




from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


__UpperCamelCase   :			Tuple          = logging.get_logger(__name__)

class 						__lowerCAmelCase      (      __magic_name__					):
							def __init__(		self		:str						, __magic_name__		:int						, __magic_name__		:int						, __magic_name__		:float						, **__magic_name__		:Optional[Any]							):







								'''simple docstring'''







								a									=				feature_size
								a									=				sampling_rate
								a									=				padding_value

								a									=				kwargs.pop("""padding_side"""						, """right"""							)
								a									=				kwargs.pop("""return_attention_mask"""						, __magic_name__							)

								super().__init__(**__magic_name__							)
							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:Union[
							        BatchFeature,
							        List[BatchFeature],
							        Dict[str, BatchFeature],
							        Dict[str, List[BatchFeature]],
							        List[Dict[str, BatchFeature]],
							    ]						, __magic_name__		:Union[bool, str, PaddingStrategy] = True						, __magic_name__		:Optional[int] = None						, __magic_name__		:bool = False						, __magic_name__		:Optional[int] = None						, __magic_name__		:Optional[bool] = None						, __magic_name__		:Optional[Union[str, TensorType]] = None						, ):







								'''simple docstring'''







								if isinstance(__magic_name__						, (list, tuple)							) and isinstance(processed_features[0]						, (dict, BatchFeature)							):
									a									=				{
									    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
									}

								# The model's main input name, usually `input_values`, has be passed for padding
								if self.model_input_names[0] not in processed_features:
									raise ValueError(
									    """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
									    F' to this method that includes {self.model_input_names[0]}, but you provided'
									    F' {list(processed_features.keys()							)}'							)

								a									=				processed_features[self.model_input_names[0]]
								a									=				(
								    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
								)

								if len(__magic_name__							) == 0:
									if return_attention_mask:
										a									=				[]
									return processed_features

								# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
								# and rebuild them afterwards if no return_tensors is specified
								# Note that we lose the specific device the tensor may be on for PyTorch

								a									=				required_input[0]
								if isinstance(__magic_name__						, (list, tuple)							):
									# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
									a									=				0
									while len(required_input[index]							) == 0:
										index += 1
									if index < len(__magic_name__							):
										a									=				required_input[index][0]

								if return_tensors is None:
									if is_tf_tensor(__magic_name__							):
										a									=				"""tf"""
									elif is_torch_tensor(__magic_name__							):
										a									=				"""pt"""
									elif isinstance(__magic_name__						, (int, float, list, tuple, np.ndarray)							):
										a									=				"""np"""
									else:
										raise ValueError(
										    F'type of {first_element} unknown: {type(__magic_name__							)}. '
										    """Should be one of a python, numpy, pytorch or tensorflow object."""							)

								for key, value in processed_features.items():
									if isinstance(value[0]						, (int, float)							):
										a									=				to_numpy(__magic_name__							)
									else:
										a									=				[to_numpy(__magic_name__							) for v in value]

        # Convert padding_strategy in PaddingStrategy
								a									=				self._get_padding_strategies(padding=__magic_name__						, max_length=__magic_name__							)

								a									=				processed_features[self.model_input_names[0]]

								a									=				len(__magic_name__							)
								if not all(len(__magic_name__							) == batch_size for v in processed_features.values()							):
									raise ValueError("""Some items in the output dictionary have a different batch size than others."""							)

								a									=				[]
								for i in range(__magic_name__							):
									a									=				{k: v[i] for k, v in processed_features.items()}
									# truncation
									a									=				self._truncate(
									    __magic_name__						, max_length=__magic_name__						, pad_to_multiple_of=__magic_name__						, truncation=__magic_name__						, )
									truncated_inputs.append(__magic_name__							)

								if padding_strategy == PaddingStrategy.LONGEST:
									# make sure that `max_length` cannot be longer than the longest truncated length
									a									=				max(len(input_slice[self.model_input_names[0]]							) for input_slice in truncated_inputs							)
									a									=				PaddingStrategy.MAX_LENGTH

								a									=				{}
								for i in range(__magic_name__							):
									# padding
									a									=				self._pad(
									    truncated_inputs[i]						, max_length=__magic_name__						, padding_strategy=__magic_name__						, pad_to_multiple_of=__magic_name__						, return_attention_mask=__magic_name__						, )

									for key, value in outputs.items():
										if key not in batch_outputs:
											a									=				[]
										if value.dtype is np.dtype(np.floataa							):
											a									=				value.astype(np.floataa							)
										batch_outputs[key].append(__magic_name__							)

								return BatchFeature(__magic_name__						, tensor_type=__magic_name__							)
							def  lowerCamelCase__				(		self		:Union[str, Any]						, __magic_name__		:Union[Dict[str, np.ndarray], BatchFeature]						, __magic_name__		:Optional[int] = None						, __magic_name__		:PaddingStrategy = PaddingStrategy.DO_NOT_PAD						, __magic_name__		:Optional[int] = None						, __magic_name__		:Optional[bool] = None						, ):







								'''simple docstring'''







								a									=				processed_features[self.model_input_names[0]]

								if padding_strategy == PaddingStrategy.LONGEST:
									a									=				len(__magic_name__							)

								if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
									a									=				((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

								a									=				padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__magic_name__							) < max_length

								if return_attention_mask and "attention_mask" not in processed_features:
									a									=				np.ones(len(__magic_name__							)						, dtype=np.intaa							)

								if needs_to_be_padded:
									a									=				max_length - len(__magic_name__							)
									if self.padding_side == "right":
										if return_attention_mask:
											a									=				np.pad(
											    processed_features["""attention_mask"""]						, (0, difference)							)
										a									=				((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
										a									=				np.pad(
										    __magic_name__						, __magic_name__						, """constant"""						, constant_values=self.padding_value							)
									elif self.padding_side == "left":
										if return_attention_mask:
											a									=				np.pad(
											    processed_features["""attention_mask"""]						, (difference, 0)							)
										a									=				((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
										a									=				np.pad(
										    __magic_name__						, __magic_name__						, """constant"""						, constant_values=self.padding_value							)
									else:
										raise ValueError("""Invalid padding strategy:""" + str(self.padding_side							)							)

								return processed_features
							def  lowerCamelCase__				(		self		:List[Any]						, __magic_name__		:Union[Dict[str, np.ndarray], BatchFeature]						, __magic_name__		:Optional[int] = None						, __magic_name__		:Optional[int] = None						, __magic_name__		:Optional[bool] = None						, ):







								'''simple docstring'''







								if not truncation:
									return processed_features
								elif truncation and max_length is None:
									raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""							)

								a									=				processed_features[self.model_input_names[0]]

								# find `max_length` that fits `pad_to_multiple_of`
								if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
									a									=				((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

								a									=				len(__magic_name__							) > max_length

								if needs_to_be_truncated:
									a									=				processed_features[self.model_input_names[0]][:max_length]
									if "attention_mask" in processed_features:
										a									=				processed_features["""attention_mask"""][:max_length]

								return processed_features





							def  lowerCamelCase__				(		self		:Union[str, Any]						, __magic_name__		:Any=False						, __magic_name__		:Dict=None							):







								'''simple docstring'''







								if padding is not False:
									if padding is True:
										a									=				PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
									elif not isinstance(__magic_name__						, __magic_name__							):
										a									=				PaddingStrategy(__magic_name__							)
									elif isinstance(__magic_name__						, __magic_name__							):
										a									=				padding
								else:
									a									=				PaddingStrategy.DO_NOT_PAD

								# Set max length if needed
								if max_length is None:
									if padding_strategy == PaddingStrategy.MAX_LENGTH:
										raise ValueError(
										    F'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'							)

        # Test if we have a padding value
								if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
									raise ValueError(
									    """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
									    """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""							)

								return padding_strategy







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

import requests





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Tuple:
	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(__lowerCamelCase				,     headers=__lowerCamelCase       ).json()
	a									=				{}

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

		for i in range(__lowerCamelCase       ):
			a									=				requests.get(url + f'&page={i + 2}'				,     headers=__lowerCamelCase       ).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						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Dict:
	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(__lowerCamelCase				,     headers=__lowerCamelCase       ).json()
	a									=				{}

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

		for i in range(__lowerCamelCase       ):
			a									=				requests.get(url + f'&page={i + 2}'				,     headers=__lowerCamelCase       ).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						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Optional[int]:
	a									=				None
	if token is not None:
		a									=				{"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

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





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Tuple:
	a									=				[]
	a									=				[]
	a									=				None

	with zipfile.ZipFile(__lowerCamelCase       ) as z:
		for filename in z.namelist():
			if not os.path.isdir(__lowerCamelCase       ):
				# read the file
				if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
					with z.open(__lowerCamelCase       ) 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(__lowerCamelCase       )
							elif filename == "job_name.txt":
								a									=				line

	if len(__lowerCamelCase       ) != len(__lowerCamelCase       ):
		raise ValueError(
		    f'`errors` and `failed_tests` should have the same number of elements. Got {len(__lowerCamelCase       )} for `errors` '
		    f'and {len(__lowerCamelCase       )} 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(__lowerCamelCase				,     __lowerCamelCase       )

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

	return result





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Dict:
	a									=				[]

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

	return errors





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Tuple:
	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 __lowerCamelCase       : item[1]["count"]				,     reverse=__lowerCamelCase       )       )
	return r





def 						__A						(      __lowerCamelCase       ) ->			List[str]:
	a									=				test.split("""::"""       )[0]
	if test.startswith("""tests/models/"""       ):
		a									=				test.split("""/"""       )[2]
	else:
		a									=				None

	return test





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Any:
	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 __lowerCamelCase       : item[1]["count"]				,     reverse=__lowerCamelCase       )       )
	return r





def 						__A						(      __lowerCamelCase       ) ->			Optional[int]:
	a									=				"""| no. | error | status |"""
	a									=				"""|-:|:-|:-|"""
	a									=				[header, sep]
	for error in reduced_by_error:
		a									=				reduced_by_error[error]["""count"""]
		a									=				f'| {count} | {error[:100]} |  |'
		lines.append(__lowerCamelCase       )

	return "\n".join(__lowerCamelCase       )





def 						__A						(      __lowerCamelCase       ) ->			int:
	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[:60]} | {_count} |'
		lines.append(__lowerCamelCase       )

	return "\n".join(__lowerCamelCase       )


if __name__ == "__main__":
		__UpperCamelCase   :			Dict          = 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.")
		__UpperCamelCase   :			Any          = parser.parse_args()

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

		__UpperCamelCase   :			Optional[Any]          = get_job_links(args.workflow_run_id, token=args.token)
		__UpperCamelCase   :			str          = {}
		# 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:
								__UpperCamelCase   :			List[str]          = k.find(" / ")
								__UpperCamelCase   :			List[Any]          = k[index + len(" / ") :]
						__UpperCamelCase   :			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)

		__UpperCamelCase   :			Tuple          = 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)

		__UpperCamelCase   :			int          = get_all_errors(args.output_dir, job_links=job_links)

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

		# print the top 30 most common test errors
		__UpperCamelCase   :			Any          = 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)

		__UpperCamelCase   :			Union[str, Any]          = reduce_by_error(errors)
		__UpperCamelCase   :			Dict          = reduce_by_model(errors)

		__UpperCamelCase   :			Union[str, Any]          = make_github_table(reduced_by_error)
		__UpperCamelCase   :			Union[str, Any]          = 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)

