
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


_lowercase											=    logging.getLogger(__name__)


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						"token-classification"


       def __init__(    self						,       __A				)						->    Any:
              if type(UpperCAmelCase_				) == dict:
                     SCREAMING_SNAKE_CASE_							:				str		=Namespace(**UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=import_module('''tasks'''				)
              try:
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=getattr(UpperCAmelCase_						,       hparams.task_type				)
                     SCREAMING_SNAKE_CASE_							:				List[str]		=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_							:				List[str]		=self.token_classification_task.get_labels(hparams.labels				)
              SCREAMING_SNAKE_CASE_							:				int		=CrossEntropyLoss().ignore_index
              super().__init__(UpperCAmelCase_						,       len(self.labels				)						,       self.mode				)


       def 	_snake_case				(    self						,       **__A				)						->    List[Any]:
              return self.model(**UpperCAmelCase_				)


       def 	_snake_case				(    self						,       __A						,       __A				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		={'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
              if self.config.model_type != "distilbert":
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=(
                         batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
                     )  # XLM and RoBERTa don"t use token_type_ids

              SCREAMING_SNAKE_CASE_							:				Tuple		=self(**UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=outputs[0]
              # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
              return {"loss": loss}


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.hparams
              for mode in ["train", "dev", "test"]:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=self._feature_file(UpperCAmelCase_				)
                     if os.path.exists(UpperCAmelCase_				) and not args.overwrite_cache:
                            logger.info('''Loading features from cached file %s'''						,       UpperCAmelCase_				)
                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.load(UpperCAmelCase_				)
                     else:
                            logger.info('''Creating features from dataset file at %s'''						,       args.data_dir				)
                            SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.token_classification_task.read_examples_from_file(args.data_dir						,       UpperCAmelCase_				)
                            SCREAMING_SNAKE_CASE_							:				Tuple		=self.token_classification_task.convert_examples_to_features(
                                UpperCAmelCase_						,       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=UpperCAmelCase_						,       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'''						,       UpperCAmelCase_				)
                            torch.save(UpperCAmelCase_						,       UpperCAmelCase_				)


       def 	_snake_case				(    self						,       __A						,       __A						,       __A = False				)						->    DataLoader:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self._feature_file(UpperCAmelCase_				)
              logger.info('''Loading features from cached file %s'''						,       UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.load(UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.tensor([f.input_ids for f in features]						,       dtype=torch.long				)
              SCREAMING_SNAKE_CASE_							:				Any		=torch.tensor([f.attention_mask for f in features]						,       dtype=torch.long				)
              if features[0].token_type_ids is not None:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.tensor([f.token_type_ids for f in features]						,       dtype=torch.long				)
              else:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=torch.tensor([0 for f in features]						,       dtype=torch.long				)
                     # HACK(we will not use this anymore soon)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.tensor([f.label_ids for f in features]						,       dtype=torch.long				)
              return DataLoader(
                  TensorDataset(UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_				)						,       batch_size=UpperCAmelCase_				)


       def 	_snake_case				(    self						,       __A						,       __A				)						->    Dict:
              """Compute validation""" ""
              SCREAMING_SNAKE_CASE_							:				str		={'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
              if self.config.model_type != "distilbert":
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=(
                         batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
                     )  # XLM and RoBERTa don"t use token_type_ids
              SCREAMING_SNAKE_CASE_							:				str		=self(**UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=outputs[:2]
              SCREAMING_SNAKE_CASE_							:				List[str]		=logits.detach().cpu().numpy()
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=inputs['''labels'''].detach().cpu().numpy()
              return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}


       def 	_snake_case				(    self						,       __A				)						->    int:
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.stack([x['''val_loss'''] for x in outputs]				).mean()
              SCREAMING_SNAKE_CASE_							:				str		=np.concatenate([x['''pred'''] for x in outputs]						,       axis=0				)
              SCREAMING_SNAKE_CASE_							:				Dict		=np.argmax(UpperCAmelCase_						,       axis=2				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=np.concatenate([x['''target'''] for x in outputs]						,       axis=0				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=dict(enumerate(self.labels				)				)
              SCREAMING_SNAKE_CASE_							:				Dict		=[[] for _ in range(out_label_ids.shape[0]				)]
              SCREAMING_SNAKE_CASE_							:				List[str]		=[[] 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_							:				str		={
                  '''val_loss''': val_loss_mean,
                  '''accuracy_score''': accuracy_score(UpperCAmelCase_						,       UpperCAmelCase_				),
                  '''precision''': precision_score(UpperCAmelCase_						,       UpperCAmelCase_				),
                  '''recall''': recall_score(UpperCAmelCase_						,       UpperCAmelCase_				),
                  '''f1''': fa_score(UpperCAmelCase_						,       UpperCAmelCase_				),
              }

              SCREAMING_SNAKE_CASE_							:				List[str]		=dict(results.items()				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=results
              return ret, preds_list, out_label_list


       def 	_snake_case				(    self						,       __A				)						->    Any:
              # when stable
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self._eval_end(UpperCAmelCase_				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=ret['''log''']
              return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


       def 	_snake_case				(    self						,       __A				)						->    Any:
              # updating to test_epoch_end instead of deprecated test_end
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[str]		=self._eval_end(UpperCAmelCase_				)

              # Converting to the dict required by pl
              # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
              # pytorch_lightning/trainer/logging.py#L139
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=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 	_snake_case				(    __A						,       __A				)						->    Union[str, Any]:
              # Add NER specific options
              BaseTransformer.add_model_specific_args(UpperCAmelCase_						,       UpperCAmelCase_				)
              parser.add_argument(
                  '''--task_type'''						,       default='''NER'''						,       type=UpperCAmelCase_						,       help='''Task type to fine tune in training (e.g. NER, POS, etc)'''				)
              parser.add_argument(
                  '''--max_seq_length'''						,       default=128						,       type=UpperCAmelCase_						,       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=UpperCAmelCase_						,       help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''						,       )
              parser.add_argument(
                  '''--gpus'''						,       default=0						,       type=UpperCAmelCase_						,       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__":
 _lowercase											=    argparse.ArgumentParser()
 add_generic_args(parser, os.getcwd())
 _lowercase											=    NERTransformer.add_model_specific_args(parser, os.getcwd())
 _lowercase											=    parser.parse_args()
 _lowercase											=    NERTransformer(args)
 _lowercase											=    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
  _lowercase											=    sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
  _lowercase											=    model.load_from_checkpoint(checkpoints[-1])
  trainer.test(model)





'''simple docstring'''





import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard


@slow
@require_flax
class 					snake_case			(unittest.TestCase ):



       def 				_a   (					self   )	->				Tuple:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()



       def 				_a   (					self   )	->				List[Any]:
           lowercase__							,							lowercase__									=    FlaxControlNetModel.from_pretrained(
               "lllyasviel/sd-controlnet-canny"  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__							,							lowercase__									=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
               "runwayml/stable-diffusion-v1-5"  ,controlnet=UpperCAmelCase_  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__									=    controlnet_params

           lowercase__									=    "bird"
           lowercase__									=    jax.device_count()
           lowercase__									=    pipe.prepare_text_inputs([prompts] * num_samples   )

           lowercase__									=    load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"   )
           lowercase__									=    pipe.prepare_image_inputs([canny_image] * num_samples   )

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

           lowercase__									=    pipe(
               prompt_ids=UpperCAmelCase_  ,image=UpperCAmelCase_  ,params=UpperCAmelCase_  ,prng_seed=UpperCAmelCase_  ,num_inference_steps=50  ,jit=UpperCAmelCase_  ,).images
           assert images.shape == (jax.device_count(), 1, 768, 512, 3)

           lowercase__									=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
           lowercase__									=    images[0, 253:256, 253:256, -1]

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    jnp.array(
               [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]   )
           print(F'''output_slice: {output_slice}'''   )
           assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2







       def 				_a   (					self   )	->				List[Any]:
           lowercase__							,							lowercase__									=    FlaxControlNetModel.from_pretrained(
               "lllyasviel/sd-controlnet-openpose"  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__							,							lowercase__									=    FlaxStableDiffusionControlNetPipeline.from_pretrained(
               "runwayml/stable-diffusion-v1-5"  ,controlnet=UpperCAmelCase_  ,from_pt=UpperCAmelCase_  ,dtype=jnp.bfloataa   )
           lowercase__									=    controlnet_params

           lowercase__									=    "Chef in the kitchen"
           lowercase__									=    jax.device_count()
           lowercase__									=    pipe.prepare_text_inputs([prompts] * num_samples   )

           lowercase__									=    load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"   )
           lowercase__									=    pipe.prepare_image_inputs([pose_image] * num_samples   )

           lowercase__									=    jax.random.PRNGKey(0   )
           lowercase__									=    jax.random.split(UpperCAmelCase_  ,jax.device_count()   )

           lowercase__									=    replicate(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )
           lowercase__									=    shard(UpperCAmelCase_   )

           lowercase__									=    pipe(
               prompt_ids=UpperCAmelCase_  ,image=UpperCAmelCase_  ,params=UpperCAmelCase_  ,prng_seed=UpperCAmelCase_  ,num_inference_steps=50  ,jit=UpperCAmelCase_  ,).images
           assert images.shape == (jax.device_count(), 1, 768, 512, 3)

           lowercase__									=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
           lowercase__									=    images[0, 253:256, 253:256, -1]

           lowercase__									=    jnp.asarray(jax.device_get(image_slice.flatten()   )   )
           lowercase__									=    jnp.array(
               [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]   )
           print(F'''output_slice: {output_slice}'''   )
           assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2




