



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_		:  Dict										=  logging.getLogger()


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 a							=		argparse.ArgumentParser()
 parser.add_argument("-f"       )
 a							=		parser.parse_args()
 return args.f


def 						UpperCAmelCase__   (   UpperCAmelCase__			:List[str]       ):






 '''simple docstring'''

 a							=		{}
 a							=		os.path.join(UpperCAmelCase__			,       "all_results.json"       )
 if os.path.exists(UpperCAmelCase__       ):
  with open(UpperCAmelCase__			,       "r"       ) as f:
   a							=		json.load(UpperCAmelCase__       )
 else:
  raise ValueError(F"""can't find {path}"""       )
 return results


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

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


A_		:  Optional[int]										=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class 		_lowercase		(							UpperCamelCase_						):

   @skip("Skipping until shape inference for to_tf_dataset PR is merged."				)
   def 		A				(   self      :							List[str]				)							->					Dict:


    """simple docstring"""






    a							=		self.get_auto_remove_tmp_dir()
    a							=		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"				)
     a							=		get_results(__a				)
     self.assertGreaterEqual(result["eval_accuracy"]				, 0.7_5				)

   def 		A				(   self      :							List[str]				)							->					str:


    """simple docstring"""






    a							=		self.get_auto_remove_tmp_dir()
    a							=		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()
     a							=		get_results(__a				)
     self.assertLess(result["eval_perplexity"]				, 100				)

   def 		A				(   self      :							Dict				)							->					str:


    """simple docstring"""






    a							=		self.get_auto_remove_tmp_dir()
    a							=		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()
     a							=		get_results(__a				)
     self.assertLess(result["eval_perplexity"]				, 42				)

   def 		A				(   self      :							Optional[int]				)							->					Tuple:


    """simple docstring"""






    a							=		7 if get_gpu_count() > 1 else 2

    a							=		self.get_auto_remove_tmp_dir()
    a							=		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()
     a							=		get_results(__a				)
     self.assertGreaterEqual(result["accuracy"]				, 0.7_5				)

   def 		A				(   self      :							int				)							->					Tuple:


    """simple docstring"""






    a							=		self.get_auto_remove_tmp_dir()
    a							=		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()
     a							=		get_results(__a				)
     self.assertGreaterEqual(result["f1"]				, 30				)
     self.assertGreaterEqual(result["exact"]				, 30				)

   def 		A				(   self      :							List[str]				)							->					Optional[Any]:


    """simple docstring"""






    a							=		self.get_auto_remove_tmp_dir()
    a							=		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()
     a							=		get_results(__a				)
     self.assertGreaterEqual(result["val_accuracy"]				, 0.8				)

   @slow
   def 		A				(   self      :							Any				)							->					List[str]:


    """simple docstring"""






    a							=		self.get_auto_remove_tmp_dir()
    a							=		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()
     a							=		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 		A				(   self      :							Optional[Any]				)							->					Tuple:


    """simple docstring"""






    a							=		self.get_auto_remove_tmp_dir()
    a							=		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()
     a							=		get_results(__a				)
     self.assertGreaterEqual(result["bleu"]				, 30				)




   def 		A				(   self      :							Dict				)							->					Any:


    """simple docstring"""






    a							=		self.get_auto_remove_tmp_dir()
    a							=		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()
     a							=		get_results(__a				)
     self.assertGreaterEqual(result["accuracy"]				, 0.7				)









'''simple docstring'''






import bsa
import requests




def 							_SCREAMING_SNAKE_CASE	(					A	:							bsa.element.ResultSet	)     ->			dict[str, str]:







   """simple docstring"""
   return {
       "name": soup.ha.a.text,
       "genre": soup.find('span'	,		class_='genre'	).text.strip(),
       "rating": soup.strong.text,
       "page_link": F"""https://www.imdb.com{soup.a.get("href"	)}""",
   }




def 							_SCREAMING_SNAKE_CASE	(					A	:							int = 5	)     ->			tuple:







   """simple docstring"""
   __snake_case					:   Optional[int]  				=			int(float(A	)	)
   if num_movies < 1:
      return ()
   __snake_case					:   Union[str, Any]  				=			(
       'https://www.imdb.com/search/title?title_type='
       F"""feature&sort=num_votes,desc&count={num_movies}"""
   )
   __snake_case					:   List[Any]  				=			bsa.BeautifulSoup(requests.get(A	).content	,		'html.parser'	)
   return tuple(
       get_movie_data_from_soup(A	)
       for movie in source.find_all('div'	,		class_='lister-item mode-advanced'	)	)


if __name__ == "__main__":
      import json

      __A     						= int(input('''How many movies would you like to see? '''))
      print(
          ''', '''.join(
              json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
          )
      )