





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


_A				:							List[Any]							= logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def        __snake_case       (						lowerCAmelCase_  )							->	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        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    return max(metric_fn(lowerCAmelCase_   ,       lowerCAmelCase_  ) for gt in ground_truths  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    SCREAMING_SNAKE_CASE__       							= [line.strip() for line in open(lowerCAmelCase_   ,       '''r'''  ).readlines()]
    SCREAMING_SNAKE_CASE__       							= []

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

    SCREAMING_SNAKE_CASE__       							= SCREAMING_SNAKE_CASE__       							= SCREAMING_SNAKE_CASE__       							= 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_  )

    SCREAMING_SNAKE_CASE__       							= 1_00.0 * em / total
    SCREAMING_SNAKE_CASE__       							= 1_00.0 * fa / total

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


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Optional[int]:
    SCREAMING_SNAKE_CASE__       							= args.k
    SCREAMING_SNAKE_CASE__       							= [line.strip() for line in open(lowerCAmelCase_   ,       '''r'''  ).readlines()]
    SCREAMING_SNAKE_CASE__       							= [line.strip() for line in open(lowerCAmelCase_   ,       '''r'''  ).readlines()]

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

    SCREAMING_SNAKE_CASE__       							= 1_00.0 * em / total
    logger.info(f'''Precision@{k}: {em: .2f}'''  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	List[Any]:
    def strip_title(lowerCAmelCase_  ):
        if title.startswith('''"'''  ):
            SCREAMING_SNAKE_CASE__       							= title[1:]
        if title.endswith('''"'''  ):
            SCREAMING_SNAKE_CASE__       							= title[:-1]
        return title

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

    SCREAMING_SNAKE_CASE__       							= rag_model.rag.question_encoder(lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= question_enc_outputs[0]

    SCREAMING_SNAKE_CASE__       							= 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'''   ,       )
    SCREAMING_SNAKE_CASE__       							= rag_model.retriever.index.get_doc_dicts(result.doc_ids  )
    SCREAMING_SNAKE_CASE__       							= []
    for docs in all_docs:
        SCREAMING_SNAKE_CASE__       							= [strip_title(lowerCAmelCase_  ) for title in docs['''title''']]
        provenance_strings.append('''\t'''.join(lowerCAmelCase_  )  )
    return provenance_strings


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    with torch.no_grad():
        SCREAMING_SNAKE_CASE__       							= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
            lowerCAmelCase_   ,       return_tensors='''pt'''   ,       padding=lowerCAmelCase_   ,       truncation=lowerCAmelCase_  )

        SCREAMING_SNAKE_CASE__       							= inputs_dict.input_ids.to(args.device  )
        SCREAMING_SNAKE_CASE__       							= inputs_dict.attention_mask.to(args.device  )
        SCREAMING_SNAKE_CASE__       							= 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]]   ,       )
        SCREAMING_SNAKE_CASE__       							= 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        __snake_case       (						)							->	Union[str, Any]:
    SCREAMING_SNAKE_CASE__       							= 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=5_0   ,       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.'''   ,       )
    SCREAMING_SNAKE_CASE__       							= parser.parse_args()
    SCREAMING_SNAKE_CASE__       							= torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''  )
    return args


def        __snake_case       (						lowerCAmelCase_  )							->	Any:
    SCREAMING_SNAKE_CASE__       							= {}
    if args.model_type is None:
        SCREAMING_SNAKE_CASE__       							= infer_model_type(args.model_name_or_path  )
        assert args.model_type is not None
    if args.model_type.startswith('''rag'''  ):
        SCREAMING_SNAKE_CASE__       							= RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
        SCREAMING_SNAKE_CASE__       							= args.n_docs
        if args.index_name is not None:
            SCREAMING_SNAKE_CASE__       							= args.index_name
        if args.index_path is not None:
            SCREAMING_SNAKE_CASE__       							= args.index_path
    else:
        SCREAMING_SNAKE_CASE__       							= BartForConditionalGeneration

    SCREAMING_SNAKE_CASE__       							= (
        [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_  )

    SCREAMING_SNAKE_CASE__       							= get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
    SCREAMING_SNAKE_CASE__       							= 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'''  ):
            SCREAMING_SNAKE_CASE__       							= RagRetriever.from_pretrained(lowerCAmelCase_   ,       **lowerCAmelCase_  )
            SCREAMING_SNAKE_CASE__       							= model_class.from_pretrained(lowerCAmelCase_   ,       retriever=lowerCAmelCase_   ,       **lowerCAmelCase_  )
            model.retriever.init_retrieval()
        else:
            SCREAMING_SNAKE_CASE__       							= 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:
            SCREAMING_SNAKE_CASE__       							= []
            for line in tqdm(lowerCAmelCase_  ):
                questions.append(line.strip()  )
                if len(lowerCAmelCase_  ) == args.eval_batch_size:
                    SCREAMING_SNAKE_CASE__       							= evaluate_batch_fn(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
                    preds_file.write('''\n'''.join(lowerCAmelCase_  ) + '''\n'''  )
                    preds_file.flush()
                    SCREAMING_SNAKE_CASE__       							= []
            if len(lowerCAmelCase_  ) > 0:
                SCREAMING_SNAKE_CASE__       							= 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__":
  _A				:							Dict							= get_args()
  main(args)







import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.in_proj_weight'''].chunk(3   ,       dim=0  )
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.in_proj_bias'''].chunk(3   ,       dim=0  )

    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.out_proj.weight''']
    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.out_proj.bias''']

    SCREAMING_SNAKE_CASE__       							= q_proj
    SCREAMING_SNAKE_CASE__       							= q_proj_bias

    SCREAMING_SNAKE_CASE__       							= k_proj
    SCREAMING_SNAKE_CASE__       							= k_proj_bias

    SCREAMING_SNAKE_CASE__       							= v_proj
    SCREAMING_SNAKE_CASE__       							= v_proj_bias

    SCREAMING_SNAKE_CASE__       							= out_proj_weights
    SCREAMING_SNAKE_CASE__       							= out_proj_bias


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Dict:
    copy_linear(hf_mlp.fca   ,       lowerCAmelCase_   ,       f'''{prefix}.c_fc'''  )
    copy_linear(hf_mlp.fca   ,       lowerCAmelCase_   ,       f'''{prefix}.c_proj'''  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.weight'''].data
    SCREAMING_SNAKE_CASE__       							= pt_weights[f'''{prefix}.bias'''].data


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Dict:
    # copy layer norms
    copy_linear(hf_layer.layer_norma   ,       lowerCAmelCase_   ,       f'''{prefix}.ln_1'''  )
    copy_linear(hf_layer.layer_norma   ,       lowerCAmelCase_   ,       f'''{prefix}.ln_2'''  )

    # copy MLP
    copy_mlp(hf_layer.mlp   ,       lowerCAmelCase_   ,       f'''{prefix}.mlp'''  )

    # copy attn
    copy_attn_layer(hf_layer.self_attn   ,       lowerCAmelCase_   ,       f'''{prefix}.attn'''  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Optional[int]:
    for layer_id, hf_layer in enumerate(lowerCAmelCase_  ):
        copy_layer(lowerCAmelCase_   ,       lowerCAmelCase_   ,       f'''{prefix}.{layer_id}'''  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Any:
    # copy projection
    SCREAMING_SNAKE_CASE__       							= pt_weights['''text_projection'''].data.T

    # copy text encoder
    for name, param in hf_model.text_model.named_parameters():
        SCREAMING_SNAKE_CASE__       							= pt_weights[f'''bert.{name}'''].data


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	List[str]:
    # copy projection
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.proj'''].data.T

    # copy layer norms
    copy_linear(hf_model.vision_model.pre_layrnorm   ,       lowerCAmelCase_   ,       '''visual.ln_pre'''  )
    copy_linear(hf_model.vision_model.post_layernorm   ,       lowerCAmelCase_   ,       '''visual.ln_post'''  )

    # copy embeddings
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.conv1.weight'''].data
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.class_embedding'''].data
    SCREAMING_SNAKE_CASE__       							= pt_weights['''visual.positional_embedding'''].data

    # copy encoder
    copy_layers(hf_model.vision_model.encoder.layers   ,       lowerCAmelCase_   ,       '''visual.transformer.resblocks'''  )


@torch.no_grad()
def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_=None  )							->	str:
    assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
    SCREAMING_SNAKE_CASE__       							= ChineseCLIPConfig.from_pretrained(lowerCAmelCase_  )

    SCREAMING_SNAKE_CASE__       							= ChineseCLIPModel(lowerCAmelCase_  ).eval()

    SCREAMING_SNAKE_CASE__       							= torch.load(lowerCAmelCase_   ,       map_location='''cpu'''  )['''state_dict''']
    SCREAMING_SNAKE_CASE__       							= {(name[7:] if name.startswith('''module.'''  ) else name): value for name, value in pt_weights.items()}

    copy_text_model_and_projection(lowerCAmelCase_   ,       lowerCAmelCase_  )
    copy_vision_model_and_projection(lowerCAmelCase_   ,       lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= pt_weights['''logit_scale'''].data

    hf_model.save_pretrained(lowerCAmelCase_  )


if __name__ == "__main__":
  _A				:							Union[str, Any]							= argparse.ArgumentParser()
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=str,
      help="""Path to the output folder storing converted hf PyTorch model.""",
  )
  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, help="""Path to original github format ChineseCLIP checkpoint."""
  )
  parser.add_argument(
      """--config_path""", default=None, required=True, type=str, help="""Path to hf config.json of model to convert."""
  )
  _A				:							Union[str, Any]							= parser.parse_args()

  convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
  print("""The conversion is finished!""")
