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

import requests



def 						lowercase_      (       A__			,		A__=None		)							->							Union[str, Any]:
    """simple docstring"""

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

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

    try:
        job_links.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}'			,		headers=A__		).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 						lowercase_      (       A__			,		A__=None		)							->							int:
    """simple docstring"""

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

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

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

        for i in range(A__		):
            snake_case								= requests.get(url + F'&page={i + 2}'			,		headers=A__		).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 						lowercase_      (       A__			,		A__			,		A__			,		A__		)							->							str:
    """simple docstring"""

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

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



def 						lowercase_      (       A__			,		A__=None		)							->							str:
    """simple docstring"""

    snake_case								= []
    snake_case								= []
    snake_case								= None

    with zipfile.ZipFile(A__		) as z:
        for filename in z.namelist():
            if not os.path.isdir(A__		):
                # read the file
                if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                    with z.open(A__		) as f:
                        for line in f:
                            snake_case								= line.decode("UTF-8"		).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    snake_case								= line[: line.index(": "		)]
                                    snake_case								= 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
                                snake_case								= line[len("FAILED "		) :]
                                failed_tests.append(A__		)
                            elif filename == "job_name.txt":
                                snake_case								= line

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

    snake_case								= None
    if job_name and job_links:
        snake_case								= job_links.get(A__			,		A__		)

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

    return result



def 						lowercase_      (       A__			,		A__=None		)							->							Union[str, Any]:
    """simple docstring"""

    snake_case								= []

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

    return errors



def 						lowercase_      (       A__			,		A__=None		)							->							Optional[int]:
    """simple docstring"""

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

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



def 						lowercase_      (       A__		)							->							str:
    """simple docstring"""

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

    return test



def 						lowercase_      (       A__			,		A__=None		)							->							List[str]:
    """simple docstring"""

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

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

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



def 						lowercase_      (       A__		)							->							List[str]:
    """simple docstring"""

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

    return "\n".join(A__		)



def 						lowercase_      (       A__		)							->							Dict:
    """simple docstring"""

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

    return "\n".join(A__		)


if __name__ == "__main__":
   _A   =			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.")
   _A   =			parser.parse_args()

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

   _A   =			get_job_links(args.workflow_run_id, token=args.token)
   _A   =			{}
   # 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:
            _A   =			k.find(" / ")
            _A   =			k[index + len(" / ") :]
         _A   =			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)

   _A   =			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)

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

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

   # print the top 30 most common test errors
   _A   =			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)

   _A   =			reduce_by_error(errors)
   _A   =			reduce_by_model(errors)

   _A   =			make_github_table(reduced_by_error)
   _A   =			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)



import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


_A   =			"bert-base-cased"
_A   =			"google/pegasus-xsum"
_A   =			[" Sam ate lunch today.", "Sams lunch ingredients."]
_A   =			["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_A   =			"patrickvonplaten/t5-tiny-random"
_A   =			"sshleifer/bart-tiny-random"
_A   =			"sshleifer/tiny-mbart"
_A   =			"sshleifer/tiny-marian-en-de"



def 						lowercase_      (       A__			,		A__		)							->							Optional[int]:
    """simple docstring"""

    snake_case								= "\n".join(A__		)
    Path(A__		).open("w"		).writelines(A__		)



def 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(A__			,		F'{split}.source'		)			,		A__		)
        _dump_articles(os.path.join(A__			,		F'{split}.target'		)			,		A__		)
    return tmp_dir







class 					lowerCamelCase		(  A_  ):





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,   )
     @slow
     def 		UpperCAmelCase(self				:   Tuple    ,   _A				:   List[str]       )						->						Optional[int]:
         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in ARTICLES       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in SUMMARIES       )
         snake_case								= 4
         snake_case								= 8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         snake_case					,							snake_case								= "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
         snake_case								= SeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=_A    ,   max_target_length=_A    ,   src_lang=_A    ,   tgt_lang=_A    ,   )
         snake_case								= DataLoader(_A    ,   batch_size=2    ,   collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
             assert isinstance(_A    ,   _A       )
             assert batch["attention_mask"].shape == batch["input_ids"].shape
             # show that articles were trimmed.
             assert batch["input_ids"].shape[1] == max_src_len
             # show that targets are the same len
             assert batch["labels"].shape[1] == max_tgt_len
             if tok_name != MBART_TINY:
                 continue
             # check language codes in correct place
             snake_case								= shift_tokens_right(batch["labels"]    ,   tokenizer.pad_token_id       )
             assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
             assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
             assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
             assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

             break  # No need to test every batch





     @parameterized.expand([BART_TINY, BERT_BASE_CASED]       )
     def 		UpperCAmelCase(self				:   str    ,   _A				:   Dict       )						->						Dict:
         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in ARTICLES       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in SUMMARIES       )
         snake_case								= 4
         snake_case								= LegacySeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=2_0    ,   max_target_length=_A    ,   )
         snake_case								= DataLoader(_A    ,   batch_size=2    ,   collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
             assert batch["attention_mask"].shape == batch["input_ids"].shape
             # show that articles were trimmed.
             assert batch["input_ids"].shape[1] == max_len_source
             assert 2_0 >= batch["input_ids"].shape[1]  # trimmed significantly
             # show that targets were truncated
             assert batch["labels"].shape[1] == trunc_target  # Truncated
             assert max_len_target > trunc_target  # Truncated
             break  # No need to test every batch





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Optional[Any]:
         snake_case								= AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"       )

         snake_case								= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         snake_case								= tmp_dir.joinpath("train.source"       ).open().readlines()
         snake_case								= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         pack_data_dir(_A    ,   _A    ,   1_2_8    ,   _A       )
         snake_case								= {x.name for x in tmp_dir.iterdir()}
         snake_case								= {x.name for x in save_dir.iterdir()}
         snake_case								= save_dir.joinpath("train.source"       ).open().readlines()
         # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
         # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
         assert len(_A       ) < len(_A       )
         assert len(_A       ) == 1
         assert len(packed_examples[0]       ) == sum(len(_A       ) for x in orig_examples       )
         assert orig_paths == new_paths





     @pytest.mark.skipif(not FAIRSEQ_AVAILABLE    ,   reason="This test requires fairseq"       )
     def 		UpperCAmelCase(self				:   Optional[int]       )						->						Union[str, Any]:
         if not FAIRSEQ_AVAILABLE:
             return
         snake_case					,							snake_case					,							snake_case								= self._get_dataset(max_len=6_4       )
         snake_case								= 6_4
         snake_case								= ds.make_dynamic_sampler(_A    ,   required_batch_size_multiple=_A       )
         snake_case								= [len(_A       ) for x in batch_sampler]
         assert len(set(_A       )       ) > 1  # it's not dynamic batch size if every batch is the same length
         assert sum(_A       ) == len(_A       )  # no dropped or added examples
         snake_case								= DataLoader(_A    ,   batch_sampler=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2       )
         snake_case								= []
         snake_case								= []
         for batch in data_loader:
             snake_case								= batch["input_ids"].shape
             snake_case								= src_shape[0]
             assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
             snake_case								= np.product(batch["input_ids"].shape       )
             num_src_per_batch.append(_A       )
             if num_src_tokens > (max_tokens * 1.1):
                 failures.append(_A       )
         assert num_src_per_batch[0] == max(_A       )
         if failures:
             raise AssertionError(f'too many tokens in {len(_A       )} batches'       )





     def 		UpperCAmelCase(self				:   int       )						->						str:
         snake_case					,							snake_case					,							snake_case								= self._get_dataset(max_len=5_1_2       )
         snake_case								= 2
         snake_case								= ds.make_sortish_sampler(_A    ,   shuffle=_A       )

         snake_case								= DataLoader(_A    ,   batch_size=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2       )
         snake_case								= DataLoader(_A    ,   batch_size=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2    ,   sampler=_A       )

         snake_case								= tokenizer.pad_token_id

         def count_pad_tokens(_A				:   Dict    ,   _A				:   Union[str, Any]="input_ids"       ):
             return [batch[k].eq(_A       ).sum().item() for batch in data_loader]

         assert sum(count_pad_tokens(_A    ,   k="labels"       )       ) < sum(count_pad_tokens(_A    ,   k="labels"       )       )
         assert sum(count_pad_tokens(_A       )       ) < sum(count_pad_tokens(_A       )       )
         assert len(_A       ) == len(_A       )





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   Union[str, Any]=1_0_0_0    ,   _A				:   Optional[int]=1_2_8       )						->						List[Any]:
         if os.getenv("USE_REAL_DATA"    ,   _A       ):
             snake_case								= "examples/seq2seq/wmt_en_ro"
             snake_case								= max_len * 2 * 6_4
             if not Path(_A       ).joinpath("train.len"       ).exists():
                 save_len_file(_A    ,   _A       )
         else:
             snake_case								= "examples/seq2seq/test_data/wmt_en_ro"
             snake_case								= max_len * 4
             save_len_file(_A    ,   _A       )

         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= SeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=_A    ,   max_target_length=_A    ,   n_obs=_A    ,   )
         return ds, max_tokens, tokenizer





     def 		UpperCAmelCase(self				:   List[Any]       )						->						Union[str, Any]:
         snake_case					,							snake_case					,							snake_case								= self._get_dataset()
         snake_case								= set(DistributedSortishSampler(_A    ,   2_5_6    ,   num_replicas=2    ,   rank=0    ,   add_extra_examples=_A       )       )
         snake_case								= set(DistributedSortishSampler(_A    ,   2_5_6    ,   num_replicas=2    ,   rank=1    ,   add_extra_examples=_A       )       )
         assert idsa.intersection(_A       ) == set()





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,   )
     def 		UpperCAmelCase(self				:   Any    ,   _A				:   Optional[Any]       )						->						Union[str, Any]:
         snake_case								= AutoTokenizer.from_pretrained(_A    ,   use_fast=_A       )
         if tok_name == MBART_TINY:
             snake_case								= SeqaSeqDataset(
                 _A    ,   data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )    ,   type_path="train"    ,   max_source_length=4    ,   max_target_length=8    ,   src_lang="EN"    ,   tgt_lang="FR"    ,   )
             snake_case								= train_dataset.dataset_kwargs
             assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
             snake_case								= SeqaSeqDataset(
                 _A    ,   data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )    ,   type_path="train"    ,   max_source_length=4    ,   max_target_length=8    ,   )
             snake_case								= train_dataset.dataset_kwargs
             assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
             assert len(_A       ) == 1 if tok_name == BART_TINY else len(_A       ) == 0

