"""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


UpperCAmelCase__     						=  logging.getLogger(__name__)


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



@dataclass
class 				a :
    _snake_case   :     str   							=							field(
        metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}      )
    _snake_case   :     Optional[str]   							=							field(
        default=lowerCAmelCase_		,							metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}		,							)
    _snake_case   :     int   							=							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.'
            )
        }		,							)
    _snake_case   :     bool   							=							field(
        default=lowerCAmelCase_		,							metadata={'help': 'Overwrite the cached training and evaluation sets'}      )






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.

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

  _UpperCAmelCase         =					import_module("""tasks"""						)
  try:
    _UpperCAmelCase         =					getattr(lowercase      ,model_args.task_type						)
    _UpperCAmelCase         =					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"""      ,lowercase						)

  # Set seed
  set_seed(training_args.seed						)

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

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

  _UpperCAmelCase         =					AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,num_labels=lowercase      ,idalabel=lowercase      ,labelaid={label: i for i, label in enumerate(lowercase						)}      ,cache_dir=model_args.cache_dir      ,)
  _UpperCAmelCase         =					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      ,)
  _UpperCAmelCase         =					AutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path      ,from_tf=bool(""".ckpt""" in model_args.model_name_or_path						)      ,config=lowercase      ,cache_dir=model_args.cache_dir      ,)

  # Get datasets
  _UpperCAmelCase         =					(
      TokenClassificationDataset(
          token_classification_task=lowercase      ,data_dir=data_args.data_dir      ,tokenizer=lowercase      ,labels=lowercase      ,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
  )
  _UpperCAmelCase         =					(
      TokenClassificationDataset(
          token_classification_task=lowercase      ,data_dir=data_args.data_dir      ,tokenizer=lowercase      ,labels=lowercase      ,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]]:
    _UpperCAmelCase         =					np.argmax(lowercase      ,axis=2						)

    _UpperCAmelCase       , _UpperCAmelCase         =					preds.shape

    _UpperCAmelCase         =					[[] for _ in range(lowercase						)]
    _UpperCAmelCase         =					[[] for _ in range(lowercase						)]

    for i in range(lowercase						):
      for j in range(lowercase						):
        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:
    _UpperCAmelCase       , _UpperCAmelCase         =					align_predictions(p.predictions      ,p.label_ids						)
    return {
        "accuracy_score": accuracy_score(lowercase      ,lowercase						),
        "precision": precision_score(lowercase      ,lowercase						),
        "recall": recall_score(lowercase      ,lowercase						),
        "f1": fa_score(lowercase      ,lowercase						),
    }

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

  # Initialize our Trainer
  _UpperCAmelCase         =					Trainer(
      model=lowercase      ,args=lowercase      ,train_dataset=lowercase      ,eval_dataset=lowercase      ,compute_metrics=lowercase      ,data_collator=lowercase      ,)

  # 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
  _UpperCAmelCase         =					{}
  if training_args.do_eval:
    logger.info("""*** Evaluate ***"""						)

    _UpperCAmelCase         =					trainer.evaluate()

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

      results.update(lowercase						)

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

    _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase         =					trainer.predict(lowercase						)
    _UpperCAmelCase       , _UpperCAmelCase         =					align_predictions(lowercase      ,lowercase						)

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

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

  return results






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
    main()

"""simple docstring"""


import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class 				a (						lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     int   							=							StableUnCLIPPipeline
    _snake_case   :     str   							=							TEXT_TO_IMAGE_PARAMS
    _snake_case   :     Any   							=							TEXT_TO_IMAGE_BATCH_PARAMS
    _snake_case   :     Optional[Any]   							=							TEXT_TO_IMAGE_IMAGE_PARAMS
    _snake_case   :     str   							=							TEXT_TO_IMAGE_IMAGE_PARAMS

    # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
    _snake_case   :     str   							=							False


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      _UpperCAmelCase         =					32
      _UpperCAmelCase         =					embedder_hidden_size

      # prior components

      torch.manual_seed(0    )
      _UpperCAmelCase         =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					CLIPTextModelWithProjection(
          CLIPTextConfig(
              bos_token_id=0      , eos_token_id=2      , hidden_size=__lowerCAmelCase      , projection_dim=__lowerCAmelCase      , intermediate_size=37      , layer_norm_eps=1e-0_5      , num_attention_heads=4      , num_hidden_layers=5      , pad_token_id=1      , vocab_size=1000      , )    )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					PriorTransformer(
          num_attention_heads=2      , attention_head_dim=12      , embedding_dim=__lowerCAmelCase      , num_layers=1      , )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					DDPMScheduler(
          variance_type="""fixed_small_log"""      , prediction_type="""sample"""      , num_train_timesteps=1000      , clip_sample=__lowerCAmelCase      , clip_sample_range=5.0      , beta_schedule="""squaredcos_cap_v2"""      , )

      # regular denoising components

      torch.manual_seed(0    )
      _UpperCAmelCase         =					StableUnCLIPImageNormalizer(embedding_dim=__lowerCAmelCase    )
      _UpperCAmelCase         =					DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""    )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0      , eos_token_id=2      , hidden_size=__lowerCAmelCase      , projection_dim=32      , intermediate_size=37      , layer_norm_eps=1e-0_5      , num_attention_heads=4      , num_hidden_layers=5      , pad_token_id=1      , vocab_size=1000      , )    )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					UNetaDConditionModel(
          sample_size=32      , in_channels=4      , out_channels=4      , down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")      , up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")      , block_out_channels=(32, 64)      , attention_head_dim=(2, 4)      , class_embed_type="""projection"""      , projection_class_embeddings_input_dim=embedder_projection_dim * 2      , cross_attention_dim=__lowerCAmelCase      , layers_per_block=1      , upcast_attention=__lowerCAmelCase      , use_linear_projection=__lowerCAmelCase      , )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					DDIMScheduler(
          beta_schedule="""scaled_linear"""      , beta_start=0.00_085      , beta_end=0.012      , prediction_type="""v_prediction"""      , set_alpha_to_one=__lowerCAmelCase      , steps_offset=1      , )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					AutoencoderKL()

      _UpperCAmelCase         =					{
          # prior components
          """prior_tokenizer""": prior_tokenizer,
          """prior_text_encoder""": prior_text_encoder,
          """prior""": prior,
          """prior_scheduler""": prior_scheduler,
          # image noising components
          """image_normalizer""": image_normalizer,
          """image_noising_scheduler""": image_noising_scheduler,
          # regular denoising components
          """tokenizer""": tokenizer,
          """text_encoder""": text_encoder,
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
      }

      return components


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     List[Any]      , __lowerCAmelCase		:     str=0    ):
      if str(__lowerCAmelCase    ).startswith("""mps"""    ):
        _UpperCAmelCase         =					torch.manual_seed(__lowerCAmelCase    )
      else:
        _UpperCAmelCase         =					torch.Generator(device=__lowerCAmelCase    ).manual_seed(__lowerCAmelCase    )
      _UpperCAmelCase         =					{
          """prompt""": """A painting of a squirrel eating a burger""",
          """generator""": generator,
          """num_inference_steps""": 2,
          """prior_num_inference_steps""": 2,
          """output_type""": """numpy""",
      }
      return inputs


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      _UpperCAmelCase         =					torch_device == """cpu"""

      self._test_attention_slicing_forward_pass(test_max_difference=__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      _UpperCAmelCase         =					torch_device in ["""cpu""", """mps"""]

      self._test_inference_batch_single_identical(test_max_difference=__lowerCAmelCase    )



@slow
@require_torch_gpu
class 				a (						unittest.TestCase      ):


    def    lowerCAmelCase_     (    self		:     str    ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""    )

      _UpperCAmelCase         =					StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""      , torch_dtype=torch.floataa    )
      pipe.to(__lowerCAmelCase    )
      pipe.set_progress_bar_config(disable=__lowerCAmelCase    )
      # stable unclip will oom when integration tests are run on a V100,
      # so turn on memory savings
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _UpperCAmelCase         =					torch.Generator(device="""cpu"""    ).manual_seed(0    )
      _UpperCAmelCase         =					pipe("""anime turle"""      , generator=__lowerCAmelCase      , output_type="""np"""    )

      _UpperCAmelCase         =					output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__lowerCAmelCase      , __lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Any    ):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      _UpperCAmelCase         =					StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""      , torch_dtype=torch.floataa    )
      _UpperCAmelCase         =					pipe.to(__lowerCAmelCase    )
      pipe.set_progress_bar_config(disable=__lowerCAmelCase    )
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      _UpperCAmelCase         =					pipe(
          """anime turtle"""      , prior_num_inference_steps=2      , num_inference_steps=2      , output_type="""np"""      , )

      _UpperCAmelCase         =					torch.cuda.max_memory_allocated()
      # make sure that less than 7 GB is allocated
      assert mem_bytes < 7 * 10**9
