
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


A     :	List[str]    = 'sshleifer/mar_enro_6_3_student'







class 			__A( a			):




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


								'''simple docstring'''




								super().setUp()

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




				@slow
				@require_torch_gpu
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								MarianMTModel.from_pretrained(_snake_case				)




				@slow
				@require_torch_gpu
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




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

								# bash_script = bash_script.replace("--fp16 ", "")
								__a										=    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										=    ['''finetune.py'''] + bash_script.split() + args
								with patch.object(_snake_case		,     '''argv'''		,     _snake_case				):
												__a										=    argparse.ArgumentParser()
												__a										=    pl.Trainer.add_argparse_args(_snake_case				)
												__a										=    SummarizationModule.add_model_specific_args(_snake_case		,     os.getcwd()				)
												__a										=    parser.parse_args()
												__a										=    main(_snake_case				)

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

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




				@timeout_decorator.timeout(600				)
				@slow
				@require_torch_gpu
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    F"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
								__a										=    {
								    '''--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										=    (
								    (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''				)[1].strip()
								)
								__a										=    bash_script.replace('''\\\n'''		,     ''''''				).strip().replace('''"$@"'''		,     ''''''				)
								__a										=    bash_script.replace('''--fp16 '''		,     ''' '''				)

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

												__a										=    distill_main(_snake_case				)

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

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

import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
					import os

					from flax.core.frozen_dict import unfreeze
					from flax.traverse_util import flatten_dict

					from transformers import FlaxBertModel

					A     :	int    = '0.12'  # assumed parallelism: 8







@require_flax
@is_staging_test
class 			__A( unittest.TestCase			):




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    TOKEN
								HfFolder.save_token(_snake_case				)




				@classmethod
				def 				SCREAMING_SNAKE_CASE_		(     cls				)    ->      Union[str, Any]:


								'''simple docstring'''




								try:
												delete_repo(token=cls._token		,     repo_id='''test-model-flax'''				)
								except HTTPError:
												pass

								try:
												delete_repo(token=cls._token		,     repo_id='''valid_org/test-model-flax-org'''				)
								except HTTPError:
												pass




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


								'''simple docstring'''




								__a										=    BertConfig(
								    vocab_size=99		,     hidden_size=32		,     num_hidden_layers=5		,     num_attention_heads=4		,     intermediate_size=37				)
								__a										=    FlaxBertModel(_snake_case				)
								model.push_to_hub('''test-model-flax'''		,     use_auth_token=self._token				)

								__a										=    FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""				)

								__a										=    flatten_dict(unfreeze(model.params				)				)
								__a										=    flatten_dict(unfreeze(new_model.params				)				)

								for key in base_params.keys():
												__a										=    (base_params[key] - new_params[key]).sum().item()
												self.assertLessEqual(_snake_case		,     1E-3		,     msg=F"""{key} not identical"""				)

								# Reset repo
								delete_repo(token=self._token		,     repo_id='''test-model-flax'''				)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
												model.save_pretrained(_snake_case		,     repo_id='''test-model-flax'''		,     push_to_hub=_snake_case		,     use_auth_token=self._token				)

								__a										=    FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""				)

								__a										=    flatten_dict(unfreeze(model.params				)				)
								__a										=    flatten_dict(unfreeze(new_model.params				)				)

								for key in base_params.keys():
												__a										=    (base_params[key] - new_params[key]).sum().item()
												self.assertLessEqual(_snake_case		,     1E-3		,     msg=F"""{key} not identical"""				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    BertConfig(
								    vocab_size=99		,     hidden_size=32		,     num_hidden_layers=5		,     num_attention_heads=4		,     intermediate_size=37				)
								__a										=    FlaxBertModel(_snake_case				)
								model.push_to_hub('''valid_org/test-model-flax-org'''		,     use_auth_token=self._token				)

								__a										=    FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''				)

								__a										=    flatten_dict(unfreeze(model.params				)				)
								__a										=    flatten_dict(unfreeze(new_model.params				)				)

								for key in base_params.keys():
												__a										=    (base_params[key] - new_params[key]).sum().item()
												self.assertLessEqual(_snake_case		,     1E-3		,     msg=F"""{key} not identical"""				)

								# Reset repo
								delete_repo(token=self._token		,     repo_id='''valid_org/test-model-flax-org'''				)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
												model.save_pretrained(
												    _snake_case		,     repo_id='''valid_org/test-model-flax-org'''		,     push_to_hub=_snake_case		,     use_auth_token=self._token				)

								__a										=    FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''				)

								__a										=    flatten_dict(unfreeze(model.params				)				)
								__a										=    flatten_dict(unfreeze(new_model.params				)				)

								for key in base_params.keys():
												__a										=    (base_params[key] - new_params[key]).sum().item()
												self.assertLessEqual(_snake_case		,     1E-3		,     msg=F"""{key} not identical"""				)



def     __lowerCAmelCase			(			a__						,    a__  )       ->  str:
				__a										=    True
				__a										=    flatten_dict(modela.params  )
				__a										=    flatten_dict(modela.params  )
				for key in flat_params_a.keys():
								if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]  )  ) > 1e-4:
												__a										=    False

				return models_are_equal







@require_flax
class 			__A( unittest.TestCase			):




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''				)
								__a										=    FlaxBertModel(_snake_case				)

								__a										=    '''bert'''
								with tempfile.TemporaryDirectory() as tmp_dir:
												model.save_pretrained(os.path.join(_snake_case		,     _snake_case				)				)

												with self.assertRaises(_snake_case				):
																__a										=    FlaxBertModel.from_pretrained(_snake_case				)

												__a										=    FlaxBertModel.from_pretrained(_snake_case		,     subfolder=_snake_case				)

								self.assertTrue(check_models_equal(_snake_case		,     _snake_case				)				)




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


								'''simple docstring'''




								__a										=    BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''				)
								__a										=    FlaxBertModel(_snake_case				)

								__a										=    '''bert'''
								with tempfile.TemporaryDirectory() as tmp_dir:
												model.save_pretrained(os.path.join(_snake_case		,     _snake_case				)		,     max_shard_size='''10KB'''				)

												with self.assertRaises(_snake_case				):
																__a										=    FlaxBertModel.from_pretrained(_snake_case				)

												__a										=    FlaxBertModel.from_pretrained(_snake_case		,     subfolder=_snake_case				)

								self.assertTrue(check_models_equal(_snake_case		,     _snake_case				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								__a										=    '''bert'''
								__a										=    '''hf-internal-testing/tiny-random-bert-subfolder'''

								with self.assertRaises(_snake_case				):
												__a										=    FlaxBertModel.from_pretrained(_snake_case				)

								__a										=    FlaxBertModel.from_pretrained(_snake_case		,     subfolder=_snake_case				)

								self.assertIsNotNone(_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    '''bert'''
								__a										=    '''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
								with self.assertRaises(_snake_case				):
												__a										=    FlaxBertModel.from_pretrained(_snake_case				)

								__a										=    FlaxBertModel.from_pretrained(_snake_case		,     subfolder=_snake_case				)

								self.assertIsNotNone(_snake_case				)