9_8_5,
  													    0.3_6_2_8_3_3_9_2,
  													    0.3_6_1_9_1_2_4_6,
  													    0.4_3_3_6_4_4_6_5,
  													    0.4_3_0_0_1_7_3_2,
  													]
  
  													assert image.shape == (1, 512, 512, 3)
  
  													assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      'cuda'
  													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'	)
  													__SCREAMING_SNAKE_CASE								=      pipe.to(_A	)
  													pipe.set_progress_bar_config(disable=_A	)
  
  													__SCREAMING_SNAKE_CASE								=      'an image of a crowded boulevard, realistic, 4k'
  													__SCREAMING_SNAKE_CASE								=      {
  													    'editing_prompt': 'crowd, crowded, people',
  													    'reverse_editing_direction': True,
  													    'edit_warmup_steps': 10,
  													    'edit_guidance_scale': 8.3,
  													    'edit_threshold': 0.9,
  													    'edit_momentum_scale': 0.5,
  													    'edit_mom_beta': 0.6,
  													}
  
  													__SCREAMING_SNAKE_CASE								=      9
  													__SCREAMING_SNAKE_CASE								=      7
  
  													# no sega enabled
  													__SCREAMING_SNAKE_CASE								=      torch.Generator(_A	)
  													generator.manual_seed(_A	)
  													__SCREAMING_SNAKE_CASE								=      pipe(
  													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							)
  
  													__SCREAMING_SNAKE_CASE								=      output.images
  													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
  													__SCREAMING_SNAKE_CASE								=      [
  													    0.4_3_4_9_7_9_9_8,
  													    0.9_1_8_1_4_0_6_5,
  													    0.7_5_4_0_7_3_9,
  													    0.5_5_5_8_0_2_0_5,
  													    0.8_4_6_7_2_6_5,
  													    0.5_3_8_9_6_9_1,
  													    0.6_2_5_7_4_5_0_6,
  													    0.5_8_8_9_7_7_6_3,
  													    0.5_0_9_2_6_7_5_7,
  													]
  
  													assert image.shape == (1, 512, 512, 3)
  
  													assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
  
  													# with sega enabled
  													# generator = torch.manual_seed(seed)
  													generator.manual_seed(_A	)
  													__SCREAMING_SNAKE_CASE								=      pipe(
  													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							**_A   ,							)
  
  													__SCREAMING_SNAKE_CASE								=      output.images
  													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
  													__SCREAMING_SNAKE_CASE								=      [
  													    0.3_0_8_9_7_1_9,
  													    0.3_0_5_0_0_1_4_4,
  													    0.2_9_0_1_6_0_4_2,
  													    0.3_0_6_3_0_9_6_4,
  													    0.3_2_5_6_8_7,
  													    0.2_9_4_1_9_2_2_5,
  													    0.2_9_0_8_0_9_1,
  													    0.2_8_7_2_3_5_9_8,
  													    0.2_7_6_9_6_2_9_4,
  													]
  
  													assert image.shape == (1, 512, 512, 3)
  
  													assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      'cuda'
  													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'	)
  													__SCREAMING_SNAKE_CASE								=      pipe.to(_A	)
  													pipe.set_progress_bar_config(disable=_A	)
  
  													__SCREAMING_SNAKE_CASE								=      'a castle next to a river'
  													__SCREAMING_SNAKE_CASE								=      {
  													    'editing_prompt': ['boat on a river, boat', 'monet, impression, sunrise'],
  													    'reverse_editing_direction': False,
  													    'edit_warmup_steps': [15, 18],
  													    'edit_guidance_scale': 6,
  													    'edit_threshold': [0.9, 0.8],
  													    'edit_momentum_scale': 0.5,
  													    'edit_mom_beta': 0.6,
  													}
  
  													__SCREAMING_SNAKE_CASE								=      48
  													__SCREAMING_SNAKE_CASE								=      7
  
  													# no sega enabled
  													__SCREAMING_SNAKE_CASE								=      torch.Generator(_A	)
  													generator.manual_seed(_A	)
  													__SCREAMING_SNAKE_CASE								=      pipe(
  													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							)
  
  													__SCREAMING_SNAKE_CASE								=      output.images
  													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
  													__SCREAMING_SNAKE_CASE								=      [
  													    0.7_5_1_6_3_5_5_5,
  													    0.7_6_0_3_7_1_4_5,
  													    0.6_1_7_8_5,
  													    0.9_1_8_9_6_7_3,
  													    0.8_6_2_7_7_0_1,
  													    0.8_5_1_8_9_6_9_4,
  													    0.8_5_1_2_8_1_3,
  													    0.8_7_0_1_2_0_7_6,
  													    0.8_3_1_2_8_5_7,
  													]
  
  													assert image.shape == (1, 512, 512, 3)
  
  													assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
  
  													# with sega enabled
  													# generator = torch.manual_seed(seed)
  													generator.manual_seed(_A	)
  													__SCREAMING_SNAKE_CASE								=      pipe(
  													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							**_A   ,							)
  
  													__SCREAMING_SNAKE_CASE								=      output.images
  													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
  													__SCREAMING_SNAKE_CASE								=      [
  													    0.7_3_5_5_3_3_6_5,
  													    0.7_5_3_7_2_7_1,
  													    0.7_4_3_4_1_9_0_5,
  													    0.6_6_4_8_0_3_5_6,
  													    0.6_4_7_2_9_2_5,
  													    0.6_3_0_3_9_4_1_6,
  													    0.6_4_8_1_2_9_0_5,
  													    0.6_7_4_9_7_1_7,
  													    0.6_5_1_7_1_0_2,
  													]
  
  													assert image.shape == (1, 512, 512, 3)
  
  													assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      'cuda'
  													__SCREAMING_SNAKE_CASE								=      StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'   ,							torch_dtype=torch.floataa	)
  													__SCREAMING_SNAKE_CASE								=      pipe.to(_A	)
  													pipe.set_progress_bar_config(disable=_A	)
  
  													__SCREAMING_SNAKE_CASE								=      'a photo of a cat'
  													__SCREAMING_SNAKE_CASE								=      {
  													    'editing_prompt': ['sunglasses'],
  													    'reverse_editing_direction': [False],
  													    'edit_warmup_steps': 10,
  													    'edit_guidance_scale': 6,
  													    'edit_threshold': 0.9_5,
  													    'edit_momentum_scale': 0.5,
  													    'edit_mom_beta': 0.6,
  													}
  
  													__SCREAMING_SNAKE_CASE								=      3
  													__SCREAMING_SNAKE_CASE								=      7
  
  													# no sega enabled
  													__SCREAMING_SNAKE_CASE								=      torch.Generator(_A	)
  													generator.manual_seed(_A	)
  													__SCREAMING_SNAKE_CASE								=      pipe(
  													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							)
  
  													__SCREAMING_SNAKE_CASE								=      output.images
  													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
  													__SCREAMING_SNAKE_CASE								=      [
  													    0.3_4_8_8_7_6_9_5,
  													    0.3_8_7_6_9_5_3,
  													    0.3_7_5,
  													    0.3_4_4_2_3_8_2_8,
  													    0.3_5_8_1_5_4_3,
  													    0.3_5_7_1_7_7_7_3,
  													    0.3_3_8_3_7_8_9,
  													    0.3_4_5_7_0_3_1_2,
  													    0.3_5_9_3_7_5,
  													]
  
  													assert image.shape == (1, 512, 512, 3)
  
  													assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
  
  													# with sega enabled
  													# generator = torch.manual_seed(seed)
  													generator.manual_seed(_A	)
  													__SCREAMING_SNAKE_CASE								=      pipe(
  													    [prompt]   ,							generator=_A   ,							guidance_scale=_A   ,							num_inference_steps=50   ,							output_type='np'   ,							width=512   ,							height=512   ,							**_A   ,							)
  
  													__SCREAMING_SNAKE_CASE								=      output.images
  													__SCREAMING_SNAKE_CASE								=      image[0, -3:, -3:, -1]
  													__SCREAMING_SNAKE_CASE								=      [
  													    0.4_2_2_8_5_1_5_6,
  													    0.3_6_9_1_4_0_6_2,
  													    0.2_9_0_7_7_1_4_8,
  													    0.4_2_0_4_1_0_1_6,
  													    0.4_1_9_1_8_9_4_5,
  													    0.3_5_4_9_8_0_4_7,
  													    0.3_6_1_8_1_6_4,
  													    0.4_4_2_3_8_2_8,
  													    0.4_3_1_1_5_2_3_4,
  													]
  
  													assert image.shape == (1, 512, 512, 3)
  
  													assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
  
  
  
  
  
  
  