






"""simple docstring"""
import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


_lowerCAmelCase         =							"sshleifer/mar_enro_6_3_student"



class      SCREAMING_SNAKE_CASE  (				__lowercase						):





	"""simple docstring"""




	def    lowerCamelCase(self							):
		super().setUp()

		A_		:						Optional[int]	    =   cached_path(
		    """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""     ,     extract_compressed_file=__A     ,     )
		A_		:						int	    =   f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""





	@slow
	@require_torch_gpu
	def    lowerCamelCase(self							):
		MarianMTModel.from_pretrained(__A							)





	@slow
	@require_torch_gpu
	def    lowerCamelCase(self							):
		A_		:						Dict	    =   {
		    """$MAX_LEN""": 64,
		    """$BS""": 64,
		    """$GAS""": 1,
		    """$ENRO_DIR""": self.data_dir,
		    """facebook/mbart-large-cc25""": MARIAN_MODEL,
		    # "val_check_interval=0.25": "val_check_interval=1.0",
		    """--learning_rate=3e-5""": """--learning_rate 3e-4""",
		    """--num_train_epochs 6""": """--num_train_epochs 1""",
		}

		# Clean up bash script
		A_		:						int	    =   (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""							)[1].strip()
		A_		:						str	    =   bash_script.replace("""\\\n"""     ,     """"""							).strip().replace("""\"$@\""""     ,     """"""							)
		for k, v in env_vars_to_replace.items():
			A_		:						Optional[Any]	    =   bash_script.replace(__A     ,     str(__A							)							)
		A_		:						List[str]	    =   self.get_auto_remove_tmp_dir()

		# bash_script = bash_script.replace("--fp16 ", "")
		A_		:						int	    =   f"""
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        """.split()
		# XXX: args.gpus > 1 : handle multi_gpu in the future

		A_		:						Dict	    =   ["""finetune.py"""] + bash_script.split() + args
		with patch.object(__A     ,     """argv"""     ,     __A							):
			A_		:						Union[str, Any]	    =   argparse.ArgumentParser()
			A_		:						str	    =   pl.Trainer.add_argparse_args(__A							)
			A_		:						Tuple	    =   SummarizationModule.add_model_specific_args(__A     ,     os.getcwd()							)
			A_		:						Optional[int]	    =   parser.parse_args()
			A_		:						Tuple	    =   main(__A							)

		# Check metrics
		A_		:						List[Any]	    =   load_json(model.metrics_save_path							)
		A_		:						Tuple	    =   metrics["""val"""][0]
		A_		:						Tuple	    =   metrics["""val"""][-1]
		self.assertEqual(len(metrics["""val"""]							)     ,     (args.max_epochs / args.val_check_interval)							)
		assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]     ,     __A							)

		self.assertGreater(last_step_stats["""val_avg_gen_time"""]     ,     0.01							)
		# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
		self.assertLessEqual(last_step_stats["""val_avg_gen_time"""]     ,     1.0							)

		# test learning requirements:

		# 1. BLEU improves over the course of training by more than 2 pts
		self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""]     ,     2							)

		# 2. BLEU finishes above 17
		self.assertGreater(last_step_stats["""val_avg_bleu"""]     ,     17							)

		# 3. test BLEU and val BLEU within ~1.1 pt.
		self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""]							)     ,     1.1							)

		# check lightning ckpt can be loaded and has a reasonable statedict
		A_		:						int	    =   os.listdir(__A							)
		A_		:						List[str]	    =   [x for x in contents if x.endswith(""".ckpt"""							)][0]
		A_		:						List[Any]	    =   os.path.join(args.output_dir     ,     __A							)
		A_		:						List[str]	    =   torch.load(__A     ,     map_location="""cpu"""							)
		A_		:						Optional[Any]	    =   """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
		assert expected_key in ckpt["state_dict"]
		assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

		# TODO: turn on args.do_predict when PL bug fixed.
		if args.do_predict:
			A_		:						int	    =   {os.path.basename(__A							) for p in contents}
			assert "test_generations.txt" in contents
			assert "test_results.txt" in contents
			# assert len(metrics["val"]) ==  desired_n_evals
			assert len(metrics["""test"""]							) == 1






class      SCREAMING_SNAKE_CASE  (				__lowercase						):





	"""simple docstring"""




	@timeout_decorator.timeout(600							)
	@slow
	@require_torch_gpu
	def    lowerCamelCase(self							):
		A_		:						int	    =   f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
		A_		:						List[Any]	    =   {
		    """--fp16_opt_level=O1""": """""",
		    """$MAX_LEN""": 128,
		    """$BS""": 16,
		    """$GAS""": 1,
		    """$ENRO_DIR""": data_dir,
		    """$m""": """sshleifer/student_marian_en_ro_6_1""",
		    """val_check_interval=0.25""": """val_check_interval=1.0""",
		}

		# Clean up bash script
		A_		:						Tuple	    =   (
		    (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""							)[1].strip()
		)
		A_		:						List[str]	    =   bash_script.replace("""\\\n"""     ,     """"""							).strip().replace("""\"$@\""""     ,     """"""							)
		A_		:						int	    =   bash_script.replace("""--fp16 """     ,     """ """							)

		for k, v in env_vars_to_replace.items():
			A_		:						Union[str, Any]	    =   bash_script.replace(__A     ,     str(__A							)							)
		A_		:						Union[str, Any]	    =   self.get_auto_remove_tmp_dir()
		A_		:						Any	    =   bash_script.replace("""--fp16"""     ,     """"""							)
		A_		:						Optional[Any]	    =   6
		A_		:						List[str]	    =   (
		    ["""distillation.py"""]
		    + bash_script.split()
		    + [
		        f"""--output_dir={output_dir}""",
		        """--gpus=1""",
		        """--learning_rate=1e-3""",
		        f"""--num_train_epochs={epochs}""",
		        """--warmup_steps=10""",
		        """--val_check_interval=1.0""",
		        """--do_predict""",
		    ]
		)
		with patch.object(__A     ,     """argv"""     ,     __A							):
			A_		:						int	    =   argparse.ArgumentParser()
			A_		:						List[Any]	    =   pl.Trainer.add_argparse_args(__A							)
			A_		:						Dict	    =   SummarizationDistiller.add_model_specific_args(__A     ,     os.getcwd()							)
			A_		:						Dict	    =   parser.parse_args()
			# assert args.gpus == gpus THIS BREAKS for multi_gpu

			A_		:						Dict	    =   distill_main(__A							)

		# Check metrics
		A_		:						int	    =   load_json(model.metrics_save_path							)
		A_		:						Optional[Any]	    =   metrics["""val"""][0]
		A_		:						Tuple	    =   metrics["""val"""][-1]
		assert len(metrics["""val"""]							) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

		assert last_step_stats["val_avg_gen_time"] >= 0.01

		assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
		assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
		assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]     ,     __A							)

		# check lightning ckpt can be loaded and has a reasonable statedict
		A_		:						str	    =   os.listdir(__A							)
		A_		:						Optional[Any]	    =   [x for x in contents if x.endswith(""".ckpt"""							)][0]
		A_		:						List[str]	    =   os.path.join(args.output_dir     ,     __A							)
		A_		:						int	    =   torch.load(__A     ,     map_location="""cpu"""							)
		A_		:						str	    =   """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
		assert expected_key in ckpt["state_dict"]
		assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

		# TODO: turn on args.do_predict when PL bug fixed.
		if args.do_predict:
			A_		:						Tuple	    =   {os.path.basename(__A							) for p in contents}
			assert "test_generations.txt" in contents
			assert "test_results.txt" in contents
			# assert len(metrics["val"]) ==  desired_n_evals
			assert len(metrics["""test"""]							) == 1









"""simple docstring"""
import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
							print("Googling.....")
							_lowerCAmelCase         =							"https://www.google.com/search?q=" + " ".join(sys.argv[1:])
							_lowerCAmelCase         =							requests.get(url, headers={"UserAgent": UserAgent().random})
							# res.raise_for_status()
							with open("project1a.html", "wb") as out_file:  # only for knowing the class
														for data in res.iter_content(10_000):
																					out_file.write(data)
							_lowerCAmelCase         =							BeautifulSoup(res.text, "html.parser")
							_lowerCAmelCase         =							list(soup.select(".eZt8xd"))[:5]

							print(len(links))
							for link in links:
														if link.text == "Maps":
																					webbrowser.open(link.get("href"))
														else:
																					webbrowser.open(F'https://google.com{link.get("href")}')

