






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)

__A      =				logging.getLogger()




def      __A					(     ):

					'''simple docstring'''







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




def      __A					(     _lowercase ):

					'''simple docstring'''







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




def      __A					(     ):

					'''simple docstring'''







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


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





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							@classmethod
							def       __A (  cls:   List[Any]   )    ->				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       __A (  cls:   List[str]   )    ->				Optional[Any]:
												shutil.rmtree(cls.tmpdir   )

							@mock.patch.dict(os.environ		,     {'''WANDB_MODE''': '''offline'''}   )
							def       __A (  self:   Optional[int]   )    ->				List[Any]:
												_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       __A (  self:   int   )    ->				List[str]:
												_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       __A (  self:   int   )    ->				Union[str, Any]:
												_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       __A (  self:   Optional[int]   )    ->				List[str]:
												# 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       __A (  self:   Tuple   )    ->				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       __A (  self:   List[str]   )    ->				Tuple:
												_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       __A (  self:   List[Any]   )    ->				Optional[Any]:
												_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       __A (  self:   Any   )    ->				Union[str, 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       __A (  self:   Union[str, Any]   )    ->				Tuple:
												_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       __A (  self:   str   )    ->				Dict:
												_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'''   )   )   )











def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= word.split()

					def justify(_lowercase					,			_lowercase					,			_lowercase ) -> str:
										_A								= max_width - width
										_A								= len(_lowercase )
										if len(_lowercase ) == 1:
															# if there is only word in line
															# just insert overall_spaces_count for the remainder of line
															return line[0] + " " * overall_spaces_count
										else:
															_A								= words_count - 1
															# num_spaces_between_words_list[i] : tells you to insert
															# num_spaces_between_words_list[i] spaces
															# after word on line[i]
															_A								= spaces_to_insert_between_words * [
															    overall_spaces_count // spaces_to_insert_between_words
															]
															_A								= (
															    overall_spaces_count % spaces_to_insert_between_words
															)
															# distribute spaces via round robin to the left words
															for i in range(_lowercase ):
																				num_spaces_between_words_list[i] += 1
															_A								= []
															for i in range(_lowercase ):
																				# add the word
																				aligned_words_list.append(line[i] )
																				# add the spaces to insert
																				aligned_words_list.append(num_spaces_between_words_list[i] * ''' ''' )
															# just add the last word to the sentence
															aligned_words_list.append(line[-1] )
															# join the aligned words list to form a justified line
															return "".join(_lowercase )

					_A								= []
					_A								= []
					_A								= 0
					for word in words:
										if width + len(_lowercase ) + len(_lowercase ) <= max_width:
															# keep adding words until we can fill out max_width
															# width = sum of length of all words (without overall_spaces_count)
															# len(word) = length of current word
															# len(line) = number of overall_spaces_count to insert between words
															line.append(_lowercase )
															width += len(_lowercase )
										else:
															# justify the line and add it to result
															answer.append(justify(_lowercase					,			_lowercase					,			_lowercase ) )
															# reset new line and new width
															_A							,_A								= [word], len(_lowercase )
					_A								= max_width - width - len(_lowercase )
					answer.append(''' '''.join(_lowercase ) + (remaining_spaces + 1) * ''' ''' )
					return answer


if __name__ == "__main__":
				from doctest import testmod

				testmod()



