"""simple docstring"""





from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

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


__A  =				'''CompVis/stable-diffusion-v1-1'''
__A  =				'''CompVis/stable-diffusion-v1-2'''
__A  =				'''CompVis/stable-diffusion-v1-3'''
__A  =				'''CompVis/stable-diffusion-v1-4'''
class  _snake_case     (					a__		):






						def __init__(       self      :			int							,					UpperCAmelCase      :			AutoencoderKL							,					UpperCAmelCase      :			CLIPTextModel							,					UpperCAmelCase      :			CLIPTokenizer							,					UpperCAmelCase      :			UNetaDConditionModel							,					UpperCAmelCase      :			Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]							,					UpperCAmelCase      :			StableDiffusionSafetyChecker							,					UpperCAmelCase      :			CLIPImageProcessor							,					UpperCAmelCase      :			bool = True							,					):
													super()._init_()

													__lowerCamelCase					:		Optional[int]		   =     StableDiffusionPipeline.from_pretrained(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     StableDiffusionPipeline.from_pretrained(UpperCAmelCase    )
													__lowerCamelCase					:		Tuple		   =     StableDiffusionPipeline.from_pretrained(UpperCAmelCase    )
													__lowerCamelCase					:		List[str]		   =     StableDiffusionPipeline(
													    vae=UpperCAmelCase							,					text_encoder=UpperCAmelCase							,					tokenizer=UpperCAmelCase							,					unet=UpperCAmelCase							,					scheduler=UpperCAmelCase							,					safety_checker=UpperCAmelCase							,					feature_extractor=UpperCAmelCase							,					requires_safety_checker=UpperCAmelCase							,					)

													self.register_modules(pipelinea=self.pipea							,					pipelinea=self.pipea							,					pipelinea=self.pipea							,					pipelinea=self.pipea    )






						@property
						def       lowerCamelCase__     (       self      :			Dict    ):
													return {k: getattr(self							,					UpperCAmelCase    ) for k in self.config.keys() if not k.startswith("_"    )}






						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			Optional[Union[str, int]] = "auto"    ):
													if slice_size == "auto":
																				# half the attention head size is usually a good trade-off between
																				# speed and memory
																				__lowerCamelCase					:		Optional[int]		   =     self.unet.config.attention_head_dim // 2
													self.unet.set_attention_slice(UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Tuple    ):
													self.enable_attention_slicing(UpperCAmelCase    )






						@torch.no_grad()
						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			Union[str, List[str]]							,					UpperCAmelCase      :			int = 512							,					UpperCAmelCase      :			int = 512							,					UpperCAmelCase      :			int = 50							,					UpperCAmelCase      :			float = 7.5							,					UpperCAmelCase      :			Optional[Union[str, List[str]]] = None							,					UpperCAmelCase      :			Optional[int] = 1							,					UpperCAmelCase      :			float = 0.0							,					UpperCAmelCase      :			Optional[torch.Generator] = None							,					UpperCAmelCase      :			Optional[torch.FloatTensor] = None							,					UpperCAmelCase      :			Optional[str] = "pil"							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,					UpperCAmelCase      :			int = 1							,					**UpperCAmelCase      :			Optional[Any]							,					):
													return self.pipea(
													    prompt=UpperCAmelCase							,					height=UpperCAmelCase							,					width=UpperCAmelCase							,					num_inference_steps=UpperCAmelCase							,					guidance_scale=UpperCAmelCase							,					negative_prompt=UpperCAmelCase							,					num_images_per_prompt=UpperCAmelCase							,					eta=UpperCAmelCase							,					generator=UpperCAmelCase							,					latents=UpperCAmelCase							,					output_type=UpperCAmelCase							,					return_dict=UpperCAmelCase							,					callback=UpperCAmelCase							,					callback_steps=UpperCAmelCase							,					**UpperCAmelCase							,					)






						@torch.no_grad()
						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			Union[str, List[str]]							,					UpperCAmelCase      :			int = 512							,					UpperCAmelCase      :			int = 512							,					UpperCAmelCase      :			int = 50							,					UpperCAmelCase      :			float = 7.5							,					UpperCAmelCase      :			Optional[Union[str, List[str]]] = None							,					UpperCAmelCase      :			Optional[int] = 1							,					UpperCAmelCase      :			float = 0.0							,					UpperCAmelCase      :			Optional[torch.Generator] = None							,					UpperCAmelCase      :			Optional[torch.FloatTensor] = None							,					UpperCAmelCase      :			Optional[str] = "pil"							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,					UpperCAmelCase      :			int = 1							,					**UpperCAmelCase      :			Union[str, Any]							,					):
													return self.pipea(
													    prompt=UpperCAmelCase							,					height=UpperCAmelCase							,					width=UpperCAmelCase							,					num_inference_steps=UpperCAmelCase							,					guidance_scale=UpperCAmelCase							,					negative_prompt=UpperCAmelCase							,					num_images_per_prompt=UpperCAmelCase							,					eta=UpperCAmelCase							,					generator=UpperCAmelCase							,					latents=UpperCAmelCase							,					output_type=UpperCAmelCase							,					return_dict=UpperCAmelCase							,					callback=UpperCAmelCase							,					callback_steps=UpperCAmelCase							,					**UpperCAmelCase							,					)






						@torch.no_grad()
						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			Union[str, List[str]]							,					UpperCAmelCase      :			int = 512							,					UpperCAmelCase      :			int = 512							,					UpperCAmelCase      :			int = 50							,					UpperCAmelCase      :			float = 7.5							,					UpperCAmelCase      :			Optional[Union[str, List[str]]] = None							,					UpperCAmelCase      :			Optional[int] = 1							,					UpperCAmelCase      :			float = 0.0							,					UpperCAmelCase      :			Optional[torch.Generator] = None							,					UpperCAmelCase      :			Optional[torch.FloatTensor] = None							,					UpperCAmelCase      :			Optional[str] = "pil"							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,					UpperCAmelCase      :			int = 1							,					**UpperCAmelCase      :			Dict							,					):
													return self.pipea(
													    prompt=UpperCAmelCase							,					height=UpperCAmelCase							,					width=UpperCAmelCase							,					num_inference_steps=UpperCAmelCase							,					guidance_scale=UpperCAmelCase							,					negative_prompt=UpperCAmelCase							,					num_images_per_prompt=UpperCAmelCase							,					eta=UpperCAmelCase							,					generator=UpperCAmelCase							,					latents=UpperCAmelCase							,					output_type=UpperCAmelCase							,					return_dict=UpperCAmelCase							,					callback=UpperCAmelCase							,					callback_steps=UpperCAmelCase							,					**UpperCAmelCase							,					)






						@torch.no_grad()
						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Union[str, List[str]]							,					UpperCAmelCase      :			int = 512							,					UpperCAmelCase      :			int = 512							,					UpperCAmelCase      :			int = 50							,					UpperCAmelCase      :			float = 7.5							,					UpperCAmelCase      :			Optional[Union[str, List[str]]] = None							,					UpperCAmelCase      :			Optional[int] = 1							,					UpperCAmelCase      :			float = 0.0							,					UpperCAmelCase      :			Optional[torch.Generator] = None							,					UpperCAmelCase      :			Optional[torch.FloatTensor] = None							,					UpperCAmelCase      :			Optional[str] = "pil"							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,					UpperCAmelCase      :			int = 1							,					**UpperCAmelCase      :			List[str]							,					):
													return self.pipea(
													    prompt=UpperCAmelCase							,					height=UpperCAmelCase							,					width=UpperCAmelCase							,					num_inference_steps=UpperCAmelCase							,					guidance_scale=UpperCAmelCase							,					negative_prompt=UpperCAmelCase							,					num_images_per_prompt=UpperCAmelCase							,					eta=UpperCAmelCase							,					generator=UpperCAmelCase							,					latents=UpperCAmelCase							,					output_type=UpperCAmelCase							,					return_dict=UpperCAmelCase							,					callback=UpperCAmelCase							,					callback_steps=UpperCAmelCase							,					**UpperCAmelCase							,					)







						@torch.no_grad()
						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			Union[str, List[str]]							,					UpperCAmelCase      :			int = 512							,					UpperCAmelCase      :			int = 512							,					UpperCAmelCase      :			int = 50							,					UpperCAmelCase      :			float = 7.5							,					UpperCAmelCase      :			Optional[Union[str, List[str]]] = None							,					UpperCAmelCase      :			Optional[int] = 1							,					UpperCAmelCase      :			float = 0.0							,					UpperCAmelCase      :			Optional[torch.Generator] = None							,					UpperCAmelCase      :			Optional[torch.FloatTensor] = None							,					UpperCAmelCase      :			Optional[str] = "pil"							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,					UpperCAmelCase      :			int = 1							,					**UpperCAmelCase      :			Tuple							,					):
													__lowerCamelCase					:		Optional[int]		   =     "cuda" if torch.cuda.is_available() else "cpu"
													self.to(UpperCAmelCase    )

													# Checks if the height and width are divisible by 8 or not
													if height % 8 != 0 or width % 8 != 0:
																				raise ValueError(F"""`height` and `width` must be divisible by 8 but are {height} and {width}."""    )

													# Get first result from Stable Diffusion Checkpoint v1.1
													__lowerCamelCase					:		str		   =     self.textaimg_sda_a(
													    prompt=UpperCAmelCase							,					height=UpperCAmelCase							,					width=UpperCAmelCase							,					num_inference_steps=UpperCAmelCase							,					guidance_scale=UpperCAmelCase							,					negative_prompt=UpperCAmelCase							,					num_images_per_prompt=UpperCAmelCase							,					eta=UpperCAmelCase							,					generator=UpperCAmelCase							,					latents=UpperCAmelCase							,					output_type=UpperCAmelCase							,					return_dict=UpperCAmelCase							,					callback=UpperCAmelCase							,					callback_steps=UpperCAmelCase							,					**UpperCAmelCase							,					)

													# Get first result from Stable Diffusion Checkpoint v1.2
													__lowerCamelCase					:		Optional[int]		   =     self.textaimg_sda_a(
													    prompt=UpperCAmelCase							,					height=UpperCAmelCase							,					width=UpperCAmelCase							,					num_inference_steps=UpperCAmelCase							,					guidance_scale=UpperCAmelCase							,					negative_prompt=UpperCAmelCase							,					num_images_per_prompt=UpperCAmelCase							,					eta=UpperCAmelCase							,					generator=UpperCAmelCase							,					latents=UpperCAmelCase							,					output_type=UpperCAmelCase							,					return_dict=UpperCAmelCase							,					callback=UpperCAmelCase							,					callback_steps=UpperCAmelCase							,					**UpperCAmelCase							,					)

													# Get first result from Stable Diffusion Checkpoint v1.3
													__lowerCamelCase					:		Any		   =     self.textaimg_sda_a(
													    prompt=UpperCAmelCase							,					height=UpperCAmelCase							,					width=UpperCAmelCase							,					num_inference_steps=UpperCAmelCase							,					guidance_scale=UpperCAmelCase							,					negative_prompt=UpperCAmelCase							,					num_images_per_prompt=UpperCAmelCase							,					eta=UpperCAmelCase							,					generator=UpperCAmelCase							,					latents=UpperCAmelCase							,					output_type=UpperCAmelCase							,					return_dict=UpperCAmelCase							,					callback=UpperCAmelCase							,					callback_steps=UpperCAmelCase							,					**UpperCAmelCase							,					)

													# Get first result from Stable Diffusion Checkpoint v1.4
													__lowerCamelCase					:		Optional[Any]		   =     self.textaimg_sda_a(
													    prompt=UpperCAmelCase							,					height=UpperCAmelCase							,					width=UpperCAmelCase							,					num_inference_steps=UpperCAmelCase							,					guidance_scale=UpperCAmelCase							,					negative_prompt=UpperCAmelCase							,					num_images_per_prompt=UpperCAmelCase							,					eta=UpperCAmelCase							,					generator=UpperCAmelCase							,					latents=UpperCAmelCase							,					output_type=UpperCAmelCase							,					return_dict=UpperCAmelCase							,					callback=UpperCAmelCase							,					callback_steps=UpperCAmelCase							,					**UpperCAmelCase							,					)

													# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
													return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]]    )
"""simple docstring"""





from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('''3.8'''):
				import importlib_metadata
else:
				import importlib.metadata as importlib_metadata


__A  =				''''''


if version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):
				class  _snake_case     (					tr.AbstractTransform		):






										def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			str = " "    ):
																	__lowerCamelCase					:		List[str]		   =     sentence_delimiter






										def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			str    ):
																	return list(UpperCAmelCase    )







										def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			List[str]    ):
																	__lowerCamelCase					:		Any		   =     []
																	for sent_idx, sentence in enumerate(UpperCAmelCase    ):
																								chars.extend(self.process_string(UpperCAmelCase    )    )
																								if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCAmelCase    ) - 1:
																															chars.append(self.sentence_delimiter    )
																	return chars

				__A  =				tr.Compose(
				    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
				)
else:
				__A  =				tr.Compose(
				    [
				        tr.RemoveMultipleSpaces(),
				        tr.Strip(),
				        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
				        tr.ReduceToListOfListOfChars(),
				    ]
				)


__A  =				'''\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''

__A  =				'''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
'''

__A  =				'''
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> cer = datasets.load_metric("cer")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				, _KWARGS_DESCRIPTION		)
class  _snake_case     (					datasets.Metric		):






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													return datasets.MetricInfo(
													    description=_DESCRIPTION							,					citation=_CITATION							,					inputs_description=_KWARGS_DESCRIPTION							,					features=datasets.Features(
													        {
													            "predictions": datasets.Value("string"							,					id="sequence"    ),
													            "references": datasets.Value("string"							,					id="sequence"    ),
													        }    )							,					codebase_urls=["https://github.com/jitsi/jiwer/"]							,					reference_urls=[
													        "https://en.wikipedia.org/wiki/Word_error_rate",
													        "https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates",
													    ]							,					)







						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			List[str]=False    ):
													if concatenate_texts:
																				return jiwer.compute_measures(
																				    UpperCAmelCase							,					UpperCAmelCase							,					truth_transform=UpperCAmelCase							,					hypothesis_transform=UpperCAmelCase							,					)["wer"]

													__lowerCamelCase					:		Optional[int]		   =     0
													__lowerCamelCase					:		Optional[int]		   =     0
													for prediction, reference in zip(UpperCAmelCase							,					UpperCAmelCase    ):
																				__lowerCamelCase					:		Dict		   =     jiwer.compute_measures(
																				    UpperCAmelCase							,					UpperCAmelCase							,					truth_transform=UpperCAmelCase							,					hypothesis_transform=UpperCAmelCase							,					)
																				incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																				total += measures["substitutions"] + measures["deletions"] + measures["hits"]

													return incorrect / total