
"""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)

_a		       =							logging.getLogger()







def 	__a     (		):
   UpperCAmelCase_					:					Tuple			     =    argparse.ArgumentParser()
   parser.add_argument("-f"  )
   UpperCAmelCase_					:					List[Any]			     =    parser.parse_args()
   return args.f







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					List[str]			     =    {}
   UpperCAmelCase_					:					Optional[int]			     =    os.path.join(__lowerCamelCase,		"all_results.json"  )
   if os.path.exists(__lowerCamelCase  ):
      with open(__lowerCamelCase,		"r"  ) as f:
         UpperCAmelCase_					:					List[Any]			     =    json.load(__lowerCamelCase  )
   else:
      raise ValueError(f"""can't find {path}"""  )
   return results







def 	__a     (		):
   UpperCAmelCase_					:					str			     =    torch.cuda.is_available() and torch_device == "cuda"
   return is_using_cuda and is_apex_available()


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


class A_	(lowercase__      ):




  '''simple docstring'''







  @classmethod
  def        UpperCamelCase__					(		cls  ):

     """simple docstring"""
     # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
     UpperCAmelCase_					:					Any			     =    tempfile.mkdtemp()
     UpperCAmelCase_					:					Dict			     =    os.path.join(cls.tmpdir				,   "default_config.yml"  )
     write_basic_config(save_location=cls.configPath  )
     UpperCAmelCase_					:					int			     =    ["accelerate", "launch", "--config_file", cls.configPath]







  @classmethod
  def        UpperCamelCase__					(		cls  ):

     """simple docstring"""
     shutil.rmtree(cls.tmpdir  )







  @mock.patch.dict(os.environ				,   {"WANDB_MODE": "offline"}  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    self.get_auto_remove_tmp_dir()
     UpperCAmelCase_					:					Union[str, 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  )
     UpperCAmelCase_					:					int			     =    get_results(lowercase_  )
     self.assertGreaterEqual(result["eval_accuracy"]				,   0.75  )
     self.assertTrue(os.path.exists(os.path.join(lowercase_				,   "epoch_0"  )  )  )
     self.assertTrue(os.path.exists(os.path.join(lowercase_				,   "glue_no_trainer"  )  )  )







  @mock.patch.dict(os.environ				,   {"WANDB_MODE": "offline"}  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.get_auto_remove_tmp_dir()
     UpperCAmelCase_					:					Optional[Any]			     =    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_					:					List[str]			     =    get_results(lowercase_  )
     self.assertLess(result["perplexity"]				,   100  )
     self.assertTrue(os.path.exists(os.path.join(lowercase_				,   "epoch_0"  )  )  )
     self.assertTrue(os.path.exists(os.path.join(lowercase_				,   "clm_no_trainer"  )  )  )







  @mock.patch.dict(os.environ				,   {"WANDB_MODE": "offline"}  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    self.get_auto_remove_tmp_dir()
     UpperCAmelCase_					:					str			     =    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_					:					int			     =    get_results(lowercase_  )
     self.assertLess(result["perplexity"]				,   42  )
     self.assertTrue(os.path.exists(os.path.join(lowercase_				,   "epoch_0"  )  )  )
     self.assertTrue(os.path.exists(os.path.join(lowercase_				,   "mlm_no_trainer"  )  )  )







  @mock.patch.dict(os.environ				,   {"WANDB_MODE": "offline"}  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
     UpperCAmelCase_					:					Union[str, Any]			     =    7 if get_gpu_count() > 1 else 2

     UpperCAmelCase_					:					int			     =    self.get_auto_remove_tmp_dir()
     UpperCAmelCase_					:					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  )
     UpperCAmelCase_					:					List[Any]			     =    get_results(lowercase_  )
     self.assertGreaterEqual(result["eval_accuracy"]				,   0.75  )
     self.assertLess(result["train_loss"]				,   0.5  )
     self.assertTrue(os.path.exists(os.path.join(lowercase_				,   "epoch_0"  )  )  )
     self.assertTrue(os.path.exists(os.path.join(lowercase_				,   "ner_no_trainer"  )  )  )







  @unittest.skip(reason="Fix me @muellerzr"  )
  @mock.patch.dict(os.environ				,   {"WANDB_MODE": "offline"}  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    self.get_auto_remove_tmp_dir()
     UpperCAmelCase_					:					Tuple			     =    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_					:					int			     =    get_results(lowercase_  )
     # 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(lowercase_				,   "epoch_0"  )  )  )
     self.assertTrue(os.path.exists(os.path.join(lowercase_				,   "qa_no_trainer"  )  )  )







  @mock.patch.dict(os.environ				,   {"WANDB_MODE": "offline"}  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    self.get_auto_remove_tmp_dir()
     UpperCAmelCase_					:					Any			     =    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_					:					Optional[int]			     =    get_results(lowercase_  )
     self.assertGreaterEqual(result["eval_accuracy"]				,   0.8  )
     self.assertTrue(os.path.exists(os.path.join(lowercase_				,   "swag_no_trainer"  )  )  )







  @slow
  @mock.patch.dict(os.environ				,   {"WANDB_MODE": "offline"}  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.get_auto_remove_tmp_dir()
     UpperCAmelCase_					:					List[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  )
     UpperCAmelCase_					:					str			     =    get_results(lowercase_  )
     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(lowercase_				,   "epoch_0"  )  )  )
     self.assertTrue(os.path.exists(os.path.join(lowercase_				,   "summarization_no_trainer"  )  )  )







  @slow
  @mock.patch.dict(os.environ				,   {"WANDB_MODE": "offline"}  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.get_auto_remove_tmp_dir()
     UpperCAmelCase_					:					List[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  )
     UpperCAmelCase_					:					Any			     =    get_results(lowercase_  )
     self.assertGreaterEqual(result["eval_bleu"]				,   30  )
     self.assertTrue(os.path.exists(os.path.join(lowercase_				,   "epoch_0"  )  )  )
     self.assertTrue(os.path.exists(os.path.join(lowercase_				,   "translation_no_trainer"  )  )  )







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    logging.StreamHandler(sys.stdout  )
     logger.addHandler(lowercase_  )

     UpperCAmelCase_					:					Union[str, Any]			     =    self.get_auto_remove_tmp_dir()
     UpperCAmelCase_					:					int			     =    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_					:					Tuple			     =    get_results(lowercase_  )
     self.assertGreaterEqual(result["eval_overall_accuracy"]				,   0.10  )







  @mock.patch.dict(os.environ				,   {"WANDB_MODE": "offline"}  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.get_auto_remove_tmp_dir()
     UpperCAmelCase_					:					Optional[int]			     =    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_					:					Optional[Any]			     =    get_results(lowercase_  )
     # The base model scores a 25%
     self.assertGreaterEqual(result["eval_accuracy"]				,   0.6  )
     self.assertTrue(os.path.exists(os.path.join(lowercase_				,   "step_1"  )  )  )
     self.assertTrue(os.path.exists(os.path.join(lowercase_				,   "image_classification_no_trainer"  )  )  )




"""simple docstring"""





def 	__a     (		__lowerCamelCase = 3,		__lowerCamelCase = 7,		__lowerCamelCase = 100_0000  ):
   UpperCAmelCase_					:					Dict			     =    0
   UpperCAmelCase_					:					List[Any]			     =    1

   for current_denominator in range(1,		limit + 1  ):
      UpperCAmelCase_					:					Dict			     =    current_denominator * numerator // denominator
      if current_denominator % denominator == 0:
         current_numerator -= 1
      if current_numerator * max_denominator > current_denominator * max_numerator:
         UpperCAmelCase_					:					List[Any]			     =    current_numerator
         UpperCAmelCase_					:					Optional[int]			     =    current_denominator
   return max_numerator


if __name__ == "__main__":
    print(solution(numerator=3, denominator=7, limit=1_000_000))


