





"""simple docstring"""





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''': 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__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						"\n".join(_SCREAMING_SNAKE_CASE     )
		Path(_SCREAMING_SNAKE_CASE     ).open("w"     ).writelines(_SCREAMING_SNAKE_CASE     )


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    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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





class 							_lowerCamelCase   (							_lowercase			):


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


						@slow
						@require_torch_gpu
						def      snake_case_ (self		)			-> Union[str, Any]:
								UpperCamelCase =						list_models()
								UpperCamelCase =						"sshleifer"
								UpperCamelCase =						[x.modelId for x in model_list if x.modelId.startswith(__a		)]
								UpperCamelCase =						["sshleifer/blenderbot-3B", "sshleifer/blenderbot-90M"]
								UpperCamelCase =						[]
								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      snake_case_ (self		)			-> Optional[int]:
								UpperCamelCase =						{"student_encoder_layers": 2, "student_decoder_layers": 1, "no_teacher": True}
								self._test_distiller_cli(__a		)


						def      snake_case_ (self		)			-> str:
								UpperCamelCase =						{
								    "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",
								}
								UpperCamelCase =						self._test_distiller_cli(__a ,				check_contents=__a		)

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

								UpperCamelCase =						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      snake_case_ (self		)			-> Any:
								UpperCamelCase =						AutoModelForSeqaSeqLM.from_pretrained(__a		)
								UpperCamelCase ,					UpperCamelCase =						model.dummy_inputs["input_ids"], model.dummy_inputs["attention_mask"]
								UpperCamelCase =						torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]] ,				dtype=torch.long ,				device=model.device		)
								UpperCamelCase =						target_ids[:, :-1].contiguous()  # Why this line?
								UpperCamelCase =						target_ids[:, 1:].clone()  # why clone?
								UpperCamelCase =						model(
								    __a ,				attention_mask=__a ,				decoder_input_ids=__a ,				labels=__a ,				use_cache=__a		).loss

								UpperCamelCase =						model(__a ,				attention_mask=__a ,				decoder_input_ids=__a ,				use_cache=__a		).logits

								UpperCamelCase =						nn.functional.log_softmax(__a ,				dim=-1		)
								UpperCamelCase ,					UpperCamelCase =						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      snake_case_ (self		)			-> List[Any]:
								UpperCamelCase =						{
								    "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",
								}
								UpperCamelCase =						self._test_distiller_cli(__a ,				check_contents=__a		)
								assert model.model.config.model_type == "mbart"

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


						def      snake_case_ (self		)			-> Tuple:
								UpperCamelCase =						{
								    "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      snake_case_ (self		)			-> str:
								UpperCamelCase =						{
								    "teacher": T5_TINY,
								    "student": T5_TINIER,
								    "model_name_or_path": T5_TINIER,
								    "tokenizer_name": T5_TINIER,
								}
								self._test_distiller_cli(__a		)




						def      snake_case_ (self ,				__a ,				__a=True		)			-> Any:
								UpperCamelCase =						{
								    "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		)
								UpperCamelCase =						CHEAP_ARGS.copy()
								UpperCamelCase =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
								UpperCamelCase =						self.get_auto_remove_tmp_dir()

								args_d.update(data_dir=__a ,				output_dir=__a ,				**__a		)
								UpperCamelCase =						distill_main(argparse.Namespace(**__a		)		)
								if not check_contents:
										return model
								UpperCamelCase =						os.listdir(__a		)
								UpperCamelCase =						{os.path.basename(__a		) for p in contents}
								UpperCamelCase =						[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		)

								UpperCamelCase =						load_json(model.metrics_save_path		)
								UpperCamelCase =						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		)
								UpperCamelCase =						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 							_lowerCamelCase   (							_lowercase			):


						@parameterized.expand(
						    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY] ,				)
						def      snake_case_ (self ,				__a		)			-> int:
								UpperCamelCase =						CHEAP_ARGS.copy()
								UpperCamelCase =						"translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization"
								UpperCamelCase =						0.1 if task == "translation" else 0

								UpperCamelCase =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
								UpperCamelCase =						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
								UpperCamelCase =						argparse.Namespace(**__a		)
								UpperCamelCase =						main(__a		)

								UpperCamelCase =						module.model.get_input_embeddings()
								assert not input_embeds.weight.requires_grad
								if model == T5_TINY:
										UpperCamelCase =						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 =						module.model.model
										UpperCamelCase =						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 =						module.model.model
										UpperCamelCase =						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 =						load_json(module.output_dir / "text_batch.json"		)
								assert isinstance(__a ,				__a		)
								assert len(__a		) >= 4


						def      snake_case_ (self		)			-> Optional[int]:
								UpperCamelCase =						CHEAP_ARGS.copy()

								UpperCamelCase =						"summarization"
								UpperCamelCase =						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
								UpperCamelCase =						BART_TINY
								UpperCamelCase =						self.get_auto_remove_tmp_dir()
								UpperCamelCase =						args_d.copy()
								args_da.update(
								    model_name_or_path=__a ,				output_dir=__a ,				)
								UpperCamelCase =						("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
								for p in extra_model_params:
										UpperCamelCase =						0.5
								UpperCamelCase =						argparse.Namespace(**__a		)
								UpperCamelCase =						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
								UpperCamelCase =						T5_TINY
								UpperCamelCase =						self.get_auto_remove_tmp_dir()
								UpperCamelCase =						args_d.copy()
								args_da.update(
								    model_name_or_path=__a ,				output_dir=__a ,				)
								UpperCamelCase =						"encoder_layerdrop"
								UpperCamelCase =						0.5
								UpperCamelCase =						argparse.Namespace(**__a		)
								with pytest.raises(__a		) as excinfo:
										UpperCamelCase =						main(__a		)
								assert str(excinfo.value		) == F"model config doesn't have a `{unsupported_param}` attribute"




						def      snake_case_ (self		)			-> Any:
								UpperCamelCase =						CHEAP_ARGS.copy()

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

								UpperCamelCase =						BART_TINY
								UpperCamelCase =						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
								UpperCamelCase =						argparse.ArgumentParser()
								UpperCamelCase =						pl.Trainer.add_argparse_args(__a		)
								UpperCamelCase =						SummarizationModule.add_model_specific_args(__a ,				os.getcwd()		)
								UpperCamelCase =						{"--help": True}

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













"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__        =      {
    '''configuration_openai''': ['''OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OpenAIGPTConfig'''],
    '''tokenization_openai''': ['''OpenAIGPTTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      ['''OpenAIGPTTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''OpenAIGPTDoubleHeadsModel''',
						    '''OpenAIGPTForSequenceClassification''',
						    '''OpenAIGPTLMHeadModel''',
						    '''OpenAIGPTModel''',
						    '''OpenAIGPTPreTrainedModel''',
						    '''load_tf_weights_in_openai_gpt''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFOpenAIGPTDoubleHeadsModel''',
						    '''TFOpenAIGPTForSequenceClassification''',
						    '''TFOpenAIGPTLMHeadModel''',
						    '''TFOpenAIGPTMainLayer''',
						    '''TFOpenAIGPTModel''',
						    '''TFOpenAIGPTPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
						from .tokenization_openai import OpenAIGPTTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_openai_fast import OpenAIGPTTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_openai import (
												    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    OpenAIGPTDoubleHeadsModel,
												    OpenAIGPTForSequenceClassification,
												    OpenAIGPTLMHeadModel,
												    OpenAIGPTModel,
												    OpenAIGPTPreTrainedModel,
												    load_tf_weights_in_openai_gpt,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_openai import (
												    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFOpenAIGPTDoubleHeadsModel,
												    TFOpenAIGPTForSequenceClassification,
												    TFOpenAIGPTLMHeadModel,
												    TFOpenAIGPTMainLayer,
												    TFOpenAIGPTModel,
												    TFOpenAIGPTPreTrainedModel,
												)

else:
						import sys

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






