'''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)

lowercase__  :      Tuple        =					logging.getLogger()



def  __lowerCamelCase       (      ):


      '''simple docstring'''


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



def  __lowerCamelCase       (      _UpperCamelCase  :  Optional[int]					):


      '''simple docstring'''


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



def  __lowerCamelCase       (      ):


      '''simple docstring'''


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


lowercase__  :      Tuple        =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  @classmethod
  def  lowerCAmelCase__ (      cls			:       List[str]  )				->List[Any]:
        UpperCAmelCase_            =  tempfile.mkdtemp()
        UpperCAmelCase_            =  os.path.join(cls.tmpdir  ,							'''default_config.yml'''  )
        write_basic_config(save_location=cls.configPath  )
        UpperCAmelCase_            =  ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]






  @classmethod
  def  lowerCAmelCase__ (      cls			:       Optional[int]  )				->Optional[Any]:
        shutil.rmtree(cls.tmpdir  )






  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       List[Any]  )				->str:
        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  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  )
        UpperCAmelCase_            =  get_results(_SCREAMING_SNAKE_CASE  )
        self.assertGreaterEqual(result['''eval_accuracy''']  ,							0.75  )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,							'''epoch_0'''  )  )  )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,							'''glue_no_trainer'''  )  )  )






  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       Any  )				->Optional[Any]:
        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  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  )
        UpperCAmelCase_            =  get_results(_SCREAMING_SNAKE_CASE  )
        self.assertLess(result['''perplexity''']  ,							100  )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,							'''epoch_0'''  )  )  )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,							'''clm_no_trainer'''  )  )  )






  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       str  )				->int:
        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  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  )
        UpperCAmelCase_            =  get_results(_SCREAMING_SNAKE_CASE  )
        self.assertLess(result['''perplexity''']  ,							42  )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,							'''epoch_0'''  )  )  )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,							'''mlm_no_trainer'''  )  )  )






  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Any:
        UpperCAmelCase_            =  7 if get_gpu_count() > 1 else 2

        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  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  )
        UpperCAmelCase_            =  get_results(_SCREAMING_SNAKE_CASE  )
        self.assertGreaterEqual(result['''eval_accuracy''']  ,							0.75  )
        self.assertLess(result['''train_loss''']  ,							0.5  )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,							'''epoch_0'''  )  )  )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,							'''ner_no_trainer'''  )  )  )






  @unittest.skip(reason='''Fix me @muellerzr'''  )
  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       Dict  )				->Optional[int]:
        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  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  )
        UpperCAmelCase_            =  get_results(_SCREAMING_SNAKE_CASE  )
        # 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(_SCREAMING_SNAKE_CASE  ,							'''epoch_0'''  )  )  )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,							'''qa_no_trainer'''  )  )  )






  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->Tuple:
        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  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  )
        UpperCAmelCase_            =  get_results(_SCREAMING_SNAKE_CASE  )
        self.assertGreaterEqual(result['''eval_accuracy''']  ,							0.8  )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,							'''swag_no_trainer'''  )  )  )






  @slow
  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       str  )				->Optional[int]:
        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  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  )
        UpperCAmelCase_            =  get_results(_SCREAMING_SNAKE_CASE  )
        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(_SCREAMING_SNAKE_CASE  ,							'''epoch_0'''  )  )  )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,							'''summarization_no_trainer'''  )  )  )






  @slow
  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       int  )				->Union[str, Any]:
        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  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  )
        UpperCAmelCase_            =  get_results(_SCREAMING_SNAKE_CASE  )
        self.assertGreaterEqual(result['''eval_bleu''']  ,							30  )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,							'''epoch_0'''  )  )  )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,							'''translation_no_trainer'''  )  )  )






  @slow
  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Union[str, Any]:
        UpperCAmelCase_            =  logging.StreamHandler(sys.stdout  )
        logger.addHandler(_SCREAMING_SNAKE_CASE  )

        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  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  )
        UpperCAmelCase_            =  get_results(_SCREAMING_SNAKE_CASE  )
        self.assertGreaterEqual(result['''eval_overall_accuracy''']  ,							0.10  )






  @mock.patch.dict(os.environ  ,							{'''WANDB_MODE''': '''offline'''}  )
  def  lowerCAmelCase__ (      self			:       int  )				->List[Any]:
        UpperCAmelCase_            =  self.get_auto_remove_tmp_dir()
        UpperCAmelCase_            =  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  )
        UpperCAmelCase_            =  get_results(_SCREAMING_SNAKE_CASE  )
        # The base model scores a 25%
        self.assertGreaterEqual(result['''eval_accuracy''']  ,							0.6  )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,							'''step_1'''  )  )  )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE  ,							'''image_classification_no_trainer'''  )  )  )






'''simple docstring'''
from __future__ import annotations

from typing import TypedDict







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	42
  lowerCAmelCase__										=	42






def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter s type must be str.'''					)

      return [s[i:] + s[:i] for i in range(len(_UpperCamelCase					)					)]



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter s type must be str.'''					)
      if not s:
            raise ValueError('''The parameter s must not be empty.'''					)

      UpperCAmelCase_            =  all_rotations(_UpperCamelCase					)
      rotations.sort()  # sort the list of rotations in alphabetically order
      # make a string composed of the last char of each rotation
      UpperCAmelCase_            =  {
          "bwt_string": "".join([word[-1] for word in rotations]					),
          "idx_original_string": rotations.index(_UpperCamelCase					),
      }
      return response



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter bwt_string type must be str.'''					)
      if not bwt_string:
            raise ValueError('''The parameter bwt_string must not be empty.'''					)
      try:
            UpperCAmelCase_            =  int(_UpperCamelCase					)
      except ValueError:
            raise TypeError(
                '''The parameter idx_original_string type must be int or passive'''
                ''' of cast to int.'''					)
      if idx_original_string < 0:
            raise ValueError('''The parameter idx_original_string must not be lower than 0.'''					)
      if idx_original_string >= len(_UpperCamelCase					):
            raise ValueError(
                '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''					)

      UpperCAmelCase_            =  [''''''] * len(_UpperCamelCase					)
      for _ in range(len(_UpperCamelCase					)					):
            for i in range(len(_UpperCamelCase					)					):
                  UpperCAmelCase_            =  bwt_string[i] + ordered_rotations[i]
            ordered_rotations.sort()
      return ordered_rotations[idx_original_string]


if __name__ == "__main__":
      lowercase__  :      Optional[int]        =					"Provide a string that I will generate its BWT transform: "
      lowercase__  :      List[Any]        =					input(entry_msg).strip()
      lowercase__  :      Any        =					bwt_transform(s)
      print(
          F'''Burrows Wheeler transform for string \'{s}\' results '''
          F'''in \'{result['bwt_string']}\''''
      )
      lowercase__  :      Dict        =					reverse_bwt(result["bwt_string"], result["idx_original_string"])
      print(
          F'''Reversing Burrows Wheeler transform for entry \'{result['bwt_string']}\' '''
          F'''we get original string \'{original_string}\''''
      )





