

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class   __UpperCamelCase      (   lowercase  ):
	SCREAMING_SNAKE_CASE__      			=       (VQDiffusionScheduler,)




	def  __A   (						self		:  Dict						,		**lowerCAmelCase		:  str							):


		'''simple docstring'''







		UpperCAmelCase_    			=     {
		    "num_vec_classes": 4_097,
		    "num_train_timesteps": 100,
		}

		config.update(**lowerCAmelCase							)
		return config




	def  __A   (						self		:  Optional[Any]						,		lowerCAmelCase		:  List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     4
		UpperCAmelCase_    			=     8
		UpperCAmelCase_    			=     8

		UpperCAmelCase_    			=     torch.randint(0						,		lowerCAmelCase						,		(batch_size, height * width)							)

		return sample




	@property
	def  __A   (						self		:  int							):


		'''simple docstring'''







		assert False




	def  __A   (						self		:  Optional[int]						,		lowerCAmelCase		:  List[str]							):


		'''simple docstring'''







		def model(lowerCAmelCase		:  str						,		lowerCAmelCase		:  List[str]						,		*lowerCAmelCase		:  int							):
			UpperCAmelCase_			,				UpperCAmelCase_    			=     sample.shape
			UpperCAmelCase_    			=     torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)							)
			UpperCAmelCase_    			=     F.log_softmax(logits.double()						,		dim=1							).float()
			return return_value

		return model




	def  __A   (						self		:  int							):


		'''simple docstring'''







		for timesteps in [2, 5, 100, 1_000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase							)




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


		'''simple docstring'''







		for num_vec_classes in [5, 100, 1_000, 4_000]:
			self.check_over_configs(num_vec_classes=lowerCAmelCase							)




	def  __A   (						self		:  str							):


		'''simple docstring'''







		for t in [0, 50, 99]:
			self.check_over_forward(time_step=lowerCAmelCase							)




	def  __A   (						self		:  Dict							):


		'''simple docstring'''







		pass


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)

_a:      Tuple								=     logging.getLogger()
_a:      Union[str, Any]								=     torch.cuda.is_available()
_a:      Dict								=     {
    """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  __lowerCAmelCase (  A    ,       A      ):
	UpperCAmelCase_    			=     "\n".join(A      )
	Path(A      ).open("w"      ).writelines(A      )


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


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



def  __lowerCAmelCase (  A      ):
	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   __UpperCamelCase      (   lowercase  ):




	@classmethod
	def  __A   (						cls		:  Dict							):


		'''simple docstring'''







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




	@slow
	@require_torch_gpu
	def  __A   (						self		:  List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     list_models()
		UpperCAmelCase_    			=     "sshleifer"
		UpperCAmelCase_    			=     [x.modelId for x in model_list if x.modelId.startswith(lowerCAmelCase							)]
		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(lowerCAmelCase							)
			except Exception:
				failures.append(lowerCAmelCase							)
		assert not failures, F"The following models could not be loaded through AutoConfig: {failures}"




	def  __A   (						self		:  Any							):


		'''simple docstring'''







		UpperCAmelCase_    			=     {"student_encoder_layers": 2, "student_decoder_layers": 1, "no_teacher": True}
		self._test_distiller_cli(lowerCAmelCase							)




	def  __A   (						self		:  List[Any]							):


		'''simple docstring'''







		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(lowerCAmelCase						,		check_contents=lowerCAmelCase							)

		UpperCAmelCase_    			=     list(Path(model.output_dir							).glob("*.ckpt"							)							)
		self.assertEqual(1						,		len(lowerCAmelCase							)							)
		UpperCAmelCase_    			=     list(Path(model.output_dir							).glob("**/*.bin"							)							)
		self.assertEqual(len(lowerCAmelCase							)						,		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(lowerCAmelCase						,		lowerCAmelCase						,		str(model.output_dir / "best_tfmr"							)							)
		self.assertTrue(Path(lowerCAmelCase							).exists()							)

		UpperCAmelCase_    			=     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  __A   (						self		:  List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase							)
		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(
		    lowerCAmelCase						,		attention_mask=lowerCAmelCase						,		decoder_input_ids=lowerCAmelCase						,		labels=lowerCAmelCase						,		use_cache=lowerCAmelCase							).loss

		UpperCAmelCase_    			=     model(lowerCAmelCase						,		attention_mask=lowerCAmelCase						,		decoder_input_ids=lowerCAmelCase						,		use_cache=lowerCAmelCase							).logits

		UpperCAmelCase_    			=     nn.functional.log_softmax(lowerCAmelCase						,		dim=-1							)
		UpperCAmelCase_			,				UpperCAmelCase_    			=     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  __A   (						self		:  Optional[int]							):


		'''simple docstring'''







		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(lowerCAmelCase						,		check_contents=lowerCAmelCase							)
		assert model.model.config.model_type == "mbart"

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




	def  __A   (						self		:  int							):


		'''simple docstring'''







		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(lowerCAmelCase							)




	def  __A   (						self		:  int							):


		'''simple docstring'''







		UpperCAmelCase_    			=     {
		    "teacher": T5_TINY,
		    "student": T5_TINIER,
		    "model_name_or_path": T5_TINIER,
		    "tokenizer_name": T5_TINIER,
		}
		self._test_distiller_cli(lowerCAmelCase							)




	def  __A   (						self		:  Optional[int]						,		lowerCAmelCase		:  Tuple						,		lowerCAmelCase		:  Any=True							):


		'''simple docstring'''







		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(lowerCAmelCase							)
		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=lowerCAmelCase						,		output_dir=lowerCAmelCase						,		**lowerCAmelCase							)
		UpperCAmelCase_    			=     distill_main(argparse.Namespace(**lowerCAmelCase							)							)
		if not check_contents:
			return model
		UpperCAmelCase_    			=     os.listdir(lowerCAmelCase							)
		UpperCAmelCase_    			=     {os.path.basename(lowerCAmelCase							) for p in contents}
		UpperCAmelCase_    			=     [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							)

		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}"]						,		lowerCAmelCase							)
		UpperCAmelCase_    			=     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   __UpperCamelCase      (   lowercase  ):




	@parameterized.expand(
	    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]						,		)
	def  __A   (						self		:  Any						,		lowerCAmelCase		:  Tuple							):


		'''simple docstring'''







		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=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
		UpperCAmelCase_    			=     argparse.Namespace(**lowerCAmelCase							)
		UpperCAmelCase_    			=     main(lowerCAmelCase							)

		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(lowerCAmelCase						,		lowerCAmelCase							)
		assert len(lowerCAmelCase							) >= 4




	def  __A   (						self		:  Tuple							):


		'''simple docstring'''







		UpperCAmelCase_    			=     CHEAP_ARGS.copy()

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




	def  __A   (						self		:  Dict							):


		'''simple docstring'''







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

		# --help test
		with pytest.raises(lowerCAmelCase							) as excinfo:
			with CaptureStdout() as cs:
				UpperCAmelCase_    			=     parser.parse_args(lowerCAmelCase							)
			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(lowerCAmelCase							) as excinfo:
			with CaptureStderr() as cs:
				UpperCAmelCase_    			=     parser.parse_args(lowerCAmelCase							)
			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(**lowerCAmelCase							)
		UpperCAmelCase_    			=     main(lowerCAmelCase							)
		assert (
		    getattr(model.hparams						,		"lr_scheduler"							) == supported_param
		), F"lr_scheduler={supported_param} shouldn't fail"