



'''simple docstring'''

import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

__lowerCAmelCase						      =       logging.getLogger()



def 					__lowerCamelCase					(				)      ->				int:
		_a					:				Tuple				=							argparse.ArgumentParser()
		parser.add_argument('-f'  )
		_a					:				Optional[int]				=							parser.parse_args()
		return args.f



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Union[str, Any]:
		_a					:				Union[str, Any]				=							{}
		_a					:				Dict				=							os.path.join(lowerCAmelCase_						,      'all_results.json'  )
		if os.path.exists(lowerCAmelCase_  ):
				with open(lowerCAmelCase_						,      'r'  ) as f:
						_a					:				str				=							json.load(lowerCAmelCase_  )
		else:
				raise ValueError(f"""can't find {path}"""  )
		return results



def 					__lowerCamelCase					(				)      ->				List[str]:
		_a					:				List[Any]				=							torch.cuda.is_available() and torch_device == 'cuda'
		return is_using_cuda and is_apex_available()


__lowerCAmelCase						      =       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class      __magic_name__						(   _UpperCamelCase       ):







							@classmethod
							def 							__lowercase      (							cls :	List[str]		):
									# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
									_a					:				int				=							tempfile.mkdtemp()
									_a					:				List[Any]				=							os.path.join(cls.tmpdir     ,'default_config.yml'		)
									write_basic_config(save_location=cls.configPath		)
									_a					:				Union[str, Any]				=							['accelerate', 'launch', '--config_file', cls.configPath]







							@classmethod
							def 							__lowercase      (							cls :	Tuple		):
									shutil.rmtree(cls.tmpdir		)







							@mock.patch.dict(os.environ     ,{'WANDB_MODE': 'offline'}		)
							def 							__lowercase      (							self :	Optional[Any]		):
									_a					:				List[Any]				=							self.get_auto_remove_tmp_dir()
									_a					:				Any				=							F"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

									if is_cuda_and_apex_available():
											testargs.append('--fp16'		)

									run_command(self._launch_args + testargs		)
									_a					:				List[str]				=							get_results(_UpperCAmelCase		)
									self.assertGreaterEqual(result['eval_accuracy']     ,0.75		)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase     ,'epoch_0'		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase     ,'glue_no_trainer'		)		)		)







							@mock.patch.dict(os.environ     ,{'WANDB_MODE': 'offline'}		)
							def 							__lowercase      (							self :	Optional[Any]		):
									_a					:				Optional[int]				=							self.get_auto_remove_tmp_dir()
									_a					:				Any				=							F"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.split()

									if torch.cuda.device_count() > 1:
											# Skipping because there are not enough batches to train the model + would need a drop_last to work.
											return

									run_command(self._launch_args + testargs		)
									_a					:				Optional[Any]				=							get_results(_UpperCAmelCase		)
									self.assertLess(result['perplexity']     ,100		)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase     ,'epoch_0'		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase     ,'clm_no_trainer'		)		)		)







							@mock.patch.dict(os.environ     ,{'WANDB_MODE': 'offline'}		)
							def 							__lowercase      (							self :	Dict		):
									_a					:				List[Any]				=							self.get_auto_remove_tmp_dir()
									_a					:				Dict				=							F"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

									run_command(self._launch_args + testargs		)
									_a					:				List[Any]				=							get_results(_UpperCAmelCase		)
									self.assertLess(result['perplexity']     ,42		)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase     ,'epoch_0'		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase     ,'mlm_no_trainer'		)		)		)







							@mock.patch.dict(os.environ     ,{'WANDB_MODE': 'offline'}		)
							def 							__lowercase      (							self :	Union[str, Any]		):
									# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
									_a					:				Optional[int]				=							7 if get_gpu_count() > 1 else 2

									_a					:				Tuple				=							self.get_auto_remove_tmp_dir()
									_a					:				Any				=							F"""
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        """.split()

									run_command(self._launch_args + testargs		)
									_a					:				Any				=							get_results(_UpperCAmelCase		)
									self.assertGreaterEqual(result['eval_accuracy']     ,0.75		)
									self.assertLess(result['train_loss']     ,0.5		)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase     ,'epoch_0'		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase     ,'ner_no_trainer'		)		)		)







							@unittest.skip(reason='Fix me @muellerzr'		)
							@mock.patch.dict(os.environ     ,{'WANDB_MODE': 'offline'}		)
							def 							__lowercase      (							self :	int		):
									_a					:				List[Any]				=							self.get_auto_remove_tmp_dir()
									_a					:				int				=							F"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

									run_command(self._launch_args + testargs		)
									_a					:				List[str]				=							get_results(_UpperCAmelCase		)
									# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
									self.assertGreaterEqual(result['eval_f1']     ,28		)
									self.assertGreaterEqual(result['eval_exact']     ,28		)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase     ,'epoch_0'		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase     ,'qa_no_trainer'		)		)		)







							@mock.patch.dict(os.environ     ,{'WANDB_MODE': 'offline'}		)
							def 							__lowercase      (							self :	Tuple		):
									_a					:				Optional[int]				=							self.get_auto_remove_tmp_dir()
									_a					:				List[str]				=							F"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

									run_command(self._launch_args + testargs		)
									_a					:				Tuple				=							get_results(_UpperCAmelCase		)
									self.assertGreaterEqual(result['eval_accuracy']     ,0.8		)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase     ,'swag_no_trainer'		)		)		)







							@slow
							@mock.patch.dict(os.environ     ,{'WANDB_MODE': 'offline'}		)
							def 							__lowercase      (							self :	str		):
									_a					:				List[Any]				=							self.get_auto_remove_tmp_dir()
									_a					:				List[str]				=							F"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

									run_command(self._launch_args + testargs		)
									_a					:				Tuple				=							get_results(_UpperCAmelCase		)
									self.assertGreaterEqual(result['eval_rouge1']     ,10		)
									self.assertGreaterEqual(result['eval_rouge2']     ,2		)
									self.assertGreaterEqual(result['eval_rougeL']     ,7		)
									self.assertGreaterEqual(result['eval_rougeLsum']     ,7		)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase     ,'epoch_0'		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase     ,'summarization_no_trainer'		)		)		)







							@slow
							@mock.patch.dict(os.environ     ,{'WANDB_MODE': 'offline'}		)
							def 							__lowercase      (							self :	List[str]		):
									_a					:				Any				=							self.get_auto_remove_tmp_dir()
									_a					:				Optional[Any]				=							F"""
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        """.split()

									run_command(self._launch_args + testargs		)
									_a					:				List[str]				=							get_results(_UpperCAmelCase		)
									self.assertGreaterEqual(result['eval_bleu']     ,30		)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase     ,'epoch_0'		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase     ,'translation_no_trainer'		)		)		)







							@slow
							def 							__lowercase      (							self :	Any		):
									_a					:				Optional[int]				=							logging.StreamHandler(sys.stdout		)
									logger.addHandler(_UpperCAmelCase		)

									_a					:				List[str]				=							self.get_auto_remove_tmp_dir()
									_a					:				List[str]				=							F"""
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        """.split()

									run_command(self._launch_args + testargs		)
									_a					:				int				=							get_results(_UpperCAmelCase		)
									self.assertGreaterEqual(result['eval_overall_accuracy']     ,0.10		)







							@mock.patch.dict(os.environ     ,{'WANDB_MODE': 'offline'}		)
							def 							__lowercase      (							self :	Any		):
									_a					:				Optional[Any]				=							self.get_auto_remove_tmp_dir()
									_a					:				List[str]				=							F"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

									if is_cuda_and_apex_available():
											testargs.append('--fp16'		)

									run_command(self._launch_args + testargs		)
									_a					:				Any				=							get_results(_UpperCAmelCase		)
									# The base model scores a 25%
									self.assertGreaterEqual(result['eval_accuracy']     ,0.6		)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase     ,'step_1'		)		)		)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase     ,'image_classification_no_trainer'		)		)		)









'''simple docstring'''

from __future__ import annotations

import os
from typing import Any

import requests

__lowerCAmelCase						      =       '''https://api.github.com'''

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
__lowerCAmelCase						      =       BASE_URL + '''/user'''

# https://github.com/settings/tokens
__lowerCAmelCase						      =       os.environ.get('''USER_TOKEN''', '''''')



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				dict[Any, Any]:
		_a					:				Union[str, Any]				=							{
		    'Authorization': f"""token {auth_token}""",
		    'Accept': 'application/vnd.github.v3+json',
		}
		return requests.get(lowerCAmelCase_						,      headers=lowerCAmelCase_  ).json()


if __name__ == "__main__":  # pragma: no cover
							if USER_TOKEN:
														for key, value in fetch_github_info(USER_TOKEN).items():
																					print(f"""{key}: {value}""")
							else:
														raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')




