
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__		:Optional[int]   =    logging.getLogger()



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						argparse.ArgumentParser()
					parser.add_argument('''-f'''		)
					lowercase							=						parser.parse_args()
					return args.f



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						{}
					lowercase							=						os.path.join(lowerCAmelCase__ ,			'''all_results.json'''		)
					if os.path.exists(lowerCAmelCase__		):
										with open(lowerCAmelCase__ ,			'''r'''		) as f:
															lowercase							=						json.load(lowerCAmelCase__		)
					else:
										raise ValueError(f'can\'t find {path}'		)
					return results



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						torch.cuda.is_available() and torch_device == '''cuda'''
					return is_using_cuda and is_apex_available()


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







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							@classmethod
							def  A__      (				cls):
												# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
												lowercase							=						tempfile.mkdtemp()
												lowercase							=						os.path.join(cls.tmpdir   ,'''default_config.yml''')
												write_basic_config(save_location=cls.configPath)
												lowercase							=						['''accelerate''', '''launch''', '''--config_file''', cls.configPath]




							@classmethod
							def  A__      (				cls):
												shutil.rmtree(cls.tmpdir)




							@mock.patch.dict(os.environ   ,{'''WANDB_MODE''': '''offline'''})
							def  A__      (				self):
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						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):
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						get_results(A__)
												self.assertLess(result['''perplexity''']   ,1_0_0)
												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):
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						get_results(A__)
												self.assertLess(result['''perplexity''']   ,4_2)
												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):
												# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
												lowercase							=						7 if get_gpu_count() > 1 else 2

												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						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):
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						get_results(A__)
												# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
												self.assertGreaterEqual(result['''eval_f1''']   ,2_8)
												self.assertGreaterEqual(result['''eval_exact''']   ,2_8)
												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):
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						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):
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						get_results(A__)
												self.assertGreaterEqual(result['''eval_rouge1''']   ,1_0)
												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):
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						get_results(A__)
												self.assertGreaterEqual(result['''eval_bleu''']   ,3_0)
												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):
												lowercase							=						logging.StreamHandler(sys.stdout)
												logger.addHandler(A__)

												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						get_results(A__)
												self.assertGreaterEqual(result['''eval_overall_accuracy''']   ,0.10)





							@mock.patch.dict(os.environ   ,{'''WANDB_MODE''': '''offline'''})
							def  A__      (				self):
												lowercase							=						self.get_auto_remove_tmp_dir()
												lowercase							=						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)
												lowercase							=						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''')))








'''simple docstring'''
from datetime import datetime

import matplotlib.pyplot as plt
import torch


def 			a      (			__a   )  ->	int:



					'''simple docstring'''






					for param in module.parameters():
										UpperCamelCase__   :Dict												= False


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



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= '''cuda''' if torch.cuda.is_available() else '''cpu'''
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										UpperCamelCase__   :Optional[int]												= '''mps'''
					if device == "mps":
										print(
										    '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
										    ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
										    ''' with generations.'''   )
					return device


def 			a      (			__a   )  ->	Any:



					'''simple docstring'''






					UpperCamelCase__   :Dict												= plt.imshow(__a   )
					fig.axes.get_xaxis().set_visible(__a   )
					fig.axes.get_yaxis().set_visible(__a   )
					plt.show()







def 			a      (			)  ->	str:



					'''simple docstring'''






					UpperCamelCase__   :int												= datetime.now()
					UpperCamelCase__   :str												= current_time.strftime('''%H:%M:%S'''   )
					return timestamp