






'''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      A     (   SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, unittest.TestCase    ):






 """simple docstring"""





 __a      :    List[str]			  =    StableUnCLIPPipeline
 __a      :    Union[str, Any]			  =    TEXT_TO_IMAGE_PARAMS
 __a      :    Dict			  =    TEXT_TO_IMAGE_BATCH_PARAMS
 __a      :    List[str]			  =    TEXT_TO_IMAGE_IMAGE_PARAMS
 __a      :    Union[str, Any]			  =    TEXT_TO_IMAGE_IMAGE_PARAMS

 # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
 __a      :    List[Any]			  =    False
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[str]            =				32
       UpperCamelCase_					: List[Any]            =				embedder_hidden_size

       # prior components

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

       torch.manual_seed(0			)
       UpperCamelCase_					: str            =				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=10_00      ,  )			)

       torch.manual_seed(0			)
       UpperCamelCase_					: List[Any]            =				PriorTransformer(
           num_attention_heads=2      ,  attention_head_dim=12      ,  embedding_dim=__lowerCAmelCase      ,  num_layers=1      ,  )

       torch.manual_seed(0			)
       UpperCamelCase_					: List[Any]            =				DDPMScheduler(
           variance_type="""fixed_small_log"""      ,  prediction_type="""sample"""      ,  num_train_timesteps=10_00      ,  clip_sample=__lowerCAmelCase      ,  clip_sample_range=5.0      ,  beta_schedule="""squaredcos_cap_v2"""      ,  )

       # regular denoising components

       torch.manual_seed(0			)
       UpperCamelCase_					: Dict            =				StableUnCLIPImageNormalizer(embedding_dim=__lowerCAmelCase			)
       UpperCamelCase_					: Optional[int]            =				DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""			)

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

       torch.manual_seed(0			)
       UpperCamelCase_					: 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=10_00      ,  )			)

       torch.manual_seed(0			)
       UpperCamelCase_					: Union[str, 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			)
       UpperCamelCase_					: Union[str, Any]            =				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			)
       UpperCamelCase_					: Tuple            =				AutoencoderKL()

       UpperCamelCase_					: List[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 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=0			):
       if str(__lowerCAmelCase			).startswith("""mps"""			):
             UpperCamelCase_					: List[str]            =				torch.manual_seed(__lowerCAmelCase			)
       else:
             UpperCamelCase_					: Optional[Any]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(__lowerCAmelCase			)
       UpperCamelCase_					: str            =				{
           """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 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: str            =				torch_device == """cpu"""

       self._test_attention_slicing_forward_pass(test_max_difference=__lowerCAmelCase			)





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[int]            =				torch_device in ["""cpu""", """mps"""]

       self._test_inference_batch_single_identical(test_max_difference=__lowerCAmelCase			)






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






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""			)

       UpperCamelCase_					: int            =				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()

       UpperCamelCase_					: Optional[int]            =				torch.Generator(device="""cpu"""			).manual_seed(0			)
       UpperCamelCase_					: Tuple            =				pipe("""anime turle"""      ,  generator=__lowerCAmelCase      ,  output_type="""np"""			)

       UpperCamelCase_					: List[str]            =				output.images[0]

       assert image.shape == (7_68, 7_68, 3)

       assert_mean_pixel_difference(__lowerCAmelCase      ,  __lowerCAmelCase			)





 def 	_UpperCAmelCase							(				self			):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       UpperCamelCase_					: List[Any]            =				StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""      ,  torch_dtype=torch.floataa			)
       UpperCamelCase_					: int            =				pipe.to(__lowerCAmelCase			)
       pipe.set_progress_bar_config(disable=__lowerCAmelCase			)
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       UpperCamelCase_					: Dict            =				pipe(
           """anime turtle"""      ,  prior_num_inference_steps=2      ,  num_inference_steps=2      ,  output_type="""np"""      ,  )

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














'''simple docstring'''

# Lint as: python3
import itertools
import os
import re


UpperCamelCase      					=re.compile(R"([A-Z]+)([A-Z][a-z])")
UpperCamelCase      					=re.compile(R"([a-z\d])([A-Z])")

UpperCamelCase      					=re.compile(R"(?<!_)_(?!_)")
UpperCamelCase      					=re.compile(R"(_{2,})")

UpperCamelCase      					=R"^\w+(\.\w+)*$"

UpperCamelCase      					=R"<>:/\|?*"



def 				snake_case       (  a_				:			Union[str, Any]						)							->					List[Any]:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				_uppercase_uppercase_re.sub(r"""\1_\2"""     , a_						)
      UpperCamelCase_					: Any            =				_lowercase_uppercase_re.sub(r"""\1_\2"""     , a_						)
      return name.lower()



def 				snake_case       (  a_				:			str						)							->					Optional[int]:





      """simple docstring"""

      UpperCamelCase_					: Dict            =				_single_underscore_re.split(a_						)
      UpperCamelCase_					: Any            =				[_multiple_underscores_re.split(a_						) for n in name]
      return "".join(n.capitalize() for n in itertools.chain.from_iterable(a_						) if n != """"""						)



def 				snake_case       (  a_				:			str						)							->					List[Any]:





      """simple docstring"""

      if os.path.basename(a_						) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"						)
      return camelcase_to_snakecase(a_						)



def 				snake_case       (  a_				:			int     , a_				:			List[Any]						)							->					List[Any]:





      """simple docstring"""

      if os.path.basename(a_						) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"						)
      if not re.match(_split_re     , a_						):
            raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."						)
      return f"{filename_prefix_for_name(a_						)}-{split}"



def 				snake_case       (  a_				:			str     , a_				:			Optional[int]     , a_				:			List[Any]     , a_				:			Optional[int]=None						)							->					Tuple:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				filename_prefix_for_split(a_     , a_						)
      if filetype_suffix:
            prefix += f".{filetype_suffix}"
      UpperCamelCase_					: List[str]            =				os.path.join(a_     , a_						)
      return f"{filepath}*"



def 				snake_case       (  a_				:			List[Any]     , a_				:			Optional[int]     , a_				:			List[Any]     , a_				:			int=None     , a_				:			Dict=None						)							->					Any:





      """simple docstring"""

      UpperCamelCase_					: Union[str, Any]            =				filename_prefix_for_split(a_     , a_						)
      UpperCamelCase_					: Union[str, Any]            =				os.path.join(a_     , a_						)

      if shard_lengths:
            UpperCamelCase_					: List[Any]            =				len(a_						)
            UpperCamelCase_					: Optional[int]            =				[f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(a_						)]
            if filetype_suffix:
                  UpperCamelCase_					: Tuple            =				[filename + f".{filetype_suffix}" for filename in filenames]
            return filenames
      else:
            UpperCamelCase_					: List[Any]            =				prefix
            if filetype_suffix:
                  filename += f".{filetype_suffix}"
            return [filename]






