

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import PoolFormerImageProcessor





class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def __init__(       self					:			str     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			List[Any]=7     , __lowerCamelCase					:			Any=3     , __lowerCamelCase					:			Any=30     , __lowerCamelCase					:			Any=400     , __lowerCamelCase					:			Union[str, Any]=True     , __lowerCamelCase					:			List[Any]=None     , __lowerCamelCase					:			Optional[int]=0.9     , __lowerCamelCase					:			Dict=None     , __lowerCamelCase					:			Dict=True     , __lowerCamelCase					:			List[Any]=[0.5, 0.5, 0.5]     , __lowerCamelCase					:			Dict=[0.5, 0.5, 0.5]     , ):
       SCREAMING_SNAKE_CASE										=   size if size is not None else {"shortest_edge": 30}
       SCREAMING_SNAKE_CASE										=   crop_size if crop_size is not None else {"height": 30, "width": 30}
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   batch_size
       SCREAMING_SNAKE_CASE										=   num_channels
       SCREAMING_SNAKE_CASE										=   min_resolution
       SCREAMING_SNAKE_CASE										=   max_resolution
       SCREAMING_SNAKE_CASE										=   do_resize_and_center_crop
       SCREAMING_SNAKE_CASE										=   size
       SCREAMING_SNAKE_CASE										=   crop_pct
       SCREAMING_SNAKE_CASE										=   crop_size
       SCREAMING_SNAKE_CASE										=   do_normalize
       SCREAMING_SNAKE_CASE										=   image_mean
       SCREAMING_SNAKE_CASE										=   image_std





   def      _snake_case		(       self					:			Dict			):
       return {
           "size": self.size,
           "do_resize_and_center_crop": self.do_resize_and_center_crop,
           "crop_pct": self.crop_pct,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }





@require_torch
@require_vision
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      PoolFormerImageProcessor if is_vision_available() else None





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   PoolFormerImageProcessingTester(self			)





   @property
   def      _snake_case		(       self					:			Optional[int]			):
       return self.image_processor_tester.prepare_image_processor_dict()





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       self.assertTrue(hasattr(__lowerCamelCase     , "do_resize_and_center_crop"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "size"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "crop_pct"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "do_normalize"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "image_mean"			)			)
       self.assertTrue(hasattr(__lowerCamelCase     , "image_std"			)			)





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   self.image_processing_class.from_dict(self.image_processor_dict			)
       self.assertEqual(image_processor.size     , {"shortest_edge": 30}			)
       self.assertEqual(image_processor.crop_size     , {"height": 30, "width": 30}			)

       SCREAMING_SNAKE_CASE										=   self.image_processing_class.from_dict(self.image_processor_dict     , size=42     , crop_size=84			)
       self.assertEqual(image_processor.size     , {"shortest_edge": 42}			)
       self.assertEqual(image_processor.crop_size     , {"height": 84, "width": 84}			)





   def      _snake_case		(       self					:			List[str]			):
       pass





   def      _snake_case		(       self					:			List[Any]			):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       # create random PIL images
       SCREAMING_SNAKE_CASE										=   prepare_image_inputs(self.image_processor_tester     , equal_resolution=__lowerCamelCase			)
       for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase     , Image.Image			)

       # Test not batched input
       SCREAMING_SNAKE_CASE										=   image_processing(image_inputs[0]     , return_tensors="pt"			).pixel_values

       self.assertEqual(
           encoded_images.shape     , (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )     , )

       # Test batched
       SCREAMING_SNAKE_CASE										=   image_processing(__lowerCamelCase     , return_tensors="pt"			).pixel_values
       self.assertEqual(
           encoded_images.shape     , (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )     , )





   def      _snake_case		(       self					:			Optional[int]			):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE										=   prepare_image_inputs(self.image_processor_tester     , equal_resolution=__lowerCamelCase     , numpify=__lowerCamelCase			)
       for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase     , np.ndarray			)

       # Test not batched input
       SCREAMING_SNAKE_CASE										=   image_processing(image_inputs[0]     , return_tensors="pt"			).pixel_values
       self.assertEqual(
           encoded_images.shape     , (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )     , )

       # Test batched
       SCREAMING_SNAKE_CASE										=   image_processing(__lowerCamelCase     , return_tensors="pt"			).pixel_values
       self.assertEqual(
           encoded_images.shape     , (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )     , )





   def      _snake_case		(       self					:			str			):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE										=   self.image_processing_class(**self.image_processor_dict			)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE										=   prepare_image_inputs(self.image_processor_tester     , equal_resolution=__lowerCamelCase     , torchify=__lowerCamelCase			)
       for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase     , torch.Tensor			)

       # Test not batched input
       SCREAMING_SNAKE_CASE										=   image_processing(image_inputs[0]     , return_tensors="pt"			).pixel_values
       self.assertEqual(
           encoded_images.shape     , (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )     , )

       # Test batched
       SCREAMING_SNAKE_CASE										=   image_processing(__lowerCamelCase     , return_tensors="pt"			).pixel_values
       self.assertEqual(
           encoded_images.shape     , (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )     , )


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

import requests





def 	__a     (      A__			:  str			,  A__			:  List[Any]=None       ):
    SCREAMING_SNAKE_CASE										=   None
    if token is not None:
        SCREAMING_SNAKE_CASE										=   {"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}

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

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

        for i in range(A__       ):
            SCREAMING_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 	__a     (      A__			:  List[Any]			,  A__			:  Optional[int]=None       ):
    SCREAMING_SNAKE_CASE										=   None
    if token is not None:
        SCREAMING_SNAKE_CASE										=   {"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}

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

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

        for i in range(A__       ):
            SCREAMING_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 	__a     (      A__			:  Any			,  A__			:  str			,  A__			:  List[str]			,  A__			:  Union[str, Any]       ):
    SCREAMING_SNAKE_CASE										=   None
    if token is not None:
        SCREAMING_SNAKE_CASE										=   {"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}

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





def 	__a     (      A__			:  List[Any]			,  A__			:  List[Any]=None       ):
    SCREAMING_SNAKE_CASE										=   []
    SCREAMING_SNAKE_CASE										=   []
    SCREAMING_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:
                            SCREAMING_SNAKE_CASE										=   line.decode("UTF-8"       ).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    SCREAMING_SNAKE_CASE										=   line[: line.index(": "       )]
                                    SCREAMING_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
                                SCREAMING_SNAKE_CASE										=   line[len("FAILED "       ) :]
                                failed_tests.append(A__       )
                            elif filename == "job_name.txt":
                                SCREAMING_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."       )

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

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

    return result





def 	__a     (      A__			:  Union[str, Any]			,  A__			:  Union[str, Any]=None       ):
    SCREAMING_SNAKE_CASE										=   []

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

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





def 	__a     (      A__			:  str       ):
    SCREAMING_SNAKE_CASE										=   test.split("::"       )[0]
    if test.startswith("tests/models/"       ):
        SCREAMING_SNAKE_CASE										=   test.split("/"       )[2]
    else:
        SCREAMING_SNAKE_CASE										=   None

    return test





def 	__a     (      A__			:  List[str]			,  A__			:  Dict=None       ):
    SCREAMING_SNAKE_CASE										=   [(x[0], x[1], get_model(x[2]       )) for x in logs]
    SCREAMING_SNAKE_CASE										=   [x for x in logs if x[2] is not None]
    SCREAMING_SNAKE_CASE										=   {x[2] for x in logs}

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

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





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

    return "\n".join(A__       )





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

    return "\n".join(A__       )


if __name__ == "__main__":
    __A			:					List[str]						=			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			:					Union[str, Any]						=			parser.parse_args()

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

    __A			:					int						=			get_job_links(args.workflow_run_id, token=args.token)
    __A			:					Dict						=			{}
    # 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			:					Union[str, Any]						=			k.find(' / ')
                __A			:					Optional[int]						=			k[index + len(' / ') :]
            __A			:					Optional[int]						=			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			:					int						=			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			:					Optional[int]						=			get_all_errors(args.output_dir, job_links=job_links)

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

    # print the top 30 most common test errors
    __A			:					Optional[Any]						=			counter.most_common(3_0)
    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			:					str						=			reduce_by_error(errors)
    __A			:					int						=			reduce_by_model(errors)

    __A			:					Any						=			make_github_table(reduced_by_error)
    __A			:					List[str]						=			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)