
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__					:     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__					:     List[Any]   = parser.parse_json_file(
              json_file=os.path.abspath(sys.argv[1]    )    )
     else:
          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()




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"

