


import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 	__lowercase					(	__snake_case			,       unittest.TestCase	):
      UpperCamelCase										=							UnCLIPImageVariationPipeline
      UpperCamelCase										=							IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
      UpperCamelCase										=							IMAGE_VARIATION_BATCH_PARAMS

      UpperCamelCase										=							[
          '''generator''',
          '''return_dict''',
          '''decoder_num_inference_steps''',
          '''super_res_num_inference_steps''',
      ]
      UpperCamelCase										=							False





      @property
      def 							_lowercase					(		self    :       Optional[int]							)     -> List[str]:






         """simple docstring"""







         return 3_2





      @property
      def 							_lowercase					(		self    :       Optional[int]							)     -> str:






         """simple docstring"""







         return 3_2





      @property
      def 							_lowercase					(		self    :       List[str]							)     -> Any:






         """simple docstring"""







         return self.time_input_dim





      @property
      def 							_lowercase					(		self    :       List[str]							)     -> Optional[Any]:






         """simple docstring"""







         return self.time_input_dim * 4





      @property
      def 							_lowercase					(		self    :       Tuple							)     -> Union[str, Any]:






         """simple docstring"""







         return 1_0_0





      @property
      def 							_lowercase					(		self    :       List[Any]							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
         return tokenizer





      @property
      def 							_lowercase					(		self    :       Dict							)     -> Optional[Any]:






         """simple docstring"""







         torch.manual_seed(0							)
         UpperCAmelCase      					=  CLIPTextConfig(
             bos_token_id=0       ,							eos_token_id=2       ,							hidden_size=self.text_embedder_hidden_size       ,							projection_dim=self.text_embedder_hidden_size       ,							intermediate_size=3_7       ,							layer_norm_eps=1e-0_5       ,							num_attention_heads=4       ,							num_hidden_layers=5       ,							pad_token_id=1       ,							vocab_size=1_0_0_0       ,							)
         return CLIPTextModelWithProjection(__lowerCamelCase							)





      @property
      def 							_lowercase					(		self    :       Dict							)     -> Any:






         """simple docstring"""







         torch.manual_seed(0							)
         UpperCAmelCase      					=  CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size       ,							projection_dim=self.text_embedder_hidden_size       ,							num_hidden_layers=5       ,							num_attention_heads=4       ,							image_size=3_2       ,							intermediate_size=3_7       ,							patch_size=1       ,							)
         return CLIPVisionModelWithProjection(__lowerCamelCase							)





      @property
      def 							_lowercase					(		self    :       str							)     -> Dict:






         """simple docstring"""







         torch.manual_seed(0							)

         UpperCAmelCase      					=  {
             """clip_embeddings_dim""": self.text_embedder_hidden_size,
             """time_embed_dim""": self.time_embed_dim,
             """cross_attention_dim""": self.cross_attention_dim,
         }

         UpperCAmelCase      					=  UnCLIPTextProjModel(**__lowerCamelCase							)
         return model





      @property
      def 							_lowercase					(		self    :       Optional[int]							)     -> int:






         """simple docstring"""







         torch.manual_seed(0							)

         UpperCAmelCase      					=  {
             """sample_size""": 3_2,
             # RGB in channels
             """in_channels""": 3,
             # Out channels is double in channels because predicts mean and variance
             """out_channels""": 6,
             """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
             """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
             """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
             """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
             """layers_per_block""": 1,
             """cross_attention_dim""": self.cross_attention_dim,
             """attention_head_dim""": 4,
             """resnet_time_scale_shift""": """scale_shift""",
             """class_embed_type""": """identity""",
         }

         UpperCAmelCase      					=  UNetaDConditionModel(**__lowerCamelCase							)
         return model





      @property
      def 							_lowercase					(		self    :       Tuple							)     -> Optional[Any]:






         """simple docstring"""







         return {
             "sample_size": 6_4,
             "layers_per_block": 1,
             "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
             "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
             "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
             "in_channels": 6,
             "out_channels": 3,
         }





      @property
      def 							_lowercase					(		self    :       Optional[int]							)     -> Optional[Any]:






         """simple docstring"""







         torch.manual_seed(0							)

         UpperCAmelCase      					=  UNetaDModel(**self.dummy_super_res_kwargs							)
         return model





      @property
      def 							_lowercase					(		self    :       int							)     -> Tuple:






         """simple docstring"""







         torch.manual_seed(1							)

         UpperCAmelCase      					=  UNetaDModel(**self.dummy_super_res_kwargs							)
         return model





      def 							_lowercase					(		self    :       Optional[Any]							)     -> int:






         """simple docstring"""







         UpperCAmelCase      					=  self.dummy_decoder
         UpperCAmelCase      					=  self.dummy_text_proj
         UpperCAmelCase      					=  self.dummy_text_encoder
         UpperCAmelCase      					=  self.dummy_tokenizer
         UpperCAmelCase      					=  self.dummy_super_res_first
         UpperCAmelCase      					=  self.dummy_super_res_last

         UpperCAmelCase      					=  UnCLIPScheduler(
             variance_type="""learned_range"""       ,							prediction_type="""epsilon"""       ,							num_train_timesteps=1_0_0_0       ,							)

         UpperCAmelCase      					=  UnCLIPScheduler(
             variance_type="""fixed_small_log"""       ,							prediction_type="""epsilon"""       ,							num_train_timesteps=1_0_0_0       ,							)

         UpperCAmelCase      					=  CLIPImageProcessor(crop_size=3_2       ,							size=3_2							)

         UpperCAmelCase      					=  self.dummy_image_encoder

         return {
             "decoder": decoder,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "text_proj": text_proj,
             "feature_extractor": feature_extractor,
             "image_encoder": image_encoder,
             "super_res_first": super_res_first,
             "super_res_last": super_res_last,
             "decoder_scheduler": decoder_scheduler,
             "super_res_scheduler": super_res_scheduler,
         }





      def 							_lowercase					(		self    :       Optional[int]       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       Any=0       ,							__lowerCamelCase    :       str=True							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  floats_tensor((1, 3, 3_2, 3_2)       ,							rng=random.Random(__lowerCamelCase							)							).to(__lowerCamelCase							)
         if str(__lowerCamelCase							).startswith("""mps"""							):
            UpperCAmelCase      					=  torch.manual_seed(__lowerCamelCase							)
         else:
            UpperCAmelCase      					=  torch.Generator(device=__lowerCamelCase							).manual_seed(__lowerCamelCase							)

         if pil_image:
            UpperCAmelCase      					=  input_image * 0.5 + 0.5
            UpperCAmelCase      					=  input_image.clamp(0       ,							1							)
            UpperCAmelCase      					=  input_image.cpu().permute(0       ,							2       ,							3       ,							1							).float().numpy()
            UpperCAmelCase      					=  DiffusionPipeline.numpy_to_pil(__lowerCamelCase							)[0]

         return {
             "image": input_image,
             "generator": generator,
             "decoder_num_inference_steps": 2,
             "super_res_num_inference_steps": 2,
             "output_type": "np",
         }





      def 							_lowercase					(		self    :       Tuple							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  """cpu"""

         UpperCAmelCase      					=  self.get_dummy_components()

         UpperCAmelCase      					=  self.pipeline_class(**__lowerCamelCase							)
         UpperCAmelCase      					=  pipe.to(__lowerCamelCase							)

         pipe.set_progress_bar_config(disable=__lowerCamelCase							)

         UpperCAmelCase      					=  self.get_dummy_inputs(__lowerCamelCase       ,							pil_image=__lowerCamelCase							)

         UpperCAmelCase      					=  pipe(**__lowerCamelCase							)
         UpperCAmelCase      					=  output.images

         UpperCAmelCase      					=  self.get_dummy_inputs(__lowerCamelCase       ,							pil_image=__lowerCamelCase							)

         UpperCAmelCase      					=  pipe(
             **__lowerCamelCase       ,							return_dict=__lowerCamelCase       ,							)[0]

         UpperCAmelCase      					=  image[0, -3:, -3:, -1]
         UpperCAmelCase      					=  image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         UpperCAmelCase      					=  np.array(
             [
                 0.9_997,
                 0.0_002,
                 0.9_997,
                 0.9_997,
                 0.9_969,
                 0.0_023,
                 0.9_997,
                 0.9_969,
                 0.9_970,
             ]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2





      def 							_lowercase					(		self    :       int							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  """cpu"""

         UpperCAmelCase      					=  self.get_dummy_components()

         UpperCAmelCase      					=  self.pipeline_class(**__lowerCamelCase							)
         UpperCAmelCase      					=  pipe.to(__lowerCamelCase							)

         pipe.set_progress_bar_config(disable=__lowerCamelCase							)

         UpperCAmelCase      					=  self.get_dummy_inputs(__lowerCamelCase       ,							pil_image=__lowerCamelCase							)

         UpperCAmelCase      					=  pipe(**__lowerCamelCase							)
         UpperCAmelCase      					=  output.images

         UpperCAmelCase      					=  self.get_dummy_inputs(__lowerCamelCase       ,							pil_image=__lowerCamelCase							)

         UpperCAmelCase      					=  pipe(
             **__lowerCamelCase       ,							return_dict=__lowerCamelCase       ,							)[0]

         UpperCAmelCase      					=  image[0, -3:, -3:, -1]
         UpperCAmelCase      					=  image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         UpperCAmelCase      					=  np.array([0.9_997, 0.0_003, 0.9_997, 0.9_997, 0.9_970, 0.0_024, 0.9_997, 0.9_971, 0.9_971]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2





      def 							_lowercase					(		self    :       Any							)     -> int:






         """simple docstring"""







         UpperCAmelCase      					=  """cpu"""

         UpperCAmelCase      					=  self.get_dummy_components()

         UpperCAmelCase      					=  self.pipeline_class(**__lowerCamelCase							)
         UpperCAmelCase      					=  pipe.to(__lowerCamelCase							)

         pipe.set_progress_bar_config(disable=__lowerCamelCase							)

         UpperCAmelCase      					=  self.get_dummy_inputs(__lowerCamelCase       ,							pil_image=__lowerCamelCase							)
         UpperCAmelCase      					=  [
             pipeline_inputs["""image"""],
             pipeline_inputs["""image"""],
         ]

         UpperCAmelCase      					=  pipe(**__lowerCamelCase							)
         UpperCAmelCase      					=  output.images

         UpperCAmelCase      					=  self.get_dummy_inputs(__lowerCamelCase       ,							pil_image=__lowerCamelCase							)
         UpperCAmelCase      					=  [
             tuple_pipeline_inputs["""image"""],
             tuple_pipeline_inputs["""image"""],
         ]

         UpperCAmelCase      					=  pipe(
             **__lowerCamelCase       ,							return_dict=__lowerCamelCase       ,							)[0]

         UpperCAmelCase      					=  image[0, -3:, -3:, -1]
         UpperCAmelCase      					=  image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (2, 6_4, 6_4, 3)

         UpperCAmelCase      					=  np.array(
             [
                 0.9_997,
                 0.9_989,
                 0.0_008,
                 0.0_021,
                 0.9_960,
                 0.0_018,
                 0.0_014,
                 0.0_002,
                 0.9_933,
             ]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2





      def 							_lowercase					(		self    :       Optional[Any]							)     -> List[str]:






         """simple docstring"""







         UpperCAmelCase      					=  torch.device("""cpu"""							)






         class 	__lowercase					:
               UpperCamelCase										=							1

         UpperCAmelCase      					=  self.get_dummy_components()

         UpperCAmelCase      					=  self.pipeline_class(**__lowerCamelCase							)
         UpperCAmelCase      					=  pipe.to(__lowerCamelCase							)

         pipe.set_progress_bar_config(disable=__lowerCamelCase							)

         UpperCAmelCase      					=  torch.Generator(device=__lowerCamelCase							).manual_seed(0							)
         UpperCAmelCase      					=  pipe.decoder.dtype
         UpperCAmelCase      					=  1

         UpperCAmelCase      					=  (
             batch_size,
             pipe.decoder.config.in_channels,
             pipe.decoder.config.sample_size,
             pipe.decoder.config.sample_size,
         )
         UpperCAmelCase      					=  pipe.prepare_latents(
             __lowerCamelCase       ,							dtype=__lowerCamelCase       ,							device=__lowerCamelCase       ,							generator=__lowerCamelCase       ,							latents=__lowerCamelCase       ,							scheduler=DummyScheduler()							)

         UpperCAmelCase      					=  (
             batch_size,
             pipe.super_res_first.config.in_channels // 2,
             pipe.super_res_first.config.sample_size,
             pipe.super_res_first.config.sample_size,
         )
         UpperCAmelCase      					=  pipe.prepare_latents(
             __lowerCamelCase       ,							dtype=__lowerCamelCase       ,							device=__lowerCamelCase       ,							generator=__lowerCamelCase       ,							latents=__lowerCamelCase       ,							scheduler=DummyScheduler()							)

         UpperCAmelCase      					=  self.get_dummy_inputs(__lowerCamelCase       ,							pil_image=__lowerCamelCase							)

         UpperCAmelCase      					=  pipe(
             **__lowerCamelCase       ,							decoder_latents=__lowerCamelCase       ,							super_res_latents=__lowerCamelCase							).images

         UpperCAmelCase      					=  self.get_dummy_inputs(__lowerCamelCase       ,							pil_image=__lowerCamelCase							)
         # Don't pass image, instead pass embedding
         UpperCAmelCase      					=  pipeline_inputs.pop("""image"""							)
         UpperCAmelCase      					=  pipe.image_encoder(__lowerCamelCase							).image_embeds

         UpperCAmelCase      					=  pipe(
             **__lowerCamelCase       ,							decoder_latents=__lowerCamelCase       ,							super_res_latents=__lowerCamelCase       ,							image_embeddings=__lowerCamelCase       ,							).images

         # make sure passing text embeddings manually is identical
         assert np.abs(img_out_a - img_out_a							).max() < 1e-4





      @skip_mps
      def 							_lowercase					(		self    :       int							)     -> Any:






         """simple docstring"""







         UpperCAmelCase      					=  torch_device == """cpu"""

         # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
         UpperCAmelCase      					=  1e-2

         self._test_attention_slicing_forward_pass(
             test_max_difference=__lowerCamelCase       ,							expected_max_diff=__lowerCamelCase							)





      @skip_mps
      def 							_lowercase					(		self    :       Dict							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  torch_device == """cpu"""
         UpperCAmelCase      					=  True
         UpperCAmelCase      					=  [
             """decoder_num_inference_steps""",
             """super_res_num_inference_steps""",
         ]

         self._test_inference_batch_single_identical(
             test_max_difference=__lowerCamelCase       ,							relax_max_difference=__lowerCamelCase       ,							additional_params_copy_to_batched_inputs=__lowerCamelCase       ,							)





      def 							_lowercase					(		self    :       Dict							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  [
             """decoder_num_inference_steps""",
             """super_res_num_inference_steps""",
         ]

         if torch_device == "mps":
            # TODO: MPS errors with larger batch sizes
            UpperCAmelCase      					=  [2, 3]
            self._test_inference_batch_consistent(
                batch_sizes=__lowerCamelCase       ,							additional_params_copy_to_batched_inputs=__lowerCamelCase       ,							)
         else:
            self._test_inference_batch_consistent(
                additional_params_copy_to_batched_inputs=__lowerCamelCase							)





      @skip_mps
      def 							_lowercase					(		self    :       List[str]							)     -> Optional[Any]:






         """simple docstring"""







         return super().test_dict_tuple_outputs_equivalent()





      @skip_mps
      def 							_lowercase					(		self    :       int							)     -> Optional[Any]:






         """simple docstring"""







         return super().test_save_load_local()





      @skip_mps
      def 							_lowercase					(		self    :       int							)     -> Optional[Any]:






         """simple docstring"""







         return super().test_save_load_optional_components()






@slow
@require_torch_gpu
class 	__lowercase					(	unittest.TestCase	):





      def 							_lowercase					(		self    :       Any							)     -> Optional[int]:






         """simple docstring"""







         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





      def 							_lowercase					(		self    :       Any							)     -> List[str]:






         """simple docstring"""







         UpperCAmelCase      					=  load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"""							)
         UpperCAmelCase      					=  load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/unclip/karlo_v1_alpha_cat_variation_fp16.npy"""							)

         UpperCAmelCase      					=  UnCLIPImageVariationPipeline.from_pretrained(
             """kakaobrain/karlo-v1-alpha-image-variations"""       ,							torch_dtype=torch.floataa							)
         UpperCAmelCase      					=  pipeline.to(__lowerCamelCase							)
         pipeline.set_progress_bar_config(disable=__lowerCamelCase							)

         UpperCAmelCase      					=  torch.Generator(device="""cpu"""							).manual_seed(0							)
         UpperCAmelCase      					=  pipeline(
             __lowerCamelCase       ,							generator=__lowerCamelCase       ,							output_type="""np"""       ,							)

         UpperCAmelCase      					=  output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         assert_mean_pixel_difference(__lowerCamelCase       ,							__lowerCamelCase       ,							1_5							)







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


__a	 =      datasets.logging.get_logger(__name__)


__a	 =      """\
@InProceedings{moosavi2019minimum,
    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
    year = {2019},
    booktitle = {Proceedings of the 57th Annual Meeting of
        the Association for Computational Linguistics (Volume 1: Long Papers)},
    publisher = {Association for Computational Linguistics},
    address = {Florence, Italy},
}

@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}

@INPROCEEDINGS{Bagga98algorithmsfor,
    author = {Amit Bagga and Breck Baldwin},
    title = {Algorithms for Scoring Coreference Chains},
    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
    year = {1998},
    pages = {563--566}
}

@INPROCEEDINGS{Luo05oncoreference,
    author = {Xiaoqiang Luo},
    title = {On coreference resolution performance metrics},
    booktitle = {In Proc. of HLT/EMNLP},
    year = {2005},
    pages = {25--32},
    publisher = {URL}
}

@inproceedings{moosavi-strube-2016-coreference,
    title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",
    author = \"Moosavi, Nafise Sadat  and
      Strube, Michael\",
    booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",
    month = aug,
    year = \"2016\",
    address = \"Berlin, Germany\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/P16-1060\",
    doi = \"10.18653/v1/P16-1060\",
    pages = \"632--642\",
}

"""

__a	 =      """\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].

This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column	Type	Description
1	Document ID	This is a variation on the document filename
2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3	Word number
4	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.
5	Part-of-Speech
6	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.
7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"
8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.
9	Word sense	This is the word sense of the word in Column 3.
10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11	Named Entities	These columns identifies the spans representing various named entities.
12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N	Coreference	Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html

Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md

CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
"""

__a	 =      """
Calculates coreference evaluation metrics.
Args:
    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
        Each prediction is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
        Each reference is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    keep_singletons: After extracting all mentions of key or system files,
        mentions whose corresponding coreference chain is of size one,
        are considered as singletons. The default evaluation mode will include
        singletons in evaluations if they are included in the key or the system files.
        By setting 'keep_singletons=False', all singletons in the key and system files
        will be excluded from the evaluation.
    NP_only: Most of the recent coreference resolvers only resolve NP mentions and
        leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.
    min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.
        Minimum spans are determined using the MINA algorithm.

Returns:
    'mentions': mentions
    'muc': MUC metric [Vilain et al, 1995]
    'bcub': B-cubed [Bagga and Baldwin, 1998]
    'ceafe': CEAFe [Luo et al., 2005]
    'lea': LEA [Moosavi and Strube, 2016]
    'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)

Examples:

    >>> coval = datasets.load_metric('coval')
    >>> words = ['bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -',
    ... 'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)',
    ... 'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)',
    ... 'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -',
    ... 'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -',
    ... 'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -']
    >>> references = [words]
    >>> predictions = [words]
    >>> results = coval.compute(predictions=predictions, references=references)
    >>> print(results) # doctest:+ELLIPSIS
    {'mentions/recall': 1.0,[...] 'conll_score': 100.0}
"""
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_=False	,    lowerCAmelCase_=False	,    lowerCAmelCase_=True	,    lowerCAmelCase_=False	,    lowerCAmelCase_="dummy_doc" )    ->Optional[Any]:
   UpperCAmelCase      					=  {doc: key_lines}
   UpperCAmelCase      					=  {doc: sys_lines}

   UpperCAmelCase      					=  {}

   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0

   UpperCAmelCase					,	UpperCAmelCase      					=  reader.get_doc_mentions(lowerCAmelCase_	,    key_doc_lines[doc]	,    lowerCAmelCase_ )
   key_singletons_num += singletons_num

   if NP_only or min_span:
      UpperCAmelCase      					=  reader.set_annotated_parse_trees(lowerCAmelCase_	,    key_doc_lines[doc]	,    lowerCAmelCase_	,    lowerCAmelCase_ )

   UpperCAmelCase					,	UpperCAmelCase      					=  reader.get_doc_mentions(lowerCAmelCase_	,    sys_doc_lines[doc]	,    lowerCAmelCase_ )
   sys_singletons_num += singletons_num

   if NP_only or min_span:
      UpperCAmelCase      					=  reader.set_annotated_parse_trees(lowerCAmelCase_	,    key_doc_lines[doc]	,    lowerCAmelCase_	,    lowerCAmelCase_ )

   if remove_nested:
      UpperCAmelCase					,	UpperCAmelCase      					=  reader.remove_nested_coref_mentions(lowerCAmelCase_	,    lowerCAmelCase_ )
      key_nested_coref_num += nested_mentions
      key_removed_nested_clusters += removed_clusters

      UpperCAmelCase					,	UpperCAmelCase      					=  reader.remove_nested_coref_mentions(lowerCAmelCase_	,    lowerCAmelCase_ )
      sys_nested_coref_num += nested_mentions
      sys_removed_nested_clusters += removed_clusters

   UpperCAmelCase      					=  reader.get_mention_assignments(lowerCAmelCase_	,    lowerCAmelCase_ )
   UpperCAmelCase      					=  reader.get_mention_assignments(lowerCAmelCase_	,    lowerCAmelCase_ )

   UpperCAmelCase      					=  (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 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  get_coref_infos(lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )

   UpperCAmelCase      					=  {}
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  0

   for name, metric in metrics:
      UpperCAmelCase					,	UpperCAmelCase					,	UpperCAmelCase      					=  evaluator.evaluate_documents(lowerCAmelCase_	,    lowerCAmelCase_	,    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(1_0 )	,    F"""Recall: {recall * 1_0_0:.2f}"""	,    F""" Precision: {precision * 1_0_0:.2f}"""	,    F""" F1: {fa * 1_0_0:.2f}"""	,    )

   if conll_subparts_num == 3:
      UpperCAmelCase      					=  (conll / 3) * 1_0_0
      logger.info(F"""CoNLL score: {conll:.2f}""" )
      output_scores.update({"""conll_score""": conll} )

   return output_scores
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->List[Any]:
   UpperCAmelCase      					=  False
   for line in key_lines:
      if not line.startswith("""#""" ):
         if len(line.split() ) > 6:
            UpperCAmelCase      					=  line.split()[5]
            if not parse_col == "-":
               UpperCAmelCase      					=  True
               break
            else:
               break
   return has_gold_parse







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





      def 							_lowercase					(		self    :       int							)     -> str:






         """simple docstring"""







         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 							_lowercase					(		self    :       Union[str, Any]       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       Optional[int]=True       ,							__lowerCamelCase    :       Any=False       ,							__lowerCamelCase    :       List[Any]=False       ,							__lowerCamelCase    :       List[Any]=False							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  [
             ("""mentions""", evaluator.mentions),
             ("""muc""", evaluator.muc),
             ("""bcub""", evaluator.b_cubed),
             ("""ceafe""", evaluator.ceafe),
             ("""lea""", evaluator.lea),
         ]

         if min_span:
            UpperCAmelCase      					=  util.check_gold_parse_annotation(__lowerCamelCase							)
            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"

         UpperCAmelCase      					=  evaluate(
             key_lines=__lowerCamelCase       ,							sys_lines=__lowerCamelCase       ,							metrics=__lowerCamelCase       ,							NP_only=__lowerCamelCase       ,							remove_nested=__lowerCamelCase       ,							keep_singletons=__lowerCamelCase       ,							min_span=__lowerCamelCase       ,							)

         return score



