
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)

SCREAMING_SNAKE_CASE				:Optional[int]									=							logging.getLogger()


def 		UpperCAmelCase     (      )						->      Any:







				"""simple docstring"""



				__A       =					argparse.ArgumentParser()
				parser.add_argument("-f"		)
				__A       =					parser.parse_args()
				return args.f


def 		UpperCAmelCase     (      a_		)						->      Union[str, Any]:







				"""simple docstring"""



				__A       =					{}
				__A       =					os.path.join(a_						,  "all_results.json"		)
				if os.path.exists(a_		):
								with open(a_						,  "r"		) as f:
												__A       =					json.load(a_		)
				else:
								raise ValueError(F'''can\'t find {path}'''		)
				return results


def 		UpperCAmelCase     (      )						->      List[Any]:







				"""simple docstring"""



				__A       =					torch.cuda.is_available() and torch_device == "cuda"
				return is_using_cuda and is_apex_available()


SCREAMING_SNAKE_CASE				:Dict									=							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





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






	@classmethod
	def 							UpperCamelCase_    (			cls   :					Union[str, Any]					):
					shutil.rmtree(cls.tmpdir					)






	@mock.patch.dict(os.environ				,{"WANDB_MODE": "offline"}					)
	def 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					self.get_auto_remove_tmp_dir()
					__A       =					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       =					get_results(A					)
					self.assertGreaterEqual(result["eval_accuracy"]				,0.75					)
					self.assertTrue(os.path.exists(os.path.join(A				,"epoch_0"					)					)					)
					self.assertTrue(os.path.exists(os.path.join(A				,"glue_no_trainer"					)					)					)






	@mock.patch.dict(os.environ				,{"WANDB_MODE": "offline"}					)
	def 							UpperCamelCase_    (			self   :					Tuple					):
					__A       =					self.get_auto_remove_tmp_dir()
					__A       =					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       =					get_results(A					)
					self.assertLess(result["perplexity"]				,1_00					)
					self.assertTrue(os.path.exists(os.path.join(A				,"epoch_0"					)					)					)
					self.assertTrue(os.path.exists(os.path.join(A				,"clm_no_trainer"					)					)					)






	@mock.patch.dict(os.environ				,{"WANDB_MODE": "offline"}					)
	def 							UpperCamelCase_    (			self   :					int					):
					__A       =					self.get_auto_remove_tmp_dir()
					__A       =					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       =					get_results(A					)
					self.assertLess(result["perplexity"]				,42					)
					self.assertTrue(os.path.exists(os.path.join(A				,"epoch_0"					)					)					)
					self.assertTrue(os.path.exists(os.path.join(A				,"mlm_no_trainer"					)					)					)






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

					__A       =					self.get_auto_remove_tmp_dir()
					__A       =					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       =					get_results(A					)
					self.assertGreaterEqual(result["eval_accuracy"]				,0.75					)
					self.assertLess(result["train_loss"]				,0.5					)
					self.assertTrue(os.path.exists(os.path.join(A				,"epoch_0"					)					)					)
					self.assertTrue(os.path.exists(os.path.join(A				,"ner_no_trainer"					)					)					)






	@unittest.skip(reason="Fix me @muellerzr"					)
	@mock.patch.dict(os.environ				,{"WANDB_MODE": "offline"}					)
	def 							UpperCamelCase_    (			self   :					int					):
					__A       =					self.get_auto_remove_tmp_dir()
					__A       =					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       =					get_results(A					)
					# 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(A				,"epoch_0"					)					)					)
					self.assertTrue(os.path.exists(os.path.join(A				,"qa_no_trainer"					)					)					)






	@mock.patch.dict(os.environ				,{"WANDB_MODE": "offline"}					)
	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A       =					self.get_auto_remove_tmp_dir()
					__A       =					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       =					get_results(A					)
					self.assertGreaterEqual(result["eval_accuracy"]				,0.8					)
					self.assertTrue(os.path.exists(os.path.join(A				,"swag_no_trainer"					)					)					)






	@slow
	@mock.patch.dict(os.environ				,{"WANDB_MODE": "offline"}					)
	def 							UpperCamelCase_    (			self   :					str					):
					__A       =					self.get_auto_remove_tmp_dir()
					__A       =					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       =					get_results(A					)
					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(A				,"epoch_0"					)					)					)
					self.assertTrue(os.path.exists(os.path.join(A				,"summarization_no_trainer"					)					)					)






	@slow
	@mock.patch.dict(os.environ				,{"WANDB_MODE": "offline"}					)
	def 							UpperCamelCase_    (			self   :					Any					):
					__A       =					self.get_auto_remove_tmp_dir()
					__A       =					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       =					get_results(A					)
					self.assertGreaterEqual(result["eval_bleu"]				,30					)
					self.assertTrue(os.path.exists(os.path.join(A				,"epoch_0"					)					)					)
					self.assertTrue(os.path.exists(os.path.join(A				,"translation_no_trainer"					)					)					)






	@slow
	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					logging.StreamHandler(sys.stdout					)
					logger.addHandler(A					)

					__A       =					self.get_auto_remove_tmp_dir()
					__A       =					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       =					get_results(A					)
					self.assertGreaterEqual(result["eval_overall_accuracy"]				,0.10					)







	@mock.patch.dict(os.environ				,{"WANDB_MODE": "offline"}					)
	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					self.get_auto_remove_tmp_dir()
					__A       =					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       =					get_results(A					)
					# The base model scores a 25%
					self.assertGreaterEqual(result["eval_accuracy"]				,0.6					)
					self.assertTrue(os.path.exists(os.path.join(A				,"step_1"					)					)					)
					self.assertTrue(os.path.exists(os.path.join(A				,"image_classification_no_trainer"					)					)					)




'''simple docstring'''





def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			if exponent == 1:
						return base
			if exponent % 2 == 0:
						__A=	_modexpt(_SCREAMING_SNAKE_CASE,exponent // 2,_SCREAMING_SNAKE_CASE ) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(_SCREAMING_SNAKE_CASE,exponent - 1,_SCREAMING_SNAKE_CASE )) % modulo_value






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int = 1777,_SCREAMING_SNAKE_CASE					: int = 1855,_SCREAMING_SNAKE_CASE					: int = 8 ):






			"""simple docstring"""




			__A=	base
			for _ in range(1,_SCREAMING_SNAKE_CASE ):
						__A=	_modexpt(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,10**digits )

			return result


if __name__ == "__main__":
					print(F"""{solution() = }""")



