


"""simple docstring"""







import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





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

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							1
         lowerCAmelCase     :     Optional[int]								=							3
         lowerCAmelCase     :     List[Any]								=							(32, 32)

         lowerCAmelCase     :     str								=							floats_tensor((batch_size, num_channels) + sizes		,     rng=random.Random(0						)						).to(snake_case__						)
         return image

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         torch.manual_seed(0						)
         lowerCAmelCase     :     Tuple								=							UNetaDConditionModel(
             block_out_channels=(32, 64)		,     layers_per_block=2		,     sample_size=32		,     in_channels=4		,     out_channels=4		,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,     cross_attention_dim=32		,     )
         return model

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         torch.manual_seed(0						)
         lowerCAmelCase     :     int								=							AutoencoderKL(
             block_out_channels=[32, 64]		,     in_channels=3		,     out_channels=3		,     down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,     latent_channels=4		,     )
         return model

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         torch.manual_seed(0						)
         lowerCAmelCase     :     str								=							CLIPTextConfig(
             bos_token_id=0		,     eos_token_id=2		,     hidden_size=32		,     intermediate_size=37		,     layer_norm_eps=1e-05		,     num_attention_heads=4		,     num_hidden_layers=5		,     pad_token_id=1		,     vocab_size=1_000		,     )
         return CLIPTextModel(snake_case__						)

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         def extract(*snake_case__		,     **snake_case__						):


           class       SCREAMING_SNAKE_CASE__				:
                  """simple docstring"""


                  def __init__(						self						):






                    """simple docstring"""





                    lowerCAmelCase     :     int								=							torch.ones([0]						)







                  def        lowercase__							(						self		,     snake_case__						):






                    """simple docstring"""





                    self.pixel_values.to(snake_case__						)
                    return self

           return Out()

         return extract

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							"cpu"  # ensure determinism for the device-dependent torch.Generator
         lowerCAmelCase     :     Dict								=							self.dummy_cond_unet
         lowerCAmelCase     :     List[str]								=							DDIMScheduler(
             beta_start=0.00085		,     beta_end=0.012		,     beta_schedule="scaled_linear"		,     clip_sample=snake_case__		,     set_alpha_to_one=snake_case__		,     )

         lowerCAmelCase     :     Union[str, Any]								=							self.dummy_vae
         lowerCAmelCase     :     List[Any]								=							self.dummy_text_encoder
         lowerCAmelCase     :     Optional[Any]								=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

         # make sure here that pndm scheduler skips prk
         lowerCAmelCase     :     Any								=							StableDiffusionPipeline(
             unet=snake_case__		,     scheduler=snake_case__		,     vae=snake_case__		,     text_encoder=snake_case__		,     tokenizer=snake_case__		,     safety_checker=snake_case__		,     feature_extractor=self.dummy_extractor		,     )
         lowerCAmelCase     :     List[str]								=							sd_pipe.to(snake_case__						)
         sd_pipe.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase     :     Union[str, Any]								=							"A painting of a squirrel eating a burger"

         lowerCAmelCase     :     str								=							torch.Generator(device=snake_case__						).manual_seed(0						)
         lowerCAmelCase     :     int								=							sd_pipe([prompt]		,     generator=snake_case__		,     guidance_scale=6.0		,     num_inference_steps=2		,     output_type="np"						)
         lowerCAmelCase     :     str								=							output.images

         lowerCAmelCase     :     List[str]								=							torch.Generator(device=snake_case__						).manual_seed(0						)
         lowerCAmelCase     :     Optional[int]								=							sd_pipe(
             [prompt]		,     generator=snake_case__		,     guidance_scale=6.0		,     num_inference_steps=2		,     output_type="np"		,     return_dict=snake_case__		,     )[0]

         lowerCAmelCase     :     Any								=							image[0, -3:, -3:, -1]
         lowerCAmelCase     :     List[str]								=							image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         lowerCAmelCase     :     Any								=							np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]						)

         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						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							"cpu"  # ensure determinism for the device-dependent torch.Generator
         lowerCAmelCase     :     Optional[Any]								=							self.dummy_cond_unet
         lowerCAmelCase     :     Any								=							PNDMScheduler(skip_prk_steps=snake_case__						)
         lowerCAmelCase     :     Union[str, Any]								=							self.dummy_vae
         lowerCAmelCase     :     str								=							self.dummy_text_encoder
         lowerCAmelCase     :     Any								=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

         # make sure here that pndm scheduler skips prk
         lowerCAmelCase     :     Union[str, Any]								=							StableDiffusionPipeline(
             unet=snake_case__		,     scheduler=snake_case__		,     vae=snake_case__		,     text_encoder=snake_case__		,     tokenizer=snake_case__		,     safety_checker=snake_case__		,     feature_extractor=self.dummy_extractor		,     )
         lowerCAmelCase     :     Optional[int]								=							sd_pipe.to(snake_case__						)
         sd_pipe.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase     :     Union[str, Any]								=							"A painting of a squirrel eating a burger"
         lowerCAmelCase     :     List[str]								=							torch.Generator(device=snake_case__						).manual_seed(0						)
         lowerCAmelCase     :     str								=							sd_pipe([prompt]		,     generator=snake_case__		,     guidance_scale=6.0		,     num_inference_steps=2		,     output_type="np"						)

         lowerCAmelCase     :     Union[str, Any]								=							output.images

         lowerCAmelCase     :     str								=							torch.Generator(device=snake_case__						).manual_seed(0						)
         lowerCAmelCase     :     Tuple								=							sd_pipe(
             [prompt]		,     generator=snake_case__		,     guidance_scale=6.0		,     num_inference_steps=2		,     output_type="np"		,     return_dict=snake_case__		,     )[0]

         lowerCAmelCase     :     List[str]								=							image[0, -3:, -3:, -1]
         lowerCAmelCase     :     Optional[Any]								=							image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         lowerCAmelCase     :     List[Any]								=							np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]						)

         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						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							StableDiffusionPipeline.from_pretrained(
             "hf-internal-testing/tiny-stable-diffusion-lms-pipe"		,     safety_checker=snake_case__						)
         assert isinstance(snake_case__		,     snake_case__						)
         assert isinstance(pipe.scheduler		,     snake_case__						)
         assert pipe.safety_checker is None

         lowerCAmelCase     :     Dict								=							pipe("example prompt"		,     num_inference_steps=2						).images[0]
         assert image is not None

         # check that there's no error when saving a pipeline with one of the models being None
         with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(snake_case__						)
           lowerCAmelCase     :     Dict								=							StableDiffusionPipeline.from_pretrained(snake_case__						)

         # sanity check that the pipeline still works
         assert pipe.safety_checker is None
         lowerCAmelCase     :     str								=							pipe("example prompt"		,     num_inference_steps=2						).images[0]
         assert image is not None







       @unittest.skipIf(torch_device != "cuda"		,     "This test requires a GPU"						)
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							self.dummy_cond_unet
         lowerCAmelCase     :     Dict								=							PNDMScheduler(skip_prk_steps=snake_case__						)
         lowerCAmelCase     :     str								=							self.dummy_vae
         lowerCAmelCase     :     List[Any]								=							self.dummy_text_encoder
         lowerCAmelCase     :     Union[str, Any]								=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

         # put models in fp16
         lowerCAmelCase     :     str								=							unet.half()
         lowerCAmelCase     :     Union[str, Any]								=							vae.half()
         lowerCAmelCase     :     Any								=							bert.half()

         # make sure here that pndm scheduler skips prk
         lowerCAmelCase     :     str								=							StableDiffusionPipeline(
             unet=snake_case__		,     scheduler=snake_case__		,     vae=snake_case__		,     text_encoder=snake_case__		,     tokenizer=snake_case__		,     safety_checker=snake_case__		,     feature_extractor=self.dummy_extractor		,     )
         lowerCAmelCase     :     Optional[Any]								=							sd_pipe.to(snake_case__						)
         sd_pipe.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase     :     List[Any]								=							"A painting of a squirrel eating a burger"
         lowerCAmelCase     :     Any								=							sd_pipe([prompt]		,     num_inference_steps=2		,     output_type="np"						).images

         assert image.shape == (1, 64, 64, 3)


@nightly
@require_torch_gpu
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





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

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"		,     safety_checker=snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config						)
         lowerCAmelCase     :     Tuple								=							sd_pipe.to(snake_case__						)
         sd_pipe.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase     :     Dict								=							(
             "portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
             " coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
             " anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
             " children from bahnhof zoo, detailed "
         )
         lowerCAmelCase     :     Any								=							4_003_660_346
         lowerCAmelCase     :     List[str]								=							7

         # without safety guidance (sld_guidance_scale = 0)
         lowerCAmelCase     :     Dict								=							torch.manual_seed(snake_case__						)
         lowerCAmelCase     :     str								=							sd_pipe(
             [prompt]		,     generator=snake_case__		,     guidance_scale=snake_case__		,     num_inference_steps=50		,     output_type="np"		,     width=512		,     height=512		,     sld_guidance_scale=0		,     )

         lowerCAmelCase     :     Optional[int]								=							output.images
         lowerCAmelCase     :     List[str]								=							image[0, -3:, -3:, -1]
         lowerCAmelCase     :     str								=							[0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

         assert image.shape == (1, 512, 512, 3)

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

         # without safety guidance (strong configuration)
         lowerCAmelCase     :     int								=							torch.manual_seed(snake_case__						)
         lowerCAmelCase     :     Optional[int]								=							sd_pipe(
             [prompt]		,     generator=snake_case__		,     guidance_scale=snake_case__		,     num_inference_steps=50		,     output_type="np"		,     width=512		,     height=512		,     sld_guidance_scale=2_000		,     sld_warmup_steps=7		,     sld_threshold=0.025		,     sld_momentum_scale=0.5		,     sld_mom_beta=0.7		,     )

         lowerCAmelCase     :     Optional[Any]								=							output.images
         lowerCAmelCase     :     Tuple								=							image[0, -3:, -3:, -1]
         lowerCAmelCase     :     Tuple								=							[0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

         assert image.shape == (1, 512, 512, 3)

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

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"		,     safety_checker=snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config						)
         lowerCAmelCase     :     Optional[Any]								=							sd_pipe.to(snake_case__						)
         sd_pipe.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase     :     str								=							"padme amidala taking a bath artwork, safe for work, no nudity"
         lowerCAmelCase     :     Tuple								=							2_734_971_755
         lowerCAmelCase     :     Dict								=							7

         lowerCAmelCase     :     str								=							torch.manual_seed(snake_case__						)
         lowerCAmelCase     :     List[str]								=							sd_pipe(
             [prompt]		,     generator=snake_case__		,     guidance_scale=snake_case__		,     num_inference_steps=50		,     output_type="np"		,     width=512		,     height=512		,     sld_guidance_scale=0		,     )

         lowerCAmelCase     :     List[Any]								=							output.images
         lowerCAmelCase     :     List[str]								=							image[0, -3:, -3:, -1]
         lowerCAmelCase     :     Any								=							[0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

         assert image.shape == (1, 512, 512, 3)

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

         lowerCAmelCase     :     List[str]								=							torch.manual_seed(snake_case__						)
         lowerCAmelCase     :     Union[str, Any]								=							sd_pipe(
             [prompt]		,     generator=snake_case__		,     guidance_scale=snake_case__		,     num_inference_steps=50		,     output_type="np"		,     width=512		,     height=512		,     sld_guidance_scale=2_000		,     sld_warmup_steps=7		,     sld_threshold=0.025		,     sld_momentum_scale=0.5		,     sld_mom_beta=0.7		,     )

         lowerCAmelCase     :     List[Any]								=							output.images
         lowerCAmelCase     :     List[str]								=							image[0, -3:, -3:, -1]
         lowerCAmelCase     :     Optional[Any]								=							[0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

         assert image.shape == (1, 512, 512, 3)

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







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"						)
         lowerCAmelCase     :     Union[str, Any]								=							sd_pipe.to(snake_case__						)
         sd_pipe.set_progress_bar_config(disable=snake_case__						)

         lowerCAmelCase     :     Optional[int]								=							(
             "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
             " leyendecker"
         )
         lowerCAmelCase     :     Dict								=							1_044_355_234
         lowerCAmelCase     :     Any								=							12

         lowerCAmelCase     :     Tuple								=							torch.manual_seed(snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							sd_pipe(
             [prompt]		,     generator=snake_case__		,     guidance_scale=snake_case__		,     num_inference_steps=50		,     output_type="np"		,     width=512		,     height=512		,     sld_guidance_scale=0		,     )

         lowerCAmelCase     :     int								=							output.images
         lowerCAmelCase     :     Optional[Any]								=							image[0, -3:, -3:, -1]
         lowerCAmelCase     :     int								=							np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]						)

         assert image.shape == (1, 512, 512, 3)

         assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-7

         lowerCAmelCase     :     Optional[int]								=							torch.manual_seed(snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							sd_pipe(
             [prompt]		,     generator=snake_case__		,     guidance_scale=snake_case__		,     num_inference_steps=50		,     output_type="np"		,     width=512		,     height=512		,     sld_guidance_scale=2_000		,     sld_warmup_steps=7		,     sld_threshold=0.025		,     sld_momentum_scale=0.5		,     sld_mom_beta=0.7		,     )

         lowerCAmelCase     :     Optional[int]								=							output.images
         lowerCAmelCase     :     Union[str, Any]								=							image[0, -3:, -3:, -1]
         lowerCAmelCase     :     int								=							np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]						)
         assert image.shape == (1, 512, 512, 3)

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









"""simple docstring"""







import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


lowerCAmelCase__      				=   logging.getLogger(__name__)


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Tuple   ="token-classification"

       def __init__(						self		,     snake_case__						):






         """simple docstring"""





         if type(snake_case__						) == dict:
           lowerCAmelCase     :     List[str]								=							Namespace(**snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							import_module("tasks"						)
         try:
           lowerCAmelCase     :     Dict								=							getattr(snake_case__		,     hparams.task_type						)
           lowerCAmelCase     :     TokenClassificationTask								=							token_classification_task_clazz()
         except AttributeError:
           raise ValueError(
               f"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
               f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""						)
         lowerCAmelCase     :     Dict								=							self.token_classification_task.get_labels(hparams.labels						)
         lowerCAmelCase     :     str								=							CrossEntropyLoss().ignore_index
         super().__init__(snake_case__		,     len(self.labels						)		,     self.mode						)

       def        lowercase__							(						self		,     **snake_case__						):






         """simple docstring"""





         return self.model(**snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
         if self.config.model_type != "distilbert":
           lowerCAmelCase     :     Optional[int]								=							(
               batch[2] if self.config.model_type in ["bert", "xlnet"] else None
           )  # XLM and RoBERTa don"t use token_type_ids

         lowerCAmelCase     :     str								=							self(**snake_case__						)
         lowerCAmelCase     :     Tuple								=							outputs[0]
         # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss}

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							self.hparams
         for mode in ["train", "dev", "test"]:
           lowerCAmelCase     :     Optional[int]								=							self._feature_file(snake_case__						)
           if os.path.exists(snake_case__						) and not args.overwrite_cache:
             logger.info("Loading features from cached file %s"		,     snake_case__						)
             lowerCAmelCase     :     Dict								=							torch.load(snake_case__						)
           else:
             logger.info("Creating features from dataset file at %s"		,     args.data_dir						)
             lowerCAmelCase     :     Optional[int]								=							self.token_classification_task.read_examples_from_file(args.data_dir		,     snake_case__						)
             lowerCAmelCase     :     Optional[int]								=							self.token_classification_task.convert_examples_to_features(
                 snake_case__		,     self.labels		,     args.max_seq_length		,     self.tokenizer		,     cls_token_at_end=bool(self.config.model_type in ["xlnet"]						)		,     cls_token=self.tokenizer.cls_token		,     cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0		,     sep_token=self.tokenizer.sep_token		,     sep_token_extra=snake_case__		,     pad_on_left=bool(self.config.model_type in ["xlnet"]						)		,     pad_token=self.tokenizer.pad_token_id		,     pad_token_segment_id=self.tokenizer.pad_token_type_id		,     pad_token_label_id=self.pad_token_label_id		,     )
             logger.info("Saving features into cached file %s"		,     snake_case__						)
             torch.save(snake_case__		,     snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__ = False						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							self._feature_file(snake_case__						)
         logger.info("Loading features from cached file %s"		,     snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							torch.load(snake_case__						)
         lowerCAmelCase     :     Optional[int]								=							torch.tensor([f.input_ids for f in features]		,     dtype=torch.long						)
         lowerCAmelCase     :     int								=							torch.tensor([f.attention_mask for f in features]		,     dtype=torch.long						)
         if features[0].token_type_ids is not None:
           lowerCAmelCase     :     Tuple								=							torch.tensor([f.token_type_ids for f in features]		,     dtype=torch.long						)
         else:
           lowerCAmelCase     :     Optional[Any]								=							torch.tensor([0 for f in features]		,     dtype=torch.long						)
           # HACK(we will not use this anymore soon)
         lowerCAmelCase     :     Optional[int]								=							torch.tensor([f.label_ids for f in features]		,     dtype=torch.long						)
         return DataLoader(
             TensorDataset(snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						)		,     batch_size=snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         """Compute validation""" ""
         lowerCAmelCase     :     str								=							{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
         if self.config.model_type != "distilbert":
           lowerCAmelCase     :     List[Any]								=							(
               batch[2] if self.config.model_type in ["bert", "xlnet"] else None
           )  # XLM and RoBERTa don"t use token_type_ids
         lowerCAmelCase     :     Any								=							self(**snake_case__						)
         lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							outputs[:2]
         lowerCAmelCase     :     Dict								=							logits.detach().cpu().numpy()
         lowerCAmelCase     :     Optional[Any]								=							inputs["labels"].detach().cpu().numpy()
         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							torch.stack([x["val_loss"] for x in outputs]						).mean()
         lowerCAmelCase     :     Optional[Any]								=							np.concatenate([x["pred"] for x in outputs]		,     axis=0						)
         lowerCAmelCase     :     List[Any]								=							np.argmax(snake_case__		,     axis=2						)
         lowerCAmelCase     :     Optional[int]								=							np.concatenate([x["target"] for x in outputs]		,     axis=0						)

         lowerCAmelCase     :     str								=							dict(enumerate(self.labels						)						)
         lowerCAmelCase     :     Optional[int]								=							[[] for _ in range(out_label_ids.shape[0]						)]
         lowerCAmelCase     :     Dict								=							[[] for _ in range(out_label_ids.shape[0]						)]

         for i in range(out_label_ids.shape[0]						):
           for j in range(out_label_ids.shape[1]						):
             if out_label_ids[i, j] != self.pad_token_label_id:
               out_label_list[i].append(label_map[out_label_ids[i][j]]						)
               preds_list[i].append(label_map[preds[i][j]]						)

         lowerCAmelCase     :     Dict								=							{
             "val_loss": val_loss_mean,
             "accuracy_score": accuracy_score(snake_case__		,     snake_case__						),
             "precision": precision_score(snake_case__		,     snake_case__						),
             "recall": recall_score(snake_case__		,     snake_case__						),
             "f1": fa_score(snake_case__		,     snake_case__						),
         }

         lowerCAmelCase     :     int								=							dict(results.items()						)
         lowerCAmelCase     :     Tuple								=							results
         return ret, preds_list, out_label_list

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     Dict								=							self._eval_end(snake_case__						)
         lowerCAmelCase     :     int								=							ret["log"]
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     int								=							self._eval_end(snake_case__						)

         # Converting to the dict required by pl
         # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
         # pytorch_lightning/trainer/logging.py#L139
         lowerCAmelCase     :     str								=							ret["log"]
         # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
         return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







       @staticmethod
       def        lowercase__							(						snake_case__		,     snake_case__						):






         """simple docstring"""





         BaseTransformer.add_model_specific_args(snake_case__		,     snake_case__						)
         parser.add_argument(
             "--task_type"		,     default="NER"		,     type=snake_case__		,     help="Task type to fine tune in training (e.g. NER, POS, etc)"						)
         parser.add_argument(
             "--max_seq_length"		,     default=128		,     type=snake_case__		,     help=(
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )		,     )

         parser.add_argument(
             "--labels"		,     default=""		,     type=snake_case__		,     help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."		,     )
         parser.add_argument(
             "--gpus"		,     default=0		,     type=snake_case__		,     help="The number of GPUs allocated for this, it is by default 0 meaning none"		,     )

         parser.add_argument(
             "--overwrite_cache"		,     action="store_true"		,     help="Overwrite the cached training and evaluation sets"						)

         return parser


if __name__ == "__main__":
     lowerCAmelCase__      				=   argparse.ArgumentParser()
     add_generic_args(parser, os.getcwd())
     lowerCAmelCase__      				=   NERTransformer.add_model_specific_args(parser, os.getcwd())
     lowerCAmelCase__      				=   parser.parse_args()
     lowerCAmelCase__      				=   NERTransformer(args)
     lowerCAmelCase__      				=   generic_train(model, args)

     if args.do_predict:
          # See https://github.com/huggingface/transformers/issues/3159
          # pl use this default format to create a checkpoint:
          # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
          # /pytorch_lightning/callbacks/model_checkpoint.py#L322
          lowerCAmelCase__      				=   sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
          lowerCAmelCase__      				=   model.load_from_checkpoint(checkpoints[-1])
          trainer.test(model)





