

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 itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


a_						     =    datasets.utils.logging.get_logger(__name__)






@dataclass
class _UpperCamelCase							(							datasets.BuilderConfig   ):
			'''simple docstring'''







			lowerCamelCase__           =10000
			lowerCamelCase__           =None
			lowerCamelCase__           =None






class _UpperCamelCase							(							datasets.ArrowBasedBuilder   ):
			'''simple docstring'''







			lowerCamelCase__           =ParquetConfig
			def 							__UpperCamelCase (			self							: str	)							->       str:

				"""simple docstring"""

				return datasets.DatasetInfo(features=self.config.features	)
			def 							__UpperCamelCase (			self							: Dict      ,      a							: List[Any]	)							->       Tuple:

				"""simple docstring"""

				if not self.config.data_files:
					raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       dl_manager.download_and_extract(self.config.data_files	)
				if isinstance(a      ,      (str, list, tuple)	):
					SCREAMING_SNAKE_CASE   :      Dict				     =       data_files
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [dl_manager.iter_files(a	) for file in files]
					return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,      gen_kwargs={"files": files}	)]
				SCREAMING_SNAKE_CASE   :      str				     =       []
				for split_name, files in data_files.items():
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Tuple				     =       [dl_manager.iter_files(a	) for file in files]
					# Infer features is they are stoed in the arrow schema
					if self.info.features is None:
						for file in itertools.chain.from_iterable(a	):
							with open(a      ,      "rb"	) as f:
								SCREAMING_SNAKE_CASE   :      Dict				     =       datasets.Features.from_arrow_schema(pq.read_schema(a	)	)
							break
					splits.append(datasets.SplitGenerator(name=a      ,      gen_kwargs={"files": files}	)	)
				return splits
			def 							__UpperCamelCase (			self							: Dict      ,      a							: pa.Table	)							->       pa.Table:

				"""simple docstring"""

				if self.info.features is not None:
					# more expensive cast to support nested features with keys in a different order
					# allows str <-> int/float or str to Audio for example
					SCREAMING_SNAKE_CASE   :      str				     =       table_cast(a      ,      self.info.features.arrow_schema	)
				return pa_table






			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Optional[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.info.features.arrow_schema if self.info.features is not None else None
				if self.info.features is not None and self.config.columns is not None:
					if sorted(field.name for field in schema	) != sorted(self.config.columns	):
						raise ValueError(
						    F"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"	)
				for file_idx, file in enumerate(itertools.chain.from_iterable(a	)	):
					with open(a      ,      "rb"	) as f:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       pq.ParquetFile(a	)
						try:
							for batch_idx, record_batch in enumerate(
							    parquet_file.iter_batches(batch_size=self.config.batch_size      ,      columns=self.config.columns	)	):
								SCREAMING_SNAKE_CASE   :      int				     =       pa.Table.from_batches([record_batch]	)
								# Uncomment for debugging (will print the Arrow table size and elements)
								# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
								# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
								yield F"{file_idx}_{batch_idx}", self._cast_table(a	)
						except ValueError as e:
							logger.error(F"Failed to read file '{file}' with error {type(a	)}: {e}"	)
							raise