

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)

lowerCAmelCase__             =		logging.getLogger()
lowerCAmelCase__             =		torch.cuda.is_available()
lowerCAmelCase__             =		{
    """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""": 4_2,
    """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""": 1_2,
    """max_target_length""": 1_2,
    """val_max_target_length""": 1_2,
    """test_max_target_length""": 1_2,
    """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        lowerCamelCase_  (				UpperCAmelCase_				:  Path				,							UpperCAmelCase_				:  list						)    ->					str:




	'''simple docstring'''




	_UpperCamelCase :		Any								=    '\n'.join(UpperCAmelCase_						)
	Path(UpperCAmelCase_						).open('w'						).writelines(UpperCAmelCase_						)


lowerCAmelCase__             =		[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
lowerCAmelCase__             =		["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
lowerCAmelCase__             =		"""patrickvonplaten/t5-tiny-random"""
lowerCAmelCase__             =		"""sshleifer/t5-tinier-random"""
lowerCAmelCase__             =		"""sshleifer/bart-tiny-random"""
lowerCAmelCase__             =		"""sshleifer/tiny-mbart"""
lowerCAmelCase__             =		"""sshleifer/tiny-marian-en-de"""
lowerCAmelCase__             =		"""stas/tiny-wmt19-en-de"""


lowerCAmelCase__             =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






def        lowerCamelCase_  (				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 						lowercase     (					_lowercase				):


		"""simple docstring"""




		@classmethod
		def 		A__       (					cls):
			logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
			return cls


		@slow
		@require_torch_gpu
		def 		A__       (					self):
			_UpperCamelCase :		int								=    list_models()
			_UpperCamelCase :		Dict								=    'sshleifer'
			_UpperCamelCase :		Optional[Any]								=    [x.modelId for x in model_list if x.modelId.startswith(__snake_case)]
			_UpperCamelCase :		Any								=    ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
			_UpperCamelCase :		Union[str, Any]								=    []
			for m in model_ids:
				if m in allowed_to_be_broken:
					continue
				try:
					AutoConfig.from_pretrained(__snake_case)
				except Exception:
					failures.append(__snake_case)
			assert not failures, f'''The following models could not be loaded through AutoConfig: {failures}'''


		def 		A__       (					self):
			_UpperCamelCase :		str								=    {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
			self._test_distiller_cli(__snake_case)


		def 		A__       (					self):
			_UpperCamelCase :		List[Any]								=    {
			    'student_encoder_layers': 2,
			    'student_decoder_layers': 1,
			    'max_epochs': 4,
			    'val_check_interval': 0.2_5,
			    'alpha_hid': 2.0,
			    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
			}
			_UpperCamelCase :		int								=    self._test_distiller_cli(__snake_case						,							check_contents=__snake_case)

			_UpperCamelCase :		str								=    list(Path(model.output_dir).glob('*.ckpt'))
			self.assertEqual(1						,							len(__snake_case))
			_UpperCamelCase :		Union[str, Any]								=    list(Path(model.output_dir).glob('**/*.bin'))
			self.assertEqual(len(__snake_case)						,							2)
			_UpperCamelCase :		List[str]								=    lmap(str.strip						,							Path(model.hparams.data_dir).joinpath('test.source').open().readlines())
			_UpperCamelCase :		List[str]								=    tempfile.mktemp()  # XXX: not being cleaned up
			generate_summaries_or_translations(__snake_case						,							__snake_case						,							str(model.output_dir / 'best_tfmr'))
			self.assertTrue(Path(__snake_case).exists())

			_UpperCamelCase :		Tuple								=    self.get_auto_remove_tmp_dir()
			convert_pl_to_hf(ckpts[0]						,							transformer_ckpts[0].parent						,							__snake_case)
			assert os.path.exists(os.path.join(__snake_case						,							'pytorch_model.bin'))


		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    AutoModelForSeqaSeqLM.from_pretrained(__snake_case)
			_UpperCamelCase						,		_UpperCamelCase :		List[str]								=    model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
			_UpperCamelCase :		str								=    torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]						,							dtype=torch.long						,							device=model.device)
			_UpperCamelCase :		Any								=    target_ids[:, :-1].contiguous()  # Why this line?
			_UpperCamelCase :		Union[str, Any]								=    target_ids[:, 1:].clone()  # why clone?
			_UpperCamelCase :		List[Any]								=    model(
			    __snake_case						,							attention_mask=__snake_case						,							decoder_input_ids=__snake_case						,							labels=__snake_case						,							use_cache=__snake_case).loss

			_UpperCamelCase :		Dict								=    model(__snake_case						,							attention_mask=__snake_case						,							decoder_input_ids=__snake_case						,							use_cache=__snake_case).logits

			_UpperCamelCase :		Any								=    nn.functional.log_softmax(__snake_case						,							dim=-1)
			_UpperCamelCase						,		_UpperCamelCase :		Union[str, Any]								=    label_smoothed_nll_loss(
			    __snake_case						,							__snake_case						,							0.1						,							ignore_index=model.config.pad_token_id)
			with self.assertRaises(__snake_case):
				# TODO: understand why this breaks
				self.assertEqual(__snake_case						,							__snake_case)


		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    {
			    'student_encoder_layers': 2,
			    'student_decoder_layers': 1,
			    'num_train_epochs': 4,
			    'val_check_interval': 0.2_5,
			    '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',
			}
			_UpperCamelCase :		List[str]								=    self._test_distiller_cli(__snake_case						,							check_contents=__snake_case)
			assert model.model.config.model_type == "mbart"

			_UpperCamelCase :		List[Any]								=    list(Path(model.output_dir).glob('*.ckpt'))
			self.assertEqual(1						,							len(__snake_case))
			_UpperCamelCase :		List[Any]								=    list(Path(model.output_dir).glob('**/*.bin'))
			_UpperCamelCase :		Tuple								=    list(Path(model.output_dir).glob('best_tfmr/*'))
			assert len(__snake_case) > 2
			self.assertEqual(len(__snake_case)						,							2)


		def 		A__       (					self):
			_UpperCamelCase :		List[str]								=    {
			    '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(__snake_case)


		def 		A__       (					self):
			_UpperCamelCase :		int								=    {
			    'teacher': T5_TINY,
			    'student': T5_TINIER,
			    'model_name_or_path': T5_TINIER,
			    'tokenizer_name': T5_TINIER,
			}
			self._test_distiller_cli(__snake_case)






		def 		A__       (					self						,							__snake_case						,							__snake_case=True):
			_UpperCamelCase :		Optional[Any]								=    {
			    '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(__snake_case)
			_UpperCamelCase :		dict								=    CHEAP_ARGS.copy()
			_UpperCamelCase :		Union[str, Any]								=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
			_UpperCamelCase :		Dict								=    self.get_auto_remove_tmp_dir()

			args_d.update(data_dir=__snake_case						,							output_dir=__snake_case						,							**__snake_case)
			_UpperCamelCase :		Tuple								=    distill_main(argparse.Namespace(**__snake_case))
			if not check_contents:
				return model
			_UpperCamelCase :		Any								=    os.listdir(__snake_case)
			_UpperCamelCase :		Union[str, Any]								=    {os.path.basename(__snake_case) for p in contents}
			_UpperCamelCase :		Any								=    [p for p in contents if p.endswith('ckpt')]
			assert len(__snake_case) > 0

			self.assertIn('test_generations.txt'						,							__snake_case)
			self.assertIn('test_results.txt'						,							__snake_case)

			_UpperCamelCase :		str								=    load_json(model.metrics_save_path)
			_UpperCamelCase :		Any								=    metrics['val'][-1]
			self.assertGreaterEqual(last_step_stats['val_avg_gen_time']						,							0.0_1)
			self.assertGreaterEqual(1.0						,							last_step_stats['val_avg_gen_time'])
			self.assertIsInstance(last_step_stats[f'''val_avg_{model.val_metric}''']						,							__snake_case)
			_UpperCamelCase :		Optional[int]								=    int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1)
			self.assertEqual(len(metrics['val'])						,							__snake_case)
			self.assertEqual(len(metrics['test'])						,							1)
			return model







class 						lowercase     (					_lowercase				):


		"""simple docstring"""




		@parameterized.expand(
		    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]						,							)
		def 		A__       (					self						,							__snake_case):
			_UpperCamelCase :		dict								=    CHEAP_ARGS.copy()
			_UpperCamelCase :		Optional[Any]								=    'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
			_UpperCamelCase :		str								=    0.1 if task == 'translation' else 0

			_UpperCamelCase :		Union[str, Any]								=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
			_UpperCamelCase :		List[str]								=    self.get_auto_remove_tmp_dir()
			args_d.update(
			    data_dir=__snake_case						,							model_name_or_path=__snake_case						,							tokenizer_name=__snake_case						,							train_batch_size=2						,							eval_batch_size=2						,							output_dir=__snake_case						,							do_predict=__snake_case						,							task=__snake_case						,							src_lang='en_XX'						,							tgt_lang='ro_RO'						,							freeze_encoder=__snake_case						,							freeze_embeds=__snake_case						,							)
			assert "n_train" in args_d
			_UpperCamelCase :		Optional[int]								=    argparse.Namespace(**__snake_case)
			_UpperCamelCase :		List[str]								=    main(__snake_case)

			_UpperCamelCase :		Any								=    module.model.get_input_embeddings()
			assert not input_embeds.weight.requires_grad
			if model == T5_TINY:
				_UpperCamelCase :		List[Any]								=    module.model.lm_head
				assert not lm_head.weight.requires_grad
				assert (lm_head.weight == input_embeds.weight).all().item()
			elif model == FSMT_TINY:
				_UpperCamelCase :		int								=    module.model.model
				_UpperCamelCase :		Any								=    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:
				_UpperCamelCase :		Union[str, Any]								=    module.model.model
				_UpperCamelCase :		Any								=    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

			_UpperCamelCase :		int								=    load_json(module.output_dir / 'text_batch.json')
			assert isinstance(__snake_case						,							__snake_case)
			assert len(__snake_case) >= 4


		def 		A__       (					self):
			_UpperCamelCase :		dict								=    CHEAP_ARGS.copy()

			_UpperCamelCase :		Optional[Any]								=    'summarization'
			_UpperCamelCase :		str								=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())

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

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

			# test models whose config doesn't include the extra_model_args
			_UpperCamelCase :		Union[str, Any]								=    T5_TINY
			_UpperCamelCase :		Tuple								=    self.get_auto_remove_tmp_dir()
			_UpperCamelCase :		int								=    args_d.copy()
			args_da.update(
			    model_name_or_path=__snake_case						,							output_dir=__snake_case						,							)
			_UpperCamelCase :		Union[str, Any]								=    'encoder_layerdrop'
			_UpperCamelCase :		Optional[int]								=    0.5
			_UpperCamelCase :		Optional[Any]								=    argparse.Namespace(**__snake_case)
			with pytest.raises(__snake_case) as excinfo:
				_UpperCamelCase :		Optional[Any]								=    main(__snake_case)
			assert str(excinfo.value) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''






		def 		A__       (					self):
			_UpperCamelCase :		dict								=    CHEAP_ARGS.copy()

			_UpperCamelCase :		Union[str, Any]								=    'summarization'
			_UpperCamelCase :		Union[str, Any]								=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())

			_UpperCamelCase :		int								=    BART_TINY
			_UpperCamelCase :		Dict								=    self.get_auto_remove_tmp_dir()

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

			# emulate finetune.py
			_UpperCamelCase :		List[Any]								=    argparse.ArgumentParser()
			_UpperCamelCase :		Optional[Any]								=    pl.Trainer.add_argparse_args(__snake_case)
			_UpperCamelCase :		Any								=    SummarizationModule.add_model_specific_args(__snake_case						,							os.getcwd())
			_UpperCamelCase :		Optional[int]								=    {'--help': True}

			# --help test
			with pytest.raises(__snake_case) as excinfo:
				with CaptureStdout() as cs:
					_UpperCamelCase :		Tuple								=    parser.parse_args(__snake_case)
				assert False, "--help is expected to sys.exit"
			assert excinfo.type == SystemExit
			_UpperCamelCase :		str								=    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
			_UpperCamelCase :		Dict								=    'non_existing_scheduler'
			_UpperCamelCase :		str								=    {f'''--lr_scheduler={unsupported_param}'''}
			with pytest.raises(__snake_case) as excinfo:
				with CaptureStderr() as cs:
					_UpperCamelCase :		Dict								=    parser.parse_args(__snake_case)
				assert False, "invalid argument is expected to sys.exit"
			assert excinfo.type == SystemExit
			_UpperCamelCase :		List[Any]								=    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
			_UpperCamelCase :		Any								=    'cosine'
			_UpperCamelCase :		int								=    args_d.copy()
			_UpperCamelCase :		Optional[Any]								=    supported_param
			_UpperCamelCase :		Dict								=    argparse.Namespace(**__snake_case)
			_UpperCamelCase :		int								=    main(__snake_case)
			assert (
			    getattr(model.hparams						,							'lr_scheduler') == supported_param
			), f'''lr_scheduler={supported_param} shouldn\'t fail'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__             =		{"""configuration_vit_hybrid""": ["""VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTHybridConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		[
							    """VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """ViTHybridForImageClassification""",
							    """ViTHybridModel""",
							    """ViTHybridPreTrainedModel""",
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		["""ViTHybridImageProcessor"""]


if TYPE_CHECKING:
							from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_hybrid import (
														    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTHybridForImageClassification,
														    ViTHybridModel,
														    ViTHybridPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
							import sys

							lowerCAmelCase__             =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




