




"""simple docstring"""

import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata



def 					UpperCamelCase__       (						lowercase__					:       str				):
		snake_case						:  Dict        =			min(re.search("^ +"    ,       lowercase__				).end() if t.startswith(" "				) else 0 for t in string.splitlines()				)
		return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(lowercase__				)]				)


__A							=     "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

__A							=     "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


__A							=     "\\n# Begin of markdown\n\nSome cool dataset card\n"
class      lowerCamelCase__      (					unittest.TestCase     ):


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					with tempfile.TemporaryDirectory() as tmp_dir:
							snake_case						:  Optional[Any]        =			Path(SCREAMING_SNAKE_CASE							) / "README.md"
							with open(SCREAMING_SNAKE_CASE			,	"w+"							) as readme_file:
									readme_file.write(SCREAMING_SNAKE_CASE							)
							snake_case						:  Optional[int]        =			DatasetMetadata.from_readme(SCREAMING_SNAKE_CASE							)
							self.assertDictEqual(SCREAMING_SNAKE_CASE			,	{"language": ["zh", "en"], "task_ids": ["sentiment-classification"]}							)

							with open(SCREAMING_SNAKE_CASE			,	"w+"							) as readme_file:
									readme_file.write(SCREAMING_SNAKE_CASE							)
							snake_case						:  Tuple        =			DatasetMetadata.from_readme(SCREAMING_SNAKE_CASE							)
							self.assertDictEqual(SCREAMING_SNAKE_CASE			,	{}							)

							with open(SCREAMING_SNAKE_CASE			,	"w+"							) as readme_file:
									readme_file.write(SCREAMING_SNAKE_CASE							)
							snake_case						:  Dict        =			DatasetMetadata.from_readme(SCREAMING_SNAKE_CASE							)
							self.assertEqual(SCREAMING_SNAKE_CASE			,	{}							)



			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  List[Any]        =			_dedent(
					    "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            "							)
					assert DatasetMetadata.from_yaml_string(SCREAMING_SNAKE_CASE							)

					snake_case						:  int        =			_dedent(
					    "\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            "							)
					with self.assertRaises(SCREAMING_SNAKE_CASE							):
							DatasetMetadata.from_yaml_string(SCREAMING_SNAKE_CASE							)

					snake_case						:  str        =			_dedent(
					    "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            "							)
					assert DatasetMetadata.from_yaml_string(SCREAMING_SNAKE_CASE							)







"""simple docstring"""

from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__A							=     logging.get_logger(__name__)  # pylint: disable=invalid-name


__A							=     "\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = \"cuda\"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")\n        ```\n"
class      lowerCamelCase__      (					lowerCamelCase_     ):


			def __init__(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					super().__init__()

					self.register_modules(
					    unet=SCREAMING_SNAKE_CASE			,	scheduler=SCREAMING_SNAKE_CASE			,	)

					snake_case						:  List[str]        =			None


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE=0							):



					"""simple docstring"""







					if is_accelerate_available() and is_accelerate_version(">="			,	"0.14.0"							):
							from accelerate import cpu_offload
					else:
							raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"							)

					snake_case						:  Tuple        =			torch.device(F'''cuda:{gpu_id}'''							)

					if self.device.type != "cpu":
							self.to("cpu"			,	silence_dtype_warnings=SCREAMING_SNAKE_CASE							)
							torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

					for cpu_offloaded_model in [self.unet]:
							cpu_offload(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

					if self.safety_checker is not None:
							cpu_offload(self.safety_checker			,	execution_device=SCREAMING_SNAKE_CASE			,	offload_buffers=SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE=0							):



					"""simple docstring"""







					if is_accelerate_available() and is_accelerate_version(">="			,	"0.17.0.dev0"							):
							from accelerate import cpu_offload_with_hook
					else:
							raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."							)

					snake_case						:  Union[str, Any]        =			torch.device(F'''cuda:{gpu_id}'''							)

					if self.device.type != "cpu":
							self.to("cpu"			,	silence_dtype_warnings=SCREAMING_SNAKE_CASE							)
							torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

					snake_case						:  List[str]        =			None
					for cpu_offloaded_model in [self.unet]:
							snake_case     ,			snake_case						:  Optional[int]        =			cpu_offload_with_hook(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	prev_module_hook=SCREAMING_SNAKE_CASE							)

					if self.safety_checker is not None:
							snake_case     ,			snake_case						:  List[str]        =			cpu_offload_with_hook(self.safety_checker			,	SCREAMING_SNAKE_CASE			,	prev_module_hook=SCREAMING_SNAKE_CASE							)

					# We'll offload the last model manually.
					snake_case						:  Optional[Any]        =			hook


			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					if not hasattr(self.unet			,	"_hf_hook"							):
							return self.device
					for module in self.unet.modules():
							if (
							    hasattr(SCREAMING_SNAKE_CASE			,	"_hf_hook"							)
							    and hasattr(module._hf_hook			,	"execution_device"							)
							    and module._hf_hook.execution_device is not None
							):
									return torch.device(module._hf_hook.execution_device							)
					return self.device


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=None							):



					"""simple docstring"""







					snake_case						:  Any        =			(batch_size, num_channels, height, width)
					if isinstance(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							) and len(SCREAMING_SNAKE_CASE							) != batch_size:
							raise ValueError(
							    F'''You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE							)}, but requested an effective batch'''
							    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''							)

					if latents is None:
							snake_case						:  Any        =			randn_tensor(SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE			,	device=SCREAMING_SNAKE_CASE			,	dtype=SCREAMING_SNAKE_CASE							)
					else:
							snake_case						:  Tuple        =			latents.to(device=SCREAMING_SNAKE_CASE			,	dtype=SCREAMING_SNAKE_CASE							)

					# scale the initial noise by the standard deviation required by the scheduler
					snake_case						:  Optional[int]        =			latents * self.scheduler.init_noise_sigma
					return latents


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = "pil"							):



					"""simple docstring"""







					if output_type not in ["pt", "np", "pil"]:
							raise ValueError(
							    F'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''							)

					# Equivalent to diffusers.VaeImageProcessor.denormalize
					snake_case						:  List[str]        =			(sample / 2 + 0.5).clamp(0			,	1							)
					if output_type == "pt":
							return sample

					# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
					snake_case						:  Optional[int]        =			sample.cpu().permute(0			,	2			,	3			,	1							).numpy()
					if output_type == "np":
							return sample

					# Output_type must be 'pil'
					snake_case						:  List[str]        =			self.numpy_to_pil(SCREAMING_SNAKE_CASE							)
					return sample


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=None							):



					"""simple docstring"""







					if self.unet.config.num_class_embeds is not None:
							if isinstance(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):
									snake_case						:  Optional[Any]        =			torch.tensor(SCREAMING_SNAKE_CASE			,	dtype=torch.int							)
							elif isinstance(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):
									assert batch_size == 1, "Batch size must be 1 if classes is an int"
									snake_case						:  str        =			torch.tensor([class_labels]			,	dtype=torch.int							)
							elif class_labels is None:
									# Randomly generate batch_size class labels
									# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
									snake_case						:  Tuple        =			torch.randint(0			,	self.unet.config.num_class_embeds			,	size=(batch_size,)							)
							snake_case						:  int        =			class_labels.to(SCREAMING_SNAKE_CASE							)
					else:
							snake_case						:  str        =			None
					return class_labels


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if num_inference_steps is None and timesteps is None:
							raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."							)

					if num_inference_steps is not None and timesteps is not None:
							logger.warning(
							    F'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
							    " `timesteps` will be used over `num_inference_steps`."							)

					if latents is not None:
							snake_case						:  str        =			(batch_size, 3, img_size, img_size)
							if latents.shape != expected_shape:
									raise ValueError(F'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''							)

					if (callback_steps is None) or (
					    callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							) or callback_steps <= 0)
					):
							raise ValueError(
							    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
							    F''' {type(SCREAMING_SNAKE_CASE							)}.'''							)



			@torch.no_grad()
			@replace_example_docstring(SCREAMING_SNAKE_CASE							)
			def __call__(       self			,	SCREAMING_SNAKE_CASE = 1			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = 1			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = "pil"			,	SCREAMING_SNAKE_CASE = True			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = 1			,	):



					"""simple docstring"""







					snake_case						:  List[str]        =			self.unet.config.sample_size
					snake_case						:  Dict        =			self._execution_device

					# 1. Check inputs
					self.check_inputs(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

					# 2. Prepare image latents
					# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
					snake_case						:  Any        =			self.prepare_latents(
					    batch_size=SCREAMING_SNAKE_CASE			,	num_channels=self.unet.config.in_channels			,	height=SCREAMING_SNAKE_CASE			,	width=SCREAMING_SNAKE_CASE			,	dtype=self.unet.dtype			,	device=SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE			,	latents=SCREAMING_SNAKE_CASE			,	)

					# 3. Handle class_labels for class-conditional models
					snake_case						:  Union[str, Any]        =			self.prepare_class_labels(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	class_labels=SCREAMING_SNAKE_CASE							)

					# 4. Prepare timesteps
					if timesteps is not None:
							self.scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE			,	device=SCREAMING_SNAKE_CASE							)
							snake_case						:  Dict        =			self.scheduler.timesteps
							snake_case						:  str        =			len(SCREAMING_SNAKE_CASE							)
					else:
							self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE							)
							snake_case						:  str        =			self.scheduler.timesteps

					# 5. Denoising loop
					# Multistep sampling: implements Algorithm 1 in the paper
					with self.progress_bar(total=SCREAMING_SNAKE_CASE							) as progress_bar:
							for i, t in enumerate(SCREAMING_SNAKE_CASE							):
									snake_case						:  Any        =			self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)
									snake_case						:  Dict        =			self.unet(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	class_labels=SCREAMING_SNAKE_CASE			,	return_dict=SCREAMING_SNAKE_CASE							)[0]

									snake_case						:  List[Any]        =			self.scheduler.step(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE							)[0]

									# call the callback, if provided
									progress_bar.update()
									if callback is not None and i % callback_steps == 0:
											callback(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

        # 6. Post-process image sample
					snake_case						:  Dict        =			self.postprocess_image(SCREAMING_SNAKE_CASE			,	output_type=SCREAMING_SNAKE_CASE							)

					# Offload last model to CPU
					if hasattr(self			,	"final_offload_hook"							) and self.final_offload_hook is not None:
							self.final_offload_hook.offload()

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE							)

