




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


UpperCAmelCase__    :							List[Any]       		=     '\nDistills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class\nnames. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but\nwhen only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled\ndataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided\nclass names.\n'

UpperCAmelCase__    :							Tuple       		=     logging.getLogger(__name__)

@dataclass
class   UpperCAmelCase    :
   __SCREAMING_SNAKE_CASE		:				Optional[str]      					=       field(
       default="roberta-large-mnli" ,       metadata={"help": "The NLI/zero-shot teacher model to be distilled."} )
   __SCREAMING_SNAKE_CASE		:				Optional[str]      					=       field(
       default="This example is {}." ,       metadata={
           "help": (
               "Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}"
               "where class name is inserted."
           )
       } ,       )
   __SCREAMING_SNAKE_CASE		:				Optional[int]      					=       field(
       default=32 ,       metadata={"help": "Batch size for generating teacher predictions."} )
   __SCREAMING_SNAKE_CASE		:				Optional[bool]      					=       field(
       default=A ,       metadata={
           "help": (
               "Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"
               "multi-class multi-label classification)."
           )
       } ,       )
   __SCREAMING_SNAKE_CASE		:				Optional[float]      					=       field(
       default=1.0 ,       metadata={"help": "Temperature applied to teacher softmax for distillation."} )

@dataclass
class   UpperCAmelCase    :
   __SCREAMING_SNAKE_CASE		:				Optional[str]      					=       field(
       default="distilbert-base-uncased" ,       metadata={"help": "The NLI/zero-shot teacher model to be distilled."} )

@dataclass
class   UpperCAmelCase    :
   __SCREAMING_SNAKE_CASE		:				str      					=       field(metadata={"help": "Text file with one unlabeled instance per line."} )
   __SCREAMING_SNAKE_CASE		:				str      					=       field(metadata={"help": "Text file with one class name per line."} )
   __SCREAMING_SNAKE_CASE		:				bool      					=       field(
       default=A ,       metadata={"help": "Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."} ,       )

@dataclass
class   UpperCAmelCase    (      A ):
   __SCREAMING_SNAKE_CASE		:				Optional[str]      					=       field(
       default=A ,       metadata={"help": "The output directory where the model predictions and checkpoints will be written."} ,       )
   __SCREAMING_SNAKE_CASE		:				int      					=       field(
       default=32 ,       metadata={"help": "Batch size per GPU/TPU core/CPU for training."} )
   __SCREAMING_SNAKE_CASE		:				int      					=       field(
       default=1_28 ,       metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."} )
   __SCREAMING_SNAKE_CASE		:				float      					=       field(default=1.0 ,       metadata={"help": "Total number of training epochs to perform."} )
   __SCREAMING_SNAKE_CASE		:				bool      					=       field(default=A ,       metadata={"help": "Whether to run training of student model."} )
   __SCREAMING_SNAKE_CASE		:				bool      					=       field(
       default=A ,       metadata={
           "help": (
               "Whether to evaluate the agreement of the final student predictions and the teacher predictions"
               "after training."
           )
       } ,       )
   __SCREAMING_SNAKE_CASE		:				Optional[int]      					=       field(
       default=0 ,       metadata={
           "help": (
               "Limit the total amount of checkpoints."
               "Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints)."
           )
       } ,       )

class   UpperCAmelCase    (      A ):






   def 			_a    (						self :							Dict    ,       UpperCamelCase_ :							List[Any]    ,       UpperCamelCase_ :							Optional[int]    ,       UpperCamelCase_ :							Union[str, Any]=False  )->      Optional[int]:
     _lowercase:			List[Any]      =     inputs['labels']
     _lowercase:			Dict      =     model(inputs['input_ids']    ,       attention_mask=inputs['attention_mask']  )
     _lowercase:			List[str]      =     outputs[0]

     _lowercase:			Optional[int]      =     -torch.sum(target_p * logits.log_softmax(dim=-1  )    ,       axis=-1  ).mean()

     if return_outputs:
       return loss, outputs

     return loss


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		Optional[int]:





  """simple docstring"""





  _lowercase:			Tuple      =     []
  with open(UpperCamelCase       ,  'r'				) as f:
    for line in f:
      _lowercase:			Tuple      =     line.strip()
      if len(UpperCamelCase				) > 0:
        lines.append(UpperCamelCase				)
  return lines


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		Optional[int]:





  """simple docstring"""





  _lowercase:			int      =     []
  _lowercase:			Union[str, Any]      =     []
  for example in examples:
    for name in class_names:
      premises.append(UpperCamelCase				)
      hypotheses.append(hypothesis_template.format(UpperCamelCase				)				)
  return premises, hypotheses


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		Optional[Any]:





  """simple docstring"""





  for label, ind in config.labelaid.items():
    if label.lower().startswith('entail'				):
      return ind
  logger.warning('Could not identify entailment dimension from teacher config label2id. Setting to -1.'				)
  return -1


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  )      ->		int:





  """simple docstring"""





  _lowercase:			Union[str, Any]      =     AutoModelForSequenceClassification.from_pretrained(UpperCamelCase				)
  _lowercase:			Optional[Any]      =     model.config
  if not no_cuda and torch.cuda.is_available():
    _lowercase:			List[Any]      =     nn.DataParallel(model.cuda()				)
    batch_size *= len(model.device_ids				)
  _lowercase:			Any      =     AutoTokenizer.from_pretrained(UpperCamelCase       ,  use_fast=UpperCamelCase				)

  _lowercase  ,       _lowercase:			Optional[int]      =     get_premise_hypothesis_pairs(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)
  _lowercase:			List[str]      =     []

  for i in tqdm(range(0       ,  len(UpperCamelCase				)       ,  UpperCamelCase				)				):
    _lowercase:			Optional[Any]      =     premises[i : i + batch_size]
    _lowercase:			Optional[int]      =     hypotheses[i : i + batch_size]

    _lowercase:			Any      =     tokenizer(
        UpperCamelCase       ,  UpperCamelCase       ,  padding=UpperCamelCase       ,  truncation='only_first'       ,  return_tensors='pt'       ,  )

    with torch.cuda.amp.autocast(enabled=UpperCamelCase				):
      with torch.no_grad():
        _lowercase:			Optional[Any]      =     model(**UpperCamelCase				)
    logits.append(outputs.logits.detach().cpu().float()				)

  _lowercase:			int      =     get_entailment_id(UpperCamelCase				)
  _lowercase:			Union[str, Any]      =     -1 if entail_id == 0 else 0
  _lowercase:			List[str]      =     torch.cat(UpperCamelCase       ,  dim=0				)  # N*K x 3
  _lowercase:			Any      =     logits.reshape(len(UpperCamelCase				)       ,  len(UpperCamelCase				)       ,  -1				)[..., [contr_id, entail_id]]  # N x K x 2

  if multi_label:
    # softmax over (contr, entail) logits for each class independently
    _lowercase:			Any      =     (nli_logits / temperature).softmax(-1				)
  else:
    # softmax over entail logits across classes s.t. class probabilities sum to 1.
    _lowercase:			Optional[int]      =     (nli_logits / temperature).softmax(1				)

  return nli_prob[..., 1]  # N x K


def 		_lowerCamelCase					(       )      ->		Union[str, Any]:





  """simple docstring"""





  _lowercase:			Dict      =     HfArgumentParser(
      (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)       ,  description=UpperCamelCase       ,  )

  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  ,       _lowercase:			List[Any]      =     parser.parse_json_file(
        json_file=os.path.abspath(sys.argv[1]				)				)
  else:
    _lowercase  ,       _lowercase  ,       _lowercase  ,       _lowercase:			Dict      =     parser.parse_args_into_dataclasses()

  # Detecting last checkpoint.
  _lowercase:			Optional[int]      =     None
  if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
    _lowercase:			Dict      =     get_last_checkpoint(training_args.output_dir				)
    if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
      raise ValueError(
          F'Output directory ({training_args.output_dir}) already exists and is not empty. '
          'Use --overwrite_output_dir to overcome.'				)
    elif last_checkpoint is not None:
      logger.info(
          F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
          'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'				)

    # Setup logging
  logging.basicConfig(
      format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'       ,  datefmt='%m/%d/%Y %H:%M:%S'       ,  handlers=[logging.StreamHandler(sys.stdout				)]       ,  )
  logger.setLevel(logging.INFO if is_main_process(training_args.local_rank				) else logging.WARN				)

  # Log on each process the small summary:
  logger.warning(
      F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
      + F'distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}'				)
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank				):
    utils.logging.set_verbosity_info()
    utils.logging.enable_default_handler()
    utils.logging.enable_explicit_format()

  if training_args.local_rank != -1:
    raise ValueError('Distributed training is not currently supported.'				)
  if training_args.tpu_num_cores is not None:
    raise ValueError('TPU acceleration is not currently supported.'				)

  logger.info(F'Training/evaluation parameters {training_args}'				)

  # Set seed before initializing model.
  set_seed(training_args.seed				)

  # 1. read in data
  _lowercase:			List[Any]      =     read_lines(data_args.data_file				)
  _lowercase:			Any      =     read_lines(data_args.class_names_file				)

  # 2. get teacher predictions and load into dataset
  logger.info('Generating predictions from zero-shot teacher model'				)
  _lowercase:			Optional[int]      =     get_teacher_predictions(
      teacher_args.teacher_name_or_path       ,  UpperCamelCase       ,  UpperCamelCase       ,  teacher_args.hypothesis_template       ,  teacher_args.teacher_batch_size       ,  teacher_args.temperature       ,  teacher_args.multi_label       ,  data_args.use_fast_tokenizer       ,  training_args.no_cuda       ,  training_args.fpaa       ,  )
  _lowercase:			Any      =     Dataset.from_dict(
      {
          'text': examples,
          'labels': teacher_soft_preds,
      }				)

  # 3. create student
  logger.info('Initializing student model'				)
  _lowercase:			Union[str, Any]      =     AutoModelForSequenceClassification.from_pretrained(
      student_args.student_name_or_path       ,  num_labels=len(UpperCamelCase				)				)
  _lowercase:			int      =     AutoTokenizer.from_pretrained(student_args.student_name_or_path       ,  use_fast=data_args.use_fast_tokenizer				)
  _lowercase:			Any      =     dict(enumerate(UpperCamelCase				)				)
  _lowercase:			Dict      =     {label: i for i, label in enumerate(UpperCamelCase				)}

  # 4. train student on teacher predictions
  _lowercase:			Tuple      =     dataset.map(UpperCamelCase       ,  input_columns='text'				)
  dataset.set_format('torch'				)

  def compute_metrics(UpperCamelCase       ,  UpperCamelCase=False				):
    _lowercase:			Dict      =     p.predictions.argmax(-1				)
    _lowercase:			List[Any]      =     p.label_ids.argmax(-1				)  # "label_ids" are actually distributions
    return {"agreement": (preds == proxy_labels).mean().item()}

  _lowercase:			List[str]      =     DistillationTrainer(
      model=UpperCamelCase       ,  tokenizer=UpperCamelCase       ,  args=UpperCamelCase       ,  train_dataset=UpperCamelCase       ,  compute_metrics=UpperCamelCase       ,  )

  if training_args.do_train:
    logger.info('Training student model on teacher predictions'				)
    trainer.train()

  if training_args.do_eval:
    _lowercase:			Dict      =     trainer.evaluate(eval_dataset=UpperCamelCase				)['eval_agreement']
    logger.info(F'Agreement of student and teacher predictions: {agreement * 100:0.2f}%'				)

  trainer.save_model()


if __name__ == "__main__":
   main()






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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   UpperCAmelCase    (      A ,       unittest.TestCase ):
   __SCREAMING_SNAKE_CASE		:				int      					=       KandinskyPipeline
   __SCREAMING_SNAKE_CASE		:				Union[str, Any]      					=       [
       "prompt",
       "image_embeds",
       "negative_image_embeds",
   ]
   __SCREAMING_SNAKE_CASE		:				Tuple      					=       ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
   __SCREAMING_SNAKE_CASE		:				Tuple      					=       [
       "generator",
       "height",
       "width",
       "latents",
       "guidance_scale",
       "negative_prompt",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   __SCREAMING_SNAKE_CASE		:				Dict      					=       False






   @property
   def 			_a    (						self :							str  )->      Dict:
     return 32






   @property
   def 			_a    (						self :							Tuple  )->      Any:
     return 32






   @property
   def 			_a    (						self :							Union[str, Any]  )->      List[str]:
     return self.time_input_dim






   @property
   def 			_a    (						self :							Optional[Any]  )->      Tuple:
     return self.time_input_dim * 4






   @property
   def 			_a    (						self :							int  )->      Dict:
     return 1_00






   @property
   def 			_a    (						self :							Tuple  )->      Tuple:
     _lowercase:			List[Any]      =     XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
     return tokenizer






   @property
   def 			_a    (						self :							List[str]  )->      Optional[int]:
     torch.manual_seed(0  )
     _lowercase:			int      =     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=10_05    ,       )

     _lowercase:			Optional[Any]      =     MultilingualCLIP(UpperCamelCase_  )
     _lowercase:			int      =     text_encoder.eval()

     return text_encoder






   @property
   def 			_a    (						self :							Optional[int]  )->      Optional[Any]:
     torch.manual_seed(0  )

     _lowercase:			Optional[Any]      =     {
         'in_channels': 4,
         # 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:			str      =     UNetaDConditionModel(**UpperCamelCase_  )
     return model






   @property
   def 			_a    (						self :							Dict  )->      Optional[Any]:
     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 			_a    (						self :							List[str]  )->      int:
     torch.manual_seed(0  )
     _lowercase:			List[str]      =     VQModel(**self.dummy_movq_kwargs  )
     return model






   def 			_a    (						self :							int  )->      List[Any]:
     _lowercase:			List[str]      =     self.dummy_text_encoder
     _lowercase:			List[Any]      =     self.dummy_tokenizer
     _lowercase:			Optional[Any]      =     self.dummy_unet
     _lowercase:			List[str]      =     self.dummy_movq

     _lowercase:			Optional[Any]      =     DDIMScheduler(
         num_train_timesteps=10_00    ,       beta_schedule='linear'    ,       beta_start=0.00_085    ,       beta_end=0.012    ,       clip_sample=UpperCamelCase_    ,       set_alpha_to_one=UpperCamelCase_    ,       steps_offset=1    ,       prediction_type='epsilon'    ,       thresholding=UpperCamelCase_    ,       )

     _lowercase:			List[Any]      =     {
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'unet': unet,
         'scheduler': scheduler,
         'movq': movq,
     }
     return components






   def 			_a    (						self :							str    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							Optional[int]=0  )->      Union[str, Any]:
     _lowercase:			str      =     floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(UpperCamelCase_  )  ).to(UpperCamelCase_  )
     _lowercase:			Optional[int]      =     floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(seed + 1  )  ).to(UpperCamelCase_  )
     if str(UpperCamelCase_  ).startswith('mps'  ):
       _lowercase:			Any      =     torch.manual_seed(UpperCamelCase_  )
     else:
       _lowercase:			Tuple      =     torch.Generator(device=UpperCamelCase_  ).manual_seed(UpperCamelCase_  )
     _lowercase:			List[str]      =     {
         'prompt': 'horse',
         'image_embeds': image_embeds,
         'negative_image_embeds': negative_image_embeds,
         'generator': generator,
         'height': 64,
         'width': 64,
         'guidance_scale': 4.0,
         'num_inference_steps': 2,
         'output_type': 'np',
     }
     return inputs






   def 			_a    (						self :							List[str]  )->      List[str]:
     _lowercase:			str      =     'cpu'

     _lowercase:			Optional[Any]      =     self.get_dummy_components()

     _lowercase:			int      =     self.pipeline_class(**UpperCamelCase_  )
     _lowercase:			List[str]      =     pipe.to(UpperCamelCase_  )

     pipe.set_progress_bar_config(disable=UpperCamelCase_  )

     _lowercase:			Tuple      =     pipe(**self.get_dummy_inputs(UpperCamelCase_  )  )
     _lowercase:			Union[str, Any]      =     output.images

     _lowercase:			Union[str, Any]      =     pipe(
         **self.get_dummy_inputs(UpperCamelCase_  )    ,       return_dict=UpperCamelCase_    ,       )[0]

     _lowercase:			int      =     image[0, -3:, -3:, -1]
     _lowercase:			List[str]      =     image_from_tuple[0, -3:, -3:, -1]

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

     _lowercase:			Union[str, Any]      =     np.array(
         [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]  )

     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()}'

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






   def 			_a    (						self :							Optional[int]  )->      Any:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






   def 			_a    (						self :							List[Any]  )->      Optional[int]:
     _lowercase:			Optional[int]      =     load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinsky/kandinsky_text2img_cat_fp16.npy'  )

     _lowercase:			List[Any]      =     KandinskyPriorPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-1-prior'    ,       torch_dtype=torch.floataa  )
     pipe_prior.to(UpperCamelCase_  )

     _lowercase:			str      =     KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'    ,       torch_dtype=torch.floataa  )
     _lowercase:			List[str]      =     pipeline.to(UpperCamelCase_  )
     pipeline.set_progress_bar_config(disable=UpperCamelCase_  )

     _lowercase:			Dict      =     'red cat, 4k photo'

     _lowercase:			List[Any]      =     torch.Generator(device='cuda'  ).manual_seed(0  )
     _lowercase  ,       _lowercase:			Dict      =     pipe_prior(
         UpperCamelCase_    ,       generator=UpperCamelCase_    ,       num_inference_steps=5    ,       negative_prompt=''    ,       ).to_tuple()

     _lowercase:			str      =     torch.Generator(device='cuda'  ).manual_seed(0  )
     _lowercase:			Optional[int]      =     pipeline(
         UpperCamelCase_    ,       image_embeds=UpperCamelCase_    ,       negative_image_embeds=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       num_inference_steps=1_00    ,       output_type='np'    ,       )

     _lowercase:			Optional[int]      =     output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(UpperCamelCase_    ,       UpperCamelCase_  )
