


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

from tabulate import DataRow, TableFormat, tabulate


__UpperCamelCase    :						Union[str, Any]     =							TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow('', '|', '|'),
    datarow=DataRow('', '|', '|'),
    padding=1,
    with_header_hide=None,
)


__UpperCamelCase    :						Union[str, Any]     =							[]
__UpperCamelCase    :						List[str]     =							[]

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

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

__UpperCamelCase    :						Optional[Any]     =							0
for log in Path().glob('*.log'):
    __UpperCamelCase    :						Optional[Any]     =							0
    with open(log, 'r') as f:
        for line in f:
            __UpperCamelCase    :						List[str]     =							json.loads(line)
            if line.get('nodeid', '') != "":
                __UpperCamelCase    :						List[str]     =							line['nodeid']
                if line.get('duration', None) is not None:
                    __UpperCamelCase    :						Tuple     =							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])
    __UpperCamelCase    :						Optional[Any]     =							[]
    log.unlink()

__UpperCamelCase    :						List[Any]     =							''
__UpperCamelCase    :						List[Any]     =							[]
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"
            __UpperCamelCase    :						Optional[Any]     =							[]
            __UpperCamelCase    :						List[str]     =							{}
            for test in failed_tests:
                __UpperCamelCase    :						Optional[int]     =							test[0].split('::')
                __UpperCamelCase    :						Optional[Any]     =							data[0].split('/')[-1]
                if data[0] not in filesafailed:
                    __UpperCamelCase    :						Dict     =							[data[1:]]
                else:
                    filesafailed[data[0]] += [data[1:]]
                failed_table.append(data)

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

            __UpperCamelCase    :						Any     =							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:
        __UpperCamelCase    :						Dict     =							'Too many failed tests, please see the full report in the Action results.'
        __UpperCamelCase    :						Optional[Any]     =							len(err) + 10
        __UpperCamelCase    :						Optional[Any]     =							message[: 3000 - offset] + F'''\n...\n```\n{err}'''
    print(F'''### {message}''')
else:
    __UpperCamelCase    :						Union[str, Any]     =							'No failed tests! 🤗'
    print(F'''## {message}''')
    payload.append(no_error_payload)

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

    __UpperCamelCase    :						Dict     =							WebClient(token=os.environ['SLACK_API_TOKEN'])
    if message != "No failed tests! 🤗":
        __UpperCamelCase    :						Optional[int]     =							{
            'type': 'section',
            'text': {
                'type': 'mrkdwn',
                'text': message,
            },
        }
        payload.append(md_report)
        __UpperCamelCase    :						str     =							{
            '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)
        __UpperCamelCase    :						Union[str, Any]     =							{
            'type': 'context',
            'elements': [
                {
                    'type': 'plain_text',
                    'text': F'''Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}''',
                }
            ],
        }
        payload.append(date_report)
    __UpperCamelCase    :						Optional[Any]     =							client.chat_postMessage(channel='#accelerate-ci-daily', text=message, blocks=payload)
    __UpperCamelCase    :						List[Any]     =							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
            __UpperCamelCase    :						Union[str, Any]     =							''
            for i, row in enumerate(test_failures):
                if row[0] != test_class:
                    __UpperCamelCase    :						List[str]     =							row[0]
                else:
                    __UpperCamelCase    :						Dict     =							''

            __UpperCamelCase    :						Dict     =							{
                '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],
            )







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase    :						Any     =							{
    'configuration_mgp_str': ['MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MgpstrConfig'],
    'processing_mgp_str': ['MgpstrProcessor'],
    'tokenization_mgp_str': ['MgpstrTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						Tuple     =							[
        'MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MgpstrModel',
        'MgpstrPreTrainedModel',
        'MgpstrForSceneTextRecognition',
    ]

if TYPE_CHECKING:
    from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
    from .processing_mgp_str import MgpstrProcessor
    from .tokenization_mgp_str import MgpstrTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mgp_str import (
            MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
            MgpstrForSceneTextRecognition,
            MgpstrModel,
            MgpstrPreTrainedModel,
        )
else:
    import sys

    __UpperCamelCase    :						Optional[Any]     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



