
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)

lowercase__							:							List[Any]									=			logging.getLogger()







def 	A_ (							)      ->    List[Any]:



						'''simple docstring'''

						__UpperCamelCase       =			argparse.ArgumentParser()
						parser.add_argument('''-f''' )
						__UpperCamelCase       =			parser.parse_args()
						return args.f







def 	A_ (							snake_case							:   List[Any] )      ->    Tuple:



						'''simple docstring'''

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







def 	A_ (							)      ->    int:



						'''simple docstring'''

						__UpperCamelCase       =			torch.cuda.is_available() and torch_device == '''cuda'''
						return is_using_cuda and is_apex_available()


lowercase__							:							Tuple									=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							@classmethod
							def     A__		(  cls			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			tempfile.mkdtemp()
													__UpperCamelCase       =			os.path.join(cls.tmpdir     ,	'''default_config.yml'''			)
													write_basic_config(save_location=cls.configPath			)
													__UpperCamelCase       =			['''accelerate''', '''launch''', '''--config_file''', cls.configPath]



							@classmethod
							def     A__		(  cls			)->   Optional[Any]:





													'''simple docstring'''






													shutil.rmtree(cls.tmpdir			)



							@mock.patch.dict(os.environ     ,	{'''WANDB_MODE''': '''offline'''}			)
							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_auto_remove_tmp_dir()
													__UpperCamelCase       =			F"\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

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

													run_command(self._launch_args + testargs			)
													__UpperCamelCase       =			get_results(_SCREAMING_SNAKE_CASE			)
													self.assertGreaterEqual(result['''eval_accuracy''']     ,	0.7_5			)
													self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,	'''epoch_0'''			)			)			)
													self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,	'''glue_no_trainer'''			)			)			)



							@mock.patch.dict(os.environ     ,	{'''WANDB_MODE''': '''offline'''}			)
							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_auto_remove_tmp_dir()
													__UpperCamelCase       =			F"\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        ".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			)
													__UpperCamelCase       =			get_results(_SCREAMING_SNAKE_CASE			)
													self.assertLess(result['''perplexity''']     ,	100			)
													self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,	'''epoch_0'''			)			)			)
													self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,	'''clm_no_trainer'''			)			)			)



							@mock.patch.dict(os.environ     ,	{'''WANDB_MODE''': '''offline'''}			)
							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_auto_remove_tmp_dir()
													__UpperCamelCase       =			F"\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

													run_command(self._launch_args + testargs			)
													__UpperCamelCase       =			get_results(_SCREAMING_SNAKE_CASE			)
													self.assertLess(result['''perplexity''']     ,	42			)
													self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,	'''epoch_0'''			)			)			)
													self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,	'''mlm_no_trainer'''			)			)			)



							@mock.patch.dict(os.environ     ,	{'''WANDB_MODE''': '''offline'''}			)
							def     A__		(  self			)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			7 if get_gpu_count() > 1 else 2

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

													run_command(self._launch_args + testargs			)
													__UpperCamelCase       =			get_results(_SCREAMING_SNAKE_CASE			)
													self.assertGreaterEqual(result['''eval_accuracy''']     ,	0.7_5			)
													self.assertLess(result['''train_loss''']     ,	0.5			)
													self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,	'''epoch_0'''			)			)			)
													self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,	'''ner_no_trainer'''			)			)			)



							@unittest.skip(reason='''Fix me @muellerzr'''			)
							@mock.patch.dict(os.environ     ,	{'''WANDB_MODE''': '''offline'''}			)
							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_auto_remove_tmp_dir()
													__UpperCamelCase       =			F"\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

													run_command(self._launch_args + testargs			)
													__UpperCamelCase       =			get_results(_SCREAMING_SNAKE_CASE			)
													# 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(_SCREAMING_SNAKE_CASE     ,	'''epoch_0'''			)			)			)
													self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,	'''qa_no_trainer'''			)			)			)



							@mock.patch.dict(os.environ     ,	{'''WANDB_MODE''': '''offline'''}			)
							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_auto_remove_tmp_dir()
													__UpperCamelCase       =			F"\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        ".split()

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



							@slow
							@mock.patch.dict(os.environ     ,	{'''WANDB_MODE''': '''offline'''}			)
							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_auto_remove_tmp_dir()
													__UpperCamelCase       =			F"\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

													run_command(self._launch_args + testargs			)
													__UpperCamelCase       =			get_results(_SCREAMING_SNAKE_CASE			)
													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(_SCREAMING_SNAKE_CASE     ,	'''epoch_0'''			)			)			)
													self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,	'''summarization_no_trainer'''			)			)			)



							@slow
							@mock.patch.dict(os.environ     ,	{'''WANDB_MODE''': '''offline'''}			)
							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_auto_remove_tmp_dir()
													__UpperCamelCase       =			F"\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

													run_command(self._launch_args + testargs			)
													__UpperCamelCase       =			get_results(_SCREAMING_SNAKE_CASE			)
													self.assertGreaterEqual(result['''eval_bleu''']     ,	30			)
													self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,	'''epoch_0'''			)			)			)
													self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,	'''translation_no_trainer'''			)			)			)



							@slow
							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       =			logging.StreamHandler(sys.stdout			)
													logger.addHandler(_SCREAMING_SNAKE_CASE			)

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

													run_command(self._launch_args + testargs			)
													__UpperCamelCase       =			get_results(_SCREAMING_SNAKE_CASE			)
													self.assertGreaterEqual(result['''eval_overall_accuracy''']     ,	0.1_0			)



							@mock.patch.dict(os.environ     ,	{'''WANDB_MODE''': '''offline'''}			)
							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_auto_remove_tmp_dir()
													__UpperCamelCase       =			F"\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        ".split()

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

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






def 	A_ (							snake_case							:   int = 10 ,      snake_case							:   int = 1000 ,      snake_case							:   bool = True )      ->    int:



						'''simple docstring'''

						assert (
						    isinstance(snake_case ,      snake_case )
						    and isinstance(snake_case ,      snake_case )
						    and isinstance(snake_case ,      snake_case )
						), "Invalid type of value(s) specified to function!"

						if min_val > max_val:
												raise ValueError('''Invalid value for min_val or max_val (min_value < max_value)''' )
						return min_val if option else max_val




def 	A_ (							snake_case							:   int ,      snake_case							:   int )      ->    int:



						'''simple docstring'''

						return int((number_a + number_a) / 2 )




def 	A_ (							snake_case							:   int ,      snake_case							:   int ,      snake_case							:   int )      ->    None:



						'''simple docstring'''

						assert (
						    isinstance(snake_case ,      snake_case ) and isinstance(snake_case ,      snake_case ) and isinstance(snake_case ,      snake_case )
						), 'argument values must be type of "int"'

						if lower > higher:
												raise ValueError('''argument value for lower and higher must be(lower > higher)''' )

						if not lower < to_guess < higher:
												raise ValueError(
												    '''guess value must be within the range of lower and higher value''' )

						def answer(snake_case							:   int ) -> str:
												if number > to_guess:
																		return "high"
												elif number < to_guess:
																		return "low"
												else:
																		return "same"

						print('''started...''' )

						__UpperCamelCase       =			lower
						__UpperCamelCase       =			higher

						__UpperCamelCase       =			[]

						while True:
												__UpperCamelCase       =			get_avg(snake_case ,      snake_case )
												last_numbers.append(snake_case )

												if answer(snake_case ) == "low":
																		__UpperCamelCase       =			number
												elif answer(snake_case ) == "high":
																		__UpperCamelCase       =			number
												else:
																		break

						print(f"guess the number : {last_numbers[-1]}" )
						print(f"details : {last_numbers!s}" )




def 	A_ (							)      ->    None:



						'''simple docstring'''

						__UpperCamelCase       =			int(input('''Enter lower value : ''' ).strip() )
						__UpperCamelCase       =			int(input('''Enter high value : ''' ).strip() )
						__UpperCamelCase       =			int(input('''Enter value to guess : ''' ).strip() )
						guess_the_number(snake_case ,      snake_case ,      snake_case )


if __name__ == "__main__":
				main()




