





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     unittest.TestCase				):
			_UpperCamelCase   :		Tuple			 =	KandinskyInpaintPipeline
			_UpperCamelCase   :		Optional[Any]			 =	['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
			_UpperCamelCase   :		Optional[int]			 =	[
			    'prompt',
			    'negative_prompt',
			    'image_embeds',
			    'negative_image_embeds',
			    'image',
			    'mask_image',
			]
			_UpperCamelCase   :		Tuple			 =	[
			    'generator',
			    'height',
			    'width',
			    'latents',
			    'guidance_scale',
			    'negative_prompt',
			    'num_inference_steps',
			    'return_dict',
			    'guidance_scale',
			    'num_images_per_prompt',
			    'output_type',
			    'return_dict',
			]
			_UpperCamelCase   :		Any			 =	False


			@property
			def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       Union[str, Any]:





					"""simple docstring"""







					return 32


			@property
			def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       int:





					"""simple docstring"""







					return 32


			@property
			def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       Dict:





					"""simple docstring"""







					return self.time_input_dim


			@property
			def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Optional[Any]:





					"""simple docstring"""







					return self.time_input_dim * 4


			@property
			def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       List[Any]:





					"""simple docstring"""







					return 100


			@property
			def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       str:





					"""simple docstring"""







					lowercase__           =  XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'	)
					return tokenizer


			@property
			def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Dict:





					"""simple docstring"""







					torch.manual_seed(0	)
					lowercase__           =  MCLIPConfig(
					    numDims=self.cross_attention_dim      ,					transformerDimensions=self.text_embedder_hidden_size      ,					hidden_size=self.text_embedder_hidden_size      ,					intermediate_size=37      ,					num_attention_heads=4      ,					num_hidden_layers=5      ,					vocab_size=1_005      ,					)

					lowercase__           =  MultilingualCLIP(a	)
					lowercase__           =  text_encoder.eval()

					return text_encoder


			@property
			def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       List[Any]:





					"""simple docstring"""







					torch.manual_seed(0	)

					lowercase__           =  {
					    'in_channels': 9,
					    # Out channels is double in channels because predicts mean and variance
					    'out_channels': 8,
					    'addition_embed_type': 'text_image',
					    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
					    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
					    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
					    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
					    'layers_per_block': 1,
					    'encoder_hid_dim': self.text_embedder_hidden_size,
					    'encoder_hid_dim_type': 'text_image_proj',
					    'cross_attention_dim': self.cross_attention_dim,
					    'attention_head_dim': 4,
					    'resnet_time_scale_shift': 'scale_shift',
					    'class_embed_type': None,
					}

					lowercase__           =  UNetaDConditionModel(**a	)
					return model


			@property
			def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       Any:





					"""simple docstring"""







					return {
					    "block_out_channels": [32, 64],
					    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
					    "in_channels": 3,
					    "latent_channels": 4,
					    "layers_per_block": 1,
					    "norm_num_groups": 8,
					    "norm_type": "spatial",
					    "num_vq_embeddings": 12,
					    "out_channels": 3,
					    "up_block_types": [
					        "AttnUpDecoderBlock2D",
					        "UpDecoderBlock2D",
					    ],
					    "vq_embed_dim": 4,
					}


			@property
			def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       List[str]:





					"""simple docstring"""







					torch.manual_seed(0	)
					lowercase__           =  VQModel(**self.dummy_movq_kwargs	)
					return model


			def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       str:





					"""simple docstring"""







					lowercase__           =  self.dummy_text_encoder
					lowercase__           =  self.dummy_tokenizer
					lowercase__           =  self.dummy_unet
					lowercase__           =  self.dummy_movq

					lowercase__           =  DDIMScheduler(
					    num_train_timesteps=1_000      ,					beta_schedule='linear'      ,					beta_start=0.00085      ,					beta_end=0.012      ,					clip_sample=a      ,					set_alpha_to_one=a      ,					steps_offset=1      ,					prediction_type='epsilon'      ,					thresholding=a      ,					)

					lowercase__           =  {
					    'text_encoder': text_encoder,
					    'tokenizer': tokenizer,
					    'unet': unet,
					    'scheduler': scheduler,
					    'movq': movq,
					}

					return components


			def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    List[str]      ,					a      :    List[str]=0	)->       str:





					"""simple docstring"""







					lowercase__           =  floats_tensor((1, self.cross_attention_dim)      ,					rng=random.Random(a	)	).to(a	)
					lowercase__           =  floats_tensor((1, self.cross_attention_dim)      ,					rng=random.Random(seed + 1	)	).to(a	)
					# create init_image
					lowercase__           =  floats_tensor((1, 3, 64, 64)      ,					rng=random.Random(a	)	).to(a	)
					lowercase__           =  image.cpu().permute(0      ,					2      ,					3      ,					1	)[0]
					lowercase__           =  Image.fromarray(np.uinta(a	)	).convert('RGB'	).resize((256, 256)	)
					# create mask
					lowercase__           =  np.ones((64, 64)      ,					dtype=np.floataa	)
					lowercase__           =  0

					if str(a	).startswith('mps'	):
							lowercase__           =  torch.manual_seed(a	)
					else:
							lowercase__           =  torch.Generator(device=a	).manual_seed(a	)
					lowercase__           =  {
					    'prompt': 'horse',
					    'image': init_image,
					    'mask_image': mask,
					    'image_embeds': image_embeds,
					    'negative_image_embeds': negative_image_embeds,
					    'generator': generator,
					    'height': 64,
					    'width': 64,
					    'num_inference_steps': 2,
					    'guidance_scale': 4.0,
					    'output_type': 'np',
					}
					return inputs


			def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       int:





					"""simple docstring"""







					lowercase__           =  'cpu'

					lowercase__           =  self.get_dummy_components()

					lowercase__           =  self.pipeline_class(**a	)
					lowercase__           =  pipe.to(a	)

					pipe.set_progress_bar_config(disable=a	)

					lowercase__           =  pipe(**self.get_dummy_inputs(a	)	)
					lowercase__           =  output.images

					lowercase__           =  pipe(
					    **self.get_dummy_inputs(a	)      ,					return_dict=a      ,					)[0]

					lowercase__           =  image[0, -3:, -3:, -1]
					lowercase__           =  image_from_tuple[0, -3:, -3:, -1]

					print(f"""image.shape {image.shape}"""	)

					assert image.shape == (1, 64, 64, 3)

					lowercase__           =  np.array(
					    [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]	)

					assert (
					    np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
					), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
					assert (
					    np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2
					), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""


			def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       Dict:





					"""simple docstring"""







					super().test_inference_batch_single_identical(expected_max_diff=3E-3	)






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


			def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       Dict:





					"""simple docstring"""







					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()


			def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       List[Any]:





					"""simple docstring"""







					lowercase__           =  load_numpy(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'	)

					lowercase__           =  load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'	)
					lowercase__           =  np.ones((768, 768)      ,					dtype=np.floataa	)
					lowercase__           =  0

					lowercase__           =  'a hat'

					lowercase__           =  KandinskyPriorPipeline.from_pretrained(
					    'kandinsky-community/kandinsky-2-1-prior'      ,					torch_dtype=torch.floataa	)
					pipe_prior.to(a	)

					lowercase__           =  KandinskyInpaintPipeline.from_pretrained(
					    'kandinsky-community/kandinsky-2-1-inpaint'      ,					torch_dtype=torch.floataa	)
					lowercase__           =  pipeline.to(a	)
					pipeline.set_progress_bar_config(disable=a	)

					lowercase__           =  torch.Generator(device='cpu'	).manual_seed(0	)
					lowercase__      ,   lowercase__           =  pipe_prior(
					    a      ,					generator=a      ,					num_inference_steps=5      ,					negative_prompt=''      ,					).to_tuple()

					lowercase__           =  pipeline(
					    a      ,					image=a      ,					mask_image=a      ,					image_embeds=a      ,					negative_image_embeds=a      ,					generator=a      ,					num_inference_steps=100      ,					height=768      ,					width=768      ,					output_type='np'      ,					)

					lowercase__           =  output.images[0]

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

					assert_mean_pixel_difference(a      ,					a	)









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


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






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






def       __UpperCamelCase  ()    ->       str:
  # 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.

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

  lowercase__           =  import_module('tasks'		)
  try:
    lowercase__           =  getattr(_SCREAMING_SNAKE_CASE		,						model_args.task_type		)
    lowercase__           =  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'		,						_SCREAMING_SNAKE_CASE		)

  # Set seed
  set_seed(training_args.seed		)

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

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

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

  # Get datasets
  lowercase__           =  (
      TokenClassificationDataset(
          token_classification_task=_SCREAMING_SNAKE_CASE		,						data_dir=data_args.data_dir		,						tokenizer=_SCREAMING_SNAKE_CASE		,						labels=_SCREAMING_SNAKE_CASE		,						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
  )
  lowercase__           =  (
      TokenClassificationDataset(
          token_classification_task=_SCREAMING_SNAKE_CASE		,						data_dir=data_args.data_dir		,						tokenizer=_SCREAMING_SNAKE_CASE		,						labels=_SCREAMING_SNAKE_CASE		,						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(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		) -> Tuple[List[int], List[int]]:
    lowercase__           =  np.argmax(_SCREAMING_SNAKE_CASE		,						axis=2		)

    lowercase__      ,   lowercase__           =  preds.shape

    lowercase__           =  [[] for _ in range(_SCREAMING_SNAKE_CASE		)]
    lowercase__           =  [[] for _ in range(_SCREAMING_SNAKE_CASE		)]

    for i in range(_SCREAMING_SNAKE_CASE		):
      for j in range(_SCREAMING_SNAKE_CASE		):
        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(_SCREAMING_SNAKE_CASE		) -> Dict:
    lowercase__      ,   lowercase__           =  align_predictions(p.predictions		,						p.label_ids		)
    return {
        "accuracy_score": accuracy_score(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		),
        "precision": precision_score(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		),
        "recall": recall_score(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		),
        "f1": fa_score(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		),
    }

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

  # Initialize our Trainer
  lowercase__           =  Trainer(
      model=_SCREAMING_SNAKE_CASE		,						args=_SCREAMING_SNAKE_CASE		,						train_dataset=_SCREAMING_SNAKE_CASE		,						eval_dataset=_SCREAMING_SNAKE_CASE		,						compute_metrics=_SCREAMING_SNAKE_CASE		,						data_collator=_SCREAMING_SNAKE_CASE		,						)

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

    lowercase__           =  trainer.evaluate()

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

      results.update(_SCREAMING_SNAKE_CASE		)

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

    lowercase__      ,   lowercase__      ,   lowercase__           =  trainer.predict(_SCREAMING_SNAKE_CASE		)
    lowercase__      ,   lowercase__           =  align_predictions(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)

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

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

  return results




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Any:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
      main()


