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


UpperCamelCase     					=      logging.getLogger(__name__)


@dataclass
class _a  :

	'''simple docstring'''



	lowerCamelCase_       :     List[Any]          =			field(
	    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
	lowerCamelCase_       :     Any          =			field(
	    default=_lowerCamelCase       ,				metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
	lowerCamelCase_       :     int          =			field(
	    default="""NER"""       ,				metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}   )
	lowerCamelCase_       :     Dict          =			field(
	    default=_lowerCamelCase       ,				metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
	lowerCamelCase_       :     Optional[int]          =			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.
	lowerCamelCase_       :     Tuple          =			field(
	    default=_lowerCamelCase       ,				metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,				)


@dataclass
class _a  :

	'''simple docstring'''



	lowerCamelCase_       :     Optional[Any]          =			field(
	    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}   )
	lowerCamelCase_       :     Union[str, Any]          =			field(
	    default=_lowerCamelCase       ,				metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}       ,				)
	lowerCamelCase_       :     Dict          =			field(
	    default=1_2_8       ,				metadata={
	        """help""": (
	            """The maximum total input sequence length after tokenization. Sequences longer """
	            """than this will be truncated, sequences shorter will be padded."""
	        )
	    }       ,				)
	lowerCamelCase_       :     int          =			field(
	    default=_lowerCamelCase       ,				metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )



def        lowerCamelCase_       (    )				->			Dict:
		__A				:       Any			    = 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.
				__A    ,  __A    ,  __A				:       Any			    = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
		else:
				__A    ,  __A    ,  __A				:       List[str]			    = 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."							)

		__A				:       Optional[int]			    = import_module("tasks"							)
		try:
				__A				:       Tuple			    = getattr(__UpperCAmelCase   ,      model_args.task_type							)
				__A				:       Any			    = 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
		__A				:       str			    = token_classification_task.get_labels(data_args.labels							)
		__A				:       Union[str, Any]			    = dict(enumerate(__UpperCAmelCase							)							)
		__A				:       Any			    = len(__UpperCAmelCase							)

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

		__A				:       Optional[int]			    = 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   ,      )
		__A				:       List[Any]			    = 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   ,      )
		__A				:       Dict			    = 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
		__A				:       Optional[int]			    = (
		    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
		)
		__A				:       Optional[int]			    = (
		    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(_lowercase   ,      _lowercase							) -> Tuple[List[int], List[int]]:
				__A				:       str			    = np.argmax(__UpperCAmelCase   ,      axis=2							)

				__A    ,  __A				:       int			    = preds.shape

				__A				:       str			    = [[] for _ in range(__UpperCAmelCase							)]
				__A				:       Dict			    = [[] 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(_lowercase							) -> Dict:
				__A    ,  __A				:       Dict			    = 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
		__A				:       Tuple			    = DataCollatorWithPadding(__UpperCAmelCase   ,      pad_to_multiple_of=8							) if training_args.fpaa else None

		# Initialize our Trainer
		__A				:       Union[str, Any]			    = 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
		__A				:       Dict			    = {}
		if training_args.do_eval:
				logger.info("*** Evaluate ***"							)

				__A				:       Any			    = trainer.evaluate()

				__A				:       List[str]			    = 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:
				__A				:       int			    = 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   ,      )

				__A    ,  __A    ,  __A				:       str			    = trainer.predict(__UpperCAmelCase							)
				__A    ,  __A				:       Dict			    = align_predictions(__UpperCAmelCase   ,      __UpperCAmelCase							)

				__A				:       List[str]			    = 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
				__A				:       str			    = 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        lowerCamelCase_       (    _lowercase							)				->			Optional[int]:
		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)

