






"""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     :     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)

_lowerCAmelCase     :     str            =	logging.getLogger()


def      SCREAMING_SNAKE_CASE__    (   )->						Tuple:






     '''simple docstring'''



     UpperCAmelCase__		:  int				=				argparse.ArgumentParser()
     parser.add_argument("-f"							)
     UpperCAmelCase__		:  Dict				=				parser.parse_args()
     return args.f


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      List[str]							)->						Optional[Any]:






     '''simple docstring'''



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


def      SCREAMING_SNAKE_CASE__    (   )->						Tuple:






     '''simple docstring'''



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


_lowerCAmelCase     :     Union[str, Any]            =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class        lowerCAmelCase__       (  __magic_name__					):
   @skip("Skipping until shape inference for to_tf_dataset PR is merged."				)
   def 	__a				(						self	:     Tuple				):


        '''simple docstring'''
        UpperCAmelCase__		:  int				=				self.get_auto_remove_tmp_dir()
        UpperCAmelCase__		:  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(snake_case__				,       "argv"				,       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"				)
             UpperCAmelCase__		:  Dict				=				get_results(snake_case__				)
             self.assertGreaterEqual(result["eval_accuracy"]				,       0.75				)
   def 	__a				(						self	:     int				):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[int]				=				self.get_auto_remove_tmp_dir()
        UpperCAmelCase__		:  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(snake_case__				,       "argv"				,       snake_case__				):
             run_clm.main()
             UpperCAmelCase__		:  Any				=				get_results(snake_case__				)
             self.assertLess(result["eval_perplexity"]				,       1_0_0				)
   def 	__a				(						self	:     Dict				):


        '''simple docstring'''
        UpperCAmelCase__		:  Dict				=				self.get_auto_remove_tmp_dir()
        UpperCAmelCase__		:  List[Any]				=				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(snake_case__				,       "argv"				,       snake_case__				):
             run_mlm.main()
             UpperCAmelCase__		:  Union[str, Any]				=				get_results(snake_case__				)
             self.assertLess(result["eval_perplexity"]				,       4_2				)
   def 	__a				(						self	:     Optional[int]				):


        '''simple docstring'''
        # 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__		:  Dict				=				self.get_auto_remove_tmp_dir()
        UpperCAmelCase__		:  Optional[Any]				=				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(snake_case__				,       "argv"				,       snake_case__				):
             run_ner.main()
             UpperCAmelCase__		:  Any				=				get_results(snake_case__				)
             self.assertGreaterEqual(result["accuracy"]				,       0.75				)
   def 	__a				(						self	:     List[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  str				=				self.get_auto_remove_tmp_dir()
        UpperCAmelCase__		:  Optional[int]				=				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(snake_case__				,       "argv"				,       snake_case__				):
             run_squad.main()
             UpperCAmelCase__		:  Optional[int]				=				get_results(snake_case__				)
             self.assertGreaterEqual(result["f1"]				,       3_0				)
             self.assertGreaterEqual(result["exact"]				,       3_0				)
   def 	__a				(						self	:     int				):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[int]				=				self.get_auto_remove_tmp_dir()
        UpperCAmelCase__		:  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(snake_case__				,       "argv"				,       snake_case__				):
             run_swag.main()
             UpperCAmelCase__		:  Dict				=				get_results(snake_case__				)
             self.assertGreaterEqual(result["val_accuracy"]				,       0.8				)
   @slow
   def 	__a				(						self	:     Union[str, Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[int]				=				self.get_auto_remove_tmp_dir()
        UpperCAmelCase__		:  Optional[int]				=				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(snake_case__				,       "argv"				,       snake_case__				):
             run_summarization.main()
             UpperCAmelCase__		:  Any				=				get_results(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 	__a				(						self	:     List[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[Any]				=				self.get_auto_remove_tmp_dir()
        UpperCAmelCase__		:  int				=				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(snake_case__				,       "argv"				,       snake_case__				):
             run_translation.main()
             UpperCAmelCase__		:  List[Any]				=				get_results(snake_case__				)
             self.assertGreaterEqual(result["bleu"]				,       3_0				)




   def 	__a				(						self	:     List[str]				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[Any]				=				self.get_auto_remove_tmp_dir()
        UpperCAmelCase__		:  int				=				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(snake_case__				,       "argv"				,       snake_case__				):
             run_image_classification.main()
             UpperCAmelCase__		:  Any				=				get_results(snake_case__				)
             self.assertGreaterEqual(result["accuracy"]				,       0.7				)










"""simple docstring"""



import shutil

import requests


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str       , snake_case    :      bool = False       , snake_case    :      str = "."							)->						dict:






     '''simple docstring'''



     UpperCAmelCase__		:  Union[str, Any]				=				"https://api.nasa.gov/planetary/apod"
     return requests.get(snake_case       , params={"api_key": api_key}							).json()


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str       , snake_case    :      str = "."							)->						dict:






     '''simple docstring'''



     UpperCAmelCase__		:  List[Any]				=				get_apod_data(snake_case							)
     UpperCAmelCase__		:  str				=				apod_data["url"]
     UpperCAmelCase__		:  Tuple				=				img_url.split("/"							)[-1]
     UpperCAmelCase__		:  Dict				=				requests.get(snake_case       , stream=snake_case							)

     with open(f'{path}/{img_name}'       , "wb+"							) as img_file:
          shutil.copyfileobj(response.raw       , snake_case							)
     del response
     return apod_data


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str							)->						dict:






     '''simple docstring'''



     UpperCAmelCase__		:  List[str]				=				"https://images-api.nasa.gov/search"
     return requests.get(snake_case       , params={"q": query}							).json()


if __name__ == "__main__":
 print(save_apod("""YOUR API KEY"""))
 _lowerCAmelCase     :     List[Any]            =	get_archive_data("""apollo 2011""")["""collection"""]["""items"""]
 print(apollo_aaaa_items[0]["""data"""][0]["""description"""])


