





'''simple docstring'''


import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import (
           MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
           MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
           MODEL_MAPPING,
           DeiTForImageClassification,
           DeiTForImageClassificationWithTeacher,
           DeiTForMaskedImageModeling,
           DeiTModel,
       )
       from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import DeiTImageProcessor

class  lowerCAmelCase_		:




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=13      ,  _lowerCAmelCase=30      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=5      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=37      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=10      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=2      ,  )     ->				int:
     _lowerCAmelCase						      =   parent
     _lowerCAmelCase						      =   batch_size
     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   is_training
     _lowerCAmelCase						      =   use_labels
     _lowerCAmelCase						      =   hidden_size
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   intermediate_size
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   type_sequence_label_size
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   scope
     _lowerCAmelCase						      =   encoder_stride

     # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
     _lowerCAmelCase						      =   (image_size // patch_size) ** 2
     _lowerCAmelCase						      =   num_patches + 2




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

     _lowerCAmelCase						      =   None
     if self.use_labels:
         _lowerCAmelCase						      =   ids_tensor([self.batch_size]      ,  self.type_sequence_label_size		)

     _lowerCAmelCase						      =   self.get_config()

     return config, pixel_values, labels




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     return DeiTConfig(
         image_size=self.image_size      ,  patch_size=self.patch_size      ,  num_channels=self.num_channels      ,  hidden_size=self.hidden_size      ,  num_hidden_layers=self.num_hidden_layers      ,  num_attention_heads=self.num_attention_heads      ,  intermediate_size=self.intermediate_size      ,  hidden_act=self.hidden_act      ,  hidden_dropout_prob=self.hidden_dropout_prob      ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,  is_decoder=_lowerCAmelCase      ,  initializer_range=self.initializer_range      ,  encoder_stride=self.encoder_stride      ,  )




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				List[str]:
     _lowerCAmelCase						      =   DeiTModel(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)
     self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   DeiTForMaskedImageModeling(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)
     self.parent.assertEqual(
         result.reconstruction.shape      ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)		)

     # test greyscale images
     _lowerCAmelCase						      =   1
     _lowerCAmelCase						      =   DeiTForMaskedImageModeling(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()

     _lowerCAmelCase						      =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)
     self.parent.assertEqual(result.reconstruction.shape      ,  (self.batch_size, 1, self.image_size, self.image_size)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Any:
     _lowerCAmelCase						      =   self.type_sequence_label_size
     _lowerCAmelCase						      =   DeiTForImageClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  labels=_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.type_sequence_label_size)		)

     # test greyscale images
     _lowerCAmelCase						      =   1
     _lowerCAmelCase						      =   DeiTForImageClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()

     _lowerCAmelCase						      =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  labels=_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.type_sequence_label_size)		)





 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   self.prepare_config_and_inputs()
     (
         (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      
     )						      =   config_and_inputs
     _lowerCAmelCase						      =   {"pixel_values": pixel_values}
     return config, inputs_dict







@require_torch
class  lowerCAmelCase_		(		__magic_name__    ,__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Tuple								=       (
     (
         DeiTModel,
         DeiTForImageClassification,
         DeiTForImageClassificationWithTeacher,
         DeiTForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 __lowerCamelCase    :				int								=       (
     {
         "feature-extraction": DeiTModel,
         "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
     }
     if is_torch_available()
     else {}
 )

 __lowerCamelCase    :				Dict								=       False
 __lowerCamelCase    :				List[Any]								=       False
 __lowerCamelCase    :				Union[str, Any]								=       False




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   DeiTModelTester(self		)
     _lowerCAmelCase						      =   ConfigTester(self      ,  config_class=_lowerCAmelCase      ,  has_text_modality=_lowerCAmelCase      ,  hidden_size=37		)




 def 				_snake_case (						self		)     ->				int:
     self.config_tester.run_common_tests()




 @unittest.skip(reason="DeiT does not use inputs_embeds"		)
 def 				_snake_case (						self		)     ->				List[Any]:
     pass




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         self.assertIsInstance(model.get_input_embeddings()      ,  (nn.Module)		)
         _lowerCAmelCase						      =   model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(_lowerCAmelCase      ,  nn.Linear		)		)




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         _lowerCAmelCase						      =   inspect.signature(model.forward		)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowerCAmelCase						      =   [*signature.parameters.keys()]

         _lowerCAmelCase						      =   ["pixel_values"]
         self.assertListEqual(arg_names[:1]      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=False		)     ->				int:
     _lowerCAmelCase						      =   super()._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase      ,  return_labels=_lowerCAmelCase		)

     if return_labels:
         if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
             del inputs_dict["labels"]

     return inputs_dict




 def 				_snake_case (						self		)     ->				Tuple:
     if not self.model_tester.is_training:
         return

     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()
     _lowerCAmelCase						      =   True

     for model_class in self.all_model_classes:
         # DeiTForImageClassificationWithTeacher supports inference-only
         if (
             model_class in get_values(_lowerCAmelCase		)
             or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
         ):
             continue
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         model.to(_lowerCAmelCase		)
         model.train()
         _lowerCAmelCase						      =   self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase      ,  return_labels=_lowerCAmelCase		)
         _lowerCAmelCase						      =   model(**_lowerCAmelCase		).loss
         loss.backward()




 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()
     if not self.model_tester.is_training:
         return

     _lowerCAmelCase						      =   False
     _lowerCAmelCase						      =   True

     for model_class in self.all_model_classes:
         if model_class in get_values(_lowerCAmelCase		) or not model_class.supports_gradient_checkpointing:
             continue
         # DeiTForImageClassificationWithTeacher supports inference-only
         if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
             continue
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         model.gradient_checkpointing_enable()
         model.to(_lowerCAmelCase		)
         model.train()
         _lowerCAmelCase						      =   self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase      ,  return_labels=_lowerCAmelCase		)
         _lowerCAmelCase						      =   model(**_lowerCAmelCase		).loss
         loss.backward()




 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()

     _lowerCAmelCase						      =   [
         {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
         {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
         {"title": "regression", "num_labels": 1, "dtype": torch.float},
     ]

     for model_class in self.all_model_classes:
         if (
             model_class
             not in [
                 *get_values(_lowerCAmelCase		),
                 *get_values(_lowerCAmelCase		),
             ]
             or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
         ):
             continue

         for problem_type in problem_types:
             with self.subTest(msg=f'''Testing {model_class} with {problem_type['title']}'''		):
                 _lowerCAmelCase						      =   problem_type["title"]
                 _lowerCAmelCase						      =   problem_type["num_labels"]

                 _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
                 model.to(_lowerCAmelCase		)
                 model.train()

                 _lowerCAmelCase						      =   self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase      ,  return_labels=_lowerCAmelCase		)

                 if problem_type["num_labels"] > 1:
                     _lowerCAmelCase						      =   inputs["labels"].unsqueeze(1		).repeat(1      ,  problem_type["num_labels"]		)

                 _lowerCAmelCase						      =   inputs["labels"].to(problem_type["dtype"]		)

                 # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                 # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                 # they have the same size." which is a symptom something in wrong for the regression problem.
                 # See https://github.com/huggingface/transformers/issues/11780
                 with warnings.catch_warnings(record=_lowerCAmelCase		) as warning_list:
                     _lowerCAmelCase						      =   model(**_lowerCAmelCase		).loss
                 for w in warning_list:
                     if "Using a target size that is different to the input size" in str(w.message		):
                         raise ValueError(
                             f'''Something is going wrong in the regression problem: intercepted {w.message}'''		)

                 loss.backward()





 @slow
 def 				_snake_case (						self		)     ->				Any:
     for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						      =   DeiTModel.from_pretrained(_lowerCAmelCase		)
         self.assertIsNotNone(_lowerCAmelCase		)







def 						__a():



    '''simple docstring'''





    _lowerCAmelCase						      =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
    return image





@require_torch
@require_vision
class  lowerCAmelCase_		(		unittest.TestCase		):




 @cached_property
 def 				_snake_case (						self		)     ->				Any:
     return (
         DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"		)
         if is_vision_available()
         else None
     )




 @slow
 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"		).to(
         _lowerCAmelCase		)

     _lowerCAmelCase						      =   self.default_image_processor
     _lowerCAmelCase						      =   prepare_img()
     _lowerCAmelCase						      =   image_processor(images=_lowerCAmelCase      ,  return_tensors="pt"		).to(_lowerCAmelCase		)

     # forward pass
     with torch.no_grad():
         _lowerCAmelCase						      =   model(**_lowerCAmelCase		)

     # verify the logits
     _lowerCAmelCase						      =   torch.Size((1, 1000)		)
     self.assertEqual(outputs.logits.shape      ,  _lowerCAmelCase		)

     _lowerCAmelCase						      =   torch.tensor([-1.0266, 0.1912, -1.2861]		).to(_lowerCAmelCase		)

     self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,  _lowerCAmelCase      ,  atol=1E-4		)		)





 @slow
 @require_accelerate
 @require_torch_gpu
 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   DeiTModel.from_pretrained(
         "facebook/deit-base-distilled-patch16-224"      ,  torch_dtype=torch.floataa      ,  device_map="auto"		)
     _lowerCAmelCase						      =   self.default_image_processor

     _lowerCAmelCase						      =   prepare_img()
     _lowerCAmelCase						      =   image_processor(images=_lowerCAmelCase      ,  return_tensors="pt"		)
     _lowerCAmelCase						      =   inputs.pixel_values.to(_lowerCAmelCase		)

     # forward pass to make sure inference works in fp16
     with torch.no_grad():
         _lowerCAmelCase						      =   model(_lowerCAmelCase		)









'''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)


