


import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


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


class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      def __init__( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : AutoencoderKL  ,							SCREAMING_SNAKE_CASE__       : CLIPTextModel  ,							SCREAMING_SNAKE_CASE__       : CLIPTokenizer  ,							SCREAMING_SNAKE_CASE__       : UNetaDConditionModel  ,							SCREAMING_SNAKE_CASE__       : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]  ,							SCREAMING_SNAKE_CASE__       : StableDiffusionSafetyChecker  ,							SCREAMING_SNAKE_CASE__       : CLIPImageProcessor  ,							)	->    Optional[Any]:
             super().__init__()
             self.register_modules(
                 vae=SCREAMING_SNAKE_CASE__  ,							text_encoder=SCREAMING_SNAKE_CASE__  ,							tokenizer=SCREAMING_SNAKE_CASE__  ,							unet=SCREAMING_SNAKE_CASE__  ,							scheduler=SCREAMING_SNAKE_CASE__  ,							safety_checker=SCREAMING_SNAKE_CASE__  ,							feature_extractor=SCREAMING_SNAKE_CASE__  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[str, int]] = "auto"  )	->    str:
             if slice_size == "auto":
                    # half the attention head size is usually a good trade-off between
                    # speed and memory
                    a_      :	str							=						self.unet.config.attention_head_dim // 2
             self.unet.set_attention_slice(SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    str:
             self.enable_attention_slicing(SCREAMING_SNAKE_CASE__  )

      @torch.no_grad()
      def __call__( self       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Union[str, List[str]]  ,							SCREAMING_SNAKE_CASE__       : int = 5_1_2  ,							SCREAMING_SNAKE_CASE__       : int = 5_1_2  ,							SCREAMING_SNAKE_CASE__       : int = 5_0  ,							SCREAMING_SNAKE_CASE__       : float = 7.5  ,							SCREAMING_SNAKE_CASE__       : Optional[Union[str, List[str]]] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[int] = 1  ,							SCREAMING_SNAKE_CASE__       : float = 0.0  ,							SCREAMING_SNAKE_CASE__       : Optional[torch.Generator] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[torch.FloatTensor] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[str] = "pil"  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,							SCREAMING_SNAKE_CASE__       : int = 1  ,							SCREAMING_SNAKE_CASE__       : Optional[torch.FloatTensor] = None  ,							**SCREAMING_SNAKE_CASE__       : List[str]  ,							)	->    List[str]:

             if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	Tuple							=						1
             elif isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ):
                    a_      :	Optional[int]							=						len(SCREAMING_SNAKE_CASE__  )
             else:
                    raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(SCREAMING_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(SCREAMING_SNAKE_CASE__  ,							SCREAMING_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(SCREAMING_SNAKE_CASE__  )}."""  )

             # get prompt text embeddings
             a_      :	int							=						self.tokenizer(
                 SCREAMING_SNAKE_CASE__  ,							padding='max_length'  ,							max_length=self.tokenizer.model_max_length  ,							return_tensors='pt'  ,							)
             a_      :	Tuple							=						text_inputs.input_ids

             if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                    a_      :	Any							=						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}"""  )
                    a_      :	Dict							=						text_input_ids[:, : self.tokenizer.model_max_length]

             if text_embeddings is None:
                    a_      :	List[str]							=						self.text_encoder(text_input_ids.to(self.device  )  )[0]

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

                    a_      :	Tuple							=						text_input_ids.shape[-1]
                    a_      :	str							=						self.tokenizer(
                        SCREAMING_SNAKE_CASE__  ,							padding='max_length'  ,							max_length=SCREAMING_SNAKE_CASE__  ,							truncation=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  ,							)
                    a_      :	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
                    a_      :	Dict							=						uncond_embeddings.shape[1]
                    a_      :	Tuple							=						uncond_embeddings.repeat(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							1  )
                    a_      :	List[str]							=						uncond_embeddings.view(batch_size * num_images_per_prompt  ,							SCREAMING_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
                    a_      :	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`.
             a_      :	Tuple							=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
             a_      :	Any							=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
             a_      :	Dict							=						text_embeddings.dtype
             if latents is None:
                    if self.device.type == "mps":
                           # randn does not exist on mps
                           a_      :	Union[str, Any]							=						torch.randn(
                               SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							device='cpu'  ,							dtype=SCREAMING_SNAKE_CASE__  ).to(self.device  )
                           a_      :	Any							=						torch.randn(SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							device='cpu'  ,							dtype=SCREAMING_SNAKE_CASE__  ).to(
                               self.device  )
                    else:
                           a_      :	Tuple							=						torch.randn(
                               SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							device=self.device  ,							dtype=SCREAMING_SNAKE_CASE__  )
                           a_      :	List[Any]							=						torch.randn(SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							device=self.device  ,							dtype=SCREAMING_SNAKE_CASE__  )
             else:
                    if latents_reference.shape != latents_shape:
                           raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""  )
                    a_      :	str							=						latents_reference.to(self.device  )
                    a_      :	Optional[Any]							=						latents.to(self.device  )

             # This is the key part of the pipeline where we
             # try to ensure that the generated images w/ the same seed
             # but different sizes actually result in similar images
             a_      :	Union[str, Any]							=						(latents_shape[3] - latents_shape_reference[3]) // 2
             a_      :	Tuple							=						(latents_shape[2] - latents_shape_reference[2]) // 2
             a_      :	Any							=						latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
             a_      :	Union[str, Any]							=						latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
             a_      :	List[str]							=						0 if dx < 0 else dx
             a_      :	Any							=						0 if dy < 0 else dy
             a_      :	List[Any]							=						max(-dx  ,							0  )
             a_      :	Any							=						max(-dy  ,							0  )
             # import pdb
             # pdb.set_trace()
             a_      :	Tuple							=						latents_reference[:, :, dy : dy + h, dx : dx + w]

             # set timesteps
             self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  )

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

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

             for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE__  )  ):
                    # expand the latents if we are doing classifier free guidance
                    a_      :	Union[str, Any]							=						torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents
                    a_      :	str							=						self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    # predict the noise residual
                    a_      :	str							=						self.unet(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							encoder_hidden_states=SCREAMING_SNAKE_CASE__  ).sample

                    # perform guidance
                    if do_classifier_free_guidance:
                           a_      :	Dict							=						noise_pred.chunk(2  )
                           a_      :	List[Any]							=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                    # compute the previous noisy sample x_t -> x_t-1
                    a_      :	Union[str, Any]							=						self.scheduler.step(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  ).prev_sample

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

             a_      :	Dict							=						1 / 0.18215 * latents
             a_      :	Optional[int]							=						self.vae.decode(SCREAMING_SNAKE_CASE__  ).sample

             a_      :	int							=						(image / 2 + 0.5).clamp(0  ,							1  )

             # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
             a_      :	Optional[int]							=						image.cpu().permute(0  ,							2  ,							3  ,							1  ).float().numpy()

             if self.safety_checker is not None:
                    a_      :	List[Any]							=						self.feature_extractor(self.numpy_to_pil(SCREAMING_SNAKE_CASE__  )  ,							return_tensors='pt'  ).to(
                        self.device  )
                    a_      :	List[str]							=						self.safety_checker(
                        images=SCREAMING_SNAKE_CASE__  ,							clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype  )  )
             else:
                    a_      :	Any							=						None

             if output_type == "pil":
                    a_      :	Tuple							=						self.numpy_to_pil(SCREAMING_SNAKE_CASE__  )

             if not return_dict:
                    return (image, has_nsfw_concept)

             return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE__  ,							nsfw_content_detected=SCREAMING_SNAKE_CASE__  )






import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


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


UpperCAmelCase_   :				Dict						      = '\\n@InProceedings{moosavi2019minimum,\n    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n    year = {2019},\n    booktitle = {Proceedings of the 57th Annual Meeting of\n        the Association for Computational Linguistics (Volume 1: Long Papers)},\n    publisher = {Association for Computational Linguistics},\n    address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n    author = {Amit Bagga and Breck Baldwin},\n    title = {Algorithms for Scoring Coreference Chains},\n    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n    year = {1998},\n    pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n    author = {Xiaoqiang Luo},\n    title = {On coreference resolution performance metrics},\n    booktitle = {In Proc. of HLT/EMNLP},\n    year = {2005},\n    pages = {25--32},\n    publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n    title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",\n    author = "Moosavi, Nafise Sadat  and\n      Strube, Michael",\n    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",\n    month = aug,\n    year = "2016",\n    address = "Berlin, Germany",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/P16-1060",\n    doi = "10.18653/v1/P16-1060",\n    pages = "632--642",\n}\n\n'

UpperCAmelCase_   :				Tuple						      = '\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn	Type	Description\n1	Document ID	This is a variation on the document filename\n2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3	Word number\n4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.\n5	Part-of-Speech\n6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.\n7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"\n8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.\n9	Word sense	This is the word sense of the word in Column 3.\n10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11	Named Entities	These columns identifies the spans representing various named entities.\n12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN	Coreference	Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n'

UpperCAmelCase_   :				Optional[Any]						      = '\nCalculates coreference evaluation metrics.\nArgs:\n    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n        Each prediction is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n        Each reference is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    keep_singletons: After extracting all mentions of key or system files,\n        mentions whose corresponding coreference chain is of size one,\n        are considered as singletons. The default evaluation mode will include\n        singletons in evaluations if they are included in the key or the system files.\n        By setting \'keep_singletons=False\', all singletons in the key and system files\n        will be excluded from the evaluation.\n    NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n        leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.\n    min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.\n        Minimum spans are determined using the MINA algorithm.\n\nReturns:\n    \'mentions\': mentions\n    \'muc\': MUC metric [Vilain et al, 1995]\n    \'bcub\': B-cubed [Bagga and Baldwin, 1998]\n    \'ceafe\': CEAFe [Luo et al., 2005]\n    \'lea\': LEA [Moosavi and Strube, 2016]\n    \'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n    >>> coval = datasets.load_metric(\'coval\')\n    >>> words = [\'bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -\',\n    ... \'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)\',\n    ... \'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)\',\n    ... \'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -\',\n    ... \'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -\',\n    ... \'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -\']\n    >>> references = [words]\n    >>> predictions = [words]\n    >>> results = coval.compute(predictions=predictions, references=references)\n    >>> print(results) # doctest:+ELLIPSIS\n    {\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}\n'







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]						,					__A				:    Any						,					__A				:    List[Any]=False						,					__A				:    Tuple=False						,					__A				:    Any=True						,					__A				:    Any=False						,					__A				:    Any="dummy_doc"       )			->	List[str]:

       """simple docstring"""

       a_      :	List[str]							=						{doc: key_lines}
       a_      :	Optional[Any]							=						{doc: sys_lines}

       a_      :	List[Any]							=						{}

       a_      :	Tuple							=						0
       a_      :	List[str]							=						0
       a_      :	Union[str, Any]							=						0
       a_      :	List[Any]							=						0
       a_      :	List[str]							=						0
       a_      :	Union[str, Any]							=						0

       a_							,	a_      :	List[Any]							=						reader.get_doc_mentions(__A						,					key_doc_lines[doc]						,					__A       )
       key_singletons_num += singletons_num

       if NP_only or min_span:
              a_      :	Union[str, Any]							=						reader.set_annotated_parse_trees(__A						,					key_doc_lines[doc]						,					__A						,					__A       )

       a_							,	a_      :	Union[str, Any]							=						reader.get_doc_mentions(__A						,					sys_doc_lines[doc]						,					__A       )
       sys_singletons_num += singletons_num

       if NP_only or min_span:
              a_      :	Dict							=						reader.set_annotated_parse_trees(__A						,					key_doc_lines[doc]						,					__A						,					__A       )

       if remove_nested:
              a_							,	a_      :	Optional[Any]							=						reader.remove_nested_coref_mentions(__A						,					__A       )
              key_nested_coref_num += nested_mentions
              key_removed_nested_clusters += removed_clusters

              a_							,	a_      :	Optional[Any]							=						reader.remove_nested_coref_mentions(__A						,					__A       )
              sys_nested_coref_num += nested_mentions
              sys_removed_nested_clusters += removed_clusters

       a_      :	int							=						reader.get_mention_assignments(__A						,					__A       )
       a_      :	List[Any]							=						reader.get_mention_assignments(__A						,					__A       )

       a_      :	List[Any]							=						(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

       if remove_nested:
              logger.info(
                  'Number of removed nested coreferring mentions in the key '
                  F"""annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}"""       )
              logger.info(
                  'Number of resulting singleton clusters in the key '
                  F"""annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}"""       )

       if not keep_singletons:
              logger.info(
                  F"""{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system """
                  'files, respectively'       )

       return doc_coref_infos







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    Optional[int]						,					__A				:    Optional[Any]						,					__A				:    Optional[int]						,					__A				:    Tuple						,					__A				:    Dict						,					__A				:    Optional[int]       )			->	List[Any]:

       """simple docstring"""

       a_      :	int							=						get_coref_infos(__A						,					__A						,					__A						,					__A						,					__A						,					__A       )

       a_      :	List[Any]							=						{}
       a_      :	int							=						0
       a_      :	Optional[int]							=						0

       for name, metric in metrics:
              a_							,	a_							,	a_      :	Tuple							=						evaluator.evaluate_documents(__A						,					__A						,					beta=1       )
              if name in ["muc", "bcub", "ceafe"]:
                     conll += fa
                     conll_subparts_num += 1
              output_scores.update({F"""{name}/recall""": recall, F"""{name}/precision""": precision, F"""{name}/f1""": fa}       )

              logger.info(
                  name.ljust(10       )						,					F"""Recall: {recall * 1_00:.2f}"""						,					F""" Precision: {precision * 1_00:.2f}"""						,					F""" F1: {fa * 1_00:.2f}"""						,					)

       if conll_subparts_num == 3:
              a_      :	List[str]							=						(conll / 3) * 1_00
              logger.info(F"""CoNLL score: {conll:.2f}"""       )
              output_scores.update({'conll_score': conll}       )

       return output_scores







def  SCREAMING_SNAKE_CASE_		(    __A				:    int       )			->	Dict:

       """simple docstring"""

       a_      :	List[Any]							=						False
       for line in key_lines:
              if not line.startswith('#'       ):
                     if len(line.split()       ) > 6:
                            a_      :	List[Any]							=						line.split()[5]
                            if not parse_col == "-":
                                   a_      :	List[Any]							=						True
                                   break
                            else:
                                   break
       return has_gold_parse
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,					_KWARGS_DESCRIPTION    )
class 				SCREAMING_SNAKE_CASE__						(       datasets.Metric    ):
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    int:
             return datasets.MetricInfo(
                 description=_DESCRIPTION  ,							citation=_CITATION  ,							inputs_description=_KWARGS_DESCRIPTION  ,							features=datasets.Features(
                     {
                         'predictions': datasets.Sequence(datasets.Value('string'  )  ),
                         'references': datasets.Sequence(datasets.Value('string'  )  ),
                     }  )  ,							codebase_urls=['https://github.com/ns-moosavi/coval']  ,							reference_urls=[
                     'https://github.com/ns-moosavi/coval',
                     'https://www.aclweb.org/anthology/P16-1060',
                     'http://www.conll.cemantix.org/2012/data.html',
                 ]  ,							)

      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : List[str]=True  ,							SCREAMING_SNAKE_CASE__       : Tuple=False  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=False  ,							SCREAMING_SNAKE_CASE__       : int=False  )	->    Tuple:
             a_      :	List[str]							=						[
                 ('mentions', evaluator.mentions),
                 ('muc', evaluator.muc),
                 ('bcub', evaluator.b_cubed),
                 ('ceafe', evaluator.ceafe),
                 ('lea', evaluator.lea),
             ]

             if min_span:
                    a_      :	Union[str, Any]							=						util.check_gold_parse_annotation(SCREAMING_SNAKE_CASE__  )
                    if not has_gold_parse:
                           raise NotImplementedError('References should have gold parse annotation to use \'min_span\'.'  )
                           # util.parse_key_file(key_file)
                           # key_file = key_file + ".parsed"

             a_      :	List[Any]							=						evaluate(
                 key_lines=SCREAMING_SNAKE_CASE__  ,							sys_lines=SCREAMING_SNAKE_CASE__  ,							metrics=SCREAMING_SNAKE_CASE__  ,							NP_only=SCREAMING_SNAKE_CASE__  ,							remove_nested=SCREAMING_SNAKE_CASE__  ,							keep_singletons=SCREAMING_SNAKE_CASE__  ,							min_span=SCREAMING_SNAKE_CASE__  ,							)

             return score


