
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


a__				:      List[Any]    		=     getLogger(__name__)


a__				:      Optional[Any]    		=     '''cuda''' if torch.cuda.is_available() else '''cpu'''







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__ = 8 ,						a__ = DEFAULT_DEVICE ,						a__=False ,						a__="summarization" ,						a__=None ,						**a__ ,						):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				int				      = Path(a__   ).open('''w''' ,						encoding='''utf-8'''   )
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = str(a__   )
    SCREAMING_SNAKE_CASE				:				int				      = AutoModelForSeqaSeqLM.from_pretrained(a__   ).to(a__   )
    if fpaa:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = model.half()

    SCREAMING_SNAKE_CASE				:				List[Any]				      = AutoTokenizer.from_pretrained(a__   )
    logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}"""   )  # if this is wrong, check config.model_type.

    SCREAMING_SNAKE_CASE				:				Optional[int]				      = time.time()
    # update config with task specific params
    use_task_specific_params(a__ ,						a__   )
    if prefix is None:
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = prefix or getattr(model.config ,						'''prefix''' ,						''''''   ) or ''''''
    for examples_chunk in tqdm(list(chunks(a__ ,						a__   )   )   ):
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = [prefix + text for text in examples_chunk]
        SCREAMING_SNAKE_CASE				:				List[str]				      = tokenizer(a__ ,						return_tensors='''pt''' ,						truncation=a__ ,						padding='''longest'''   ).to(a__   )
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = model.generate(
            input_ids=batch.input_ids ,						attention_mask=batch.attention_mask ,						**a__ ,						)
        SCREAMING_SNAKE_CASE				:				str				      = tokenizer.batch_decode(a__ ,						skip_special_tokens=a__ ,						clean_up_tokenization_spaces=a__   )
        for hypothesis in dec:
            fout.write(hypothesis + '''\n'''   )
            fout.flush()
    fout.close()
    SCREAMING_SNAKE_CASE				:				Any				      = int(time.time() - start_time   )  # seconds
    SCREAMING_SNAKE_CASE				:				List[Any]				      = len(a__   )
    return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,						4   )}







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''   )







def 				UpperCAmelCase_(    a__=True   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = argparse.ArgumentParser()
    parser.add_argument('''model_name''' ,						type=a__ ,						help='''like facebook/bart-large-cnn,t5-base, etc.'''   )
    parser.add_argument('''input_path''' ,						type=a__ ,						help='''like cnn_dm/test.source'''   )
    parser.add_argument('''save_path''' ,						type=a__ ,						help='''where to save summaries'''   )
    parser.add_argument('''--reference_path''' ,						type=a__ ,						required=a__ ,						help='''like cnn_dm/test.target'''   )
    parser.add_argument('''--score_path''' ,						type=a__ ,						required=a__ ,						default='''metrics.json''' ,						help='''where to save metrics'''   )
    parser.add_argument('''--device''' ,						type=a__ ,						required=a__ ,						default=a__ ,						help='''cuda, cuda:1, cpu etc.'''   )
    parser.add_argument(
        '''--prefix''' ,						type=a__ ,						required=a__ ,						default=a__ ,						help='''will be added to the begininng of src examples'''   )
    parser.add_argument('''--task''' ,						type=a__ ,						default='''summarization''' ,						help='''used for task_specific_params + metrics'''   )
    parser.add_argument('''--bs''' ,						type=a__ ,						default=8 ,						required=a__ ,						help='''batch size'''   )
    parser.add_argument(
        '''--n_obs''' ,						type=a__ ,						default=-1 ,						required=a__ ,						help='''How many observations. Defaults to all.'''   )
    parser.add_argument('''--fp16''' ,						action='''store_true'''   )
    parser.add_argument('''--dump-args''' ,						action='''store_true''' ,						help='''print the custom hparams with the results'''   )
    parser.add_argument(
        '''--info''' ,						nargs='''?''' ,						type=a__ ,						const=datetime_now() ,						help=(
            '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
            ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
        ) ,						)
    # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				str				      = parser.parse_known_args()
    SCREAMING_SNAKE_CASE				:				int				      = parse_numeric_n_bool_cl_kwargs(a__   )
    if parsed_args and verbose:
        print(F"""parsed the following generate kwargs: {parsed_args}"""   )
    with open(args.input_path   ) as f:
        SCREAMING_SNAKE_CASE				:				int				      = [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
    if args.n_obs > 0:
        SCREAMING_SNAKE_CASE				:				List[Any]				      = examples[: args.n_obs]
    Path(args.save_path   ).parent.mkdir(exist_ok=a__   )
    if args.reference_path is None and Path(args.score_path   ).exists():
        warnings.warn(F"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""   )
    SCREAMING_SNAKE_CASE				:				Any				      = generate_summaries_or_translations(
        a__ ,						args.save_path ,						args.model_name ,						batch_size=args.bs ,						device=args.device ,						fpaa=args.fpaa ,						task=args.task ,						prefix=args.prefix ,						**a__ ,						)

    if args.reference_path is None:
        return {}

    # Compute scores
    SCREAMING_SNAKE_CASE				:				Tuple				      = calculate_bleu if '''translation''' in args.task else calculate_rouge
    SCREAMING_SNAKE_CASE				:				Dict				      = [x.rstrip() for x in open(args.save_path   ).readlines()]
    SCREAMING_SNAKE_CASE				:				str				      = [x.rstrip() for x in open(args.reference_path   ).readlines()][: len(a__   )]
    SCREAMING_SNAKE_CASE				:				dict				      = score_fn(a__ ,						a__   )
    scores.update(a__   )

    if args.dump_args:
        scores.update(a__   )
    if args.info:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = args.info

    if verbose:
        print(a__   )

    if args.score_path is not None:
        json.dump(a__ ,						open(args.score_path ,						'''w'''   )   )

    return scores


if __name__ == "__main__":
      # Usage for MT:
      # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
      run_generate(verbose=True)


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
      raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
a__				:      Dict    		=     logging.get_logger(__name__)

a__				:      str    		=     '''Hello world! cécé herlolip'''







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				List[str]				      = os.path.split(a__   )
    SCREAMING_SNAKE_CASE				:				Any				      = DataaVecTextModel.from_pretrained(
        a__ ,						checkpoint_file=a__   )
    dataavec.eval()  # disable dropout
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec.models[0]
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_model.encoder.sentence_encoder
    SCREAMING_SNAKE_CASE				:				List[str]				      = DataaVecTextConfig(
        vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings ,						hidden_size=dataavec_model.args.encoder_embed_dim ,						num_hidden_layers=dataavec_model.args.encoder_layers ,						num_attention_heads=dataavec_model.args.encoder_attention_heads ,						intermediate_size=dataavec_model.args.encoder_ffn_embed_dim ,						max_position_embeddings=514 ,						type_vocab_size=1 ,						layer_norm_eps=1e-5 ,						)
    if classification_head:
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
    print('''Our BERT config:''' ,						a__   )

    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = DataaVecTextForSequenceClassification(a__   ) if classification_head else DataaVecTextForMaskedLM(a__   )
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec_sent_encoder.embed_tokens.weight
    SCREAMING_SNAKE_CASE				:				int				      = dataavec_sent_encoder.embed_positions.weight
    SCREAMING_SNAKE_CASE				:				int				      = torch.zeros_like(
        model.dataavec_text.embeddings.token_type_embeddings.weight   )  # just zero them out b/c data2vec doesn't use them.
    SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_sent_encoder.layernorm_embedding.weight
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_sent_encoder.layernorm_embedding.bias

    for i in range(config.num_hidden_layers   ):
        # Encoder: start of layer
        SCREAMING_SNAKE_CASE				:				BertLayer				      = model.dataavec_text.encoder.layer[i]
        SCREAMING_SNAKE_CASE				:				TransformerSentenceEncoderLayer				      = dataavec_sent_encoder.layers[i]

        # self attention
        SCREAMING_SNAKE_CASE				:				BertSelfAttention				      = layer.attention.self
        assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
            (config.hidden_size, config.hidden_size)   ), (
            "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
            F""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
        )
        assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
            (config.hidden_size, config.hidden_size)   ), (
            "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
            F""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
        )
        assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
            (config.hidden_size, config.hidden_size)   ), (
            "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
            F""" {torch.Size((config.hidden_size, config.hidden_size)   )}"""
        )

        SCREAMING_SNAKE_CASE				:				str				      = dataavec_layer.self_attn.q_proj.weight
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_layer.self_attn.q_proj.bias
        SCREAMING_SNAKE_CASE				:				str				      = dataavec_layer.self_attn.k_proj.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_layer.self_attn.k_proj.bias
        SCREAMING_SNAKE_CASE				:				List[Any]				      = dataavec_layer.self_attn.v_proj.weight
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.self_attn.v_proj.bias

        # self-attention output
        SCREAMING_SNAKE_CASE				:				BertSelfOutput				      = layer.attention.output
        assert (
            self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
        ), F"""Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}"""
        SCREAMING_SNAKE_CASE				:				List[Any]				      = dataavec_layer.self_attn.out_proj.weight
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_layer.self_attn.out_proj.bias
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.self_attn_layer_norm.weight
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.self_attn_layer_norm.bias

        # intermediate
        SCREAMING_SNAKE_CASE				:				BertIntermediate				      = layer.intermediate
        assert (
            intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
        ), F"""Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}"""
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.fca.weight
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_layer.fca.bias

        # output
        SCREAMING_SNAKE_CASE				:				BertOutput				      = layer.output
        assert (
            bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
        ), F"""Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}"""
        SCREAMING_SNAKE_CASE				:				int				      = dataavec_layer.fca.weight
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.fca.bias
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.final_layer_norm.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_layer.final_layer_norm.bias
        # end of layer

    if classification_head:
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''].dense.weight
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''].dense.bias
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec.model.classification_heads['''mnli'''].out_proj.weight
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''].out_proj.bias
    else:
        # LM Head
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_model.encoder.lm_head.dense.weight
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_model.encoder.lm_head.dense.bias
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_model.encoder.lm_head.layer_norm.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_model.encoder.lm_head.layer_norm.bias
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_model.encoder.lm_head.weight
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_model.encoder.lm_head.bias

    # Let's check that we get the same results.
    SCREAMING_SNAKE_CASE				:				torch.Tensor				      = dataavec.encode(a__   ).unsqueeze(0   )  # batch of size 1

    SCREAMING_SNAKE_CASE				:				List[str]				      = model(a__   )[0]
    if classification_head:
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(a__   )   )
    else:
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_model(a__   )[0]
    print(our_output.shape ,						their_output.shape   )
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.max(torch.abs(our_output - their_output   )   ).item()
    print(F"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.allclose(a__ ,						a__ ,						atol=1e-3   )
    print('''Do both models output the same tensors?''' ,						'''🔥''' if success else '''💩'''   )
    if not success:
        raise Exception('''Something went wRoNg'''   )

    pathlib.Path(a__   ).mkdir(parents=a__ ,						exist_ok=a__   )
    print(F"""Saving model to {pytorch_dump_folder_path}"""   )
    model.save_pretrained(a__   )


if __name__ == "__main__":
      a__				:      str    		=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      parser.add_argument(
          '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
      )
      a__				:      int    		=     parser.parse_args()
      convert_dataavec_checkpoint_to_pytorch(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )
