



import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


A__								=						TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow('''''', '''|''', '''|'''),
    datarow=DataRow('''''', '''|''', '''|'''),
    padding=1,
    with_header_hide=None,
)


A__								=						[]
A__								=						[]

A__								=						{'''type''': '''section''', '''text''': {'''type''': '''plain_text''', '''text''': '''No failed tests! 🤗''', '''emoji''': True}}

A__								=						[
    {
        '''type''': '''header''',
        '''text''': {
            '''type''': '''plain_text''',
            '''text''': f"""🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results""",
            '''emoji''': True,
        },
    }
]

A__								=						0
for log in Path().glob('''*.log'''):
				A__								=						0
				with open(log, '''r''') as f:
								for line in f:
												A__								=						json.loads(line)
												if line.get('''nodeid''', '''''') != "":
																A__								=						line['''nodeid''']
																if line.get('''duration''', None) is not None:
																				A__								=						f"""{line['duration']:.4f}"""
																				if line.get('''outcome''', '''''') == "failed":
																								section_num_failed += 1
																								failed.append([test, duration, log.name.split('''_''')[0]])
																								total_num_failed += 1
				group_info.append([str(log), section_num_failed, failed])
				A__								=						[]
				log.unlink()

A__								=						''''''
A__								=						[]
if total_num_failed > 0:
				for name, num_failed, failed_tests in group_info:
								if num_failed > 0:
												if num_failed == 1:
																message += f"*{name[1:]}: {num_failed} failed test*\n"
												else:
																message += f"*{name[1:]}: {num_failed} failed tests*\n"
												A__								=						[]
												A__								=						{}
												for test in failed_tests:
																A__								=						test[0].split('''::''')
																A__								=						data[0].split('''/''')[-1]
																if data[0] not in filesafailed:
																				A__								=						[data[1:]]
																else:
																				filesafailed[data[0]] += [data[1:]]
																failed_table.append(data)

												A__								=						[test[0] for test in failed_table]
												A__								=						list(set(files))
												# Count number of instances in failed_tests
												A__								=						[]
												for file in individual_files:
																table.append([file, len(filesafailed[file])])

												A__								=						tabulate(
												    table,
												    headers=['''Test Location''', '''Num Failed'''],
												    tablefmt=hf_table_format,
												    stralign='''right''',
												)
												message += f"\n```\n{failed_table}\n```"
												all_filesafailed.append(filesafailed)
				if len(message) > 3000:
								A__								=						'''Too many failed tests, please see the full report in the Action results.'''
								A__								=						len(err) + 10
								A__								=						message[: 3000 - offset] + f"""\n...\n```\n{err}"""
				print(f"""### {message}""")
else:
				A__								=						'''No failed tests! 🤗'''
				print(f"""## {message}""")
				payload.append(no_error_payload)

if os.environ.get('''TEST_TYPE''', '''''') != "":
				from slack_sdk import WebClient

				A__								=						WebClient(token=os.environ['''SLACK_API_TOKEN'''])
				if message != "No failed tests! 🤗":
								A__								=						{
								    '''type''': '''section''',
								    '''text''': {
								        '''type''': '''mrkdwn''',
								        '''text''': message,
								    },
								}
								payload.append(md_report)
								A__								=						{
								    '''type''': '''section''',
								    '''text''': {
								        '''type''': '''mrkdwn''',
								        '''text''': '''*For more details:*''',
								    },
								    '''accessory''': {
								        '''type''': '''button''',
								        '''text''': {
								            '''type''': '''plain_text''',
								            '''text''': '''Check Action results''',
								            '''emoji''': True,
								        },
								        '''url''': f"""https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}""",
								    },
								}
								payload.append(action_button)
								A__								=						{
								    '''type''': '''context''',
								    '''elements''': [
								        {
								            '''type''': '''plain_text''',
								            '''text''': f"""Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}""",
								        }
								    ],
								}
								payload.append(date_report)
				A__								=						client.chat_postMessage(channel='''#accelerate-ci-daily''', text=message, blocks=payload)
				A__								=						response.data['''ts''']
				for failed_file in all_filesafailed:
								for test_location, test_failures in failed_file.items():
												# Keep only the first instance of the test name
												A__								=						''''''
												for i, row in enumerate(test_failures):
																if row[0] != test_class:
																				A__								=						row[0]
																else:
																				A__								=						''''''

												A__								=						{
												    '''type''': '''section''',
												    '''text''': {
												        '''type''': '''mrkdwn''',
												        '''text''': f"""Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```""",
												    },
												}

												client.chat_postMessage(
												    channel='''#accelerate-ci-daily''',
												    thread_ts=ts,
												    blocks=[payload],
												)






import json
import multiprocessing
import os
import re
from collections import defaultdict

import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList


A__								=						['''\nclass''', '''\ndef''', '''\n#''', '''\n@''', '''\nprint''', '''\nif''']


class       a							(			__lowerCamelCase				):



		def __init__(     self     :str   ,__lowercase     :Dict   ,__lowercase     :str   ,__lowercase     :Any=None   ,__lowercase     :Union[str, Any]=1  ):
						snake_case__	:      Optional[int]     		=	tokenizer
						snake_case__	:      List[Any]     		=	dataset
						snake_case__	:      Any     		=	len(__lowercase  ) if n_tasks is None else n_tasks
						snake_case__	:      List[Any]     		=	n_copies






		def __iter__(     self     :Tuple  ):
						snake_case__	:      Union[str, Any]     		=	[]
						for task in range(self.n_tasks  ):
										# without strip, the model generate commented codes ...
										prompts.append(self.tokenizer.eos_token + self.dataset[task]['''prompt'''].strip()  )
						snake_case__	:      Optional[int]     		=	self.tokenizer(__lowercase   ,padding=__lowercase   ,return_tensors='''pt'''  )
						for task in range(self.n_tasks  ):
										for _ in range(self.n_copies  ):
														yield {
														    "ids": outputs.input_ids[task],
														    "task_id": task,
														    "input_len": outputs.attention_mask[task].sum(),
														}


class       a							(			__lowerCamelCase				):



		def __init__(     self     :Union[str, Any]   ,__lowercase     :List[Any]   ,__lowercase     :int   ,__lowercase     :List[str]  ):
						snake_case__	:      List[str]     		=	start_length
						snake_case__	:      Any     		=	eof_strings
						snake_case__	:      Optional[Any]     		=	tokenizer






		def __call__(     self     :str   ,__lowercase     :str   ,__lowercase     :Any   ,**__lowercase     :Optional[int]  ):
						snake_case__	:      Tuple     		=	self.tokenizer.batch_decode(input_ids[:, self.start_length :]  )
						snake_case__	:      Union[str, Any]     		=	[]
						for decoded_generation in decoded_generations:
										done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings  )  )
						return all(__lowercase  )



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					List[Any]:


				"""simple docstring"""

				snake_case__	:      List[Any]     		=	re.split('''(%s)''' % '''|'''.join(__lowerCAmelCase      )  ,  __lowerCAmelCase      )
				# last string should be ""
				return "".join(string_list[:-2]      )



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase=20  ,  **__lowerCAmelCase      )   ->					List[Any]:


				"""simple docstring"""

				snake_case__	:      Optional[int]     		=	defaultdict(__lowerCAmelCase      )  # dict of list of generated tokens
				for step, batch in tqdm(enumerate(__lowerCAmelCase      )      ):
								with torch.no_grad():
												snake_case__	:      List[Any]     		=	batch['''ids'''].shape[-1]
												snake_case__	:      str     		=	accelerator.unwrap_model(__lowerCAmelCase      ).generate(
												    input_ids=batch['''ids'''][:, : batch['''input_len''']]  ,  num_return_sequences=__lowerCAmelCase  ,  **__lowerCAmelCase      )
												# each task is generated batch_size times
												snake_case__	:      str     		=	batch['''task_id'''].repeat(__lowerCAmelCase      )
												snake_case__	:      Optional[int]     		=	accelerator.pad_across_processes(
												    __lowerCAmelCase  ,  dim=1  ,  pad_index=tokenizer.pad_token_id      )

												snake_case__		, snake_case__	:      Any     		=	accelerator.gather((generated_tokens, generated_tasks)      )
												snake_case__	:      Union[str, Any]     		=	generated_tokens.cpu().numpy()
												snake_case__	:      Optional[Any]     		=	generated_tasks.cpu().numpy()

												for task, generated_tokens in zip(__lowerCAmelCase  ,  __lowerCAmelCase      ):
																gen_token_dict[task].append(__lowerCAmelCase      )

				snake_case__	:      List[Any]     		=	[[] for _ in range(__lowerCAmelCase      )]
				for task, generated_tokens in gen_token_dict.items():
								for s in generated_tokens:
												snake_case__	:      int     		=	tokenizer.decode(__lowerCAmelCase  ,  skip_special_tokens=__lowerCAmelCase  ,  clean_up_tokenization_spaces=__lowerCAmelCase      )
												code_gens[task].append(remove_last_block(__lowerCAmelCase      )      )
				return code_gens



def 			_lowerCAmelCase       (					)   ->					Optional[int]:


				"""simple docstring"""

				snake_case__	:      List[str]     		=	HfArgumentParser(__lowerCAmelCase      )
				snake_case__	:      Optional[Any]     		=	parser.parse_args()

				transformers.logging.set_verbosity_error()
				# enables code execution in code_eval metric
				snake_case__	:      Union[str, Any]     		=	args.HF_ALLOW_CODE_EVAL
				# make sure tokenizer plays nice with multiprocessing
				snake_case__	:      Union[str, Any]     		=	'''false'''

				if args.num_workers is None:
								snake_case__	:      Union[str, Any]     		=	multiprocessing.cpu_count()

				# Use dataset load to feed to accelerate
				snake_case__	:      Dict     		=	Accelerator()
				set_seed(args.seed  ,  device_specific=__lowerCAmelCase      )

				# Load model and tokenizer
				snake_case__	:      str     		=	AutoTokenizer.from_pretrained(args.model_ckpt      )
				snake_case__	:      List[str]     		=	tokenizer.eos_token
				snake_case__	:      Optional[Any]     		=	AutoModelForCausalLM.from_pretrained(args.model_ckpt      )

				# Generation settings
				snake_case__	:      List[Any]     		=	{
				    '''do_sample''': args.do_sample,
				    '''temperature''': args.temperature,
				    '''max_new_tokens''': args.max_new_tokens,
				    '''top_p''': args.top_p,
				    '''top_k''': args.top_k,
				    '''stopping_criteria''': StoppingCriteriaList([EndOfFunctionCriteria(0  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )]      ),
				}

				# Load evaluation dataset and metric
				snake_case__	:      List[str]     		=	load_dataset('''openai_humaneval'''      )
				snake_case__	:      str     		=	load_metric('''code_eval'''      )

				snake_case__	:      List[Any]     		=	args.num_tasks if args.num_tasks is not None else len(human_eval['''test''']      )
				snake_case__	:      str     		=	args.n_samples // args.batch_size

				snake_case__	:      Tuple     		=	TokenizedDataset(__lowerCAmelCase  ,  human_eval['''test''']  ,  n_copies=__lowerCAmelCase  ,  n_tasks=__lowerCAmelCase      )
				# do not confuse args.batch_size, which is actually the num_return_sequences
				snake_case__	:      Tuple     		=	DataLoader(__lowerCAmelCase  ,  batch_size=1      )

				# Run a quick test to see if code evaluation is enabled
				try:
								snake_case__	:      Optional[Any]     		=	code_eval_metric.compute(references=['''''']  ,  predictions=[['''''']]      )
				except ValueError as exception:
								print(
								    '''Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'''
								    ''' flag to enable code evaluation.'''      )
								raise exception

				snake_case__		, snake_case__	:      Optional[Any]     		=	accelerator.prepare(__lowerCAmelCase  ,  __lowerCAmelCase      )

				snake_case__	:      Dict     		=	complete_code(
				    __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  n_tasks=__lowerCAmelCase  ,  batch_size=args.batch_size  ,  **__lowerCAmelCase  ,  )

				if accelerator.is_main_process:
								snake_case__	:      Optional[Any]     		=	[]

								for task in tqdm(range(__lowerCAmelCase      )      ):
												snake_case__	:      str     		=	human_eval['''test'''][task]['''test''']
												snake_case__	:      int     		=	f"""check({human_eval['test'][task]['entry_point']})"""
												references.append('''\n''' + test_func + '''\n''' + entry_point      )

								# Evaluate completions with "code_eval" metric
								snake_case__		, snake_case__	:      Any     		=	code_eval_metric.compute(
								    references=__lowerCAmelCase  ,  predictions=__lowerCAmelCase  ,  num_workers=args.num_workers      )
								print(f"""Results: {pass_at_k}"""      )

								# Save results to json file
								with open(args.output_file  ,  '''w'''      ) as fp:
												json.dump(__lowerCAmelCase  ,  __lowerCAmelCase      )


# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
				main()

