

from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
			import PIL

if is_torch_available():
			import torch


a_     =			logging.get_logger(__name__)






class _UpperCamelCase							(							UpperCamelCase__      ):
			'''simple docstring'''







			lowerCamelCase__							=['pixel_values']
			def __init__(	self							:       str					,					a							:       List[Any] = True					,					a							:       Optional[int] = None					,					a							:       Dict = PILImageResampling.BILINEAR					,					a							:       Dict = True					,					a							:       Optional[Any] = 1 / 255					,					a							:       Any = True					,					a							:       List[str] = None					,					a							:       List[str] = True					,					**a							:       List[Any]					,					)							->				None:

				"""simple docstring"""

				super().__init__(**_a		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				size if size is not None else {"""shortest_edge""": 224}
				SCREAMING_SNAKE_CASE      :					int    =				get_size_dict(_a					,					default_to_square=_a		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
				SCREAMING_SNAKE_CASE      :					str    =				get_size_dict(_a					,					param_name="crop_size"		)

				SCREAMING_SNAKE_CASE      :					List[Any]    =				do_resize
				SCREAMING_SNAKE_CASE      :					Dict    =				size
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				resample
				SCREAMING_SNAKE_CASE      :					int    =				do_rescale
				SCREAMING_SNAKE_CASE      :					Any    =				rescale_factor
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				do_center_crop
				SCREAMING_SNAKE_CASE      :					str    =				crop_size
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				do_flip_channel_order
			def 							__UpperCamelCase       (	self							:       int					,					a							:       Tuple					,					a							:       Dict					,					a							:       Dict = PIL.Image.BILINEAR					,					a							:       Optional[Any] = None					,					**a							:       Optional[Any]					,					)							->				np.ndarray:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				get_size_dict(_a					,					default_to_square=_a		)
				if "shortest_edge" not in size:
					raise ValueError(F"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				get_resize_output_image_size(_a					,					size=size["shortest_edge"]					,					default_to_square=_a		)
				return resize(_a					,					size=_a					,					resample=_a					,					data_format=_a					,					**_a		)
			def 							__UpperCamelCase       (	self							:       Any					,					a							:       int					,					a							:       List[str]					,					a							:       int = None					,					**a							:       Optional[int]					,					)							->				np.ndarray:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				get_size_dict(_a		)
				if "height" not in size or "width" not in size:
					raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"		)
				return center_crop(_a					,					size=(size["height"], size["width"])					,					data_format=_a					,					**_a		)
			def 							__UpperCamelCase       (	self							:       List[Any]					,					a							:       Optional[Any]					,					a							:       Dict					,					a							:       List[Any] = None					,					**a							:       Optional[int]					,					)							->				List[Any]:

				"""simple docstring"""

				return rescale(_a					,					scale=_a					,					data_format=_a					,					**_a		)
			def 							__UpperCamelCase       (	self							:       Optional[int]					,					a							:       List[Any]					,					a							:       Union[str, Any] = None		)							->				np.ndarray:

				"""simple docstring"""

				return flip_channel_order(_a					,					data_format=_a		)
			def 							__UpperCamelCase       (	self							:       Dict					,					a							:       Tuple					,					a							:       List[Any] = None					,					a							:       List[Any] = None					,					a							:       Optional[int] = None					,					a							:       List[Any] = None					,					a							:       Optional[Any] = None					,					a							:       List[str] = None					,					a							:       Union[str, Any] = None					,					a							:       int = None					,					a							:       int = None					,					a							:       Union[str, Any] = ChannelDimension.FIRST					,					**a							:       List[str]					,					)							->				PIL.Image.Image:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				do_resize if do_resize is not None else self.do_resize
				SCREAMING_SNAKE_CASE      :					List[str]    =				resample if resample is not None else self.resample
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				do_rescale if do_rescale is not None else self.do_rescale
				SCREAMING_SNAKE_CASE      :					Tuple    =				rescale_factor if rescale_factor is not None else self.rescale_factor
				SCREAMING_SNAKE_CASE      :					Any    =				do_center_crop if do_center_crop is not None else self.do_center_crop
				SCREAMING_SNAKE_CASE      :					List[Any]    =				(
				    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
				)

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				size if size is not None else self.size
				SCREAMING_SNAKE_CASE      :					List[str]    =				get_size_dict(_a					,					default_to_square=_a		)
				SCREAMING_SNAKE_CASE      :					Any    =				crop_size if crop_size is not None else self.crop_size
				SCREAMING_SNAKE_CASE      :					Dict    =				get_size_dict(_a					,					param_name="crop_size"		)

				SCREAMING_SNAKE_CASE      :					int    =				make_list_of_images(_a		)

				if not valid_images(_a		):
					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_rescale and rescale_factor is None:
					raise ValueError("Rescale factor must be specified if do_rescale is True."		)

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

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

				if do_resize:
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				[self.resize(image=_a					,					size=_a					,					resample=_a		) for image in images]

				if do_center_crop:
					SCREAMING_SNAKE_CASE      :					str    =				[self.center_crop(image=_a					,					size=_a		) for image in images]

				if do_rescale:
					SCREAMING_SNAKE_CASE      :					List[Any]    =				[self.rescale(image=_a					,					scale=_a		) for image in images]

				# the pretrained checkpoints assume images are BGR, not RGB
				if do_flip_channel_order:
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				[self.flip_channel_order(image=_a		) for image in images]

				SCREAMING_SNAKE_CASE      :					List[str]    =				[to_channel_dimension_format(_a					,					_a		) for image in images]

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				{"""pixel_values""": images}
				return BatchFeature(data=_a					,					tensor_type=_a		)






			def 							__UpperCamelCase       (	self							:       Any					,					a							:       Optional[Any]					,					a							:       Tuple = None		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				outputs.logits

				# Resize logits and compute semantic segmentation maps
				if target_sizes is not None:
					if len(_a		) != len(_a		):
						raise ValueError(
						    "Make sure that you pass in as many target sizes as the batch dimension of the logits"		)

					if is_torch_tensor(_a		):
						SCREAMING_SNAKE_CASE      :					Optional[int]    =				target_sizes.numpy()

					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				[]

					for idx in range(len(_a		)		):
						SCREAMING_SNAKE_CASE      :					Any    =				torch.nn.functional.interpolate(
						    logits[idx].unsqueeze(dim=0		)					,					size=target_sizes[idx]					,					mode="bilinear"					,					align_corners=_a		)
						SCREAMING_SNAKE_CASE      :					str    =				resized_logits[0].argmax(dim=0		)
						semantic_segmentation.append(_a		)
				else:
					SCREAMING_SNAKE_CASE      :					List[str]    =				logits.argmax(dim=1		)
					SCREAMING_SNAKE_CASE      :					List[str]    =				[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

				return semantic_segmentation





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,
)


_snake_case										=       getLogger(__name__)







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_ = 8,snake_case_ = 1024,snake_case_="val",snake_case_=None,snake_case_=False,snake_case_="summarization",snake_case_=None,snake_case_=1,snake_case_ = None,snake_case_="",**snake_case_,):
      _A							:     Dict			=						str(snake_case_     )
      assert local_rank is not None
      torch.distributed.init_process_group(backend="""nccl""",rank=snake_case_     )

      _A							:     Tuple			=						Path(snake_case_     )
      _A							:     List[Any]			=						save_dir.joinpath(f'''rank_{local_rank}_output.json'''     )
      torch.cuda.set_device(snake_case_     )
      _A							:     Union[str, Any]			=						AutoModelForSeqaSeqLM.from_pretrained(snake_case_     ).cuda()
      if fpaa:
            _A							:     Any			=						model.half()
      # determine if we need to increase num_beams
      use_task_specific_params(snake_case_,snake_case_     )  # update config with task specific params
      _A							:     str			=						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(snake_case_     )
      logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''     )  # if this is wrong, check config.model_type.

      if max_source_length is None:
            _A							:     Optional[int]			=						tokenizer.model_max_length
      if prefix is None:
            _A							:     Tuple			=						prefix or getattr(model.config,"""prefix""",""""""     ) or """"""
      _A							:     Optional[int]			=						SeqaSeqDataset(
          snake_case_,snake_case_,snake_case_,max_target_length=1024,type_path=snake_case_,n_obs=snake_case_,prefix=snake_case_,**snake_case_,)
      # 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							:     Optional[int]			=						ds.make_sortish_sampler(snake_case_,distributed=snake_case_,add_extra_examples=snake_case_,shuffle=snake_case_     )
      _A							:     Dict			=						DataLoader(snake_case_,sampler=snake_case_,batch_size=snake_case_,collate_fn=ds.collate_fn     )
      _A							:     Optional[Any]			=						[]
      for batch in tqdm(snake_case_     ):
            _A							:     Tuple			=						model.generate(
                input_ids=batch["""input_ids"""].to(model.device     ),attention_mask=batch["""attention_mask"""].to(model.device     ),num_return_sequences=snake_case_,num_beams=snake_case_,**snake_case_,)
            _A							:     Any			=						tokenizer.batch_decode(snake_case_,skip_special_tokens=snake_case_,clean_up_tokenization_spaces=snake_case_     )
            _A							:     Dict			=						batch["""ids"""]
            if num_return_sequences > 1:
                  _A							:     Any			=						chunks(snake_case_,snake_case_     )  # batch size chunks, each of size num_return_seq
            for i, pred in enumerate(snake_case_     ):
                  results.append({"""pred""": pred, """id""": ids[i].item()}     )
      save_json(snake_case_,snake_case_     )
      return results, sampler.num_replicas







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

      parser.add_argument(
          """--n_obs""",type=snake_case_,default=snake_case_,required=snake_case_,help="""How many observations. Defaults to all."""     )
      parser.add_argument(
          """--num_return_sequences""",type=snake_case_,default=1,required=snake_case_,help="""How many sequences to return"""     )
      parser.add_argument(
          """--sync_timeout""",type=snake_case_,default=600,required=snake_case_,help="""How long should master process wait for other processes to finish.""",)
      parser.add_argument("""--src_lang""",type=snake_case_,default=snake_case_,required=snake_case_     )
      parser.add_argument("""--tgt_lang""",type=snake_case_,default=snake_case_,required=snake_case_     )
      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("""--fp16""",action="""store_true"""     )
      parser.add_argument("""--debug""",action="""store_true"""     )
      _A							:     Union[str, Any]			=						time.time()
      _A			,   _A							:     List[str]			=						parser.parse_known_args()
      _A							:     List[str]			=						parse_numeric_n_bool_cl_kwargs(snake_case_     )
      if generate_kwargs and args.local_rank <= 0:
            print(f'''parsed the following generate kwargs: {generate_kwargs}'''     )
      _A							:     Dict			=						Path(args.save_dir + """_tmp"""     )
      Path(snake_case_     ).mkdir(exist_ok=snake_case_     )  # this handles locking.
      _A							:     int			=						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							:     Any			=						{}
      if args.src_lang is not None:
            _A							:     int			=						args.src_lang
      if args.tgt_lang is not None:
            _A							:     Dict			=						args.tgt_lang

      Path(args.save_dir     ).mkdir(exist_ok=snake_case_     )
      _A			,   _A							:     str			=						eval_data_dir(
          args.data_dir,snake_case_,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=snake_case_,**snake_case_,)

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

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







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Dict			=						[]
      for partial_result in partial_results:
            records.extend(snake_case_     )
      _A							:     Optional[Any]			=						sorted(snake_case_,key=lambda snake_case_     : x["id"]     )
      _A							:     List[str]			=						[x["""pred"""] for x in records]
      return preds







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      # WAIT FOR lots of .json files
      _A							:     Optional[Any]			=						time.time()
      logger.info("""waiting for all nodes to finish"""     )
      _A							:     List[str]			=						None
      while (time.time() - start_wait) < timeout:
            _A							:     str			=						list(save_dir.glob("""rank_*.json"""     )     )
            if len(snake_case_     ) < num_replicas:
                  continue
            try:
                  # make sure all json files are fully saved
                  _A							:     List[str]			=						lmap(snake_case_,snake_case_     )
                  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()

