
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


__a        = [
    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)

__a        = logging.getLogger()




def     a						(							):


				'''simple docstring'''
				lowercase_					  = argparse.ArgumentParser()
				parser.add_argument('''-f''' )
				lowercase_					  = parser.parse_args()
				return args.f




def     a						(							snake_case__: List[Any] ):


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




def     a						(							):


				'''simple docstring'''
				return bool(tf.config.list_physical_devices('''GPU''' ) )


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


class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




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

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

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_text_classification.main()
									# Reset the mixed precision policy so we don't break other tests
									tf.keras.mixed_precision.set_global_policy('''float32'''	)
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''eval_accuracy''']	,							0.75	)
	def 						_lowercase  (	self  :      Optional[Any]	)	->					int:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.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(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_clm.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertLess(result['''eval_perplexity''']	,							1_0_0	)
	def 						_lowercase  (	self  :      Any	)	->					Union[str, Any]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_mlm.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertLess(result['''eval_perplexity''']	,							4_2	)
	def 						_lowercase  (	self  :      Any	)	->					str:
					# 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'''
            run_ner.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}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_ner.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''accuracy''']	,							0.75	)
	def 						_lowercase  (	self  :      Any	)	->					Optional[Any]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_qa.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}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_squad.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''f1''']	,							3_0	)
									self.assertGreaterEqual(result['''exact''']	,							3_0	)
	def 						_lowercase  (	self  :      str	)	->					Dict:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_swag.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}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_swag.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''val_accuracy''']	,							0.8	)
	@slow
	def 						_lowercase  (	self  :      int	)	->					int:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_summarization.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}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_summarization.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''rouge1''']	,							1_0	)
									self.assertGreaterEqual(result['''rouge2''']	,							2	)
									self.assertGreaterEqual(result['''rougeL''']	,							7	)
									self.assertGreaterEqual(result['''rougeLsum''']	,							7	)
	@slow
	def 						_lowercase  (	self  :      Dict	)	->					Any:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_translation.py
            --model_name_or_path Rocketknight1/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}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_translation.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''bleu''']	,							3_0	)




	def 						_lowercase  (	self  :      int	)	->					List[str]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_image_classification.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''accuracy''']	,							0.7	)






class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      str	)	->					None:
					lowercase_					  = [0, 1]




	def 						_lowercase  (	self  :      str	,							SCREAMING_SNAKE_CASE_  :      int	)	->					list:
					if (difference := index - (len(self.sequence	) - 2)) >= 1:
									for _ in range(SCREAMING_SNAKE_CASE_	):
													self.sequence.append(self.sequence[-1] + self.sequence[-2]	)
					return self.sequence[:index]




def     a						(							):


				'''simple docstring'''
				print(
				    '''Fibonacci Series Using Dynamic Programming\n'''						,       '''Enter the index of the Fibonacci number you want to calculate '''						,       '''in the prompt below. (To exit enter exit or Ctrl-C)\n'''						,       sep=''''''						,       )
				lowercase_					  = Fibonacci()

				while True:
								lowercase_					  = input('''>> ''' )
								if prompt in {"exit", "quit"}:
												break

								try:
												lowercase_					  = int(snake_case__ )
								except ValueError:
												print('''Enter a number or \'exit\'''' )
												continue

								print(fibonacci.get(snake_case__ ) )


if __name__ == "__main__":
						main()




