




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		:    Union[str, Any]	      =		logging.getLogger()
__lowerCAmelCase		:    Any	      =		torch.cuda.is_available()
__lowerCAmelCase		:    Tuple	      =		{
    '''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       __magic_name__    (			A		: str,  A		: str       ):






			'''simple docstring'''







			a   =      "\n".join(A_       )
			Path(A_       ).open("w"       ).writelines(A_       )


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


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




def       __magic_name__    (			A		: Any       ):






			'''simple docstring'''







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




class    snake_case__							(UpperCamelCase__       ):
				"""simple docstring"""



				@classmethod
				def 							__UpperCAmelCase							(				cls    :					str							) ->					Any:
							logging.disable(logging.CRITICAL							)  # remove noisy download output from tracebacks
							return cls





				@slow
				@require_torch_gpu
				def 							__UpperCAmelCase							(				self    :					Any							) ->					Tuple:
							a   =      list_models()
							a   =      "sshleifer"
							a   =      [x.modelId for x in model_list if x.modelId.startswith(__lowerCamelCase							)]
							a   =      ["sshleifer/blenderbot-3B", "sshleifer/blenderbot-90M"]
							a   =      []
							for m in model_ids:
										if m in allowed_to_be_broken:
													continue
										try:
													AutoConfig.from_pretrained(__lowerCamelCase							)
										except Exception:
													failures.append(__lowerCamelCase							)
							assert not failures, f"""The following models could not be loaded through AutoConfig: {failures}"""





				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					Tuple:
							a   =      {"student_encoder_layers": 2, "student_decoder_layers": 1, "no_teacher": True}
							self._test_distiller_cli(__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					Optional[Any]:
							a   =      {
							    "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",
							}
							a   =      self._test_distiller_cli(__lowerCamelCase ,				check_contents=__lowerCamelCase							)

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

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





				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					List[str]:
							a   =      AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase							)
							a   =      model.dummy_inputs["input_ids"], model.dummy_inputs["attention_mask"]
							a   =      torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]] ,				dtype=torch.long ,				device=model.device							)
							a   =      target_ids[:, :-1].contiguous()  # Why this line?
							a   =      target_ids[:, 1:].clone()  # why clone?
							a   =      model(
							    __lowerCamelCase ,				attention_mask=__lowerCamelCase ,				decoder_input_ids=__lowerCamelCase ,				labels=__lowerCamelCase ,				use_cache=__lowerCamelCase							).loss

							a   =      model(__lowerCamelCase ,				attention_mask=__lowerCamelCase ,				decoder_input_ids=__lowerCamelCase ,				use_cache=__lowerCamelCase							).logits

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





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					str:
							a   =      {
							    "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",
							}
							a   =      self._test_distiller_cli(__lowerCamelCase ,				check_contents=__lowerCamelCase							)
							assert model.model.config.model_type == "mbart"

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





				def 							__UpperCAmelCase							(				self    :					str							) ->					Dict:
							a   =      {
							    "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(__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					str							) ->					Dict:
							a   =      {
							    "teacher": T5_TINY,
							    "student": T5_TINIER,
							    "model_name_or_path": T5_TINIER,
							    "tokenizer_name": T5_TINIER,
							}
							self._test_distiller_cli(__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					List[str]=True							) ->					List[str]:
							a   =      {
							    "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(__lowerCamelCase							)
							a   =      CHEAP_ARGS.copy()
							a   =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
							a   =      self.get_auto_remove_tmp_dir()

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

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

							a   =      load_json(model.metrics_save_path							)
							a   =      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}"""] ,				__lowerCamelCase							)
							a   =      int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1							)
							self.assertEqual(len(metrics["val"]							) ,				__lowerCamelCase							)
							self.assertEqual(len(metrics["test"]							) ,				1							)
							return model




class    snake_case__							(UpperCamelCase__       ):
				"""simple docstring"""



				@parameterized.expand(
				    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY] ,				)
				def 							__UpperCAmelCase							(				self    :					List[str] ,				__lowerCamelCase    :					Optional[Any]							) ->					int:
							a   =      CHEAP_ARGS.copy()
							a   =      "translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization"
							a   =      0.1 if task == "translation" else 0

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

							a   =      module.model.get_input_embeddings()
							assert not input_embeds.weight.requires_grad
							if model == T5_TINY:
										a   =      module.model.lm_head
										assert not lm_head.weight.requires_grad
										assert (lm_head.weight == input_embeds.weight).all().item()
							elif model == FSMT_TINY:
										a   =      module.model.model
										a   =      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:
										a   =      module.model.model
										a   =      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

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





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Optional[int]:
							a   =      CHEAP_ARGS.copy()

							a   =      "summarization"
							a   =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)

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

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

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





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Union[str, Any]:
							a   =      CHEAP_ARGS.copy()

							a   =      "summarization"
							a   =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)

							a   =      BART_TINY
							a   =      self.get_auto_remove_tmp_dir()

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

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

							# --help test
							with pytest.raises(__lowerCamelCase							) as excinfo:
										with CaptureStdout() as cs:
													a   =      parser.parse_args(__lowerCamelCase							)
										assert False, "--help is expected to sys.exit"
							assert excinfo.type == SystemExit
							a   =      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
							a   =      "non_existing_scheduler"
							a   =      {f"""--lr_scheduler={unsupported_param}"""}
							with pytest.raises(__lowerCamelCase							) as excinfo:
										with CaptureStderr() as cs:
													a   =      parser.parse_args(__lowerCamelCase							)
										assert False, "invalid argument is expected to sys.exit"
							assert excinfo.type == SystemExit
							a   =      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
							a   =      "cosine"
							a   =      args_d.copy()
							a   =      supported_param
							a   =      argparse.Namespace(**__lowerCamelCase							)
							a   =      main(__lowerCamelCase							)
							assert (
							    getattr(model.hparams ,				"lr_scheduler"							) == supported_param
							), f"""lr_scheduler={supported_param} shouldn't fail"""












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					List[str]:
							a   =      CamembertModel.from_pretrained("camembert-base"							)
							model.to(__lowerCamelCase							)

							a   =      torch.tensor(
							    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] ,				device=__lowerCamelCase ,				dtype=torch.long ,				)  # J'aime le camembert !
							with torch.no_grad():
										a   =      model(__lowerCamelCase							)["last_hidden_state"]
							a   =      torch.Size((1, 10, 7_68)							)
							self.assertEqual(output.shape ,				__lowerCamelCase							)
							# compare the actual values for a slice.
							a   =      torch.tensor(
							    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] ,				device=__lowerCamelCase ,				dtype=torch.float ,				)
							# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
							# camembert.eval()
							# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

							self.assertTrue(torch.allclose(output[:, :3, :3] ,				__lowerCamelCase ,				atol=1e-4							)							)






