


'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=None ):
							A_			    =		None
							if token is not None:
														A_			    =		{"Accept": "application/vnd.github+json", "Authorization": f"Bearer {token}"}

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

							try:
														job_links.update({job["name"]: job["html_url"] for job in result["jobs"]} )
														A_			    =		math.ceil((result["total_count"] - 1_0_0) / 1_0_0 )

														for i in range(UpperCamelCase_ ):
																					A_			    =		requests.get(url + f"&page={i + 2}"						,  headers=UpperCamelCase_ ).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_				(	UpperCamelCase_						,  UpperCamelCase_=None ):
							A_			    =		None
							if token is not None:
														A_			    =		{"Accept": "application/vnd.github+json", "Authorization": f"Bearer {token}"}

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

							try:
														artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]} )
														A_			    =		math.ceil((result["total_count"] - 1_0_0) / 1_0_0 )

														for i in range(UpperCamelCase_ ):
																					A_			    =		requests.get(url + f"&page={i + 2}"						,  headers=UpperCamelCase_ ).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_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		None
							if token is not None:
														A_			    =		{"Accept": "application/vnd.github+json", "Authorization": f"Bearer {token}"}

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







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=None ):
							A_			    =		[]
							A_			    =		[]
							A_			    =		None

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

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

							A_			    =		None
							if job_name and job_links:
														A_			    =		job_links.get(UpperCamelCase_						,  UpperCamelCase_ )

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

							return result







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=None ):
							A_			    =		[]

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

							return errors







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

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







def       a_				(	UpperCamelCase_ ):
							A_			    =		test.split("::" )[0]
							if test.startswith("tests/models/" ):
														A_			    =		test.split("/" )[2]
							else:
														A_			    =		None

							return test







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=None ):
							A_			    =		[(x[0], x[1], get_model(x[2] )) for x in logs]
							A_			    =		[x for x in logs if x[2] is not None]
							A_			    =		{x[2] for x in logs}

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

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







def       a_				(	UpperCamelCase_ ):
							A_			    =		"| no. | error | status |"
							A_			    =		"|-:|:-|:-|"
							A_			    =		[header, sep]
							for error in reduced_by_error:
														A_			    =		reduced_by_error[error]["count"]
														A_			    =		f"| {count} | {error[:1_0_0]} |  |"
														lines.append(UpperCamelCase_ )

							return "\n".join(UpperCamelCase_ )







def       a_				(	UpperCamelCase_ ):
							A_			    =		"| model | no. of errors | major error | count |"
							A_			    =		"|-:|-:|-:|-:|"
							A_			    =		[header, sep]
							for model in reduced_by_model:
														A_			    =		reduced_by_model[model]["count"]
														A_    ,					A_			    =		list(reduced_by_model[model]["errors"].items() )[0]
														A_			    =		f"| {model} | {count} | {error[:6_0]} | {_count} |"
														lines.append(UpperCamelCase_ )

							return "\n".join(UpperCamelCase_ )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		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					:				Optional[int]									=		parser.parse_args()

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

							__SCREAMING_SNAKE_CASE					:				str									=		get_job_links(args.workflow_run_id, token=args.token)
							__SCREAMING_SNAKE_CASE					:				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:
																												__SCREAMING_SNAKE_CASE					:				Optional[int]									=		k.find(''' / ''')
																												__SCREAMING_SNAKE_CASE					:				Optional[int]									=		k[index + len(''' / ''') :]
																					__SCREAMING_SNAKE_CASE					:				List[str]									=		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					:				str									=		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					:				List[str]									=		get_all_errors(args.output_dir, job_links=job_links)

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

							# print the top 30 most common test errors
							__SCREAMING_SNAKE_CASE					:				str									=		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					:				Optional[int]									=		reduce_by_error(errors)
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		reduce_by_model(errors)

							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		make_github_table(reduced_by_error)
							__SCREAMING_SNAKE_CASE					:				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)









'''simple docstring'''
from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
							import PIL

# soft dependency
if is_pytesseract_available():
							import pytesseract

__SCREAMING_SNAKE_CASE					:				List[str]									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							return [
							    int(1_0_0_0 * (box[0] / width) ),
							    int(1_0_0_0 * (box[1] / height) ),
							    int(1_0_0_0 * (box[2] / width) ),
							    int(1_0_0_0 * (box[3] / height) ),
							]







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		to_pil_image(UpperCamelCase_ )
							A_    ,					A_			    =		pil_image.size
							A_			    =		pytesseract.image_to_data(UpperCamelCase_						,  lang=UpperCamelCase_						,  output_type="dict"						,  config=UpperCamelCase_ )
							A_    ,					A_    ,					A_    ,					A_    ,					A_			    =		data["text"], data["left"], data["top"], data["width"], data["height"]

							# filter empty words and corresponding coordinates
							A_			    =		[idx for idx, word in enumerate(UpperCamelCase_ ) if not word.strip()]
							A_			    =		[word for idx, word in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]

							# turn coordinates into (left, top, left+width, top+height) format
							A_			    =		[]
							for x, y, w, h in zip(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
														A_			    =		[x, y, x + w, y + h]
														actual_boxes.append(UpperCamelCase_ )

							# finally, normalize the bounding boxes
							A_			    =		[]
							for box in actual_boxes:
														normalized_boxes.append(normalize_box(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ) )

							assert len(UpperCamelCase_ ) == len(UpperCamelCase_ ), "Not as many words as there are bounding boxes"

							return words, normalized_boxes







class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Any   						=["pixel_values"]






						def __init__(  self							:      List[Any] ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BILINEAR ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      float = 1 / 2_55 ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[str] = "" ,  **lowerCAmelCase							:      int ,  ):
													super().__init__(**lowerCAmelCase	)
													A_			    =		size if size is not None else {"height": 2_24, "width": 2_24}
													A_			    =		get_size_dict(lowerCAmelCase	)

													A_			    =		do_resize
													A_			    =		size
													A_			    =		resample
													A_			    =		do_rescale
													A_			    =		rescale_value
													A_			    =		do_normalize
													A_			    =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													A_			    =		image_std if image_std is not None else IMAGENET_STANDARD_STD
													A_			    =		apply_ocr
													A_			    =		ocr_lang
													A_			    =		tesseract_config






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Dict[str, int] ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BILINEAR ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      List[str] ,  ):
													A_			    =		get_size_dict(lowerCAmelCase	)
													if "height" not in size or "width" not in size:
																				raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"	)
													A_			    =		(size["height"], size["width"])
													return resize(lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[int, float] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Tuple ,  ):
													return rescale(lowerCAmelCase ,  scale=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      int ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[float, Iterable[float]] ,  lowerCAmelCase							:      Union[float, Iterable[float]] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      int ,  ):
													return normalize(lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      ImageInput ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      Any=None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      float = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[Union[str, TensorType]] = None ,  lowerCAmelCase							:      ChannelDimension = ChannelDimension.FIRST ,  **lowerCAmelCase							:      int ,  ):
													A_			    =		do_resize if do_resize is not None else self.do_resize
													A_			    =		size if size is not None else self.size
													A_			    =		get_size_dict(lowerCAmelCase	)
													A_			    =		resample if resample is not None else self.resample
													A_			    =		do_rescale if do_rescale is not None else self.do_rescale
													A_			    =		rescale_factor if rescale_factor is not None else self.rescale_factor
													A_			    =		do_normalize if do_normalize is not None else self.do_normalize
													A_			    =		image_mean if image_mean is not None else self.image_mean
													A_			    =		image_std if image_std is not None else self.image_std
													A_			    =		apply_ocr if apply_ocr is not None else self.apply_ocr
													A_			    =		ocr_lang if ocr_lang is not None else self.ocr_lang
													A_			    =		tesseract_config if tesseract_config is not None else self.tesseract_config

													A_			    =		make_list_of_images(lowerCAmelCase	)

													if not valid_images(lowerCAmelCase	):
																				raise ValueError(
																				    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																				    "torch.Tensor, tf.Tensor or jax.ndarray."	)

													if do_resize and size is None:
																				raise ValueError("Size must be specified if do_resize is True."	)

													if do_rescale and rescale_factor is None:
																				raise ValueError("Rescale factor must be specified if do_rescale is True."	)

													if do_normalize and (image_mean is None or image_std is None):
																				raise ValueError("If do_normalize is True, image_mean and image_std must be specified."	)

													# All transformations expect numpy arrays.
													A_			    =		[to_numpy_array(lowerCAmelCase	) for image in images]

													# Tesseract OCR to get words + normalized bounding boxes
													if apply_ocr:
																				requires_backends(self ,  "pytesseract"	)
																				A_			    =		[]
																				A_			    =		[]
																				for image in images:
																											A_    ,					A_			    =		apply_tesseract(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase	)
																											words_batch.append(lowerCAmelCase	)
																											boxes_batch.append(lowerCAmelCase	)

													if do_resize:
																				A_			    =		[self.resize(image=lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase	) for image in images]

													if do_rescale:
																				A_			    =		[self.rescale(image=lowerCAmelCase ,  scale=lowerCAmelCase	) for image in images]

													if do_normalize:
																				A_			    =		[self.normalize(image=lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase	) for image in images]

													A_			    =		[to_channel_dimension_format(lowerCAmelCase ,  lowerCAmelCase	) for image in images]

													A_			    =		BatchFeature(data={"pixel_values": images} ,  tensor_type=lowerCAmelCase	)

													if apply_ocr:
																				A_			    =		words_batch
																				A_			    =		boxes_batch
													return data





