
import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase__				:	int					=						logging.getLogger()
UpperCAmelCase__				:	Tuple					=						torch.cuda.is_available()
UpperCAmelCase__				:	int					=						{
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 42,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1e-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}







def 					A       ( UpperCamelCase_   :       Any  ,		UpperCamelCase_   :       Tuple )   ->    List[Any]:







			'''simple docstring'''



			lowerCAmelCase__									=							'\n'.join(UpperCamelCase_ )
			Path(UpperCamelCase_ ).open("w" ).writelines(UpperCamelCase_ )


UpperCAmelCase__				:	Any					=						[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
UpperCAmelCase__				:	List[str]					=						['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
UpperCAmelCase__				:	Dict					=						'''patrickvonplaten/t5-tiny-random'''
UpperCAmelCase__				:	Any					=						'''sshleifer/t5-tinier-random'''
UpperCAmelCase__				:	Any					=						'''sshleifer/bart-tiny-random'''
UpperCAmelCase__				:	str					=						'''sshleifer/tiny-mbart'''
UpperCAmelCase__				:	Union[str, Any]					=						'''sshleifer/tiny-marian-en-de'''
UpperCAmelCase__				:	Any					=						'''stas/tiny-wmt19-en-de'''


UpperCAmelCase__				:	Optional[Any]					=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







def 					A       ( UpperCamelCase_   :       str )   ->    Tuple:







			'''simple docstring'''



			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(UpperCamelCase_  ,		F"""{split}.source""" )  ,		UpperCamelCase_ )
						_dump_articles(os.path.join(UpperCamelCase_  ,		F"""{split}.target""" )  ,		UpperCamelCase_ )
			return tmp_dir






class 				A       (		snake_case__      ):





	@classmethod
	def 							__SCREAMING_SNAKE_CASE					(   cls     :    int							):


				"""simple docstring"""

				logging.disable(logging.CRITICAL							)  # remove noisy download output from tracebacks
				return cls





	@slow
	@require_torch_gpu
	def 							__SCREAMING_SNAKE_CASE					(   self     :    str							):


				"""simple docstring"""

				lowerCAmelCase__									=							list_models()
				lowerCAmelCase__									=							'sshleifer'
				lowerCAmelCase__									=							[x.modelId for x in model_list if x.modelId.startswith(_A							)]
				lowerCAmelCase__									=							['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
				lowerCAmelCase__									=							[]
				for m in model_ids:
							if m in allowed_to_be_broken:
										continue
							try:
										AutoConfig.from_pretrained(_A							)
							except Exception:
										failures.append(_A							)
				assert not failures, f"""The following models could not be loaded through AutoConfig: {failures}"""





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__									=							{'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
				self._test_distiller_cli(_A							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							{
				    'student_encoder_layers': 2,
				    'student_decoder_layers': 1,
				    'max_epochs': 4,
				    'val_check_interval': 0.25,
				    'alpha_hid': 2.0,
				    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
				}
				lowerCAmelCase__									=							self._test_distiller_cli(_A ,							check_contents=_A							)

				lowerCAmelCase__									=							list(Path(model.output_dir							).glob("*.ckpt"							)							)
				self.assertEqual(1 ,							len(_A							)							)
				lowerCAmelCase__									=							list(Path(model.output_dir							).glob("**/*.bin"							)							)
				self.assertEqual(len(_A							) ,							2							)
				lowerCAmelCase__									=							lmap(str.strip ,							Path(model.hparams.data_dir							).joinpath("test.source"							).open().readlines()							)
				lowerCAmelCase__									=							tempfile.mktemp()  # XXX: not being cleaned up
				generate_summaries_or_translations(_A ,							_A ,							str(model.output_dir / "best_tfmr"							)							)
				self.assertTrue(Path(_A							).exists()							)

				lowerCAmelCase__									=							self.get_auto_remove_tmp_dir()
				convert_pl_to_hf(ckpts[0] ,							transformer_ckpts[0].parent ,							_A							)
				assert os.path.exists(os.path.join(_A ,							"pytorch_model.bin"							)							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							AutoModelForSeqaSeqLM.from_pretrained(_A							)
				lowerCAmelCase__									=							model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
				lowerCAmelCase__									=							torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]] ,							dtype=torch.long ,							device=model.device							)
				lowerCAmelCase__									=							target_ids[:, :-1].contiguous()  # Why this line?
				lowerCAmelCase__									=							target_ids[:, 1:].clone()  # why clone?
				lowerCAmelCase__									=							model(
				    _A ,							attention_mask=_A ,							decoder_input_ids=_A ,							labels=_A ,							use_cache=_A							).loss

				lowerCAmelCase__									=							model(_A ,							attention_mask=_A ,							decoder_input_ids=_A ,							use_cache=_A							).logits

				lowerCAmelCase__									=							nn.functional.log_softmax(_A ,							dim=-1							)
				lowerCAmelCase__									=							label_smoothed_nll_loss(
				    _A ,							_A ,							0.1 ,							ignore_index=model.config.pad_token_id							)
				with self.assertRaises(_A							):
							# TODO: understand why this breaks
							self.assertEqual(_A ,							_A							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

				lowerCAmelCase__									=							{
				    'student_encoder_layers': 2,
				    'student_decoder_layers': 1,
				    'num_train_epochs': 4,
				    'val_check_interval': 0.25,
				    'alpha_hid': 2.0,
				    'task': 'translation',
				    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
				    'tokenizer_name': MBART_TINY,
				    'teacher': MBART_TINY,
				    'src_lang': 'en_XX',
				    'tgt_lang': 'ro_RO',
				}
				lowerCAmelCase__									=							self._test_distiller_cli(_A ,							check_contents=_A							)
				assert model.model.config.model_type == "mbart"

				lowerCAmelCase__									=							list(Path(model.output_dir							).glob("*.ckpt"							)							)
				self.assertEqual(1 ,							len(_A							)							)
				lowerCAmelCase__									=							list(Path(model.output_dir							).glob("**/*.bin"							)							)
				lowerCAmelCase__									=							list(Path(model.output_dir							).glob("best_tfmr/*"							)							)
				assert len(_A							) > 2
				self.assertEqual(len(_A							) ,							2							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							{
				    'student_encoder_layers': 1,
				    'student_decoder_layers': 1,
				    'alpha_hid': 2.0,
				    'teacher': T5_TINY,
				    'model_name_or_path': T5_TINY,
				    'tokenizer_name': T5_TINY,
				}
				self._test_distiller_cli(_A							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

				lowerCAmelCase__									=							{
				    'teacher': T5_TINY,
				    'student': T5_TINIER,
				    'model_name_or_path': T5_TINIER,
				    'tokenizer_name': T5_TINIER,
				}
				self._test_distiller_cli(_A							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any] ,							__magic_name__     :    Dict ,							__magic_name__     :    Tuple=True							):


				"""simple docstring"""

				lowerCAmelCase__									=							{
				    'label_smoothing': 0.0,
				    'early_stopping_patience': -1,
				    'train_batch_size': 1,
				    'eval_batch_size': 2,
				    'max_epochs': 2,
				    'alpha_mlm': 0.2,
				    'alpha_ce': 0.8,
				    'do_predict': True,
				    'model_name_or_path': 'sshleifer/tinier_bart',
				    'teacher': CHEAP_ARGS['model_name_or_path'],
				    'val_check_interval': 0.5,
				}
				default_updates.update(_A							)
				lowerCAmelCase__									=							CHEAP_ARGS.copy()
				lowerCAmelCase__									=							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
				lowerCAmelCase__									=							self.get_auto_remove_tmp_dir()

				args_d.update(data_dir=_A ,							output_dir=_A ,							**_A							)
				lowerCAmelCase__									=							distill_main(argparse.Namespace(**_A							)							)
				if not check_contents:
							return model
				lowerCAmelCase__									=							os.listdir(_A							)
				lowerCAmelCase__									=							{os.path.basename(_A							) for p in contents}
				lowerCAmelCase__									=							[p for p in contents if p.endswith("ckpt"							)]
				assert len(_A							) > 0

				self.assertIn("test_generations.txt" ,							_A							)
				self.assertIn("test_results.txt" ,							_A							)

				lowerCAmelCase__									=							load_json(model.metrics_save_path							)
				lowerCAmelCase__									=							metrics['val'][-1]
				self.assertGreaterEqual(last_step_stats["val_avg_gen_time"] ,							0.01							)
				self.assertGreaterEqual(1.0 ,							last_step_stats["val_avg_gen_time"]							)
				self.assertIsInstance(last_step_stats[f"""val_avg_{model.val_metric}"""] ,							_A							)
				lowerCAmelCase__									=							int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1							)
				self.assertEqual(len(metrics["val"]							) ,							_A							)
				self.assertEqual(len(metrics["test"]							) ,							1							)
				return model






class 				A       (		snake_case__      ):





	@parameterized.expand(
	    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY] ,							)
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Union[str, Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							CHEAP_ARGS.copy()
				lowerCAmelCase__									=							'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
				lowerCAmelCase__									=							0.1 if task == 'translation' else 0

				lowerCAmelCase__									=							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
				lowerCAmelCase__									=							self.get_auto_remove_tmp_dir()
				args_d.update(
				    data_dir=_A ,							model_name_or_path=_A ,							tokenizer_name=_A ,							train_batch_size=2 ,							eval_batch_size=2 ,							output_dir=_A ,							do_predict=_A ,							task=_A ,							src_lang="en_XX" ,							tgt_lang="ro_RO" ,							freeze_encoder=_A ,							freeze_embeds=_A ,							)
				assert "n_train" in args_d
				lowerCAmelCase__									=							argparse.Namespace(**_A							)
				lowerCAmelCase__									=							main(_A							)

				lowerCAmelCase__									=							module.model.get_input_embeddings()
				assert not input_embeds.weight.requires_grad
				if model == T5_TINY:
							lowerCAmelCase__									=							module.model.lm_head
							assert not lm_head.weight.requires_grad
							assert (lm_head.weight == input_embeds.weight).all().item()
				elif model == FSMT_TINY:
							lowerCAmelCase__									=							module.model.model
							lowerCAmelCase__									=							fsmt.decoder.embed_positions
							assert not embed_pos.weight.requires_grad
							assert not fsmt.decoder.embed_tokens.weight.requires_grad
							# check that embeds are not the same
							assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
				else:
							lowerCAmelCase__									=							module.model.model
							lowerCAmelCase__									=							bart.decoder.embed_positions
							assert not embed_pos.weight.requires_grad
							assert not bart.shared.weight.requires_grad
							# check that embeds are the same
							assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
							assert bart.decoder.embed_tokens == bart.shared

				lowerCAmelCase__									=							load_json(module.output_dir / "text_batch.json"							)
				assert isinstance(_A ,							_A							)
				assert len(_A							) >= 4





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							CHEAP_ARGS.copy()

				lowerCAmelCase__									=							'summarization'
				lowerCAmelCase__									=							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)

				args_d.update(
				    data_dir=_A ,							tokenizer_name=_A ,							train_batch_size=2 ,							eval_batch_size=2 ,							do_predict=_A ,							task=_A ,							src_lang="en_XX" ,							tgt_lang="ro_RO" ,							freeze_encoder=_A ,							freeze_embeds=_A ,							)

				# test models whose config includes the extra_model_args
				lowerCAmelCase__									=							BART_TINY
				lowerCAmelCase__									=							self.get_auto_remove_tmp_dir()
				lowerCAmelCase__									=							args_d.copy()
				args_da.update(
				    model_name_or_path=_A ,							output_dir=_A ,							)
				lowerCAmelCase__									=							('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
				for p in extra_model_params:
							lowerCAmelCase__									=							0.5
				lowerCAmelCase__									=							argparse.Namespace(**_A							)
				lowerCAmelCase__									=							main(_A							)
				for p in extra_model_params:
							assert getattr(model.config ,							_A							) == 0.5, f"""failed to override the model config for param {p}"""

				# test models whose config doesn't include the extra_model_args
				lowerCAmelCase__									=							T5_TINY
				lowerCAmelCase__									=							self.get_auto_remove_tmp_dir()
				lowerCAmelCase__									=							args_d.copy()
				args_da.update(
				    model_name_or_path=_A ,							output_dir=_A ,							)
				lowerCAmelCase__									=							'encoder_layerdrop'
				lowerCAmelCase__									=							0.5
				lowerCAmelCase__									=							argparse.Namespace(**_A							)
				with pytest.raises(_A							) as excinfo:
							lowerCAmelCase__									=							main(_A							)
				assert str(excinfo.value							) == f"""model config doesn't have a `{unsupported_param}` attribute"""







	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							CHEAP_ARGS.copy()

				lowerCAmelCase__									=							'summarization'
				lowerCAmelCase__									=							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)

				lowerCAmelCase__									=							BART_TINY
				lowerCAmelCase__									=							self.get_auto_remove_tmp_dir()

				args_d.update(
				    data_dir=_A ,							model_name_or_path=_A ,							output_dir=_A ,							tokenizer_name=_A ,							train_batch_size=2 ,							eval_batch_size=2 ,							do_predict=_A ,							task=_A ,							src_lang="en_XX" ,							tgt_lang="ro_RO" ,							freeze_encoder=_A ,							freeze_embeds=_A ,							)

				# emulate finetune.py
				lowerCAmelCase__									=							argparse.ArgumentParser()
				lowerCAmelCase__									=							pl.Trainer.add_argparse_args(_A							)
				lowerCAmelCase__									=							SummarizationModule.add_model_specific_args(_A ,							os.getcwd()							)
				lowerCAmelCase__									=							{'--help': True}

				# --help test
				with pytest.raises(_A							) as excinfo:
							with CaptureStdout() as cs:
										lowerCAmelCase__									=							parser.parse_args(_A							)
							assert False, "--help is expected to sys.exit"
				assert excinfo.type == SystemExit
				lowerCAmelCase__									=							lightning_base.arg_to_scheduler_metavar
				assert expected in cs.out, "--help is expected to list the supported schedulers"

				# --lr_scheduler=non_existing_scheduler test
				lowerCAmelCase__									=							'non_existing_scheduler'
				lowerCAmelCase__									=							{f"""--lr_scheduler={unsupported_param}"""}
				with pytest.raises(_A							) as excinfo:
							with CaptureStderr() as cs:
										lowerCAmelCase__									=							parser.parse_args(_A							)
							assert False, "invalid argument is expected to sys.exit"
				assert excinfo.type == SystemExit
				lowerCAmelCase__									=							f"""invalid choice: '{unsupported_param}'"""
				assert expected in cs.err, f"""should have bailed on invalid choice of scheduler {unsupported_param}"""

				# --lr_scheduler=existing_scheduler test
				lowerCAmelCase__									=							'cosine'
				lowerCAmelCase__									=							args_d.copy()
				lowerCAmelCase__									=							supported_param
				lowerCAmelCase__									=							argparse.Namespace(**_A							)
				lowerCAmelCase__									=							main(_A							)
				assert (
				    getattr(model.hparams ,							"lr_scheduler"							) == supported_param
				), f"""lr_scheduler={supported_param} shouldn't fail"""







'''simple docstring'''

UpperCAmelCase__				:	List[str]					=						"\n# Transformers installation\n! pip install transformers datasets evaluate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

UpperCAmelCase__				:	Any					=						[{"type": "code", "content": INSTALL_CONTENT}]
UpperCAmelCase__				:	Union[str, Any]					=						{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





