


'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCAmelCase_ :     Any      							=      'src/diffusers'
UpperCAmelCase_ :     str      							=      '.'


# This is to make sure the diffusers module imported is the one in the repo.
UpperCAmelCase_ :     Dict      							=      importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(DIFFUSERS_PATH, '__init__.py'),
    submodule_search_locations=[DIFFUSERS_PATH],
)
UpperCAmelCase_ :     Dict      							=      spec.loader.load_module()


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					List[str]			):
 return line.startswith(_snake_case			) or len(_snake_case			) <= 1 or re.search(r'^\s*\)(\s*->.*:|:)\s*$',				_snake_case			) is not None


def 			_lowercase       (				UpperCamelCase__       :					Union[str, Any]			):
 __A  :		List[str]       =			object_name.split('.'			)
 __A  :		Tuple       =			0

 # First let's find the module where our object lives.
 __A  :		int       =			parts[i]
 while i < len(_snake_case			) and not os.path.isfile(os.path.join(_snake_case,				f"""{module}.py"""			)			):
  i += 1
  if i < len(_snake_case			):
   __A  :		Any       =			os.path.join(_snake_case,				parts[i]			)
 if i >= len(_snake_case			):
  raise ValueError(f"""`object_name` should begin with the name of a module of diffusers but got {object_name}."""			)

 with open(os.path.join(_snake_case,				f"""{module}.py"""			),				'r',				encoding='utf-8',				newline='\n'			) as f:
  __A  :		Union[str, Any]       =			f.readlines()

 # Now let's find the class / func in the code!
 __A  :		str       =			''
 __A  :		Optional[int]       =			0
 for name in parts[i + 1 :]:
  while (
      line_index < len(_snake_case			) and re.search(rf"""^{indent}(class|def)\s+{name}(\(|\:)""",				lines[line_index]			) is None
  ):
   line_index += 1
  indent += "    "
  line_index += 1

 if line_index >= len(_snake_case			):
  raise ValueError(f""" {object_name} does not match any function or class in {module}."""			)

 # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
 __A  :		int       =			line_index
 while line_index < len(_snake_case			) and _should_continue(lines[line_index],				_snake_case			):
  line_index += 1
 # Clean up empty lines at the end (if any).
 while len(lines[line_index - 1]			) <= 1:
  line_index -= 1

 __A  :		Dict       =			lines[start_index:line_index]
 return "".join(_snake_case			)


UpperCAmelCase_ :     str      							=      re.compile(R'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
UpperCAmelCase_ :     Dict      							=      re.compile(R'^\s*(\S+)->(\S+)(\s+.*|$)')
UpperCAmelCase_ :     Optional[Any]      							=      re.compile(R'<FILL\s+[^>]*>')


def 			_lowercase       (				UpperCamelCase__       :					Optional[int]			):
 __A  :		Optional[int]       =			code.split('\n'			)
 __A  :		int       =			0
 while idx < len(_snake_case			) and len(lines[idx]			) == 0:
  idx += 1
 if idx < len(_snake_case			):
  return re.search(r'^(\s*)\S',				lines[idx]			).groups()[0]
 return ""


def 			_lowercase       (				UpperCamelCase__       :					str			):
 __A  :		List[Any]       =			len(get_indent(_snake_case			)			) > 0
 if has_indent:
  __A  :		Any       =			f"""class Bla:\n{code}"""
 __A  :		List[str]       =			black.Mode(target_versions={black.TargetVersion.PYaa},				line_length=119,				preview=_snake_case			)
 __A  :		str       =			black.format_str(_snake_case,				mode=_snake_case			)
 __A     ,__A  :		Optional[Any]       =			style_docstrings_in_code(_snake_case			)
 return result[len('class Bla:\n'			) :] if has_indent else result


def 			_lowercase       (				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					Union[str, Any]=False			):
 with open(_snake_case,				'r',				encoding='utf-8',				newline='\n'			) as f:
  __A  :		str       =			f.readlines()
 __A  :		Union[str, Any]       =			[]
 __A  :		Optional[Any]       =			0
 # Not a for loop cause `lines` is going to change (if `overwrite=True`).
 while line_index < len(_snake_case			):
  __A  :		List[str]       =			_re_copy_warning.search(lines[line_index]			)
  if search is None:
   line_index += 1
   continue

  # There is some copied code here, let's retrieve the original.
  __A     ,__A     ,__A  :		int       =			search.groups()
  __A  :		Optional[Any]       =			find_code_in_diffusers(_snake_case			)
  __A  :		List[Any]       =			get_indent(_snake_case			)

  __A  :		List[str]       =			line_index + 1 if indent == theoretical_indent else line_index + 2
  __A  :		Any       =			theoretical_indent
  __A  :		List[Any]       =			start_index

  # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
  __A  :		int       =			True
  while line_index < len(_snake_case			) and should_continue:
   line_index += 1
   if line_index >= len(_snake_case			):
    break
   __A  :		Optional[Any]       =			lines[line_index]
   __A  :		Tuple       =			_should_continue(_snake_case,				_snake_case			) and re.search(f"""^{indent}# End copy""",				_snake_case			) is None
  # Clean up empty lines at the end (if any).
  while len(lines[line_index - 1]			) <= 1:
   line_index -= 1

  __A  :		int       =			lines[start_index:line_index]
  __A  :		int       =			''.join(_snake_case			)

  # Remove any nested `Copied from` comments to avoid circular copies
  __A  :		int       =			[line for line in theoretical_code.split('\n'			) if _re_copy_warning.search(_snake_case			) is None]
  __A  :		List[Any]       =			'\n'.join(_snake_case			)

  # Before comparing, use the `replace_pattern` on the original code.
  if len(_snake_case			) > 0:
   __A  :		Optional[Any]       =			replace_pattern.replace('with',				''			).split(','			)
   __A  :		Optional[int]       =			[_re_replace_pattern.search(_snake_case			) for p in patterns]
   for pattern in patterns:
    if pattern is None:
     continue
    __A     ,__A     ,__A  :		Dict       =			pattern.groups()
    __A  :		Optional[int]       =			re.sub(_snake_case,				_snake_case,				_snake_case			)
    if option.strip() == "all-casing":
     __A  :		List[str]       =			re.sub(obja.lower(),				obja.lower(),				_snake_case			)
     __A  :		Optional[int]       =			re.sub(obja.upper(),				obja.upper(),				_snake_case			)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
   __A  :		Union[str, Any]       =			blackify(lines[start_index - 1] + theoretical_code			)
   __A  :		str       =			theoretical_code[len(lines[start_index - 1]			) :]

  # Test for a diff and act accordingly.
  if observed_code != theoretical_code:
   diffs.append([object_name, start_index]			)
   if overwrite:
    __A  :		Dict       =			lines[:start_index] + [theoretical_code] + lines[line_index:]
    __A  :		List[str]       =			start_index + 1

 if overwrite and len(_snake_case			) > 0:
  # Warn the user a file has been modified.
  print(f"""Detected changes, rewriting {filename}."""			)
  with open(_snake_case,				'w',				encoding='utf-8',				newline='\n'			) as f:
   f.writelines(_snake_case			)
 return diffs


def 			_lowercase       (				UpperCamelCase__       :					str = False			):
 __A  :		Dict       =			glob.glob(os.path.join(_snake_case,				'**/*.py'			),				recursive=_snake_case			)
 __A  :		List[Any]       =			[]
 for filename in all_files:
  __A  :		Any       =			is_copy_consistent(_snake_case,				_snake_case			)
  diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
 if not overwrite and len(_snake_case			) > 0:
  __A  :		Dict       =			'\n'.join(_snake_case			)
  raise Exception(
      'Found the following copy inconsistencies:\n'
      + diff
      + '\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.'			)


if __name__ == "__main__":
 UpperCAmelCase_ :     Optional[Any]      							=      argparse.ArgumentParser()
 parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
 UpperCAmelCase_ :     Optional[Any]      							=      parser.parse_args()

 check_copies(args.fix_and_overwrite)










'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


UpperCAmelCase_ :     Dict								=      getLogger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					str,				UpperCamelCase__       :					str,				UpperCamelCase__       :					int = 8,				UpperCamelCase__       :					int = 1024,				UpperCamelCase__       :					List[Any]="val",				UpperCamelCase__       :					int=None,				UpperCamelCase__       :					str=False,				UpperCamelCase__       :					int="summarization",				UpperCamelCase__       :					List[Any]=None,				UpperCamelCase__       :					List[Any]=1,				UpperCamelCase__       :					Dict = None,				UpperCamelCase__       :					Optional[int]="",				**UpperCamelCase__       :					str,				):
 __A  :		Dict       =			str(UpperCamelCase__			)
 assert local_rank is not None
 torch.distributed.init_process_group(backend='nccl',				rank=UpperCamelCase__			)

 __A  :		Union[str, Any]       =			Path(UpperCamelCase__			)
 __A  :		Optional[int]       =			save_dir.joinpath(f"""rank_{local_rank}_output.json"""			)
 torch.cuda.set_device(UpperCamelCase__			)
 __A  :		Dict       =			AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__			).cuda()
 if fpaa:
  __A  :		Optional[Any]       =			model.half()
 # determine if we need to increase num_beams
 use_task_specific_params(UpperCamelCase__,				UpperCamelCase__			)  # update config with task specific params
 __A  :		Any       =			generate_kwargs.pop('num_beams',				model.config.num_beams			)  # AttributeError risk?
 if num_return_sequences > num_beams:
  __A  :		int       =			num_return_sequences

 __A  :		Optional[Any]       =			AutoTokenizer.from_pretrained(UpperCamelCase__			)
 logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""			)  # if this is wrong, check config.model_type.

 if max_source_length is None:
  __A  :		Union[str, Any]       =			tokenizer.model_max_length
 if prefix is None:
  __A  :		List[Any]       =			prefix or getattr(model.config,				'prefix',				''			) or ''
 __A  :		Tuple       =			SeqaSeqDataset(
     UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				max_target_length=1024,				type_path=UpperCamelCase__,				n_obs=UpperCamelCase__,				prefix=UpperCamelCase__,				**UpperCamelCase__,				)
 # I set shuffle=True for a more accurate progress bar.
 # If all the longest samples are first, the prog bar estimate is too high at the beginning.
 __A  :		Any       =			ds.make_sortish_sampler(UpperCamelCase__,				distributed=UpperCamelCase__,				add_extra_examples=UpperCamelCase__,				shuffle=UpperCamelCase__			)
 __A  :		Union[str, Any]       =			DataLoader(UpperCamelCase__,				sampler=UpperCamelCase__,				batch_size=UpperCamelCase__,				collate_fn=ds.collate_fn			)
 __A  :		Tuple       =			[]
 for batch in tqdm(UpperCamelCase__			):
  __A  :		Any       =			model.generate(
      input_ids=batch['input_ids'].to(model.device			),				attention_mask=batch['attention_mask'].to(model.device			),				num_return_sequences=UpperCamelCase__,				num_beams=UpperCamelCase__,				**UpperCamelCase__,				)
  __A  :		Dict       =			tokenizer.batch_decode(UpperCamelCase__,				skip_special_tokens=UpperCamelCase__,				clean_up_tokenization_spaces=UpperCamelCase__			)
  __A  :		List[str]       =			batch['ids']
  if num_return_sequences > 1:
   __A  :		str       =			chunks(UpperCamelCase__,				UpperCamelCase__			)  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(UpperCamelCase__			):
   results.append({'pred': pred, 'id': ids[i].item()}			)
 save_json(UpperCamelCase__,				UpperCamelCase__			)
 return results, sampler.num_replicas


def 			_lowercase       (				):
 __A  :		Optional[Any]       =			argparse.ArgumentParser(
     epilog='Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'			)
 parser.add_argument('--data_dir',				type=UpperCamelCase__,				help='like cnn_dm/test.source'			)
 parser.add_argument(
     '--model_name',				type=UpperCamelCase__,				help='like facebook/bart-large-cnn,t5-base, etc.',				default='sshleifer/distilbart-xsum-12-3',				)
 parser.add_argument('--save_dir',				type=UpperCamelCase__,				help='where to save',				default='tmp_gen'			)
 parser.add_argument('--max_source_length',				type=UpperCamelCase__,				default=UpperCamelCase__			)
 parser.add_argument(
     '--type_path',				type=UpperCamelCase__,				default='test',				help='which subset to evaluate typically train/val/test'			)
 parser.add_argument('--task',				type=UpperCamelCase__,				default='summarization',				help='used for task_specific_params + metrics'			)
 parser.add_argument('--bs',				type=UpperCamelCase__,				default=8,				required=UpperCamelCase__,				help='batch size'			)
 parser.add_argument(
     '--local_rank',				type=UpperCamelCase__,				default=-1,				required=UpperCamelCase__,				help='should be passed by distributed.launch'			)

 parser.add_argument(
     '--n_obs',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__,				help='How many observations. Defaults to all.'			)
 parser.add_argument(
     '--num_return_sequences',				type=UpperCamelCase__,				default=1,				required=UpperCamelCase__,				help='How many sequences to return'			)
 parser.add_argument(
     '--sync_timeout',				type=UpperCamelCase__,				default=600,				required=UpperCamelCase__,				help='How long should master process wait for other processes to finish.',				)
 parser.add_argument('--src_lang',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__			)
 parser.add_argument('--tgt_lang',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__			)
 parser.add_argument(
     '--prefix',				type=UpperCamelCase__,				required=UpperCamelCase__,				default=UpperCamelCase__,				help='will be added to the begininng of src examples'			)
 parser.add_argument('--fp16',				action='store_true'			)
 parser.add_argument('--debug',				action='store_true'			)
 __A  :		int       =			time.time()
 __A     ,__A  :		int       =			parser.parse_known_args()
 __A  :		List[str]       =			parse_numeric_n_bool_cl_kwargs(UpperCamelCase__			)
 if generate_kwargs and args.local_rank <= 0:
  print(f"""parsed the following generate kwargs: {generate_kwargs}"""			)
 __A  :		List[str]       =			Path(args.save_dir + '_tmp'			)
 Path(UpperCamelCase__			).mkdir(exist_ok=UpperCamelCase__			)  # this handles locking.
 __A  :		Optional[Any]       =			list(json_save_dir.glob('rank_*.json'			)			)
 if intermediate_files:
  raise ValueError(f"""Found files at {json_save_dir} please move or remove them."""			)
  # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
 __A  :		List[str]       =			{}
 if args.src_lang is not None:
  __A  :		Dict       =			args.src_lang
 if args.tgt_lang is not None:
  __A  :		Optional[Any]       =			args.tgt_lang

 Path(args.save_dir			).mkdir(exist_ok=UpperCamelCase__			)
 __A     ,__A  :		List[Any]       =			eval_data_dir(
     args.data_dir,				UpperCamelCase__,				args.model_name,				type_path=args.type_path,				bs=args.bs,				fpaa=args.fpaa,				task=args.task,				local_rank=args.local_rank,				n_obs=args.n_obs,				max_source_length=args.max_source_length,				num_return_sequences=args.num_return_sequences,				prefix=args.prefix,				dataset_kwargs=UpperCamelCase__,				**UpperCamelCase__,				)

 if args.local_rank <= 0:
  __A  :		Tuple       =			Path(args.save_dir			)
  save_dir.mkdir(exist_ok=UpperCamelCase__			)
  __A  :		Dict       =			gather_results_from_each_node(UpperCamelCase__,				UpperCamelCase__,				args.sync_timeout			)
  __A  :		Union[str, Any]       =			combine_partial_results(UpperCamelCase__			)
  if args.num_return_sequences > 1:
   __A  :		Any       =			save_dir.joinpath('pseudolabel_results.json'			)
   print(f"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"""			)
   save_json(UpperCamelCase__,				UpperCamelCase__			)
   return
  __A  :		int       =			Path(args.data_dir			).joinpath(args.type_path + '.target'			)
  with open(UpperCamelCase__			) as f:
   __A  :		str       =			[x.rstrip() for x in f.readlines()][: len(UpperCamelCase__			)]

  # Calculate metrics, save metrics,  and save _generations.txt
  __A  :		Tuple       =			'translation' in args.task
  __A  :		Union[str, Any]       =			calculate_bleu if calc_bleu else calculate_rouge
  __A  :		Optional[Any]       =			'bleu' if calc_bleu else 'rouge'
  __A  :		Dict       =			score_fn(UpperCamelCase__,				UpperCamelCase__			)
  __A  :		Any       =			len(UpperCamelCase__			)
  __A  :		Tuple       =			time.time() - start_time
  __A  :		Tuple       =			round(runtime / metrics['n_obs'],				4			)
  __A  :		int       =			num_replicas
  # TODO(@stas00): add whatever metadata to metrics
  __A  :		List[Any]       =			save_dir.joinpath(f"""{args.type_path}_{metric_name}.json"""			)
  save_json(UpperCamelCase__,				UpperCamelCase__,				indent=UpperCamelCase__			)
  print(UpperCamelCase__			)
  write_txt_file(UpperCamelCase__,				save_dir.joinpath(f"""{args.type_path}_generations.txt"""			)			)
  if args.debug:
   write_txt_file(UpperCamelCase__,				save_dir.joinpath(f"""{args.type_path}.target"""			)			)
  else:
   shutil.rmtree(UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					List[str]			):
 __A  :		List[str]       =			[]
 for partial_result in partial_results:
  records.extend(UpperCamelCase__			)
 __A  :		List[Any]       =			sorted(UpperCamelCase__,				key=lambda UpperCamelCase__			: x["id"]			)
 __A  :		Dict       =			[x['pred'] for x in records]
 return preds


def 			_lowercase       (				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					int			):
 # WAIT FOR lots of .json files
 __A  :		Dict       =			time.time()
 logger.info('waiting for all nodes to finish'			)
 __A  :		int       =			None
 while (time.time() - start_wait) < timeout:
  __A  :		List[Any]       =			list(save_dir.glob('rank_*.json'			)			)
  if len(UpperCamelCase__			) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   __A  :		List[Any]       =			lmap(UpperCamelCase__,				UpperCamelCase__			)
   return json_data
  except JSONDecodeError:
   continue
 else:
  raise TimeoutError('Rank 0 gave up on waiting for other processes'			)
 # Unreachable


if __name__ == "__main__":
 # Usage for MT:
 run_generate()






