





'''simple docstring'''






from pathlib import Path

import fire
from tqdm import tqdm







def 					_a						(       _lowerCamelCase="ro"    ,	_lowerCamelCase="en"    ,	_lowerCamelCase="wmt16"    ,	_lowerCamelCase=None ) ->				None:



			"""simple docstring"""


			try:
						import datasets
			except (ModuleNotFoundError, ImportError):
						raise ImportError("""run pip install datasets""" )
			__snake_case					:      Dict							      =     F'''{src_lang}-{tgt_lang}'''
			print(F'''Converting {dataset}-{pair}''' )
			__snake_case					:      Tuple							      =     datasets.load_dataset(_lowerCamelCase    ,	_lowerCamelCase )
			if save_dir is None:
						__snake_case					:      Optional[Any]							      =     F'''{dataset}-{pair}'''
			__snake_case					:      List[Any]							      =     Path(_lowerCamelCase )
			save_dir.mkdir(exist_ok=_lowerCamelCase )

			for split in ds.keys():
						print(F'''Splitting {split} with {ds[split].num_rows} records''' )

						# to save to val.source, val.target like summary datasets
						__snake_case					:      List[str]							      =     """val""" if split == """validation""" else split
						__snake_case					:      List[Any]							      =     save_dir.joinpath(F'''{fn}.source''' )
						__snake_case					:      Union[str, Any]							      =     save_dir.joinpath(F'''{fn}.target''' )
						__snake_case					:      Union[str, Any]							      =     src_path.open("""w+""" )
						__snake_case					:      Dict							      =     tgt_path.open("""w+""" )

						# reader is the bottleneck so writing one record at a time doesn't slow things down
						for x in tqdm(ds[split] ):
									__snake_case					:      Optional[Any]							      =     x["""translation"""]
									src_fp.write(ex[src_lang] + """\n""" )
									tgt_fp.write(ex[tgt_lang] + """\n""" )

			print(F'''Saved {dataset} dataset to {save_dir}''' )


if __name__ == "__main__":
	fire.Fire(download_wmt_dataset)









'''simple docstring'''






import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


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




class  _A (					__lowercase		):

		def __init__(   self					:   str  ,      __magic_name__					:   WhisperForConditionalGeneration  ,      __magic_name__					:   WhisperProcessor  ,      __magic_name__					:   AutoencoderKL  ,      __magic_name__					:   CLIPTextModel  ,      __magic_name__					:   CLIPTokenizer  ,      __magic_name__					:   UNetaDConditionModel  ,      __magic_name__					:   Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]  ,      __magic_name__					:   StableDiffusionSafetyChecker  ,      __magic_name__					:   CLIPImageProcessor  ,      )					->    Union[str, Any]:


					"""simple docstring"""

					super().__init__()

					if safety_checker is None:
								logger.warning(
								    f'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
								    """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
								    """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
								    """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
								    """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
								    """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""			)

					self.register_modules(
					    speech_model=__magic_name__  ,      speech_processor=__magic_name__  ,      vae=__magic_name__  ,      text_encoder=__magic_name__  ,      tokenizer=__magic_name__  ,      unet=__magic_name__  ,      scheduler=__magic_name__  ,      feature_extractor=__magic_name__  ,      )

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   Optional[Union[str, int]] = "auto"			)					->    Union[str, Any]:


					"""simple docstring"""

					if slice_size == "auto":
								__snake_case					:      str							      =     self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(__magic_name__			)

		def 	lowercase__     (   self					:   str			)					->    Any:


					"""simple docstring"""

					self.enable_attention_slicing(__magic_name__			)

		@torch.no_grad()
		def __call__(   self					:   Optional[int]  ,      __magic_name__					:   str  ,      __magic_name__					:   Dict=1_60_00  ,      __magic_name__					:   int = 5_12  ,      __magic_name__					:   int = 5_12  ,      __magic_name__					:   int = 50  ,      __magic_name__					:   float = 7.5  ,      __magic_name__					:   Optional[Union[str, List[str]]] = None  ,      __magic_name__					:   Optional[int] = 1  ,      __magic_name__					:   float = 0.0  ,      __magic_name__					:   Optional[torch.Generator] = None  ,      __magic_name__					:   Optional[torch.FloatTensor] = None  ,      __magic_name__					:   Optional[str] = "pil"  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,      __magic_name__					:   int = 1  ,      **__magic_name__					:   List[str]  ,      )					->    int:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     self.speech_processor.feature_extractor(
					    __magic_name__  ,      return_tensors="""pt"""  ,      sampling_rate=__magic_name__			).input_features.to(self.device			)
					__snake_case					:      List[str]							      =     self.speech_model.generate(__magic_name__  ,      max_length=48_00_00			)

					__snake_case					:      List[Any]							      =     self.speech_processor.tokenizer.batch_decode(__magic_name__  ,      skip_special_tokens=__magic_name__  ,      normalize=__magic_name__			)[
					    0
					]

					if isinstance(__magic_name__  ,      __magic_name__			):
								__snake_case					:      Tuple							      =     1
					elif isinstance(__magic_name__  ,      __magic_name__			):
								__snake_case					:      Optional[int]							      =     len(__magic_name__			)
					else:
								raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(__magic_name__			)}'''			)

					if height % 8 != 0 or width % 8 != 0:
								raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''			)

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

					# get prompt text embeddings
					__snake_case					:      Dict							      =     self.tokenizer(
					    __magic_name__  ,      padding="""max_length"""  ,      max_length=self.tokenizer.model_max_length  ,      return_tensors="""pt"""  ,      )
					__snake_case					:      Optional[Any]							      =     text_inputs.input_ids

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
								__snake_case					:      Tuple							      =     self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]			)
								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}'''			)
								__snake_case					:      Any							      =     text_input_ids[:, : self.tokenizer.model_max_length]
					__snake_case					:      int							      =     self.text_encoder(text_input_ids.to(self.device			)			)[0]

					# duplicate text embeddings for each generation per prompt, using mps friendly method
					__snake_case      ,     __snake_case      ,     __snake_case					:      Any							      =     text_embeddings.shape
					__snake_case					:      List[Any]							      =     text_embeddings.repeat(1  ,      __magic_name__  ,      1			)
					__snake_case					:      Dict							      =     text_embeddings.view(bs_embed * num_images_per_prompt  ,      __magic_name__  ,      -1			)

					# 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.
					__snake_case					:      Optional[int]							      =     guidance_scale > 1.0
					# get unconditional embeddings for classifier free guidance
					if do_classifier_free_guidance:
								__snake_case					:      List[str]
								if negative_prompt is None:
											__snake_case					:      Optional[Any]							      =     [""""""] * batch_size
								elif type(__magic_name__			) is not type(__magic_name__			):
											raise TypeError(
											    f'''`negative_prompt` should be the same type to `prompt`, but got {type(__magic_name__			)} !='''
											    f''' {type(__magic_name__			)}.'''			)
								elif isinstance(__magic_name__  ,      __magic_name__			):
											__snake_case					:      Dict							      =     [negative_prompt]
								elif batch_size != len(__magic_name__			):
											raise ValueError(
											    f'''`negative_prompt`: {negative_prompt} has batch size {len(__magic_name__			)}, but `prompt`:'''
											    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
											    """ the batch size of `prompt`."""			)
								else:
											__snake_case					:      int							      =     negative_prompt

								__snake_case					:      List[str]							      =     text_input_ids.shape[-1]
								__snake_case					:      Any							      =     self.tokenizer(
								    __magic_name__  ,      padding="""max_length"""  ,      max_length=__magic_name__  ,      truncation=__magic_name__  ,      return_tensors="""pt"""  ,      )
								__snake_case					:      Dict							      =     self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

								# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
								__snake_case					:      Optional[int]							      =     uncond_embeddings.shape[1]
								__snake_case					:      Union[str, Any]							      =     uncond_embeddings.repeat(1  ,      __magic_name__  ,      1			)
								__snake_case					:      Tuple							      =     uncond_embeddings.view(batch_size * num_images_per_prompt  ,      __magic_name__  ,      -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
								__snake_case					:      Dict							      =     torch.cat([uncond_embeddings, text_embeddings]			)

					# get the initial random noise unless the user supplied it

					# Unlike in other pipelines, latents need to be generated in the target device
					# for 1-to-1 results reproducibility with the CompVis implementation.
					# However this currently doesn't work in `mps`.
					__snake_case					:      List[Any]							      =     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
					__snake_case					:      List[Any]							      =     text_embeddings.dtype
					if latents is None:
								if self.device.type == "mps":
											# randn does not exist on mps
											__snake_case					:      Optional[int]							      =     torch.randn(__magic_name__  ,      generator=__magic_name__  ,      device="""cpu"""  ,      dtype=__magic_name__			).to(
											    self.device			)
								else:
											__snake_case					:      int							      =     torch.randn(__magic_name__  ,      generator=__magic_name__  ,      device=self.device  ,      dtype=__magic_name__			)
					else:
								if latents.shape != latents_shape:
											raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''			)
								__snake_case					:      List[str]							      =     latents.to(self.device			)

					# set timesteps
					self.scheduler.set_timesteps(__magic_name__			)

					# Some schedulers like PNDM have timesteps as arrays
					# It's more optimized to move all timesteps to correct device beforehand
					__snake_case					:      Optional[int]							      =     self.scheduler.timesteps.to(self.device			)

					# scale the initial noise by the standard deviation required by the scheduler
					__snake_case					:      str							      =     latents * self.scheduler.init_noise_sigma

					# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
					# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
					# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
					# and should be between [0, 1]
					__snake_case					:      Tuple							      =     """eta""" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
					__snake_case					:      List[str]							      =     {}
					if accepts_eta:
								__snake_case					:      str							      =     eta

					for i, t in enumerate(self.progress_bar(__magic_name__			)			):
								# expand the latents if we are doing classifier free guidance
								__snake_case					:      Union[str, Any]							      =     torch.cat([latents] * 2			) if do_classifier_free_guidance else latents
								__snake_case					:      Dict							      =     self.scheduler.scale_model_input(__magic_name__  ,      __magic_name__			)

								# predict the noise residual
								__snake_case					:      Tuple							      =     self.unet(__magic_name__  ,      __magic_name__  ,      encoder_hidden_states=__magic_name__			).sample

								# perform guidance
								if do_classifier_free_guidance:
											__snake_case      ,     __snake_case					:      str							      =     noise_pred.chunk(2			)
											__snake_case					:      Any							      =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

								# compute the previous noisy sample x_t -> x_t-1
								__snake_case					:      Optional[Any]							      =     self.scheduler.step(__magic_name__  ,      __magic_name__  ,      __magic_name__  ,      **__magic_name__			).prev_sample

								# call the callback, if provided
								if callback is not None and i % callback_steps == 0:
											callback(__magic_name__  ,      __magic_name__  ,      __magic_name__			)

					__snake_case					:      int							      =     1 / 0.18215 * latents
					__snake_case					:      Optional[Any]							      =     self.vae.decode(__magic_name__			).sample

					__snake_case					:      Any							      =     (image / 2 + 0.5).clamp(0  ,      1			)

					# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
					__snake_case					:      Any							      =     image.cpu().permute(0  ,      2  ,      3  ,      1			).float().numpy()

					if output_type == "pil":
								__snake_case					:      Tuple							      =     self.numpy_to_pil(__magic_name__			)

					if not return_dict:
								return image

					return StableDiffusionPipelineOutput(images=__magic_name__  ,      nsfw_content_detected=__magic_name__			)


