





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

from tabulate import DataRow, TableFormat, tabulate


__lowercase       :str       =  TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow("", "|", "|"),
    datarow=DataRow("", "|", "|"),
    padding=1,
    with_header_hide=None,
)


__lowercase       :Tuple       =  []
__lowercase       :Dict       =  []

__lowercase       :Union[str, Any]       =  {"type": "section", "text": {"type": "plain_text", "text": "No failed tests! 🤗", "emoji": True}}

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

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

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

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

												__lowercase       :Tuple       =  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) > 3_000:
								__lowercase       :Tuple       =  "Too many failed tests, please see the full report in the Action results."
								__lowercase       :Optional[int]       =  len(err) + 10
								__lowercase       :List[Any]       =  message[: 3_000 - offset] + f"\n...\n```\n{err}"
				print(f"### {message}")
else:
				__lowercase       :Optional[int]       =  "No failed tests! 🤗"
				print(f"## {message}")
				payload.append(no_error_payload)

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

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

												__lowercase       :List[str]       =  {
												    "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 warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						["image_processor", "tokenizer"]
							snake_case_							 =						"CLIPImageProcessor"
							snake_case_							 =						("CLIPTokenizer", "CLIPTokenizerFast")

							def __init__(      self				:     Any							,	a				:     List[Any]=None							,	a				:     Any=None							,	**a				:     int    )	->int:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					None
													if "feature_extractor" in kwargs:
																			warnings.warn(
																			    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																			    " instead."							,	a							,	)
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					kwargs.pop("feature_extractor"    )

													SCREAMING_SNAKE_CASE__     :				int   =					image_processor if image_processor is not None else feature_extractor
													if image_processor is None:
																			raise ValueError("You need to specify an `image_processor`."    )
													if tokenizer is None:
																			raise ValueError("You need to specify a `tokenizer`."    )

													super().__init__(a							,	a    )

							def __call__(      self				:     Tuple							,	a				:     Tuple=None							,	a				:     Union[str, Any]=None							,	a				:     List[str]=None							,	**a				:     Optional[Any]    )	->Optional[Any]:

													if text is None and images is None:
																			raise ValueError("You have to specify either text or images. Both cannot be none."    )

													if text is not None:
																			SCREAMING_SNAKE_CASE__     :				str   =					self.tokenizer(a							,	return_tensors=a							,	**a    )

													if images is not None:
																			SCREAMING_SNAKE_CASE__     :				int   =					self.image_processor(a							,	return_tensors=a							,	**a    )

													if text is not None and images is not None:
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					image_features.pixel_values
																			return encoding
													elif text is not None:
																			return encoding
													else:
																			return BatchEncoding(data=dict(**a    )							,	tensor_type=a    )

							def      A_  (      self				:     Optional[int]							,	*a				:     Any							,	**a				:     List[str]    )	->Any:
													return self.tokenizer.batch_decode(*a							,	**a    )

							def      A_  (      self				:     Any							,	*a				:     Optional[int]							,	**a				:     Dict    )	->Any:
													return self.tokenizer.decode(*a							,	**a    )

							@property
							def      A_  (      self				:     List[str]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.tokenizer.model_input_names
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.image_processor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )

							@property
							def      A_  (      self				:     Optional[int]    )	->List[Any]:
													warnings.warn(
													    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							,	a							,	)
													return self.image_processor_class






							@property
							def      A_  (      self				:     Dict    )	->str:
													warnings.warn(
													    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,	a							,	)
													return self.image_processor