






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
  from sagemaker import Session, TrainingJobAnalytics
  from sagemaker.huggingface import HuggingFace


@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER"      ,   "False"	)	) is not True      ,   reason="Skipping test because should only be run when releasing minor transformers version"      ,   )
@pytest.mark.usefixtures("sm_env"	)
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 16_00, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 16_00, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]	)
class    __lowercase   (						unittest.TestCase	):







     '''simple docstring'''




     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         if self.framework == "pytorch":
             subprocess.run(
                 F"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split()			,						encoding="""utf-8"""			,						check=UpperCamelCase_			,						)
         assert hasattr(self			,						"""env"""					)




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      Any					):


         """simple docstring"""



         __A       =						{
             """enabled""": True,
             """processes_per_host""": 8,
         }
         __A       =						{
             """enabled""": True,
             """parameters""": {
                 """microbatches""": 4,
                 """placement_strategy""": """spread""",
                 """pipeline""": """interleaved""",
                 """optimize""": """speed""",
                 """partitions""": 4,
                 """ddp""": True,
             },
         }

         __A       =						{"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

         __A       =						"""trainer""" if self.script == """run_glue.py""" else """smtrainer"""
         # creates estimator
         return HuggingFace(
             entry_point=self.script			,						source_dir=self.env.test_path			,						role=self.env.role			,						image_uri=self.env.image_uri			,						base_job_name=F"{self.env.base_job_name}-{instance_count}-smp-{name_extension}"			,						instance_count=UpperCamelCase_			,						instance_type=self.instance_type			,						debugger_hook_config=UpperCamelCase_			,						hyperparameters={
                 **self.env.hyperparameters,
                 """model_name_or_path""": self.model_name_or_path,
                 """max_steps""": 500,
             }			,						metric_definitions=self.env.metric_definitions			,						distribution=UpperCamelCase_			,						py_version="""py36"""			,						)




     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      Optional[Any]					):


         """simple docstring"""



         TrainingJobAnalytics(UpperCamelCase_					).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv"					)





     @parameterized.expand([(1,)]					)
     def 		lowerCAmelCase_  (					self							:      Dict			,						UpperCamelCase_							:      Optional[Any]					):


         """simple docstring"""



         __A       =						self.create_estimator(UpperCamelCase_					)

         # run training
         estimator.fit()

         # result dataframe
         __A       =						TrainingJobAnalytics(estimator.latest_training_job.name					).dataframe()

         # extract kpis
         __A       =						list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]					)
         __A       =						list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]					)
         # get train time from SageMaker job, this includes starting, preprocessing, stopping
         __A       =						(
             Session().describe_training_job(estimator.latest_training_job.name					).get("""TrainingTimeInSeconds"""			,						999_999					)
         )

         # assert kpis
         assert train_runtime <= self.results["train_runtime"]
         assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy					)
         assert all(t <= self.results["""eval_loss"""] for t in eval_loss					)

         # dump tests result into json file to share in PR
         with open(F"{estimator.latest_training_job.name}.json"			,						"""w"""					) as outfile:
             json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss}			,						UpperCamelCase_					)












import argparse
import os
import re

import packaging.version


__a   :							Tuple						= "examples/"
__a   :							Any						= {
    "examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
    "doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
__a   :							Tuple						= {
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
__a   :							List[str]						= "README.md"



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      List[str]      ,					__lowercase :      Optional[Any]     )    ->   List[Any]:





    """simple docstring"""

    with open(__lowercase      ,					"""r"""      ,					encoding="""utf-8"""      ,					newline="""\n"""     ) as f:
        __A       =						f.read()
    __A     ,						__A       =						REPLACE_PATTERNS[pattern]
    __A       =						replace.replace("""VERSION"""      ,					__lowercase     )
    __A       =						re_pattern.sub(__lowercase      ,					__lowercase     )
    with open(__lowercase      ,					"""w"""      ,					encoding="""utf-8"""      ,					newline="""\n"""     ) as f:
        f.write(__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[str]     )    ->   Dict:





    """simple docstring"""

    for folder, directories, fnames in os.walk(__lowercase     ):
        # Removing some of the folders with non-actively maintained examples from the walk
        if "research_projects" in directories:
            directories.remove("""research_projects"""     )
        if "legacy" in directories:
            directories.remove("""legacy"""     )
        for fname in fnames:
            if fname.endswith(""".py"""     ):
                update_version_in_file(os.path.join(__lowercase      ,					__lowercase     )      ,					__lowercase      ,					pattern="""examples"""     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Any      ,					__lowercase :      Optional[int]=False     )    ->   Dict:





    """simple docstring"""

    for pattern, fname in REPLACE_FILES.items():
        update_version_in_file(__lowercase      ,					__lowercase      ,					__lowercase     )
    if not patch:
        update_version_in_examples(__lowercase     )



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





    """simple docstring"""

    __A       =						"""🤗 Transformers currently provides the following architectures"""
    __A       =						"""1. Want to contribute a new model?"""
    with open(__lowercase      ,					"""r"""      ,					encoding="""utf-8"""      ,					newline="""\n"""     ) as f:
        __A       =						f.readlines()

    # Find the start of the list.
    __A       =						0
    while not lines[start_index].startswith(_start_prompt     ):
        start_index += 1
    start_index += 1

    __A       =						start_index
    # Update the lines in the model list.
    while not lines[index].startswith(_end_prompt     ):
        if lines[index].startswith("""1."""     ):
            __A       =						lines[index].replace(
                """https://huggingface.co/docs/transformers/main/model_doc"""      ,					"""https://huggingface.co/docs/transformers/model_doc"""      ,					)
        index += 1

    with open(__lowercase      ,					"""w"""      ,					encoding="""utf-8"""      ,					newline="""\n"""     ) as f:
        f.writelines(__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			)    ->   Dict:





    """simple docstring"""

    with open(REPLACE_FILES["""init"""]      ,					"""r"""     ) as f:
        __A       =						f.read()
    __A       =						REPLACE_PATTERNS["""init"""][0].search(__lowercase     ).groups()[0]
    return packaging.version.parse(__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str=False     )    ->   Optional[Any]:





    """simple docstring"""

    __A       =						get_version()
    if patch and default_version.is_devrelease:
        raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!"""     )
    if default_version.is_devrelease:
        __A       =						default_version.base_version
    elif patch:
        __A       =						f"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
    else:
        __A       =						f"{default_version.major}.{default_version.minor + 1}.0"

    # Now let's ask nicely if that's the right one.
    __A       =						input(f"Which version are you releasing? [{default_version}]"     )
    if len(__lowercase     ) == 0:
        __A       =						default_version

    print(f"Updating version to {version}."     )
    global_version_update(__lowercase      ,					patch=__lowercase     )
    if not patch:
        print("""Cleaning main README, don't forget to run `make fix-copies`."""     )
        clean_main_ref_in_model_list()



def       _SCREAMING_SNAKE_CASE    (			)    ->   List[str]:





    """simple docstring"""

    __A       =						get_version()
    __A       =						f"{current_version.major}.{current_version.minor + 1}.0.dev0"
    __A       =						current_version.base_version

    # Check with the user we got that right.
    __A       =						input(f"Which version are we developing now? [{dev_version}]"     )
    if len(__lowercase     ) == 0:
        __A       =						dev_version

    print(f"Updating version to {version}."     )
    global_version_update(__lowercase     )
    print("""Cleaning main README, don't forget to run `make fix-copies`."""     )
    clean_main_ref_in_model_list()


if __name__ == "__main__":
  __a   :							str						= argparse.ArgumentParser()
  parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
  parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
  __a   :							Union[str, Any]						= parser.parse_args()
  if not args.post_release:
    pre_release_work(patch=args.patch)
  elif args.patch:
    print("Nothing to do after a patch :-)")
  else:
    post_release_work()




