






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


UpperCAmelCase_					:		Optional[int]           = [
    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)

UpperCAmelCase_					:		Dict           = logging.getLogger()

def   SCREAMING_SNAKE_CASE_				(				)						->     Any:






	"""simple docstring"""



	UpperCamelCase	:Union[str, Any]		=      argparse.ArgumentParser()
	parser.add_argument("""-f"""   )
	UpperCamelCase	:Optional[int]		=      parser.parse_args()
	return args.f

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    int   )						->     str:






	"""simple docstring"""



	UpperCamelCase	:List[Any]		=      {}
	UpperCamelCase	:Union[str, Any]		=      os.path.join(__magic_name__      ,     """all_results.json"""   )
	if os.path.exists(__magic_name__   ):
		with open(__magic_name__      ,     """r"""   ) as f:
			UpperCamelCase	:List[str]		=      json.load(__magic_name__   )
	else:
		raise ValueError(f"""can't find {path}"""   )
	return results

def   SCREAMING_SNAKE_CASE_				(				)						->     Dict:






	"""simple docstring"""



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


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

class        _SCREAMING_SNAKE_CASE			(     _a ):





	@skip("""Skipping until shape inference for to_tf_dataset PR is merged."""      )
	def 		_A					(						self							:   Optional[Any]      ):
		UpperCamelCase	:Dict		=      self.get_auto_remove_tmp_dir()
		UpperCamelCase	:List[Any]		=      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(__lowerCamelCase					,    """argv"""					,    __lowerCamelCase      ):
			run_text_classification.main()
			# Reset the mixed precision policy so we don't break other tests
			tf.keras.mixed_precision.set_global_policy("""float32"""      )
			UpperCamelCase	:Union[str, Any]		=      get_results(__lowerCamelCase      )
			self.assertGreaterEqual(result["""eval_accuracy"""]					,    0.75      )





	def 		_A					(						self							:   int      ):
		UpperCamelCase	:Optional[int]		=      self.get_auto_remove_tmp_dir()
		UpperCamelCase	:str		=      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(__lowerCamelCase					,    """argv"""					,    __lowerCamelCase      ):
			run_clm.main()
			UpperCamelCase	:str		=      get_results(__lowerCamelCase      )
			self.assertLess(result["""eval_perplexity"""]					,    100      )





	def 		_A					(						self							:   Optional[Any]      ):
		UpperCamelCase	:str		=      self.get_auto_remove_tmp_dir()
		UpperCamelCase	:Dict		=      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(__lowerCamelCase					,    """argv"""					,    __lowerCamelCase      ):
			run_mlm.main()
			UpperCamelCase	:Any		=      get_results(__lowerCamelCase      )
			self.assertLess(result["""eval_perplexity"""]					,    42      )





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

		UpperCamelCase	:Dict		=      self.get_auto_remove_tmp_dir()
		UpperCamelCase	:str		=      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(__lowerCamelCase					,    """argv"""					,    __lowerCamelCase      ):
			run_ner.main()
			UpperCamelCase	:int		=      get_results(__lowerCamelCase      )
			self.assertGreaterEqual(result["""accuracy"""]					,    0.75      )





	def 		_A					(						self							:   Optional[int]      ):
		UpperCamelCase	:Optional[Any]		=      self.get_auto_remove_tmp_dir()
		UpperCamelCase	:str		=      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(__lowerCamelCase					,    """argv"""					,    __lowerCamelCase      ):
			run_squad.main()
			UpperCamelCase	:str		=      get_results(__lowerCamelCase      )
			self.assertGreaterEqual(result["""f1"""]					,    30      )
			self.assertGreaterEqual(result["""exact"""]					,    30      )





	def 		_A					(						self							:   Optional[int]      ):
		UpperCamelCase	:Dict		=      self.get_auto_remove_tmp_dir()
		UpperCamelCase	:Tuple		=      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(__lowerCamelCase					,    """argv"""					,    __lowerCamelCase      ):
			run_swag.main()
			UpperCamelCase	:Optional[Any]		=      get_results(__lowerCamelCase      )
			self.assertGreaterEqual(result["""val_accuracy"""]					,    0.8      )





	@slow
	def 		_A					(						self							:   Union[str, Any]      ):
		UpperCamelCase	:str		=      self.get_auto_remove_tmp_dir()
		UpperCamelCase	:Tuple		=      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(__lowerCamelCase					,    """argv"""					,    __lowerCamelCase      ):
			run_summarization.main()
			UpperCamelCase	:int		=      get_results(__lowerCamelCase      )
			self.assertGreaterEqual(result["""rouge1"""]					,    10      )
			self.assertGreaterEqual(result["""rouge2"""]					,    2      )
			self.assertGreaterEqual(result["""rougeL"""]					,    7      )
			self.assertGreaterEqual(result["""rougeLsum"""]					,    7      )





	@slow
	def 		_A					(						self							:   Optional[int]      ):
		UpperCamelCase	:Optional[Any]		=      self.get_auto_remove_tmp_dir()
		UpperCamelCase	:Any		=      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(__lowerCamelCase					,    """argv"""					,    __lowerCamelCase      ):
			run_translation.main()
			UpperCamelCase	:Dict		=      get_results(__lowerCamelCase      )
			self.assertGreaterEqual(result["""bleu"""]					,    30      )





	def 		_A					(						self							:   List[Any]      ):
		UpperCamelCase	:Optional[Any]		=      self.get_auto_remove_tmp_dir()
		UpperCamelCase	:Any		=      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(__lowerCamelCase					,    """argv"""					,    __lowerCamelCase      ):
			run_image_classification.main()
			UpperCamelCase	:Tuple		=      get_results(__lowerCamelCase      )
			self.assertGreaterEqual(result["""accuracy"""]					,    0.7      )








class        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   Union[str, Any]      ):
		UpperCamelCase	:Union[str, Any]		=      [0, 1]





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

def   SCREAMING_SNAKE_CASE_				(				)						->     None:






	"""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=""""""      ,     )
	UpperCamelCase	:Any		=      Fibonacci()

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

		try:
			UpperCamelCase	:int		=      int(__magic_name__   )
		except ValueError:
			print("""Enter a number or 'exit'"""   )
			continue

		print(fibonacci.get(__magic_name__   )   )


if __name__ == "__main__":
	main()
