
'''simple docstring'''







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

import requests
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Tuple				,				__SCREAMING_SNAKE_CASE :			List[str]=None							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	None
						if token is not None:
												lowercase_     :					List[str]													=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': F'''Bearer {token}'''}

						lowercase_     :					str													=	F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
						lowercase_     :					Optional[Any]													=	requests.get(__lowerCAmelCase				,				headers=__lowerCAmelCase							).json()
						lowercase_     :					Optional[int]													=	{}

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

												for i in range(__lowerCAmelCase							):
																		lowercase_     :					Tuple													=	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  snake_case_		(    __SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			Optional[int]=None							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	None
						if token is not None:
												lowercase_     :					str													=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': F'''Bearer {token}'''}

						lowercase_     :					str													=	F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'''
						lowercase_     :					str													=	requests.get(__lowerCAmelCase				,				headers=__lowerCAmelCase							).json()
						lowercase_     :					Dict													=	{}

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

												for i in range(__lowerCAmelCase							):
																		lowercase_     :					Any													=	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  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			Dict							):



						"""simple docstring"""





						lowercase_     :					str													=	None
						if token is not None:
												lowercase_     :					str													=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': F'''Bearer {token}'''}

						lowercase_     :					str													=	requests.get(__lowerCAmelCase				,				headers=__lowerCAmelCase				,				allow_redirects=__lowerCAmelCase							)
						lowercase_     :					Dict													=	result.headers['''Location''']
						lowercase_     :					List[Any]													=	requests.get(__lowerCAmelCase				,				allow_redirects=__lowerCAmelCase							)
						lowercase_     :					Optional[Any]													=	os.path.join(__lowerCAmelCase				,				F'''{artifact_name}.zip'''							)
						with open(__lowerCAmelCase				,				'''wb'''							) as fp:
												fp.write(response.content							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[Any]				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]=None							):



						"""simple docstring"""





						lowercase_     :					Dict													=	[]
						lowercase_     :					Optional[int]													=	[]
						lowercase_     :					List[Any]													=	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:
																																										lowercase_     :					Tuple													=	line.decode('''UTF-8'''							).strip()
																																										if filename == "failures_line.txt":
																																																try:
																																																						# `error_line` is the place where `error` occurs
																																																						lowercase_     :					Optional[int]													=	line[: line.index(''': '''							)]
																																																						lowercase_     :					Optional[Any]													=	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
																																																lowercase_     :					Union[str, Any]													=	line[len('''FAILED '''							) :]
																																																failed_tests.append(__lowerCAmelCase							)
																																										elif filename == "job_name.txt":
																																																lowercase_     :					List[Any]													=	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.'''							)

						lowercase_     :					Union[str, Any]													=	None
						if job_name and job_links:
												lowercase_     :					Union[str, Any]													=	job_links.get(__lowerCAmelCase				,				__lowerCAmelCase							)

						# A list with elements of the form (line of error, error, failed test)
						lowercase_     :					Optional[int]													=	[x + [y] + [job_link] for x, y in zip(__lowerCAmelCase				,				__lowerCAmelCase							)]

						return result
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[str]				,				__SCREAMING_SNAKE_CASE :			Optional[int]=None							):



						"""simple docstring"""





						lowercase_     :					Union[str, Any]													=	[]

						lowercase_     :					Any													=	[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  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[Any]				,				__SCREAMING_SNAKE_CASE :			List[str]=None							):



						"""simple docstring"""





						lowercase_     :					str													=	Counter()
						counter.update([x[1] for x in logs]							)
						lowercase_     :					Optional[int]													=	counter.most_common()
						lowercase_     :					Optional[Any]													=	{}
						for error, count in counts:
												if error_filter is None or error not in error_filter:
																		lowercase_     :					str													=	{'''count''': count, '''failed_tests''': [(x[2], x[0]) for x in logs if x[1] == error]}

						lowercase_     :					str													=	dict(sorted(r.items()				,				key=lambda __SCREAMING_SNAKE_CASE							: item[1]["count"]				,				reverse=__lowerCAmelCase							)							)
						return r
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					List[Any]													=	test.split('''::'''							)[0]
						if test.startswith('''tests/models/'''							):
												lowercase_     :					Optional[Any]													=	test.split('''/'''							)[2]
						else:
												lowercase_     :					List[str]													=	None

						return test
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Any				,				__SCREAMING_SNAKE_CASE :			List[str]=None							):



						"""simple docstring"""





						lowercase_     :					List[Any]													=	[(x[0], x[1], get_model(x[2]							)) for x in logs]
						lowercase_     :					Union[str, Any]													=	[x for x in logs if x[2] is not None]
						lowercase_     :					Dict													=	{x[2] for x in logs}

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

						lowercase_     :					Tuple													=	dict(sorted(r.items()				,				key=lambda __SCREAMING_SNAKE_CASE							: item[1]["count"]				,				reverse=__lowerCAmelCase							)							)
						return r
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						lowercase_     :					Union[str, Any]													=	'''| no. | error | status |'''
						lowercase_     :					str													=	'''|-:|:-|:-|'''
						lowercase_     :					Optional[int]													=	[header, sep]
						for error in reduced_by_error:
												lowercase_     :					Any													=	reduced_by_error[error]['''count''']
												lowercase_     :					Any													=	F'''| {count} | {error[:100]} |  |'''
												lines.append(__lowerCAmelCase							)

						return "\n".join(__lowerCAmelCase							)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						lowercase_     :					int													=	'''| model | no. of errors | major error | count |'''
						lowercase_     :					Any													=	'''|-:|-:|-:|-:|'''
						lowercase_     :					Tuple													=	[header, sep]
						for model in reduced_by_model:
												lowercase_     :					List[str]													=	reduced_by_model[model]['''count''']
												lowercase_						,			lowercase_     :					Union[str, Any]													=	list(reduced_by_model[model]['''errors'''].items()							)[0]
												lowercase_     :					Optional[Any]													=	F'''| {model} | {count} | {error[:60]} | {_count} |'''
												lines.append(__lowerCAmelCase							)

						return "\n".join(__lowerCAmelCase							)


if __name__ == "__main__":
							_lowercase			:  Tuple       							=	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.")
							_lowercase			:  Optional[int]       							=	parser.parse_args()

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

							_lowercase			:  Union[str, Any]       							=	get_job_links(args.workflow_run_id, token=args.token)
							_lowercase			:  Optional[int]       							=	{}
							# 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:
																												_lowercase			:  int       							=	k.find(" / ")
																												_lowercase			:  Tuple       							=	k[index + len(" / ") :]
																					_lowercase			:  Dict       							=	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)

							_lowercase			:  List[Any]       							=	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)

							_lowercase			:  List[Any]       							=	get_all_errors(args.output_dir, job_links=job_links)

							# `e[1]` is the error
							_lowercase			:  Union[str, Any]       							=	Counter()
							counter.update([e[1] for e in errors])

							# print the top 30 most common test errors
							_lowercase			:  Optional[Any]       							=	counter.most_common(3_0)
							for item in most_common:
														print(item)

							with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp:
														json.dump(errors, fp, ensure_ascii=False, indent=4)

							_lowercase			:  List[Any]       							=	reduce_by_error(errors)
							_lowercase			:  str       							=	reduce_by_model(errors)

							_lowercase			:  List[Any]       							=	make_github_table(reduced_by_error)
							_lowercase			:  Tuple       							=	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'''







import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						lowercase_     :					Optional[int]													=	checkpoints.load_tax_checkpoint(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Dict													=	flatten_dict(__SCREAMING_SNAKE_CASE							)
						return flax_params
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]							):



						"""simple docstring"""





						lowercase_     :					int													=	{}

						lowercase_     :					Any													=	{
						    '''token_embedder''': '''embeddings''',
						    '''encoder_norm''': '''layernorm''',
						    '''kernel''': '''weight''',
						    '''.out''': '''.output''',
						    '''scale''': '''weight''',
						    '''embedders_0.pos_embedding''': '''row_embedder.weight''',
						    '''embedders_1.pos_embedding''': '''column_embedder.weight''',
						}

						lowercase_     :					Tuple													=	{
						    '''query''': '''attention.query''',
						    '''key''': '''attention.key''',
						    '''value''': '''attention.value''',
						    '''output.dense''': '''output''',
						    '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
						    '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
						    '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
						    '''mlp.''': '''mlp.DenseReluDense.''',
						    '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
						    '''self_attention.o''': '''self_attention.attention.o''',
						    '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
						    '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
						    '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
						    '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
						}

						for key in flax_dict.keys():
												if "target" in key:
																		# remove the first prefix from the key
																		lowercase_     :					Tuple													=	'''.'''.join(key[1:]							)

																		# rename the key
																		for old, new in CONVERSION_MAPPING.items():
																								lowercase_     :					Optional[Any]													=	new_key.replace(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														lowercase_     :					Optional[Any]													=	new_key.replace(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)

																		if "layers" in new_key and "decoder" not in new_key:
																								# use regex to replace the layer number
																								lowercase_     :					List[Any]													=	re.sub(R'''layers_(\d+)'''				,				R'''layer.\1'''				,				__SCREAMING_SNAKE_CASE							)
																								lowercase_     :					Union[str, Any]													=	new_key.replace('''encoder'''				,				'''encoder.encoder'''							)

																		elif "layers" in new_key and "decoder" in new_key:
																								# use regex to replace the layer number
																								lowercase_     :					str													=	re.sub(R'''layers_(\d+)'''				,				R'''layer.\1'''				,				__SCREAMING_SNAKE_CASE							)

																		lowercase_     :					Dict													=	flax_dict[key]

						lowercase_     :					Any													=	{}
						# convert converted_dict into torch format
						for key in converted_dict.keys():
												if ("embed_tokens" not in key) and ("embedder" not in key):
																		lowercase_     :					str													=	torch.from_numpy(converted_dict[key].T							)
												else:
																		lowercase_     :					str													=	torch.from_numpy(converted_dict[key]							)

						return converted_torch_dict
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			List[Any]				,				__SCREAMING_SNAKE_CASE :			Tuple=False				,				__SCREAMING_SNAKE_CASE :			List[Any]=False							):



						"""simple docstring"""





						lowercase_     :					List[str]													=	get_flax_param(__SCREAMING_SNAKE_CASE							)

						if not use_large:
												lowercase_     :					List[str]													=	PixaStructVisionConfig()
												lowercase_     :					Optional[Any]													=	PixaStructTextConfig()
						else:
												lowercase_     :					Optional[int]													=	PixaStructVisionConfig(
												    hidden_size=1536				,				d_ff=3968				,				num_attention_heads=24				,				num_hidden_layers=18							)
												lowercase_     :					Dict													=	PixaStructTextConfig(hidden_size=1536				,				d_ff=3968				,				num_heads=24				,				num_layers=18							)
						lowercase_     :					str													=	PixaStructConfig(
						    vision_config=encoder_config.to_dict()				,				text_config=decoder_config.to_dict()				,				is_vqa=__SCREAMING_SNAKE_CASE							)

						lowercase_     :					List[str]													=	PixaStructForConditionalGeneration(__SCREAMING_SNAKE_CASE							)

						lowercase_     :					int													=	rename_and_convert_flax_params(__SCREAMING_SNAKE_CASE							)
						model.load_state_dict(__SCREAMING_SNAKE_CASE							)

						lowercase_     :					str													=	AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''							)
						lowercase_     :					List[Any]													=	PixaStructImageProcessor()
						lowercase_     :					int													=	PixaStructProcessor(image_processor=__SCREAMING_SNAKE_CASE				,				tokenizer=__SCREAMING_SNAKE_CASE							)

						if use_large:
												lowercase_     :					Tuple													=	4096

						lowercase_     :					Optional[int]													=	True

						# mkdir if needed
						os.makedirs(__SCREAMING_SNAKE_CASE				,				exist_ok=__SCREAMING_SNAKE_CASE							)

						model.save_pretrained(__SCREAMING_SNAKE_CASE							)
						processor.save_pretrained(__SCREAMING_SNAKE_CASE							)

						print('''Model saved in {}'''.format(__SCREAMING_SNAKE_CASE							)							)


if __name__ == "__main__":
							_lowercase			:  str   							=	argparse.ArgumentParser()
							parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
							parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
							parser.add_argument("--use_large", action="store_true", help="Use large model.")
							parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
							_lowercase			:  Tuple   							=	parser.parse_args()

							convert_pixastruct_original_pytorch_checkpoint_to_hf(
							    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
							)



