



'''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


snake_case_ :	Dict         =  logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()






def A__    (     UpperCAmelCase_							):
						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 A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						return max(metric_fn(UpperCAmelCase_  ,  UpperCAmelCase_							) for gt in ground_truths							)






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		int 				=   [line.strip() for line in open(UpperCAmelCase_  ,  'r'							).readlines()]
						_UpperCamelCase					:		Union[str, Any] 				=   []

						if args.gold_data_mode == "qa":
												_UpperCamelCase					:		List[str] 				=   pd.read_csv(UpperCAmelCase_  ,  sep='\t'  ,  header=UpperCAmelCase_							)
												for answer_list in data[1]:
																		_UpperCamelCase					:		Any 				=   ast.literal_eval(UpperCAmelCase_							)
																		answers.append(UpperCAmelCase_							)
						else:
												_UpperCamelCase					:		Optional[int] 				=   [line.strip() for line in open(UpperCAmelCase_  ,  'r'							).readlines()]
												_UpperCamelCase					:		Union[str, Any] 				=   [[reference] for reference in references]

						_UpperCamelCase					:		List[str] 				=   0
						for prediction, ground_truths in zip(UpperCAmelCase_  ,  UpperCAmelCase_							):
												total += 1
												em += metric_max_over_ground_truths(UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)
												fa += metric_max_over_ground_truths(UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)

						_UpperCamelCase					:		Dict 				=   100.0 * em / total
						_UpperCamelCase					:		int 				=   100.0 * fa / total

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






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		Optional[int] 				=   args.k
						_UpperCamelCase					:		Dict 				=   [line.strip() for line in open(UpperCAmelCase_  ,  'r'							).readlines()]
						_UpperCamelCase					:		str 				=   [line.strip() for line in open(UpperCAmelCase_  ,  'r'							).readlines()]

						_UpperCamelCase					:		Union[str, Any] 				=   0
						for hypo, reference in zip(UpperCAmelCase_  ,  UpperCAmelCase_							):
												_UpperCamelCase					:		List[Any] 				=   set(hypo.split('\t'							)[:k]							)
												_UpperCamelCase					:		List[Any] 				=   set(reference.split('\t'							)							)
												total += 1
												em += len(hypo_provenance & ref_provenance							) / k

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






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						def strip_title(UpperCAmelCase_							):
												if title.startswith('"'							):
																		_UpperCamelCase					:		List[str] 				=   title[1:]
												if title.endswith('"'							):
																		_UpperCamelCase					:		Any 				=   title[:-1]
												return title

						_UpperCamelCase					:		Any 				=   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    UpperCAmelCase_  ,  return_tensors='pt'  ,  padding=UpperCAmelCase_  ,  truncation=UpperCAmelCase_  ,  )['input_ids'].to(args.device							)

						_UpperCamelCase					:		List[str] 				=   rag_model.rag.question_encoder(UpperCAmelCase_							)
						_UpperCamelCase					:		Optional[Any] 				=   question_enc_outputs[0]

						_UpperCamelCase					:		str 				=   rag_model.retriever(
						    UpperCAmelCase_  ,  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'  ,  )
						_UpperCamelCase					:		Tuple 				=   rag_model.retriever.index.get_doc_dicts(result.doc_ids							)
						_UpperCamelCase					:		Optional[Any] 				=   []
						for docs in all_docs:
												_UpperCamelCase					:		Any 				=   [strip_title(UpperCAmelCase_							) for title in docs['title']]
												provenance_strings.append('\t'.join(UpperCAmelCase_							)							)
						return provenance_strings






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						with torch.no_grad():
												_UpperCamelCase					:		int 				=   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
												    UpperCAmelCase_  ,  return_tensors='pt'  ,  padding=UpperCAmelCase_  ,  truncation=UpperCAmelCase_							)

												_UpperCamelCase					:		Union[str, Any] 				=   inputs_dict.input_ids.to(args.device							)
												_UpperCamelCase					:		Optional[int] 				=   inputs_dict.attention_mask.to(args.device							)
												_UpperCamelCase					:		str 				=   rag_model.generate(  # rag_model overwrites generate
												    UpperCAmelCase_  ,  attention_mask=UpperCAmelCase_  ,  num_beams=args.num_beams  ,  min_length=args.min_length  ,  max_length=args.max_length  ,  early_stopping=UpperCAmelCase_  ,  num_return_sequences=1  ,  bad_words_ids=[[0, 0]]  ,  )
												_UpperCamelCase					:		List[str] 				=   rag_model.retriever.generator_tokenizer.batch_decode(UpperCAmelCase_  ,  skip_special_tokens=UpperCAmelCase_							)

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

												return answers






def A__    (     ):
						_UpperCamelCase					:		str 				=   argparse.ArgumentParser()
						parser.add_argument(
						    '--model_type'  ,  choices=['rag_sequence', 'rag_token', 'bart']  ,  type=UpperCAmelCase_  ,  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=UpperCAmelCase_  ,  choices=['exact', 'compressed', 'legacy']  ,  type=UpperCAmelCase_  ,  help='RAG model retriever type'  ,  )
						parser.add_argument(
						    '--index_path'  ,  default=UpperCAmelCase_  ,  type=UpperCAmelCase_  ,  help='Path to the retrieval index'  ,  )
						parser.add_argument('--n_docs'  ,  default=5  ,  type=UpperCAmelCase_  ,  help='Number of retrieved docs'							)
						parser.add_argument(
						    '--model_name_or_path'  ,  default=UpperCAmelCase_  ,  type=UpperCAmelCase_  ,  required=UpperCAmelCase_  ,  help='Path to pretrained checkpoints or model identifier from huggingface.co/models'  ,  )
						parser.add_argument(
						    '--eval_mode'  ,  choices=['e2e', 'retrieval']  ,  default='e2e'  ,  type=UpperCAmelCase_  ,  help=(
						        'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
						        ' precision@k.'
						    )  ,  )
						parser.add_argument('--k'  ,  default=1  ,  type=UpperCAmelCase_  ,  help='k for the precision@k calculation'							)
						parser.add_argument(
						    '--evaluation_set'  ,  default=UpperCAmelCase_  ,  type=UpperCAmelCase_  ,  required=UpperCAmelCase_  ,  help='Path to a file containing evaluation samples'  ,  )
						parser.add_argument(
						    '--gold_data_path'  ,  default=UpperCAmelCase_  ,  type=UpperCAmelCase_  ,  required=UpperCAmelCase_  ,  help='Path to a tab-separated file with gold samples'  ,  )
						parser.add_argument(
						    '--gold_data_mode'  ,  default='qa'  ,  type=UpperCAmelCase_  ,  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=UpperCAmelCase_  ,  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=UpperCAmelCase_  ,  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=UpperCAmelCase_  ,  help='Number of beams to be used when generating answers'  ,  )
						parser.add_argument('--min_length'  ,  default=1  ,  type=UpperCAmelCase_  ,  help='Min length of the generated answers'							)
						parser.add_argument('--max_length'  ,  default=5_0  ,  type=UpperCAmelCase_  ,  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.'  ,  )
						_UpperCamelCase					:		Optional[Any] 				=   parser.parse_args()
						_UpperCamelCase					:		Tuple 				=   torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)
						return args






def A__    (     UpperCAmelCase_							):
						_UpperCamelCase					:		Optional[int] 				=   {}
						if args.model_type is None:
												_UpperCamelCase					:		Tuple 				=   infer_model_type(args.model_name_or_path							)
												assert args.model_type is not None
						if args.model_type.startswith('rag'							):
												_UpperCamelCase					:		str 				=   RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
												_UpperCamelCase					:		Dict 				=   args.n_docs
												if args.index_name is not None:
																		_UpperCamelCase					:		int 				=   args.index_name
												if args.index_path is not None:
																		_UpperCamelCase					:		int 				=   args.index_path
						else:
												_UpperCamelCase					:		Any 				=   BartForConditionalGeneration

						_UpperCamelCase					:		int 				=   (
						    [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'  ,  UpperCAmelCase_							)

						_UpperCamelCase					:		Tuple 				=   get_scores if args.eval_mode == 'e2e' else get_precision_at_k
						_UpperCamelCase					:		Dict 				=   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(UpperCAmelCase_  ,  args.predictions_path  ,  args.gold_data_path							)
																		continue

												logger.info('***** Running evaluation for {} *****'.format(UpperCAmelCase_							)							)
												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'							):
																		_UpperCamelCase					:		Dict 				=   RagRetriever.from_pretrained(UpperCAmelCase_  ,  **UpperCAmelCase_							)
																		_UpperCamelCase					:		Optional[Any] 				=   model_class.from_pretrained(UpperCAmelCase_  ,  retriever=UpperCAmelCase_  ,  **UpperCAmelCase_							)
																		model.retriever.init_retrieval()
												else:
																		_UpperCamelCase					:		int 				=   model_class.from_pretrained(UpperCAmelCase_  ,  **UpperCAmelCase_							)
												model.to(args.device							)

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

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


if __name__ == "__main__":
					snake_case_ :	int         =  get_args()
					main(args)





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase           =					logging.get_logger(__name__)

lowerCamelCase           =					torch.device('''cpu''')




def 			UpperCAmelCase__				(			):





  '''simple docstring'''







  a__  ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
  a__  =Image.open(requests.get(_A	,      stream=_A							).raw							)
  return im




def 			UpperCAmelCase__				(			_A      : Optional[int]							):





  '''simple docstring'''







  if swiftformer_name == "swiftformer_xs":
    return torch.tensor([-2.17_03E00, 2.11_07E00, -2.08_11E00, 8.86_85E-01, 2.43_60E-01]							)

  elif swiftformer_name == "swiftformer_s":
    return torch.tensor([3.96_36E-01, 2.34_78E-01, -1.69_63E00, -1.73_81E00, -8.63_37E-01]							)

  elif swiftformer_name == "swiftformer_l1":
    return torch.tensor([-4.27_68E-01, -4.74_29E-01, -1.08_97E00, -1.02_48E00, 3.55_23E-02]							)

  elif swiftformer_name == "swiftformer_l3":
    return torch.tensor([-2.53_30E-01, 2.42_11E-01, -6.01_85E-01, -8.27_89E-01, -6.04_46E-02]							)




def 			UpperCAmelCase__				(			_A      : List[str]	,      _A      : Union[str, Any]	,      _A      : str							):





  '''simple docstring'''







  a__  =dct.pop(_A							)
  a__  =val




def 			UpperCAmelCase__				(			_A      : Any							):





  '''simple docstring'''







  a__  =[]
  for k in state_dict.keys():
    a__  =k
    if ".pwconv" in k:
      a__  =k_new.replace('''.pwconv'''	,      '''.point_wise_conv'''							)
    if ".dwconv" in k:
      a__  =k_new.replace('''.dwconv'''	,      '''.depth_wise_conv'''							)
    if ".Proj." in k:
      a__  =k_new.replace('''.Proj.'''	,      '''.proj.'''							)
    if "patch_embed" in k_new:
      a__  =k_new.replace('''patch_embed'''	,      '''swiftformer.patch_embed.patch_embedding'''							)
    if "network" in k_new:
      a__  =k_new.split('''.'''							)
      if ls[2].isdigit():
        a__  ='''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]							)
      else:
        a__  =k_new.replace('''network'''	,      '''swiftformer.encoder.network'''							)
    rename_keys.append((k, k_new)							)
  return rename_keys




@torch.no_grad()
def 			UpperCAmelCase__				(			_A      : int	,      _A      : Union[str, Any]	,      _A      : int							):





  '''simple docstring'''







  a__  =SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  a__  =10_00
  a__  ='''huggingface/label-files'''
  a__  ='''imagenet-1k-id2label.json'''
  a__  =json.load(open(hf_hub_download(_A	,      _A	,      repo_type='''dataset'''							)	,      '''r'''							)							)
  a__  ={int(_A							): v for k, v in idalabel.items()}
  a__  =idalabel
  a__  ={v: k for k, v in idalabel.items()}

  # size of the architecture
  if swiftformer_name == "swiftformer_xs":
    a__  =[3, 3, 6, 4]
    a__  =[48, 56, 1_12, 2_20]

  elif swiftformer_name == "swiftformer_s":
    a__  =[3, 3, 9, 6]
    a__  =[48, 64, 1_68, 2_24]

  elif swiftformer_name == "swiftformer_l1":
    a__  =[4, 3, 10, 5]
    a__  =[48, 96, 1_92, 3_84]

  elif swiftformer_name == "swiftformer_l3":
    a__  =[4, 4, 12, 6]
    a__  =[64, 1_28, 3_20, 5_12]

  # load state_dict of original model, remove and rename some keys
  if original_ckpt:
    if original_ckpt.startswith('''https'''							):
      a__  =torch.hub.load_state_dict_from_url(_A	,      map_location='''cpu'''	,      check_hash=_A							)
    else:
      a__  =torch.load(_A	,      map_location='''cpu'''							)
  a__  =checkpoint

  a__  =create_rename_keys(_A							)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(_A	,      _A	,      _A							)

  # load HuggingFace model
  a__  =SwiftFormerForImageClassification(_A							).eval()
  hf_model.load_state_dict(_A							)

  # prepare test inputs
  a__  =prepare_img()
  a__  =ViTImageProcessor.from_pretrained('''preprocessor_config'''							)
  a__  =processor(images=_A	,      return_tensors='''pt'''							)

  # compare outputs from both models
  a__  =get_expected_output(_A							)
  a__  =hf_model(inputs['''pixel_values''']							).logits

  assert hf_logits.shape == torch.Size([1, 10_00]							)
  assert torch.allclose(hf_logits[0, 0:5]	,      _A	,      atol=1E-3							)

  Path(_A							).mkdir(exist_ok=_A							)
  print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""							)
  hf_model.save_pretrained(_A							)


if __name__ == "__main__":
       lowerCamelCase           =					argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--swiftformer_name''',
           default='''swiftformer_xs''',
           choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
           type=str,
           help='''Name of the SwiftFormer model you\'d like to convert.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default='''./converted_outputs/''',
           type=str,
           help='''Path to the output PyTorch model directory.''',
       )
       parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

       lowerCamelCase           =					parser.parse_args()
       convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
