



"""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)

lowercase__						:							Optional[Any]        =      logging.getLogger()
lowercase__						:							int        =      torch.cuda.is_available()
lowercase__						:							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""": 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-0_8,
    """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 							UpperCamelCase_       (       lowerCAmelCase__ :							Path       ,  lowerCAmelCase__ :							list		)		->  List[str]:

  """simple docstring"""




  lowerCAmelCase_  :							List[Any]								=					'\n'.join(lowerCAmelCase__		)
  Path(lowerCAmelCase__		).open('w'		).writelines(lowerCAmelCase__		)


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


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

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Dict		)		->  List[Any]:

  """simple docstring"""




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







class  UpperCamelCase__  (      lowercase_    ):




      """simple docstring"""




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



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



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]			):
        lowerCAmelCase_  :							Optional[int]								=					{'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
        self._test_distiller_cli(SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple			):
        lowerCAmelCase_  :							Union[str, Any]								=					{
            '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_  :							Optional[Any]								=					self._test_distiller_cli(SCREAMING_SNAKE_CASE_       ,     check_contents=SCREAMING_SNAKE_CASE_			)

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

        lowerCAmelCase_  :							Dict								=					self.get_auto_remove_tmp_dir()
        convert_pl_to_hf(ckpts[0]       ,     transformer_ckpts[0].parent       ,     SCREAMING_SNAKE_CASE_			)
        assert os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_       ,     'pytorch_model.bin'			)			)



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

        lowerCAmelCase_  :							Optional[Any]								=					model(SCREAMING_SNAKE_CASE_       ,     attention_mask=SCREAMING_SNAKE_CASE_       ,     decoder_input_ids=SCREAMING_SNAKE_CASE_       ,     use_cache=SCREAMING_SNAKE_CASE_			).logits

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



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[Any]			):
        lowerCAmelCase_  :							List[str]								=					{
            '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_  :							Optional[int]								=					self._test_distiller_cli(SCREAMING_SNAKE_CASE_       ,     check_contents=SCREAMING_SNAKE_CASE_			)
        assert model.model.config.model_type == "mbart"

        lowerCAmelCase_  :							Dict								=					list(Path(model.output_dir			).glob('*.ckpt'			)			)
        self.assertEqual(1       ,     len(SCREAMING_SNAKE_CASE_			)			)
        lowerCAmelCase_  :							Tuple								=					list(Path(model.output_dir			).glob('**/*.bin'			)			)
        lowerCAmelCase_  :							Optional[int]								=					list(Path(model.output_dir			).glob('best_tfmr/*'			)			)
        assert len(SCREAMING_SNAKE_CASE_			) > 2
        self.assertEqual(len(SCREAMING_SNAKE_CASE_			)       ,     2			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple			):
        lowerCAmelCase_  :							int								=					{
            '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(SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple			):
        lowerCAmelCase_  :							Optional[int]								=					{
            'teacher': T5_TINY,
            'student': T5_TINIER,
            'model_name_or_path': T5_TINIER,
            'tokenizer_name': T5_TINIER,
        }
        self._test_distiller_cli(SCREAMING_SNAKE_CASE_			)





      def 	SCREAMING_SNAKE_CASE__					( self							: Union[str, Any]       ,     SCREAMING_SNAKE_CASE_							: List[Any]       ,     SCREAMING_SNAKE_CASE_							: Union[str, Any]=True			):
        lowerCAmelCase_  :							Dict								=					{
            '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(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							dict								=					CHEAP_ARGS.copy()
        lowerCAmelCase_  :							List[str]								=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
        lowerCAmelCase_  :							List[Any]								=					self.get_auto_remove_tmp_dir()

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

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

        lowerCAmelCase_  :							List[Any]								=					load_json(model.metrics_save_path			)
        lowerCAmelCase_  :							Dict								=					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}"]       ,     SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Dict								=					int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1			)
        self.assertEqual(len(metrics['val']			)       ,     SCREAMING_SNAKE_CASE_			)
        self.assertEqual(len(metrics['test']			)       ,     1			)
        return model







class  UpperCamelCase__  (      lowercase_    ):




      """simple docstring"""




      @parameterized.expand(
          [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]       ,     )
      def 	SCREAMING_SNAKE_CASE__					( self							: str       ,     SCREAMING_SNAKE_CASE_							: str			):
        lowerCAmelCase_  :							dict								=					CHEAP_ARGS.copy()
        lowerCAmelCase_  :							int								=					'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
        lowerCAmelCase_  :							List[str]								=					0.1 if task == 'translation' else 0

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

        lowerCAmelCase_  :							List[str]								=					module.model.get_input_embeddings()
        assert not input_embeds.weight.requires_grad
        if model == T5_TINY:
          lowerCAmelCase_  :							Optional[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:
          lowerCAmelCase_  :							Optional[Any]								=					module.model.model
          lowerCAmelCase_  :							Optional[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:
          lowerCAmelCase_  :							Optional[Any]								=					module.model.model
          lowerCAmelCase_  :							int								=					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_  :							List[Any]								=					load_json(module.output_dir / 'text_batch.json'			)
        assert isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
        assert len(SCREAMING_SNAKE_CASE_			) >= 4



      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[Any]			):
        lowerCAmelCase_  :							dict								=					CHEAP_ARGS.copy()

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

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

        # test models whose config includes the extra_model_args
        lowerCAmelCase_  :							List[str]								=					BART_TINY
        lowerCAmelCase_  :							List[Any]								=					self.get_auto_remove_tmp_dir()
        lowerCAmelCase_  :							Dict								=					args_d.copy()
        args_da.update(
            model_name_or_path=SCREAMING_SNAKE_CASE_       ,     output_dir=SCREAMING_SNAKE_CASE_       ,     )
        lowerCAmelCase_  :							Optional[int]								=					('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
        for p in extra_model_params:
          lowerCAmelCase_  :							str								=					0.5
        lowerCAmelCase_  :							Union[str, Any]								=					argparse.Namespace(**SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Any								=					main(SCREAMING_SNAKE_CASE_			)
        for p in extra_model_params:
          assert getattr(model.config       ,     SCREAMING_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
        lowerCAmelCase_  :							Union[str, Any]								=					T5_TINY
        lowerCAmelCase_  :							int								=					self.get_auto_remove_tmp_dir()
        lowerCAmelCase_  :							Optional[int]								=					args_d.copy()
        args_da.update(
            model_name_or_path=SCREAMING_SNAKE_CASE_       ,     output_dir=SCREAMING_SNAKE_CASE_       ,     )
        lowerCAmelCase_  :							int								=					'encoder_layerdrop'
        lowerCAmelCase_  :							str								=					0.5
        lowerCAmelCase_  :							str								=					argparse.Namespace(**SCREAMING_SNAKE_CASE_			)
        with pytest.raises(SCREAMING_SNAKE_CASE_			) as excinfo:
          lowerCAmelCase_  :							Any								=					main(SCREAMING_SNAKE_CASE_			)
        assert str(excinfo.value			) == F"model config doesn't have a `{unsupported_param}` attribute"





      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]			):
        lowerCAmelCase_  :							dict								=					CHEAP_ARGS.copy()

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

        lowerCAmelCase_  :							List[str]								=					BART_TINY
        lowerCAmelCase_  :							int								=					self.get_auto_remove_tmp_dir()

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

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

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










"""simple docstring"""







import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


lowercase__						:							Any        =      logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class  UpperCamelCase__  (      lowercase_    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	42
      _SCREAMING_SNAKE_CASE    							=	None




def 							UpperCamelCase_       (       lowerCAmelCase__ :							List[Any]       ,  lowerCAmelCase__ :							Optional[Any]=0.999       ,  lowerCAmelCase__ :							Optional[int]="cosine"       ,  )		->  Optional[Any]:

  """simple docstring"""




  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowerCAmelCase__ :							Optional[Any]		):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(lowerCAmelCase__ :							Union[str, Any]		):
      return math.exp(t * -12.0		)

  else:
    raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"		)

  lowerCAmelCase_  :							List[str]								=					[]
  for i in range(lowerCAmelCase__		):
    lowerCAmelCase_  :							str								=					i / num_diffusion_timesteps
    lowerCAmelCase_  :							Optional[int]								=					(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(lowerCAmelCase__		) / alpha_bar_fn(lowerCAmelCase__		)       ,  lowerCAmelCase__		)		)
  return torch.tensor(lowerCAmelCase__       ,  dtype=torch.floataa		)







class  UpperCamelCase__  (      lowercase_,   lowercase_    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	[e.name for e in KarrasDiffusionSchedulers]
      _SCREAMING_SNAKE_CASE    							=	1



      @register_to_config
      def __init__( self							: str       ,     SCREAMING_SNAKE_CASE_							: int = 1_0_0_0       ,     SCREAMING_SNAKE_CASE_							: float = 0.00_01       ,     SCREAMING_SNAKE_CASE_							: float = 0.02       ,     SCREAMING_SNAKE_CASE_							: str = "linear"       ,     SCREAMING_SNAKE_CASE_							: Optional[Union[np.ndarray, List[float]]] = None       ,     SCREAMING_SNAKE_CASE_							: str = "epsilon"       ,     SCREAMING_SNAKE_CASE_							: str = "linspace"       ,     SCREAMING_SNAKE_CASE_							: int = 0       ,     ):
        if trained_betas is not None:
          lowerCAmelCase_  :							int								=					torch.tensor(SCREAMING_SNAKE_CASE_       ,     dtype=torch.floataa			)
        elif beta_schedule == "linear":
          lowerCAmelCase_  :							Union[str, Any]								=					torch.linspace(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     dtype=torch.floataa			)
        elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          lowerCAmelCase_  :							Dict								=					(
              torch.linspace(beta_start**0.5       ,     beta_end**0.5       ,     SCREAMING_SNAKE_CASE_       ,     dtype=torch.floataa			) ** 2
          )
        elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          lowerCAmelCase_  :							Optional[int]								=					betas_for_alpha_bar(SCREAMING_SNAKE_CASE_			)
        else:
          raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"			)

        lowerCAmelCase_  :							str								=					1.0 - self.betas
        lowerCAmelCase_  :							List[Any]								=					torch.cumprod(self.alphas       ,     dim=0			)

        lowerCAmelCase_  :							Dict								=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
        lowerCAmelCase_  :							Optional[int]								=					np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
        lowerCAmelCase_  :							Optional[int]								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			)

        # setable values
        lowerCAmelCase_  :							Union[str, Any]								=					None
        lowerCAmelCase_  :							List[Any]								=					np.linspace(0       ,     num_train_timesteps - 1       ,     SCREAMING_SNAKE_CASE_       ,     dtype=SCREAMING_SNAKE_CASE_			)[::-1].copy()
        lowerCAmelCase_  :							int								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							List[str]								=					False



      @property
      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple			):
        # standard deviation of the initial noise distribution
        if self.config.timestep_spacing in ["linspace", "trailing"]:
          return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5



      def 	SCREAMING_SNAKE_CASE__					( self							: int       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: Union[float, torch.FloatTensor]			):
        if isinstance(SCREAMING_SNAKE_CASE_       ,     torch.Tensor			):
          lowerCAmelCase_  :							List[Any]								=					timestep.to(self.timesteps.device			)
        lowerCAmelCase_  :							Any								=					(self.timesteps == timestep).nonzero().item()
        lowerCAmelCase_  :							List[str]								=					self.sigmas[step_index]
        lowerCAmelCase_  :							Optional[int]								=					sample / ((sigma**2 + 1) ** 0.5)
        lowerCAmelCase_  :							Dict								=					True
        return sample



      def 	SCREAMING_SNAKE_CASE__					( self							: str       ,     SCREAMING_SNAKE_CASE_							: int       ,     SCREAMING_SNAKE_CASE_							: Union[str, torch.device] = None			):
        lowerCAmelCase_  :							Optional[int]								=					num_inference_steps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
          lowerCAmelCase_  :							Any								=					np.linspace(0       ,     self.config.num_train_timesteps - 1       ,     SCREAMING_SNAKE_CASE_       ,     dtype=SCREAMING_SNAKE_CASE_			)[
              ::-1
          ].copy()
        elif self.config.timestep_spacing == "leading":
          lowerCAmelCase_  :							int								=					self.config.num_train_timesteps // self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          lowerCAmelCase_  :							List[Any]								=					(np.arange(0       ,     SCREAMING_SNAKE_CASE_			) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_			)
          timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
          lowerCAmelCase_  :							List[Any]								=					self.config.num_train_timesteps / self.num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # casting to int to avoid issues when num_inference_step is power of 3
          lowerCAmelCase_  :							int								=					(np.arange(self.config.num_train_timesteps       ,     0       ,     -step_ratio			)).round().copy().astype(SCREAMING_SNAKE_CASE_			)
          timesteps -= 1
        else:
          raise ValueError(
              F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."			)

        lowerCAmelCase_  :							Optional[int]								=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
        lowerCAmelCase_  :							List[str]								=					np.interp(SCREAMING_SNAKE_CASE_       ,     np.arange(0       ,     len(SCREAMING_SNAKE_CASE_			)			)       ,     SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Union[str, Any]								=					np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
        lowerCAmelCase_  :							List[str]								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_			)
        if str(SCREAMING_SNAKE_CASE_			).startswith('mps'			):
          # mps does not support float64
          lowerCAmelCase_  :							List[str]								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_       ,     dtype=torch.floataa			)
        else:
          lowerCAmelCase_  :							List[Any]								=					torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Tuple       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: Union[float, torch.FloatTensor]       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: Optional[torch.Generator] = None       ,     SCREAMING_SNAKE_CASE_							: bool = True       ,     ):

        if (
            isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			)
            or isinstance(SCREAMING_SNAKE_CASE_       ,     torch.IntTensor			)
            or isinstance(SCREAMING_SNAKE_CASE_       ,     torch.LongTensor			)
        ):
          raise ValueError(
              (
                  'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                  ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
                  ' one of the `scheduler.timesteps` as a timestep.'
              )       ,     )

        if not self.is_scale_input_called:
          logger.warning(
              'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
              'See `StableDiffusionPipeline` for a usage example.'			)

        if isinstance(SCREAMING_SNAKE_CASE_       ,     torch.Tensor			):
          lowerCAmelCase_  :							Union[str, Any]								=					timestep.to(self.timesteps.device			)

        lowerCAmelCase_  :							Optional[int]								=					(self.timesteps == timestep).nonzero().item()
        lowerCAmelCase_  :							Dict								=					self.sigmas[step_index]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
          lowerCAmelCase_  :							List[str]								=					sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          lowerCAmelCase_  :							Tuple								=					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        elif self.config.prediction_type == "sample":
          raise NotImplementedError('prediction_type not implemented yet: sample'			)
        else:
          raise ValueError(
              F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"			)

        lowerCAmelCase_  :							Any								=					self.sigmas[step_index]
        lowerCAmelCase_  :							Union[str, Any]								=					self.sigmas[step_index + 1]
        lowerCAmelCase_  :							List[str]								=					(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
        lowerCAmelCase_  :							Dict								=					(sigma_to**2 - sigma_up**2) ** 0.5

        # 2. Convert to an ODE derivative
        lowerCAmelCase_  :							Dict								=					(sample - pred_original_sample) / sigma

        lowerCAmelCase_  :							Tuple								=					sigma_down - sigma

        lowerCAmelCase_  :							List[str]								=					sample + derivative * dt

        lowerCAmelCase_  :							Any								=					model_output.device
        lowerCAmelCase_  :							Optional[int]								=					randn_tensor(model_output.shape       ,     dtype=model_output.dtype       ,     device=SCREAMING_SNAKE_CASE_       ,     generator=SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Optional[Any]								=					prev_sample + noise * sigma_up

        if not return_dict:
          return (prev_sample,)

        return EulerAncestralDiscreteSchedulerOutput(
            prev_sample=SCREAMING_SNAKE_CASE_       ,     pred_original_sample=SCREAMING_SNAKE_CASE_			)



      def 	SCREAMING_SNAKE_CASE__					( self							: Dict       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     SCREAMING_SNAKE_CASE_							: torch.FloatTensor       ,     ):
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        lowerCAmelCase_  :							int								=					self.sigmas.to(device=original_samples.device       ,     dtype=original_samples.dtype			)
        if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_			):
          # mps does not support float64
          lowerCAmelCase_  :							Any								=					self.timesteps.to(original_samples.device       ,     dtype=torch.floataa			)
          lowerCAmelCase_  :							str								=					timesteps.to(original_samples.device       ,     dtype=torch.floataa			)
        else:
          lowerCAmelCase_  :							Dict								=					self.timesteps.to(original_samples.device			)
          lowerCAmelCase_  :							List[str]								=					timesteps.to(original_samples.device			)

        lowerCAmelCase_  :							Any								=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

        lowerCAmelCase_  :							Dict								=					sigmas[step_indices].flatten()
        while len(sigma.shape			) < len(original_samples.shape			):
          lowerCAmelCase_  :							Optional[int]								=					sigma.unsqueeze(-1			)

        lowerCAmelCase_  :							List[str]								=					original_samples + noise * sigma
        return noisy_samples





      def __len__( self							: List[Any]			):
        return self.config.num_train_timesteps





