




'''simple docstring'''

import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


a    :							Tuple										=    logging.getLogger(__name__)






@dataclass
class 		a							:
				snake_case_           =						field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}	)
				snake_case_           =						field(
				    default=_lowerCamelCase  ,	metadata={"help": "Pretrained config name or path if not the same as model_name"}	)
				snake_case_           =						field(
				    default="NER"  ,	metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}	)
				snake_case_           =						field(
				    default=_lowerCamelCase  ,	metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}	)
				snake_case_           =						field(default=_lowerCamelCase  ,	metadata={"help": "Set this flag to use fast tokenization."}	)
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				snake_case_           =						field(
				    default=_lowerCamelCase  ,	metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}  ,	)







@dataclass
class 		a							:
				snake_case_           =						field(
				    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}	)
				snake_case_           =						field(
				    default=_lowerCamelCase  ,	metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}  ,	)
				snake_case_           =						field(
				    default=128  ,	metadata={
				        "help": (
				            "The maximum total input sequence length after tokenization. Sequences longer "
				            "than this will be truncated, sequences shorter will be padded."
				        )
				    }  ,	)
				snake_case_           =						field(
				    default=_lowerCamelCase  ,	metadata={"help": "Overwrite the cached training and evaluation sets"}	)







def 						__magic_name__       (      )						->   Dict:

					'''simple docstring'''

					snake_case_		     =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
					if len(sys.argv      ) == 2 and sys.argv[1].endswith('''.json'''      ):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										snake_case_     ,snake_case_     ,snake_case_		     =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
					else:
										snake_case_     ,snake_case_     ,snake_case_		     =							parser.parse_args_into_dataclasses()

					if (
					    os.path.exists(training_args.output_dir      )
					    and os.listdir(training_args.output_dir      )
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
										    ''' --overwrite_output_dir to overcome.'''      )

					snake_case_		     =							import_module('''tasks'''      )
					try:
										snake_case_		     =							getattr(__UpperCAmelCase, model_args.task_type      )
										snake_case_		     =							token_classification_task_clazz()
					except AttributeError:
										raise ValueError(
										    F"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
										    F"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"      )

					# Setup logging
					logging.basicConfig(
					    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, )
					logger.warning(
					    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''', training_args.local_rank, training_args.device, training_args.n_gpu, bool(training_args.local_rank != -1      ), training_args.fpaa, )
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank      ):
										transformers.utils.logging.set_verbosity_info()
										transformers.utils.logging.enable_default_handler()
										transformers.utils.logging.enable_explicit_format()
					logger.info('''Training/evaluation parameters %s''', __UpperCAmelCase      )

					# Set seed
					set_seed(training_args.seed      )

					# Prepare CONLL-2003 task
					snake_case_		     =							token_classification_task.get_labels(data_args.labels      )
					snake_case_		     =							dict(enumerate(__UpperCAmelCase      )      )
					snake_case_		     =							len(__UpperCAmelCase      )

					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					snake_case_		     =							AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=__UpperCAmelCase, idalabel=__UpperCAmelCase, labelaid={label: i for i, label in enumerate(__UpperCAmelCase      )}, cache_dir=model_args.cache_dir, )
					snake_case_		     =							AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast, )
					snake_case_		     =							AutoModelForTokenClassification.from_pretrained(
					    model_args.model_name_or_path, from_tf=bool('''.ckpt''' in model_args.model_name_or_path      ), config=__UpperCAmelCase, cache_dir=model_args.cache_dir, )

					# Get datasets
					snake_case_		     =							(
					    TokenClassificationDataset(
					        token_classification_task=__UpperCAmelCase, data_dir=data_args.data_dir, tokenizer=__UpperCAmelCase, labels=__UpperCAmelCase, model_type=config.model_type, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.train, )
					    if training_args.do_train
					    else None
					)
					snake_case_		     =							(
					    TokenClassificationDataset(
					        token_classification_task=__UpperCAmelCase, data_dir=data_args.data_dir, tokenizer=__UpperCAmelCase, labels=__UpperCAmelCase, model_type=config.model_type, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.dev, )
					    if training_args.do_eval
					    else None
					)

					def align_predictions(__UpperCAmelCase, __UpperCAmelCase      ) -> Tuple[List[int], List[int]]:
										snake_case_		     =							np.argmax(__UpperCAmelCase, axis=2      )

										snake_case_     ,snake_case_		     =							preds.shape

										snake_case_		     =							[[] for _ in range(__UpperCAmelCase      )]
										snake_case_		     =							[[] for _ in range(__UpperCAmelCase      )]

										for i in range(__UpperCAmelCase      ):
															for j in range(__UpperCAmelCase      ):
																				if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																									out_label_list[i].append(label_map[label_ids[i][j]]      )
																									preds_list[i].append(label_map[preds[i][j]]      )

										return preds_list, out_label_list

					def compute_metrics(__UpperCAmelCase      ) -> Dict:
										snake_case_     ,snake_case_		     =							align_predictions(p.predictions, p.label_ids      )
										return {
										    "accuracy_score": accuracy_score(__UpperCAmelCase, __UpperCAmelCase      ),
										    "precision": precision_score(__UpperCAmelCase, __UpperCAmelCase      ),
										    "recall": recall_score(__UpperCAmelCase, __UpperCAmelCase      ),
										    "f1": fa_score(__UpperCAmelCase, __UpperCAmelCase      ),
										}

					# Data collator
					snake_case_		     =							DataCollatorWithPadding(__UpperCAmelCase, pad_to_multiple_of=8      ) if training_args.fpaa else None

					# Initialize our Trainer
					snake_case_		     =							Trainer(
					    model=__UpperCAmelCase, args=__UpperCAmelCase, train_dataset=__UpperCAmelCase, eval_dataset=__UpperCAmelCase, compute_metrics=__UpperCAmelCase, data_collator=__UpperCAmelCase, )

					# Training
					if training_args.do_train:
										trainer.train(
										    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
										trainer.save_model()
										# For convenience, we also re-save the tokenizer to the same directory,
										# so that you can share your model easily on huggingface.co/models =)
										if trainer.is_world_process_zero():
															tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
					snake_case_		     =							{}
					if training_args.do_eval:
										logger.info('''*** Evaluate ***'''      )

										snake_case_		     =							trainer.evaluate()

										snake_case_		     =							os.path.join(training_args.output_dir, '''eval_results.txt'''      )
										if trainer.is_world_process_zero():
															with open(__UpperCAmelCase, '''w'''      ) as writer:
																				logger.info('''***** Eval results *****'''      )
																				for key, value in result.items():
																									logger.info('''  %s = %s''', __UpperCAmelCase, __UpperCAmelCase      )
																									writer.write('''%s = %s\n''' % (key, value)      )

															results.update(__UpperCAmelCase      )

    # Predict
					if training_args.do_predict:
										snake_case_		     =							TokenClassificationDataset(
										    token_classification_task=__UpperCAmelCase, data_dir=data_args.data_dir, tokenizer=__UpperCAmelCase, labels=__UpperCAmelCase, model_type=config.model_type, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.test, )

										snake_case_     ,snake_case_     ,snake_case_		     =							trainer.predict(__UpperCAmelCase      )
										snake_case_     ,snake_case_		     =							align_predictions(__UpperCAmelCase, __UpperCAmelCase      )

										snake_case_		     =							os.path.join(training_args.output_dir, '''test_results.txt'''      )
										if trainer.is_world_process_zero():
															with open(__UpperCAmelCase, '''w'''      ) as writer:
																				for key, value in metrics.items():
																									logger.info('''  %s = %s''', __UpperCAmelCase, __UpperCAmelCase      )
																									writer.write('''%s = %s\n''' % (key, value)      )

        # Save predictions
										snake_case_		     =							os.path.join(training_args.output_dir, '''test_predictions.txt'''      )
										if trainer.is_world_process_zero():
															with open(__UpperCAmelCase, '''w'''      ) as writer:
																				with open(os.path.join(data_args.data_dir, '''test.txt'''      ), '''r'''      ) as f:
																									token_classification_task.write_predictions_to_file(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )

					return results


def 						__magic_name__       (      __UpperCAmelCase      )						->   Optional[int]:

					'''simple docstring'''

					main()


if __name__ == "__main__":
							main()







'''simple docstring'''

import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
							a    :							List[str]										=    argparse.ArgumentParser()

							parser.add_argument(
							    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
							)
							# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
							parser.add_argument(
							    '--original_config_file',
							    default=None,
							    type=str,
							    help='The YAML config file corresponding to the original architecture.',
							)
							parser.add_argument(
							    '--num_in_channels',
							    default=None,
							    type=int,
							    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
							)
							parser.add_argument(
							    '--scheduler_type',
							    default='pndm',
							    type=str,
							    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
							)
							parser.add_argument(
							    '--pipeline_type',
							    default=None,
							    type=str,
							    help=(
							        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
							        '. If `None` pipeline will be automatically inferred.'
							    ),
							)
							parser.add_argument(
							    '--image_size',
							    default=None,
							    type=int,
							    help=(
							        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
							        ' Base. Use 768 for Stable Diffusion v2.'
							    ),
							)
							parser.add_argument(
							    '--prediction_type',
							    default=None,
							    type=str,
							    help=(
							        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
							        ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
							    ),
							)
							parser.add_argument(
							    '--extract_ema',
							    action='store_true',
							    help=(
							        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
							        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
							        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
							    ),
							)
							parser.add_argument(
							    '--upcast_attention',
							    action='store_true',
							    help=(
							        'Whether the attention computation should always be upcasted. This is necessary when running stable'
							        ' diffusion 2.1.'
							    ),
							)
							parser.add_argument(
							    '--from_safetensors',
							    action='store_true',
							    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
							)
							parser.add_argument(
							    '--to_safetensors',
							    action='store_true',
							    help='Whether to store pipeline in safetensors format or not.',
							)
							parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
							parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
							parser.add_argument(
							    '--stable_unclip',
							    type=str,
							    default=None,
							    required=False,
							    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
							)
							parser.add_argument(
							    '--stable_unclip_prior',
							    type=str,
							    default=None,
							    required=False,
							    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
							)
							parser.add_argument(
							    '--clip_stats_path',
							    type=str,
							    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
							    required=False,
							)
							parser.add_argument(
							    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
							)
							parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
							parser.add_argument(
							    '--vae_path',
							    type=str,
							    default=None,
							    required=False,
							    help='Set to a path, hub id to an already converted vae to not convert it again.',
							)
							a    :							str										=    parser.parse_args()

							a    :							List[Any]										=    download_from_original_stable_diffusion_ckpt(
							    checkpoint_path=args.checkpoint_path,
							    original_config_file=args.original_config_file,
							    image_size=args.image_size,
							    prediction_type=args.prediction_type,
							    model_type=args.pipeline_type,
							    extract_ema=args.extract_ema,
							    scheduler_type=args.scheduler_type,
							    num_in_channels=args.num_in_channels,
							    upcast_attention=args.upcast_attention,
							    from_safetensors=args.from_safetensors,
							    device=args.device,
							    stable_unclip=args.stable_unclip,
							    stable_unclip_prior=args.stable_unclip_prior,
							    clip_stats_path=args.clip_stats_path,
							    controlnet=args.controlnet,
							    vae_path=args.vae_path,
							)

							if args.half:
														pipe.to(torch_dtype=torch.floataa)

							if args.controlnet:
														# only save the controlnet model
														pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
							else:
														pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

