"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				@property
				def 						_a      ( self ):
										lowerCamelCase__   =1
										lowerCamelCase__   =3
										lowerCamelCase__   =(32, 32)

										lowerCamelCase__   =floats_tensor((batch_size, num_channels) + sizes      ,					rng=random.Random(0 ) ).to(A_ )
										return image

				@property
				def 						_a      ( self ):
										torch.manual_seed(0 )
										lowerCamelCase__   =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 						_a      ( self ):
										torch.manual_seed(0 )
										lowerCamelCase__   =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 						_a      ( self ):
										torch.manual_seed(0 )
										lowerCamelCase__   =RobertaSeriesConfig(
										    hidden_size=32      ,					project_dim=32      ,					intermediate_size=37      ,					layer_norm_eps=1E-05      ,					num_attention_heads=4      ,					num_hidden_layers=5      ,					pad_token_id=1      ,					vocab_size=5006      ,					)
										return RobertaSeriesModelWithTransformation(A_ )

				@property
				def 						_a      ( self ):

										def extract(*_lowerCamelCase      ,					**_lowerCamelCase ):






																class   __UpperCAmelCase		:

																				def __init__( self ):
																										lowerCamelCase__   =torch.ones([0] )






																				def 						_a      ( self      ,					_lowerCamelCase ):
																										self.pixel_values.to(A_ )
																										return self





																return Out()

										return extract

				def 						_a      ( self ):
										lowerCamelCase__   ="cpu"  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__   =self.dummy_cond_unet
										lowerCamelCase__   =PNDMScheduler(skip_prk_steps=A_ )
										lowerCamelCase__   =self.dummy_vae
										lowerCamelCase__   =self.dummy_text_encoder
										lowerCamelCase__   =XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
										lowerCamelCase__   =77

										lowerCamelCase__   =self.dummy_image.to(A_ )
										lowerCamelCase__   =init_image / 2 + 0.5

										# make sure here that pndm scheduler skips prk
										lowerCamelCase__   =AltDiffusionImgaImgPipeline(
										    unet=A_      ,					scheduler=A_      ,					vae=A_      ,					text_encoder=A_      ,					tokenizer=A_      ,					safety_checker=A_      ,					feature_extractor=self.dummy_extractor      ,					)
										lowerCamelCase__   =VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor      ,					do_normalize=A_ )
										lowerCamelCase__   =alt_pipe.to(A_ )
										alt_pipe.set_progress_bar_config(disable=A_ )

										lowerCamelCase__   ="A painting of a squirrel eating a burger"
										lowerCamelCase__   =torch.Generator(device=A_ ).manual_seed(0 )
										lowerCamelCase__   =alt_pipe(
										    [prompt]      ,					generator=A_      ,					guidance_scale=6.0      ,					num_inference_steps=2      ,					output_type="np"      ,					image=A_      ,					)

										lowerCamelCase__   =output.images

										lowerCamelCase__   =torch.Generator(device=A_ ).manual_seed(0 )
										lowerCamelCase__   =alt_pipe(
										    [prompt]      ,					generator=A_      ,					guidance_scale=6.0      ,					num_inference_steps=2      ,					output_type="np"      ,					image=A_      ,					return_dict=A_      ,					)[0]

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

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__   =np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9] )

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5E-3

				@unittest.skipIf(torch_device != "cuda"      ,					"This test requires a GPU" )
				def 						_a      ( self ):
										lowerCamelCase__   =self.dummy_cond_unet
										lowerCamelCase__   =PNDMScheduler(skip_prk_steps=A_ )
										lowerCamelCase__   =self.dummy_vae
										lowerCamelCase__   =self.dummy_text_encoder
										lowerCamelCase__   =XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
										lowerCamelCase__   =77

										lowerCamelCase__   =self.dummy_image.to(A_ )

										# put models in fp16
										lowerCamelCase__   =unet.half()
										lowerCamelCase__   =vae.half()
										lowerCamelCase__   =bert.half()

										# make sure here that pndm scheduler skips prk
										lowerCamelCase__   =AltDiffusionImgaImgPipeline(
										    unet=A_      ,					scheduler=A_      ,					vae=A_      ,					text_encoder=A_      ,					tokenizer=A_      ,					safety_checker=A_      ,					feature_extractor=self.dummy_extractor      ,					)
										lowerCamelCase__   =VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor      ,					do_normalize=A_ )
										lowerCamelCase__   =alt_pipe.to(A_ )
										alt_pipe.set_progress_bar_config(disable=A_ )

										lowerCamelCase__   ="A painting of a squirrel eating a burger"
										lowerCamelCase__   =torch.manual_seed(0 )
										lowerCamelCase__   =alt_pipe(
										    [prompt]      ,					generator=A_      ,					num_inference_steps=2      ,					output_type="np"      ,					image=A_      ,					).images

										assert image.shape == (1, 32, 32, 3)






				@unittest.skipIf(torch_device != "cuda"      ,					"This test requires a GPU" )
				def 						_a      ( self ):
										lowerCamelCase__   =load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg" )
										# resize to resolution that is divisible by 8 but not 16 or 32
										lowerCamelCase__   =init_image.resize((760, 504) )

										lowerCamelCase__   ="BAAI/AltDiffusion"
										lowerCamelCase__   =AltDiffusionImgaImgPipeline.from_pretrained(
										    A_      ,					safety_checker=A_      ,					)
										pipe.to(A_ )
										pipe.set_progress_bar_config(disable=A_ )
										pipe.enable_attention_slicing()

										lowerCamelCase__   ="A fantasy landscape, trending on artstation"

										lowerCamelCase__   =torch.manual_seed(0 )
										lowerCamelCase__   =pipe(
										    prompt=A_      ,					image=A_      ,					strength=0.7_5      ,					guidance_scale=7.5      ,					generator=A_      ,					output_type="np"      ,					)
										lowerCamelCase__   =output.images[0]

										lowerCamelCase__   =image[255:258, 383:386, -1]

										assert image.shape == (504, 760, 3)
										lowerCamelCase__   =np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0] )

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






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

				def 						_a      ( self ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






				def 						_a      ( self ):
										lowerCamelCase__   =load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg" )
										lowerCamelCase__   =init_image.resize((768, 512) )
										lowerCamelCase__   =load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy" )

										lowerCamelCase__   ="BAAI/AltDiffusion"
										lowerCamelCase__   =AltDiffusionImgaImgPipeline.from_pretrained(
										    A_      ,					safety_checker=A_      ,					)
										pipe.to(A_ )
										pipe.set_progress_bar_config(disable=A_ )
										pipe.enable_attention_slicing()

										lowerCamelCase__   ="A fantasy landscape, trending on artstation"

										lowerCamelCase__   =torch.manual_seed(0 )
										lowerCamelCase__   =pipe(
										    prompt=A_      ,					image=A_      ,					strength=0.7_5      ,					guidance_scale=7.5      ,					generator=A_      ,					output_type="np"      ,					)
										lowerCamelCase__   =output.images[0]

										assert image.shape == (512, 768, 3)
										# img2img is flaky across GPUs even in fp32, so using MAE here
										assert np.abs(expected_image - image ).max() < 1E-2









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


_A					:				List[str]				=			logging.getLogger(__name__)



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	"""token-classification"""



    def __init__(       self   ,				A_     ):





        '''simple docstring'''

        if type(A_     ) == dict:
            SCREAMING_SNAKE_CASE__											=			Namespace(**A_     )
        SCREAMING_SNAKE_CASE__											=			import_module('''tasks'''     )
        try:
            SCREAMING_SNAKE_CASE__											=			getattr(A_   ,				hparams.task_type     )
            SCREAMING_SNAKE_CASE__											=			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__()}'''     )
        SCREAMING_SNAKE_CASE__											=			self.token_classification_task.get_labels(hparams.labels     )
        SCREAMING_SNAKE_CASE__											=			CrossEntropyLoss().ignore_index
        super().__init__(A_   ,				len(self.labels     )   ,				self.mode     )



    def  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

        return self.model(**A_     )



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
        if self.config.model_type != "distilbert":
            SCREAMING_SNAKE_CASE__											=			(
                batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
            )  # XLM and RoBERTa don"t use token_type_ids

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



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.hparams
        for mode in ["train", "dev", "test"]:
            SCREAMING_SNAKE_CASE__											=			self._feature_file(A_     )
            if os.path.exists(A_     ) and not args.overwrite_cache:
                logger.info('''Loading features from cached file %s'''   ,				A_     )
                SCREAMING_SNAKE_CASE__											=			torch.load(A_     )
            else:
                logger.info('''Creating features from dataset file at %s'''   ,				args.data_dir     )
                SCREAMING_SNAKE_CASE__											=			self.token_classification_task.read_examples_from_file(args.data_dir   ,				A_     )
                SCREAMING_SNAKE_CASE__											=			self.token_classification_task.convert_examples_to_features(
                    A_   ,				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=A_   ,				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'''   ,				A_     )
                torch.save(A_   ,				A_     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_ = False     ):





        '''simple docstring'''

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



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        """Compute validation""" ""
        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
        if self.config.model_type != "distilbert":
            SCREAMING_SNAKE_CASE__											=			(
                batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
            )  # XLM and RoBERTa don"t use token_type_ids
        SCREAMING_SNAKE_CASE__											=			self(**A_     )
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			outputs[:2]
        SCREAMING_SNAKE_CASE__											=			logits.detach().cpu().numpy()
        SCREAMING_SNAKE_CASE__											=			inputs['''labels'''].detach().cpu().numpy()
        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			torch.stack([x['''val_loss'''] for x in outputs]     ).mean()
        SCREAMING_SNAKE_CASE__											=			np.concatenate([x['''pred'''] for x in outputs]   ,				axis=0     )
        SCREAMING_SNAKE_CASE__											=			np.argmax(A_   ,				axis=2     )
        SCREAMING_SNAKE_CASE__											=			np.concatenate([x['''target'''] for x in outputs]   ,				axis=0     )

        SCREAMING_SNAKE_CASE__											=			dict(enumerate(self.labels     )     )
        SCREAMING_SNAKE_CASE__											=			[[] for _ in range(out_label_ids.shape[0]     )]
        SCREAMING_SNAKE_CASE__											=			[[] 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]]     )

        SCREAMING_SNAKE_CASE__											=			{
            '''val_loss''': val_loss_mean,
            '''accuracy_score''': accuracy_score(A_   ,				A_     ),
            '''precision''': precision_score(A_   ,				A_     ),
            '''recall''': recall_score(A_   ,				A_     ),
            '''f1''': fa_score(A_   ,				A_     ),
        }

        SCREAMING_SNAKE_CASE__											=			dict(results.items()     )
        SCREAMING_SNAKE_CASE__											=			results
        return ret, preds_list, out_label_list



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self._eval_end(A_     )
        SCREAMING_SNAKE_CASE__											=			ret['''log''']
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self._eval_end(A_     )

        # Converting to the dict required by pl
        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
        # pytorch_lightning/trainer/logging.py#L139
        SCREAMING_SNAKE_CASE__											=			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_					(       A_   ,				A_     ):





        '''simple docstring'''

        BaseTransformer.add_model_specific_args(A_   ,				A_     )
        parser.add_argument(
            '''--task_type'''   ,				default='''NER'''   ,				type=A_   ,				help='''Task type to fine tune in training (e.g. NER, POS, etc)'''     )
        parser.add_argument(
            '''--max_seq_length'''   ,				default=1_28   ,				type=A_   ,				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=A_   ,				help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''   ,				)
        parser.add_argument(
            '''--gpus'''   ,				default=0   ,				type=A_   ,				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__":
  _A					:				List[str]				=			argparse.ArgumentParser()
  add_generic_args(parser, os.getcwd())
  _A					:				List[Any]				=			NERTransformer.add_model_specific_args(parser, os.getcwd())
  _A					:				Optional[Any]				=			parser.parse_args()
  _A					:				Any				=			NERTransformer(args)
  _A					:				Any				=			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
    _A					:				Optional[int]				=			sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
    _A					:				Optional[int]				=			model.load_from_checkpoint(checkpoints[-1])
    trainer.test(model)
