"""simple docstring"""


import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def 			lowerCamelCase_ (					)							->					Any:




						'''simple docstring'''



						def filter_log(__lowerCAmelCase				):
												return False if "This IS expected if you are initializing" in record.msg else True

						lowerCamelCase__					=datasets.utils.logging.get_logger("transformers.modeling_utils"				)
						logger.addFilter(__lowerCAmelCase				)
						try:
												yield
						finally:
												logger.removeFilter(__lowerCAmelCase				)


a							='\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'

a							='\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

a							='\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'






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

				def 		_a	(      self      ):
										return datasets.MetricInfo(
										    description=_DESCRIPTION	, citation=_CITATION	, homepage="https://github.com/Tiiiger/bert_score"	, inputs_description=_KWARGS_DESCRIPTION	, features=datasets.Features(
										        {
										            "predictions": datasets.Value("string"	, id="sequence"      ),
										            "references": datasets.Sequence(datasets.Value("string"	, id="sequence"      )	, id="references"      ),
										        }      )	, codebase_urls=["https://github.com/Tiiiger/bert_score"]	, reference_urls=[
										        "https://github.com/Tiiiger/bert_score",
										        "https://arxiv.org/abs/1904.09675",
										    ]	, )

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase=None	, _lowerCamelCase=None	, _lowerCamelCase=None	, _lowerCamelCase=False	, _lowerCamelCase=False	, _lowerCamelCase=None	, _lowerCamelCase=64	, _lowerCamelCase=4	, _lowerCamelCase=False	, _lowerCamelCase=False	, _lowerCamelCase=None	, _lowerCamelCase=False	, ):
										lowerCamelCase__					=bert_score.utils.get_hash
										lowerCamelCase__					=bert_score.BERTScorer

										if version.parse(bert_score.__version__      ) >= version.parse("0.3.10"      ):
																lowerCamelCase__					=functools.partial(_lowerCamelCase	, use_fast_tokenizer=_lowerCamelCase      )
																lowerCamelCase__					=functools.partial(_lowerCamelCase	, use_fast_tokenizer=_lowerCamelCase      )
										elif use_fast_tokenizer:
																raise ImportWarning(
																    "To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"
																    "You can install it with `pip install \"bert-score>=0.3.10\"`."      )

										if model_type is None:
																assert lang is not None, "either lang or model_type should be specified"
																lowerCamelCase__					=bert_score.utils.langamodel[lang.lower()]

										if num_layers is None:
																lowerCamelCase__					=bert_score.utils.modelalayers[model_type]

										lowerCamelCase__					=get_hash(
										    model=_lowerCamelCase	, num_layers=_lowerCamelCase	, idf=_lowerCamelCase	, rescale_with_baseline=_lowerCamelCase	, use_custom_baseline=baseline_path is not None	, )

										with filter_logging_context():
																if not hasattr(self	, "cached_bertscorer"      ) or self.cached_bertscorer.hash != hashcode:
																						lowerCamelCase__					=scorer(
																						    model_type=_lowerCamelCase	, num_layers=_lowerCamelCase	, batch_size=_lowerCamelCase	, nthreads=_lowerCamelCase	, all_layers=_lowerCamelCase	, idf=_lowerCamelCase	, device=_lowerCamelCase	, lang=_lowerCamelCase	, rescale_with_baseline=_lowerCamelCase	, baseline_path=_lowerCamelCase	, )

										((lowerCamelCase__)					,      (lowerCamelCase__)					,      (lowerCamelCase__))					=self.cached_bertscorer.score(
										    cands=_lowerCamelCase	, refs=_lowerCamelCase	, verbose=_lowerCamelCase	, batch_size=_lowerCamelCase	, )
										lowerCamelCase__					={
										    "precision": P.tolist(),
										    "recall": R.tolist(),
										    "f1": F.tolist(),
										    "hashcode": hashcode,
										}
										return output_dict

				def 		_a	(      self	, _lowerCamelCase=None	, _lowerCamelCase=None	, **_lowerCamelCase      ):
										# References can be strings or lists of strings
										# Let's change strings to lists of strings with one element
										if references is not None:
																lowerCamelCase__					=[[ref] if isinstance(_lowerCamelCase	, _lowerCamelCase      ) else ref for ref in references]
										super().add_batch(predictions=_lowerCamelCase	, references=_lowerCamelCase	, **_lowerCamelCase      )






				def 		_a	(      self	, _lowerCamelCase=None	, _lowerCamelCase=None	, **_lowerCamelCase      ):
										# References can be strings or lists of strings
										# Let's change strings to lists of strings with one element
										if isinstance(_lowerCamelCase	, _lowerCamelCase      ):
																lowerCamelCase__					=[reference]
										super().add(prediction=_lowerCamelCase	, reference=_lowerCamelCase	, **_lowerCamelCase      )



"""simple docstring"""


from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)






class    __UpperCAmelCase						(	__lowerCAmelCase	):

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, ):
										super().__init__()

										self.register_modules(vae=_lowerCamelCase	, text_encoder=_lowerCamelCase	, tokenizer=_lowerCamelCase	, unet=_lowerCamelCase	, scheduler=_lowerCamelCase      )

				def 		_a	(      self	, _lowerCamelCase      ):
										with torch.no_grad():
																lowerCamelCase__					=self.vae.encode(tfms.ToTensor()(_lowerCamelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
																lowerCamelCase__					=0.1_8_2_1_5 * latent.latent_dist.sample()
										return latent

				def 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=(1 / 0.1_8_2_1_5) * latent
										with torch.no_grad():
																lowerCamelCase__					=self.vae.decode(_lowerCamelCase      ).sample
										lowerCamelCase__					=(img / 2 + 0.5).clamp(0	, 1      )
										lowerCamelCase__					=img.detach().cpu().permute(0	, 2	, 3	, 1      ).numpy()
										lowerCamelCase__					=(img * 255).round().astype("uint8"      )
										return Image.fromarray(img[0]      )

				def 		_a	(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=self.tokenizer(
										    _lowerCamelCase	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=_lowerCamelCase	, return_tensors="pt"	, )

										lowerCamelCase__					=self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

										lowerCamelCase__					=self.tokenizer(
										    ""	, padding="max_length"	, max_length=self.tokenizer.model_max_length	, truncation=_lowerCamelCase	, return_tensors="pt"	, )

										lowerCamelCase__					=self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

										return torch.cat([uncond_embedding, text_embedding]      )






				def __call__(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase = 0.3	, _lowerCamelCase = 0.6	, _lowerCamelCase = 0.5	, _lowerCamelCase = 42	, _lowerCamelCase = 50	, _lowerCamelCase = 7.5	, ):
										lowerCamelCase__					=steps - int(kmin * steps      )
										lowerCamelCase__					=steps - int(kmax * steps      )

										lowerCamelCase__					=self.prep_text(_lowerCamelCase      )

										self.scheduler.set_timesteps(_lowerCamelCase      )

										lowerCamelCase__					,      lowerCamelCase__					=img.size
										lowerCamelCase__					=self.encode(_lowerCamelCase      )

										torch.manual_seed(_lowerCamelCase      )
										lowerCamelCase__					=torch.randn(
										    (1, self.unet.config.in_channels, height // 8, width // 8)	, ).to(self.device      )

										lowerCamelCase__					=self.scheduler.add_noise(
										    _lowerCamelCase	, _lowerCamelCase	, timesteps=self.scheduler.timesteps[tmax]	, )

										lowerCamelCase__					=torch.cat([latents] * 2      )

										lowerCamelCase__					=self.scheduler.scale_model_input(_lowerCamelCase	, self.scheduler.timesteps[tmax]      )

										with torch.no_grad():
																lowerCamelCase__					=self.unet(
																    _lowerCamelCase	, self.scheduler.timesteps[tmax]	, encoder_hidden_states=_lowerCamelCase	, ).sample

										lowerCamelCase__					,      lowerCamelCase__					=pred.chunk(2      )
										lowerCamelCase__					=pred_uncond + guidance_scale * (pred_text - pred_uncond)

										lowerCamelCase__					=self.scheduler.step(_lowerCamelCase	, self.scheduler.timesteps[tmax]	, _lowerCamelCase      ).prev_sample

										for i, t in enumerate(tqdm(self.scheduler.timesteps      )      ):
																if i > tmax:
																						if i < tmin:  # layout generation phase
																												lowerCamelCase__					=self.scheduler.add_noise(
																												    _lowerCamelCase	, _lowerCamelCase	, timesteps=_lowerCamelCase	, )

																												lowerCamelCase__					=(mix_factor * latents) + (
																												    1 - mix_factor
																												) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																												lowerCamelCase__					=torch.cat([input] * 2      )

																						else:  # content generation phase
																												lowerCamelCase__					=torch.cat([latents] * 2      )

																						lowerCamelCase__					=self.scheduler.scale_model_input(_lowerCamelCase	, _lowerCamelCase      )

																						with torch.no_grad():
																												lowerCamelCase__					=self.unet(
																												    _lowerCamelCase	, _lowerCamelCase	, encoder_hidden_states=_lowerCamelCase	, ).sample

																						lowerCamelCase__					,      lowerCamelCase__					=pred.chunk(2      )
																						lowerCamelCase__					=pred_uncond + guidance_scale * (pred_text - pred_uncond)

																						lowerCamelCase__					=self.scheduler.step(_lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ).prev_sample

										return self.decode(_lowerCamelCase      )


