
"""simple docstring"""




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__   :						Any 							=     getLogger(__name__)


A__   :						str 							=     """cuda""" if torch.cuda.is_available() else """cpu"""



def 				a__			(					lowerCAmelCase						:							List[str]							,     lowerCAmelCase						:							str							,     lowerCAmelCase						:							str							,     lowerCAmelCase						:							int = 8							,     lowerCAmelCase						:							str = DEFAULT_DEVICE							,     lowerCAmelCase						:							Tuple=False							,     lowerCAmelCase						:							Optional[Any]="summarization"							,     lowerCAmelCase						:							Tuple=None							,     **lowerCAmelCase						:							Tuple							,     ):






     '''simple docstring'''
     UpperCAmelCase__				:			int							=    Path(lowerCAmelCase		).open("w"							,     encoding="utf-8"		)
     UpperCAmelCase__				:			Any							=    str(lowerCAmelCase		)
     UpperCAmelCase__				:			Dict							=    AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase		).to(lowerCAmelCase		)
     if fpaa:
          UpperCAmelCase__				:			Optional[int]							=    model.half()

     UpperCAmelCase__				:			str							=    AutoTokenizer.from_pretrained(lowerCAmelCase		)
     logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"		)  # if this is wrong, check config.model_type.

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



def 				a__			(					):






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



def 				a__			(					lowerCAmelCase						:							Optional[Any]=True		):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[int]							=    argparse.ArgumentParser()
     parser.add_argument("model_name"							,     type=lowerCAmelCase							,     help="like facebook/bart-large-cnn,t5-base, etc."		)
     parser.add_argument("input_path"							,     type=lowerCAmelCase							,     help="like cnn_dm/test.source"		)
     parser.add_argument("save_path"							,     type=lowerCAmelCase							,     help="where to save summaries"		)
     parser.add_argument("--reference_path"							,     type=lowerCAmelCase							,     required=lowerCAmelCase							,     help="like cnn_dm/test.target"		)
     parser.add_argument("--score_path"							,     type=lowerCAmelCase							,     required=lowerCAmelCase							,     default="metrics.json"							,     help="where to save metrics"		)
     parser.add_argument("--device"							,     type=lowerCAmelCase							,     required=lowerCAmelCase							,     default=lowerCAmelCase							,     help="cuda, cuda:1, cpu etc."		)
     parser.add_argument(
         "--prefix"							,     type=lowerCAmelCase							,     required=lowerCAmelCase							,     default=lowerCAmelCase							,     help="will be added to the begininng of src examples"		)
     parser.add_argument("--task"							,     type=lowerCAmelCase							,     default="summarization"							,     help="used for task_specific_params + metrics"		)
     parser.add_argument("--bs"							,     type=lowerCAmelCase							,     default=8							,     required=lowerCAmelCase							,     help="batch size"		)
     parser.add_argument(
         "--n_obs"							,     type=lowerCAmelCase							,     default=-1							,     required=lowerCAmelCase							,     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=lowerCAmelCase							,     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
     UpperCAmelCase__				,						UpperCAmelCase__				:			List[str]							=    parser.parse_known_args()
     UpperCAmelCase__				:			Union[str, Any]							=    parse_numeric_n_bool_cl_kwargs(lowerCAmelCase		)
     if parsed_args and verbose:
          print(F"parsed the following generate kwargs: {parsed_args}"		)
     with open(args.input_path		) as f:
          UpperCAmelCase__				:			Optional[int]							=    [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
     if args.n_obs > 0:
          UpperCAmelCase__				:			Union[str, Any]							=    examples[: args.n_obs]
     Path(args.save_path		).parent.mkdir(exist_ok=lowerCAmelCase		)
     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."		)
     UpperCAmelCase__				:			Union[str, Any]							=    generate_summaries_or_translations(
         lowerCAmelCase							,     args.save_path							,     args.model_name							,     batch_size=args.bs							,     device=args.device							,     fpaa=args.fpaa							,     task=args.task							,     prefix=args.prefix							,     **lowerCAmelCase							,     )

     if args.reference_path is None:
          return {}

     # Compute scores
     UpperCAmelCase__				:			Union[str, Any]							=    calculate_bleu if "translation" in args.task else calculate_rouge
     UpperCAmelCase__				:			int							=    [x.rstrip() for x in open(args.save_path		).readlines()]
     UpperCAmelCase__				:			Dict							=    [x.rstrip() for x in open(args.reference_path		).readlines()][: len(lowerCAmelCase		)]
     UpperCAmelCase__				:			dict							=    score_fn(lowerCAmelCase							,     lowerCAmelCase		)
     scores.update(lowerCAmelCase		)

     if args.dump_args:
          scores.update(lowerCAmelCase		)
     if args.info:
          UpperCAmelCase__				:			Union[str, Any]							=    args.info

     if verbose:
          print(lowerCAmelCase		)

     if args.score_path is not None:
          json.dump(lowerCAmelCase							,     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)





"""simple docstring"""




import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()



def 				a__			(					lowerCAmelCase						:							Optional[Any]							,     lowerCAmelCase						:							Dict							,     lowerCAmelCase						:							Tuple		):






     '''simple docstring'''
     # Initialise PyTorch model
     UpperCAmelCase__				:			int							=    FNetConfig.from_json_file(lowerCAmelCase		)
     print(F"Building PyTorch model from configuration: {config}"		)
     UpperCAmelCase__				:			Union[str, Any]							=    FNetForPreTraining(lowerCAmelCase		)

     UpperCAmelCase__				:			int							=    restore_checkpoint(lowerCAmelCase							,     lowerCAmelCase		)
     UpperCAmelCase__				:			List[str]							=    checkpoint_dict["target"]

     # Embeddings
     # Position IDs
     UpperCAmelCase__				:			Union[str, Any]							=    fnet_pretraining_model.state_dict()

     UpperCAmelCase__				:			Optional[Any]							=    state_dict["fnet.embeddings.position_ids"]
     UpperCAmelCase__				:			Dict							=    {"fnet.embeddings.position_ids": position_ids}
     # Embedding Layers
     UpperCAmelCase__				:			Dict							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]		)
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]		)
     UpperCAmelCase__				:			Tuple							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]		)
     UpperCAmelCase__				:			int							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]		).T
     UpperCAmelCase__				:			List[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]		)
     UpperCAmelCase__				:			Optional[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]		)
     UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]		)

     # Encoder Layers
     for layer in range(config.num_hidden_layers		):
          UpperCAmelCase__				:			Any							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["mixing_layer_norm"]["scale"]		)
          UpperCAmelCase__				:			List[str]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["mixing_layer_norm"]["bias"]		)

          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["intermediate"]["kernel"]		).T
          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["intermediate"]["bias"]		)

          UpperCAmelCase__				:			Dict							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["output"]["kernel"]		).T
          UpperCAmelCase__				:			List[str]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["output"]["bias"]		)

          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["output_layer_norm"]["scale"]		)
          UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["output_layer_norm"]["bias"]		)

     # Pooler Layers
     UpperCAmelCase__				:			Optional[Any]							=    torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]		).T
     UpperCAmelCase__				:			Optional[int]							=    torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]		)

     # Masked LM Layers
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["predictions_dense"]["kernel"]		).T
     UpperCAmelCase__				:			int							=    torch.tensor(
         pretrained_model_params["predictions_dense"]["bias"]		)
     UpperCAmelCase__				:			Tuple							=    torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["scale"]		)
     UpperCAmelCase__				:			List[str]							=    torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["bias"]		)
     UpperCAmelCase__				:			List[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]		)
     UpperCAmelCase__				:			Optional[int]							=    torch.tensor(
         pretrained_model_params["predictions_output"]["output_bias"]		)
     UpperCAmelCase__				:			Any							=    torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]		)

     # Seq Relationship Layers
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["classification"]["output_kernel"]		)
     UpperCAmelCase__				:			str							=    torch.tensor(
         pretrained_model_params["classification"]["output_bias"]		)

     # Load State Dict
     fnet_pretraining_model.load_state_dict(lowerCAmelCase		)

     # Save PreTrained
     print(F"Saving pretrained model to {save_path}"		)
     fnet_pretraining_model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
    A__   :						Union[str, Any] 							=     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--flax_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--fnet_config_file""",
        default=None,
        type=str,
        required=True,
        help=(
            """The config json file corresponding to the pre-trained FNet model. \n"""
            """This specifies the model architecture."""
        ),
    )
    parser.add_argument("""--save_path""", default=None, type=str, required=True, help="""Path to the output model.""")
    A__   :						Dict 							=     parser.parse_args()
    convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)



