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							=      logging.getLogger()




def       __lowerCAmelCase      (						)    ->  Optional[int]:
  __UpperCAmelCase						    =       argparse.ArgumentParser()
  parser.add_argument("-f"					)
  __UpperCAmelCase						    =       parser.parse_args()
  return args.f




def       __lowerCAmelCase      (						A_   : Optional[Any]					)    ->  List[str]:
  __UpperCAmelCase						    =       {}
  __UpperCAmelCase						    =       os.path.join(lowercase__						,	"all_results.json"					)
  if os.path.exists(lowercase__					):
    with open(lowercase__						,	"r"					) as f:
      __UpperCAmelCase						    =       json.load(lowercase__					)
  else:
    raise ValueError(F'''can\'t find {path}'''					)
  return results




def       __lowerCAmelCase      (						)    ->  Tuple:
  __UpperCAmelCase						    =       torch.cuda.is_available() and torch_device == "cuda"
  return is_using_cuda and is_apex_available()


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





class 			UpperCAmelCase__    (		__snake_case					):



  """simple docstring"""






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




    '''simple docstring'''




    __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 	_UpperCAmelCase				(				cls:			str   ) -> Optional[int]:




    '''simple docstring'''




    shutil.rmtree(cls.tmpdir   )






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




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_auto_remove_tmp_dir()
    __UpperCAmelCase						    =       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   )
    __UpperCAmelCase						    =       get_results(__UpperCamelCase   )
    self.assertGreaterEqual(result["eval_accuracy"]				,				0.75   )
    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 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_auto_remove_tmp_dir()
    __UpperCAmelCase						    =       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   )
    __UpperCAmelCase						    =       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 	_UpperCAmelCase				(				self:			Any   ) -> Tuple:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_auto_remove_tmp_dir()
    __UpperCAmelCase						    =       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   )
    __UpperCAmelCase						    =       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 	_UpperCAmelCase				(				self:			Optional[int]   ) -> Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       7 if get_gpu_count() > 1 else 2

    __UpperCAmelCase						    =       self.get_auto_remove_tmp_dir()
    __UpperCAmelCase						    =       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   )
    __UpperCAmelCase						    =       get_results(__UpperCamelCase   )
    self.assertGreaterEqual(result["eval_accuracy"]				,				0.75   )
    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 	_UpperCAmelCase				(				self:			int   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_auto_remove_tmp_dir()
    __UpperCAmelCase						    =       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   )
    __UpperCAmelCase						    =       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 	_UpperCAmelCase				(				self:			Optional[int]   ) -> Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_auto_remove_tmp_dir()
    __UpperCAmelCase						    =       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   )
    __UpperCAmelCase						    =       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 	_UpperCAmelCase				(				self:			Any   ) -> int:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_auto_remove_tmp_dir()
    __UpperCAmelCase						    =       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   )
    __UpperCAmelCase						    =       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 	_UpperCAmelCase				(				self:			str   ) -> Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_auto_remove_tmp_dir()
    __UpperCAmelCase						    =       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   )
    __UpperCAmelCase						    =       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 	_UpperCAmelCase				(				self:			Optional[int]   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       logging.StreamHandler(sys.stdout   )
    logger.addHandler(__UpperCamelCase   )

    __UpperCAmelCase						    =       self.get_auto_remove_tmp_dir()
    __UpperCAmelCase						    =       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   )
    __UpperCAmelCase						    =       get_results(__UpperCamelCase   )
    self.assertGreaterEqual(result["eval_overall_accuracy"]				,				0.10   )






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




    '''simple docstring'''




    __UpperCAmelCase						    =       self.get_auto_remove_tmp_dir()
    __UpperCAmelCase						    =       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   )
    __UpperCAmelCase						    =       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"   )   )   )




def       __lowerCAmelCase      (						A_   : str					)    ->  bool:
  if not all(x.isalpha() for x in string					):
    raise ValueError("String must only contain alphabetic characters."					)

  __UpperCAmelCase						    =       sorted(string.lower()					)
  return len(A_					) == len(set(A_					)					)


if __name__ == "__main__":
       a_							=      input("""Enter a string """).strip()

       a_							=      is_isogram(input_str)
       print(F"{input_str} is {'an' if isogram else 'not an'} isogram.")



