code: 
  
  import argparse
  import json
  
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from timm.models import create_model
  
  from transformers import (
      BeitImageProcessor,
      DataaVecVisionConfig,
      DataaVecVisionForImageClassification,
      DataaVecVisionModel,
  )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=False			,  SCREAMING_SNAKE_CASE_	:					int=False			,  SCREAMING_SNAKE_CASE_	:					Optional[Any]="data2vec."    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     str   = 'backbone.' if is_semantic else ''
  
       lowercase__					:     Dict   = []
       for i in range(config.num_hidden_layers    ):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append(
                (f"""{prefix}blocks.{i}.norm1.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight""")    )
            rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias""")    )
            rename_keys.append(
                (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight""")    )
            rename_keys.append(
                (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias""")    )
            rename_keys.append(
                (f"""{prefix}blocks.{i}.norm2.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight""")    )
            rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias""")    )
            rename_keys.append(
                (f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight""")    )
            rename_keys.append(
                (f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias""")    )
            rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""{hf_prefix}encoder.layer.{i}.output.dense.weight""")    )
            rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""{hf_prefix}encoder.layer.{i}.output.dense.bias""")    )
  
       # projection layer + position embeddings
       rename_keys.extend(
           [
               (f"""{prefix}cls_token""", f"""{hf_prefix}embeddings.cls_token"""),
               (f"""{prefix}patch_embed.proj.weight""", f"""{hf_prefix}embeddings.patch_embeddings.projection.weight"""),
               (f"""{prefix}patch_embed.proj.bias""", f"""{hf_prefix}embeddings.patch_embeddings.projection.bias"""),
           ]    )
  
       if has_lm_head:
            # mask token + shared relative position bias + layernorm
            rename_keys.extend(
                [
                    ('mask_token', f"""{hf_prefix}embeddings.mask_token"""),
                    (
                        'rel_pos_bias.relative_position_bias_table',
                        f"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
                    ),
                    (
                        'rel_pos_bias.relative_position_index',
                        f"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
                    ),
                    ('norm.weight', 'layernorm.weight'),
                    ('norm.bias', 'layernorm.bias'),
                ]    )
       elif is_semantic:
            # semantic segmentation classification heads
            rename_keys.extend(
                [
                    ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
                    ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
                    ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
                    ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
                ]    )
       else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ('fc_norm.weight', f"""{hf_prefix}pooler.layernorm.weight"""),
                    ('fc_norm.bias', f"""{hf_prefix}pooler.layernorm.bias"""),
                    ('head.weight', 'classifier.weight'),
                    ('head.bias', 'classifier.bias'),
                ]    )
  
       return rename_keys
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=False			,  SCREAMING_SNAKE_CASE_	:					Dict=False			,  SCREAMING_SNAKE_CASE_	:					List[str]="data2vec_vision."    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       for i in range(config.num_hidden_layers    ):
            lowercase__					:     Optional[int]   = 'backbone.' if is_semantic else ''
            # queries, keys and values
            lowercase__					:     List[Any]   = state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""    )
            lowercase__					:     Optional[int]   = state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""    )
            lowercase__					:     Dict   = state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""    )
  
            lowercase__					:     Union[str, Any]   = in_proj_weight[
                : config.hidden_size, :
            ]
            lowercase__					:     List[str]   = q_bias
            lowercase__					:     Tuple   = in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            lowercase__					:     Union[str, Any]   = in_proj_weight[
                -config.hidden_size :, :
            ]
            lowercase__					:     Union[str, Any]   = v_bias
  
            # gamma_1 and gamma_2
            # we call them lambda because otherwise they are renamed when using .from_pretrained
            lowercase__					:     Optional[Any]   = state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""    )
            lowercase__					:     Dict   = state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""    )
  
            lowercase__					:     List[str]   = gamma_a
            lowercase__					:     Dict   = gamma_a
  
            # relative_position bias table + index
            if not has_lm_head:
                 # each layer has its own relative position bias
                 lowercase__					:     Tuple   = state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""    )
                 lowercase__					:     Optional[Any]   = state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""    )
  
                 lowercase__					:     int   = table
                 lowercase__					:     List[str]   = index
  
  def 			snake_case__    (					):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     List[str]   = argparse.ArgumentParser(
           'Convert Data2VecVision to HF for image classification and pretraining'			,  add_help=SCREAMING_SNAKE_CASE_    )
       parser.add_argument('--hf_checkpoint_name'			,  type=SCREAMING_SNAKE_CASE_    )
       parser.add_argument('--input_size'			,  default=224			,  type=SCREAMING_SNAKE_CASE_			,  help='images input size'    )
       parser.add_argument('--beit_checkpoint'			,  default=''			,  help='beit checkpoint'    )
  
       return parser.parse_args()
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					str    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       def load_state_dict(SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=""			,  SCREAMING_SNAKE_CASE_	:					List[Any]="relative_position_index"    ):
            lowercase__					:     str   = []
            lowercase__					:     str   = []
            lowercase__					:     Dict   = []
            # copy state_dict so _load_from_state_dict can modify it
            lowercase__					:     List[Any]   = getattr(SCREAMING_SNAKE_CASE_			,  '_metadata'			,  SCREAMING_SNAKE_CASE_    )
            lowercase__					:     Optional[int]   = state_dict.copy()
            if metadata is not None:
                 lowercase__					:     Dict   = metadata
  
            def load(SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=""    ):
                 lowercase__					:     Optional[Any]   = {} if metadata is None else metadata.get(prefix[:-1]			,  {}    )
                 module._load_from_state_dict(
                     SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
                 for name, child in module._modules.items():
                      if child is not None:
                           load(SCREAMING_SNAKE_CASE_			,  prefix + name + '.'    )
  
            load(SCREAMING_SNAKE_CASE_			,  prefix=SCREAMING_SNAKE_CASE_    )
  
            lowercase__					:     List[Any]   = []
            lowercase__					:     Dict   = []
            for key in missing_keys:
                 lowercase__					:     Any   = True
                 for ignore_key in ignore_missing.split('|'    ):
                      if ignore_key in key:
                           lowercase__					:     Union[str, Any]   = False
                           break
                 if keep_flag:
                      warn_missing_keys.append(SCREAMING_SNAKE_CASE_    )
                 else:
                      ignore_missing_keys.append(SCREAMING_SNAKE_CASE_    )
  
            lowercase__					:     Tuple   = warn_missing_keys
  
            if len(SCREAMING_SNAKE_CASE_    ) > 0:
                 print(
                     'Weights of {} not initialized from pretrained model: {}'.format(
                         model.__class__.__name__			,  SCREAMING_SNAKE_CASE_    )    )
            if len(SCREAMING_SNAKE_CASE_    ) > 0:
                 print('Weights from pretrained model not used in {}: {}'.format(model.__class__.__name__			,  SCREAMING_SNAKE_CASE_    )    )
            if len(SCREAMING_SNAKE_CASE_    ) > 0:
                 print(
                     'Ignored weights of {} not initialized from pretrained model: {}'.format(
                         model.__class__.__name__			,  SCREAMING_SNAKE_CASE_    )    )
            if len(SCREAMING_SNAKE_CASE_    ) > 0:
                 print('\n'.join(SCREAMING_SNAKE_CASE_    )    )
  
       lowercase__					:     int   = {
           'pretrained': False,
           'use_shared_rel_pos_bias': True,
           'use_abs_pos_emb': False,
           'init_values': 0.1,
       }
  
       if is_finetuned:
            model_kwargs.update(
                {
                    'num_classes': 1_000,
                    'use_mean_pooling': True,
                    'init_scale': 0.001,
                    'use_rel_pos_bias': True,
                }    )
  
       lowercase__					:     Dict   = create_model(
           'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'			,  **SCREAMING_SNAKE_CASE_			,  )
       lowercase__					:     int   = model.patch_embed.patch_size
       lowercase__					:     int   = (args.input_size // patch_size[0], args.input_size // patch_size[1])
       lowercase__					:     str   = torch.load(args.beit_checkpoint			,  map_location='cpu'    )
  
       print(f"""Load ckpt from {args.beit_checkpoint}"""    )
       lowercase__					:     int   = None
       for model_key in ("model", "module"):
            if model_key in checkpoint:
                 lowercase__					:     Tuple   = checkpoint[model_key]
                 print(f"""Load state_dict by model_key = {model_key}"""    )
                 break
  
       lowercase__					:     Optional[Any]   = list(checkpoint_model.keys()    )
       for key in all_keys:
            if "relative_position_index" in key:
                 checkpoint_model.pop(SCREAMING_SNAKE_CASE_    )
  
            if "relative_position_bias_table" in key:
                 lowercase__					:     Any   = checkpoint_model[key]
                 lowercase__   ,				lowercase__					:     List[Any]   = rel_pos_bias.size()
                 lowercase__   ,				lowercase__					:     Optional[int]   = model.state_dict()[key].size()
                 lowercase__					:     Dict   = model.patch_embed.patch_shape
                 if dst_patch_shape[0] != dst_patch_shape[1]:
                      raise NotImplementedError()
  
       load_state_dict(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  prefix=''    )
  
       return model
  
  def 			snake_case__    (					):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     List[str]   = get_args()
  
       lowercase__					:     Union[str, Any]   = 'ft1k' in args.hf_checkpoint_name
       lowercase__					:     Dict   = 'large' in args.hf_checkpoint_name
  
       if is_finetuned:
            # To convert Beit's data2vec_vision to HF you need to copy
            # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
            # into this folder.
            import modeling_finetune  # noqa: F401
       else:
            # To convert Beit's data2vec_vision to HF you need to copy
            # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
            # into this folder
            # IMPORTANT: Note that for now we've only converted the down-stream
            # model and not the full pretrained model. This means for the integration
            # test you need to add a `return x` after the following line:
            # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
            # to make the integration test pass.
            import modeling_cyclical  # noqa: F401
  
       # 1. Create model config
       lowercase__					:     Optional[Any]   = DataaVecVisionConfig()
       if is_finetuned:
            lowercase__					:     int   = True
            lowercase__					:     Any   = False
            lowercase__					:     Union[str, Any]   = True
            lowercase__					:     Union[str, Any]   = 1_000
  
            lowercase__					:     Any   = 'huggingface/label-files'
            lowercase__					:     Any   = 'imagenet-1k-id2label.json'
            lowercase__					:     Union[str, Any]   = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  repo_type='dataset'    )			,  'r'    )    )
            lowercase__					:     Dict   = {int(SCREAMING_SNAKE_CASE_    ): v for k, v in idalabel.items()}
            lowercase__					:     Optional[int]   = idalabel
            lowercase__					:     Optional[int]   = {v: k for k, v in idalabel.items()}
       else:
            lowercase__					:     Dict   = False
            lowercase__					:     int   = True
            lowercase__					:     List[str]   = False
  
       if is_large:
            lowercase__					:     List[str]   = 1_024
            lowercase__					:     Union[str, Any]   = 4_096
            lowercase__					:     Union[str, Any]   = 24
            lowercase__					:     Optional[int]   = 16
  
       # 2. Load Beit model
       lowercase__					:     List[Any]   = load_beit_model(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
       orig_model.eval()
  
       # 3. Forward Beit model
       lowercase__					:     Optional[Any]   = BeitImageProcessor(size=config.image_size			,  do_center_crop=SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Tuple   = Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'    )
       lowercase__					:     Tuple   = image_processor(images=SCREAMING_SNAKE_CASE_			,  return_tensors='pt'    )
       lowercase__					:     List[Any]   = encoding['pixel_values']
  
       lowercase__					:     List[Any]   = (pixel_values,) if is_finetuned else (pixel_values, None)
       with torch.no_grad():
            lowercase__					:     List[str]   = orig_model(*SCREAMING_SNAKE_CASE_    )
  
       # 4. Load HF Data2VecVision model
       if is_finetuned:
            lowercase__					:     Optional[int]   = DataaVecVisionForImageClassification(SCREAMING_SNAKE_CASE_    )
            hf_model.eval()
            lowercase__					:     Union[str, Any]   = False
            lowercase__					:     Dict   = 'data2vec_vision.'
       else:
            lowercase__					:     Tuple   = DataaVecVisionModel(SCREAMING_SNAKE_CASE_    )
            hf_model.eval()
            lowercase__					:     int   = True
            lowercase__					:     Tuple   = ''
  
       lowercase__					:     Tuple   = create_rename_keys(SCREAMING_SNAKE_CASE_			,  hf_prefix=SCREAMING_SNAKE_CASE_			,  has_lm_head=SCREAMING_SNAKE_CASE_    )
       lowercase__					:     str   = orig_model.state_dict()
       for src, dest in rename_keys:
            lowercase__					:     int   = state_dict.pop(SCREAMING_SNAKE_CASE_    )
            lowercase__					:     Dict   = val
  
       read_in_q_k_v(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  hf_prefix=SCREAMING_SNAKE_CASE_			,  has_lm_head=SCREAMING_SNAKE_CASE_    )
       lowercase__   ,				lowercase__					:     List[Any]   = hf_model.load_state_dict(SCREAMING_SNAKE_CASE_			,  strict=SCREAMING_SNAKE_CASE_    )
       print('HF missing'			,  SCREAMING_SNAKE_CASE_    )
       print('HF unexpected_keys'			,  SCREAMING_SNAKE_CASE_    )
  
       # 5. Forward HF Data2VecVision model
       with torch.no_grad():
            lowercase__					:     Optional[int]   = hf_model(SCREAMING_SNAKE_CASE_    )
  
       lowercase__					:     Optional[int]   = hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state
  
       # 6. Compare
       lowercase__					:     Dict   = torch.max(torch.abs(hf_output - orig_model_output    )    ).item()
  
       print(f"""max_absolute_diff = {max_absolute_diff}"""    )
       lowercase__					:     Dict   = torch.allclose(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  atol=1E-3    )
       print('Do both models output the same tensors?'			,  '🔥' if success else '💩'    )
       if not success:
            raise Exception('Something went wRoNg'    )
  
       # 7. Save
       print(f"""Saving to {args.hf_checkpoint_name}"""    )
       hf_model.save_pretrained(args.hf_checkpoint_name    )
       image_processor.save_pretrained(args.hf_checkpoint_name    )
  
  
  if __name__ == "__main__":
         main()
         # Run the following to convert checkpoints
         #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
         #          --beit_checkpoint ./pretrained_base.pt \
         #          --hf_checkpoint_name "./data2vec-vision-base"
         #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
         #          --beit_checkpoint ./finetuned_base.pt \
         #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
         #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
         #          --beit_checkpoint ./pretrained_large.pt \
         #          --hf_checkpoint_name "./data2vec-vision-large"
         #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
         #          --beit_checkpoint ./finetuned_large.pt \
         #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"
  
  
style_context: 
  
  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
  
  
  snake_case_    						=				'''
  Distills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class
  names. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but
  when only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled
  dataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided
  class names.
  '''
  
  snake_case_    						=				logging.getLogger(__name__)
  
  
  @dataclass
  class    SCREAMING_SNAKE_CASE__      :
      __lowerCamelCase		: Optional[str]														=  field(
          default="""roberta-large-mnli"""	, metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}  )
      __lowerCamelCase		: 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."""
              )
          }	, )
      __lowerCamelCase		: Optional[int]														=  field(
          default=32	, metadata={"""help""": """Batch size for generating teacher predictions."""}  )
      __lowerCamelCase		: Optional[bool]														=  field(
          default=__snake_case	, metadata={
              """help""": (
                  """Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"""
                  """multi-class multi-label classification)."""
              )
          }	, )
      __lowerCamelCase		: Optional[float]														=  field(
          default=1.0	, metadata={"""help""": """Temperature applied to teacher softmax for distillation."""}  )
  
  
  
  @dataclass
  class    SCREAMING_SNAKE_CASE__      :
      __lowerCamelCase		: Optional[str]														=  field(
          default="""distilbert-base-uncased"""	, metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}  )
  
  
  
  @dataclass
  class    SCREAMING_SNAKE_CASE__      :
      __lowerCamelCase		: str														=  field(metadata={"""help""": """Text file with one unlabeled instance per line."""}  )
      __lowerCamelCase		: str														=  field(metadata={"""help""": """Text file with one class name per line."""}  )
      __lowerCamelCase		: bool														=  field(
          default=__snake_case	, metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."""}	, )
  
  
  
  @dataclass
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
      __lowerCamelCase		: Optional[str]														=  field(
          default=__snake_case	, metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""}	, )
      __lowerCamelCase		: int														=  field(
          default=32	, metadata={"""help""": """Batch size per GPU/TPU core/CPU for training."""}  )
      __lowerCamelCase		: int														=  field(
          default=128	, metadata={"""help""": """Batch size per GPU/TPU core/CPU for evaluation."""}  )
      __lowerCamelCase		: float														=  field(default=1.0	, metadata={"""help""": """Total number of training epochs to perform."""}  )
      __lowerCamelCase		: bool														=  field(default=__snake_case	, metadata={"""help""": """Whether to run training of student model."""}  )
      __lowerCamelCase		: bool														=  field(
          default=__snake_case	, metadata={
              """help""": (
                  """Whether to evaluate the agreement of the final student predictions and the teacher predictions"""
                  """after training."""
              )
          }	, )
      __lowerCamelCase		: 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    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a		,     a=False):
           lowercase__					:     Tuple   = inputs['labels']
           lowercase__					:     Any   = model(inputs['input_ids']		,     attention_mask=inputs['attention_mask'])
           lowercase__					:     Optional[int]   = outputs[0]
  
           lowercase__					:     Any   = -torch.sum(target_p * logits.log_softmax(dim=-1)		,     axis=-1).mean()
  
           if return_outputs:
                return loss, outputs
  
           return loss
  
  
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Any   = []
       with open(SCREAMING_SNAKE_CASE_			,  'r'    ) as f:
            for line in f:
                 lowercase__					:     Dict   = line.strip()
                 if len(SCREAMING_SNAKE_CASE_    ) > 0:
                      lines.append(SCREAMING_SNAKE_CASE_    )
       return lines
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Optional[Any]			,  SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					int    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Optional[Any]   = []
       lowercase__					:     Tuple   = []
       for example in examples:
            for name in class_names:
                 premises.append(SCREAMING_SNAKE_CASE_    )
                 hypotheses.append(hypothesis_template.format(SCREAMING_SNAKE_CASE_    )    )
       return premises, hypotheses
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple    ):
  
  
  
       '''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 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					float			,  SCREAMING_SNAKE_CASE_	:					bool			,  SCREAMING_SNAKE_CASE_	:					bool			,  SCREAMING_SNAKE_CASE_	:					bool			,  SCREAMING_SNAKE_CASE_	:					bool			,  ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Union[str, Any]   = AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Optional[Any]   = model.config
       if not no_cuda and torch.cuda.is_available():
            lowercase__					:     str   = nn.DataParallel(model.cuda()    )
            batch_size *= len(model.device_ids    )
       lowercase__					:     List[str]   = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_			,  use_fast=SCREAMING_SNAKE_CASE_    )
  
       lowercase__   ,				lowercase__					:     List[str]   = get_premise_hypothesis_pairs(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Any   = []
  
       for i in tqdm(range(0			,  len(SCREAMING_SNAKE_CASE_    )			,  SCREAMING_SNAKE_CASE_    )    ):
            lowercase__					:     Dict   = premises[i : i + batch_size]
            lowercase__					:     Union[str, Any]   = hypotheses[i : i + batch_size]
  
            lowercase__					:     List[str]   = tokenizer(
                SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  padding=SCREAMING_SNAKE_CASE_			,  truncation='only_first'			,  return_tensors='pt'			,  )
  
            with torch.cuda.amp.autocast(enabled=SCREAMING_SNAKE_CASE_    ):
                 with torch.no_grad():
                      lowercase__					:     Optional[Any]   = model(**SCREAMING_SNAKE_CASE_    )
            logits.append(outputs.logits.detach().cpu().float()    )
  
       lowercase__					:     str   = get_entailment_id(SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Optional[Any]   = -1 if entail_id == 0 else 0
       lowercase__					:     int   = torch.cat(SCREAMING_SNAKE_CASE_			,  dim=0    )  # N*K x 3
       lowercase__					:     str   = logits.reshape(len(SCREAMING_SNAKE_CASE_    )			,  len(SCREAMING_SNAKE_CASE_    )			,  -1    )[..., [contr_id, entail_id]]  # N x K x 2
  
       if multi_label:
            # softmax over (contr, entail) logits for each class independently
            lowercase__					:     Optional[Any]   = (nli_logits / temperature).softmax(-1    )
       else:
            # softmax over entail logits across classes s.t. class probabilities sum to 1.
            lowercase__					:     Optional[Any]   = (nli_logits / temperature).softmax(1    )
  
       return nli_prob[..., 1]  # N x K
  
  def 			snake_case__    (					):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     int   = HfArgumentParser(
           (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)			,  description=SCREAMING_SNAKE_CASE_			,  )
  
       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__					:     List[Any]   = parser.parse_args_into_dataclasses()
  
       # Detecting last checkpoint.
       lowercase__					:     List[str]   = None
       if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
            lowercase__					:     Any   = 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__					:     Optional[Any]   = read_lines(data_args.data_file    )
       lowercase__					:     List[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__					:     Tuple   = get_teacher_predictions(
           teacher_args.teacher_name_or_path			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  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__					:     int   = Dataset.from_dict(
           {
               'text': examples,
               'labels': teacher_soft_preds,
           }    )
  
       # 3. create student
       logger.info('Initializing student model'    )
       lowercase__					:     str   = AutoModelForSequenceClassification.from_pretrained(
           student_args.student_name_or_path			,  num_labels=len(SCREAMING_SNAKE_CASE_    )    )
       lowercase__					:     Optional[Any]   = AutoTokenizer.from_pretrained(student_args.student_name_or_path			,  use_fast=data_args.use_fast_tokenizer    )
       lowercase__					:     Optional[int]   = dict(enumerate(SCREAMING_SNAKE_CASE_    )    )
       lowercase__					:     int   = {label: i for i, label in enumerate(SCREAMING_SNAKE_CASE_    )}
  
       # 4. train student on teacher predictions
       lowercase__					:     Any   = dataset.map(SCREAMING_SNAKE_CASE_			,  input_columns='text'    )
       dataset.set_format('torch'    )
  
       def compute_metrics(SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					List[str]=False    ):
            lowercase__					:     Any   = p.predictions.argmax(-1    )
            lowercase__					:     Dict   = p.label_ids.argmax(-1    )  # "label_ids" are actually distributions
            return {"agreement": (preds == proxy_labels).mean().item()}
  
       lowercase__					:     int   = DistillationTrainer(
           model=SCREAMING_SNAKE_CASE_			,  tokenizer=SCREAMING_SNAKE_CASE_			,  args=SCREAMING_SNAKE_CASE_			,  train_dataset=SCREAMING_SNAKE_CASE_			,  compute_metrics=SCREAMING_SNAKE_CASE_			,  )
  
       if training_args.do_train:
            logger.info('Training student model on teacher predictions'    )
            trainer.train()
  
       if training_args.do_eval:
            lowercase__					:     str   = trainer.evaluate(eval_dataset=SCREAMING_SNAKE_CASE_    )['eval_agreement']
            logger.info(f"""Agreement of student and teacher predictions: {agreement * 100:0.2f}%"""    )
  
       trainer.save_model()
  
  
  if __name__ == "__main__":
         main()
  
  