



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_		:     Dict        =     logging.getLogger()


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






 '''simple docstring'''

 a						=		argparse.ArgumentParser()
 parser.add_argument("-f"					)
 a						=		parser.parse_args()
 return args.f


def 	UpperCAmelCase__      (      UpperCAmelCase__ :List[str]					)     ->   Dict:






 '''simple docstring'''

 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 	UpperCAmelCase__      (      )     ->   Tuple:






 '''simple docstring'''

 a						=		torch.cuda.is_available() and torch_device == "cuda"
 return is_using_cuda and is_apex_available()


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




class 		_lowercase		(       UpperCAmelCase__	):

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


    """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 		A							(      cls			:       List[Any]							)       ->				Optional[Any]:


    """simple docstring"""






    shutil.rmtree(cls.tmpdir							)

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


    """simple docstring"""






    a						=		self.get_auto_remove_tmp_dir()
    a						=		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							)
    a						=		get_results(__lowerCAmelCase							)
    self.assertGreaterEqual(result["eval_accuracy"]							,   0.7_5							)
    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 		A							(      self			:       List[str]							)       ->				Tuple:


    """simple docstring"""






    a						=		self.get_auto_remove_tmp_dir()
    a						=		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							)
    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 		A							(      self			:       List[Any]							)       ->				Any:


    """simple docstring"""






    a						=		self.get_auto_remove_tmp_dir()
    a						=		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							)
    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 		A							(      self			:       Tuple							)       ->				List[str]:


    """simple docstring"""






    a						=		7 if get_gpu_count() > 1 else 2

    a						=		self.get_auto_remove_tmp_dir()
    a						=		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							)
    a						=		get_results(__lowerCAmelCase							)
    self.assertGreaterEqual(result["eval_accuracy"]							,   0.7_5							)
    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 		A							(      self			:       Union[str, Any]							)       ->				int:


    """simple docstring"""






    a						=		self.get_auto_remove_tmp_dir()
    a						=		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							)
    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 		A							(      self			:       Any							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		self.get_auto_remove_tmp_dir()
    a						=		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							)
    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 		A							(      self			:       Union[str, Any]							)       ->				str:


    """simple docstring"""






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


    """simple docstring"""






    a						=		self.get_auto_remove_tmp_dir()
    a						=		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							)
    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 		A							(      self			:       Tuple							)       ->				Tuple:


    """simple docstring"""






    a						=		logging.StreamHandler(sys.stdout							)
    logger.addHandler(__lowerCAmelCase							)

    a						=		self.get_auto_remove_tmp_dir()
    a						=		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							)
    a						=		get_results(__lowerCAmelCase							)
    self.assertGreaterEqual(result["eval_overall_accuracy"]							,   0.1_0							)




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


    """simple docstring"""






    a						=		self.get_auto_remove_tmp_dir()
    a						=		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							)
    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 	UpperCAmelCase__      (      UpperCAmelCase__ :Any					):






 '''simple docstring'''

 if not head:
  return True
 # split the list to two parts
 a		,  a						=		head.next, head
 while fast and fast.next:
  a						=		fast.next.next
  a						=		slow.next
 a						=		slow.next
 a						=		None  # Don't forget here! But forget still works!
 # reverse the second part
 a						=		None
 while second:
  a						=		second.next
  a						=		node
  a						=		second
  a						=		nxt
 # compare two parts
 # second part has the same or one less node
 while node:
  if node.val != head.val:
   return False
  a						=		node.next
  a						=		head.next
 return True


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str					):






 '''simple docstring'''

 if not head or not head.next:
  return True

 # 1. Get the midpoint (slow)
 a						=		a						=		a						=		head
 while fast and fast.next:
  a		,  a						=		fast.next.next, slow.next

 # 2. Push the second half into the stack
 a						=		[slow.val]
 while slow.next:
  a						=		slow.next
  stack.append(slow.val					)

 # 3. Comparison
 while stack:
  if stack.pop() != cur.val:
   return False
  a						=		cur.next

 return True




def 	UpperCAmelCase__      (      UpperCAmelCase__ :Any					):






 '''simple docstring'''

 if not head or not head.next:
  return True
 a						=		{}
 a						=		0
 while head:
  if head.val in d:
   d[head.val].append(UpperCAmelCase__					)
  else:
   a						=		[pos]
  a						=		head.next
  pos += 1
 a						=		pos - 1
 a						=		0
 for v in d.values():
  if len(UpperCAmelCase__					) % 2 != 0:
   middle += 1
  else:
   a						=		0
   for i in range(0 ,					len(UpperCAmelCase__					)					):
    if v[i] + v[len(UpperCAmelCase__					) - 1 - step] != checksum:
     return False
    step += 1
  if middle > 1:
   return False
 return True

