
'''simple docstring'''



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.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu




class     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



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




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					1
												_lowerCAmelCase    		=					3
												_lowerCAmelCase    		=					(32, 32)

												_lowerCAmelCase    		=					floats_tensor((batch_size, num_channels) + sizes       ,  rng=random.Random(0 ) ).to(_lowercase )
												return image




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												torch.manual_seed(0 )
												_lowerCAmelCase    		=					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    _lowercase      (   self ):

												"""simple docstring"""



												torch.manual_seed(0 )
												_lowerCAmelCase    		=					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    _lowercase      (   self ):

												"""simple docstring"""



												torch.manual_seed(0 )
												_lowerCAmelCase    		=					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(_lowercase )




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												def extract(*_lowercase       ,  **_lowercase ):




																		class     UpperCAmelCase_	:






																								'''simple docstring'''






																								def __init__(   self ):

																														"""simple docstring"""



																														_lowerCAmelCase    		=					torch.ones([0] )




																								def    _lowercase      (   self       ,  _lowercase ):

																														"""simple docstring"""



																														self.pixel_values.to(_lowercase )
																														return self






																		return Out()

												return extract




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
												_lowerCAmelCase    		=					self.dummy_cond_unet
												_lowerCAmelCase    		=					DDIMScheduler(
												    beta_start=0.0_0085       ,  beta_end=0.012       ,  beta_schedule="""scaled_linear"""       ,  clip_sample=_lowercase       ,  set_alpha_to_one=_lowercase       ,  )

												_lowerCAmelCase    		=					self.dummy_vae
												_lowerCAmelCase    		=					self.dummy_text_encoder
												_lowerCAmelCase    		=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

												# make sure here that pndm scheduler skips prk
												_lowerCAmelCase    		=					StableDiffusionPipeline(
												    unet=_lowercase       ,  scheduler=_lowercase       ,  vae=_lowercase       ,  text_encoder=_lowercase       ,  tokenizer=_lowercase       ,  safety_checker=_lowercase       ,  feature_extractor=self.dummy_extractor       ,  )
												_lowerCAmelCase    		=					sd_pipe.to(_lowercase )
												sd_pipe.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					"""A painting of a squirrel eating a burger"""

												_lowerCAmelCase    		=					torch.Generator(device=_lowercase ).manual_seed(0 )
												_lowerCAmelCase    		=					sd_pipe([prompt]       ,  generator=_lowercase       ,  guidance_scale=6.0       ,  num_inference_steps=2       ,  output_type="""np""" )
												_lowerCAmelCase    		=					output.images

												_lowerCAmelCase    		=					torch.Generator(device=_lowercase ).manual_seed(0 )
												_lowerCAmelCase    		=					sd_pipe(
												    [prompt]       ,  generator=_lowercase       ,  guidance_scale=6.0       ,  num_inference_steps=2       ,  output_type="""np"""       ,  return_dict=_lowercase       ,  )[0]

												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)
												_lowerCAmelCase    		=					np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864] )

												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    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
												_lowerCAmelCase    		=					self.dummy_cond_unet
												_lowerCAmelCase    		=					PNDMScheduler(skip_prk_steps=_lowercase )
												_lowerCAmelCase    		=					self.dummy_vae
												_lowerCAmelCase    		=					self.dummy_text_encoder
												_lowerCAmelCase    		=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

												# make sure here that pndm scheduler skips prk
												_lowerCAmelCase    		=					StableDiffusionPipeline(
												    unet=_lowercase       ,  scheduler=_lowercase       ,  vae=_lowercase       ,  text_encoder=_lowercase       ,  tokenizer=_lowercase       ,  safety_checker=_lowercase       ,  feature_extractor=self.dummy_extractor       ,  )
												_lowerCAmelCase    		=					sd_pipe.to(_lowercase )
												sd_pipe.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					"""A painting of a squirrel eating a burger"""
												_lowerCAmelCase    		=					torch.Generator(device=_lowercase ).manual_seed(0 )
												_lowerCAmelCase    		=					sd_pipe([prompt]       ,  generator=_lowercase       ,  guidance_scale=6.0       ,  num_inference_steps=2       ,  output_type="""np""" )

												_lowerCAmelCase    		=					output.images

												_lowerCAmelCase    		=					torch.Generator(device=_lowercase ).manual_seed(0 )
												_lowerCAmelCase    		=					sd_pipe(
												    [prompt]       ,  generator=_lowercase       ,  guidance_scale=6.0       ,  num_inference_steps=2       ,  output_type="""np"""       ,  return_dict=_lowercase       ,  )[0]

												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)
												_lowerCAmelCase    		=					np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993] )

												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    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					StableDiffusionPipeline.from_pretrained(
												    """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""       ,  safety_checker=_lowercase )
												assert isinstance(_lowercase       ,  _lowercase )
												assert isinstance(pipe.scheduler       ,  _lowercase )
												assert pipe.safety_checker is None

												_lowerCAmelCase    		=					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(_lowercase )
																		_lowerCAmelCase    		=					StableDiffusionPipeline.from_pretrained(_lowercase )

												# sanity check that the pipeline still works
												assert pipe.safety_checker is None
												_lowerCAmelCase    		=					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    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.dummy_cond_unet
												_lowerCAmelCase    		=					PNDMScheduler(skip_prk_steps=_lowercase )
												_lowerCAmelCase    		=					self.dummy_vae
												_lowerCAmelCase    		=					self.dummy_text_encoder
												_lowerCAmelCase    		=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

												# put models in fp16
												_lowerCAmelCase    		=					unet.half()
												_lowerCAmelCase    		=					vae.half()
												_lowerCAmelCase    		=					bert.half()

												# make sure here that pndm scheduler skips prk
												_lowerCAmelCase    		=					StableDiffusionPipeline(
												    unet=_lowercase       ,  scheduler=_lowercase       ,  vae=_lowercase       ,  text_encoder=_lowercase       ,  tokenizer=_lowercase       ,  safety_checker=_lowercase       ,  feature_extractor=self.dummy_extractor       ,  )
												_lowerCAmelCase    		=					sd_pipe.to(_lowercase )
												sd_pipe.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					"""A painting of a squirrel eating a burger"""
												_lowerCAmelCase    		=					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    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""       ,  safety_checker=_lowercase )
												_lowerCAmelCase    		=					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
												_lowerCAmelCase    		=					sd_pipe.to(_lowercase )
												sd_pipe.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					(
												    """portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
												    """ coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
												    """ anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
												    """ children from bahnhof zoo, detailed """
												)
												_lowerCAmelCase    		=					4_003_660_346
												_lowerCAmelCase    		=					7

												# without safety guidance (sld_guidance_scale = 0)
												_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												_lowerCAmelCase    		=					sd_pipe(
												    [prompt]       ,  generator=_lowercase       ,  guidance_scale=_lowercase       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=0       ,  )

												_lowerCAmelCase    		=					output.images
												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					[0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

												assert image.shape == (1, 512, 512, 3)

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2

												# without safety guidance (strong configuration)
												_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												_lowerCAmelCase    		=					sd_pipe(
												    [prompt]       ,  generator=_lowercase       ,  guidance_scale=_lowercase       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=2_000       ,  sld_warmup_steps=7       ,  sld_threshold=0.025       ,  sld_momentum_scale=0.5       ,  sld_mom_beta=0.7       ,  )

												_lowerCAmelCase    		=					output.images
												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					[0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

												assert image.shape == (1, 512, 512, 3)

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""       ,  safety_checker=_lowercase )
												_lowerCAmelCase    		=					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
												_lowerCAmelCase    		=					sd_pipe.to(_lowercase )
												sd_pipe.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					"""padme amidala taking a bath artwork, safe for work, no nudity"""
												_lowerCAmelCase    		=					2_734_971_755
												_lowerCAmelCase    		=					7

												_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												_lowerCAmelCase    		=					sd_pipe(
												    [prompt]       ,  generator=_lowercase       ,  guidance_scale=_lowercase       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=0       ,  )

												_lowerCAmelCase    		=					output.images
												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					[0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

												assert image.shape == (1, 512, 512, 3)

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2

												_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												_lowerCAmelCase    		=					sd_pipe(
												    [prompt]       ,  generator=_lowercase       ,  guidance_scale=_lowercase       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=2_000       ,  sld_warmup_steps=7       ,  sld_threshold=0.025       ,  sld_momentum_scale=0.5       ,  sld_mom_beta=0.7       ,  )

												_lowerCAmelCase    		=					output.images
												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					[0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

												assert image.shape == (1, 512, 512, 3)

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" )
												_lowerCAmelCase    		=					sd_pipe.to(_lowercase )
												sd_pipe.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					(
												    """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
												    """ leyendecker"""
												)
												_lowerCAmelCase    		=					1_044_355_234
												_lowerCAmelCase    		=					12

												_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												_lowerCAmelCase    		=					sd_pipe(
												    [prompt]       ,  generator=_lowercase       ,  guidance_scale=_lowercase       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=0       ,  )

												_lowerCAmelCase    		=					output.images
												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )

												assert image.shape == (1, 512, 512, 3)

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-7

												_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												_lowerCAmelCase    		=					sd_pipe(
												    [prompt]       ,  generator=_lowercase       ,  guidance_scale=_lowercase       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=2_000       ,  sld_warmup_steps=7       ,  sld_threshold=0.025       ,  sld_momentum_scale=0.5       ,  sld_mom_beta=0.7       ,  )

												_lowerCAmelCase    		=					output.images
												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561] )
												assert image.shape == (1, 512, 512, 3)

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2


'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase          =						logging.get_logger(__name__)

_lowercase          =						{
    """edbeeching/decision-transformer-gym-hopper-medium""": (
        """https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: List[str]										=  '''decision_transformer'''
						_lowercase						: Optional[Any]										=  ['''past_key_values''']
						_lowercase						: str										=  {
						    '''max_position_embeddings''': '''n_positions''',
						    '''num_attention_heads''': '''n_head''',
						    '''num_hidden_layers''': '''n_layer''',
						}




						def __init__(   self       ,  _lowercase=17       ,  _lowercase=4       ,  _lowercase=128       ,  _lowercase=4_096       ,  _lowercase=True       ,  _lowercase=1       ,  _lowercase=1_024       ,  _lowercase=3       ,  _lowercase=1       ,  _lowercase=None       ,  _lowercase="relu"       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=1e-5       ,  _lowercase=0.02       ,  _lowercase=True       ,  _lowercase=True       ,  _lowercase=50_256       ,  _lowercase=50_256       ,  _lowercase=False       ,  _lowercase=False       ,  **_lowercase       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					state_dim
												_lowerCAmelCase    		=					act_dim
												_lowerCAmelCase    		=					hidden_size
												_lowerCAmelCase    		=					max_ep_len
												_lowerCAmelCase    		=					action_tanh
												_lowerCAmelCase    		=					vocab_size
												_lowerCAmelCase    		=					n_positions
												_lowerCAmelCase    		=					n_layer
												_lowerCAmelCase    		=					n_head
												_lowerCAmelCase    		=					n_inner
												_lowerCAmelCase    		=					activation_function
												_lowerCAmelCase    		=					resid_pdrop
												_lowerCAmelCase    		=					embd_pdrop
												_lowerCAmelCase    		=					attn_pdrop
												_lowerCAmelCase    		=					layer_norm_epsilon
												_lowerCAmelCase    		=					initializer_range
												_lowerCAmelCase    		=					scale_attn_weights
												_lowerCAmelCase    		=					use_cache
												_lowerCAmelCase    		=					scale_attn_by_inverse_layer_idx
												_lowerCAmelCase    		=					reorder_and_upcast_attn

												_lowerCAmelCase    		=					bos_token_id
												_lowerCAmelCase    		=					eos_token_id

												super().__init__(bos_token_id=_lowercase       ,  eos_token_id=_lowercase       ,  **_lowercase )
