'''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 					__magic_name__		(					__lowerCAmelCase,  __lowerCAmelCase,  __lowerCAmelCase,  unittest.TestCase):
			UpperCamelCase__       = StableUnCLIPPipeline
			UpperCamelCase__       = TEXT_TO_IMAGE_PARAMS
			UpperCamelCase__       = TEXT_TO_IMAGE_BATCH_PARAMS
			UpperCamelCase__       = TEXT_TO_IMAGE_IMAGE_PARAMS
			UpperCamelCase__       = TEXT_TO_IMAGE_IMAGE_PARAMS

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    Any       		= 32
										lowercase_      :    int       		= embedder_hidden_size

										# prior components

										torch.manual_seed(0					)
										lowercase_      :    Optional[int]       		= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

										torch.manual_seed(0					)
										lowercase_      :    Any       		= CLIPTextModelWithProjection(
										    CLIPTextConfig(
										        bos_token_id=0       ,    eos_token_id=2       ,    hidden_size=lowerCAmelCase_       ,    projection_dim=lowerCAmelCase_       ,    intermediate_size=37       ,    layer_norm_eps=1E-05       ,    num_attention_heads=4       ,    num_hidden_layers=5       ,    pad_token_id=1       ,    vocab_size=1000       ,    )					)

										torch.manual_seed(0					)
										lowercase_      :    Any       		= PriorTransformer(
										    num_attention_heads=2       ,    attention_head_dim=12       ,    embedding_dim=lowerCAmelCase_       ,    num_layers=1       ,    )

										torch.manual_seed(0					)
										lowercase_      :    List[Any]       		= DDPMScheduler(
										    variance_type="""fixed_small_log"""       ,    prediction_type="""sample"""       ,    num_train_timesteps=1000       ,    clip_sample=lowerCAmelCase_       ,    clip_sample_range=5.0       ,    beta_schedule="""squaredcos_cap_v2"""       ,    )

										# regular denoising components

										torch.manual_seed(0					)
										lowercase_      :    Optional[int]       		= StableUnCLIPImageNormalizer(embedding_dim=lowerCAmelCase_					)
										lowercase_      :    List[str]       		= DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""					)

										torch.manual_seed(0					)
										lowercase_      :    List[str]       		= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

										torch.manual_seed(0					)
										lowercase_      :    List[str]       		= CLIPTextModel(
										    CLIPTextConfig(
										        bos_token_id=0       ,    eos_token_id=2       ,    hidden_size=lowerCAmelCase_       ,    projection_dim=32       ,    intermediate_size=37       ,    layer_norm_eps=1E-05       ,    num_attention_heads=4       ,    num_hidden_layers=5       ,    pad_token_id=1       ,    vocab_size=1000       ,    )					)

										torch.manual_seed(0					)
										lowercase_      :    List[Any]       		= 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=lowerCAmelCase_       ,    layers_per_block=1       ,    upcast_attention=lowerCAmelCase_       ,    use_linear_projection=lowerCAmelCase_       ,    )

										torch.manual_seed(0					)
										lowercase_      :    Dict       		= DDIMScheduler(
										    beta_schedule="""scaled_linear"""       ,    beta_start=0.0_00_85       ,    beta_end=0.0_12       ,    prediction_type="""v_prediction"""       ,    set_alpha_to_one=lowerCAmelCase_       ,    steps_offset=1       ,    )

										torch.manual_seed(0					)
										lowercase_      :    str       		= AutoencoderKL()

										lowercase_      :    Any       		= {
										    # 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 			SCREAMING_SNAKE_CASE_  (			self       :						int       ,    lowercase_       :						Optional[Any]       ,    lowercase_       :						List[str]=0					):
										if str(lowerCAmelCase_					).startswith("""mps"""					):
																	lowercase_      :    Dict       		= torch.manual_seed(lowerCAmelCase_					)
										else:
																	lowercase_      :    Union[str, Any]       		= torch.Generator(device=lowerCAmelCase_					).manual_seed(lowerCAmelCase_					)
										lowercase_      :    Union[str, Any]       		= {
										    """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 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										lowercase_      :    Optional[Any]       		= torch_device == """cpu"""

										self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    Tuple       		= torch_device in ["""cpu""", """mps"""]

										self._test_inference_batch_single_identical(test_max_difference=lowerCAmelCase_					)





@slow
@require_torch_gpu
class 					__magic_name__		(					unittest.TestCase):







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										lowercase_      :    int       		= load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""					)

										lowercase_      :    Tuple       		= StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""       ,    torch_dtype=torch.floataa					)
										pipe.to(lowerCAmelCase_					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_					)
										# 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()

										lowercase_      :    Any       		= torch.Generator(device="""cpu"""					).manual_seed(0					)
										lowercase_      :    Optional[int]       		= pipe("""anime turle"""       ,    generator=lowerCAmelCase_       ,    output_type="""np"""					)

										lowercase_      :    Dict       		= output.images[0]

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

										assert_mean_pixel_difference(lowerCAmelCase_       ,    lowerCAmelCase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										lowercase_      :    str       		= StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""       ,    torch_dtype=torch.floataa					)
										lowercase_      :    Union[str, Any]       		= pipe.to(lowerCAmelCase_					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_					)
										pipe.enable_attention_slicing()
										pipe.enable_sequential_cpu_offload()

										lowercase_      :    List[Any]       		= pipe(
										    """anime turtle"""       ,    prior_num_inference_steps=2       ,    num_inference_steps=2       ,    output_type="""np"""       ,    )

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






'''simple docstring'''




import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate





@dataclass(frozen=_UpperCAmelCase)
class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = field(default='''image-classification''',  metadata={'''include_in_asdict_even_if_is_default''': True})
			UpperCamelCase__       = Features({'''image''': Image()})
			UpperCamelCase__       = Features({'''labels''': ClassLabel})
			UpperCamelCase__       = "image"
			UpperCamelCase__       = "labels"







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						str					):
										if self.label_column not in features:
																	raise ValueError(f'''Column {self.label_column} is not present in features.'''					)
										if not isinstance(features[self.label_column]       ,    lowercase_					):
																	raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''					)
										lowercase_      :    List[str]       		= copy.deepcopy(self					)
										lowercase_      :    List[str]       		= self.label_schema.copy()
										lowercase_      :    List[Any]       		= features[self.label_column]
										lowercase_      :    Optional[Any]       		= label_schema
										return task_template







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										return {
										    self.image_column: "image",
										    self.label_column: "labels",
										}





