






"""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


_UpperCamelCase		:  Optional[int]						=		logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Tuple    ):





  '''simple docstring'''





  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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[int]		,					__snake_case			:  List[Any]		,					__snake_case			:  int    ):





  '''simple docstring'''





  return max(metric_fn(__snake_case		,					__snake_case    ) for gt in ground_truths    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]		,					__snake_case			:  Dict		,					__snake_case			:  List[Any]    ):





  '''simple docstring'''





  lowercase							    =       [line.strip() for line in open(__snake_case		,					'r'    ).readlines()]
  lowercase							    =       []

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

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

  lowercase							    =       100.0 * em / total
  lowercase							    =       100.0 * fa / total

  logger.info(f'F1: {fa:.2f}'    )
  logger.info(f'EM: {em:.2f}'    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Tuple		,					__snake_case			:  Dict		,					__snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       args.k
  lowercase							    =       [line.strip() for line in open(__snake_case		,					'r'    ).readlines()]
  lowercase							    =       [line.strip() for line in open(__snake_case		,					'r'    ).readlines()]

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

  lowercase							    =       100.0 * em / total
  logger.info(f'Precision@{k}: {em: .2f}'    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int		,					__snake_case			:  Optional[Any]		,					__snake_case			:  List[str]    ):





  '''simple docstring'''





  def strip_title(__snake_case			:  Union[str, Any]    ):
    if title.startswith('"'    ):
      lowercase							    =       title[1:]
    if title.endswith('"'    ):
      lowercase							    =       title[:-1]
    return title

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

  lowercase							    =       rag_model.rag.question_encoder(__snake_case    )
  lowercase							    =       question_enc_outputs[0]

  lowercase							    =       rag_model.retriever(
      __snake_case		,					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'		,					)
  lowercase							    =       rag_model.retriever.index.get_doc_dicts(result.doc_ids    )
  lowercase							    =       []
  for docs in all_docs:
    lowercase							    =       [strip_title(__snake_case    ) for title in docs['title']]
    provenance_strings.append('\t'.join(__snake_case    )    )
  return provenance_strings
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Any		,					__snake_case			:  List[Any]		,					__snake_case			:  List[Any]    ):





  '''simple docstring'''





  with torch.no_grad():
    lowercase							    =       rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        __snake_case		,					return_tensors='pt'		,					padding=__snake_case		,					truncation=__snake_case    )

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

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

    return answers
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       argparse.ArgumentParser()
  parser.add_argument(
      '--model_type'		,					choices=['rag_sequence', 'rag_token', 'bart']		,					type=__snake_case		,					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=__snake_case		,					choices=['exact', 'compressed', 'legacy']		,					type=__snake_case		,					help='RAG model retriever type'		,					)
  parser.add_argument(
      '--index_path'		,					default=__snake_case		,					type=__snake_case		,					help='Path to the retrieval index'		,					)
  parser.add_argument('--n_docs'		,					default=5		,					type=__snake_case		,					help='Number of retrieved docs'    )
  parser.add_argument(
      '--model_name_or_path'		,					default=__snake_case		,					type=__snake_case		,					required=__snake_case		,					help='Path to pretrained checkpoints or model identifier from huggingface.co/models'		,					)
  parser.add_argument(
      '--eval_mode'		,					choices=['e2e', 'retrieval']		,					default='e2e'		,					type=__snake_case		,					help=(
          'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
          ' precision@k.'
      )		,					)
  parser.add_argument('--k'		,					default=1		,					type=__snake_case		,					help='k for the precision@k calculation'    )
  parser.add_argument(
      '--evaluation_set'		,					default=__snake_case		,					type=__snake_case		,					required=__snake_case		,					help='Path to a file containing evaluation samples'		,					)
  parser.add_argument(
      '--gold_data_path'		,					default=__snake_case		,					type=__snake_case		,					required=__snake_case		,					help='Path to a tab-separated file with gold samples'		,					)
  parser.add_argument(
      '--gold_data_mode'		,					default='qa'		,					type=__snake_case		,					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=__snake_case		,					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=__snake_case		,					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=__snake_case		,					help='Number of beams to be used when generating answers'		,					)
  parser.add_argument('--min_length'		,					default=1		,					type=__snake_case		,					help='Min length of the generated answers'    )
  parser.add_argument('--max_length'		,					default=50		,					type=__snake_case		,					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.'		,					)
  lowercase							    =       parser.parse_args()
  lowercase							    =       torch.device('cuda' if torch.cuda.is_available() else 'cpu'    )
  return args
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       {}
  if args.model_type is None:
    lowercase							    =       infer_model_type(args.model_name_or_path    )
    assert args.model_type is not None
  if args.model_type.startswith('rag'    ):
    lowercase							    =       RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
    lowercase							    =       args.n_docs
    if args.index_name is not None:
      lowercase							    =       args.index_name
    if args.index_path is not None:
      lowercase							    =       args.index_path
  else:
    lowercase							    =       BartForConditionalGeneration

  lowercase							    =       (
      [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'		,					__snake_case    )

  lowercase							    =       get_scores if args.eval_mode == 'e2e' else get_precision_at_k
  lowercase							    =       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(__snake_case		,					args.predictions_path		,					args.gold_data_path    )
      continue

    logger.info('***** Running evaluation for {} *****'.format(__snake_case    )    )
    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'    ):
      lowercase							    =       RagRetriever.from_pretrained(__snake_case		,					**__snake_case    )
      lowercase							    =       model_class.from_pretrained(__snake_case		,					retriever=__snake_case		,					**__snake_case    )
      model.retriever.init_retrieval()
    else:
      lowercase							    =       model_class.from_pretrained(__snake_case		,					**__snake_case    )
    model.to(args.device    )

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

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


if __name__ == "__main__":
    _UpperCamelCase		:  Dict						=		get_args()
    main(args)










"""simple docstring"""



import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]    ):





  '''simple docstring'''





  lowercase							    =       []
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
          f'stage{idx}.patch_embed.proj.weight',
      )    )
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
          f'stage{idx}.patch_embed.proj.bias',
      )    )
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
          f'stage{idx}.patch_embed.norm.weight',
      )    )
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
          f'stage{idx}.patch_embed.norm.bias',
      )    )
  return embed
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]		,					__snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       []
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj.bias',
      )    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc1.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc1.bias')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc2.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc2.bias')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', f'stage{idx}.blocks.{cnt}.norm1.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', f'stage{idx}.blocks.{cnt}.norm1.bias')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', f'stage{idx}.blocks.{cnt}.norm2.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', f'stage{idx}.blocks.{cnt}.norm2.bias')    )
  return attention_weights
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       []
  token.append((f'cvt.encoder.stages.{idx}.cls_token', 'stage2.cls_token')    )
  return token
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       []
  head.append(('layernorm.weight', 'norm.weight')    )
  head.append(('layernorm.bias', 'norm.bias')    )
  head.append(('classifier.weight', 'head.weight')    )
  head.append(('classifier.bias', 'head.bias')    )
  return head
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]		,					__snake_case			:  Dict		,					__snake_case			:  List[str]		,					__snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  lowercase							    =       'imagenet-1k-id2label.json'
  lowercase							    =       10_00

  lowercase							    =       'huggingface/label-files'
  lowercase							    =       num_labels
  lowercase							    =       json.load(open(cached_download(hf_hub_url(__snake_case		,					__snake_case		,					repo_type='dataset'    )    )		,					'r'    )    )
  lowercase							    =       {int(__snake_case    ): v for k, v in idalabel.items()}

  lowercase							    =       idalabel
  lowercase							    =       {v: k for k, v in idalabel.items()}

  lowercase							    =       lowercase							    =       CvtConfig(num_labels=__snake_case		,					idalabel=__snake_case		,					labelaid=__snake_case    )

  # For depth size 13 (13 = 1+2+10)
  if cvt_model.rsplit('/'		,					1    )[-1][4:6] == "13":
    lowercase							    =       [1, 2, 10]

  # For depth size 21 (21 = 1+4+16)
  elif cvt_model.rsplit('/'		,					1    )[-1][4:6] == "21":
    lowercase							    =       [1, 4, 16]

  # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
  else:
    lowercase							    =       [2, 2, 20]
    lowercase							    =       [3, 12, 16]
    lowercase							    =       [1_92, 7_68, 10_24]

  lowercase							    =       CvtForImageClassification(__snake_case    )
  lowercase							    =       AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'    )
  lowercase							    =       image_size
  lowercase							    =       torch.load(__snake_case		,					map_location=torch.device('cpu'    )    )

  lowercase							    =       OrderedDict()
  lowercase							    =       []

  for idx in range(len(config.depth    )    ):
    if config.cls_token[idx]:
      lowercase							    =       list_of_state_dict + cls_token(__snake_case    )
    lowercase							    =       list_of_state_dict + embeddings(__snake_case    )
    for cnt in range(config.depth[idx]    ):
      lowercase							    =       list_of_state_dict + attention(__snake_case		,					__snake_case    )

  lowercase							    =       list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(__snake_case    )
  for i in range(len(__snake_case    )    ):
    lowercase							    =       original_weights[list_of_state_dict[i][1]]

  model.load_state_dict(__snake_case    )
  model.save_pretrained(__snake_case    )
  image_processor.save_pretrained(__snake_case    )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
    _UpperCamelCase		:  Optional[Any]						=		argparse.ArgumentParser()
    parser.add_argument(
        '--cvt_model',
        default='cvt-w24',
        type=str,
        help='Name of the cvt model you\'d like to convert.',
    )
    parser.add_argument(
        '--image_size',
        default=3_8_4,
        type=int,
        help='Input Image Size',
    )
    parser.add_argument(
        '--cvt_file_name',
        default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
        type=str,
        help='Input Image Size',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )

    _UpperCamelCase		:  Tuple						=		parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)


