

import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict

import requests
from slack_sdk import WebClient


a_     =			WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])

def        lowerCamelCase__       (    _a):
	SCREAMING_SNAKE_CASE      :					Any    =				test_results.split(" ")

	SCREAMING_SNAKE_CASE      :					str    =				0
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				0

	# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
	# When it is too long, those signs are not present.
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				expressions[-2] if "=" in expressions[-1] else expressions[-1]

	for i, expression in enumerate(UpperCamelCase__):
		if "failed" in expression:
			failed += int(expressions[i - 1])
		if "passed" in expression:
			success += int(expressions[i - 1])

	return failed, success, time_spent

def        lowerCamelCase__       (    _a):
	SCREAMING_SNAKE_CASE      :					Tuple    =				{}
	SCREAMING_SNAKE_CASE      :					int    =				None
	SCREAMING_SNAKE_CASE      :					str    =				False
	for line in failures_short_lines.split("\n"):
		if re.search(r"_ \[doctest\]"							,	UpperCamelCase__):
			SCREAMING_SNAKE_CASE      :					Optional[Any]    =				True
			SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				line.split(" ")[2]
		elif in_error and not line.split(" ")[0].isdigit():
			SCREAMING_SNAKE_CASE      :					Optional[int]    =				line
			SCREAMING_SNAKE_CASE      :					Dict    =				False

	return failures






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(	self							:       Optional[Any]					,					a							:       int					,					a							:       Optional[Any]		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Tuple    =				title

				SCREAMING_SNAKE_CASE      :					List[Any]    =				doc_test_results["time_spent"].split(","		)[0]
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				doc_test_results["success"]
				SCREAMING_SNAKE_CASE      :					Any    =				doc_test_results["failures"]
				SCREAMING_SNAKE_CASE      :					str    =				self.n_success + self.n_failures

				# Failures and success of the modeling tests
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				doc_test_results
			@property
			def 							__UpperCamelCase       (	self							:       int		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				[self._time_spent]
				SCREAMING_SNAKE_CASE      :					int    =				0

				for time in time_spent:
					SCREAMING_SNAKE_CASE      :					Dict    =				time.split(":"		)

					# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
					if len(__UpperCAmelCase		) == 1:
						SCREAMING_SNAKE_CASE      :					Tuple    =				[0, 0, time_parts[0]]

					SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Dict    =				int(time_parts[0]		), int(time_parts[1]		), float(time_parts[2]		)
					total_secs += hours * 3600 + minutes * 60 + seconds

				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					str    =				total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
				return F"{int(__UpperCAmelCase		)}h{int(__UpperCAmelCase		)}m{int(__UpperCAmelCase		)}s"
			@property
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				Dict:

				"""simple docstring"""

				return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
			@property
			def 							__UpperCamelCase       (	self							:       str		)							->				Dict:

				"""simple docstring"""

				return {
				    "type": "section",
				    "text": {
				        "type": "plain_text",
				        "text": F"🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.",
				        "emoji": True,
				    },
				    "accessory": {
				        "type": "button",
				        "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
				        "url": F"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}",
				    },
				}
			@property
			def 							__UpperCamelCase       (	self							:       Any		)							->				Dict:

				"""simple docstring"""

				return {
				    "type": "section",
				    "text": {
				        "type": "plain_text",
				        "text": (
				            F"There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in"
				            F" {self.time}."
				        ),
				        "emoji": True,
				    },
				    "accessory": {
				        "type": "button",
				        "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
				        "url": F"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}",
				    },
				}
			@property
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				40
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				{k: v["failed"] for k, v in doc_test_results.items() if isinstance(__UpperCAmelCase					,					__UpperCAmelCase		)}

				SCREAMING_SNAKE_CASE      :					List[str]    =				""
				for category, failures in category_failures.items():
					if len(__UpperCAmelCase		) == 0:
						continue

					if report != "":
						report += "\n\n"

					report += F"*{category} failures*:".ljust(line_length // 2		).rjust(line_length // 2		) + "\n"
					report += "`"
					report += "`\n`".join(__UpperCAmelCase		)
					report += "`"

				return {
				    "type": "section",
				    "text": {
				        "type": "mrkdwn",
				        "text": F"The following examples had failures:\n\n\n{report}\n",
				    },
				}
			@property
			def 							__UpperCamelCase       (	self							:       str		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				[self.header]

				if self.n_failures > 0:
					blocks.append(self.failures		)

				if self.n_failures > 0:
					blocks.extend([self.category_failures]		)

				if self.n_failures == 0:
					blocks.append(self.no_failures		)

				return json.dumps(__UpperCAmelCase		)
			@staticmethod
			def 							__UpperCamelCase       (	)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Any    =				[
				    {
				        "type": "section",
				        "text": {
				            "type": "plain_text",
				            "text": "There was an issue running the tests.",
				        },
				        "accessory": {
				            "type": "button",
				            "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
				            "url": F"https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}",
				        },
				    }
				]

				print("Sending the following payload"		)
				print(json.dumps({"blocks": json.loads(__UpperCAmelCase		)}		)		)

				client.chat_postMessage(
				    channel=os.environ["CI_SLACK_CHANNEL_ID_DAILY"]					,					text="There was an issue running the tests."					,					blocks=__UpperCAmelCase					,					)
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				int:

				"""simple docstring"""

				print("Sending the following payload"		)
				print(json.dumps({"blocks": json.loads(self.payload		)}		)		)

				SCREAMING_SNAKE_CASE      :					int    =				F"{self.n_failures} failures out of {self.n_tests} tests," if self.n_failures else "All tests passed."

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				client.chat_postMessage(
				    channel=os.environ["CI_SLACK_CHANNEL_ID_DAILY"]					,					blocks=self.payload					,					text=__UpperCAmelCase					,					)
			def 							__UpperCamelCase       (	self							:       List[str]					,					a							:       Any					,					a							:       Dict					,					a							:       Dict					,					a							:       int		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				""
				for key, value in failures.items():
					SCREAMING_SNAKE_CASE      :					Tuple    =				value[:200] + " [Truncated]" if len(__UpperCAmelCase		) > 250 else value
					failures_text += F"*{key}*\n_{value}_\n\n"

				SCREAMING_SNAKE_CASE      :					Dict    =				job_name
				SCREAMING_SNAKE_CASE      :					str    =				{"type": "section", "text": {"type": "mrkdwn", "text": text}}

				if job_link is not None:
					SCREAMING_SNAKE_CASE      :					List[Any]    =				{
					    "type": "button",
					    "text": {"type": "plain_text", "text": "GitHub Action job", "emoji": True},
					    "url": job_link,
					}

				return [
				    {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
				    content,
				    {"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
				]






			def 							__UpperCamelCase       (	self							:       List[Any]		)							->				Dict:

				"""simple docstring"""

				if self.thread_ts is None:
					raise ValueError("Can only post reply if a post has been made."		)

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.doc_test_results.pop("job_link"		)
				self.doc_test_results.pop("failures"		)
				self.doc_test_results.pop("success"		)
				self.doc_test_results.pop("time_spent"		)

				SCREAMING_SNAKE_CASE      :					List[str]    =				sorted(self.doc_test_results.items()					,					key=lambda a		: t[0]		)
				for job, job_result in sorted_dict:
					if len(job_result["failures"]		):
						SCREAMING_SNAKE_CASE      :					Optional[int]    =				F"*Num failures* :{len(job_result['failed']		)} \n"
						SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				job_result["failures"]
						SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.get_reply_blocks(__UpperCAmelCase					,					__UpperCAmelCase					,					__UpperCAmelCase					,					text=__UpperCAmelCase		)

						print("Sending the following reply"		)
						print(json.dumps({"blocks": blocks}		)		)

						client.chat_postMessage(
						    channel=os.environ["CI_SLACK_CHANNEL_ID_DAILY"]					,					text=F"Results for {job}"					,					blocks=__UpperCAmelCase					,					thread_ts=self.thread_ts["ts"]					,					)

						time.sleep(1		)

def        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					List[str]    =				os.environ["GITHUB_RUN_ID"]
	SCREAMING_SNAKE_CASE      :					int    =				f"https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				requests.get(UpperCamelCase__).json()
	SCREAMING_SNAKE_CASE      :					List[str]    =				{}

	try:
		jobs.update({job["name"]: job["html_url"] for job in result["jobs"]})
		SCREAMING_SNAKE_CASE      :					Any    =				math.ceil((result["total_count"] - 100) / 100)

		for i in range(UpperCamelCase__):
			SCREAMING_SNAKE_CASE      :					Optional[int]    =				requests.get(url + f"&page={i + 2}").json()
			jobs.update({job["name"]: job["html_url"] for job in result["jobs"]})

		return jobs
	except Exception as e:
		print("Unknown error, could not fetch links."							,	UpperCamelCase__)

	return {}

def        lowerCamelCase__       (    _a):
	SCREAMING_SNAKE_CASE      :					Tuple    =				{}

	if os.path.exists(UpperCamelCase__):
		SCREAMING_SNAKE_CASE      :					str    =				os.listdir(UpperCamelCase__)
		for file in files:
			try:
				with open(os.path.join(UpperCamelCase__							,	UpperCamelCase__)							,	encoding="utf-8") as f:
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				f.read()
			except UnicodeDecodeError as e:
				raise ValueError(f"Could not open {os.path.join(UpperCamelCase__							,	UpperCamelCase__)}.") from e

	return _artifact

def        lowerCamelCase__       (    ):






	class _UpperCamelCase							:
				'''simple docstring'''







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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE      :					Dict    =				name
					SCREAMING_SNAKE_CASE      :					int    =				[]
				def __str__(	self							:       Any		)							->				List[str]:

					"""simple docstring"""

					return self.name






				def 							__UpperCamelCase       (	self							:       Dict					,					a							:       List[str]		)							->				int:

					"""simple docstring"""

					self.paths.append({"name": self.name, "path": path}		)

	SCREAMING_SNAKE_CASE      :					Optional[int]    =				{}

	SCREAMING_SNAKE_CASE      :					List[str]    =				filter(os.path.isdir							,	os.listdir())
	for directory in directories:
		SCREAMING_SNAKE_CASE      :					str    =				directory
		if artifact_name not in _available_artifacts:
			SCREAMING_SNAKE_CASE      :					List[Any]    =				Artifact(UpperCamelCase__)

			_available_artifacts[artifact_name].add_path(UpperCamelCase__)

	return _available_artifacts


if __name__ == "__main__":
			a_     =			get_job_links()
			a_     =			retrieve_available_artifacts()

			a_     =			collections.OrderedDict(
			    [
			        ('*.py', 'API Examples'),
			        ('*.md', 'MD Examples'),
			    ]
			)

			# This dict will contain all the information relative to each doc test category:
			# - failed: list of failed tests
			# - failures: dict in the format 'test': 'error_message'
			a_     =			{
			    v: {
			        'failed': [],
			        'failures': {},
			    }
			    for v in docs.values()
			}

			# Link to the GitHub Action job
			a_     =			github_actions_job_links.get('run_doctests')

			a_     =			available_artifacts['doc_tests_gpu_test_reports'].paths[0]
			a_     =			retrieve_artifact(artifact_path['name'])
			if "stats" in artifact:
						a_   ,		a_   ,		a_     =			handle_test_results(artifact['stats'])
						a_     =			failed
						a_     =			success
						a_     =			time_spent[1:-1] + ', '

						a_     =			extract_first_line_failure(artifact['failures_short'])
						for line in artifact["summary_short"].split('\n'):
									if re.search('FAILED', line):
												a_     =			line.replace('FAILED ', '')
												a_     =			line.split()[0].replace('\n', '')

												if "::" in line:
															a_   ,		a_     =			line.split('::')
												else:
															a_   ,		a_     =			line, line

												for file_regex in docs.keys():
															if fnmatch(file_path, file_regex):
																		a_     =			docs[file_regex]
																		doc_test_results[category]["failed"].append(test)

																		a_     =			all_failures[test] if test in all_failures else 'N/A'
																		a_     =			failure
																		break

			a_     =			Message('🤗 Results of the doc tests.', doc_test_results)
			message.post()
			message.post_reply()
"""simple docstring"""




# 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase							 =     {
    "configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig"],
}
try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     ["VivitImageProcessor"]


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     [
				    "VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "VivitModel",
				    "VivitPreTrainedModel",
				    "VivitForVideoClassification",
				]


if TYPE_CHECKING:
				from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_vivit import VivitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vivit import (
								    VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VivitForVideoClassification,
								    VivitModel,
								    VivitPreTrainedModel,
								)


else:
				import sys

				__lowerCamelCase							 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





