



from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


a_    :  Dict						  =		logging.get_logger(__name__)  # pylint: disable=invalid-name





class  lowerCamelCase__	(    UpperCAmelCase_							,     UpperCAmelCase_):
     """simple docstring"""



     @register_to_config
     def __init__(self					,      __a					,      __a = None					,      __a = None     ):






           '''simple docstring'''



           super().__init__()

           lowerCamelCase             =		learnable

           if self.learnable:
                 assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
                 assert length is not None, "learnable=True requires `length` to be set"

                 lowerCamelCase             =		torch.zeros(__a					,      __a     )
           else:
                 lowerCamelCase             =		None

           lowerCamelCase             =		torch.nn.Parameter(__a     )





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     _A      =      42
     _A      =      42
     _A      =      42
     _A      =      42
     _A      =      42
     _A      =      42







     def __init__(self					,      __a					,      __a					,      __a					,      __a					,      __a					,      __a					,      ):






           '''simple docstring'''



           super().__init__()

           self.register_modules(
               vqvae=__a					,      transformer=__a					,      text_encoder=__a					,      tokenizer=__a					,      scheduler=__a					,      learned_classifier_free_sampling_embeddings=__a					,      )







     def     _a   (self					,      __a					,      __a					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		len(__a     ) if isinstance(__a					,      __a     ) else 1

           # get prompt text embeddings
           lowerCamelCase             =		self.tokenizer(
               __a					,      padding="max_length"					,      max_length=self.tokenizer.model_max_length					,      return_tensors="pt"					,      )
           lowerCamelCase             =		text_inputs.input_ids

           if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                 lowerCamelCase             =		self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]     )
                 logger.warning(
                     "The following part of your input was truncated because CLIP can only handle sequences up to"
                     F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""     )
                 lowerCamelCase             =		text_input_ids[:, : self.tokenizer.model_max_length]
           lowerCamelCase             =		self.text_encoder(text_input_ids.to(self.device     )     )[0]

           # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
           # While CLIP does normalize the pooled output of the text transformer when combining
           # the image and text embeddings, CLIP does not directly normalize the last hidden state.
           #
           # CLIP normalizing the pooled output.
           # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
           lowerCamelCase             =		prompt_embeds / prompt_embeds.norm(dim=-1					,      keepdim=__a     )

           # duplicate text embeddings for each generation per prompt
           lowerCamelCase             =		prompt_embeds.repeat_interleave(__a					,      dim=0     )

           if do_classifier_free_guidance:
                 if self.learned_classifier_free_sampling_embeddings.learnable:
                       lowerCamelCase             =		self.learned_classifier_free_sampling_embeddings.embeddings
                       lowerCamelCase             =		negative_prompt_embeds.unsqueeze(0     ).repeat(__a					,      1					,      1     )
                 else:
                       lowerCamelCase             =		[""] * batch_size

                       lowerCamelCase             =		text_input_ids.shape[-1]
                       lowerCamelCase             =		self.tokenizer(
                           __a					,      padding="max_length"					,      max_length=__a					,      truncation=__a					,      return_tensors="pt"					,      )
                       lowerCamelCase             =		self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]
                       # See comment for normalizing text embeddings
                       lowerCamelCase             =		negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1					,      keepdim=__a     )

                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 lowerCamelCase             =		negative_prompt_embeds.shape[1]
                 lowerCamelCase             =		negative_prompt_embeds.repeat(1					,      __a					,      1     )
                 lowerCamelCase             =		negative_prompt_embeds.view(batch_size * num_images_per_prompt					,      __a					,      -1     )

                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 lowerCamelCase             =		torch.cat([negative_prompt_embeds, prompt_embeds]     )

           return prompt_embeds







     @torch.no_grad()
     def __call__(self					,      __a					,      __a = 1_00					,      __a = 5.0					,      __a = 1.0					,      __a = 1					,      __a = None					,      __a = None					,      __a = "pil"					,      __a = True					,      __a = None					,      __a = 1					,      ):






           '''simple docstring'''



           if isinstance(__a					,      __a     ):
                 lowerCamelCase             =		1
           elif isinstance(__a					,      __a     ):
                 lowerCamelCase             =		len(__a     )
           else:
                 raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(__a     )}"""     )

           lowerCamelCase             =		batch_size * num_images_per_prompt

           lowerCamelCase             =		guidance_scale > 1.0

           lowerCamelCase             =		self._encode_prompt(__a					,      __a					,      __a     )

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

           # get the initial completely masked latents unless the user supplied it

           lowerCamelCase             =		(batch_size, self.transformer.num_latent_pixels)
           if latents is None:
                 lowerCamelCase             =		self.transformer.num_vector_embeds - 1
                 lowerCamelCase             =		torch.full(__a					,      __a     ).to(self.device     )
           else:
                 if latents.shape != latents_shape:
                       raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""     )
                 if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
                       raise ValueError(
                           "Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"
                           F""" {self.transformer.num_vector_embeds - 1} (inclusive)."""     )
                 lowerCamelCase             =		latents.to(self.device     )

           # set timesteps
           self.scheduler.set_timesteps(__a					,      device=self.device     )

           lowerCamelCase             =		self.scheduler.timesteps.to(self.device     )

           lowerCamelCase             =		latents

           for i, t in enumerate(self.progress_bar(__a     )     ):
                 # expand the sample if we are doing classifier free guidance
                 lowerCamelCase             =		torch.cat([sample] * 2     ) if do_classifier_free_guidance else sample

                 # predict the un-noised image
                 # model_output == `log_p_x_0`
                 lowerCamelCase             =		self.transformer(__a					,      encoder_hidden_states=__a					,      timestep=__a     ).sample

                 if do_classifier_free_guidance:
                       lowerCamelCase      ,				lowerCamelCase             =		model_output.chunk(2     )
                       lowerCamelCase             =		model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                       model_output -= torch.logsumexp(__a					,      dim=1					,      keepdim=__a     )

                 lowerCamelCase             =		self.truncate(__a					,      __a     )

                 # remove `log(0)`'s (`-inf`s)
                 lowerCamelCase             =		model_output.clamp(-70     )

                 # compute the previous noisy sample x_t -> x_t-1
                 lowerCamelCase             =		self.scheduler.step(__a					,      timestep=__a					,      sample=__a					,      generator=__a     ).prev_sample

                 # call the callback, if provided
                 if callback is not None and i % callback_steps == 0:
                       callback(__a					,      __a					,      __a     )

           lowerCamelCase             =		self.vqvae.config.vq_embed_dim
           lowerCamelCase             =		(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
           lowerCamelCase             =		self.vqvae.quantize.get_codebook_entry(__a					,      shape=__a     )
           lowerCamelCase             =		self.vqvae.decode(__a					,      force_not_quantize=__a     ).sample

           lowerCamelCase             =		(image / 2 + 0.5).clamp(0					,      1     )
           lowerCamelCase             =		image.cpu().permute(0					,      2					,      3					,      1     ).numpy()

           if output_type == "pil":
                 lowerCamelCase             =		self.numpy_to_pil(__a     )

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=__a     )







     def     _a   (self					,      __a					,      __a     ):






           '''simple docstring'''



           lowerCamelCase      ,				lowerCamelCase             =		torch.sort(__a					,      1					,      descending=__a     )
           lowerCamelCase             =		torch.exp(__a     )
           lowerCamelCase             =		sorted_p_x_0.cumsum(dim=1     ) < truncation_rate

           # Ensure that at least the largest probability is not zeroed out
           lowerCamelCase             =		torch.full_like(keep_mask[:, 0:1, :]					,      __a     )
           lowerCamelCase             =		torch.cat((all_true, keep_mask)					,      dim=1     )
           lowerCamelCase             =		keep_mask[:, :-1, :]

           lowerCamelCase             =		keep_mask.gather(1					,      indices.argsort(1     )     )

           lowerCamelCase             =		log_p_x_0.clone()

           lowerCamelCase             =		-torch.inf  # -inf = log(0)

           return rv




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_    :  Optional[int]						  =		logging.getLogger(__name__)





@dataclass
class  lowerCamelCase__	:
     """simple docstring"""



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





@dataclass
class  lowerCamelCase__	:
     """simple docstring"""



     _A      =      field(
         metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'})
     _A      =      field(
         default=UpperCAmelCase_							,     metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}							,     )
     _A      =      field(
         default=1_28							,     metadata={
             'help': (
                 'The maximum total input sequence length after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             )
         }							,     )
     _A      =      field(
         default=UpperCAmelCase_							,     metadata={'help': 'Overwrite the cached training and evaluation sets'})



def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		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.
            lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
      else:
            lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		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."			)

      lowerCamelCase             =		import_module("tasks"			)
      try:
            lowerCamelCase             =		getattr(UpperCAmelCase__ , model_args.task_type			)
            lowerCamelCase             =		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
      lowerCamelCase             =		token_classification_task.get_labels(data_args.labels			)
      lowerCamelCase             =		dict(enumerate(UpperCAmelCase__			)			)
      lowerCamelCase             =		len(UpperCAmelCase__			)

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

      lowerCamelCase             =		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 , )
      lowerCamelCase             =		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 , )
      lowerCamelCase             =		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
      lowerCamelCase             =		(
          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
      )
      lowerCamelCase             =		(
          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]]:
            lowerCamelCase             =		np.argmax(UpperCAmelCase__ , axis=2			)

            lowerCamelCase      ,				lowerCamelCase             =		preds.shape

            lowerCamelCase             =		[[] for _ in range(UpperCAmelCase__			)]
            lowerCamelCase             =		[[] 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:
            lowerCamelCase      ,				lowerCamelCase             =		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
      lowerCamelCase             =		DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8			) if training_args.fpaa else None

      # Initialize our Trainer
      lowerCamelCase             =		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
      lowerCamelCase             =		{}
      if training_args.do_eval:
            logger.info("*** Evaluate ***"			)

            lowerCamelCase             =		trainer.evaluate()

            lowerCamelCase             =		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:
            lowerCamelCase             =		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 , )

            lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		trainer.predict(UpperCAmelCase__			)
            lowerCamelCase      ,				lowerCamelCase             =		align_predictions(UpperCAmelCase__ , UpperCAmelCase__			)

            lowerCamelCase             =		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
            lowerCamelCase             =		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      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      main()


if __name__ == "__main__":
     main()