"""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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     List[Any]      ):
     lowercase_	:				int													=	test_results.split(' '      )

     lowercase_	:				Optional[int]													=	0
     lowercase_	:				List[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.
     lowercase_	:				Any													=	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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]      ):
     lowercase_	:				List[str]													=	{}
     lowercase_	:				Dict													=	None
     lowercase_	:				str													=	False
     for line in failures_short_lines.split('\n'      ):
          if re.search(R'_ \[doctest\]'     ,			lowercase_      ):
               lowercase_	:				Optional[int]													=	True
               lowercase_	:				Any													=	line.split(' '      )[2]
          elif in_error and not line.split(' '      )[0].isdigit():
               lowercase_	:				str													=	line
               lowercase_	:				Optional[int]													=	False

     return failures







class        UpperCamelCase		:
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	title

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

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


        '''simple docstring'''


        lowercase_	:				Tuple													=	[self._time_spent]
        lowercase_	:				Optional[Any]													=	0

        for time in time_spent:
             lowercase_	:				Optional[Any]													=	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(A__			) == 1:
                  lowercase_	:				str													=	[0, 0, time_parts[0]]

             lowercase_	:				Union[str, Any]													=	int(time_parts[0]			), int(time_parts[1]			), float(time_parts[2]			)
             total_secs += hours * 3600 + minutes * 60 + seconds

        lowercase_	:				str													=	total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
        return f'''{int(A__			)}h{int(A__			)}m{int(A__			)}s'''
   @property
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


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


        '''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			)	->      Tuple:


        '''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			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				Dict													=	40
        lowercase_	:				Optional[int]													=	{k: v["""failed"""] for k, v in doc_test_results.items() if isinstance(A__     ,A__			)}

        lowercase_	:				Dict													=	""""""
        for category, failures in category_failures.items():
             if len(A__			) == 0:
                  continue

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

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

        return {
            "type": "section",
            "text": {
                "type": "mrkdwn",
                "text": f'''The following examples had failures:\n\n\n{report}\n''',
            },
        }
   @property
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				List[str]													=	[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(A__			)
   @staticmethod
   def 			_UpperCAmelCase   (			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[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(A__			)}			)			)

        client.chat_postMessage(
            channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']     ,text='There was an issue running the tests.'     ,blocks=A__     ,)
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


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

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

        lowercase_	:				Optional[Any]													=	client.chat_postMessage(
            channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']     ,blocks=self.payload     ,text=A__     ,)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


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

        lowercase_	:				Dict													=	job_name
        lowercase_	:				List[Any]													=	{"""type""": """section""", """text""": {"""type""": """mrkdwn""", """text""": text}}

        if job_link is not None:
             lowercase_	:				str													=	{
                 """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			)	->      Union[str, Any]:


        '''simple docstring'''


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

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

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

                  time.sleep(1			)






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

     try:
          jobs.update({job['name']: job['html_url'] for job in result['jobs']}      )
          lowercase_	:				Tuple													=	math.ceil((result['total_count'] - 1_00) / 1_00      )

          for i in range(lowercase_      ):
               lowercase_	:				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.'     ,			lowercase_      )

     return {}



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str      ):
     lowercase_	:				List[Any]													=	{}

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

     return _artifact



def 			lowercase__(       ):







     class        UpperCamelCase		:
        def __init__(			self     ,__UpperCamelCase			)	->      int:


             '''simple docstring'''


             lowercase_	:				str													=	name
             lowercase_	:				str													=	[]
        def __str__(			self			)	->      Union[str, Any]:


             '''simple docstring'''


             return self.name







        def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      List[str]:


             '''simple docstring'''


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






     lowercase_	:				Dict[str, Artifact]													=	{}

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

               _available_artifacts[artifact_name].add_path(lowercase_      )

     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    	=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    	=line.split("::")
                            else:
                                   __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()




import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: int    ,      lowercase_: Dict    ,      lowercase_: Tuple					)     ->      Any:
	# Construct model
	if gpta_config_file == "":
		A__							:    Dict        =		GPTaConfig()
	else:
		A__							:    List[Any]        =		GPTaConfig.from_json_file(lowercase_					)
	A__							:    Tuple        =		GPTaModel(lowercase_					)

	# Load weights from numpy
	load_tf_weights_in_gpta(lowercase_    ,      lowercase_    ,      lowercase_					)

	# Save pytorch-model
	A__							:    Optional[Any]        =		pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
	A__							:    Optional[Any]        =		pytorch_dump_folder_path + """/""" + CONFIG_NAME
	print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""					)
	torch.save(model.state_dict()    ,      lowercase_					)
	print(f"""Save configuration file to {pytorch_config_dump_path}"""					)
	with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as f:
		f.write(config.to_json_string()					)


if __name__ == "__main__":
	A_       : Union[str, Any]         =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	parser.add_argument(
	    '--gpt2_config_file',
	    default='',
	    type=str,
	    help=(
	        'An optional config json file corresponding to the pre-trained OpenAI model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	A_       : str         =			parser.parse_args()
	convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



