




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_						=			logging.getLogger()






def  snake_case(    )			->							Optional[Any]:





     '''simple docstring'''


     lowercase :       Dict       =			argparse.ArgumentParser()
     parser.add_argument('''-f'''   )
     lowercase :       Optional[int]       =			parser.parse_args()
     return args.f






def  snake_case(    __magic_name__   )			->							Union[str, Any]:





     '''simple docstring'''


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






def  snake_case(    )			->							Optional[Any]:





     '''simple docstring'''


     lowercase :       int       =			torch.cuda.is_available() and torch_device == '''cuda'''
     return is_using_cuda and is_apex_available()


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



class 						_A (  _lowerCamelCase				):






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






       """simple docstring"""







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






  @classmethod
  def  __a      (  cls		:     Dict				)		->   str:






       """simple docstring"""







       shutil.rmtree(cls.tmpdir				)






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






       """simple docstring"""







       lowercase :       Tuple       =			self.get_auto_remove_tmp_dir()
       lowercase :       str       =			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				)
       lowercase :       List[Any]       =			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		:     List[Any]				)		->   Tuple:






       """simple docstring"""







       lowercase :       List[Any]       =			self.get_auto_remove_tmp_dir()
       lowercase :       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				)
       lowercase :       Optional[int]       =			get_results(_A				)
       self.assertLess(result['''perplexity''']				,						100				)
       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		:     Any				)		->   str:






       """simple docstring"""







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






       """simple docstring"""







       lowercase :       List[str]       =			7 if get_gpu_count() > 1 else 2

       lowercase :       List[Any]       =			self.get_auto_remove_tmp_dir()
       lowercase :       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				)
       lowercase :       Any       =			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		:     Dict				)		->   Dict:






       """simple docstring"""







       lowercase :       List[str]       =			self.get_auto_remove_tmp_dir()
       lowercase :       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				)
       lowercase :       str       =			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		:     Dict				)		->   List[Any]:






       """simple docstring"""







       lowercase :       int       =			self.get_auto_remove_tmp_dir()
       lowercase :       Optional[int]       =			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				)
       lowercase :       Tuple       =			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		:     Union[str, Any]				)		->   Dict:






       """simple docstring"""







       lowercase :       Dict       =			self.get_auto_remove_tmp_dir()
       lowercase :       str       =			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				)
       lowercase :       Tuple       =			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		:     Optional[Any]				)		->   Tuple:






       """simple docstring"""







       lowercase :       List[Any]       =			self.get_auto_remove_tmp_dir()
       lowercase :       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				)
       lowercase :       Tuple       =			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		:     Dict				)		->   List[Any]:






       """simple docstring"""







       lowercase :       List[str]       =			logging.StreamHandler(sys.stdout				)
       logger.addHandler(_A				)

       lowercase :       Dict       =			self.get_auto_remove_tmp_dir()
       lowercase :       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				)
       lowercase :       int       =			get_results(_A				)
       self.assertGreaterEqual(result['''eval_overall_accuracy''']				,						0.10				)






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






       """simple docstring"""







       lowercase :       Tuple       =			self.get_auto_remove_tmp_dir()
       lowercase :       Tuple       =			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				)
       lowercase :       List[str]       =			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'''				)				)				)





import os
from collections.abc import Iterator






def  snake_case(    __magic_name__ = "."   )			->							Iterator[str]:





     '''simple docstring'''


     for dir_path, dir_names, filenames in os.walk(__magic_name__   ):
          lowercase :       Tuple       =			[d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
          for filename in filenames:
               if filename == "__init__.py":
                    continue
               if os.path.splitext(__magic_name__   )[1] in (".py", ".ipynb"):
                    yield os.path.join(__magic_name__			,		__magic_name__   ).lstrip('''./'''   )






def  snake_case(    __magic_name__   )			->							Dict:





     '''simple docstring'''


     return F"""{i * '  '}*""" if i else "\n##"






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       Dict       =			old_path.split(os.sep   )
     for i, new_part in enumerate(new_path.split(os.sep   )   ):
          if (i + 1 > len(__magic_name__   ) or old_parts[i] != new_part) and new_part:
               print(F"""{md_prefix(__magic_name__   )} {new_part.replace('_'			,		' '   ).title()}"""   )
     return new_path






def  snake_case(    __magic_name__ = "."   )			->							None:





     '''simple docstring'''


     lowercase :       str       =			''''''
     for filepath in sorted(good_file_paths(__magic_name__   )   ):
          lowercase      ,	lowercase :       Optional[int]       =			os.path.split(__magic_name__   )
          if filepath != old_path:
               lowercase :       str       =			print_path(__magic_name__			,		__magic_name__   )
          lowercase :       Optional[int]       =			(filepath.count(os.sep   ) + 1) if filepath else 0
          lowercase :       Optional[Any]       =			F"""{filepath}/{filename}""".replace(''' '''			,		'''%20'''   )
          lowercase :       List[str]       =			os.path.splitext(filename.replace('''_'''			,		''' '''   ).title()   )[0]
          print(F"""{md_prefix(__magic_name__   )} [{filename}]({url})"""   )


if __name__ == "__main__":
 print_directory_md('.')