
"""simple docstring"""
from typing import List

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


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''efficientformer'''




							def __init__( self    ,				snake_case_ = [3, 2, 6, 4]    ,				snake_case_ = [48, 96, 224, 448]    ,				snake_case_ = [True, True, True, True]    ,				snake_case_ = 448    ,				snake_case_ = 32    ,				snake_case_ = 4    ,				snake_case_ = 7    ,				snake_case_ = 5    ,				snake_case_ = 8    ,				snake_case_ = 4    ,				snake_case_ = 0.0    ,				snake_case_ = 16    ,				snake_case_ = 3    ,				snake_case_ = 3    ,				snake_case_ = 3    ,				snake_case_ = 2    ,				snake_case_ = 1    ,				snake_case_ = 0.0    ,				snake_case_ = 1    ,				snake_case_ = True    ,				snake_case_ = True    ,				snake_case_ = 1e-5    ,				snake_case_ = "gelu"    ,				snake_case_ = 0.02    ,				snake_case_ = 1e-1_2    ,				snake_case_ = 224    ,				snake_case_ = 1e-0_5    ,				**snake_case_    ,				)       ->  None:
													super().__init__(**snake_case_				)

													__lowerCAmelCase     =  hidden_act
													__lowerCAmelCase     =  hidden_dropout_prob
													__lowerCAmelCase     =  hidden_sizes
													__lowerCAmelCase     =  num_hidden_layers
													__lowerCAmelCase     =  num_attention_heads
													__lowerCAmelCase     =  initializer_range
													__lowerCAmelCase     =  layer_norm_eps
													__lowerCAmelCase     =  patch_size
													__lowerCAmelCase     =  num_channels
													__lowerCAmelCase     =  depths
													__lowerCAmelCase     =  mlp_expansion_ratio
													__lowerCAmelCase     =  downsamples
													__lowerCAmelCase     =  dim
													__lowerCAmelCase     =  key_dim
													__lowerCAmelCase     =  attention_ratio
													__lowerCAmelCase     =  resolution
													__lowerCAmelCase     =  pool_size
													__lowerCAmelCase     =  downsample_patch_size
													__lowerCAmelCase     =  downsample_stride
													__lowerCAmelCase     =  downsample_pad
													__lowerCAmelCase     =  drop_path_rate
													__lowerCAmelCase     =  num_metaad_blocks
													__lowerCAmelCase     =  distillation
													__lowerCAmelCase     =  use_layer_scale
													__lowerCAmelCase     =  layer_scale_init_value
													__lowerCAmelCase     =  image_size
													__lowerCAmelCase     =  batch_norm_eps


"""simple docstring"""
import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class       lowerCAmelCase_		(				A__				,				A__				,				A__				,				unittest.TestCase ):



							'''simple docstring'''





							_snake_case						      =		StableUnCLIPPipeline
							_snake_case						      =		TEXT_TO_IMAGE_PARAMS
							_snake_case						      =		TEXT_TO_IMAGE_BATCH_PARAMS
							_snake_case						      =		TEXT_TO_IMAGE_IMAGE_PARAMS
							_snake_case						      =		TEXT_TO_IMAGE_IMAGE_PARAMS

							# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
							_snake_case						      =		False




							def    A__						( self				)       ->  Optional[Any]:
													__lowerCAmelCase     =  32
													__lowerCAmelCase     =  embedder_hidden_size

													# prior components

													torch.manual_seed(0				)
													__lowerCAmelCase     =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  CLIPTextModelWithProjection(
													    CLIPTextConfig(
													        bos_token_id=0    ,				eos_token_id=2    ,				hidden_size=snake_case_    ,				projection_dim=snake_case_    ,				intermediate_size=37    ,				layer_norm_eps=1e-0_5    ,				num_attention_heads=4    ,				num_hidden_layers=5    ,				pad_token_id=1    ,				vocab_size=1_000    ,				)				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  PriorTransformer(
													    num_attention_heads=2    ,				attention_head_dim=12    ,				embedding_dim=snake_case_    ,				num_layers=1    ,				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  DDPMScheduler(
													    variance_type="""fixed_small_log"""    ,				prediction_type="""sample"""    ,				num_train_timesteps=1_000    ,				clip_sample=snake_case_    ,				clip_sample_range=5.0    ,				beta_schedule="""squaredcos_cap_v2"""    ,				)

													# regular denoising components

													torch.manual_seed(0				)
													__lowerCAmelCase     =  StableUnCLIPImageNormalizer(embedding_dim=snake_case_				)
													__lowerCAmelCase     =  DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  CLIPTextModel(
													    CLIPTextConfig(
													        bos_token_id=0    ,				eos_token_id=2    ,				hidden_size=snake_case_    ,				projection_dim=32    ,				intermediate_size=37    ,				layer_norm_eps=1e-0_5    ,				num_attention_heads=4    ,				num_hidden_layers=5    ,				pad_token_id=1    ,				vocab_size=1_000    ,				)				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  UNetaDConditionModel(
													    sample_size=32    ,				in_channels=4    ,				out_channels=4    ,				down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")    ,				up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")    ,				block_out_channels=(32, 64)    ,				attention_head_dim=(2, 4)    ,				class_embed_type="""projection"""    ,				projection_class_embeddings_input_dim=embedder_projection_dim * 2    ,				cross_attention_dim=snake_case_    ,				layers_per_block=1    ,				upcast_attention=snake_case_    ,				use_linear_projection=snake_case_    ,				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  DDIMScheduler(
													    beta_schedule="""scaled_linear"""    ,				beta_start=0.00_085    ,				beta_end=0.012    ,				prediction_type="""v_prediction"""    ,				set_alpha_to_one=snake_case_    ,				steps_offset=1    ,				)

													torch.manual_seed(0				)
													__lowerCAmelCase     =  AutoencoderKL()

													__lowerCAmelCase     =  {
													    # prior components
													    """prior_tokenizer""": prior_tokenizer,
													    """prior_text_encoder""": prior_text_encoder,
													    """prior""": prior,
													    """prior_scheduler""": prior_scheduler,
													    # image noising components
													    """image_normalizer""": image_normalizer,
													    """image_noising_scheduler""": image_noising_scheduler,
													    # regular denoising components
													    """tokenizer""": tokenizer,
													    """text_encoder""": text_encoder,
													    """unet""": unet,
													    """scheduler""": scheduler,
													    """vae""": vae,
													}

													return components




							def    A__						( self    ,				snake_case_    ,				snake_case_=0				)       ->  Optional[Any]:
													if str(snake_case_				).startswith("""mps"""				):
																			__lowerCAmelCase     =  torch.manual_seed(snake_case_				)
													else:
																			__lowerCAmelCase     =  torch.Generator(device=snake_case_				).manual_seed(snake_case_				)
													__lowerCAmelCase     =  {
													    """prompt""": """A painting of a squirrel eating a burger""",
													    """generator""": generator,
													    """num_inference_steps""": 2,
													    """prior_num_inference_steps""": 2,
													    """output_type""": """numpy""",
													}
													return inputs




							def    A__						( self				)       ->  Tuple:
													__lowerCAmelCase     =  torch_device == """cpu"""

													self._test_attention_slicing_forward_pass(test_max_difference=snake_case_				)




							def    A__						( self				)       ->  Tuple:
													__lowerCAmelCase     =  torch_device in ["""cpu""", """mps"""]

													self._test_inference_batch_single_identical(test_max_difference=snake_case_				)
@slow
@require_torch_gpu
class       lowerCAmelCase_		(				unittest.TestCase ):



							'''simple docstring'''





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




							def    A__						( self				)       ->  Tuple:
													__lowerCAmelCase     =  load_numpy(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""				)

													__lowerCAmelCase     =  StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""    ,				torch_dtype=torch.floataa				)
													pipe.to(snake_case_				)
													pipe.set_progress_bar_config(disable=snake_case_				)
													# stable unclip will oom when integration tests are run on a V100,
													# so turn on memory savings
													pipe.enable_attention_slicing()
													pipe.enable_sequential_cpu_offload()

													__lowerCAmelCase     =  torch.Generator(device="""cpu"""				).manual_seed(0				)
													__lowerCAmelCase     =  pipe("""anime turle"""    ,				generator=snake_case_    ,				output_type="""np"""				)

													__lowerCAmelCase     =  output.images[0]

													assert image.shape == (768, 768, 3)

													assert_mean_pixel_difference(snake_case_    ,				snake_case_				)




							def    A__						( self				)       ->  Tuple:
													torch.cuda.empty_cache()
													torch.cuda.reset_max_memory_allocated()
													torch.cuda.reset_peak_memory_stats()

													__lowerCAmelCase     =  StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""    ,				torch_dtype=torch.floataa				)
													__lowerCAmelCase     =  pipe.to(snake_case_				)
													pipe.set_progress_bar_config(disable=snake_case_				)
													pipe.enable_attention_slicing()
													pipe.enable_sequential_cpu_offload()

													__lowerCAmelCase     =  pipe(
													    """anime turtle"""    ,				prior_num_inference_steps=2    ,				num_inference_steps=2    ,				output_type="""np"""    ,				)

													__lowerCAmelCase     =  torch.cuda.max_memory_allocated()
													# make sure that less than 7 GB is allocated
													assert mem_bytes < 7 * 10**9
