


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



def 	_lowerCAmelCase  (							lowerCAmelCase_							:int							)->Union[str, Any]:
   '''simple docstring'''






   snake_case_    			=    test_results.split(" "							)

   snake_case_    			=    0
   snake_case_    			=    0

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

   for i, expression in enumerate(a_							):
      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_							:str							)->Optional[int]:
   '''simple docstring'''






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

   return failures


class 			__lowerCAmelCase   :






  """simple docstring"""





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




     """simple docstring"""
     snake_case_    			=    title

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

     # Failures and success of the modeling tests
     snake_case_    			=    doc_test_results



  @property
  def     lowerCAmelCase__     (   self				:				Optional[int]     )      ->       Optional[int]:




     """simple docstring"""
     snake_case_    			=    [self._time_spent]
     snake_case_    			=    0

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

        snake_case_			,			snake_case_			,			snake_case_    			=    int(time_parts[0]     ), int(time_parts[1]     ), float(time_parts[2]     )
        total_secs += hours * 3_6_0_0 + minutes * 6_0 + seconds

     snake_case_			,			snake_case_			,			snake_case_    			=    total_secs // 3_6_0_0, (total_secs % 3_6_0_0) // 6_0, total_secs % 6_0
     return F'''{int(_lowerCAmelCase     )}h{int(_lowerCAmelCase     )}m{int(_lowerCAmelCase     )}s'''



  @property
  def     lowerCAmelCase__     (   self				:				Union[str, Any]     )      ->       Tuple:




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



  @property
  def     lowerCAmelCase__     (   self				:				Optional[Any]     )      ->       int:




     """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				:				Dict     )      ->       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				:				List[str]     )      ->       Any:




     """simple docstring"""
     snake_case_    			=    4_0
     snake_case_    			=    {k: v["failed"] for k, v in doc_test_results.items() if isinstance(_lowerCAmelCase			, _lowerCAmelCase     )}

     snake_case_    			=    ""
     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				:				List[Any]     )      ->       Tuple:




     """simple docstring"""
     snake_case_    			=    [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__     (   )      ->       Tuple:




     """simple docstring"""
     snake_case_    			=    [
         {
             "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				:				Tuple     )      ->       Optional[int]:




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

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

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



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




     """simple docstring"""
     snake_case_    			=    ""
     for key, value in failures.items():
        snake_case_    			=    value[:2_0_0] + " [Truncated]" if len(_lowerCAmelCase     ) > 2_5_0 else value
        failures_text += F'''*{key}*\n_{value}_\n\n'''

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

     if job_link is not None:
        snake_case_    			=    {
            "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				:				Tuple     )      ->       Tuple:




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

     snake_case_    			=    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"     )

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






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

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

      for i in range(a_							):
         snake_case_    			=    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."						,  a_							)

   return {}



def 	_lowerCAmelCase  (							lowerCAmelCase_							:List[str]							)->List[str]:
   '''simple docstring'''






   snake_case_    			=    {}

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

   return _artifact



def 	_lowerCAmelCase  (							)->List[str]:
   '''simple docstring'''






   class 			__lowerCAmelCase   :






     """simple docstring"""





     def __init__(   self				:				List[Any]			, _lowerCAmelCase				:				str     )      ->       Union[str, Any]:




        """simple docstring"""
        snake_case_    			=    name
        snake_case_    			=    []



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




        """simple docstring"""
        return self.name



     def     lowerCAmelCase__     (   self				:				Dict			, _lowerCAmelCase				:				str     )      ->       Union[str, Any]:




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






   snake_case_    			=    {}

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

         _available_artifacts[artifact_name].add_path(a_							)

   return _available_artifacts


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE		:int     						=   get_job_links()
   SCREAMING_SNAKE_CASE		:Optional[int]     						=   retrieve_available_artifacts()

   SCREAMING_SNAKE_CASE		:Dict     						=   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		:List[Any]     						=   {
       v: {
           'failed': [],
           'failures': {},
       }
       for v in docs.values()
   }

   # Link to the GitHub Action job
   SCREAMING_SNAKE_CASE		:Union[str, Any]     						=   github_actions_job_links.get('''run_doctests''')

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

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

            if "::" in line:
               SCREAMING_SNAKE_CASE		:Tuple     						=   line.split('''::''')
            else:
               SCREAMING_SNAKE_CASE		:List[str]     						=   line, line

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

                  SCREAMING_SNAKE_CASE		:str     						=   all_failures[test] if test in all_failures else 'N/A'
                  SCREAMING_SNAKE_CASE		:Optional[Any]     						=   failure
                  break

   SCREAMING_SNAKE_CASE		:Optional[Any]     						=   Message('''🤗 Results of the doc tests.''', doc_test_results)
   message.post()
   message.post_reply()






import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


def 						UpperCAmelCase			(	a_						)		->	List[str]:







				"""simple docstring"""



				__A   			= args.pruning_method
				__A   			= args.threshold

				__A   			= args.model_name_or_path.rstrip("/"						)
				__A   			= args.target_model_path

				print(F'''Load fine-pruned model from {model_name_or_path}'''						)
				__A   			= torch.load(os.path.join(a_		,   "pytorch_model.bin"						)						)
				__A   			= {}

				for name, tensor in model.items():
								if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
												__A   			= tensor
												print(F'''Copied layer {name}'''						)
								elif "classifier" in name or "qa_output" in name:
												__A   			= tensor
												print(F'''Copied layer {name}'''						)
								elif "bias" in name:
												__A   			= tensor
												print(F'''Copied layer {name}'''						)
								else:
												if pruning_method == "magnitude":
																__A   			= MagnitudeBinarizer.apply(inputs=a_		,   threshold=a_						)
																__A   			= tensor * mask
																print(F'''Pruned layer {name}'''						)
												elif pruning_method == "topK":
																if "mask_scores" in name:
																				continue
																__A   			= name[:-6]
																__A   			= model[F'''{prefix_}mask_scores''']
																__A   			= TopKBinarizer.apply(a_		,   a_						)
																__A   			= tensor * mask
																print(F'''Pruned layer {name}'''						)
												elif pruning_method == "sigmoied_threshold":
																if "mask_scores" in name:
																				continue
																__A   			= name[:-6]
																__A   			= model[F'''{prefix_}mask_scores''']
																__A   			= ThresholdBinarizer.apply(a_		,   a_		,   a_						)
																__A   			= tensor * mask
																print(F'''Pruned layer {name}'''						)
												elif pruning_method == "l0":
																if "mask_scores" in name:
																				continue
																__A   			= name[:-6]
																__A   			= model[F'''{prefix_}mask_scores''']
																__A			,							__A   			= -0.1, 1.1
																__A   			= torch.sigmoid(a_						)
																__A   			= s * (r - l) + l
																__A   			= s_bar.clamp(min=0.0		,   max=1.0						)
																__A   			= tensor * mask
																print(F'''Pruned layer {name}'''						)
												else:
																raise ValueError("Unknown pruning method"						)

				if target_model_path is None:
								__A   			= os.path.join(
								    os.path.dirname(a_						)		,   F'''bertarized_{os.path.basename(a_						)}'''						)

				if not os.path.isdir(a_						):
								shutil.copytree(a_		,   a_						)
								print(F'''\nCreated folder {target_model_path}'''						)

				torch.save(a_		,   os.path.join(a_		,   "pytorch_model.bin"						)						)
				print("\nPruned model saved! See you later!"						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple							 =				argparse.ArgumentParser()

					parser.add_argument(
					    '--pruning_method',
					    choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
					    type=str,
					    required=True,
					    help=(
					        'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
					        ' sigmoied_threshold = Soft movement pruning)'
					    ),
					)
					parser.add_argument(
					    '--threshold',
					    type=float,
					    required=False,
					    help=(
					        'For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
					        'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
					        'Not needed for `l0`'
					    ),
					)
					parser.add_argument(
					    '--model_name_or_path',
					    type=str,
					    required=True,
					    help='Folder containing the model that was previously fine-pruned',
					)
					parser.add_argument(
					    '--target_model_path',
					    default=None,
					    type=str,
					    required=False,
					    help='Folder containing the model that was previously fine-pruned',
					)

					SCREAMING_SNAKE_CASE							:str							 =				parser.parse_args()

					main(args)
