



'''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


a  :     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)

a  :     int       =       logging.getLogger()



def  __lowerCamelCase    (   ) ->     Dict:
							UpperCAmelCase	:						int					=						argparse.ArgumentParser()
							parser.add_argument("""-f"""						)
							UpperCAmelCase	:						Dict					=						parser.parse_args()
							return args.f



def  __lowerCamelCase    (   _lowercase						) ->     Dict:
							UpperCAmelCase	:						Dict					=						{}
							UpperCAmelCase	:						Tuple					=						os.path.join(_lowercase      ,							"""all_results.json"""						)
							if os.path.exists(_lowercase						):
														with open(_lowercase      ,							"""r"""						) as f:
																					UpperCAmelCase	:						List[Any]					=						json.load(_lowercase						)
							else:
														raise ValueError(F'''can\'t find {path}'''						)
							return results



def  __lowerCamelCase    (   ) ->     int:
							return bool(tf.config.list_physical_devices("""GPU"""						)						)


a  :     Tuple       =       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class      UpperCamelCase_					(				__magic_name__     ):





	@skip("""Skipping until shape inference for to_tf_dataset PR is merged."""       )
	def 					_lowercase(    self       )     ->     int:
								UpperCAmelCase	:						Union[str, Any]					=						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(A						,						"""argv"""						,						A       ):
															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	:						Optional[int]					=						get_results(A       )
															self.assertGreaterEqual(result["""eval_accuracy"""]						,						0.7_5       )





	def 					_lowercase(    self       )     ->     Union[str, Any]:
								UpperCAmelCase	:						Optional[int]					=						self.get_auto_remove_tmp_dir()
								UpperCAmelCase	:						Any					=						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(A						,						"""argv"""						,						A       ):
															run_clm.main()
															UpperCAmelCase	:						Any					=						get_results(A       )
															self.assertLess(result["""eval_perplexity"""]						,						100       )





	def 					_lowercase(    self       )     ->     List[str]:
								UpperCAmelCase	:						Tuple					=						self.get_auto_remove_tmp_dir()
								UpperCAmelCase	:						Any					=						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(A						,						"""argv"""						,						A       ):
															run_mlm.main()
															UpperCAmelCase	:						str					=						get_results(A       )
															self.assertLess(result["""eval_perplexity"""]						,						42       )





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

								UpperCAmelCase	:						List[str]					=						self.get_auto_remove_tmp_dir()
								UpperCAmelCase	:						List[Any]					=						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(A						,						"""argv"""						,						A       ):
															run_ner.main()
															UpperCAmelCase	:						Dict					=						get_results(A       )
															self.assertGreaterEqual(result["""accuracy"""]						,						0.7_5       )





	def 					_lowercase(    self       )     ->     Dict:
								UpperCAmelCase	:						int					=						self.get_auto_remove_tmp_dir()
								UpperCAmelCase	:						List[Any]					=						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(A						,						"""argv"""						,						A       ):
															run_squad.main()
															UpperCAmelCase	:						Tuple					=						get_results(A       )
															self.assertGreaterEqual(result["""f1"""]						,						30       )
															self.assertGreaterEqual(result["""exact"""]						,						30       )





	def 					_lowercase(    self       )     ->     int:
								UpperCAmelCase	:						str					=						self.get_auto_remove_tmp_dir()
								UpperCAmelCase	:						int					=						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(A						,						"""argv"""						,						A       ):
															run_swag.main()
															UpperCAmelCase	:						Union[str, Any]					=						get_results(A       )
															self.assertGreaterEqual(result["""val_accuracy"""]						,						0.8       )





	@slow
	def 					_lowercase(    self       )     ->     Union[str, Any]:
								UpperCAmelCase	:						Dict					=						self.get_auto_remove_tmp_dir()
								UpperCAmelCase	:						Dict					=						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(A						,						"""argv"""						,						A       ):
															run_summarization.main()
															UpperCAmelCase	:						Any					=						get_results(A       )
															self.assertGreaterEqual(result["""rouge1"""]						,						10       )
															self.assertGreaterEqual(result["""rouge2"""]						,						2       )
															self.assertGreaterEqual(result["""rougeL"""]						,						7       )
															self.assertGreaterEqual(result["""rougeLsum"""]						,						7       )





	@slow
	def 					_lowercase(    self       )     ->     str:
								UpperCAmelCase	:						List[str]					=						self.get_auto_remove_tmp_dir()
								UpperCAmelCase	:						List[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(A						,						"""argv"""						,						A       ):
															run_translation.main()
															UpperCAmelCase	:						List[str]					=						get_results(A       )
															self.assertGreaterEqual(result["""bleu"""]						,						30       )






	def 					_lowercase(    self       )     ->     Any:
								UpperCAmelCase	:						List[Any]					=						self.get_auto_remove_tmp_dir()
								UpperCAmelCase	:						Tuple					=						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(A						,						"""argv"""						,						A       ):
															run_image_classification.main()
															UpperCAmelCase	:						Tuple					=						get_results(A       )
															self.assertGreaterEqual(result["""accuracy"""]						,						0.7       )






'''simple docstring'''




class      UpperCamelCase_					:





	def __init__(    self       )     ->     None:
								UpperCAmelCase	:						int					=						[0, 1]






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




def  __lowerCamelCase    (   ) ->     None:
							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	:						Tuple					=						Fibonacci()

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

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

														print(fibonacci.get(_lowercase						)						)


if __name__ == "__main__":
							main()

