"""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       			=      getLogger(__name__)


__A       			=      """cuda""" if torch.cuda.is_available() else """cpu"""

def 						UpperCamelCase				(	_lowerCAmelCase   :							List[str]				, _lowerCAmelCase   :							str				, _lowerCAmelCase   :							str				, _lowerCAmelCase   :							int = 8				, _lowerCAmelCase   :							str = DEFAULT_DEVICE				, _lowerCAmelCase   :							List[str]=False				, _lowerCAmelCase   :							Union[str, Any]="summarization"				, _lowerCAmelCase   :							int=None				, **_lowerCAmelCase   :							List[str]				, ):
    __a			      =						Path(_lowerCAmelCase						).open("""w"""				, encoding="""utf-8"""						)
    __a			      =						str(_lowerCAmelCase						)
    __a			      =						AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase						).to(_lowerCAmelCase						)
    if fpaa:
        __a			      =						model.half()

    __a			      =						AutoTokenizer.from_pretrained(_lowerCAmelCase						)
    logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""						)  # if this is wrong, check config.model_type.

    __a			      =						time.time()
    # update config with task specific params
    use_task_specific_params(_lowerCAmelCase				, _lowerCAmelCase						)
    if prefix is None:
        __a			      =						prefix or getattr(model.config				, """prefix"""				, """"""						) or """"""
    for examples_chunk in tqdm(list(chunks(_lowerCAmelCase				, _lowerCAmelCase						)						)						):
        __a			      =						[prefix + text for text in examples_chunk]
        __a			      =						tokenizer(_lowerCAmelCase				, return_tensors="""pt"""				, truncation=_lowerCAmelCase				, padding="""longest"""						).to(_lowerCAmelCase						)
        __a			      =						model.generate(
            input_ids=batch.input_ids				, attention_mask=batch.attention_mask				, **_lowerCAmelCase				, )
        __a			      =						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()
    __a			      =						int(time.time() - start_time						)  # seconds
    __a			      =						len(_lowerCAmelCase						)
    return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs				, 4						)}

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

def 						UpperCamelCase				(	_lowerCAmelCase   :							int=True						):
    __a			      =						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
    __a      ,  __a			      =						parser.parse_known_args()
    __a			      =						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:
        __a			      =						[""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in f.readlines()]
    if args.n_obs > 0:
        __a			      =						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."""						)
    __a			      =						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
    __a			      =						calculate_bleu if """translation""" in args.task else calculate_rouge
    __a			      =						[x.rstrip() for x in open(args.save_path						).readlines()]
    __a			      =						[x.rstrip() for x in open(args.reference_path						).readlines()][: len(_lowerCAmelCase						)]
    __a			      =						score_fn(_lowerCAmelCase				, _lowerCAmelCase						)
    scores.update(_lowerCAmelCase						)

    if args.dump_args:
        scores.update(_lowerCAmelCase						)
    if args.info:
        __a			      =						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 inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


__A       			=      [nn.LayerNorm]

__A       			=      logging.get_logger(__name__)

__A       			=      version.parse(version.parse(torch.__version__).base_version)

__A       			=      parsed_torch_version_base >= version.parse("""2.0""")
__A       			=      parsed_torch_version_base >= version.parse("""1.12""")
__A       			=      parsed_torch_version_base >= version.parse("""1.11""")
__A       			=      parsed_torch_version_base < version.parse("""1.11""")

def 						UpperCamelCase				(	_lowerCAmelCase   :							Tuple				, _lowerCAmelCase   :							List[str]				, _lowerCAmelCase   :							Union[str, Any]				, _lowerCAmelCase   :							int				, self   :							Optional[Any]						):
    from torch import _softmax_backward_data

    if is_torch_less_than_1_11:
        return _softmax_backward_data(_lowerCAmelCase				, _lowerCAmelCase				, parent.dim				, self						)
    else:
        return _softmax_backward_data(_lowerCAmelCase				, _lowerCAmelCase				, parent.dim				, self.dtype						)

def 						UpperCamelCase				(	_lowerCAmelCase   :							nn.Linear				, _lowerCAmelCase   :							torch.LongTensor				, _lowerCAmelCase   :							int = 0						):
    __a			      =						index.to(layer.weight.device						)
    __a			      =						layer.weight.index_select(_lowerCAmelCase				, _lowerCAmelCase						).clone().detach()
    if layer.bias is not None:
        if dim == 1:
            __a			      =						layer.bias.clone().detach()
        else:
            __a			      =						layer.bias[index].clone().detach()
    __a			      =						list(layer.weight.size()						)
    __a			      =						len(_lowerCAmelCase						)
    __a			      =						nn.Linear(new_size[1]				, new_size[0]				, bias=layer.bias is not None						).to(layer.weight.device						)
    __a			      =						False
    new_layer.weight.copy_(W.contiguous()						)
    __a			      =						True
    if layer.bias is not None:
        __a			      =						False
        new_layer.bias.copy_(b.contiguous()						)
        __a			      =						True
    return new_layer






class 					a					(				nn.Module							):



  def __init__(		self   :    Optional[int]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    List[Any]     )      -> List[str]:
      super().__init__()
      __a			      =						nf
      __a			      =						nn.Parameter(torch.empty(lowerCamelCase_						,  lowerCamelCase_     )     )
      __a			      =						nn.Parameter(torch.zeros(lowerCamelCase_     )     )
      nn.init.normal_(self.weight						,  std=0.02     )



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    Optional[Any]     )      -> Union[str, Any]:
      __a			      =						x.size()[:-1] + (self.nf,)
      __a			      =						torch.addmm(self.bias						,  x.view(-1						,  x.size(-1     )     )						,  self.weight     )
      __a			      =						x.view(lowerCamelCase_     )
      return x

def 						UpperCamelCase				(	_lowerCAmelCase   :							ConvaD				, _lowerCAmelCase   :							torch.LongTensor				, _lowerCAmelCase   :							int = 1						):
    __a			      =						index.to(layer.weight.device						)
    __a			      =						layer.weight.index_select(_lowerCAmelCase				, _lowerCAmelCase						).clone().detach()
    if dim == 0:
        __a			      =						layer.bias.clone().detach()
    else:
        __a			      =						layer.bias[index].clone().detach()
    __a			      =						list(layer.weight.size()						)
    __a			      =						len(_lowerCAmelCase						)
    __a			      =						ConvaD(new_size[1]				, new_size[0]						).to(layer.weight.device						)
    __a			      =						False
    new_layer.weight.copy_(W.contiguous()						)
    __a			      =						True
    __a			      =						False
    new_layer.bias.copy_(b.contiguous()						)
    __a			      =						True
    return new_layer

def 						UpperCamelCase				(	_lowerCAmelCase   :							Union[nn.Linear, ConvaD]				, _lowerCAmelCase   :							torch.LongTensor				, _lowerCAmelCase   :							Optional[int] = None						):
    if isinstance(_lowerCAmelCase				, nn.Linear						):
        return prune_linear_layer(_lowerCAmelCase				, _lowerCAmelCase				, dim=0 if dim is None else dim						)
    elif isinstance(_lowerCAmelCase				, _lowerCAmelCase						):
        return prune_convad_layer(_lowerCAmelCase				, _lowerCAmelCase				, dim=1 if dim is None else dim						)
    else:
        raise ValueError(f"""Can't prune layer of class {layer.__class__}"""						)

def 						UpperCamelCase				(	_lowerCAmelCase   :							Callable[..., torch.Tensor]				, _lowerCAmelCase   :							int				, _lowerCAmelCase   :							int				, *_lowerCAmelCase   :							List[Any]						):
    assert len(_lowerCAmelCase						) > 0, f"""{input_tensors} has to be a tuple/list of tensors"""

    # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
    __a			      =						len(inspect.signature(_lowerCAmelCase						).parameters						)
    if num_args_in_forward_chunk_fn != len(_lowerCAmelCase						):
        raise ValueError(
            f"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_lowerCAmelCase						)} input """
            """tensors are given"""						)

    if chunk_size > 0:
        __a			      =						input_tensors[0].shape[chunk_dim]
        for input_tensor in input_tensors:
            if input_tensor.shape[chunk_dim] != tensor_shape:
                raise ValueError(
                    f"""All input tenors have to be of the same shape: {tensor_shape}, """
                    f"""found shape {input_tensor.shape[chunk_dim]}"""						)

        if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
            raise ValueError(
                f"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
                f"""size {chunk_size}"""						)

        __a			      =						input_tensors[0].shape[chunk_dim] // chunk_size

        # chunk input tensor into tuples
        __a			      =						tuple(input_tensor.chunk(_lowerCAmelCase				, dim=_lowerCAmelCase						) for input_tensor in input_tensors						)
        # apply forward fn to every tuple
        __a			      =						tuple(forward_fn(*_lowerCAmelCase						) for input_tensors_chunk in zip(*_lowerCAmelCase						)						)
        # concatenate output at same dimension
        return torch.cat(_lowerCAmelCase				, dim=_lowerCAmelCase						)

    return forward_fn(*_lowerCAmelCase						)

def 						UpperCamelCase				(	_lowerCAmelCase   :							List[int]				, _lowerCAmelCase   :							int				, _lowerCAmelCase   :							int				, _lowerCAmelCase   :							Set[int]						):
    __a			      =						torch.ones(_lowerCAmelCase				, _lowerCAmelCase						)
    __a			      =						set(_lowerCAmelCase						) - already_pruned_heads  # Convert to set and remove already pruned heads
    for head in heads:
        # Compute how many pruned heads are before the head and move the index accordingly
        __a			      =						head - sum(1 if h < head else 0 for h in already_pruned_heads						)
        __a			      =						0
    __a			      =						mask.view(-1						).contiguous().eq(1						)
    __a			      =						torch.arange(len(_lowerCAmelCase						)						)[mask].long()
    return heads, index

def 						UpperCamelCase				(	*_lowerCAmelCase   :							Union[torch.Tensor, List[torch.Tensor]]				, _lowerCAmelCase   :							Optional[str] = None						):
    return torch.meshgrid(*_lowerCAmelCase				, indexing=_lowerCAmelCase						)






def 						UpperCamelCase				(	_lowerCAmelCase   :							torch.Tensor						):
    return tensor.device, storage_ptr(_lowerCAmelCase						), storage_size(_lowerCAmelCase						)
