



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 					__snake_case     (      ):
				__a							=    argparse.ArgumentParser()
				parser.add_argument('''-f'''    )
				__a							=    parser.parse_args()
				return args.f





def 					__snake_case     (      _UpperCAmelCase    ):
				__a							=    {}
				__a							=    os.path.join(UpperCamelCase__       ,			'''all_results.json'''    )
				if os.path.exists(UpperCamelCase__    ):
								with open(UpperCamelCase__       ,			'''r'''    ) as f:
												__a							=    json.load(UpperCamelCase__    )
				else:
								raise ValueError(f'can\'t find {path}'    )
				return results





def 					__snake_case     (      ):
				__a							=    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    _A	(     UpperCamelCase__   ):



							@classmethod
							def 		_lowerCamelCase      (		cls							:					List[str]):


											'''simple docstring'''

											__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 		_lowerCamelCase      (		cls							:					Optional[Any]):


											'''simple docstring'''

											shutil.rmtree(cls.tmpdir)



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


											'''simple docstring'''

											__a							=    self.get_auto_remove_tmp_dir()
											__a							=    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)
											__a							=    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							:					Tuple):


											'''simple docstring'''

											__a							=    self.get_auto_remove_tmp_dir()
											__a							=    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)
											__a							=    get_results(__lowerCamelCase)
											self.assertLess(result['''perplexity''']					,		100)
											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							:					Optional[Any]):


											'''simple docstring'''

											__a							=    self.get_auto_remove_tmp_dir()
											__a							=    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)
											__a							=    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							:					Tuple):


											'''simple docstring'''

											__a							=    7 if get_gpu_count() > 1 else 2

											__a							=    self.get_auto_remove_tmp_dir()
											__a							=    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)
											__a							=    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							:					Any):


											'''simple docstring'''

											__a							=    self.get_auto_remove_tmp_dir()
											__a							=    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)
											__a							=    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							:					Union[str, Any]):


											'''simple docstring'''

											__a							=    self.get_auto_remove_tmp_dir()
											__a							=    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)
											__a							=    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							:					Tuple):


											'''simple docstring'''

											__a							=    self.get_auto_remove_tmp_dir()
											__a							=    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)
											__a							=    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							:					Union[str, Any]):


											'''simple docstring'''

											__a							=    self.get_auto_remove_tmp_dir()
											__a							=    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)
											__a							=    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							:					Optional[Any]):


											'''simple docstring'''

											__a							=    logging.StreamHandler(sys.stdout)
											logger.addHandler(__lowerCamelCase)

											__a							=    self.get_auto_remove_tmp_dir()
											__a							=    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)
											__a							=    get_results(__lowerCamelCase)
											self.assertGreaterEqual(result['''eval_overall_accuracy''']					,		0.10)





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


											'''simple docstring'''

											__a							=    self.get_auto_remove_tmp_dir()
											__a							=    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)
											__a							=    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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):

    def get_matched_characters(_UpperCAmelCase       ,			_UpperCAmelCase    ) -> str:
        __a							=    []
        __a							=    min(len(_stra    )       ,			len(_stra    )    ) // 2
        for i, l in enumerate(_stra    ):
            __a							=    int(max(0       ,			i - limit    )    )
            __a							=    int(min(i + limit + 1       ,			len(_stra    )    )    )
            if l in _stra[left:right]:
                matched.append(_UpperCAmelCase    )
                __a							=    f'{_stra[0:_stra.index(_UpperCAmelCase    )]} {_stra[_stra.index(_UpperCAmelCase    ) + 1:]}'

        return "".join(_UpperCAmelCase    )

    # matching characters
    __a							=    get_matched_characters(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    get_matched_characters(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    len(_UpperCAmelCase    )

    # transposition
    __a							=    (
        len([(ca, ca) for ca, ca in zip(_UpperCAmelCase       ,			_UpperCAmelCase    ) if ca != ca]    ) // 2
    )

    if not match_count:
        __a							=    0.0
    else:
        __a							=    (
            1
            / 3
            * (
                match_count / len(_UpperCAmelCase    )
                + match_count / len(_UpperCAmelCase    )
                + (match_count - transpositions) / match_count
            )
        )

    # common prefix up to 4 characters
    __a							=    0
    for ca, ca in zip(stra[:4]       ,			stra[:4]    ):
        if ca == ca:
            prefix_len += 1
        else:
            break

    return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(jaro_winkler('''hello''', '''world'''))





