
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


lowercase_											=	getLogger(__name__)


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


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





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					List[str]			=						Path(snake_case__    ).open('''w'''   ,	encoding='''utf-8'''    )
  __SCREAMING_SNAKE_CASE     :					Tuple			=						str(snake_case__    )
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						AutoModelForSeqaSeqLM.from_pretrained(snake_case__    ).to(snake_case__    )
  if fpaa:
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						model.half()

  __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						AutoTokenizer.from_pretrained(snake_case__    )
  logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''    )  # if this is wrong, check config.model_type.

  __SCREAMING_SNAKE_CASE     :					Tuple			=						time.time()
  # update config with task specific params
  use_task_specific_params(snake_case__   ,	snake_case__    )
  if prefix is None:
    __SCREAMING_SNAKE_CASE     :					str			=						prefix or getattr(model.config   ,	'''prefix'''   ,	''''''    ) or ''''''
  for examples_chunk in tqdm(list(chunks(snake_case__   ,	snake_case__    )    )    ):
    __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						[prefix + text for text in examples_chunk]
    __SCREAMING_SNAKE_CASE     :					Any			=						tokenizer(snake_case__   ,	return_tensors='''pt'''   ,	truncation=snake_case__   ,	padding='''longest'''    ).to(snake_case__    )
    __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						model.generate(
        input_ids=batch.input_ids   ,	attention_mask=batch.attention_mask   ,	**snake_case__   ,	)
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						tokenizer.batch_decode(snake_case__   ,	skip_special_tokens=snake_case__   ,	clean_up_tokenization_spaces=snake_case__    )
    for hypothesis in dec:
      fout.write(hypothesis + '''\n'''    )
      fout.flush()
  fout.close()
  __SCREAMING_SNAKE_CASE     :					Dict			=						int(time.time() - start_time    )  # seconds
  __SCREAMING_SNAKE_CASE     :					Dict			=						len(snake_case__    )
  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__			(    snake_case=True    ):





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

  if args.reference_path is None:
    return {}

  # Compute scores
  __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						calculate_bleu if '''translation''' in args.task else calculate_rouge
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						[x.rstrip() for x in open(args.save_path    ).readlines()]
  __SCREAMING_SNAKE_CASE     :					Dict			=						[x.rstrip() for x in open(args.reference_path    ).readlines()][: len(snake_case__    )]
  __SCREAMING_SNAKE_CASE     :					Tuple			=						score_fn(snake_case__   ,	snake_case__    )
  scores.update(snake_case__    )

  if args.dump_args:
    scores.update(snake_case__    )
  if args.info:
    __SCREAMING_SNAKE_CASE     :					str			=						args.info

  if verbose:
    print(snake_case__    )

  if args.score_path is not None:
    json.dump(snake_case__   ,	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 re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowercase_											=	"""src/transformers"""

# Matches is_xxx_available()
lowercase_											=	re.compile(R"""is\_([a-z_]*)_available()""")
# Matches from xxx import bla
lowercase_											=	re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
lowercase_											=	re.compile(R"""^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)""")


lowercase_											=	"""
{0} = None
"""

lowercase_											=	"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


lowercase_											=	"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""


def     a__			(    snake_case    ):





  """simple docstring"""
  if _re_test_backend.search(snake_case    ) is None:
    return None
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						[b[0] for b in _re_backend.findall(snake_case    )]
  backends.sort()
  return "_and_".join(snake_case    )


def     a__			(    ):





  """simple docstring"""
  with open(os.path.join(snake_case   ,	'''__init__.py'''    )   ,	'''r'''   ,	encoding='''utf-8'''   ,	newline='''\n'''    ) as f:
    __SCREAMING_SNAKE_CASE     :					Tuple			=						f.readlines()

  # Get to the point we do the actual imports for type checking
  __SCREAMING_SNAKE_CASE     :					Any			=						0
  while not lines[line_index].startswith('''if TYPE_CHECKING'''    ):
    line_index += 1

  __SCREAMING_SNAKE_CASE     :					Tuple			=						{}
  # Go through the end of the file
  while line_index < len(snake_case    ):
    # If the line is an if is_backend_available, we grab all objects associated.
    __SCREAMING_SNAKE_CASE     :					int			=						find_backend(lines[line_index]    )
    if backend is not None:
      while not lines[line_index].startswith('''    else:'''    ):
        line_index += 1
      line_index += 1

      __SCREAMING_SNAKE_CASE     :					Any			=						[]
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]    ) <= 1 or lines[line_index].startswith(''' ''' * 8    ):
        __SCREAMING_SNAKE_CASE     :					Dict			=						lines[line_index]
        __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						_re_single_line_import.search(snake_case    )
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(''', '''    )    )
        elif line.startswith(''' ''' * 12    ):
          objects.append(line[12:-2]    )
        line_index += 1

      __SCREAMING_SNAKE_CASE     :					List[str]			=						objects
    else:
      line_index += 1

  return backend_specific_objects


def     a__			(    snake_case   ,	snake_case    ):





  """simple docstring"""
  if name.isupper():
    return DUMMY_CONSTANT.format(snake_case    )
  elif name.islower():
    return DUMMY_FUNCTION.format(snake_case   ,	snake_case    )
  else:
    return DUMMY_CLASS.format(snake_case   ,	snake_case    )


def     a__			(    snake_case=None    ):





  """simple docstring"""
  if backend_specific_objects is None:
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						read_init()
  # For special correspondence backend to module name as used in the function requires_modulename
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						{}

  for backend, objects in backend_specific_objects.items():
    __SCREAMING_SNAKE_CASE     :					Tuple			=						'''[''' + ''', '''.join(F'''"{b}"''' for b in backend.split('''_and_'''    )    ) + ''']'''
    __SCREAMING_SNAKE_CASE     :					Optional[int]			=						'''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
    dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
    dummy_file += "\n".join([create_dummy_object(snake_case   ,	snake_case    ) for o in objects]    )
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						dummy_file

  return dummy_files


def     a__			(    snake_case=False    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						create_dummy_files()
  # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  __SCREAMING_SNAKE_CASE     :					str			=						{'''torch''': '''pt'''}

  # Locate actual dummy modules and read their content.
  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						os.path.join(snake_case   ,	'''utils'''    )
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						{
      backend: os.path.join(snake_case   ,	F'''dummy_{short_names.get(snake_case   ,	snake_case    )}_objects.py'''    )
      for backend in dummy_files.keys()
  }

  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						{}
  for backend, file_path in dummy_file_paths.items():
    if os.path.isfile(snake_case    ):
      with open(snake_case   ,	'''r'''   ,	encoding='''utf-8'''   ,	newline='''\n'''    ) as f:
        __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						f.read()
    else:
      __SCREAMING_SNAKE_CASE     :					Any			=						''''''

  for backend in dummy_files.keys():
    if dummy_files[backend] != actual_dummies[backend]:
      if overwrite:
        print(
            F'''Updating transformers.utils.dummy_{short_names.get(snake_case   ,	snake_case    )}_objects.py as the main '''
            '''__init__ has new objects.'''    )
        with open(dummy_file_paths[backend]   ,	'''w'''   ,	encoding='''utf-8'''   ,	newline='''\n'''    ) as f:
          f.write(dummy_files[backend]    )
      else:
        raise ValueError(
            '''The main __init__ has objects that are not present in '''
            F'''transformers.utils.dummy_{short_names.get(snake_case   ,	snake_case    )}_objects.py. Run `make fix-copies` '''
            '''to fix this.'''    )


if __name__ == "__main__":
   lowercase_											=	argparse.ArgumentParser()
   parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
   lowercase_											=	parser.parse_args()

   check_dummies(args.fix_and_overwrite)
