





from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


snake_case__      :							List[Any]									=  logging.get_logger(__name__)  # pylint: disable=invalid-name


snake_case__      :							int									=  '\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = "cuda"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample.png")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")\n        ```\n'





class A_      (    _lowerCamelCase     ):




   def __init__(self			:str					,     _UpperCamelCase			:UNetaDModel					,     _UpperCamelCase			:CMStochasticIterativeScheduler				)->      None:
         super().__init__()

         self.register_modules(
             unet=_UpperCamelCase					,     scheduler=_UpperCamelCase					,     )

         __A      =       None




   def _lowerCAmelCase   (self			:Dict					,     _UpperCamelCase			:Tuple=0				)->      Optional[Any]:
         if is_accelerate_available() and is_accelerate_version('''>='''					,     '''0.14.0'''				):
               from accelerate import cpu_offload
         else:
               raise ImportError('''`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'''				)

         __A      =       torch.device(f"""cuda:{gpu_id}"""				)

         if self.device.type != "cpu":
               self.to('''cpu'''					,     silence_dtype_warnings=_UpperCamelCase				)
               torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         for cpu_offloaded_model in [self.unet]:
               cpu_offload(_UpperCamelCase					,     _UpperCamelCase				)

         if self.safety_checker is not None:
               cpu_offload(self.safety_checker					,     execution_device=_UpperCamelCase					,     offload_buffers=_UpperCamelCase				)




   def _lowerCAmelCase   (self			:Tuple					,     _UpperCamelCase			:Tuple=0				)->      List[str]:
         if is_accelerate_available() and is_accelerate_version('''>='''					,     '''0.17.0.dev0'''				):
               from accelerate import cpu_offload_with_hook
         else:
               raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''				)

         __A      =       torch.device(f"""cuda:{gpu_id}"""				)

         if self.device.type != "cpu":
               self.to('''cpu'''					,     silence_dtype_warnings=_UpperCamelCase				)
               torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         __A      =       None
         for cpu_offloaded_model in [self.unet]:
               __A     ,		__A      =       cpu_offload_with_hook(_UpperCamelCase					,     _UpperCamelCase					,     prev_module_hook=_UpperCamelCase				)

         if self.safety_checker is not None:
               __A     ,		__A      =       cpu_offload_with_hook(self.safety_checker					,     _UpperCamelCase					,     prev_module_hook=_UpperCamelCase				)

         # We'll offload the last model manually.
         __A      =       hook




   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def _lowerCAmelCase   (self			:Optional[int]				)->      str:
         if not hasattr(self.unet					,     '''_hf_hook'''				):
               return self.device
         for module in self.unet.modules():
               if (
                   hasattr(_UpperCamelCase					,     '''_hf_hook'''				)
                   and hasattr(module._hf_hook					,     '''execution_device'''				)
                   and module._hf_hook.execution_device is not None
               ):
                     return torch.device(module._hf_hook.execution_device				)
         return self.device




   def _lowerCAmelCase   (self			:int					,     _UpperCamelCase			:Optional[Any]					,     _UpperCamelCase			:int					,     _UpperCamelCase			:Optional[Any]					,     _UpperCamelCase			:Dict					,     _UpperCamelCase			:Any					,     _UpperCamelCase			:int					,     _UpperCamelCase			:List[Any]					,     _UpperCamelCase			:int=None				)->      List[str]:
         __A      =       (batch_size, num_channels, height, width)
         if isinstance(_UpperCamelCase					,     _UpperCamelCase				) and len(_UpperCamelCase				) != batch_size:
               raise ValueError(
                   f"""You have passed a list of generators of length {len(_UpperCamelCase				)}, but requested an effective batch"""
                   f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

         if latents is None:
               __A      =       randn_tensor(_UpperCamelCase					,     generator=_UpperCamelCase					,     device=_UpperCamelCase					,     dtype=_UpperCamelCase				)
         else:
               __A      =       latents.to(device=_UpperCamelCase					,     dtype=_UpperCamelCase				)

         # scale the initial noise by the standard deviation required by the scheduler
         __A      =       latents * self.scheduler.init_noise_sigma
         return latents




   def _lowerCAmelCase   (self			:int					,     _UpperCamelCase			:torch.FloatTensor					,     _UpperCamelCase			:str = "pil"				)->      Dict:
         if output_type not in ["pt", "np", "pil"]:
               raise ValueError(
                   f"""output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"""				)

         # Equivalent to diffusers.VaeImageProcessor.denormalize
         __A      =       (sample / 2 + 0.5).clamp(0					,     1				)
         if output_type == "pt":
               return sample

         # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
         __A      =       sample.cpu().permute(0					,     2					,     3					,     1				).numpy()
         if output_type == "np":
               return sample

         # Output_type must be 'pil'
         __A      =       self.numpy_to_pil(_UpperCamelCase				)
         return sample




   def _lowerCAmelCase   (self			:int					,     _UpperCamelCase			:List[Any]					,     _UpperCamelCase			:Union[str, Any]					,     _UpperCamelCase			:Optional[Any]=None				)->      str:
         if self.unet.config.num_class_embeds is not None:
               if isinstance(_UpperCamelCase					,     _UpperCamelCase				):
                     __A      =       torch.tensor(_UpperCamelCase					,     dtype=torch.int				)
               elif isinstance(_UpperCamelCase					,     _UpperCamelCase				):
                     assert batch_size == 1, "Batch size must be 1 if classes is an int"
                     __A      =       torch.tensor([class_labels]					,     dtype=torch.int				)
               elif class_labels is None:
                     # Randomly generate batch_size class labels
                     # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
                     __A      =       torch.randint(0					,     self.unet.config.num_class_embeds					,     size=(batch_size,)				)
               __A      =       class_labels.to(_UpperCamelCase				)
         else:
               __A      =       None
         return class_labels




   def _lowerCAmelCase   (self			:Optional[Any]					,     _UpperCamelCase			:Tuple					,     _UpperCamelCase			:Optional[int]					,     _UpperCamelCase			:Tuple					,     _UpperCamelCase			:Dict					,     _UpperCamelCase			:List[str]					,     _UpperCamelCase			:Union[str, Any]				)->      Union[str, Any]:
         if num_inference_steps is None and timesteps is None:
               raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''				)

         if num_inference_steps is not None and timesteps is not None:
               logger.warning(
                   f"""Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"""
                   ''' `timesteps` will be used over `num_inference_steps`.'''				)

         if latents is not None:
               __A      =       (batch_size, 3, img_size, img_size)
               if latents.shape != expected_shape:
                     raise ValueError(f"""The shape of latents is {latents.shape} but is expected to be {expected_shape}."""				)

         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(_UpperCamelCase					,     _UpperCamelCase				) or callback_steps <= 0)
         ):
               raise ValueError(
                   f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                   f""" {type(_UpperCamelCase				)}."""				)






   @torch.no_grad()
   @replace_example_docstring(_UpperCamelCase				)
   def __call__(self			:Tuple					,     _UpperCamelCase			:int = 1					,     _UpperCamelCase			:Optional[Union[torch.Tensor, List[int], int]] = None					,     _UpperCamelCase			:int = 1					,     _UpperCamelCase			:List[int] = None					,     _UpperCamelCase			:Optional[Union[torch.Generator, List[torch.Generator]]] = None					,     _UpperCamelCase			:Optional[torch.FloatTensor] = None					,     _UpperCamelCase			:Optional[str] = "pil"					,     _UpperCamelCase			:bool = True					,     _UpperCamelCase			:Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,     _UpperCamelCase			:int = 1					,     )->      Union[str, Any]:
         __A      =       self.unet.config.sample_size
         __A      =       self._execution_device

         # 1. Check inputs
         self.check_inputs(_UpperCamelCase					,     _UpperCamelCase					,     _UpperCamelCase					,     _UpperCamelCase					,     _UpperCamelCase					,     _UpperCamelCase				)

         # 2. Prepare image latents
         # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
         __A      =       self.prepare_latents(
             batch_size=_UpperCamelCase					,     num_channels=self.unet.config.in_channels					,     height=_UpperCamelCase					,     width=_UpperCamelCase					,     dtype=self.unet.dtype					,     device=_UpperCamelCase					,     generator=_UpperCamelCase					,     latents=_UpperCamelCase					,     )

         # 3. Handle class_labels for class-conditional models
         __A      =       self.prepare_class_labels(_UpperCamelCase					,     _UpperCamelCase					,     class_labels=_UpperCamelCase				)

         # 4. Prepare timesteps
         if timesteps is not None:
               self.scheduler.set_timesteps(timesteps=_UpperCamelCase					,     device=_UpperCamelCase				)
               __A      =       self.scheduler.timesteps
               __A      =       len(_UpperCamelCase				)
         else:
               self.scheduler.set_timesteps(_UpperCamelCase				)
               __A      =       self.scheduler.timesteps

         # 5. Denoising loop
         # Multistep sampling: implements Algorithm 1 in the paper
         with self.progress_bar(total=_UpperCamelCase				) as progress_bar:
               for i, t in enumerate(_UpperCamelCase				):
                     __A      =       self.scheduler.scale_model_input(_UpperCamelCase					,     _UpperCamelCase				)
                     __A      =       self.unet(_UpperCamelCase					,     _UpperCamelCase					,     class_labels=_UpperCamelCase					,     return_dict=_UpperCamelCase				)[0]

                     __A      =       self.scheduler.step(_UpperCamelCase					,     _UpperCamelCase					,     _UpperCamelCase					,     generator=_UpperCamelCase				)[0]

                     # call the callback, if provided
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                           callback(_UpperCamelCase					,     _UpperCamelCase					,     _UpperCamelCase				)

        # 6. Post-process image sample
         __A      =       self.postprocess_image(_UpperCamelCase					,     output_type=_UpperCamelCase				)

         # Offload last model to CPU
         if hasattr(self					,     '''final_offload_hook'''				) and self.final_offload_hook is not None:
               self.final_offload_hook.offload()

         if not return_dict:
               return (image,)

         return ImagePipelineOutput(images=_UpperCamelCase				)






import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


snake_case__      :							str									=  logging.getLogger(__name__)





@dataclass
class A_      :
   lowerCAmelCase__       =				field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}     )
   lowerCAmelCase__       =				field(
       default=_lowerCamelCase				,   metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}     )
   lowerCAmelCase__       =				field(
       default="""NER"""				,   metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}     )
   lowerCAmelCase__       =				field(
       default=_lowerCamelCase				,   metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}     )
   lowerCAmelCase__       =				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__       =				field(
       default=_lowerCamelCase				,   metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}				,   )





@dataclass
class A_      :
   lowerCAmelCase__       =				field(
       metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}     )
   lowerCAmelCase__       =				field(
       metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}     )
   lowerCAmelCase__       =				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."""
           )
       }				,   )
   lowerCAmelCase__       =				field(
       default=_lowerCamelCase				,   metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )





def _a       (			)			->		int:







      '''simple docstring'''






      __A      =       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
      __A     ,		__A     ,		__A      =       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      =       import_module('''tasks'''				)

      try:
            __A      =       getattr(lowerCamelCase      ,       model_args.task_type				)
            __A      =       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      ,       )
      logger.info(
          '''n_replicas: %s, distributed training: %s, 16-bits training: %s'''      ,       training_args.n_replicas      ,       bool(training_args.n_replicas > 1				)      ,       training_args.fpaa      ,       )
      logger.info('''Training/evaluation parameters %s'''      ,       lowerCamelCase				)

      # Prepare Token Classification task
      __A      =       token_classification_task.get_labels(data_args.labels				)
      __A      =       dict(enumerate(lowerCamelCase				)				)
      __A      =       len(lowerCamelCase				)

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

      __A      =       AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,       num_labels=lowerCamelCase      ,       idalabel=lowerCamelCase      ,       labelaid={label: i for i, label in enumerate(lowerCamelCase				)}      ,       cache_dir=model_args.cache_dir      ,       )
      __A      =       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      ,       )

      with training_args.strategy.scope():
            __A      =       TFAutoModelForTokenClassification.from_pretrained(
                model_args.model_name_or_path      ,       from_pt=bool('''.bin''' in model_args.model_name_or_path				)      ,       config=lowerCamelCase      ,       cache_dir=model_args.cache_dir      ,       )

      # Get datasets
      __A      =       (
          TFTokenClassificationDataset(
              token_classification_task=lowerCamelCase      ,       data_dir=data_args.data_dir      ,       tokenizer=lowerCamelCase      ,       labels=lowerCamelCase      ,       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      =       (
          TFTokenClassificationDataset(
              token_classification_task=lowerCamelCase      ,       data_dir=data_args.data_dir      ,       tokenizer=lowerCamelCase      ,       labels=lowerCamelCase      ,       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(lowerCamelCase:     np.ndarray      ,       lowerCamelCase:     np.ndarray				) -> Tuple[List[int], List[int]]:
            __A      =       np.argmax(lowerCamelCase      ,       axis=2				)
            __A     ,		__A      =       preds.shape
            __A      =       [[] for _ in range(lowerCamelCase				)]
            __A      =       [[] for _ in range(lowerCamelCase				)]

            for i in range(lowerCamelCase				):
                  for j in range(lowerCamelCase				):
                        if label_ids[i, j] != -1_00:
                              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(lowerCamelCase:     EvalPrediction				) -> Dict:
            __A     ,		__A      =       align_predictions(p.predictions      ,       p.label_ids				)

            return {
                "precision": precision_score(lowerCamelCase      ,       lowerCamelCase				),
                "recall": recall_score(lowerCamelCase      ,       lowerCamelCase				),
                "f1": fa_score(lowerCamelCase      ,       lowerCamelCase				),
            }

      # Initialize our Trainer
      __A      =       TFTrainer(
          model=lowerCamelCase      ,       args=lowerCamelCase      ,       train_dataset=train_dataset.get_dataset() if train_dataset else None      ,       eval_dataset=eval_dataset.get_dataset() if eval_dataset else None      ,       compute_metrics=lowerCamelCase      ,       )

      # Training
      if training_args.do_train:
            trainer.train()
            trainer.save_model()
            tokenizer.save_pretrained(training_args.output_dir				)

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

            __A      =       trainer.evaluate()
            __A      =       os.path.join(training_args.output_dir      ,       '''eval_results.txt'''				)

            with open(lowerCamelCase      ,       '''w'''				) as writer:
                  logger.info('''***** Eval results *****'''				)

                  for key, value in result.items():
                        logger.info('''  %s = %s'''      ,       lowerCamelCase      ,       lowerCamelCase				)
                        writer.write('''%s = %s\n''' % (key, value)				)

                  results.update(lowerCamelCase				)

    # Predict
      if training_args.do_predict:
            __A      =       TFTokenClassificationDataset(
                token_classification_task=lowerCamelCase      ,       data_dir=data_args.data_dir      ,       tokenizer=lowerCamelCase      ,       labels=lowerCamelCase      ,       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      =       trainer.predict(test_dataset.get_dataset()				)
            __A     ,		__A      =       align_predictions(lowerCamelCase      ,       lowerCamelCase				)
            __A      =       classification_report(lowerCamelCase      ,       lowerCamelCase				)

            logger.info('''\n%s'''      ,       lowerCamelCase				)

            __A      =       os.path.join(training_args.output_dir      ,       '''test_results.txt'''				)

            with open(lowerCamelCase      ,       '''w'''				) as writer:
                  writer.write('''%s\n''' % report				)

            # Save predictions
            __A      =       os.path.join(training_args.output_dir      ,       '''test_predictions.txt'''				)

            with open(lowerCamelCase      ,       '''w'''				) as writer:
                  with open(os.path.join(data_args.data_dir      ,       '''test.txt'''				)      ,       '''r'''				) as f:
                        __A      =       0

                        for line in f:
                              if line.startswith('''-DOCSTART-'''				) or line == "" or line == "\n":
                                    writer.write(lowerCamelCase				)

                                    if not preds_list[example_id]:
                                          example_id += 1
                              elif preds_list[example_id]:
                                    __A      =       line.split()[0] + ''' ''' + preds_list[example_id].pop(0				) + '''\n'''

                                    writer.write(lowerCamelCase				)
                              else:
                                    logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.'''      ,       line.split()[0]				)

      return results


if __name__ == "__main__":
       main()





