


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	: Optional[Any]  				='sshleifer/mar_enro_6_3_student'
class        _lowercase      (			A__       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:List[str]	)		->			Tuple:
										super().setUp()

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


				@slow
				@require_torch_gpu
				def 		__magic_name__(       self			:List[Any]	)		->			List[str]:
										MarianMTModel.from_pretrained(lowerCAmelCase__	)



				@slow
				@require_torch_gpu
				def 		__magic_name__(       self			:Dict	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Any					=		{
										    '''$MAX_LEN''': 64,
										    '''$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
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		(self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''	)[1].strip()
										__SCREAMING_SNAKE_CASE						:     Any					=		bash_script.replace('''\\\n'''			,			''''''	).strip().replace('''"$@"'''			,			''''''	)
										for k, v in env_vars_to_replace.items():
																__SCREAMING_SNAKE_CASE						:     List[Any]					=		bash_script.replace(lowerCAmelCase__			,			str(lowerCAmelCase__	)	)
										__SCREAMING_SNAKE_CASE						:     Any					=		self.get_auto_remove_tmp_dir()

										# bash_script = bash_script.replace("--fp16 ", "")
										__SCREAMING_SNAKE_CASE						:     Dict					=		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

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		['''finetune.py'''] + bash_script.split() + args
										with patch.object(lowerCAmelCase__			,			'''argv'''			,			lowerCAmelCase__	):
																__SCREAMING_SNAKE_CASE						:     int					=		argparse.ArgumentParser()
																__SCREAMING_SNAKE_CASE						:     Any					=		pl.Trainer.add_argparse_args(lowerCAmelCase__	)
																__SCREAMING_SNAKE_CASE						:     List[str]					=		SummarizationModule.add_model_specific_args(lowerCAmelCase__			,			os.getcwd()	)
																__SCREAMING_SNAKE_CASE						:     int					=		parser.parse_args()
																__SCREAMING_SNAKE_CASE						:     str					=		main(lowerCAmelCase__	)

										# Check metrics
										__SCREAMING_SNAKE_CASE						:     List[str]					=		load_json(model.metrics_save_path	)
										__SCREAMING_SNAKE_CASE						:     int					=		metrics['''val'''][0]
										__SCREAMING_SNAKE_CASE						:     Dict					=		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
										__SCREAMING_SNAKE_CASE						:     Dict					=		os.listdir(lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		[x for x in contents if x.endswith('''.ckpt'''	)][0]
										__SCREAMING_SNAKE_CASE						:     int					=		os.path.join(args.output_dir			,			lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     int					=		torch.load(lowerCAmelCase__			,			map_location='''cpu'''	)
										__SCREAMING_SNAKE_CASE						:     Union[str, 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:
																__SCREAMING_SNAKE_CASE						:     Dict					=		{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        _lowercase      (			A__       ):






				'''simple docstring'''



				@timeout_decorator.timeout(600	)
				@slow
				@require_torch_gpu
				def 		__magic_name__(       self			:List[Any]	)		->			int:
										__SCREAMING_SNAKE_CASE						:     str					=		f'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
										__SCREAMING_SNAKE_CASE						:     Optional[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
										__SCREAMING_SNAKE_CASE						:     int					=		(
										    (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''	)[1].strip()
										)
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		bash_script.replace('''\\\n'''			,			''''''	).strip().replace('''"$@"'''			,			''''''	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		bash_script.replace('''--fp16 '''			,			''' '''	)

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

																__SCREAMING_SNAKE_CASE						:     Any					=		distill_main(lowerCAmelCase__	)

										# Check metrics
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		load_json(model.metrics_save_path	)
										__SCREAMING_SNAKE_CASE						:     List[str]					=		metrics['''val'''][0]
										__SCREAMING_SNAKE_CASE						:     List[str]					=		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
										__SCREAMING_SNAKE_CASE						:     List[str]					=		os.listdir(lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Any					=		[x for x in contents if x.endswith('''.ckpt'''	)][0]
										__SCREAMING_SNAKE_CASE						:     List[str]					=		os.path.join(args.output_dir			,			lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Dict					=		torch.load(lowerCAmelCase__			,			map_location='''cpu'''	)
										__SCREAMING_SNAKE_CASE						:     int					=		'''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:
																__SCREAMING_SNAKE_CASE						:     Any					=		{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




"""simple docstring"""



import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


__snake_case   			=    """hf-internal-testing/tiny-random-bert"""
__snake_case   			=    os.path.join(TRANSFORMERS_CACHE, """models--hf-internal-testing--tiny-random-bert""")
__snake_case   			=    """9b8c223d42b2188cb49d29af482996f9d0f3e5a6"""







class _lowerCAmelCase				(      unittest.TestCase	):







   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case    :      int      					=    cached_file(UpperCamelCase__						,	UpperCamelCase__						)
       # Should have downloaded the file in here
       self.assertTrue(os.path.isdir(UpperCamelCase__						)						)
       # Cache should contain at least those three subfolders:
       for subfolder in ["blobs", "refs", "snapshots"]:
           self.assertTrue(os.path.isdir(os.path.join(UpperCamelCase__						,	UpperCamelCase__						)						)						)
       with open(os.path.join(UpperCamelCase__						,	"refs"						,	"main"						)						) as f:
           snake_case    :      Dict      					=    f.read()
       self.assertEqual(UpperCamelCase__						,	os.path.join(UpperCamelCase__						,	"snapshots"						,	UpperCamelCase__						,	UpperCamelCase__						)						)
       self.assertTrue(os.path.isfile(UpperCamelCase__						)						)

       # File is cached at the same place the second time.
       snake_case    :      List[str]      					=    cached_file(UpperCamelCase__						,	UpperCamelCase__						)
       self.assertEqual(UpperCamelCase__						,	UpperCamelCase__						)

       # Using a specific revision to test the full commit hash.
       snake_case    :      Any      					=    cached_file(UpperCamelCase__						,	UpperCamelCase__						,	revision="9b8c223"						)
       self.assertEqual(UpperCamelCase__						,	os.path.join(UpperCamelCase__						,	"snapshots"						,	UpperCamelCase__						,	UpperCamelCase__						)						)







   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       with self.assertRaisesRegex(UpperCamelCase__						,	"is not a valid model identifier"						):
           snake_case    :      Optional[Any]      					=    cached_file("tiny-random-bert"						,	UpperCamelCase__						)

       with self.assertRaisesRegex(UpperCamelCase__						,	"is not a valid git identifier"						):
           snake_case    :      Optional[Any]      					=    cached_file(UpperCamelCase__						,	UpperCamelCase__						,	revision="aaaa"						)

       with self.assertRaisesRegex(UpperCamelCase__						,	"does not appear to have a file named"						):
           snake_case    :      List[Any]      					=    cached_file(UpperCamelCase__						,	"conf"						)







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       with self.assertRaisesRegex(UpperCamelCase__						,	"does not appear to have a file named"						):
           snake_case    :      Tuple      					=    cached_file(UpperCamelCase__						,	"conf"						)

       with open(os.path.join(UpperCamelCase__						,	"refs"						,	"main"						)						) as f:
           snake_case    :      Any      					=    f.read()
       self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__						,	".no_exist"						,	UpperCamelCase__						,	"conf"						)						)						)

       snake_case    :      Optional[Any]      					=    cached_file(UpperCamelCase__						,	"conf"						,	_raise_exceptions_for_missing_entries=UpperCamelCase__						)
       self.assertIsNone(UpperCamelCase__						)

       snake_case    :      Any      					=    cached_file(UpperCamelCase__						,	"conf"						,	local_files_only=UpperCamelCase__						,	_raise_exceptions_for_missing_entries=UpperCamelCase__						)
       self.assertIsNone(UpperCamelCase__						)

       snake_case    :      Any      					=    mock.Mock()
       snake_case    :      List[Any]      					=    500
       snake_case    :      int      					=    {}
       snake_case    :      Optional[int]      					=    HTTPError
       snake_case    :      Tuple      					=    {}

       # Under the mock environment we get a 500 error when trying to reach the tokenizer.
       with mock.patch("requests.Session.request"						,	return_value=UpperCamelCase__						) as mock_head:
           snake_case    :      Tuple      					=    cached_file(UpperCamelCase__						,	"conf"						,	_raise_exceptions_for_connection_errors=UpperCamelCase__						)
           self.assertIsNone(UpperCamelCase__						)
           # This check we did call the fake head request
           mock_head.assert_called()







   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       self.assertTrue(has_file("hf-internal-testing/tiny-bert-pt-only"						,	UpperCamelCase__						)						)
       self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only"						,	UpperCamelCase__						)						)
       self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only"						,	UpperCamelCase__						)						)







   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       self.assertIsNone(get_file_from_repo("bert-base-cased"						,	"ahah.txt"						)						)

       # The function raises if the repository does not exist.
       with self.assertRaisesRegex(UpperCamelCase__						,	"is not a valid model identifier"						):
           get_file_from_repo("bert-base-case"						,	UpperCamelCase__						)

       # The function raises if the revision does not exist.
       with self.assertRaisesRegex(UpperCamelCase__						,	"is not a valid git identifier"						):
           get_file_from_repo("bert-base-cased"						,	UpperCamelCase__						,	revision="ahaha"						)

       snake_case    :      int      					=    get_file_from_repo("bert-base-cased"						,	UpperCamelCase__						)
       # The name is the cached name which is not very easy to test, so instead we load the content.
       snake_case    :      str      					=    json.loads(open(UpperCamelCase__						,	"r"						).read()						)
       self.assertEqual(config["hidden_size"]						,	768						)







   def  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




       with tempfile.TemporaryDirectory() as tmp_dir:
           snake_case    :      int      					=    Path(UpperCamelCase__						) / "a.txt"
           filename.touch()
           self.assertEqual(get_file_from_repo(UpperCamelCase__						,	"a.txt"						)						,	str(UpperCamelCase__						)						)

           self.assertIsNone(get_file_from_repo(UpperCamelCase__						,	"b.txt"						)						)





