



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)

__snake_case       :	Tuple            =logging.getLogger()






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Union[str, Any]				=  argparse.ArgumentParser()
				parser.add_argument('''-f''')
				lowerCAmelCase__  :       Optional[Any]				=  parser.parse_args()
				return args.f






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       List[Any]				=  {}
				lowerCAmelCase__  :       Tuple				=  os.path.join(lowerCamelCase_ ,'''all_results.json''')
				if os.path.exists(lowerCamelCase_):
								with open(lowerCamelCase_ ,'''r''') as f:
												lowerCAmelCase__  :       Any				=  json.load(lowerCamelCase_)
				else:
								raise ValueError(f"""can't find {path}""")
				return results






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       List[str]				=  torch.cuda.is_available() and torch_device == '''cuda'''
				return is_using_cuda and is_apex_available()


__snake_case       :	Optional[Any]            =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class  lowerCamelCase__    (							lowerCamelCase__):




			'''simple docstring'''



			@classmethod
			def 	lowerCAmelCase__   (cls				)  ->    Optional[int]:

							"""simple docstring"""






							lowerCAmelCase__  :       Tuple				=  tempfile.mkdtemp()
							lowerCAmelCase__  :       str				=  os.path.join(cls.tmpdir	,'''default_config.yml'''				)
							write_basic_config(save_location=cls.configPath				)
							lowerCAmelCase__  :       Optional[Any]				=  ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]



			@classmethod
			def 	lowerCAmelCase__   (cls				)  ->    Dict:

							"""simple docstring"""






							shutil.rmtree(cls.tmpdir				)



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

							"""simple docstring"""






							lowerCAmelCase__  :       List[str]				=  self.get_auto_remove_tmp_dir()
							lowerCAmelCase__  :       List[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				)
							lowerCAmelCase__  :       List[Any]				=  get_results(__lowerCamelCase				)
							self.assertGreaterEqual(result['''eval_accuracy''']	,0.75				)
							self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase	,'''epoch_0'''				)				)				)
							self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase	,'''glue_no_trainer'''				)				)				)



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

							"""simple docstring"""






							lowerCAmelCase__  :       Optional[Any]				=  self.get_auto_remove_tmp_dir()
							lowerCAmelCase__  :       Optional[int]				=  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				)
							lowerCAmelCase__  :       Optional[Any]				=  get_results(__lowerCamelCase				)
							self.assertLess(result['''perplexity''']	,1_00				)
							self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase	,'''epoch_0'''				)				)				)
							self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase	,'''clm_no_trainer'''				)				)				)



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

							"""simple docstring"""






							lowerCAmelCase__  :       int				=  self.get_auto_remove_tmp_dir()
							lowerCAmelCase__  :       str				=  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				)
							lowerCAmelCase__  :       List[Any]				=  get_results(__lowerCamelCase				)
							self.assertLess(result['''perplexity''']	,42				)
							self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase	,'''epoch_0'''				)				)				)
							self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase	,'''mlm_no_trainer'''				)				)				)



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

							"""simple docstring"""






							lowerCAmelCase__  :       Tuple				=  7 if get_gpu_count() > 1 else 2

							lowerCAmelCase__  :       Optional[Any]				=  self.get_auto_remove_tmp_dir()
							lowerCAmelCase__  :       int				=  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				)
							lowerCAmelCase__  :       List[str]				=  get_results(__lowerCamelCase				)
							self.assertGreaterEqual(result['''eval_accuracy''']	,0.75				)
							self.assertLess(result['''train_loss''']	,0.5				)
							self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase	,'''epoch_0'''				)				)				)
							self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase	,'''ner_no_trainer'''				)				)				)



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

							"""simple docstring"""






							lowerCAmelCase__  :       Optional[int]				=  self.get_auto_remove_tmp_dir()
							lowerCAmelCase__  :       Dict				=  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				)
							lowerCAmelCase__  :       Optional[int]				=  get_results(__lowerCamelCase				)
							# 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(__lowerCamelCase	,'''epoch_0'''				)				)				)
							self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase	,'''qa_no_trainer'''				)				)				)



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

							"""simple docstring"""






							lowerCAmelCase__  :       str				=  self.get_auto_remove_tmp_dir()
							lowerCAmelCase__  :       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				)
							lowerCAmelCase__  :       Optional[Any]				=  get_results(__lowerCamelCase				)
							self.assertGreaterEqual(result['''eval_accuracy''']	,0.8				)
							self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase	,'''swag_no_trainer'''				)				)				)



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

							"""simple docstring"""






							lowerCAmelCase__  :       Any				=  self.get_auto_remove_tmp_dir()
							lowerCAmelCase__  :       Union[str, Any]				=  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				)
							lowerCAmelCase__  :       Optional[Any]				=  get_results(__lowerCamelCase				)
							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(__lowerCamelCase	,'''epoch_0'''				)				)				)
							self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase	,'''summarization_no_trainer'''				)				)				)



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

							"""simple docstring"""






							lowerCAmelCase__  :       Any				=  self.get_auto_remove_tmp_dir()
							lowerCAmelCase__  :       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				)
							lowerCAmelCase__  :       Tuple				=  get_results(__lowerCamelCase				)
							self.assertGreaterEqual(result['''eval_bleu''']	,30				)
							self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase	,'''epoch_0'''				)				)				)
							self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase	,'''translation_no_trainer'''				)				)				)



			@slow
			def 	lowerCAmelCase__   (self				)  ->    Dict:

							"""simple docstring"""






							lowerCAmelCase__  :       Dict				=  logging.StreamHandler(sys.stdout				)
							logger.addHandler(__lowerCamelCase				)

							lowerCAmelCase__  :       List[Any]				=  self.get_auto_remove_tmp_dir()
							lowerCAmelCase__  :       Optional[int]				=  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				)
							lowerCAmelCase__  :       str				=  get_results(__lowerCamelCase				)
							self.assertGreaterEqual(result['''eval_overall_accuracy''']	,0.10				)



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

							"""simple docstring"""






							lowerCAmelCase__  :       Tuple				=  self.get_auto_remove_tmp_dir()
							lowerCAmelCase__  :       Optional[int]				=  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				)
							lowerCAmelCase__  :       List[str]				=  get_results(__lowerCamelCase				)
							# The base model scores a 25%
							self.assertGreaterEqual(result['''eval_accuracy''']	,0.6				)
							self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase	,'''step_1'''				)				)				)
							self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase	,'''image_classification_no_trainer'''				)				)				)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int):





				'''simple docstring'''

				if length <= 0 or not isinstance(lowerCamelCase_ ,lowerCamelCase_):
								raise ValueError('''Length must be a positive integer.''')
				return [n * (2 * n - 1) for n in range(lowerCamelCase_)]


if __name__ == "__main__":
							print(hexagonal_numbers(length=5))
							print(hexagonal_numbers(length=1_0))

