




import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


SCREAMING_SNAKE_CASE_						:					int			   =       get_logger(__name__)
def        __lowerCAmelCase (UpperCAmelCase_		):

    '''simple docstring'''





    return TestCommand(
        args.dataset   ,    args.name   ,    args.cache_dir   ,    args.data_dir   ,    args.all_configs   ,    args.save_info or args.save_infos   ,    args.ignore_verifications   ,    args.force_redownload   ,    args.clear_cache   ,    )







class 		__UpperCAmelCase(		_SCREAMING_SNAKE_CASE ):





     """simple docstring"""



     __UpperCAmelCase	: List[Any]						=    False  # to tell pytest it's not a test class

     @staticmethod
     def 				__SCREAMING_SNAKE_CASE		(      A :       Optional[int]    )			->			Tuple:






         """simple docstring"""
         a__      =       parser.add_parser("""test"""						,				help="""Test dataset implementation."""    )
         test_parser.add_argument("""--name"""						,				type=__A						,				default=__A						,				help="""Dataset processing name"""    )
         test_parser.add_argument(
             """--cache_dir"""						,				type=__A						,				default=__A						,				help="""Cache directory where the datasets are stored."""						,				)
         test_parser.add_argument(
             """--data_dir"""						,				type=__A						,				default=__A						,				help="""Can be used to specify a manual directory to get the files from."""						,				)
         test_parser.add_argument("""--all_configs"""						,				action="""store_true"""						,				help="""Test all dataset configurations"""    )
         test_parser.add_argument(
             """--save_info"""						,				action="""store_true"""						,				help="""Save the dataset infos in the dataset card (README.md)"""    )
         test_parser.add_argument(
             """--ignore_verifications"""						,				action="""store_true"""						,				help="""Run the test without checksums and splits checks."""						,				)
         test_parser.add_argument("""--force_redownload"""						,				action="""store_true"""						,				help="""Force dataset redownload"""    )
         test_parser.add_argument(
             """--clear_cache"""						,				action="""store_true"""						,				help="""Remove downloaded files and cached datasets after each config test"""						,				)
         # aliases
         test_parser.add_argument("""--save_infos"""						,				action="""store_true"""						,				help="""alias to save_info"""    )
         test_parser.add_argument("""dataset"""						,				type=__A						,				help="""Name of the dataset to download"""    )
         test_parser.set_defaults(func=_test_command_factory    )

     def __init__(      self :       Optional[int]						,				A :       Optional[int]						,				A :       Tuple						,				A :       int						,				A :       Dict						,				A :       Optional[int]						,				A :       Any						,				A :       Any						,				A :       List[str]						,				A :       Optional[int]						,				)			->			Dict:






         """simple docstring"""
         a__      =       dataset
         a__      =       name
         a__      =       cache_dir
         a__      =       data_dir
         a__      =       all_configs
         a__      =       save_infos
         a__      =       ignore_verifications
         a__      =       force_redownload
         a__      =       clear_cache
         if clear_cache and not cache_dir:
             print(
                 """When --clear_cache is used, specifying a cache directory is mandatory.\n"""
                 """The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n"""
                 """Please provide a --cache_dir that will be used to test the dataset script."""    )
             exit(1    )
         if save_infos:
             a__      =       True






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






         """simple docstring"""
         fl_logger().setLevel(__A    )
         if self._name is not None and self._all_configs:
             print("""Both parameters `config` and `all_configs` can\'t be used at once."""    )
             exit(1    )
         a__ ,		a__      =       self._dataset, self._name
         a__      =       dataset_module_factory(__A    )
         a__      =       import_main_class(module.module_path    )
         a__      =       len(builder_cls.BUILDER_CONFIGS    ) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

         def get_builders() -> Generator[DatasetBuilder, None, None]:
             if self._all_configs and builder_cls.BUILDER_CONFIGS:
                 for i, config in enumerate(builder_cls.BUILDER_CONFIGS    ):
                     if "config_name" in module.builder_kwargs:
                         yield builder_cls(
                             cache_dir=self._cache_dir						,				data_dir=self._data_dir						,				**module.builder_kwargs						,				)
                     else:
                         yield builder_cls(
                             config_name=config.name						,				cache_dir=self._cache_dir						,				data_dir=self._data_dir						,				**module.builder_kwargs						,				)
             else:
                 if "config_name" in module.builder_kwargs:
                     yield builder_cls(cache_dir=self._cache_dir						,				data_dir=self._data_dir						,				**module.builder_kwargs    )
                 else:
                     yield builder_cls(
                         config_name=__A						,				cache_dir=self._cache_dir						,				data_dir=self._data_dir						,				**module.builder_kwargs						,				)

         for j, builder in enumerate(get_builders()    ):
             print(f"""Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})"""    )
             a__      =       os.path.exists(
                 os.path.join(builder.get_imported_module_dir()						,				datasets.config.DATASETDICT_INFOS_FILENAME    )    )  # record checksums only if we need to update a (deprecated) dataset_infos.json
             builder.download_and_prepare(
                 download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                 if not self._force_redownload
                 else DownloadMode.FORCE_REDOWNLOAD						,				verification_mode=VerificationMode.NO_CHECKS
                 if self._ignore_verifications
                 else VerificationMode.ALL_CHECKS						,				try_from_hf_gcs=__A						,				)
             builder.as_dataset()
             if self._save_infos:
                 builder._save_infos()

             # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
             # The dataset_infos are saved in the YAML part of the README.md

             # Let's move it to the original directory of the dataset script, to allow the user to
             # upload them on S3 at the same time afterwards.
             if self._save_infos:
                 a__      =       os.path.join(builder_cls.get_imported_module_dir()						,				"""README.md"""    )
                 a__      =       Path(__A    ).name + """.py"""
                 a__      =       os.path.join(__A						,				__A    )
                 if os.path.isfile(__A    ):
                     a__      =       os.path.dirname(__A    )
                 elif os.path.isfile(__A    ):
                     a__      =       path
                 elif os.path.isdir(__A    ):  # for local directories containing only data files
                     a__      =       path
                 else:  # in case of a remote dataset
                     a__      =       None
                     print(f"""Dataset card saved at {dataset_readme_path}"""    )

                 # Move dataset_info back to the user
                 if dataset_dir is not None:
                     a__      =       os.path.join(__A						,				"""README.md"""    )
                     copyfile(__A						,				__A    )
                     print(f"""Dataset card saved at {user_dataset_readme_path}"""    )

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
             if self._clear_cache:
                 if os.path.isdir(builder._cache_dir    ):
                     logger.warning(f"""Clearing cache at {builder._cache_dir}"""    )
                     rmtree(builder._cache_dir    )
                 a__      =       os.path.join(self._cache_dir						,				datasets.config.DOWNLOADED_DATASETS_DIR    )
                 if os.path.isdir(__A    ):
                     logger.warning(f"""Clearing cache at {download_dir}"""    )
                     rmtree(__A    )

         print("""Test successful."""    )












from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       getLogger(__name__)


SCREAMING_SNAKE_CASE_						:					Union[str, Any]			   =       4
SCREAMING_SNAKE_CASE_						:					List[str]			   =       7

SCREAMING_SNAKE_CASE_						:					List[str]			   =       {
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}
def        __lowerCAmelCase (UpperCAmelCase_		):
    if isinstance(UpperCAmelCase_   ,    torch.Tensor		):
        return image
    elif isinstance(UpperCAmelCase_   ,    PIL.Image.Image		):
        a__      =       [image]

    if isinstance(image[0]   ,    PIL.Image.Image		):
        a__ ,		a__      =       image[0].size
        a__ ,		a__      =       (x - x % 64 for x in (w, h))  # resize to integer multiple of 32

        a__      =       [np.array(i.resize((w, h)		)		)[None, :] for i in image]
        a__      =       np.concatenate(UpperCAmelCase_   ,    axis=0		)
        a__      =       np.array(UpperCAmelCase_		).astype(np.floataa		) / 2_5_5.0
        a__      =       image.transpose(0   ,    3   ,    1   ,    2		)
        a__      =       2.0 * image - 1.0
        a__      =       torch.from_numpy(UpperCAmelCase_		)
    elif isinstance(image[0]   ,    torch.Tensor		):
        a__      =       torch.cat(UpperCAmelCase_   ,    dim=0		)

    return image







class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     def __init__(      self :       int						,				A :       OnnxRuntimeModel						,				A :       OnnxRuntimeModel						,				A :       Any						,				A :       OnnxRuntimeModel						,				A :       DDPMScheduler						,				A :       Any						,				A :       int = 3_50						,				)			->			List[Any]:






         """simple docstring"""
         super().__init__(
             vae=A						,				text_encoder=A						,				tokenizer=A						,				unet=A						,				low_res_scheduler=A						,				scheduler=A						,				safety_checker=A						,				feature_extractor=A						,				watermarker=A						,				max_noise_level=A						,				)

     def __call__(      self :       Optional[int]						,				A :       Union[str, List[str]]						,				A :       Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]						,				A :       int = 75						,				A :       float = 9.0						,				A :       int = 20						,				A :       Optional[Union[str, List[str]]] = None						,				A :       Optional[int] = 1						,				A :       float = 0.0						,				A :       Optional[Union[torch.Generator, List[torch.Generator]]] = None						,				A :       Optional[torch.FloatTensor] = None						,				A :       Optional[np.ndarray] = None						,				A :       Optional[np.ndarray] = None						,				A :       Optional[str] = "pil"						,				A :       bool = True						,				A :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,				A :       Optional[int] = 1						,				)			->			Tuple:






         """simple docstring"""
         self.check_inputs(A						,				A						,				A						,				A    )

         # 2. Define call parameters
         if prompt is not None and isinstance(A						,				A    ):
             a__      =       1
         elif prompt is not None and isinstance(A						,				A    ):
             a__      =       len(A    )
         else:
             a__      =       prompt_embeds.shape[0]

         a__      =       self._execution_device
         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         a__      =       guidance_scale > 1.0

         # 3. Encode input prompt
         a__      =       self._encode_prompt(
             A						,				A						,				A						,				A						,				A						,				prompt_embeds=A						,				negative_prompt_embeds=A						,				)

         a__      =       ORT_TO_PT_TYPE[str(text_embeddings.dtype    )]

         # 4. Preprocess image
         a__      =       preprocess(A    )
         a__      =       image.cpu()

         # 5. set timesteps
         self.scheduler.set_timesteps(A						,				device=A    )
         a__      =       self.scheduler.timesteps

         # 5. Add noise to image
         a__      =       torch.tensor([noise_level]						,				dtype=torch.long						,				device=A    )
         a__      =       torch.randn(image.shape						,				generator=A						,				device=A						,				dtype=A    )
         a__      =       self.low_res_scheduler.add_noise(A						,				A						,				A    )

         a__      =       2 if do_classifier_free_guidance else 1
         a__      =       np.concatenate([image] * batch_multiplier * num_images_per_prompt    )
         a__      =       np.concatenate([noise_level] * image.shape[0]    )

         # 6. Prepare latent variables
         a__ ,		a__      =       image.shape[2:]
         a__      =       self.prepare_latents(
             batch_size * num_images_per_prompt						,				A						,				A						,				A						,				A						,				A						,				A						,				A						,				)

         # 7. Check that sizes of image and latents match
         a__      =       image.shape[1]
         if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
             raise ValueError(
                 """Incorrect configuration settings! The config of `pipeline.unet` expects"""
                 f""" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"""
                 f""" `num_channels_image`: {num_channels_image} """
                 f""" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"""
                 """ `pipeline.unet` or your `image` input."""    )

         # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         a__      =       self.prepare_extra_step_kwargs(A						,				A    )

         a__      =       next(
             (input.type for input in self.unet.model.get_inputs() if input.name == """timestep""")						,				"""tensor(float)"""    )
         a__      =       ORT_TO_NP_TYPE[timestep_dtype]

         # 9. Denoising loop
         a__      =       len(A    ) - num_inference_steps * self.scheduler.order
         with self.progress_bar(total=A    ) as progress_bar:
             for i, t in enumerate(A    ):
                 # expand the latents if we are doing classifier free guidance
                 a__      =       np.concatenate([latents] * 2    ) if do_classifier_free_guidance else latents

                 # concat latents, mask, masked_image_latents in the channel dimension
                 a__      =       self.scheduler.scale_model_input(A						,				A    )
                 a__      =       np.concatenate([latent_model_input, image]						,				axis=1    )

                 # timestep to tensor
                 a__      =       np.array([t]						,				dtype=A    )

                 # predict the noise residual
                 a__      =       self.unet(
                     sample=A						,				timestep=A						,				encoder_hidden_states=A						,				class_labels=noise_level.astype(np.intaa    )						,				)[0]

                 # perform guidance
                 if do_classifier_free_guidance:
                     a__ ,		a__      =       np.split(A						,				2    )
                 a__      =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                 # compute the previous noisy sample x_t -> x_t-1
                 a__      =       self.scheduler.step(
                     torch.from_numpy(A    )						,				A						,				A						,				**A    ).prev_sample

                 # call the callback, if provided
                 if i == len(A    ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(A						,				A						,				A    )

        # 10. Post-processing
         a__      =       self.decode_latents(latents.float()    )

         # 11. Convert to PIL
         if output_type == "pil":
             a__      =       self.numpy_to_pil(A    )

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[Any]						,				A :       Dict    )			->			List[str]:






         """simple docstring"""
         a__      =       1 / 0.0_8_3_3_3 * latents
         a__      =       self.vae(latent_sample=A    )[0]
         a__      =       np.clip(image / 2 + 0.5						,				0						,				1    )
         a__      =       image.transpose((0, 2, 3, 1)    )
         return image






     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]						,				A :       Union[str, List[str]]						,				A :       str						,				A :       Optional[int]						,				A :       bool						,				A :       Optional[str]						,				A :       Optional[torch.FloatTensor] = None						,				A :       Optional[torch.FloatTensor] = None						,				)			->			Tuple:






         """simple docstring"""
         if prompt is not None and isinstance(A						,				A    ):
             a__      =       1
         elif prompt is not None and isinstance(A						,				A    ):
             a__      =       len(A    )
         else:
             a__      =       prompt_embeds.shape[0]

         if prompt_embeds is None:
             a__      =       self.tokenizer(
                 A						,				padding="""max_length"""						,				max_length=self.tokenizer.model_max_length						,				truncation=A						,				return_tensors="""pt"""						,				)
             a__      =       text_inputs.input_ids
             a__      =       self.tokenizer(A						,				padding="""longest"""						,				return_tensors="""pt"""    ).input_ids

             if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                 A						,				A    ):
                 a__      =       self.tokenizer.batch_decode(
                     untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]    )
                 logger.warning(
                     """The following part of your input was truncated because CLIP can only handle sequences up to"""
                     f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""    )

             # no positional arguments to text_encoder
             a__      =       self.text_encoder(
                 input_ids=text_input_ids.int().to(A    )						,				)
             a__      =       prompt_embeds[0]

         a__ ,		a__ ,		a__      =       prompt_embeds.shape
         # duplicate text embeddings for each generation per prompt, using mps friendly method
         a__      =       prompt_embeds.repeat(1						,				A    )
         a__      =       prompt_embeds.reshape(bs_embed * num_images_per_prompt						,				A						,				-1    )

         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance and negative_prompt_embeds is None:
             a__      =       42
             if negative_prompt is None:
                 a__      =       [""""""] * batch_size
             elif type(A    ) is not type(A    ):
                 raise TypeError(
                     f"""`negative_prompt` should be the same type to `prompt`, but got {type(A    )} !="""
                     f""" {type(A    )}."""    )
             elif isinstance(A						,				A    ):
                 a__      =       [negative_prompt]
             elif batch_size != len(A    ):
                 raise ValueError(
                     f"""`negative_prompt`: {negative_prompt} has batch size {len(A    )}, but `prompt`:"""
                     f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                     """ the batch size of `prompt`."""    )
             else:
                 a__      =       negative_prompt

             a__      =       text_input_ids.shape[-1]
             a__      =       self.tokenizer(
                 A						,				padding="""max_length"""						,				max_length=A						,				truncation=A						,				return_tensors="""pt"""						,				)

             # if hasattr(uncond_input, "attention_mask"):
             #     attention_mask = uncond_input.attention_mask.to(device)
             # else:
             #     attention_mask = None

             a__      =       self.text_encoder(
                 input_ids=uncond_input.input_ids.int().to(A    )						,				)
             a__      =       uncond_embeddings[0]

         if do_classifier_free_guidance:
             a__      =       uncond_embeddings.shape[1]
             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             a__      =       uncond_embeddings.repeat(1						,				A    )
             a__      =       uncond_embeddings.reshape(batch_size * num_images_per_prompt						,				A						,				-1    )

             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             a__      =       np.concatenate([uncond_embeddings, prompt_embeds]    )

         return prompt_embeds






