






"""simple docstring"""





import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


_SCREAMING_SNAKE_CASE :    int    =						logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()







def 					lowerCamelCase__	(							_lowerCamelCase	:		List[Any]			)				->    List[Any]:
  if "token" in model_name_or_path:
    return "rag_token"
  if "sequence" in model_name_or_path:
    return "rag_sequence"
  if "bart" in model_name_or_path:
    return "bart"
  return None







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		int			)				->    Optional[int]:
  return max(metric_fn(_lowerCamelCase		,    _lowerCamelCase			) for gt in ground_truths			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		List[Any]		,    _lowerCamelCase	:		Optional[int]		,    _lowerCamelCase	:		Union[str, Any]			)				->    Dict:
  lowerCamelCase_   					=						[line.strip() for line in open(_lowerCamelCase		,    'r'			).readlines()]
  lowerCamelCase_   					=						[]

  if args.gold_data_mode == "qa":
    lowerCamelCase_   					=						pd.read_csv(_lowerCamelCase		,    sep='\t'		,    header=_lowerCamelCase			)
    for answer_list in data[1]:
      lowerCamelCase_   					=						ast.literal_eval(_lowerCamelCase			)
      answers.append(_lowerCamelCase			)
  else:
    lowerCamelCase_   					=						[line.strip() for line in open(_lowerCamelCase		,    'r'			).readlines()]
    lowerCamelCase_   					=						[[reference] for reference in references]

  lowerCamelCase_   					=						lowerCamelCase_   					=						lowerCamelCase_   					=						0
  for prediction, ground_truths in zip(_lowerCamelCase		,    _lowerCamelCase			):
    total += 1
    em += metric_max_over_ground_truths(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
    fa += metric_max_over_ground_truths(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)

  lowerCamelCase_   					=						100.0 * em / total
  lowerCamelCase_   					=						100.0 * fa / total

  logger.info(F'''F1: {fa:.2f}'''			)
  logger.info(F'''EM: {em:.2f}'''			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		Dict			)				->    int:
  lowerCamelCase_   					=						args.k
  lowerCamelCase_   					=						[line.strip() for line in open(_lowerCamelCase		,    'r'			).readlines()]
  lowerCamelCase_   					=						[line.strip() for line in open(_lowerCamelCase		,    'r'			).readlines()]

  lowerCamelCase_   					=						lowerCamelCase_   					=						0
  for hypo, reference in zip(_lowerCamelCase		,    _lowerCamelCase			):
    lowerCamelCase_   					=						set(hypo.split('\t'			)[:k]			)
    lowerCamelCase_   					=						set(reference.split('\t'			)			)
    total += 1
    em += len(hypo_provenance & ref_provenance			) / k

  lowerCamelCase_   					=						100.0 * em / total
  logger.info(F'''Precision@{k}: {em: .2f}'''			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		Any			)				->    int:
  def strip_title(_lowerCamelCase	:		List[Any]			):
    if title.startswith('"'			):
      lowerCamelCase_   					=						title[1:]
    if title.endswith('"'			):
      lowerCamelCase_   					=						title[:-1]
    return title

  lowerCamelCase_   					=						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
      _lowerCamelCase		,    return_tensors='pt'		,    padding=_lowerCamelCase		,    truncation=_lowerCamelCase		,    )['input_ids'].to(args.device			)

  lowerCamelCase_   					=						rag_model.rag.question_encoder(_lowerCamelCase			)
  lowerCamelCase_   					=						question_enc_outputs[0]

  lowerCamelCase_   					=						rag_model.retriever(
      _lowerCamelCase		,    question_enc_pool_output.cpu().detach().to(torch.floataa			).numpy()		,    prefix=rag_model.rag.generator.config.prefix		,    n_docs=rag_model.config.n_docs		,    return_tensors='pt'		,    )
  lowerCamelCase_   					=						rag_model.retriever.index.get_doc_dicts(result.doc_ids			)
  lowerCamelCase_   					=						[]
  for docs in all_docs:
    lowerCamelCase_   					=						[strip_title(_lowerCamelCase			) for title in docs['title']]
    provenance_strings.append('\t'.join(_lowerCamelCase			)			)
  return provenance_strings







def 					lowerCamelCase__	(							_lowerCamelCase	:		Union[str, Any]		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		int			)				->    Dict:
  with torch.no_grad():
    lowerCamelCase_   					=						rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        _lowerCamelCase		,    return_tensors='pt'		,    padding=_lowerCamelCase		,    truncation=_lowerCamelCase			)

    lowerCamelCase_   					=						inputs_dict.input_ids.to(args.device			)
    lowerCamelCase_   					=						inputs_dict.attention_mask.to(args.device			)
    lowerCamelCase_   					=						rag_model.generate(  # rag_model overwrites generate
        _lowerCamelCase		,    attention_mask=_lowerCamelCase		,    num_beams=args.num_beams		,    min_length=args.min_length		,    max_length=args.max_length		,    early_stopping=_lowerCamelCase		,    num_return_sequences=1		,    bad_words_ids=[[0, 0]]		,    )
    lowerCamelCase_   					=						rag_model.retriever.generator_tokenizer.batch_decode(_lowerCamelCase		,    skip_special_tokens=_lowerCamelCase			)

    if args.print_predictions:
      for q, a in zip(_lowerCamelCase		,    _lowerCamelCase			):
        logger.info('Q: {} - A: {}'.format(_lowerCamelCase		,    _lowerCamelCase			)			)

    return answers







def 					lowerCamelCase__	(							)				->    Optional[int]:
  lowerCamelCase_   					=						argparse.ArgumentParser()
  parser.add_argument(
      '--model_type'		,    choices=['rag_sequence', 'rag_token', 'bart']		,    type=_lowerCamelCase		,    help=(
          'RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'
          ' model_name_or_path'
      )		,    )
  parser.add_argument(
      '--index_name'		,    default=_lowerCamelCase		,    choices=['exact', 'compressed', 'legacy']		,    type=_lowerCamelCase		,    help='RAG model retriever type'		,    )
  parser.add_argument(
      '--index_path'		,    default=_lowerCamelCase		,    type=_lowerCamelCase		,    help='Path to the retrieval index'		,    )
  parser.add_argument('--n_docs'		,    default=5		,    type=_lowerCamelCase		,    help='Number of retrieved docs'			)
  parser.add_argument(
      '--model_name_or_path'		,    default=_lowerCamelCase		,    type=_lowerCamelCase		,    required=_lowerCamelCase		,    help='Path to pretrained checkpoints or model identifier from huggingface.co/models'		,    )
  parser.add_argument(
      '--eval_mode'		,    choices=['e2e', 'retrieval']		,    default='e2e'		,    type=_lowerCamelCase		,    help=(
          'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
          ' precision@k.'
      )		,    )
  parser.add_argument('--k'		,    default=1		,    type=_lowerCamelCase		,    help='k for the precision@k calculation'			)
  parser.add_argument(
      '--evaluation_set'		,    default=_lowerCamelCase		,    type=_lowerCamelCase		,    required=_lowerCamelCase		,    help='Path to a file containing evaluation samples'		,    )
  parser.add_argument(
      '--gold_data_path'		,    default=_lowerCamelCase		,    type=_lowerCamelCase		,    required=_lowerCamelCase		,    help='Path to a tab-separated file with gold samples'		,    )
  parser.add_argument(
      '--gold_data_mode'		,    default='qa'		,    type=_lowerCamelCase		,    choices=['qa', 'ans']		,    help=(
          'Format of the gold data file'
          'qa - a single line in the following format: question [tab] answer_list'
          'ans - a single line of the gold file contains the expected answer string'
      )		,    )
  parser.add_argument(
      '--predictions_path'		,    type=_lowerCamelCase		,    default='predictions.txt'		,    help='Name of the predictions file, to be stored in the checkpoints directory'		,    )
  parser.add_argument(
      '--eval_all_checkpoints'		,    action='store_true'		,    help='Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'		,    )
  parser.add_argument(
      '--eval_batch_size'		,    default=8		,    type=_lowerCamelCase		,    help='Batch size per GPU/CPU for evaluation.'		,    )
  parser.add_argument(
      '--recalculate'		,    help='Recalculate predictions even if the prediction file exists'		,    action='store_true'		,    )
  parser.add_argument(
      '--num_beams'		,    default=4		,    type=_lowerCamelCase		,    help='Number of beams to be used when generating answers'		,    )
  parser.add_argument('--min_length'		,    default=1		,    type=_lowerCamelCase		,    help='Min length of the generated answers'			)
  parser.add_argument('--max_length'		,    default=50		,    type=_lowerCamelCase		,    help='Max length of the generated answers'			)

  parser.add_argument(
      '--print_predictions'		,    action='store_true'		,    help='If True, prints predictions while evaluating.'		,    )
  parser.add_argument(
      '--print_docs'		,    action='store_true'		,    help='If True, prints docs retried while generating.'		,    )
  lowerCamelCase_   					=						parser.parse_args()
  lowerCamelCase_   					=						torch.device('cuda' if torch.cuda.is_available() else 'cpu'			)
  return args







def 					lowerCamelCase__	(							_lowerCamelCase	:		List[str]			)				->    Optional[int]:
  lowerCamelCase_   					=						{}
  if args.model_type is None:
    lowerCamelCase_   					=						infer_model_type(args.model_name_or_path			)
    assert args.model_type is not None
  if args.model_type.startswith('rag'			):
    lowerCamelCase_   					=						RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
    lowerCamelCase_   					=						args.n_docs
    if args.index_name is not None:
      lowerCamelCase_   					=						args.index_name
    if args.index_path is not None:
      lowerCamelCase_   					=						args.index_path
  else:
    lowerCamelCase_   					=						BartForConditionalGeneration

  lowerCamelCase_   					=						(
      [f.path for f in os.scandir(args.model_name_or_path			) if f.is_dir()]
      if args.eval_all_checkpoints
      else [args.model_name_or_path]
  )

  logger.info('Evaluate the following checkpoints: %s'		,    _lowerCamelCase			)

  lowerCamelCase_   					=						get_scores if args.eval_mode == 'e2e' else get_precision_at_k
  lowerCamelCase_   					=						evaluate_batch_eae if args.eval_mode == 'e2e' else evaluate_batch_retrieval

  for checkpoint in checkpoints:
    if os.path.exists(args.predictions_path			) and (not args.recalculate):
      logger.info('Calculating metrics based on an existing predictions file: {}'.format(args.predictions_path			)			)
      score_fn(_lowerCamelCase		,    args.predictions_path		,    args.gold_data_path			)
      continue

    logger.info('***** Running evaluation for {} *****'.format(_lowerCamelCase			)			)
    logger.info('  Batch size = %d'		,    args.eval_batch_size			)
    logger.info('  Predictions will be stored under {}'.format(args.predictions_path			)			)

    if args.model_type.startswith('rag'			):
      lowerCamelCase_   					=						RagRetriever.from_pretrained(_lowerCamelCase		,    **_lowerCamelCase			)
      lowerCamelCase_   					=						model_class.from_pretrained(_lowerCamelCase		,    retriever=_lowerCamelCase		,    **_lowerCamelCase			)
      model.retriever.init_retrieval()
    else:
      lowerCamelCase_   					=						model_class.from_pretrained(_lowerCamelCase		,    **_lowerCamelCase			)
    model.to(args.device			)

    with open(args.evaluation_set		,    'r'			) as eval_file, open(args.predictions_path		,    'w'			) as preds_file:
      lowerCamelCase_   					=						[]
      for line in tqdm(_lowerCamelCase			):
        questions.append(line.strip()			)
        if len(_lowerCamelCase			) == args.eval_batch_size:
          lowerCamelCase_   					=						evaluate_batch_fn(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
          preds_file.write('\n'.join(_lowerCamelCase			) + '\n'			)
          preds_file.flush()
          lowerCamelCase_   					=						[]
      if len(_lowerCamelCase			) > 0:
        lowerCamelCase_   					=						evaluate_batch_fn(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
        preds_file.write('\n'.join(_lowerCamelCase			)			)
        preds_file.flush()

      score_fn(_lowerCamelCase		,    args.predictions_path		,    args.gold_data_path			)


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE :    List[str]    =						get_args()
  main(args)












"""simple docstring"""





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 					lowerCamelCase__	(							_lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		List[str]=False		,    _lowerCamelCase	:		List[Any]=False		,    _lowerCamelCase	:		Dict="data2vec."			)				->    List[Any]:
  lowerCamelCase_   					=						'backbone.' if is_semantic else ''

  lowerCamelCase_   					=						[]
  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 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[int]		,    _lowerCamelCase	:		str		,    _lowerCamelCase	:		Union[str, Any]=False		,    _lowerCamelCase	:		Dict=False		,    _lowerCamelCase	:		Union[str, Any]="data2vec_vision."			)				->    List[str]:
  for i in range(config.num_hidden_layers			):
    lowerCamelCase_   					=						'backbone.' if is_semantic else ''
    # queries, keys and values
    lowerCamelCase_   					=						state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''			)
    lowerCamelCase_   					=						state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''			)
    lowerCamelCase_   					=						state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''			)

    lowerCamelCase_   					=						in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCamelCase_   					=						q_bias
    lowerCamelCase_   					=						in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCamelCase_   					=						in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCamelCase_   					=						v_bias

    # gamma_1 and gamma_2
    # we call them lambda because otherwise they are renamed when using .from_pretrained
    lowerCamelCase_   					=						state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''			)
    lowerCamelCase_   					=						state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''			)

    lowerCamelCase_   					=						gamma_a
    lowerCamelCase_   					=						gamma_a

    # relative_position bias table + index
    if not has_lm_head:
      # each layer has its own relative position bias
      lowerCamelCase_   					=						state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''			)
      lowerCamelCase_   					=						state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''			)

      lowerCamelCase_   					=						table
      lowerCamelCase_   					=						index







def 					lowerCamelCase__	(							)				->    Union[str, Any]:
  lowerCamelCase_   					=						argparse.ArgumentParser(
      'Convert Data2VecVision to HF for image classification and pretraining'		,    add_help=_lowerCamelCase			)
  parser.add_argument('--hf_checkpoint_name'		,    type=_lowerCamelCase			)
  parser.add_argument('--input_size'		,    default=224		,    type=_lowerCamelCase		,    help='images input size'			)
  parser.add_argument('--beit_checkpoint'		,    default=''		,    help='beit checkpoint'			)

  return parser.parse_args()







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		Any			)				->    List[str]:
  def load_state_dict(_lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		List[Any]		,    _lowerCamelCase	:		str=""		,    _lowerCamelCase	:		Any="relative_position_index"			):
    lowerCamelCase_   					=						[]
    lowerCamelCase_   					=						[]
    lowerCamelCase_   					=						[]
    # copy state_dict so _load_from_state_dict can modify it
    lowerCamelCase_   					=						getattr(_lowerCamelCase		,    '_metadata'		,    _lowerCamelCase			)
    lowerCamelCase_   					=						state_dict.copy()
    if metadata is not None:
      lowerCamelCase_   					=						metadata

    def load(_lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		List[str]=""			):
      lowerCamelCase_   					=						{} if metadata is None else metadata.get(prefix[:-1]		,    {}			)
      module._load_from_state_dict(
          _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
      for name, child in module._modules.items():
        if child is not None:
          load(_lowerCamelCase		,    prefix + name + '.'			)

    load(_lowerCamelCase		,    prefix=_lowerCamelCase			)

    lowerCamelCase_   					=						[]
    lowerCamelCase_   					=						[]
    for key in missing_keys:
      lowerCamelCase_   					=						True
      for ignore_key in ignore_missing.split('|'			):
        if ignore_key in key:
          lowerCamelCase_   					=						False
          break
      if keep_flag:
        warn_missing_keys.append(_lowerCamelCase			)
      else:
        ignore_missing_keys.append(_lowerCamelCase			)

    lowerCamelCase_   					=						warn_missing_keys

    if len(_lowerCamelCase			) > 0:
      print(
          'Weights of {} not initialized from pretrained model: {}'.format(
              model.__class__.__name__		,    _lowerCamelCase			)			)
    if len(_lowerCamelCase			) > 0:
      print('Weights from pretrained model not used in {}: {}'.format(model.__class__.__name__		,    _lowerCamelCase			)			)
    if len(_lowerCamelCase			) > 0:
      print(
          'Ignored weights of {} not initialized from pretrained model: {}'.format(
              model.__class__.__name__		,    _lowerCamelCase			)			)
    if len(_lowerCamelCase			) > 0:
      print('\n'.join(_lowerCamelCase			)			)

  lowerCamelCase_   					=						{
      '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': 1000,
            'use_mean_pooling': True,
            'init_scale': 0.0_01,
            'use_rel_pos_bias': True,
        }			)

  lowerCamelCase_   					=						create_model(
      'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'		,    **_lowerCamelCase		,    )
  lowerCamelCase_   					=						model.patch_embed.patch_size
  lowerCamelCase_   					=						(args.input_size // patch_size[0], args.input_size // patch_size[1])
  lowerCamelCase_   					=						torch.load(args.beit_checkpoint		,    map_location='cpu'			)

  print(F'''Load ckpt from {args.beit_checkpoint}'''			)
  lowerCamelCase_   					=						None
  for model_key in ("model", "module"):
    if model_key in checkpoint:
      lowerCamelCase_   					=						checkpoint[model_key]
      print(F'''Load state_dict by model_key = {model_key}'''			)
      break

  lowerCamelCase_   					=						list(checkpoint_model.keys()			)
  for key in all_keys:
    if "relative_position_index" in key:
      checkpoint_model.pop(_lowerCamelCase			)

    if "relative_position_bias_table" in key:
      lowerCamelCase_   					=						checkpoint_model[key]
      lowerCamelCase_					,     lowerCamelCase_   					=						rel_pos_bias.size()
      lowerCamelCase_					,     lowerCamelCase_   					=						model.state_dict()[key].size()
      lowerCamelCase_   					=						model.patch_embed.patch_shape
      if dst_patch_shape[0] != dst_patch_shape[1]:
        raise NotImplementedError()

  load_state_dict(_lowerCamelCase		,    _lowerCamelCase		,    prefix=''			)

  return model







def 					lowerCamelCase__	(							)				->    Dict:
  lowerCamelCase_   					=						get_args()

  lowerCamelCase_   					=						'ft1k' in args.hf_checkpoint_name
  lowerCamelCase_   					=						'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
  lowerCamelCase_   					=						DataaVecVisionConfig()
  if is_finetuned:
    lowerCamelCase_   					=						True
    lowerCamelCase_   					=						False
    lowerCamelCase_   					=						True
    lowerCamelCase_   					=						1000

    lowerCamelCase_   					=						'huggingface/label-files'
    lowerCamelCase_   					=						'imagenet-1k-id2label.json'
    lowerCamelCase_   					=						json.load(open(hf_hub_download(_lowerCamelCase		,    _lowerCamelCase		,    repo_type='dataset'			)		,    'r'			)			)
    lowerCamelCase_   					=						{int(_lowerCamelCase			): v for k, v in idalabel.items()}
    lowerCamelCase_   					=						idalabel
    lowerCamelCase_   					=						{v: k for k, v in idalabel.items()}
  else:
    lowerCamelCase_   					=						False
    lowerCamelCase_   					=						True
    lowerCamelCase_   					=						False

  if is_large:
    lowerCamelCase_   					=						1024
    lowerCamelCase_   					=						4096
    lowerCamelCase_   					=						24
    lowerCamelCase_   					=						16

  # 2. Load Beit model
  lowerCamelCase_   					=						load_beit_model(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
  orig_model.eval()

  # 3. Forward Beit model
  lowerCamelCase_   					=						BeitImageProcessor(size=config.image_size		,    do_center_crop=_lowerCamelCase			)
  lowerCamelCase_   					=						Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'			)
  lowerCamelCase_   					=						image_processor(images=_lowerCamelCase		,    return_tensors='pt'			)
  lowerCamelCase_   					=						encoding['pixel_values']

  lowerCamelCase_   					=						(pixel_values,) if is_finetuned else (pixel_values, None)
  with torch.no_grad():
    lowerCamelCase_   					=						orig_model(*_lowerCamelCase			)

  # 4. Load HF Data2VecVision model
  if is_finetuned:
    lowerCamelCase_   					=						DataaVecVisionForImageClassification(_lowerCamelCase			)
    hf_model.eval()
    lowerCamelCase_   					=						False
    lowerCamelCase_   					=						'data2vec_vision.'
  else:
    lowerCamelCase_   					=						DataaVecVisionModel(_lowerCamelCase			)
    hf_model.eval()
    lowerCamelCase_   					=						True
    lowerCamelCase_   					=						''

  lowerCamelCase_   					=						create_rename_keys(_lowerCamelCase		,    hf_prefix=_lowerCamelCase		,    has_lm_head=_lowerCamelCase			)
  lowerCamelCase_   					=						orig_model.state_dict()
  for src, dest in rename_keys:
    lowerCamelCase_   					=						state_dict.pop(_lowerCamelCase			)
    lowerCamelCase_   					=						val

  read_in_q_k_v(_lowerCamelCase		,    _lowerCamelCase		,    hf_prefix=_lowerCamelCase		,    has_lm_head=_lowerCamelCase			)
  lowerCamelCase_					,     lowerCamelCase_   					=						hf_model.load_state_dict(_lowerCamelCase		,    strict=_lowerCamelCase			)
  print('HF missing'		,    _lowerCamelCase			)
  print('HF unexpected_keys'		,    _lowerCamelCase			)

  # 5. Forward HF Data2VecVision model
  with torch.no_grad():
    lowerCamelCase_   					=						hf_model(_lowerCamelCase			)

  lowerCamelCase_   					=						hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

  # 6. Compare
  lowerCamelCase_   					=						torch.max(torch.abs(hf_output - orig_model_output			)			).item()

  print(F'''max_absolute_diff = {max_absolute_diff}'''			)
  lowerCamelCase_   					=						torch.allclose(_lowerCamelCase		,    _lowerCamelCase		,    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"




