






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase_ :						int      =      logging.get_logger(__name__)


if is_vision_available():
	import PIL

class     _SCREAMING_SNAKE_CASE       (			__lowercase      ):
	snake_case__							:     Union[str, Any]     	=  ["pixel_values"]





	def __init__(		self				:					Any ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					Dict[str, int] = None ,  __lowerCamelCase				:					PILImageResampling = PILImageResampling.BICUBIC ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					Dict[str, int] = None ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					Union[int, float] = 1 / 255 ,  __lowerCamelCase				:					bool = True ,  __lowerCamelCase				:					Optional[Union[float, List[float]]] = None ,  __lowerCamelCase				:					Optional[Union[float, List[float]]] = None ,  __lowerCamelCase				:					bool = True ,  **__lowerCamelCase				:					Dict ,  ):
		super().__init__(**snake_case_	)
		UpperCamelCase     :Optional[Any]          =	size if size is not None else {'''shortest_edge''': 224}
		UpperCamelCase     :List[str]          =	get_size_dict(snake_case_ ,  default_to_square=snake_case_	)
		UpperCamelCase     :Any          =	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
		UpperCamelCase     :Tuple          =	get_size_dict(snake_case_ ,  default_to_square=snake_case_ ,  param_name="""crop_size"""	)

		UpperCamelCase     :int          =	do_resize
		UpperCamelCase     :str          =	size
		UpperCamelCase     :List[Any]          =	resample
		UpperCamelCase     :Optional[Any]          =	do_center_crop
		UpperCamelCase     :Union[str, Any]          =	crop_size
		UpperCamelCase     :str          =	do_rescale
		UpperCamelCase     :int          =	rescale_factor
		UpperCamelCase     :List[Any]          =	do_normalize
		UpperCamelCase     :int          =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
		UpperCamelCase     :Optional[int]          =	image_std if image_std is not None else OPENAI_CLIP_STD
		UpperCamelCase     :str          =	do_convert_rgb





	def 						_A (		self				:					Tuple ,  __lowerCamelCase				:					np.ndarray ,  __lowerCamelCase				:					Dict[str, int] ,  __lowerCamelCase				:					PILImageResampling = PILImageResampling.BICUBIC ,  __lowerCamelCase				:					Optional[Union[str, ChannelDimension]] = None ,  **__lowerCamelCase				:					Optional[int] ,  ):
		UpperCamelCase     :Any          =	get_size_dict(snake_case_ ,  default_to_square=snake_case_	)
		if "shortest_edge" not in size:
			raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""	)
		UpperCamelCase     :Dict          =	get_resize_output_image_size(snake_case_ ,  size=size["""shortest_edge"""] ,  default_to_square=snake_case_	)
		return resize(snake_case_ ,  size=snake_case_ ,  resample=snake_case_ ,  data_format=snake_case_ ,  **snake_case_	)





	def 						_A (		self				:					Optional[int] ,  __lowerCamelCase				:					np.ndarray ,  __lowerCamelCase				:					Dict[str, int] ,  __lowerCamelCase				:					Optional[Union[str, ChannelDimension]] = None ,  **__lowerCamelCase				:					Tuple ,  ):
		UpperCamelCase     :int          =	get_size_dict(snake_case_	)
		if "height" not in size or "width" not in size:
			raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""	)
		return center_crop(snake_case_ ,  size=(size["""height"""], size["""width"""]) ,  data_format=snake_case_ ,  **snake_case_	)





	def 						_A (		self				:					str ,  __lowerCamelCase				:					np.ndarray ,  __lowerCamelCase				:					Union[int, float] ,  __lowerCamelCase				:					Optional[Union[str, ChannelDimension]] = None ,  **__lowerCamelCase				:					Optional[int] ,  ):
		return rescale(snake_case_ ,  scale=snake_case_ ,  data_format=snake_case_ ,  **snake_case_	)





	def 						_A (		self				:					int ,  __lowerCamelCase				:					np.ndarray ,  __lowerCamelCase				:					Union[float, List[float]] ,  __lowerCamelCase				:					Union[float, List[float]] ,  __lowerCamelCase				:					Optional[Union[str, ChannelDimension]] = None ,  **__lowerCamelCase				:					List[Any] ,  ):
		return normalize(snake_case_ ,  mean=snake_case_ ,  std=snake_case_ ,  data_format=snake_case_ ,  **snake_case_	)





	def 						_A (		self				:					str ,  __lowerCamelCase				:					ImageInput ,  __lowerCamelCase				:					bool = None ,  __lowerCamelCase				:					Dict[str, int] = None ,  __lowerCamelCase				:					PILImageResampling = None ,  __lowerCamelCase				:					bool = None ,  __lowerCamelCase				:					int = None ,  __lowerCamelCase				:					bool = None ,  __lowerCamelCase				:					float = None ,  __lowerCamelCase				:					bool = None ,  __lowerCamelCase				:					Optional[Union[float, List[float]]] = None ,  __lowerCamelCase				:					Optional[Union[float, List[float]]] = None ,  __lowerCamelCase				:					bool = None ,  __lowerCamelCase				:					Optional[Union[str, TensorType]] = None ,  __lowerCamelCase				:					Optional[ChannelDimension] = ChannelDimension.FIRST ,  **__lowerCamelCase				:					str ,  ):
		UpperCamelCase     :str          =	do_resize if do_resize is not None else self.do_resize
		UpperCamelCase     :List[Any]          =	size if size is not None else self.size
		UpperCamelCase     :Optional[int]          =	get_size_dict(snake_case_ ,  param_name="""size""" ,  default_to_square=snake_case_	)
		UpperCamelCase     :Any          =	resample if resample is not None else self.resample
		UpperCamelCase     :Dict          =	do_center_crop if do_center_crop is not None else self.do_center_crop
		UpperCamelCase     :Dict          =	crop_size if crop_size is not None else self.crop_size
		UpperCamelCase     :List[str]          =	get_size_dict(snake_case_ ,  param_name="""crop_size""" ,  default_to_square=snake_case_	)
		UpperCamelCase     :Optional[Any]          =	do_rescale if do_rescale is not None else self.do_rescale
		UpperCamelCase     :Optional[int]          =	rescale_factor if rescale_factor is not None else self.rescale_factor
		UpperCamelCase     :Optional[Any]          =	do_normalize if do_normalize is not None else self.do_normalize
		UpperCamelCase     :Optional[int]          =	image_mean if image_mean is not None else self.image_mean
		UpperCamelCase     :str          =	image_std if image_std is not None else self.image_std
		UpperCamelCase     :str          =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

		UpperCamelCase     :Optional[int]          =	make_list_of_images(snake_case_	)

		if not valid_images(snake_case_	):
			raise ValueError(
			    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
			    """torch.Tensor, tf.Tensor or jax.ndarray."""	)

		if do_resize and size is None:
			raise ValueError("""Size must be specified if do_resize is True."""	)

		if do_center_crop and crop_size is None:
			raise ValueError("""Crop size must be specified if do_center_crop is True."""	)

		if do_rescale and rescale_factor is None:
			raise ValueError("""Rescale factor must be specified if do_rescale is True."""	)

		if do_normalize and (image_mean is None or image_std is None):
			raise ValueError("""Image mean and std must be specified if do_normalize is True."""	)

		# PIL RGBA images are converted to RGB
		if do_convert_rgb:
			UpperCamelCase     :Optional[int]          =	[convert_to_rgb(snake_case_	) for image in images]

		# All transformations expect numpy arrays.
		UpperCamelCase     :List[Any]          =	[to_numpy_array(snake_case_	) for image in images]

		if do_resize:
			UpperCamelCase     :Optional[int]          =	[self.resize(image=snake_case_ ,  size=snake_case_ ,  resample=snake_case_	) for image in images]

		if do_center_crop:
			UpperCamelCase     :int          =	[self.center_crop(image=snake_case_ ,  size=snake_case_	) for image in images]

		if do_rescale:
			UpperCamelCase     :Tuple          =	[self.rescale(image=snake_case_ ,  scale=snake_case_	) for image in images]

		if do_normalize:
			UpperCamelCase     :List[str]          =	[self.normalize(image=snake_case_ ,  mean=snake_case_ ,  std=snake_case_	) for image in images]

		UpperCamelCase     :List[Any]          =	[to_channel_dimension_format(snake_case_ ,  snake_case_	) for image in images]

		UpperCamelCase     :str          =	{'''pixel_values''': images}
		return BatchFeature(data=snake_case_ ,  tensor_type=snake_case_	)


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,
)


lowercase__       =getLogger(__name__)




def    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				int = 8  ,   lowerCAmelCase__							:				int = 1_0_2_4  ,   lowerCAmelCase__							:				Union[str, Any]="val"  ,   lowerCAmelCase__							:				int=None  ,   lowerCAmelCase__							:				Any=False  ,   lowerCAmelCase__							:				Union[str, Any]="summarization"  ,   lowerCAmelCase__							:				Tuple=None  ,   lowerCAmelCase__							:				Optional[int]=1  ,   lowerCAmelCase__							:				Dict = None  ,   lowerCAmelCase__							:				int=""  ,   **lowerCAmelCase__							:				int  ,   ):
 __a :  List[Any]						=  str(lowerCAmelCase__  )
 assert local_rank is not None
 torch.distributed.init_process_group(backend='''nccl'''  ,   rank=lowerCAmelCase__  )

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

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

 if max_source_length is None:
  __a :  Dict						=  tokenizer.model_max_length
 if prefix is None:
  __a :  Dict						=  prefix or getattr(model.config  ,   '''prefix'''  ,   ''''''  ) or ''''''
 __a :  List[Any]						=  SeqaSeqDataset(
     lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   max_target_length=1_0_2_4  ,   type_path=lowerCAmelCase__  ,   n_obs=lowerCAmelCase__  ,   prefix=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )
 # 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 :  Tuple						=  ds.make_sortish_sampler(lowerCAmelCase__  ,   distributed=lowerCAmelCase__  ,   add_extra_examples=lowerCAmelCase__  ,   shuffle=lowerCAmelCase__  )
 __a :  List[Any]						=  DataLoader(lowerCAmelCase__  ,   sampler=lowerCAmelCase__  ,   batch_size=lowerCAmelCase__  ,   collate_fn=ds.collate_fn  )
 __a :  List[Any]						=  []
 for batch in tqdm(lowerCAmelCase__  ):
  __a :  Any						=  model.generate(
      input_ids=batch['''input_ids'''].to(model.device  )  ,   attention_mask=batch['''attention_mask'''].to(model.device  )  ,   num_return_sequences=lowerCAmelCase__  ,   num_beams=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )
  __a :  List[Any]						=  tokenizer.batch_decode(lowerCAmelCase__  ,   skip_special_tokens=lowerCAmelCase__  ,   clean_up_tokenization_spaces=lowerCAmelCase__  )
  __a :  int						=  batch['''ids''']
  if num_return_sequences > 1:
   __a :  List[str]						=  chunks(lowerCAmelCase__  ,   lowerCAmelCase__  )  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(lowerCAmelCase__  ):
   results.append({'''pred''': pred, '''id''': ids[i].item()}  )
 save_json(lowerCAmelCase__  ,   lowerCAmelCase__  )
 return results, sampler.num_replicas




def    __UpperCamelCase     (				):
 __a :  str						=  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=lowerCAmelCase__  ,   help='''like cnn_dm/test.source'''  )
 parser.add_argument(
     '''--model_name'''  ,   type=lowerCAmelCase__  ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''  ,   default='''sshleifer/distilbart-xsum-12-3'''  ,   )
 parser.add_argument('''--save_dir'''  ,   type=lowerCAmelCase__  ,   help='''where to save'''  ,   default='''tmp_gen'''  )
 parser.add_argument('''--max_source_length'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  )
 parser.add_argument(
     '''--type_path'''  ,   type=lowerCAmelCase__  ,   default='''test'''  ,   help='''which subset to evaluate typically train/val/test'''  )
 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(
     '''--local_rank'''  ,   type=lowerCAmelCase__  ,   default=-1  ,   required=lowerCAmelCase__  ,   help='''should be passed by distributed.launch'''  )

 parser.add_argument(
     '''--n_obs'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  ,   help='''How many observations. Defaults to all.'''  )
 parser.add_argument(
     '''--num_return_sequences'''  ,   type=lowerCAmelCase__  ,   default=1  ,   required=lowerCAmelCase__  ,   help='''How many sequences to return'''  )
 parser.add_argument(
     '''--sync_timeout'''  ,   type=lowerCAmelCase__  ,   default=6_0_0  ,   required=lowerCAmelCase__  ,   help='''How long should master process wait for other processes to finish.'''  ,   )
 parser.add_argument('''--src_lang'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  )
 parser.add_argument('''--tgt_lang'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  )
 parser.add_argument(
     '''--prefix'''  ,   type=lowerCAmelCase__  ,   required=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   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 :  Tuple						=  parser.parse_known_args()
 __a :  Optional[int]						=  parse_numeric_n_bool_cl_kwargs(lowerCAmelCase__  )
 if generate_kwargs and args.local_rank <= 0:
  print(f"parsed the following generate kwargs: {generate_kwargs}"  )
 __a :  Union[str, Any]						=  Path(args.save_dir + '''_tmp'''  )
 Path(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )  # this handles locking.
 __a :  Dict						=  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 :  Optional[Any]						=  {}
 if args.src_lang is not None:
  __a :  int						=  args.src_lang
 if args.tgt_lang is not None:
  __a :  Optional[Any]						=  args.tgt_lang

 Path(args.save_dir  ).mkdir(exist_ok=lowerCAmelCase__  )
 __a				,       __a :  Tuple						=  eval_data_dir(
     args.data_dir  ,   lowerCAmelCase__  ,   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=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )

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

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




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[int]  ):
 __a :  Optional[int]						=  []
 for partial_result in partial_results:
  records.extend(lowerCAmelCase__  )
 __a :  Tuple						=  sorted(lowerCAmelCase__  ,   key=lambda lowerCAmelCase__  : x["id"]  )
 __a :  Tuple						=  [x['''pred'''] for x in records]
 return preds




def    __UpperCamelCase     (				lowerCAmelCase__							:				Any  ,   lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				Dict  ):
 # WAIT FOR lots of .json files
 __a :  Tuple						=  time.time()
 logger.info('''waiting for all nodes to finish'''  )
 __a :  Optional[int]						=  None
 while (time.time() - start_wait) < timeout:
  __a :  Optional[int]						=  list(save_dir.glob('''rank_*.json'''  )  )
  if len(lowerCAmelCase__  ) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   __a :  Tuple						=  lmap(lowerCAmelCase__  ,   lowerCAmelCase__  )
   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()
