


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


snake_case__  				=			[
    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)

snake_case__  				=			logging.getLogger()
def 			__SCREAMING_SNAKE_CASE							(					)							->	Optional[Any]:
						__magic_name__					:				Optional[Any]				=   argparse.ArgumentParser()
						parser.add_argument('-f'					)
						__magic_name__					:				Dict				=   parser.parse_args()
						return args.f
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:int					)							->	str:
						__magic_name__					:				Tuple				=   {}
						__magic_name__					:				int				=   os.path.join(lowercase__   ,    'all_results.json'					)
						if os.path.exists(lowercase__					):
												with open(lowercase__   ,    'r'					) as f:
																		__magic_name__					:				Optional[int]				=   json.load(lowercase__					)
						else:
												raise ValueError(f"can't find {path}"					)
						return results
def 			__SCREAMING_SNAKE_CASE							(					)							->	str:
						return bool(tf.config.list_physical_devices('GPU'					)					)


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



class      snake_case__	(   _A	):
				@skip('Skipping until shape inference for to_tf_dataset PR is merged.'   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Optional[Any]				=   self.get_auto_remove_tmp_dir()
										__magic_name__					:				Union[str, Any]				=   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'   )
																__magic_name__					:				int				=   get_results(_UpperCAmelCase   )
																self.assertGreaterEqual(result['eval_accuracy']  ,   0.75   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Tuple				=   self.get_auto_remove_tmp_dir()
										__magic_name__					:				Tuple				=   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()
																__magic_name__					:				Dict				=   get_results(_UpperCAmelCase   )
																self.assertLess(result['eval_perplexity']  ,   100   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				str				=   self.get_auto_remove_tmp_dir()
										__magic_name__					:				str				=   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()
																__magic_name__					:				Any				=   get_results(_UpperCAmelCase   )
																self.assertLess(result['eval_perplexity']  ,   42   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Dict				=   7 if get_gpu_count() > 1 else 2

										__magic_name__					:				str				=   self.get_auto_remove_tmp_dir()
										__magic_name__					:				Dict				=   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()
																__magic_name__					:				Optional[int]				=   get_results(_UpperCAmelCase   )
																self.assertGreaterEqual(result['accuracy']  ,   0.75   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   self.get_auto_remove_tmp_dir()
										__magic_name__					:				Tuple				=   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()
																__magic_name__					:				Optional[Any]				=   get_results(_UpperCAmelCase   )
																self.assertGreaterEqual(result['f1']  ,   30   )
																self.assertGreaterEqual(result['exact']  ,   30   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Optional[int]				=   self.get_auto_remove_tmp_dir()
										__magic_name__					:				Optional[Any]				=   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()
																__magic_name__					:				int				=   get_results(_UpperCAmelCase   )
																self.assertGreaterEqual(result['val_accuracy']  ,   0.8   )
				@slow
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				str				=   self.get_auto_remove_tmp_dir()
										__magic_name__					:				Union[str, Any]				=   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()
																__magic_name__					:				Any				=   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 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				int				=   self.get_auto_remove_tmp_dir()
										__magic_name__					:				Any				=   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()
																__magic_name__					:				Tuple				=   get_results(_UpperCAmelCase   )
																self.assertGreaterEqual(result['bleu']  ,   30   )





				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Optional[Any]				=   self.get_auto_remove_tmp_dir()
										__magic_name__					:				Any				=   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()
																__magic_name__					:				int				=   get_results(_UpperCAmelCase   )
																self.assertGreaterEqual(result['accuracy']  ,   0.7   )






from __future__ import annotations

from collections.abc import Callable
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Callable[[int | float], int | float]   ,    lowercase__	:int | float   ,    lowercase__	:int | float   ,    lowercase__	:int = 1_0_0   ,    )							->	float:
						__magic_name__					:				str				=   x_start
						__magic_name__					:				str				=   fnc(lowercase__					)
						__magic_name__					:				Union[str, Any]				=   0.0

						for _ in range(lowercase__					):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												__magic_name__					:				Optional[int]				=   (x_end - x_start) / steps + xa
												__magic_name__					:				List[Any]				=   fnc(lowercase__					)
												area += abs(fxa + fxa					) * (xa - xa) / 2

												# Increment step
												__magic_name__					:				Optional[Any]				=   xa
												__magic_name__					:				Union[str, Any]				=   fxa
						return area


if __name__ == "__main__":
			def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Dict					)							->	Optional[int]:
									return x**3

			print('f(x) = x^3')
			print('The area between the curve, x = -10, x = 10 and the x axis is:')
			snake_case__  				=			10
			while i <= 10_0000:
						snake_case__  				=			trapezoidal_area(f, -5, 5, i)
						print(F"""with {i} steps: {area}""")
						i *= 10


