




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__UpperCamelCase   :			Union[str, Any]          = logging.get_logger(__name__)





def 						__A						(      __lowerCamelCase       ) ->			List[int]:
	if isinstance(__lowerCamelCase				,     np.ndarray       ):
		return list(tensor.shape       )

	a									=				tf.shape(__lowerCamelCase       )

	if tensor.shape == tf.TensorShape(__lowerCamelCase       ):
		return dynamic

	a									=				tensor.shape.as_list()

	return [dynamic[i] if s is None else s for i, s in enumerate(__lowerCamelCase       )]





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase = None				,     __lowerCamelCase = None       ) ->			tf.Tensor:
	return tf.nn.softmax(logits=logits + 1E-9				,     axis=__lowerCamelCase				,     name=__lowerCamelCase       )





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase=1E-5				,     __lowerCamelCase=-1       ) ->			int:
	# This is a very simplified functional layernorm, designed to duplicate
	# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
	# models in Transformers.

	if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__lowerCamelCase				,     __lowerCamelCase       ):
		raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis."""       )

	# Get mean and variance on the axis to be normalized
	a							,					a									=				tf.nn.moments(__lowerCamelCase				,     axes=[axis]				,     keepdims=__lowerCamelCase       )

	if axis != -1:
		# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
		# on every dimension except axis
		a									=				[1] * inputs.shape.rank
		a									=				shape_list(__lowerCamelCase       )[axis]
		a									=				tf.reshape(__lowerCamelCase				,     __lowerCamelCase       )
		a									=				tf.reshape(__lowerCamelCase				,     __lowerCamelCase       )

	# Compute layer normalization using the batch_normalization
	# function.
	a									=				tf.nn.batch_normalization(
	    __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     offset=__lowerCamelCase				,     scale=__lowerCamelCase				,     variance_epsilon=__lowerCamelCase				,     )
	return outputs





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=0				,     __lowerCamelCase=-1       ) ->			Tuple:
	# Replicates the behavior of torch.flatten in TF

	# If end_dim or start_dim is negative, count them from the end
	if end_dim < 0:
		end_dim += input.shape.rank
	if start_dim < 0:
		start_dim += input.shape.rank

	if start_dim == end_dim:
		return input

	a									=				tf.shape(__lowerCamelCase       )
	a									=				tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]       )
	a									=				tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]				,     axis=0       )
	return tf.reshape(__lowerCamelCase				,     __lowerCamelCase       )





def 						__A						(      __lowerCamelCase       ) ->			tf.Tensor:
	if not isinstance(__lowerCamelCase				,     tf.Tensor       ):
		a									=				tf.convert_to_tensor(__lowerCamelCase       )  # Catches stray NumPy inputs
	if encoder_attention_mask.shape.rank == 3:
		a									=				encoder_attention_mask[:, None, :, :]
	if encoder_attention_mask.shape.rank == 2:
		a									=				encoder_attention_mask[:, None, None, :]
	# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
	# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
	# /transformer/transformer_layers.py#L270
	# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
	# encoder_extended_attention_mask.transpose(-1, -2))
	a									=				(
	    tf.cast(1				,     encoder_attention_mask.dtype       ) - encoder_extended_attention_mask
	) * encoder_extended_attention_mask.dtype.min

	return encoder_extended_attention_mask





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase = "input_ids"       ) ->			None:
	tf.debugging.assert_less(
	    __lowerCamelCase				,     tf.cast(__lowerCamelCase				,     dtype=tensor.dtype       )				,     message=(
	        f'The maximum value of {tensor_name} ({tf.math.reduce_max(__lowerCamelCase       )}) must be smaller than the embedding '
	        f'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
	    )				,     )





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Union[str, Any]:
	a									=				6_4512
	# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
	# because in that case even chunking the array would not make the saving
	# possible.
	a									=				[x for x in data if len(__lowerCamelCase       ) > HDF5_OBJECT_HEADER_LIMIT]

	# Expecting this to never be true.
	if bad_attributes:
		raise RuntimeError(
		    """The following attributes cannot be saved to HDF5 file because """
		    f'they are larger than {HDF5_OBJECT_HEADER_LIMIT} '
		    f'bytes: {bad_attributes}'       )

	a									=				np.asarray(__lowerCamelCase       )

	a									=				1
	a									=				np.array_split(__lowerCamelCase				,     __lowerCamelCase       )

	# This will never loop forever thanks to the test above.
	while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data       ):
		num_chunks += 1
		a									=				np.array_split(__lowerCamelCase				,     __lowerCamelCase       )

	if num_chunks > 1:
		for chunk_id, chunk_data in enumerate(__lowerCamelCase       ):
			a									=				chunk_data
	else:
		a									=				data





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			Optional[Any]:
	if name in group.attrs:
		a									=				[n.decode("""utf8"""       ) if hasattr(__lowerCamelCase				,     """decode"""       ) else n for n in group.attrs[name]]
	else:
		a									=				[]
		a									=				0
		while "%s%d" % (name, chunk_id) in group.attrs:
			data.extend(
			    [n.decode("""utf8"""       ) if hasattr(__lowerCamelCase				,     """decode"""       ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]]       )
			chunk_id += 1
	return data





def 						__A						(      __lowerCamelCase       ) ->			Optional[Any]:

	def _expand_single_ad_tensor(__lowerCamelCase       ):
		if isinstance(__lowerCamelCase				,     tf.Tensor       ) and t.shape.rank == 1:
			return tf.expand_dims(__lowerCamelCase				,     axis=-1       )
		return t

	return tf.nest.map_structure(_expand_single_ad_tensor				,     __lowerCamelCase       )







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)

