



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


UpperCAmelCase_									=							"sshleifer/mar_enro_6_3_student"





class 		UpperCAmelCase						(						snake_case_				):





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

         _lowerCAmelCase 			=  cached_path(
             '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''       ,     extract_compressed_file=_lowerCAmelCase       ,     )
         _lowerCAmelCase 			=  F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''





   @slow
   @require_torch_gpu
   def 	__lowerCAmelCase							(	self			):
         MarianMTModel.from_pretrained(_lowerCAmelCase			)





   @slow
   @require_torch_gpu
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  {
             '''$MAX_LEN''': 64,
             '''$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
         _lowerCAmelCase 			=  (self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''			)[1].strip()
         _lowerCAmelCase 			=  bash_script.replace('''\\\n'''       ,     ''''''			).strip().replace('''"$@"'''       ,     ''''''			)
         for k, v in env_vars_to_replace.items():
               _lowerCAmelCase 			=  bash_script.replace(_lowerCAmelCase       ,     str(_lowerCAmelCase			)			)
         _lowerCAmelCase 			=  self.get_auto_remove_tmp_dir()

         # bash_script = bash_script.replace("--fp16 ", "")
         _lowerCAmelCase 			=  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

         _lowerCAmelCase 			=  ['''finetune.py'''] + bash_script.split() + args
         with patch.object(_lowerCAmelCase       ,     '''argv'''       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  argparse.ArgumentParser()
               _lowerCAmelCase 			=  pl.Trainer.add_argparse_args(_lowerCAmelCase			)
               _lowerCAmelCase 			=  SummarizationModule.add_model_specific_args(_lowerCAmelCase       ,     os.getcwd()			)
               _lowerCAmelCase 			=  parser.parse_args()
               _lowerCAmelCase 			=  main(_lowerCAmelCase			)

         # Check metrics
         _lowerCAmelCase 			=  load_json(model.metrics_save_path			)
         _lowerCAmelCase 			=  metrics['''val'''][0]
         _lowerCAmelCase 			=  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}''']       ,     _lowerCAmelCase			)

         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
         _lowerCAmelCase 			=  os.listdir(_lowerCAmelCase			)
         _lowerCAmelCase 			=  [x for x in contents if x.endswith('''.ckpt'''			)][0]
         _lowerCAmelCase 			=  os.path.join(args.output_dir       ,     _lowerCAmelCase			)
         _lowerCAmelCase 			=  torch.load(_lowerCAmelCase       ,     map_location='''cpu'''			)
         _lowerCAmelCase 			=  '''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:
               _lowerCAmelCase 			=  {os.path.basename(_lowerCAmelCase			) 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 		UpperCAmelCase						(						snake_case_				):





   @timeout_decorator.timeout(600			)
   @slow
   @require_torch_gpu
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
         _lowerCAmelCase 			=  {
             '''--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
         _lowerCAmelCase 			=  (
             (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''			)[1].strip()
         )
         _lowerCAmelCase 			=  bash_script.replace('''\\\n'''       ,     ''''''			).strip().replace('''"$@"'''       ,     ''''''			)
         _lowerCAmelCase 			=  bash_script.replace('''--fp16 '''       ,     ''' '''			)

         for k, v in env_vars_to_replace.items():
               _lowerCAmelCase 			=  bash_script.replace(_lowerCAmelCase       ,     str(_lowerCAmelCase			)			)
         _lowerCAmelCase 			=  self.get_auto_remove_tmp_dir()
         _lowerCAmelCase 			=  bash_script.replace('''--fp16'''       ,     ''''''			)
         _lowerCAmelCase 			=  6
         _lowerCAmelCase 			=  (
             ['''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(_lowerCAmelCase       ,     '''argv'''       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  argparse.ArgumentParser()
               _lowerCAmelCase 			=  pl.Trainer.add_argparse_args(_lowerCAmelCase			)
               _lowerCAmelCase 			=  SummarizationDistiller.add_model_specific_args(_lowerCAmelCase       ,     os.getcwd()			)
               _lowerCAmelCase 			=  parser.parse_args()
               # assert args.gpus == gpus THIS BREAKS for multi_gpu

               _lowerCAmelCase 			=  distill_main(_lowerCAmelCase			)

         # Check metrics
         _lowerCAmelCase 			=  load_json(model.metrics_save_path			)
         _lowerCAmelCase 			=  metrics['''val'''][0]
         _lowerCAmelCase 			=  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}''']       ,     _lowerCAmelCase			)

         # check lightning ckpt can be loaded and has a reasonable statedict
         _lowerCAmelCase 			=  os.listdir(_lowerCAmelCase			)
         _lowerCAmelCase 			=  [x for x in contents if x.endswith('''.ckpt'''			)][0]
         _lowerCAmelCase 			=  os.path.join(args.output_dir       ,     _lowerCAmelCase			)
         _lowerCAmelCase 			=  torch.load(_lowerCAmelCase       ,     map_location='''cpu'''			)
         _lowerCAmelCase 			=  '''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:
               _lowerCAmelCase 			=  {os.path.basename(_lowerCAmelCase			) 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




from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCAmelCase_									=							{"UserAgent": UserAgent().random}
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Dict				)->dict:
      _lowerCAmelCase 			=  script.contents[0]
      _lowerCAmelCase 			=  json.loads(data[data.find('''{"config"'''				) : -1]				)
      return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]





class 		UpperCAmelCase						:





   def __init__(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  F'''https://www.instagram.com/{username}/'''
         _lowerCAmelCase 			=  self.get_json()





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  requests.get(self.url       ,     headers=_lowerCAmelCase			).text
         _lowerCAmelCase 			=  BeautifulSoup(_lowerCAmelCase       ,     '''html.parser'''			).find_all('''script'''			)
         try:
               return extract_user_profile(scripts[4]			)
         except (json.decoder.JSONDecodeError, KeyError):
               return extract_user_profile(scripts[3]			)





   def __repr__(	self			):
         return F'''{self.__class__.__name__}(\'{self.username}\')'''





   def __str__(	self			):
         return F'''{self.fullname} ({self.username}) is {self.biography}'''





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["username"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["full_name"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["biography"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["business_email"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["external_url"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["edge_followed_by"]["count"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["edge_follow"]["count"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["edge_owner_to_timeline_media"]["count"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["profile_pic_url_hd"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["is_verified"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["is_private"]






def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str = "github"				)->None:
      import os

      if os.environ.get('''CI'''				):
            return  # test failing on GitHub Actions
      _lowerCAmelCase 			=  InstagramUser(_SCREAMING_SNAKE_CASE				)
      assert instagram_user.user_data
      assert isinstance(instagram_user.user_data     ,				_SCREAMING_SNAKE_CASE				)
      assert instagram_user.username == username
      if username != "github":
            return
      assert instagram_user.fullname == "GitHub"
      assert instagram_user.biography == "Built for developers."
      assert instagram_user.number_of_posts > 1_5_0
      assert instagram_user.number_of_followers > 1_2_0_0_0_0
      assert instagram_user.number_of_followings > 1_5
      assert instagram_user.email == "support@github.com"
      assert instagram_user.website == "https://github.com/readme"
      assert instagram_user.profile_picture_url.startswith('''https://instagram.'''				)
      assert instagram_user.is_verified is True
      assert instagram_user.is_private is False


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    UpperCAmelCase_									=							InstagramUser("github")
    print(instagram_user)
    print(F"""{instagram_user.number_of_posts = }""")
    print(F"""{instagram_user.number_of_followers = }""")
    print(F"""{instagram_user.number_of_followings = }""")
    print(F"""{instagram_user.email = }""")
    print(F"""{instagram_user.website = }""")
    print(F"""{instagram_user.profile_picture_url = }""")
    print(F"""{instagram_user.is_verified = }""")
    print(F"""{instagram_user.is_private = }""")