




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,
)


_lowerCamelCase      			=						getLogger(__name__)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						str	,					__UpperCamelCase      :						str	,					__UpperCamelCase      :						int = 8	,					__UpperCamelCase      :						int = 1024	,					__UpperCamelCase      :						Union[str, Any]="val"	,					__UpperCamelCase      :						str=None	,					__UpperCamelCase      :						Optional[Any]=False	,					__UpperCamelCase      :						Optional[Any]="summarization"	,					__UpperCamelCase      :						List[Any]=None	,					__UpperCamelCase      :						Tuple=1	,					__UpperCamelCase      :						Dict = None	,					__UpperCamelCase      :						List[str]=""	,					**__UpperCamelCase      :						Union[str, Any]	,					)  ->							Dict:
    UpperCAmelCase_										=					str(__UpperCamelCase				)
    assert local_rank is not None
    torch.distributed.init_process_group(backend='''nccl'''	,					rank=__UpperCamelCase				)

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

    UpperCAmelCase_										=					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:
        UpperCAmelCase_										=					tokenizer.model_max_length
    if prefix is None:
        UpperCAmelCase_										=					prefix or getattr(model.config	,					'''prefix'''	,					''''''				) or ''''''
    UpperCAmelCase_										=					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.
    UpperCAmelCase_										=					ds.make_sortish_sampler(__UpperCamelCase	,					distributed=__UpperCamelCase	,					add_extra_examples=__UpperCamelCase	,					shuffle=__UpperCamelCase				)
    UpperCAmelCase_										=					DataLoader(__UpperCamelCase	,					sampler=__UpperCamelCase	,					batch_size=__UpperCamelCase	,					collate_fn=ds.collate_fn				)
    UpperCAmelCase_										=					[]
    for batch in tqdm(__UpperCamelCase				):
        UpperCAmelCase_										=					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	,					)
        UpperCAmelCase_										=					tokenizer.batch_decode(__UpperCamelCase	,					skip_special_tokens=__UpperCamelCase	,					clean_up_tokenization_spaces=__UpperCamelCase				)
        UpperCAmelCase_										=					batch['''ids''']
        if num_return_sequences > 1:
            UpperCAmelCase_										=					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  SCREAMING_SNAKE_CASE     (	)  ->							Optional[int]:
    UpperCAmelCase_										=					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'''				)
    UpperCAmelCase_										=					time.time()
    UpperCAmelCase_			,							UpperCAmelCase_										=					parser.parse_known_args()
    UpperCAmelCase_										=					parse_numeric_n_bool_cl_kwargs(__UpperCamelCase				)
    if generate_kwargs and args.local_rank <= 0:
        print(f'parsed the following generate kwargs: {generate_kwargs}'				)
    UpperCAmelCase_										=					Path(args.save_dir + '''_tmp'''				)
    Path(__UpperCamelCase				).mkdir(exist_ok=__UpperCamelCase				)  # this handles locking.
    UpperCAmelCase_										=					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.
    UpperCAmelCase_										=					{}
    if args.src_lang is not None:
        UpperCAmelCase_										=					args.src_lang
    if args.tgt_lang is not None:
        UpperCAmelCase_										=					args.tgt_lang

    Path(args.save_dir				).mkdir(exist_ok=__UpperCamelCase				)
    UpperCAmelCase_			,							UpperCAmelCase_										=					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:
        UpperCAmelCase_										=					Path(args.save_dir				)
        save_dir.mkdir(exist_ok=__UpperCamelCase				)
        UpperCAmelCase_										=					gather_results_from_each_node(__UpperCamelCase	,					__UpperCamelCase	,					args.sync_timeout				)
        UpperCAmelCase_										=					combine_partial_results(__UpperCamelCase				)
        if args.num_return_sequences > 1:
            UpperCAmelCase_										=					save_dir.joinpath('''pseudolabel_results.json'''				)
            print(f'Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'				)
            save_json(__UpperCamelCase	,					__UpperCamelCase				)
            return
        UpperCAmelCase_										=					Path(args.data_dir				).joinpath(args.type_path + '''.target'''				)
        with open(__UpperCamelCase				) as f:
            UpperCAmelCase_										=					[x.rstrip() for x in f.readlines()][: len(__UpperCamelCase				)]

        # Calculate metrics, save metrics,  and save _generations.txt
        UpperCAmelCase_										=					'''translation''' in args.task
        UpperCAmelCase_										=					calculate_bleu if calc_bleu else calculate_rouge
        UpperCAmelCase_										=					'''bleu''' if calc_bleu else '''rouge'''
        UpperCAmelCase_										=					score_fn(__UpperCamelCase	,					__UpperCamelCase				)
        UpperCAmelCase_										=					len(__UpperCamelCase				)
        UpperCAmelCase_										=					time.time() - start_time
        UpperCAmelCase_										=					round(runtime / metrics['''n_obs''']	,					4				)
        UpperCAmelCase_										=					num_replicas
        # TODO(@stas00): add whatever metadata to metrics
        UpperCAmelCase_										=					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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]				)  ->							List:
    UpperCAmelCase_										=					[]
    for partial_result in partial_results:
        records.extend(__UpperCamelCase				)
    UpperCAmelCase_										=					sorted(__UpperCamelCase	,					key=lambda __UpperCamelCase				: x["id"]				)
    UpperCAmelCase_										=					[x['''pred'''] for x in records]
    return preds




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











import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import MobileNetVaImageProcessor



class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def __init__(     self       :      Tuple ,   __snake_case       :      int ,   __snake_case       :      List[Any]=7 ,   __snake_case       :      Any=3 ,   __snake_case       :      Any=18 ,   __snake_case       :      str=30 ,   __snake_case       :      Any=4_00 ,   __snake_case       :      Optional[int]=True ,   __snake_case       :      str=None ,   __snake_case       :      Any=True ,   __snake_case       :      List[Any]=None ,   ):
         UpperCAmelCase_										=					size if size is not None else {'''shortest_edge''': 20}
         UpperCAmelCase_										=					crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
         UpperCAmelCase_										=					parent
         UpperCAmelCase_										=					batch_size
         UpperCAmelCase_										=					num_channels
         UpperCAmelCase_										=					image_size
         UpperCAmelCase_										=					min_resolution
         UpperCAmelCase_										=					max_resolution
         UpperCAmelCase_										=					do_resize
         UpperCAmelCase_										=					size
         UpperCAmelCase_										=					do_center_crop
         UpperCAmelCase_										=					crop_size




     def   lowerCamelCase_       (     self       :      Any    ):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
         }



@require_torch
@require_vision
class       a					(       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Union[str, Any]       		=  MobileNetVaImageProcessor if is_vision_available() else None
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					MobileNetVaImageProcessingTester(self    )
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         return self.image_processor_tester.prepare_image_processor_dict()
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(__snake_case ,   '''do_resize'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''size'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''do_center_crop'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''crop_size'''    )    )
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					self.image_processing_class.from_dict(self.image_processor_dict    )
         self.assertEqual(image_processor.size ,   {'''shortest_edge''': 20}    )
         self.assertEqual(image_processor.crop_size ,   {'''height''': 18, '''width''': 18}    )

         UpperCAmelCase_										=					self.image_processing_class.from_dict(self.image_processor_dict ,   size=42 ,   crop_size=84    )
         self.assertEqual(image_processor.size ,   {'''shortest_edge''': 42}    )
         self.assertEqual(image_processor.crop_size ,   {'''height''': 84, '''width''': 84}    )
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         pass
     def   lowerCamelCase_       (     self       :      Tuple    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_tester ,   equal_resolution=__snake_case    )
         for image in image_inputs:
             self.assertIsInstance(__snake_case ,   Image.Image    )

         # Test not batched input
         UpperCAmelCase_										=					image_processing(image_inputs[0] ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )

         # Test batched
         UpperCAmelCase_										=					image_processing(__snake_case ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )
     def   lowerCamelCase_       (     self       :      str    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_tester ,   equal_resolution=__snake_case ,   numpify=__snake_case    )
         for image in image_inputs:
             self.assertIsInstance(__snake_case ,   np.ndarray    )

         # Test not batched input
         UpperCAmelCase_										=					image_processing(image_inputs[0] ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )

         # Test batched
         UpperCAmelCase_										=					image_processing(__snake_case ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )




     def   lowerCamelCase_       (     self       :      int    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_tester ,   equal_resolution=__snake_case ,   torchify=__snake_case    )
         for image in image_inputs:
             self.assertIsInstance(__snake_case ,   torch.Tensor    )

         # Test not batched input
         UpperCAmelCase_										=					image_processing(image_inputs[0] ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )

         # Test batched
         UpperCAmelCase_										=					image_processing(__snake_case ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )





