'''simple docstring'''







import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


_lowerCamelCase							:						Union[str, Any]							     =  [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-generation',
        'text-classification',
        'token-classification',
        'language-modeling',
        'multiple-choice',
        'question-answering',
        'summarization',
        'translation',
        'image-classification',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
						import run_clm
						import run_image_classification
						import run_mlm
						import run_ner
						import run_qa as run_squad
						import run_summarization
						import run_swag
						import run_text_classification
						import run_translation


logging.basicConfig(level=logging.DEBUG)

_lowerCamelCase							:						Optional[Any]							     =  logging.getLogger()






def 		_lowercase		(     ):



		'''simple docstring'''







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






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







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






def 		_lowercase		(     ):



		'''simple docstring'''







		return bool(tf.config.list_physical_devices("GPU"  )  )


_lowerCamelCase							:						Optional[Any]							     =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 					lowerCamelCase_							(	lowercase_							):




					'''simple docstring'''






					@skip("Skipping until shape inference for to_tf_dataset PR is merged."			)
					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.get_auto_remove_tmp_dir()
							_snake_case     	=	f'''\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '''.split()

							if is_cuda_available():
									testargs.append("--fp16"			)

							with patch.object(__UpperCamelCase,  "argv",  __UpperCamelCase			):
									run_text_classification.main()
									# Reset the mixed precision policy so we don't break other tests
									tf.keras.mixed_precision.set_global_policy("float32"			)
									_snake_case     	=	get_results(__UpperCamelCase			)
									self.assertGreaterEqual(result["eval_accuracy"],  0.75			)






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.get_auto_remove_tmp_dir()
							_snake_case     	=	f'''\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '''.split()

							if len(tf.config.list_physical_devices("GPU"			)			) > 1:
									# Skipping because there are not enough batches to train the model + would need a drop_last to work.
									return

							with patch.object(__UpperCamelCase,  "argv",  __UpperCamelCase			):
									run_clm.main()
									_snake_case     	=	get_results(__UpperCamelCase			)
									self.assertLess(result["eval_perplexity"],  100			)






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.get_auto_remove_tmp_dir()
							_snake_case     	=	f'''\n            run_mlm.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            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        '''.split()

							with patch.object(__UpperCamelCase,  "argv",  __UpperCamelCase			):
									run_mlm.main()
									_snake_case     	=	get_results(__UpperCamelCase			)
									self.assertLess(result["eval_perplexity"],  42			)






					def     __SCREAMING_SNAKE_CASE     (   self			):
							# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
							_snake_case     	=	7 if get_gpu_count() > 1 else 2

							_snake_case     	=	self.get_auto_remove_tmp_dir()
							_snake_case     	=	f'''\n            run_ner.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            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\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        '''.split()

							with patch.object(__UpperCamelCase,  "argv",  __UpperCamelCase			):
									run_ner.main()
									_snake_case     	=	get_results(__UpperCamelCase			)
									self.assertGreaterEqual(result["accuracy"],  0.75			)






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.get_auto_remove_tmp_dir()
							_snake_case     	=	f'''\n            run_qa.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            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '''.split()

							with patch.object(__UpperCamelCase,  "argv",  __UpperCamelCase			):
									run_squad.main()
									_snake_case     	=	get_results(__UpperCamelCase			)
									self.assertGreaterEqual(result["f1"],  30			)
									self.assertGreaterEqual(result["exact"],  30			)






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.get_auto_remove_tmp_dir()
							_snake_case     	=	f'''\n            run_swag.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            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '''.split()

							with patch.object(__UpperCamelCase,  "argv",  __UpperCamelCase			):
									run_swag.main()
									_snake_case     	=	get_results(__UpperCamelCase			)
									self.assertGreaterEqual(result["val_accuracy"],  0.8			)






					@slow
					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.get_auto_remove_tmp_dir()
							_snake_case     	=	f'''\n            run_summarization.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            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '''.split()

							with patch.object(__UpperCamelCase,  "argv",  __UpperCamelCase			):
									run_summarization.main()
									_snake_case     	=	get_results(__UpperCamelCase			)
									self.assertGreaterEqual(result["rouge1"],  10			)
									self.assertGreaterEqual(result["rouge2"],  2			)
									self.assertGreaterEqual(result["rougeL"],  7			)
									self.assertGreaterEqual(result["rougeLsum"],  7			)






					@slow
					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.get_auto_remove_tmp_dir()
							_snake_case     	=	f'''\n            run_translation.py\n            --model_name_or_path Rocketknight1/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            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\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        '''.split()

							with patch.object(__UpperCamelCase,  "argv",  __UpperCamelCase			):
									run_translation.main()
									_snake_case     	=	get_results(__UpperCamelCase			)
									self.assertGreaterEqual(result["bleu"],  30			)






					def     __SCREAMING_SNAKE_CASE     (   self			):
							_snake_case     	=	self.get_auto_remove_tmp_dir()
							_snake_case     	=	f'''\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            '''.split()

							with patch.object(__UpperCamelCase,  "argv",  __UpperCamelCase			):
									run_image_classification.main()
									_snake_case     	=	get_results(__UpperCamelCase			)
									self.assertGreaterEqual(result["accuracy"],  0.7			)











'''simple docstring'''







import math





class 					lowerCamelCase_							:




					'''simple docstring'''






					def __init__(   self,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase			):
							_snake_case     	=	x
							_snake_case     	=	y
							_snake_case     	=	z






					def __repr__(   self			):
							return f'''Point({self.x}, {self.y}, {self.z})'''






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2  )  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





