
import datasets

from .evaluate import evaluate


__A				='''\
@article{hendrycks2021cuad,
      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
      journal={arXiv preprint arXiv:2103.06268},
      year={2021}
}
'''

__A				='''
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
'''

__A				='''
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair as given in the references (see below)
        - \'prediction_text\': list of possible texts for the answer, as a list of strings
        depending on a threshold on the confidence probability of each prediction.
    references: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair (see above),
        - \'answers\': a Dict in the CUAD dataset format
            {
                \'text\': list of possible texts for the answer, as a list of strings
                \'answer_start\': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'aupr\': Area Under the Precision-Recall curve
    \'prec_at_80_recall\': Precision at 80% recall
    \'prec_at_90_recall\': Precision at 90% recall
Examples:
    >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]
    >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]
    >>> cuad_metric = datasets.load_metric("cuad")
    >>> results = cuad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}
'''




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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					return datasets.MetricInfo(
					    description=_DESCRIPTION						,  citation=_CITATION						,  inputs_description=_KWARGS_DESCRIPTION						,  features=datasets.Features(
					        {
					            "predictions": {
					                "id": datasets.Value("string" ),
					                "prediction_text": datasets.features.Sequence(datasets.Value("string" ) ),
					            },
					            "references": {
					                "id": datasets.Value("string" ),
					                "answers": datasets.features.Sequence(
					                    {
					                        "text": datasets.Value("string" ),
					                        "answer_start": datasets.Value("int32" ),
					                    } ),
					            },
					        } )						,  codebase_urls=["https://www.atticusprojectai.org/cuad"]						,  reference_urls=["https://www.atticusprojectai.org/cuad"]						,  )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       Any:
					lowerCamelCase_						=			{prediction["id"]: prediction["prediction_text"] for prediction in predictions}
					lowerCamelCase_						=			[
					    {
					        "paragraphs": [
					            {
					                "qas": [
					                    {
					                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
					                        "id": ref["id"],
					                    }
					                    for ref in references
					                ]
					            }
					        ]
					    }
					]
					lowerCamelCase_						=			evaluate(dataset=lowercase						,  predictions=lowercase )
					return score




from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


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




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       List[Any]:
					super().__init__()

					if hasattr(scheduler.config						,  "steps_offset" ) and scheduler.config.steps_offset != 1:
									lowerCamelCase_						=			(
									    f'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
									    f' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
									    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
									    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
									    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
									    " file"
									)
									deprecate("steps_offset!=1"						,  "1.0.0"						,  lowercase						,  standard_warn=lowercase )
									lowerCamelCase_						=			dict(scheduler.config )
									lowerCamelCase_						=			1
									lowerCamelCase_						=			FrozenDict(lowercase )

					if hasattr(scheduler.config						,  "skip_prk_steps" ) and scheduler.config.skip_prk_steps is False:
									lowerCamelCase_						=			(
									    f'The configuration file of this scheduler: {scheduler} has not set the configuration'
									    " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
									    " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
									    " incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
									    " Hub, it would be very nice if you could open a Pull request for the"
									    " `scheduler/scheduler_config.json` file"
									)
									deprecate("skip_prk_steps not set"						,  "1.0.0"						,  lowercase						,  standard_warn=lowercase )
									lowerCamelCase_						=			dict(scheduler.config )
									lowerCamelCase_						=			True
									lowerCamelCase_						=			FrozenDict(lowercase )

					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(
					    segmentation_model=lowercase						,  segmentation_processor=lowercase						,  vae=lowercase						,  text_encoder=lowercase						,  tokenizer=lowercase						,  unet=lowercase						,  scheduler=lowercase						,  safety_checker=lowercase						,  feature_extractor=lowercase						,  )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase = "auto" )				->       Tuple:
					if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									lowerCamelCase_						=			self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					self.enable_attention_slicing(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					if is_accelerate_available():
									from accelerate import cpu_offload
					else:
									raise ImportError("Please install accelerate via `pip install accelerate`" )

					lowerCamelCase_						=			torch.device("cuda" )

					for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
									if cpu_offloaded_model is not None:
													cpu_offload(lowercase						,  lowercase )



	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					if self.device != torch.device("meta" ) or not hasattr(self.unet						,  "_hf_hook" ):
									return self.device
					for module in self.unet.modules():
									if (
									    hasattr(lowercase						,  "_hf_hook" )
									    and hasattr(module._hf_hook						,  "execution_device" )
									    and module._hf_hook.execution_device is not None
									):
													return torch.device(module._hf_hook.execution_device )
					return self.device



	@torch.no_grad()
	def __call__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase = 512						,  lowercase = 512						,  lowercase = 50						,  lowercase = 7.5						,  lowercase = None						,  lowercase = 1						,  lowercase = 0.0						,  lowercase = None						,  lowercase = None						,  lowercase = "pil"						,  lowercase = True						,  lowercase = None						,  lowercase = 1						,  **lowercase						,  )				->       int:
					lowerCamelCase_						=			self.segmentation_processor(
					    text=[text]						,  images=[image]						,  padding="max_length"						,  return_tensors="pt" ).to(self.device )
					lowerCamelCase_						=			self.segmentation_model(**lowercase )
					lowerCamelCase_						=			torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
					lowerCamelCase_						=			self.numpy_to_pil(lowercase )[0].resize(image.size )

					# Run inpainting pipeline with the generated mask
					lowerCamelCase_						=			StableDiffusionInpaintPipeline(
					    vae=self.vae						,  text_encoder=self.text_encoder						,  tokenizer=self.tokenizer						,  unet=self.unet						,  scheduler=self.scheduler						,  safety_checker=self.safety_checker						,  feature_extractor=self.feature_extractor						,  )
					return inpainting_pipeline(
					    prompt=lowercase						,  image=lowercase						,  mask_image=lowercase						,  height=lowercase						,  width=lowercase						,  num_inference_steps=lowercase						,  guidance_scale=lowercase						,  negative_prompt=lowercase						,  num_images_per_prompt=lowercase						,  eta=lowercase						,  generator=lowercase						,  latents=lowercase						,  output_type=lowercase						,  return_dict=lowercase						,  callback=lowercase						,  callback_steps=lowercase						,  )


