
"""simple docstring"""




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)

UpperCAmelCase__      :    Optional[Any]    							= logging.getLogger()

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       List[str]           =   argparse.ArgumentParser()
							parser.add_argument("""-f"""						)
							SCREAMING_SNAKE_CASE__						:       str           =   parser.parse_args()
							return args.f

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       List[str]           =   {}
							SCREAMING_SNAKE_CASE__						:       Tuple           =   os.path.join(_snake_case   ,"""all_results.json"""						)
							if os.path.exists(_snake_case						):
														with open(_snake_case   ,"""r"""						) as f:
																					SCREAMING_SNAKE_CASE__						:       int           =   json.load(_snake_case						)
							else:
														raise ValueError(f'''can\'t find {path}'''						)
							return results

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.cuda.is_available() and torch_device == """cuda"""
							return is_using_cuda and is_apex_available()


UpperCAmelCase__      :    str    							= logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class 	lowerCAmelCase_						(_UpperCamelCase  ):


				"""simple docstring"""



				@classmethod
				def 	__magic_name__    (cls    )					->		Union[str, Any]:



											"""simple docstring"""




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



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



											"""simple docstring"""




											shutil.rmtree(cls.tmpdir    )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.get_auto_remove_tmp_dir()
											SCREAMING_SNAKE_CASE__						:       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    )
											SCREAMING_SNAKE_CASE__						:       str           =   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 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   self.get_auto_remove_tmp_dir()
											SCREAMING_SNAKE_CASE__						:       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    )
											SCREAMING_SNAKE_CASE__						:       List[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 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.get_auto_remove_tmp_dir()
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   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    )
											SCREAMING_SNAKE_CASE__						:       Optional[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 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   7 if get_gpu_count() > 1 else 2

											SCREAMING_SNAKE_CASE__						:       Dict           =   self.get_auto_remove_tmp_dir()
											SCREAMING_SNAKE_CASE__						:       List[str]           =   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    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   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 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.get_auto_remove_tmp_dir()
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   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    )
											SCREAMING_SNAKE_CASE__						:       Any           =   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 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_auto_remove_tmp_dir()
											SCREAMING_SNAKE_CASE__						:       Dict           =   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    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   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 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_auto_remove_tmp_dir()
											SCREAMING_SNAKE_CASE__						:       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    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   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 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   self.get_auto_remove_tmp_dir()
											SCREAMING_SNAKE_CASE__						:       str           =   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    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   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 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   logging.StreamHandler(sys.stdout    )
											logger.addHandler(__lowerCamelCase    )

											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.get_auto_remove_tmp_dir()
											SCREAMING_SNAKE_CASE__						:       List[str]           =   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    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   get_results(__lowerCamelCase    )
											self.assertGreaterEqual(result["""eval_overall_accuracy"""]   ,       0.10    )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.get_auto_remove_tmp_dir()
											SCREAMING_SNAKE_CASE__						:       Dict           =   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    )
											SCREAMING_SNAKE_CASE__						:       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"""    )    )    )










from bisect import bisect
from itertools import accumulate




def 			__magic_name__				( A		:					Optional[Any],  A		:					List[str],  A		:					Tuple,  A		:					Optional[Any]   ):






			'''simple docstring'''







			a    =			sorted(zip(A,  A   ),  key=lambda A   : x[0] / x[1],  reverse=A   )
			a    ,		a    =			[i[0] for i in r], [i[1] for i in r]
			a    =			list(accumulate(A   )   )
			a    =			bisect(A,  A   )
			return (
			    0
			    if k == 0
			    else sum(vl[:k]   ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
			    if k != n
			    else sum(vl[:k]   )
			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






