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					:   str							=		logging.getLogger()





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       argparse.ArgumentParser()
					parser.add_argument("-f"  )
					SCREAMING_SNAKE_CASE    			=       parser.parse_args()
					return args.f





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





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       torch.cuda.is_available() and torch_device == "cuda"
					return is_using_cuda and is_apex_available()


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


class 						lowercase    (     a     ):




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



												'''simple docstring'''

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




							@classmethod
							def 	__snake_case(						cls   :					List[str]      )					->				Dict:



												'''simple docstring'''

												shutil.rmtree(cls.tmpdir      )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE    			=       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      )
												SCREAMING_SNAKE_CASE    			=       get_results(_UpperCamelCase      )
												self.assertGreaterEqual(result["eval_accuracy"]	,							0.7_5      )
												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 	__snake_case(						self   :					Dict      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE    			=       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      )
												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					Optional[Any]      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE    			=       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      )
												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					str      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       7 if get_gpu_count() > 1 else 2

												SCREAMING_SNAKE_CASE    			=       self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE    			=       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      )
												SCREAMING_SNAKE_CASE    			=       get_results(_UpperCamelCase      )
												self.assertGreaterEqual(result["eval_accuracy"]	,							0.7_5      )
												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 	__snake_case(						self   :					List[str]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE    			=       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      )
												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					List[str]      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE    			=       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      )
												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					Any      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE    			=       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      )
												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					Any      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE    			=       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      )
												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					int      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       logging.StreamHandler(sys.stdout      )
												logger.addHandler(_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE    			=       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      )
												SCREAMING_SNAKE_CASE    			=       get_results(_UpperCamelCase      )
												self.assertGreaterEqual(result["eval_overall_accuracy"]	,							0.1_0      )





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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_auto_remove_tmp_dir()
												SCREAMING_SNAKE_CASE    			=       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      )
												SCREAMING_SNAKE_CASE    			=       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"      )      )      )





import functools





def 	__lowerCamelCase							(UpperCAmelCase__     :							list[int]      , UpperCAmelCase__     :							list[int]  ):

					# Validation
					if not isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) or not all(isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) for day in days  ):
										raise ValueError("The parameter days should be a list of integers"  )

					if len(UpperCAmelCase__  ) != 3 or not all(isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) for cost in costs  ):
										raise ValueError("The parameter costs should be a list of three integers"  )

					if len(UpperCAmelCase__  ) == 0:
										return 0

					if min(UpperCAmelCase__  ) <= 0:
										raise ValueError("All days elements should be greater than 0"  )

					if max(UpperCAmelCase__  ) >= 3_6_6:
										raise ValueError("All days elements should be less than 366"  )

					SCREAMING_SNAKE_CASE    			=       set(UpperCAmelCase__  )

					@functools.cache
					def dynamic_programming(UpperCAmelCase__     :							int  ) -> int:
										if index > 3_6_5:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1  )

										return min(
										    costs[0] + dynamic_programming(index + 1  )      , costs[1] + dynamic_programming(index + 7  )      , costs[2] + dynamic_programming(index + 3_0  )      , )

					return dynamic_programming(1  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




