






from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel







@require_tf
class 				UpperCAmelCase_  :
     __lowerCamelCase      				=  BlenderbotConfig
     __lowerCamelCase      				=  {}
     __lowerCamelCase      				=  'gelu'




     def __init__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase=13	,						_lowerCAmelCase=7	,						_lowerCAmelCase=True	,						_lowerCAmelCase=False	,						_lowerCAmelCase=99	,						_lowerCAmelCase=32	,						_lowerCAmelCase=2	,						_lowerCAmelCase=4	,						_lowerCAmelCase=37	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=20	,						_lowerCAmelCase=2	,						_lowerCAmelCase=1	,						_lowerCAmelCase=0	,						):
          UpperCAmelCase__      :  List[str]				      =			parent
          UpperCAmelCase__      :  str				      =			batch_size
          UpperCAmelCase__      :  Optional[int]				      =			seq_length
          UpperCAmelCase__      :  Dict				      =			is_training
          UpperCAmelCase__      :  List[Any]				      =			use_labels
          UpperCAmelCase__      :  List[Any]				      =			vocab_size
          UpperCAmelCase__      :  Optional[int]				      =			hidden_size
          UpperCAmelCase__      :  Optional[Any]				      =			num_hidden_layers
          UpperCAmelCase__      :  int				      =			num_attention_heads
          UpperCAmelCase__      :  Any				      =			intermediate_size

          UpperCAmelCase__      :  Dict				      =			hidden_dropout_prob
          UpperCAmelCase__      :  int				      =			attention_probs_dropout_prob
          UpperCAmelCase__      :  Any				      =			max_position_embeddings
          UpperCAmelCase__      :  Dict				      =			eos_token_id
          UpperCAmelCase__      :  List[Any]				      =			pad_token_id
          UpperCAmelCase__      :  Optional[int]				      =			bos_token_id




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  List[Any]				      =			ids_tensor([self.batch_size, self.seq_length - 1]	,						self.vocab_size		)
          UpperCAmelCase__      :  List[str]				      =			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)	,						1		)
          UpperCAmelCase__      :  Any				      =			tf.concat([input_ids, eos_tensor]	,						axis=1		)

          UpperCAmelCase__      :  List[Any]				      =			ids_tensor([self.batch_size, self.seq_length]	,						self.vocab_size		)

          UpperCAmelCase__      :  Dict				      =			self.config_cls(
              vocab_size=self.vocab_size	,						d_model=self.hidden_size	,						encoder_layers=self.num_hidden_layers	,						decoder_layers=self.num_hidden_layers	,						encoder_attention_heads=self.num_attention_heads	,						decoder_attention_heads=self.num_attention_heads	,						encoder_ffn_dim=self.intermediate_size	,						decoder_ffn_dim=self.intermediate_size	,						dropout=self.hidden_dropout_prob	,						attention_dropout=self.attention_probs_dropout_prob	,						max_position_embeddings=self.max_position_embeddings	,						eos_token_ids=[2]	,						bos_token_id=self.bos_token_id	,						pad_token_id=self.pad_token_id	,						decoder_start_token_id=self.pad_token_id	,						**self.config_updates	,						)
          UpperCAmelCase__      :  Tuple				      =			prepare_blenderbot_inputs_dict(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		)
          return config, inputs_dict




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Union[str, Any]				      =			TFBlenderbotModel(config=_lowerCAmelCase		).get_decoder()
          UpperCAmelCase__      :  Dict				      =			inputs_dict["""input_ids"""]

          UpperCAmelCase__      :  Union[str, Any]				      =			input_ids[:1, :]
          UpperCAmelCase__      :  Dict				      =			inputs_dict["""attention_mask"""][:1, :]
          UpperCAmelCase__      :  List[Any]				      =			inputs_dict["""head_mask"""]
          UpperCAmelCase__      :  Optional[Any]				      =			1

          # first forward pass
          UpperCAmelCase__      :  List[str]				      =			model(_lowerCAmelCase	,						attention_mask=_lowerCAmelCase	,						head_mask=_lowerCAmelCase	,						use_cache=_lowerCAmelCase		)

          UpperCAmelCase__      ,		UpperCAmelCase__      :  List[Any]				      =			outputs.to_tuple()

          # create hypothetical next token and extent to next_input_ids
          UpperCAmelCase__      :  List[str]				      =			ids_tensor((self.batch_size, 3)	,						config.vocab_size		)
          UpperCAmelCase__      :  Tuple				      =			tf.cast(ids_tensor((self.batch_size, 3)	,						2		)	,						tf.inta		)

          # append to next input_ids and
          UpperCAmelCase__      :  str				      =			tf.concat([input_ids, next_tokens]	,						axis=-1		)
          UpperCAmelCase__      :  int				      =			tf.concat([attention_mask, next_attn_mask]	,						axis=-1		)

          UpperCAmelCase__      :  List[str]				      =			model(_lowerCAmelCase	,						attention_mask=_lowerCAmelCase		)[0]
          UpperCAmelCase__      :  Optional[Any]				      =			model(_lowerCAmelCase	,						attention_mask=_lowerCAmelCase	,						past_key_values=_lowerCAmelCase		)[0]

          self.parent.assertEqual(next_tokens.shape[1]	,						output_from_past.shape[1]		)

          # select random slice
          UpperCAmelCase__      :  Any				      =			int(ids_tensor((1,)	,						output_from_past.shape[-1]		)		)
          UpperCAmelCase__      :  Any				      =			output_from_no_past[:, -3:, random_slice_idx]
          UpperCAmelCase__      :  Any				      =			output_from_past[:, :, random_slice_idx]

          # test that outputs are equal for slice
          tf.debugging.assert_near(_lowerCAmelCase	,						_lowerCAmelCase	,						rtol=1e-3		)





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=None ,							__lowerCamelCase=None ,							__lowerCamelCase=None ,							__lowerCamelCase=None ,							__lowerCamelCase=None ,							) ->			List[Any]:

     '''simple docstring'''
     if attention_mask is None:
          UpperCAmelCase__      :  Tuple				      =			tf.cast(tf.math.not_equal(__lowerCamelCase ,							config.pad_token_id       ) ,							tf.inta       )
     if decoder_attention_mask is None:
          UpperCAmelCase__      :  Tuple				      =			tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape ,							dtype=tf.inta       ),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,							config.pad_token_id       ) ,							tf.inta       ),
              ] ,							axis=-1 ,							)
     if head_mask is None:
          UpperCAmelCase__      :  Any				      =			tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
     if decoder_head_mask is None:
          UpperCAmelCase__      :  int				      =			tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
     if cross_attn_head_mask is None:
          UpperCAmelCase__      :  int				      =			tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }







@require_tf
class 				UpperCAmelCase_  (		__lowerCamelCase							,      __lowerCamelCase							,      unittest.TestCase		):
     __lowerCamelCase      				=  (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
     __lowerCamelCase      				=  (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
     __lowerCamelCase      				=  (
         {
             'conversational': TFBlenderbotForConditionalGeneration,
             'feature-extraction': TFBlenderbotModel,
             'summarization': TFBlenderbotForConditionalGeneration,
             'text2text-generation': TFBlenderbotForConditionalGeneration,
             'translation': TFBlenderbotForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __lowerCamelCase      				=  True
     __lowerCamelCase      				=  False
     __lowerCamelCase      				=  False




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Union[str, Any]				      =			TFBlenderbotModelTester(self		)
          UpperCAmelCase__      :  int				      =			ConfigTester(self	,						config_class=_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          self.config_tester.run_common_tests()




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Dict				      =			self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase		)







@require_tokenizers
@require_tf
class 				UpperCAmelCase_  (		unittest.TestCase		):
     __lowerCamelCase      				=  ['My friends are cool but they eat too many carbs.']
     __lowerCamelCase      				=  'facebook/blenderbot-400M-distill'




     @cached_property
     def   __UpperCAmelCase  (				self		):
          return BlenderbotTokenizer.from_pretrained(self.model_name		)




     @cached_property
     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Tuple				      =			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
          return model




     @slow
     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Optional[int]				      =			self.tokenizer(self.src_text	,						return_tensors="""tf"""		)
          UpperCAmelCase__      :  Union[str, Any]				      =			self.model.generate(
              model_inputs.input_ids	,						)
          UpperCAmelCase__      :  List[str]				      =			self.tokenizer.batch_decode(generated_ids.numpy()	,						skip_special_tokens=_lowerCAmelCase		)[0]
          assert (
              generated_words
              == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
          )













'''simple docstring'''


import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests


def 						__a(SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       Tuple=None     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   None
    if token is not None:
        _lowerCAmelCase						      =   {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

    _lowerCAmelCase						      =   F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
    _lowerCAmelCase						      =   requests.get(SCREAMING_SNAKE_CASE_		,							headers=SCREAMING_SNAKE_CASE_     ).json()
    _lowerCAmelCase						      =   {}

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

        for i in range(SCREAMING_SNAKE_CASE_     ):
            _lowerCAmelCase						      =   requests.get(url + F'''&page={i + 2}'''		,							headers=SCREAMING_SNAKE_CASE_     ).json()
            job_links.update({job["name"]: job["html_url"] for job in result["jobs"]}     )

        return job_links
    except Exception:
        print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}'''     )

    return {}


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       Dict=None     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   None
    if token is not None:
        _lowerCAmelCase						      =   {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

    _lowerCAmelCase						      =   F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'''
    _lowerCAmelCase						      =   requests.get(SCREAMING_SNAKE_CASE_		,							headers=SCREAMING_SNAKE_CASE_     ).json()
    _lowerCAmelCase						      =   {}

    try:
        artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]}     )
        _lowerCAmelCase						      =   math.ceil((result["total_count"] - 100) / 100     )

        for i in range(SCREAMING_SNAKE_CASE_     ):
            _lowerCAmelCase						      =   requests.get(url + F'''&page={i + 2}'''		,							headers=SCREAMING_SNAKE_CASE_     ).json()
            artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]}     )

        return artifacts
    except Exception:
        print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}'''     )

    return {}


def 						__a(SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       Dict     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   None
    if token is not None:
        _lowerCAmelCase						      =   {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

    _lowerCAmelCase						      =   requests.get(SCREAMING_SNAKE_CASE_		,							headers=SCREAMING_SNAKE_CASE_		,							allow_redirects=SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   result.headers["Location"]
    _lowerCAmelCase						      =   requests.get(SCREAMING_SNAKE_CASE_		,							allow_redirects=SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   os.path.join(SCREAMING_SNAKE_CASE_		,							F'''{artifact_name}.zip'''     )
    with open(SCREAMING_SNAKE_CASE_		,							"wb"     ) as fp:
        fp.write(response.content     )


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       Optional[Any]=None     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   []
    _lowerCAmelCase						      =   []
    _lowerCAmelCase						      =   None

    with zipfile.ZipFile(SCREAMING_SNAKE_CASE_     ) as z:
        for filename in z.namelist():
            if not os.path.isdir(SCREAMING_SNAKE_CASE_     ):
                # read the file
                if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                    with z.open(SCREAMING_SNAKE_CASE_     ) as f:
                        for line in f:
                            _lowerCAmelCase						      =   line.decode("UTF-8"     ).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    _lowerCAmelCase						      =   line[: line.index(": "     )]
                                    _lowerCAmelCase						      =   line[line.index(": "     ) + len(": "     ) :]
                                    errors.append([error_line, error]     )
                                except Exception:
                                    # skip un-related lines
                                    pass
                            elif filename == "summary_short.txt" and line.startswith("FAILED "     ):
                                # `test` is the test method that failed
                                _lowerCAmelCase						      =   line[len("FAILED "     ) :]
                                failed_tests.append(SCREAMING_SNAKE_CASE_     )
                            elif filename == "job_name.txt":
                                _lowerCAmelCase						      =   line

    if len(SCREAMING_SNAKE_CASE_     ) != len(SCREAMING_SNAKE_CASE_     ):
        raise ValueError(
            F'''`errors` and `failed_tests` should have the same number of elements. Got {len(SCREAMING_SNAKE_CASE_     )} for `errors` '''
            F'''and {len(SCREAMING_SNAKE_CASE_     )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'''
            " problem."     )

    _lowerCAmelCase						      =   None
    if job_name and job_links:
        _lowerCAmelCase						      =   job_links.get(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )

    # A list with elements of the form (line of error, error, failed test)
    _lowerCAmelCase						      =   [x + [y] + [job_link] for x, y in zip(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )]

    return result


def 						__a(SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       Tuple=None     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   []

    _lowerCAmelCase						      =   [os.path.join(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     ) for p in os.listdir(SCREAMING_SNAKE_CASE_     ) if p.endswith(".zip"     )]
    for p in paths:
        errors.extend(get_errors_from_single_artifact(SCREAMING_SNAKE_CASE_		,							job_links=SCREAMING_SNAKE_CASE_     )     )

    return errors


def 						__a(SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       str=None     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   Counter()
    counter.update([x[1] for x in logs]     )
    _lowerCAmelCase						      =   counter.most_common()
    _lowerCAmelCase						      =   {}
    for error, count in counts:
        if error_filter is None or error not in error_filter:
            _lowerCAmelCase						      =   {"count": count, "failed_tests": [(x[2], x[0]) for x in logs if x[1] == error]}

    _lowerCAmelCase						      =   dict(sorted(r.items()		,							key=lambda SCREAMING_SNAKE_CASE_     : item[1]["count"]		,							reverse=SCREAMING_SNAKE_CASE_     )     )
    return r


def 						__a(SCREAMING_SNAKE_CASE_	:       List[str]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   test.split("::"     )[0]
    if test.startswith("tests/models/"     ):
        _lowerCAmelCase						      =   test.split("/"     )[2]
    else:
        _lowerCAmelCase						      =   None

    return test


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       Tuple=None     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   [(x[0], x[1], get_model(x[2]     )) for x in logs]
    _lowerCAmelCase						      =   [x for x in logs if x[2] is not None]
    _lowerCAmelCase						      =   {x[2] for x in logs}

    _lowerCAmelCase						      =   {}
    for test in tests:
        _lowerCAmelCase						      =   Counter()
        # count by errors in `test`
        counter.update([x[1] for x in logs if x[2] == test]     )
        _lowerCAmelCase						      =   counter.most_common()
        _lowerCAmelCase						      =   {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
        _lowerCAmelCase						      =   sum(error_counts.values()     )
        if n_errors > 0:
            _lowerCAmelCase						      =   {"count": n_errors, "errors": error_counts}

    _lowerCAmelCase						      =   dict(sorted(r.items()		,							key=lambda SCREAMING_SNAKE_CASE_     : item[1]["count"]		,							reverse=SCREAMING_SNAKE_CASE_     )     )
    return r


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[int]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   "| no. | error | status |"
    _lowerCAmelCase						      =   "|-:|:-|:-|"
    _lowerCAmelCase						      =   [header, sep]
    for error in reduced_by_error:
        _lowerCAmelCase						      =   reduced_by_error[error]["count"]
        _lowerCAmelCase						      =   F'''| {count} | {error[:100]} |  |'''
        lines.append(SCREAMING_SNAKE_CASE_     )

    return "\n".join(SCREAMING_SNAKE_CASE_     )


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[int]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   "| model | no. of errors | major error | count |"
    _lowerCAmelCase						      =   "|-:|-:|-:|-:|"
    _lowerCAmelCase						      =   [header, sep]
    for model in reduced_by_model:
        _lowerCAmelCase						      =   reduced_by_model[model]["count"]
        _lowerCAmelCase      ,      _lowerCAmelCase						      =   list(reduced_by_model[model]["errors"].items()     )[0]
        _lowerCAmelCase						      =   F'''| {model} | {count} | {error[:60]} | {_count} |'''
        lines.append(SCREAMING_SNAKE_CASE_     )

    return "\n".join(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE									=				argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
       parser.add_argument(
           "--output_dir",
           type=str,
           required=True,
           help="Where to store the downloaded artifacts and other result files.",
       )
       parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
       _SCREAMING_SNAKE_CASE									=				parser.parse_args()

       os.makedirs(args.output_dir, exist_ok=True)

       _SCREAMING_SNAKE_CASE									=				get_job_links(args.workflow_run_id, token=args.token)
       _SCREAMING_SNAKE_CASE									=				{}
       # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
       # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
       if _job_links:
              for k, v in _job_links.items():
                     # This is how GitHub actions combine job names.
                     if " / " in k:
                            _SCREAMING_SNAKE_CASE									=				k.find(" / ")
                            _SCREAMING_SNAKE_CASE									=				k[index + len(" / ") :]
                     _SCREAMING_SNAKE_CASE									=				v
       with open(os.path.join(args.output_dir, "job_links.json"), "w", encoding="UTF-8") as fp:
              json.dump(job_links, fp, ensure_ascii=False, indent=4)

       _SCREAMING_SNAKE_CASE									=				get_artifacts_links(args.workflow_run_id, token=args.token)
       with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
              json.dump(artifacts, fp, ensure_ascii=False, indent=4)

       for idx, (name, url) in enumerate(artifacts.items()):
              download_artifact(name, url, args.output_dir, args.token)
              # Be gentle to GitHub
              time.sleep(1)

       _SCREAMING_SNAKE_CASE									=				get_all_errors(args.output_dir, job_links=job_links)

       # `e[1]` is the error
       _SCREAMING_SNAKE_CASE									=				Counter()
       counter.update([e[1] for e in errors])

       # print the top 30 most common test errors
       _SCREAMING_SNAKE_CASE									=				counter.most_common(30)
       for item in most_common:
              print(item)

       with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp:
              json.dump(errors, fp, ensure_ascii=False, indent=4)

       _SCREAMING_SNAKE_CASE									=				reduce_by_error(errors)
       _SCREAMING_SNAKE_CASE									=				reduce_by_model(errors)

       _SCREAMING_SNAKE_CASE									=				make_github_table(reduced_by_error)
       _SCREAMING_SNAKE_CASE									=				make_github_table_per_model(reduced_by_model)

       with open(os.path.join(args.output_dir, "reduced_by_error.txt"), "w", encoding="UTF-8") as fp:
              fp.write(sa)
       with open(os.path.join(args.output_dir, "reduced_by_model.txt"), "w", encoding="UTF-8") as fp:
              fp.write(sa)


