
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__   :					int							=   """CompVis/stable-diffusion-v1-1"""
A__   :					Tuple							=   """CompVis/stable-diffusion-v1-2"""
A__   :					int							=   """CompVis/stable-diffusion-v1-3"""
A__   :					str							=   """CompVis/stable-diffusion-v1-4"""


class 			__magic_name__       (       snake_case_ ):
       def __init__(     self ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ = True ,				)     ->	Any:





         """simple docstring"""




         super()._init_()

         _lowercase:			Optional[int]       =     StableDiffusionPipeline.from_pretrained(A_       )
         _lowercase:			Dict       =     StableDiffusionPipeline.from_pretrained(A_       )
         _lowercase:			List[Any]       =     StableDiffusionPipeline.from_pretrained(A_       )
         _lowercase:			Any       =     StableDiffusionPipeline(
             vae=A_ ,				text_encoder=A_ ,				tokenizer=A_ ,				unet=A_ ,				scheduler=A_ ,				safety_checker=A_ ,				feature_extractor=A_ ,				requires_safety_checker=A_ ,				)

         self.register_modules(pipelinea=self.pipea ,				pipelinea=self.pipea ,				pipelinea=self.pipea ,				pipelinea=self.pipea       )
       @property
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         return {k: getattr(self ,				A_       ) for k in self.config.keys() if not k.startswith('''_'''       )}
       def 							lowercase_							(     self ,				A_ = "auto"       )     ->	Optional[int]:





         """simple docstring"""




         if slice_size == "auto":
           # half the attention head size is usually a good trade-off between
           # speed and memory
           _lowercase:			int       =     self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(A_       )
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         self.enable_attention_slicing(A_       )
       @torch.no_grad()
       def 							lowercase_							(     self ,				A_ ,				A_ = 512 ,				A_ = 512 ,				A_ = 50 ,				A_ = 7.5 ,				A_ = None ,				A_ = 1 ,				A_ = 0.0 ,				A_ = None ,				A_ = None ,				A_ = "pil" ,				A_ = True ,				A_ = None ,				A_ = 1 ,				**A_ ,				)     ->	Any:





         """simple docstring"""




         return self.pipea(
             prompt=A_ ,				height=A_ ,				width=A_ ,				num_inference_steps=A_ ,				guidance_scale=A_ ,				negative_prompt=A_ ,				num_images_per_prompt=A_ ,				eta=A_ ,				generator=A_ ,				latents=A_ ,				output_type=A_ ,				return_dict=A_ ,				callback=A_ ,				callback_steps=A_ ,				**A_ ,				)
       @torch.no_grad()
       def 							lowercase_							(     self ,				A_ ,				A_ = 512 ,				A_ = 512 ,				A_ = 50 ,				A_ = 7.5 ,				A_ = None ,				A_ = 1 ,				A_ = 0.0 ,				A_ = None ,				A_ = None ,				A_ = "pil" ,				A_ = True ,				A_ = None ,				A_ = 1 ,				**A_ ,				)     ->	List[str]:





         """simple docstring"""




         return self.pipea(
             prompt=A_ ,				height=A_ ,				width=A_ ,				num_inference_steps=A_ ,				guidance_scale=A_ ,				negative_prompt=A_ ,				num_images_per_prompt=A_ ,				eta=A_ ,				generator=A_ ,				latents=A_ ,				output_type=A_ ,				return_dict=A_ ,				callback=A_ ,				callback_steps=A_ ,				**A_ ,				)
       @torch.no_grad()
       def 							lowercase_							(     self ,				A_ ,				A_ = 512 ,				A_ = 512 ,				A_ = 50 ,				A_ = 7.5 ,				A_ = None ,				A_ = 1 ,				A_ = 0.0 ,				A_ = None ,				A_ = None ,				A_ = "pil" ,				A_ = True ,				A_ = None ,				A_ = 1 ,				**A_ ,				)     ->	Tuple:





         """simple docstring"""




         return self.pipea(
             prompt=A_ ,				height=A_ ,				width=A_ ,				num_inference_steps=A_ ,				guidance_scale=A_ ,				negative_prompt=A_ ,				num_images_per_prompt=A_ ,				eta=A_ ,				generator=A_ ,				latents=A_ ,				output_type=A_ ,				return_dict=A_ ,				callback=A_ ,				callback_steps=A_ ,				**A_ ,				)
       @torch.no_grad()
       def 							lowercase_							(     self ,				A_ ,				A_ = 512 ,				A_ = 512 ,				A_ = 50 ,				A_ = 7.5 ,				A_ = None ,				A_ = 1 ,				A_ = 0.0 ,				A_ = None ,				A_ = None ,				A_ = "pil" ,				A_ = True ,				A_ = None ,				A_ = 1 ,				**A_ ,				)     ->	Any:





         """simple docstring"""




         return self.pipea(
             prompt=A_ ,				height=A_ ,				width=A_ ,				num_inference_steps=A_ ,				guidance_scale=A_ ,				negative_prompt=A_ ,				num_images_per_prompt=A_ ,				eta=A_ ,				generator=A_ ,				latents=A_ ,				output_type=A_ ,				return_dict=A_ ,				callback=A_ ,				callback_steps=A_ ,				**A_ ,				)



       @torch.no_grad()
       def 							lowercase_							(     self ,				A_ ,				A_ = 512 ,				A_ = 512 ,				A_ = 50 ,				A_ = 7.5 ,				A_ = None ,				A_ = 1 ,				A_ = 0.0 ,				A_ = None ,				A_ = None ,				A_ = "pil" ,				A_ = True ,				A_ = None ,				A_ = 1 ,				**A_ ,				)     ->	Dict:





         """simple docstring"""




         _lowercase:			str       =     '''cuda''' if torch.cuda.is_available() else '''cpu'''
         self.to(A_       )

         # 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
         _lowercase:			Tuple       =     self.textaimg_sda_a(
             prompt=A_ ,				height=A_ ,				width=A_ ,				num_inference_steps=A_ ,				guidance_scale=A_ ,				negative_prompt=A_ ,				num_images_per_prompt=A_ ,				eta=A_ ,				generator=A_ ,				latents=A_ ,				output_type=A_ ,				return_dict=A_ ,				callback=A_ ,				callback_steps=A_ ,				**A_ ,				)

         # Get first result from Stable Diffusion Checkpoint v1.2
         _lowercase:			Dict       =     self.textaimg_sda_a(
             prompt=A_ ,				height=A_ ,				width=A_ ,				num_inference_steps=A_ ,				guidance_scale=A_ ,				negative_prompt=A_ ,				num_images_per_prompt=A_ ,				eta=A_ ,				generator=A_ ,				latents=A_ ,				output_type=A_ ,				return_dict=A_ ,				callback=A_ ,				callback_steps=A_ ,				**A_ ,				)

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

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

         # 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"""




import comet  # From: unbabel-comet
import torch

import datasets


A__   :					int							=   datasets.logging.get_logger(__name__)

A__   :					Optional[Any]							=   '\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = "{COMET}: A Neural Framework for {MT} Evaluation",\n   author = "Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon",\n   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n   month = nov,\n   year = "2020",\n   address = "Online",\n   publisher = "Association for Computational Linguistics",\n   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n   pages = "2685--2702",\n}\n'

A__   :					Union[str, Any]							=   '\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

A__   :					int							=   '\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,					_KWARGS_DESCRIPTION )
class 			__magic_name__       (       datasets.Metric ):
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         return datasets.MetricInfo(
             description=_DESCRIPTION ,				citation=_CITATION ,				homepage='''https://unbabel.github.io/COMET/html/index.html''' ,				inputs_description=_KWARGS_DESCRIPTION ,				features=datasets.Features(
                 {
                     '''sources''': datasets.Value('''string''' ,				id='''sequence'''       ),
                     '''predictions''': datasets.Value('''string''' ,				id='''sequence'''       ),
                     '''references''': datasets.Value('''string''' ,				id='''sequence'''       ),
                 }       ) ,				codebase_urls=['''https://github.com/Unbabel/COMET'''] ,				reference_urls=[
                 '''https://github.com/Unbabel/COMET''',
                 '''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
                 '''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
             ] ,				)
       def 							lowercase_							(     self ,				A_       )     ->	Dict:





         """simple docstring"""




         if self.config_name == "default":
           _lowercase:			str       =     comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da'''       )       )
         else:
           _lowercase:			Optional[Any]       =     comet.load_from_checkpoint(comet.download_model(self.config_name       )       )



       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_=None ,				A_=False       )     ->	List[str]:





         """simple docstring"""




         if gpus is None:
           _lowercase:			Union[str, Any]       =     1 if torch.cuda.is_available() else 0
         _lowercase:			Any       =     {'''src''': sources, '''mt''': predictions, '''ref''': references}
         _lowercase:			int       =     [dict(zip(A_ ,				A_       )       ) for t in zip(*data.values()       )]
         _lowercase ,				_lowercase:			Tuple       =     self.scorer.predict(A_ ,				gpus=A_ ,				progress_bar=A_       )
         return {"mean_score": mean_score, "scores": scores}
