





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


__lowercase	: List[Any]       =WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])




def      a__			(       lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =test_results.split(" "	)

   UpperCAmelCase_           =0
   UpperCAmelCase_           =0

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

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

   return failed, success, time_spent




def      a__			(       lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           ={}
   UpperCAmelCase_           =None
   UpperCAmelCase_           =False
   for line in failures_short_lines.split("\n"	):
      if re.search(R"_ \[doctest\]"	,     lowercase__	):
         UpperCAmelCase_           =True
         UpperCAmelCase_           =line.split(" "	)[2]
      elif in_error and not line.split(" "	)[0].isdigit():
         UpperCAmelCase_           =line
         UpperCAmelCase_           =False

   return failures

class   A							:



      def __init__(						self:    Optional[Any]		,	_lowerCAmelCase:    str		,	_lowerCAmelCase:    Dict							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           =title

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

         # Failures and success of the modeling tests
         UpperCAmelCase_           =doc_test_results



      @property
      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           =[self._time_spent]
         UpperCAmelCase_           =0

         for time in time_spent:
            UpperCAmelCase_           =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:
               UpperCAmelCase_           =[0, 0, time_parts[0]]

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

         UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_           =total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
         return F'{int(_lowerCAmelCase							)}h{int(_lowerCAmelCase							)}m{int(_lowerCAmelCase							)}s'



      @property
      def 						lowerCAmelCase__     (						self:    int							)    ->     Dict:
         '''simple docstring'''

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



      @property
      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     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 						lowerCAmelCase__     (						self:    Optional[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 						lowerCAmelCase__     (						self:    Tuple							)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_           =40
         UpperCAmelCase_           ={k: v["failed"] for k, v in doc_test_results.items() if isinstance(_lowerCAmelCase		,	_lowerCAmelCase							)}

         UpperCAmelCase_           =""
         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 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           =[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 						lowerCAmelCase__     (						)    ->     Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_           =[
             {
                 "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 						lowerCAmelCase__     (						self:    Dict							)    ->     List[str]:
         '''simple docstring'''

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

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

         UpperCAmelCase_           =client.chat_postMessage(
             channel=os.environ["CI_SLACK_CHANNEL_ID_DAILY"]		,	blocks=self.payload		,	text=_lowerCAmelCase		,	)



      def 						lowerCAmelCase__     (						self:    List[str]		,	_lowerCAmelCase:    Optional[Any]		,	_lowerCAmelCase:    List[Any]		,	_lowerCAmelCase:    List[str]		,	_lowerCAmelCase:    int							)    ->     Optional[int]:
         '''simple docstring'''

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

         UpperCAmelCase_           =job_name
         UpperCAmelCase_           ={"type": "section", "text": {"type": "mrkdwn", "text": text}}

         if job_link is not None:
            UpperCAmelCase_           ={
                "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 						lowerCAmelCase__     (						self:    Any							)    ->     List[str]:
         '''simple docstring'''

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

         UpperCAmelCase_           =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"							)

         UpperCAmelCase_           =sorted(self.doc_test_results.items()		,	key=lambda _lowerCAmelCase							: t[0]							)
         for job, job_result in sorted_dict:
            if len(job_result["failures"]							):
               UpperCAmelCase_           =F'*Num failures* :{len(job_result["failed"]							)} \n'
               UpperCAmelCase_           =job_result["failures"]
               UpperCAmelCase_           =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      a__			(       ):






   '''simple docstring'''






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

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

      for i in range(lowercase__	):
         UpperCAmelCase_           =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."	,     lowercase__	)

   return {}




def      a__			(       lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           ={}

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

   return _artifact




def      a__			(       ):






   '''simple docstring'''






   class   A							:



         def __init__(						self:    Tuple		,	_lowerCAmelCase:    str							)    ->     Any:
            '''simple docstring'''

            UpperCAmelCase_           =name
            UpperCAmelCase_           =[]



         def __str__(						self:    Optional[int]							)    ->     Tuple:
            '''simple docstring'''

            return self.name






         def 						lowerCAmelCase__     (						self:    int		,	_lowerCAmelCase:    str							)    ->     List[Any]:
            '''simple docstring'''

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

   UpperCAmelCase_           ={}

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

         _available_artifacts[artifact_name].add_path(lowercase__	)

   return _available_artifacts


if __name__ == "__main__":
     __lowercase	: str       =get_job_links()
     __lowercase	: Dict       =retrieve_available_artifacts()

     __lowercase	: Optional[int]       =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'
     __lowercase	: Any       ={
         v: {
             """failed""": [],
             """failures""": {},
         }
         for v in docs.values()
     }

     # Link to the GitHub Action job
     __lowercase	: Tuple       =github_actions_job_links.get("""run_doctests""")

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

          __lowercase	: str       =extract_first_line_failure(artifact["""failures_short"""])
          for line in artifact["summary_short"].split("""\n"""):
               if re.search("""FAILED""", line):
                    __lowercase	: int       =line.replace("""FAILED """, """""")
                    __lowercase	: List[Any]       =line.split()[0].replace("""\n""", """""")

                    if "::" in line:
                         __lowercase   ,      __lowercase	: Any       =line.split("""::""")
                    else:
                         __lowercase   ,      __lowercase	: Dict       =line, line

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

                              __lowercase	: Tuple       =all_failures[test] if test in all_failures else """N/A"""
                              __lowercase	: Optional[int]       =failure
                              break

     __lowercase	: Optional[int]       =Message("""🤗 Results of the doc tests.""", doc_test_results)
     message.post()
     message.post_reply()








import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A			:						Optional[Any]        =  1_6
A			:						str        =  3_2






def 					UpperCamelCase   (      __magic_name__	:				Accelerator       ,       __magic_name__	:				int = 16	)  ->    List[str]:
		"""simple docstring"""




		lowercase__	     =						AutoTokenizer.from_pretrained("""bert-base-cased"""	)
		lowercase__	     =						load_dataset("""glue"""       ,       """mrpc"""	)

		def tokenize_function(__magic_name__	:				int	):
				# max_length=None => use the model max length (it's actually the default)
				lowercase__	     =						tokenizer(examples["""sentence1"""]       ,       examples["""sentence2"""]       ,       truncation=__magic_name__       ,       max_length=__magic_name__	)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				lowercase__	     =						datasets.map(
				    __magic_name__       ,       batched=__magic_name__       ,       remove_columns=["""idx""", """sentence1""", """sentence2"""]       ,       )

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		lowercase__	     =						tokenized_datasets.rename_column("""label"""       ,       """labels"""	)

		def collate_fn(__magic_name__	:				Optional[int]	):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				lowercase__	     =						128 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						lowercase__	     =						16
				elif accelerator.mixed_precision != "no":
						lowercase__	     =						8
				else:
						lowercase__	     =						None

				return tokenizer.pad(
				    __magic_name__       ,       padding="""longest"""       ,       max_length=__magic_name__       ,       pad_to_multiple_of=__magic_name__       ,       return_tensors="""pt"""       ,       )

		# Instantiate dataloaders.
		lowercase__	     =						DataLoader(
		    tokenized_datasets["""train"""]       ,       shuffle=__magic_name__       ,       collate_fn=__magic_name__       ,       batch_size=__magic_name__	)
		lowercase__	     =						DataLoader(
		    tokenized_datasets["""validation"""]       ,       shuffle=__magic_name__       ,       collate_fn=__magic_name__       ,       batch_size=__magic_name__	)

		return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	A			:						Union[str, Any]        =  mocked_dataloaders  # noqa: F811






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]       ,       __magic_name__	:				List[Any]	)  ->    Dict:
		"""simple docstring"""




		if os.environ.get("""TESTING_MOCKED_DATALOADERS"""       ,       __magic_name__	) == "1":
				lowercase__	     =						2
		# New Code #
		lowercase__	     =						int(args.gradient_accumulation_steps	)
		# Initialize accelerator
		lowercase__	     =						Accelerator(
		    cpu=args.cpu       ,       mixed_precision=args.mixed_precision       ,       gradient_accumulation_steps=__magic_name__	)
		if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
				raise NotImplementedError(
				    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""	)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowercase__	     =						config["""lr"""]
		lowercase__	     =						int(config["""num_epochs"""]	)
		lowercase__	     =						int(config["""seed"""]	)
		lowercase__	     =						int(config["""batch_size"""]	)

		lowercase__	     =						evaluate.load("""glue"""       ,       """mrpc"""	)

		set_seed(__magic_name__	)
		lowercase__     ,      lowercase__	     =						get_dataloaders(__magic_name__       ,       __magic_name__	)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowercase__	     =						AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""       ,       return_dict=__magic_name__	)

		# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
		# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
		# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
		lowercase__	     =						model.to(accelerator.device	)

		# Instantiate optimizer
		lowercase__	     =						AdamW(params=model.parameters()       ,       lr=__magic_name__	)

		# Instantiate scheduler
		lowercase__	     =						get_linear_schedule_with_warmup(
		    optimizer=__magic_name__       ,       num_warmup_steps=100       ,       num_training_steps=(len(__magic_name__	) * num_epochs)       ,       )

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__	     =						accelerator.prepare(
		    __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__	)

		# Now we train the model
		for epoch in range(__magic_name__	):
				model.train()
				for step, batch in enumerate(__magic_name__	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						# New code #
						# We use the new `accumulate` context manager to perform gradient accumulation
						# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
						with accelerator.accumulate(__magic_name__	):
								lowercase__	     =						model(**__magic_name__	)
								lowercase__	     =						output.loss
								accelerator.backward(__magic_name__	)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				for step, batch in enumerate(__magic_name__	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						with torch.no_grad():
								lowercase__	     =						model(**__magic_name__	)
						lowercase__	     =						outputs.logits.argmax(dim=-1	)
						lowercase__     ,      lowercase__	     =						accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
						metric.add_batch(
						    predictions=__magic_name__       ,       references=__magic_name__       ,       )

				lowercase__	     =						metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}:'''       ,       __magic_name__	)






def 					UpperCamelCase   (      )  ->    Optional[Any]:
		"""simple docstring"""




		lowercase__	     =						argparse.ArgumentParser(description="""Simple example of training script."""	)
		parser.add_argument(
		    """--mixed_precision"""       ,       type=__magic_name__       ,       default=__magic_name__       ,       choices=["""no""", """fp16""", """bf16""", """fp8"""]       ,       help="""Whether to use mixed precision. Choose"""
		    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
		    """and an Nvidia Ampere GPU."""       ,       )
		# New Code #
		parser.add_argument(
		    """--gradient_accumulation_steps"""       ,       type=__magic_name__       ,       default=1       ,       help="""The number of minibatches to be ran before gradients are accumulated."""       ,       )
		parser.add_argument("""--cpu"""       ,       action="""store_true"""       ,       help="""If passed, will train on the CPU."""	)
		lowercase__	     =						parser.parse_args()
		lowercase__	     =						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
		training_function(__magic_name__       ,       __magic_name__	)


if __name__ == "__main__":
	main()

