





'''simple docstring'''



import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_SCREAMING_SNAKE_CASE					=    logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')

_SCREAMING_SNAKE_CASE					=    list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
_SCREAMING_SNAKE_CASE					=    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class 							__lowercase       :







	'''simple docstring'''

	a    :				Optional[str]											=       field(
	    default="cifar10" ,      metadata={"help": "Name of a dataset from the datasets package"}						)
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}						)
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."} ,      )
	a    :				Optional[str]											=       field(default=lowerCAmelCase__ ,      metadata={"help": "A folder containing the training data."}						)
	a    :				Optional[str]											=       field(default=lowerCAmelCase__ ,      metadata={"help": "A folder containing the validation data."}						)
	a    :				Optional[float]											=       field(
	    default=0.15 ,      metadata={"help": "Percent to split off of train for validation."}						)
	a    :				int											=       field(default=32 ,      metadata={"help": "The size of the square patches to use for masking."}						)
	a    :				float											=       field(
	    default=0.6 ,      metadata={"help": "Percentage of patches to mask."} ,      )
	a    :				Optional[int]											=       field(
	    default=lowerCAmelCase__ ,      metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of training examples to this "
	            "value if set."
	        )
	    } ,      )
	a    :				Optional[int]											=       field(
	    default=lowerCAmelCase__ ,      metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
	            "value if set."
	        )
	    } ,      )


	def 						_UpperCAmelCase     (self )							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							{}
				if self.train_dir is not None:
							__lowercase										=							self.train_dir
				if self.validation_dir is not None:
							__lowercase										=							self.validation_dir
				__lowercase										=							data_files if data_files else None






@dataclass
class 							__lowercase       :







	'''simple docstring'''

	a    :				str											=       field(
	    default=lowerCAmelCase__ ,      metadata={
	        "help": (
	            "The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a "
	            "checkpoint identifier on the hub. "
	            "Don't set if you want to train a model from scratch."
	        )
	    } ,      )
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(lowerCAmelCase__						)} ,      )
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={
	        "help": (
	            "Override some existing default config settings when a model is trained from scratch. Example: "
	            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
	        )
	    } ,      )
	a    :				Optional[str]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"} ,      )
	a    :				str											=       field(
	    default="main" ,      metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} ,      )
	a    :				str											=       field(default=lowerCAmelCase__ ,      metadata={"help": "Name or path of preprocessor config."}						)
	a    :				bool											=       field(
	    default=lowerCAmelCase__ ,      metadata={
	        "help": (
	            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
	            "with private models)."
	        )
	    } ,      )
	a    :				Optional[int]											=       field(
	    default=lowerCAmelCase__ ,      metadata={
	        "help": (
	            "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
	        )
	    } ,      )
	a    :				Optional[int]											=       field(
	    default=lowerCAmelCase__ ,      metadata={
	        "help": (
	            "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
	        )
	    } ,      )
	a    :				Optional[int]											=       field(
	    default=lowerCAmelCase__ ,      metadata={"help": "Stride to use for the encoder."} ,      )






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase=192     ,_lowerCamelCase=32     ,_lowerCamelCase=4     ,_lowerCamelCase=0.6 )							->							str:







				'''simple docstring'''





				__lowercase										=							input_size
				__lowercase										=							mask_patch_size
				__lowercase										=							model_patch_size
				__lowercase										=							mask_ratio

				if self.input_size % self.mask_patch_size != 0:
							raise ValueError('''Input size must be divisible by mask patch size''' )
				if self.mask_patch_size % self.model_patch_size != 0:
							raise ValueError('''Mask patch size must be divisible by model patch size''' )

				__lowercase										=							self.input_size // self.mask_patch_size
				__lowercase										=							self.mask_patch_size // self.model_patch_size

				__lowercase										=							self.rand_size**2
				__lowercase										=							int(np.ceil(self.token_count * self.mask_ratio ) )


	def __call__(self )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							np.random.permutation(self.token_count )[: self.mask_count]
				__lowercase										=							np.zeros(self.token_count     ,dtype=_lowerCamelCase )
				__lowercase										=							1

				__lowercase										=							mask.reshape((self.rand_size, self.rand_size) )
				__lowercase										=							mask.repeat(self.scale     ,axis=0 ).repeat(self.scale     ,axis=1 )

				return torch.tensor(mask.flatten() )





def    _lowerCAmelCase						(				lowerCamelCase_			:  Any				):
			__lowercase										=							torch.stack([example['''pixel_values'''] for example in examples]				)
			__lowercase										=							torch.stack([example['''mask'''] for example in examples]				)
			return {"pixel_values": pixel_values, "bool_masked_pos": mask}




def    _lowerCAmelCase						(				):
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			__lowercase										=							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
			if len(sys.argv				) == 2 and sys.argv[1].endswith('''.json'''				):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						__lowercase						,		__lowercase						,		__lowercase										=							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
			else:
						__lowercase						,		__lowercase						,		__lowercase										=							parser.parse_args_into_dataclasses()

			# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
			# information sent is the one passed as arguments along with your Python/PyTorch versions.
			send_example_telemetry('''run_mim'''		,       lowerCamelCase_		,       lowerCamelCase_				)

			# Setup logging
			logging.basicConfig(
			    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''		,       datefmt='''%m/%d/%Y %H:%M:%S'''		,       handlers=[logging.StreamHandler(sys.stdout				)]		,       )

			if training_args.should_log:
						# The default of training_args.log_level is passive, so we set log level at info here to have that default.
						transformers.utils.logging.set_verbosity_info()

			__lowercase										=							training_args.get_process_log_level()
			logger.setLevel(lowerCamelCase_				)
			transformers.utils.logging.set_verbosity(lowerCamelCase_				)
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()

			# Log on each process the small summary:
			logger.warning(
			    f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
			    + f"distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}"				)
			logger.info(f"Training/evaluation parameters {training_args}"				)

			# Detecting last checkpoint.
			__lowercase										=							None
			if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
						__lowercase										=							get_last_checkpoint(training_args.output_dir				)
						if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
									raise ValueError(
									    f"Output directory ({training_args.output_dir}) already exists and is not empty. "
									    '''Use --overwrite_output_dir to overcome.'''				)
						elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
									logger.info(
									    f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
									    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''				)

    # Initialize our dataset.
			__lowercase										=							load_dataset(
			    data_args.dataset_name		,       data_args.dataset_config_name		,       data_files=data_args.data_files		,       cache_dir=model_args.cache_dir		,       use_auth_token=True if model_args.use_auth_token else None		,       )

			# If we don't have a validation split, split off a percentage of train as validation.
			__lowercase										=							None if '''validation''' in ds.keys() else data_args.train_val_split
			if isinstance(data_args.train_val_split		,       lowerCamelCase_				) and data_args.train_val_split > 0.0:
						__lowercase										=							ds['''train'''].train_test_split(data_args.train_val_split				)
						__lowercase										=							split['''train''']
						__lowercase										=							split['''test''']

			# Create config
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			__lowercase										=							{
			    '''cache_dir''': model_args.cache_dir,
			    '''revision''': model_args.model_revision,
			    '''use_auth_token''': True if model_args.use_auth_token else None,
			}
			if model_args.config_name_or_path:
						__lowercase										=							AutoConfig.from_pretrained(model_args.config_name_or_path		,       **lowerCamelCase_				)
			elif model_args.model_name_or_path:
						__lowercase										=							AutoConfig.from_pretrained(model_args.model_name_or_path		,       **lowerCamelCase_				)
			else:
						__lowercase										=							CONFIG_MAPPING[model_args.model_type]()
						logger.warning('''You are instantiating a new config instance from scratch.'''				)
						if model_args.config_overrides is not None:
									logger.info(f"Overriding config: {model_args.config_overrides}"				)
									config.update_from_string(model_args.config_overrides				)
									logger.info(f"New config: {config}"				)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
			if hasattr(lowerCamelCase_		,       '''decoder_type'''				):
						__lowercase										=							'''simmim'''

			# adapt config
			__lowercase										=							model_args.image_size if model_args.image_size is not None else config.image_size
			__lowercase										=							model_args.patch_size if model_args.patch_size is not None else config.patch_size
			__lowercase										=							(
			    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
			)

			config.update(
			    {
			        '''image_size''': model_args.image_size,
			        '''patch_size''': model_args.patch_size,
			        '''encoder_stride''': model_args.encoder_stride,
			    }				)

			# create image processor
			if model_args.image_processor_name:
						__lowercase										=							AutoImageProcessor.from_pretrained(model_args.image_processor_name		,       **lowerCamelCase_				)
			elif model_args.model_name_or_path:
						__lowercase										=							AutoImageProcessor.from_pretrained(model_args.model_name_or_path		,       **lowerCamelCase_				)
			else:
						__lowercase										=							{
						    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
						}
						__lowercase										=							IMAGE_PROCESSOR_TYPES[model_args.model_type]()

			# create model
			if model_args.model_name_or_path:
						__lowercase										=							AutoModelForMaskedImageModeling.from_pretrained(
						    model_args.model_name_or_path		,       from_tf=bool('''.ckpt''' in model_args.model_name_or_path				)		,       config=lowerCamelCase_		,       cache_dir=model_args.cache_dir		,       revision=model_args.model_revision		,       use_auth_token=True if model_args.use_auth_token else None		,       )
			else:
						logger.info('''Training new model from scratch'''				)
						__lowercase										=							AutoModelForMaskedImageModeling.from_config(lowerCamelCase_				)

			if training_args.do_train:
						__lowercase										=							ds['''train'''].column_names
			else:
						__lowercase										=							ds['''validation'''].column_names

			if data_args.image_column_name is not None:
						__lowercase										=							data_args.image_column_name
			elif "image" in column_names:
						__lowercase										=							'''image'''
			elif "img" in column_names:
						__lowercase										=							'''img'''
			else:
						__lowercase										=							column_names[0]

			# transformations as done in original SimMIM paper
			# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
			__lowercase										=							Compose(
			    [
			        Lambda(lambda lowerCamelCase_				: img.convert('''RGB'''				) if img.mode != "RGB" else img				),
			        RandomResizedCrop(model_args.image_size		,       scale=(0.67, 1.0)		,       ratio=(3.0 / 4.0, 4.0 / 3.0)				),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        Normalize(mean=image_processor.image_mean		,       std=image_processor.image_std				),
			    ]				)

			# create mask generator
			__lowercase										=							MaskGenerator(
			    input_size=model_args.image_size		,       mask_patch_size=data_args.mask_patch_size		,       model_patch_size=model_args.patch_size		,       mask_ratio=data_args.mask_ratio		,       )

			def preprocess_images(lowerCamelCase_			:  Any				):
						__lowercase										=							[transforms(lowerCamelCase_				) for image in examples[image_column_name]]
						__lowercase										=							[mask_generator() for i in range(len(examples[image_column_name]				)				)]

						return examples

			if training_args.do_train:
						if "train" not in ds:
									raise ValueError('''--do_train requires a train dataset'''				)
						if data_args.max_train_samples is not None:
									__lowercase										=							ds['''train'''].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
						# Set the training transforms
						ds["train"].set_transform(lowerCamelCase_				)

			if training_args.do_eval:
						if "validation" not in ds:
									raise ValueError('''--do_eval requires a validation dataset'''				)
						if data_args.max_eval_samples is not None:
									__lowercase										=							(
									    ds['''validation'''].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
									)
						# Set the validation transforms
						ds["validation"].set_transform(lowerCamelCase_				)

			# Initialize our trainer
			__lowercase										=							Trainer(
			    model=lowerCamelCase_		,       args=lowerCamelCase_		,       train_dataset=ds['''train'''] if training_args.do_train else None		,       eval_dataset=ds['''validation'''] if training_args.do_eval else None		,       tokenizer=lowerCamelCase_		,       data_collator=lowerCamelCase_		,       )

			# Training
			if training_args.do_train:
						__lowercase										=							None
						if training_args.resume_from_checkpoint is not None:
									__lowercase										=							training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									__lowercase										=							last_checkpoint
						__lowercase										=							trainer.train(resume_from_checkpoint=lowerCamelCase_				)
						trainer.save_model()
						trainer.log_metrics('''train'''		,       train_result.metrics				)
						trainer.save_metrics('''train'''		,       train_result.metrics				)
						trainer.save_state()

			# Evaluation
			if training_args.do_eval:
						__lowercase										=							trainer.evaluate()
						trainer.log_metrics('''eval'''		,       lowerCamelCase_				)
						trainer.save_metrics('''eval'''		,       lowerCamelCase_				)

			# Write model card and (optionally) push to hub
			__lowercase										=							{
			    '''finetuned_from''': model_args.model_name_or_path,
			    '''tasks''': '''masked-image-modeling''',
			    '''dataset''': data_args.dataset_name,
			    '''tags''': ['''masked-image-modeling'''],
			}
			if training_args.push_to_hub:
						trainer.push_to_hub(**lowerCamelCase_				)
			else:
						trainer.create_model_card(**lowerCamelCase_				)


if __name__ == "__main__":
	main()












'''simple docstring'''



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


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




def    _lowerCAmelCase						(				lowerCamelCase_			:  Any				):
			__lowercase										=							test_results.split(''' '''				)

			__lowercase										=							0
			__lowercase										=							0

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

			for i, expression in enumerate(lowerCamelCase_				):
						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						(				lowerCamelCase_			:  Union[str, Any]				):
			__lowercase										=							{}
			__lowercase										=							None
			__lowercase										=							False
			for line in failures_short_lines.split('''\n'''				):
						if re.search(r'''_ \[doctest\]'''		,       lowerCamelCase_				):
									__lowercase										=							True
									__lowercase										=							line.split(''' '''				)[2]
						elif in_error and not line.split(''' '''				)[0].isdigit():
									__lowercase										=							line
									__lowercase										=							False

			return failures






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase )							->							Any:







				'''simple docstring'''





				__lowercase										=							title

				__lowercase										=							doc_test_results['''time_spent'''].split(''',''' )[0]
				__lowercase										=							doc_test_results['''success''']
				__lowercase										=							doc_test_results['''failures''']
				__lowercase										=							self.n_success + self.n_failures

				# Failures and success of the modeling tests
				__lowercase										=							doc_test_results


	@property
	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							[self._time_spent]
				__lowercase										=							0

				for time in time_spent:
							__lowercase										=							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(_lowerCamelCase ) == 1:
										__lowercase										=							[0, 0, time_parts[0]]

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

				__lowercase						,		__lowercase						,		__lowercase										=							total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
				return f"{int(_lowerCamelCase )}h{int(_lowerCamelCase )}m{int(_lowerCamelCase )}s"


	@property
	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				return {"type": "header", "text": {"type": "plain_text", "text": self.title}}


	@property
	def 						_UpperCAmelCase     (self )							->							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 )							->							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 )							->							Dict:







				'''simple docstring'''





				__lowercase										=							40
				__lowercase										=							{k: v['''failed'''] for k, v in doc_test_results.items() if isinstance(_lowerCamelCase     ,_lowerCamelCase )}

				__lowercase										=							''''''
				for category, failures in category_failures.items():
							if len(_lowerCamelCase ) == 0:
										continue

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

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

				return {
				    "type": "section",
				    "text": {
				        "type": "mrkdwn",
				        "text": f"The following examples had failures:\n\n\n{report}\n",
				    },
				}


	@property
	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							[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(_lowerCamelCase )


	@staticmethod
	def 						_UpperCAmelCase     ()							->							List[str]:







				'''simple docstring'''





				__lowercase										=							[
				    {
				        '''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(_lowerCamelCase )} ) )

				client.chat_postMessage(
				    channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']     ,text='''There was an issue running the tests.'''     ,blocks=_lowerCamelCase     ,)


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





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

				__lowercase										=							f"{self.n_failures} failures out of {self.n_tests} tests," if self.n_failures else '''All tests passed.'''

				__lowercase										=							client.chat_postMessage(
				    channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY''']     ,blocks=self.payload     ,text=_lowerCamelCase     ,)


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							''''''
				for key, value in failures.items():
							__lowercase										=							value[:200] + ''' [Truncated]''' if len(_lowerCamelCase ) > 250 else value
							failures_text += f"*{key}*\n_{value}_\n\n"

				__lowercase										=							job_name
				__lowercase										=							{'''type''': '''section''', '''text''': {'''type''': '''mrkdwn''', '''text''': text}}

				if job_link is not None:
							__lowercase										=							{
							    '''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 )							->							Any:







				'''simple docstring'''





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

				__lowercase										=							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''' )

				__lowercase										=							sorted(self.doc_test_results.items()     ,key=lambda _lowerCamelCase : t[0] )
				for job, job_result in sorted_dict:
							if len(job_result['''failures'''] ):
										__lowercase										=							f"*Num failures* :{len(job_result['failed'] )} \n"
										__lowercase										=							job_result['''failures''']
										__lowercase										=							self.get_reply_blocks(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,text=_lowerCamelCase )

										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=_lowerCamelCase     ,thread_ts=self.thread_ts['''ts''']     ,)

										time.sleep(1 )





def    _lowerCAmelCase						(				):
			__lowercase										=							os.environ['''GITHUB_RUN_ID''']
			__lowercase										=							f"https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100"
			__lowercase										=							requests.get(lowerCamelCase_				).json()
			__lowercase										=							{}

			try:
						jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}				)
						__lowercase										=							math.ceil((result['''total_count'''] - 1_0_0) / 1_0_0				)

						for i in range(lowerCamelCase_				):
									__lowercase										=							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.'''		,       lowerCamelCase_				)

			return {}




def    _lowerCAmelCase						(				lowerCamelCase_			:  str				):
			__lowercase										=							{}

			if os.path.exists(lowerCamelCase_				):
						__lowercase										=							os.listdir(lowerCamelCase_				)
						for file in files:
									try:
												with open(os.path.join(lowerCamelCase_		,       lowerCamelCase_				)		,       encoding='''utf-8'''				) as f:
															__lowercase										=							f.read()
									except UnicodeDecodeError as e:
												raise ValueError(f"Could not open {os.path.join(lowerCamelCase_		,       lowerCamelCase_				)}."				) from e

			return _artifact




def    _lowerCAmelCase						(				):






			class 							__lowercase       :







				'''simple docstring'''

				def __init__(self     ,_lowerCamelCase )							->							Dict:







							'''simple docstring'''





							__lowercase										=							name
							__lowercase										=							[]


				def __str__(self )							->							List[str]:







							'''simple docstring'''





							return self.name


				def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Dict:







							'''simple docstring'''





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





			__lowercase										=							{}

			__lowercase										=							filter(os.path.isdir		,       os.listdir()				)
			for directory in directories:
						__lowercase										=							directory
						if artifact_name not in _available_artifacts:
									__lowercase										=							Artifact(lowerCamelCase_				)

									_available_artifacts[artifact_name].add_path(lowerCamelCase_				)

			return _available_artifacts


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

	_SCREAMING_SNAKE_CASE					=    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'
	_SCREAMING_SNAKE_CASE					=    {
	    v: {
	        '''failed''': [],
	        '''failures''': {},
	    }
	    for v in docs.values()
	}

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

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

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

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

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

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

	_SCREAMING_SNAKE_CASE					=    Message('''🤗 Results of the doc tests.''', doc_test_results)
	message.post()
	message.post_reply()





