


"""simple docstring"""
import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__SCREAMING_SNAKE_CASE							:      List[Any]						=      'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
__SCREAMING_SNAKE_CASE							:      int						=      direct_transformers_import(PATH_TO_TRANSFORMERS)

__SCREAMING_SNAKE_CASE							:      List[str]						=      transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
__SCREAMING_SNAKE_CASE							:      Dict						=      re.compile(R'\[(.+?)\]\((https://huggingface\.co/.+?)\)')


__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      {
    'DecisionTransformerConfig',
    'EncoderDecoderConfig',
    'MusicgenConfig',
    'RagConfig',
    'SpeechEncoderDecoderConfig',
    'TimmBackboneConfig',
    'VisionEncoderDecoderConfig',
    'VisionTextDualEncoderConfig',
    'LlamaConfig',
}







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Any:
							snake_case_          =						None

							# source code of `config_class`
							snake_case_          =						inspect.getsource(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						_re_checkpoint.findall(_SCREAMING_SNAKE_CASE			)

							# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
							# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
							for ckpt_name, ckpt_link in checkpoints:
														# allow the link to end with `/`
														if ckpt_link.endswith("""/"""			):
																					snake_case_          =						ckpt_link[:-1]

														# verify the checkpoint name corresponds to the checkpoint link
														snake_case_          =						f"""https://huggingface.co/{ckpt_name}"""
														if ckpt_link == ckpt_link_from_name:
																					snake_case_          =						ckpt_name
																					break

							return checkpoint







def   _a			( )      ->			int:
							snake_case_          =						[]

							for config_class in list(CONFIG_MAPPING.values()			):
														# Skip deprecated models
														if "models.deprecated" in config_class.__module__:
																					continue
														snake_case_          =						get_checkpoint_from_config_class(_SCREAMING_SNAKE_CASE			)

														snake_case_          =						config_class.__name__
														if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
																					configs_without_checkpoint.append(_SCREAMING_SNAKE_CASE			)

							if len(_SCREAMING_SNAKE_CASE			) > 0:
														snake_case_          =						"""\n""".join(sorted(_SCREAMING_SNAKE_CASE			)			)
														raise ValueError(f"""The following configurations don't contain any valid checkpoint:\n{message}"""			)


if __name__ == "__main__":
				check_config_docstrings_have_checkpoints()







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

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class        __A       (snake_case__  ,						snake_case__  ,						unittest.TestCase):





							'''simple docstring'''

							__lowercase:						List[Any]							 =      CycleDiffusionPipeline
							__lowercase:						int							 =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
							    """negative_prompt""",
							    """height""",
							    """width""",
							    """negative_prompt_embeds""",
							}
							__lowercase:						str							 =      PipelineTesterMixin.required_optional_params - {"""latents"""}
							__lowercase:						Any							 =      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""})
							__lowercase:						Any							 =      IMAGE_TO_IMAGE_IMAGE_PARAMS
							__lowercase:						Any							 =      IMAGE_TO_IMAGE_IMAGE_PARAMS



							def 						lowerCAmelCase    (			self							: Dict    )   ->Any:



														"""simple docstring"""



														torch.manual_seed(0    )
														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						,    )
														snake_case_          =						DDIMScheduler(
														    beta_start=0.00_085						,    beta_end=0.012						,    beta_schedule="""scaled_linear"""						,    num_train_timesteps=1_000						,    clip_sample=UpperCAmelCase_						,    set_alpha_to_one=UpperCAmelCase_						,    )
														torch.manual_seed(0    )
														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						,    )
														torch.manual_seed(0    )
														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						,    )
														snake_case_          =						CLIPTextModel(UpperCAmelCase_    )
														snake_case_          =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

														snake_case_          =						{
														    """unet""": unet,
														    """scheduler""": scheduler,
														    """vae""": vae,
														    """text_encoder""": text_encoder,
														    """tokenizer""": tokenizer,
														    """safety_checker""": None,
														    """feature_extractor""": None,
														}
														return components



							def 						lowerCAmelCase    (			self							: int						,    UpperCAmelCase_							: str						,    UpperCAmelCase_							: List[str]=0    )   ->str:



														"""simple docstring"""



														snake_case_          =						floats_tensor((1, 3, 32, 32)						,    rng=random.Random(UpperCAmelCase_    )    ).to(UpperCAmelCase_    )
														snake_case_          =						image / 2 + 0.5
														if str(UpperCAmelCase_    ).startswith("""mps"""    ):
																					snake_case_          =						torch.manual_seed(UpperCAmelCase_    )
														else:
																					snake_case_          =						torch.Generator(device=UpperCAmelCase_    ).manual_seed(UpperCAmelCase_    )
														snake_case_          =						{
														    """prompt""": """An astronaut riding an elephant""",
														    """source_prompt""": """An astronaut riding a horse""",
														    """image""": image,
														    """generator""": generator,
														    """num_inference_steps""": 2,
														    """eta""": 0.1,
														    """strength""": 0.8,
														    """guidance_scale""": 3,
														    """source_guidance_scale""": 1,
														    """output_type""": """numpy""",
														}
														return inputs



							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->Union[str, Any]:



														"""simple docstring"""



														snake_case_          =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator

														snake_case_          =						self.get_dummy_components()
														snake_case_          =						CycleDiffusionPipeline(**UpperCAmelCase_    )
														snake_case_          =						pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )

														snake_case_          =						self.get_dummy_inputs(UpperCAmelCase_    )
														snake_case_          =						pipe(**UpperCAmelCase_    )
														snake_case_          =						output.images

														snake_case_          =						images[0, -3:, -3:, -1]

														assert images.shape == (1, 32, 32, 3)
														snake_case_          =						np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]    )

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



							@unittest.skipIf(torch_device != """cuda"""						,    """This test requires a GPU"""    )
							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->str:



														"""simple docstring"""



														snake_case_          =						self.get_dummy_components()
														for name, module in components.items():
																					if hasattr(UpperCAmelCase_						,    """half"""    ):
																												snake_case_          =						module.half()
														snake_case_          =						CycleDiffusionPipeline(**UpperCAmelCase_    )
														snake_case_          =						pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )

														snake_case_          =						self.get_dummy_inputs(UpperCAmelCase_    )
														snake_case_          =						pipe(**UpperCAmelCase_    )
														snake_case_          =						output.images

														snake_case_          =						images[0, -3:, -3:, -1]

														assert images.shape == (1, 32, 32, 3)
														snake_case_          =						np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]    )

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



							@skip_mps
							def 						lowerCAmelCase    (			self							: Tuple    )   ->Optional[int]:



														"""simple docstring"""



														return super().test_save_load_local()



							@unittest.skip("""non-deterministic pipeline"""    )
							def 						lowerCAmelCase    (			self							: int    )   ->Optional[int]:



														"""simple docstring"""



														return super().test_inference_batch_single_identical()



							@skip_mps
							def 						lowerCAmelCase    (			self							: Tuple    )   ->List[Any]:



														"""simple docstring"""



														return super().test_dict_tuple_outputs_equivalent()



							@skip_mps
							def 						lowerCAmelCase    (			self							: Any    )   ->Tuple:



														"""simple docstring"""



														return super().test_save_load_optional_components()



							@skip_mps
							def 						lowerCAmelCase    (			self							: List[Any]    )   ->str:



														"""simple docstring"""



														return super().test_attention_slicing_forward_pass()





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





							'''simple docstring'''

							def 						lowerCAmelCase    (			self							: List[Any]    )   ->Optional[int]:



														"""simple docstring"""



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



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->str:



														"""simple docstring"""



														snake_case_          =						load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/cycle-diffusion/black_colored_car.png"""    )
														snake_case_          =						load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""    )
														snake_case_          =						init_image.resize((512, 512)    )

														snake_case_          =						"""CompVis/stable-diffusion-v1-4"""
														snake_case_          =						DDIMScheduler.from_pretrained(UpperCAmelCase_						,    subfolder="""scheduler"""    )
														snake_case_          =						CycleDiffusionPipeline.from_pretrained(
														    UpperCAmelCase_						,    scheduler=UpperCAmelCase_						,    safety_checker=UpperCAmelCase_						,    torch_dtype=torch.floataa						,    revision="""fp16"""    )

														pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
														pipe.enable_attention_slicing()

														snake_case_          =						"""A black colored car"""
														snake_case_          =						"""A blue colored car"""

														snake_case_          =						torch.manual_seed(0    )
														snake_case_          =						pipe(
														    prompt=UpperCAmelCase_						,    source_prompt=UpperCAmelCase_						,    image=UpperCAmelCase_						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=UpperCAmelCase_						,    output_type="""np"""						,    )
														snake_case_          =						output.images

														# the values aren't exactly equal, but the images look the same visually
														assert np.abs(image - expected_image    ).max() < 5E-1



							def 						lowerCAmelCase    (			self							: Tuple    )   ->List[str]:



														"""simple docstring"""



														snake_case_          =						load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/cycle-diffusion/black_colored_car.png"""    )
														snake_case_          =						load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""    )
														snake_case_          =						init_image.resize((512, 512)    )

														snake_case_          =						"""CompVis/stable-diffusion-v1-4"""
														snake_case_          =						DDIMScheduler.from_pretrained(UpperCAmelCase_						,    subfolder="""scheduler"""    )
														snake_case_          =						CycleDiffusionPipeline.from_pretrained(UpperCAmelCase_						,    scheduler=UpperCAmelCase_						,    safety_checker=UpperCAmelCase_    )

														pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
														pipe.enable_attention_slicing()

														snake_case_          =						"""A black colored car"""
														snake_case_          =						"""A blue colored car"""

														snake_case_          =						torch.manual_seed(0    )
														snake_case_          =						pipe(
														    prompt=UpperCAmelCase_						,    source_prompt=UpperCAmelCase_						,    image=UpperCAmelCase_						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=UpperCAmelCase_						,    output_type="""np"""						,    )
														snake_case_          =						output.images

														assert np.abs(image - expected_image    ).max() < 2E-2



