






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__      			=						getLogger(__name__)







def     a_   (__A				,       __A				,       __A				,       __A = 8				,       __A = 1_024				,       __A="val"				,       __A=None				,       __A=False				,       __A="summarization"				,       __A=None				,       __A=1				,       __A = None				,       __A=""				,       **__A				,       )				->						Dict:



					"""simple docstring"""
					__a					:						Optional[Any]      =					str(__UpperCAmelCase      )
					assert local_rank is not None
					torch.distributed.init_process_group(backend="nccl"				,       rank=__UpperCAmelCase      )

					__a					:						Optional[Any]      =					Path(__UpperCAmelCase      )
					__a					:						Tuple      =					save_dir.joinpath(f'rank_{local_rank}_output.json'      )
					torch.cuda.set_device(__UpperCAmelCase      )
					__a					:						List[Any]      =					AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      ).cuda()
					if fpaa:
										__a					:						Union[str, Any]      =					model.half()
					# determine if we need to increase num_beams
					use_task_specific_params(__UpperCAmelCase				,       __UpperCAmelCase      )  # 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					:						Union[str, 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					:						List[Any]      =					tokenizer.model_max_length
					if prefix is None:
										__a					:						Dict      =					prefix or getattr(model.config				,       "prefix"				,       ""      ) or ""
					__a					:						str      =					SeqaSeqDataset(
					    __UpperCAmelCase				,       __UpperCAmelCase				,       __UpperCAmelCase				,       max_target_length=1_024				,       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					:						Union[str, Any]      =					ds.make_sortish_sampler(__UpperCAmelCase				,       distributed=__UpperCAmelCase				,       add_extra_examples=__UpperCAmelCase				,       shuffle=__UpperCAmelCase      )
					__a					:						Optional[int]      =					DataLoader(__UpperCAmelCase				,       sampler=__UpperCAmelCase				,       batch_size=__UpperCAmelCase				,       collate_fn=ds.collate_fn      )
					__a					:						Any      =					[]
					for batch in tqdm(__UpperCAmelCase      ):
										__a					:						Union[str, 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					:						Optional[int]      =					tokenizer.batch_decode(__UpperCAmelCase				,       skip_special_tokens=__UpperCAmelCase				,       clean_up_tokenization_spaces=__UpperCAmelCase      )
										__a					:						List[Any]      =					batch["ids"]
										if num_return_sequences > 1:
															__a					:						Tuple      =					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     a_   ()				->						Dict:



					"""simple docstring"""
					__a					:						Dict      =					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					:						Dict      =					time.time()
					__a      ,			__a					:						Any      =					parser.parse_known_args()
					__a					:						int      =					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					:						List[str]      =					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[int]      =					{}
					if args.src_lang is not None:
										__a					:						Tuple      =					args.src_lang
					if args.tgt_lang is not None:
										__a					:						Optional[int]      =					args.tgt_lang

					Path(args.save_dir      ).mkdir(exist_ok=__UpperCAmelCase      )
					__a      ,			__a					:						Dict      =					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					:						Optional[Any]      =					Path(args.save_dir      )
										save_dir.mkdir(exist_ok=__UpperCAmelCase      )
										__a					:						List[Any]      =					gather_results_from_each_node(__UpperCAmelCase				,       __UpperCAmelCase				,       args.sync_timeout      )
										__a					:						Optional[Any]      =					combine_partial_results(__UpperCAmelCase      )
										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(__UpperCAmelCase				,       __UpperCAmelCase      )
															return
										__a					:						Dict      =					Path(args.data_dir      ).joinpath(args.type_path + ".target"      )
										with open(__UpperCAmelCase      ) as f:
															__a					:						Union[str, Any]      =					[x.rstrip() for x in f.readlines()][: len(__UpperCAmelCase      )]

										# Calculate metrics, save metrics,  and save _generations.txt
										__a					:						Tuple      =					"translation" in args.task
										__a					:						List[str]      =					calculate_bleu if calc_bleu else calculate_rouge
										__a					:						Any      =					"bleu" if calc_bleu else "rouge"
										__a					:						List[Any]      =					score_fn(__UpperCAmelCase				,       __UpperCAmelCase      )
										__a					:						List[str]      =					len(__UpperCAmelCase      )
										__a					:						Optional[Any]      =					time.time() - start_time
										__a					:						Any      =					round(runtime / metrics["n_obs"]				,       4      )
										__a					:						Optional[int]      =					num_replicas
										# TODO(@stas00): add whatever metadata to metrics
										__a					:						Optional[int]      =					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     a_   (__A      )				->						List:



					"""simple docstring"""
					__a					:						List[str]      =					[]
					for partial_result in partial_results:
										records.extend(__UpperCAmelCase      )
					__a					:						Union[str, Any]      =					sorted(__UpperCAmelCase				,       key=lambda __A      : x["id"]      )
					__a					:						Any      =					[x["pred"] for x in records]
					return preds







def     a_   (__A				,       __A				,       __A      )				->						List[Dict[str, List]]:



					"""simple docstring"""
					# WAIT FOR lots of .json files
					__a					:						Any      =					time.time()
					logger.info("waiting for all nodes to finish"      )
					__a					:						Union[str, Any]      =					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					:						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()











import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch





@require_torch
class 			lowerCamelCase_				:


 '''simple docstring'''







 @property
 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       return self.get_dummy_input()



 @property
 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       if self.block_type == "down":
             return (4, 32, 16, 16)
       elif self.block_type == "mid":
             return (4, 32, 32, 32)
       elif self.block_type == "up":
             return (4, 32, 64, 64)

       raise ValueError(F"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."		)



 def  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						Optional[Any]=True							,							_lowerCAmelCase    :						str=False							,							_lowerCAmelCase    :						Optional[int]=False							,							_lowerCAmelCase    :						Dict=False							,							):
       SCREAMING_SNAKE_CASE_      		=     4
       SCREAMING_SNAKE_CASE_      		=     32
       SCREAMING_SNAKE_CASE_      		=     (32, 32)

       SCREAMING_SNAKE_CASE_      		=     torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     torch.device(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     (batch_size, num_channels) + sizes
       SCREAMING_SNAKE_CASE_      		=     randn_tensor(_lowerCAmelCase							,							generator=_lowerCAmelCase							,							device=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     {'hidden_states': hidden_states}

       if include_temb:
             SCREAMING_SNAKE_CASE_      		=     128
             SCREAMING_SNAKE_CASE_      		=     randn_tensor((batch_size, temb_channels)							,							generator=_lowerCAmelCase							,							device=_lowerCAmelCase		)

       if include_res_hidden_states_tuple:
             SCREAMING_SNAKE_CASE_      		=     torch.manual_seed(1		)
             SCREAMING_SNAKE_CASE_      		=     (randn_tensor(_lowerCAmelCase							,							generator=_lowerCAmelCase							,							device=_lowerCAmelCase		),)

       if include_encoder_hidden_states:
             SCREAMING_SNAKE_CASE_      		=     floats_tensor((batch_size, 32, 32)		).to(_lowerCAmelCase		)

       if include_skip_sample:
             SCREAMING_SNAKE_CASE_      		=     randn_tensor(((batch_size, 3) + sizes)							,							generator=_lowerCAmelCase							,							device=_lowerCAmelCase		)

       return dummy_input



 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     {
           'in_channels': 32,
           'out_channels': 32,
           'temb_channels': 128,
       }
       if self.block_type == "up":
             SCREAMING_SNAKE_CASE_      		=     32

       if self.block_type == "mid":
             init_dict.pop('out_channels'		)

       SCREAMING_SNAKE_CASE_      		=     self.dummy_input
       return init_dict, inputs_dict



 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.prepare_init_args_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_      		=     self.block_class(**_lowerCAmelCase		)
       unet_block.to(_lowerCAmelCase		)
       unet_block.eval()

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     unet_block(**_lowerCAmelCase		)

       if isinstance(_lowerCAmelCase							,							_lowerCAmelCase		):
             SCREAMING_SNAKE_CASE_      		=     output[0]

       self.assertEqual(output.shape							,							self.output_shape		)

       SCREAMING_SNAKE_CASE_      		=     output[0, -1, -3:, -3:]
       SCREAMING_SNAKE_CASE_      		=     torch.tensor(_lowerCAmelCase		).to(_lowerCAmelCase		)
       assert torch_all_close(output_slice.flatten()							,							_lowerCAmelCase							,							atol=5E-3		)




 @unittest.skipIf(torch_device == 'mps'							,							'Training is not supported in mps'		)
 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.prepare_init_args_and_inputs_for_common()
       SCREAMING_SNAKE_CASE_      		=     self.block_class(**_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.train()
       SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)

       if isinstance(_lowerCAmelCase							,							_lowerCAmelCase		):
             SCREAMING_SNAKE_CASE_      		=     output[0]

       SCREAMING_SNAKE_CASE_      		=     torch.device(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     randn_tensor(output.shape							,							device=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.functional.mse_loss(_lowerCAmelCase							,							_lowerCAmelCase		)
       loss.backward()