


'''simple docstring'''




import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


_lowerCAmelCase  :		Any		     =	'\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n    author = "Lin, Chin-Yew  and\n      Och, Franz Josef",\n    booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n    month = "aug 23{--}aug 27",\n    year = "2004",\n    address = "Geneva, Switzerland",\n    publisher = "COLING",\n    url = "https://www.aclweb.org/anthology/C04-1072",\n    pages = "501--507",\n}\n'

_lowerCAmelCase  :		List[Any]		     =	'\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n'

_lowerCAmelCase  :		List[Any]		     =	'\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    \'bleu\': bleu score,\n    \'precisions\': geometric mean of n-gram precisions,\n    \'brevity_penalty\': brevity penalty,\n    \'length_ratio\': ratio of lengths,\n    \'translation_length\': translation_length,\n    \'reference_length\': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample\n    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)\n    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric("bleu")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results["bleu"])\n    1.0\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,				_KWARGS_DESCRIPTION							)
class  lowerCAmelCase				(				datasets.Metric							):



	def 				lowercase     (					self    ):
		return datasets.MetricInfo(
		    description=_DESCRIPTION ,					citation=_CITATION ,					inputs_description=_KWARGS_DESCRIPTION ,					features=datasets.Features(
		        {
		            'predictions': datasets.Sequence(datasets.Value('string' ,					id='token'    ) ,					id='sequence'    ),
		            'references': datasets.Sequence(
		                datasets.Sequence(datasets.Value('string' ,					id='token'    ) ,					id='sequence'    ) ,					id='references'    ),
		        }    ) ,					codebase_urls=['https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py'] ,					reference_urls=[
		        'https://en.wikipedia.org/wiki/BLEU',
		        'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
		    ] ,					)



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__=4 ,					snake_case__=False    ):
		lowerCAmelCase					:    Dict      							= compute_bleu(
		    reference_corpus=snake_case__ ,					translation_corpus=snake_case__ ,					max_order=snake_case__ ,					smooth=snake_case__    )
		((lowerCAmelCase)							,   (lowerCAmelCase)							,   (lowerCAmelCase)							,   (lowerCAmelCase)							,   (lowerCAmelCase)							,   (lowerCAmelCase))					:    List[Any]      							= score
		return {
		    "bleu": bleu,
		    "precisions": precisions,
		    "brevity_penalty": bp,
		    "length_ratio": ratio,
		    "translation_length": translation_length,
		    "reference_length": reference_length,
		}




'''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


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



class  lowerCAmelCase				(				a							):



	def __init__(					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					):
		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=snake_case__ ,					speech_processor=snake_case__ ,					vae=snake_case__ ,					text_encoder=snake_case__ ,					tokenizer=snake_case__ ,					unet=snake_case__ ,					scheduler=snake_case__ ,					feature_extractor=snake_case__ ,					)



	def 				lowercase     (					self ,					snake_case__ = "auto"    ):
		if slice_size == "auto":
			lowerCAmelCase					:    Union[str, Any]      							= self.unet.config.attention_head_dim // 2
		self.unet.set_attention_slice(snake_case__    )



	def 				lowercase     (					self    ):
		self.enable_attention_slicing(snake_case__    )



	@torch.no_grad()
	def __call__(					self ,					snake_case__ ,					snake_case__=1_6000 ,					snake_case__ = 512 ,					snake_case__ = 512 ,					snake_case__ = 50 ,					snake_case__ = 7.5 ,					snake_case__ = None ,					snake_case__ = 1 ,					snake_case__ = 0.0 ,					snake_case__ = None ,					snake_case__ = None ,					snake_case__ = "pil" ,					snake_case__ = True ,					snake_case__ = None ,					snake_case__ = 1 ,					**snake_case__ ,					):
		lowerCAmelCase					:    List[str]      							= self.speech_processor.feature_extractor(
		    snake_case__ ,					return_tensors='pt' ,					sampling_rate=snake_case__    ).input_features.to(self.device    )
		lowerCAmelCase					:    Optional[Any]      							= self.speech_model.generate(snake_case__ ,					max_length=48_0000    )

		lowerCAmelCase					:    str      							= self.speech_processor.tokenizer.batch_decode(snake_case__ ,					skip_special_tokens=snake_case__ ,					normalize=snake_case__    )[
		    0
		]

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

		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(snake_case__ ,					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(snake_case__    )}."    )

		# get prompt text embeddings
		lowerCAmelCase					:    str      							= self.tokenizer(
		    snake_case__ ,					padding='max_length' ,					max_length=self.tokenizer.model_max_length ,					return_tensors='pt' ,					)
		lowerCAmelCase					:    Tuple      							= text_inputs.input_ids

		if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
			lowerCAmelCase					:    str      							= 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}"    )
			lowerCAmelCase					:    Union[str, Any]      							= text_input_ids[:, : self.tokenizer.model_max_length]
		lowerCAmelCase					:    Union[str, Any]      							= self.text_encoder(text_input_ids.to(self.device    )    )[0]

		# duplicate text embeddings for each generation per prompt, using mps friendly method
		lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					:    int      							= text_embeddings.shape
		lowerCAmelCase					:    Any      							= text_embeddings.repeat(1 ,					snake_case__ ,					1    )
		lowerCAmelCase					:    Optional[int]      							= text_embeddings.view(bs_embed * num_images_per_prompt ,					snake_case__ ,					-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.
		lowerCAmelCase					:    List[str]      							= guidance_scale > 1.0
		# get unconditional embeddings for classifier free guidance
		if do_classifier_free_guidance:
			lowerCAmelCase					:    List[str]
			if negative_prompt is None:
				lowerCAmelCase					:    Any      							= [''] * batch_size
			elif type(snake_case__    ) is not type(snake_case__    ):
				raise TypeError(
				    f"`negative_prompt` should be the same type to `prompt`, but got {type(snake_case__    )} !="
				    f" {type(snake_case__    )}."    )
			elif isinstance(snake_case__ ,					snake_case__    ):
				lowerCAmelCase					:    Union[str, Any]      							= [negative_prompt]
			elif batch_size != len(snake_case__    ):
				raise ValueError(
				    f"`negative_prompt`: {negative_prompt} has batch size {len(snake_case__    )}, but `prompt`:"
				    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
				    ' the batch size of `prompt`.'    )
			else:
				lowerCAmelCase					:    Dict      							= negative_prompt

			lowerCAmelCase					:    Optional[int]      							= text_input_ids.shape[-1]
			lowerCAmelCase					:    int      							= self.tokenizer(
			    snake_case__ ,					padding='max_length' ,					max_length=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt' ,					)
			lowerCAmelCase					:    Union[str, Any]      							= self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

			# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
			lowerCAmelCase					:    List[Any]      							= uncond_embeddings.shape[1]
			lowerCAmelCase					:    List[str]      							= uncond_embeddings.repeat(1 ,					snake_case__ ,					1    )
			lowerCAmelCase					:    Optional[Any]      							= uncond_embeddings.view(batch_size * num_images_per_prompt ,					snake_case__ ,					-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
			lowerCAmelCase					:    List[str]      							= 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`.
		lowerCAmelCase					:    Union[str, Any]      							= (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
		lowerCAmelCase					:    Dict      							= text_embeddings.dtype
		if latents is None:
			if self.device.type == "mps":
				# randn does not exist on mps
				lowerCAmelCase					:    str      							= torch.randn(snake_case__ ,					generator=snake_case__ ,					device='cpu' ,					dtype=snake_case__    ).to(
				    self.device    )
			else:
				lowerCAmelCase					:    Tuple      							= torch.randn(snake_case__ ,					generator=snake_case__ ,					device=self.device ,					dtype=snake_case__    )
		else:
			if latents.shape != latents_shape:
				raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"    )
			lowerCAmelCase					:    str      							= latents.to(self.device    )

		# set timesteps
		self.scheduler.set_timesteps(snake_case__    )

		# Some schedulers like PNDM have timesteps as arrays
		# It's more optimized to move all timesteps to correct device beforehand
		lowerCAmelCase					:    Union[str, Any]      							= self.scheduler.timesteps.to(self.device    )

		# scale the initial noise by the standard deviation required by the scheduler
		lowerCAmelCase					:    Any      							= 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]
		lowerCAmelCase					:    Tuple      							= 'eta' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
		lowerCAmelCase					:    Union[str, Any]      							= {}
		if accepts_eta:
			lowerCAmelCase					:    int      							= eta

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

			# predict the noise residual
			lowerCAmelCase					:    List[str]      							= self.unet(snake_case__ ,					snake_case__ ,					encoder_hidden_states=snake_case__    ).sample

			# perform guidance
			if do_classifier_free_guidance:
				lowerCAmelCase							,   lowerCAmelCase					:    Dict      							= noise_pred.chunk(2    )
				lowerCAmelCase					:    Tuple      							= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

			# compute the previous noisy sample x_t -> x_t-1
			lowerCAmelCase					:    int      							= self.scheduler.step(snake_case__ ,					snake_case__ ,					snake_case__ ,					**snake_case__    ).prev_sample

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

		lowerCAmelCase					:    List[Any]      							= 1 / 0.1_8_2_1_5 * latents
		lowerCAmelCase					:    Dict      							= self.vae.decode(snake_case__    ).sample

		lowerCAmelCase					:    List[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
		lowerCAmelCase					:    Union[str, Any]      							= image.cpu().permute(0 ,					2 ,					3 ,					1    ).float().numpy()

		if output_type == "pil":
			lowerCAmelCase					:    Dict      							= self.numpy_to_pil(snake_case__    )

		if not return_dict:
			return image

		return StableDiffusionPipelineOutput(images=snake_case__ ,					nsfw_content_detected=snake_case__    )
