



"""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)

__UpperCAmelCase      :      Union[str, Any]   					=					logging.getLogger()
__UpperCAmelCase      :      Optional[int]   					=					torch.cuda.is_available()
__UpperCAmelCase      :      Union[str, Any]   					=					{
    '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      (			_A,     _A							):




     """simple docstring"""
     snake_case_						:Any								=						"\n".join(_A							)
     Path(_A							).open("w"							).writelines(_A							)


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


__UpperCAmelCase      :      Any   					=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
def      A      (			_A							):




     """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 	__lowerCAmelCase							(__UpperCamelCase  ):


     '''simple docstring'''


     @classmethod
     def 		_a (      cls   ):




          """simple docstring"""





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







     @slow
     @require_torch_gpu
     def 		_a (      self   ):




          """simple docstring"""





          snake_case_						:Optional[Any]								=						list_models()
          snake_case_						:List[str]								=						"sshleifer"
          snake_case_						:Optional[Any]								=						[x.modelId for x in model_list if x.modelId.startswith(a   )]
          snake_case_						:Tuple								=						["sshleifer/blenderbot-3B", "sshleifer/blenderbot-90M"]
          snake_case_						:List[Any]								=						[]
          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 		_a (      self   ):




          """simple docstring"""





          snake_case_						:int								=						{"student_encoder_layers": 2, "student_decoder_layers": 1, "no_teacher": True}
          self._test_distiller_cli(a   )







     def 		_a (      self   ):




          """simple docstring"""





          snake_case_						:int								=						{
              "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",
          }
          snake_case_						:Optional[int]								=						self._test_distiller_cli(a    ,       check_contents=a   )

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

          snake_case_						:str								=						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 		_a (      self   ):




          """simple docstring"""





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

          snake_case_						:List[Any]								=						model(a    ,       attention_mask=a    ,       decoder_input_ids=a    ,       use_cache=a   ).logits

          snake_case_						:List[str]								=						nn.functional.log_softmax(a    ,       dim=-1   )
          snake_case_						:List[Any]								=						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 		_a (      self   ):




          """simple docstring"""





          snake_case_						:Optional[Any]								=						{
              "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",
          }
          snake_case_						:Tuple								=						self._test_distiller_cli(a    ,       check_contents=a   )
          assert model.model.config.model_type == "mbart"

          snake_case_						:Any								=						list(Path(model.output_dir   ).glob("*.ckpt"   )   )
          self.assertEqual(1    ,       len(a   )   )
          snake_case_						:Union[str, Any]								=						list(Path(model.output_dir   ).glob("**/*.bin"   )   )
          snake_case_						:Union[str, Any]								=						list(Path(model.output_dir   ).glob("best_tfmr/*"   )   )
          assert len(a   ) > 2
          self.assertEqual(len(a   )    ,       2   )







     def 		_a (      self   ):




          """simple docstring"""





          snake_case_						:Tuple								=						{
              "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 		_a (      self   ):




          """simple docstring"""





          snake_case_						:Optional[Any]								=						{
              "teacher": T5_TINY,
              "student": T5_TINIER,
              "model_name_or_path": T5_TINIER,
              "tokenizer_name": T5_TINIER,
          }
          self._test_distiller_cli(a   )







     def 		_a (      self    ,       a    ,       a=True   ):




          """simple docstring"""





          snake_case_						:Union[str, Any]								=						{
              "label_smoothing": 0.0,
              "early_stopping_patience": -1,
              "train_batch_size": 1,
              "eval_batch_size": 2,
              "max_epochs": 2,
              "alpha_mlm": 0.2,
              "alpha_ce": 0.8,
              "do_predict": True,
              "model_name_or_path": "sshleifer/tinier_bart",
              "teacher": CHEAP_ARGS["model_name_or_path"],
              "val_check_interval": 0.5,
          }
          default_updates.update(a   )
          snake_case_						:dict								=						CHEAP_ARGS.copy()
          snake_case_						:str								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
          snake_case_						:Optional[Any]								=						self.get_auto_remove_tmp_dir()

          args_d.update(data_dir=a    ,       output_dir=a    ,       **a   )
          snake_case_						:Optional[Any]								=						distill_main(argparse.Namespace(**a   )   )
          if not check_contents:
               return model
          snake_case_						:Union[str, Any]								=						os.listdir(a   )
          snake_case_						:List[str]								=						{os.path.basename(a   ) for p in contents}
          snake_case_						:Dict								=						[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   )

          snake_case_						:Dict								=						load_json(model.metrics_save_path   )
          snake_case_						:Optional[int]								=						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   )
          snake_case_						:List[Any]								=						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							(__UpperCamelCase  ):


     '''simple docstring'''


     @parameterized.expand(
         [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]    ,       )
     def 		_a (      self    ,       a   ):




          """simple docstring"""





          snake_case_						:dict								=						CHEAP_ARGS.copy()
          snake_case_						:Optional[int]								=						"translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization"
          snake_case_						:Tuple								=						0.1 if task == "translation" else 0

          snake_case_						:str								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
          snake_case_						:Tuple								=						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
          snake_case_						:List[str]								=						argparse.Namespace(**a   )
          snake_case_						:Optional[int]								=						main(a   )

          snake_case_						:Union[str, Any]								=						module.model.get_input_embeddings()
          assert not input_embeds.weight.requires_grad
          if model == T5_TINY:
               snake_case_						:Union[str, 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:
               snake_case_						:List[str]								=						module.model.model
               snake_case_						:Union[str, 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:
               snake_case_						:Any								=						module.model.model
               snake_case_						:Any								=						bart.decoder.embed_positions
               assert not embed_pos.weight.requires_grad
               assert not bart.shared.weight.requires_grad
               # check that embeds are the same
               assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
               assert bart.decoder.embed_tokens == bart.shared

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







     def 		_a (      self   ):




          """simple docstring"""





          snake_case_						:dict								=						CHEAP_ARGS.copy()

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







     def 		_a (      self   ):




          """simple docstring"""





          snake_case_						:dict								=						CHEAP_ARGS.copy()

          snake_case_						:Tuple								=						"summarization"
          snake_case_						:Union[str, Any]								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )

          snake_case_						:Optional[int]								=						BART_TINY
          snake_case_						:int								=						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
          snake_case_						:str								=						argparse.ArgumentParser()
          snake_case_						:Union[str, Any]								=						pl.Trainer.add_argparse_args(a   )
          snake_case_						:str								=						SummarizationModule.add_model_specific_args(a    ,       os.getcwd()   )
          snake_case_						:List[Any]								=						{"--help": True}

          # --help test
          with pytest.raises(a   ) as excinfo:
               with CaptureStdout() as cs:
                    snake_case_						:Any								=						parser.parse_args(a   )
               assert False, "--help is expected to sys.exit"
          assert excinfo.type == SystemExit
          snake_case_						:int								=						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
          snake_case_						:Any								=						"non_existing_scheduler"
          snake_case_						:int								=						{F'''--lr_scheduler={unsupported_param}'''}
          with pytest.raises(a   ) as excinfo:
               with CaptureStderr() as cs:
                    snake_case_						:Dict								=						parser.parse_args(a   )
               assert False, "invalid argument is expected to sys.exit"
          assert excinfo.type == SystemExit
          snake_case_						:Any								=						F'''invalid choice: \'{unsupported_param}\''''
          assert expected in cs.err, F'''should have bailed on invalid choice of scheduler {unsupported_param}'''

          # --lr_scheduler=existing_scheduler test
          snake_case_						:Dict								=						"cosine"
          snake_case_						:Optional[int]								=						args_d.copy()
          snake_case_						:Union[str, Any]								=						supported_param
          snake_case_						:Optional[Any]								=						argparse.Namespace(**a   )
          snake_case_						:str								=						main(a   )
          assert (
              getattr(model.hparams    ,       "lr_scheduler"   ) == supported_param
          ), F'''lr_scheduler={supported_param} shouldn\'t fail'''






"""simple docstring"""






import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					a__        =     CONFIG_NAME







					@register_to_config
					def __init__(      self    ,       a = True    ,       a = 8    ,       a = "lanczos"    ,       a = True    ,       a = False    ,       ):




										"""simple docstring"""





										super().__init__()







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										if images.ndim == 3:
															snake_case_						:Union[str, Any]								=						images[None, ...]
										snake_case_						:Optional[int]								=						(images * 2_55).round().astype("uint8"   )
										if images.shape[-1] == 1:
															# special case for grayscale (single channel) images
															snake_case_						:List[str]								=						[Image.fromarray(image.squeeze()    ,       mode="L"   ) for image in images]
										else:
															snake_case_						:str								=						[Image.fromarray(a   ) for image in images]

										return pil_images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										if not isinstance(a    ,       a   ):
															snake_case_						:Dict								=						[images]
										snake_case_						:Optional[int]								=						[np.array(a   ).astype(np.floataa   ) / 255.0 for image in images]
										snake_case_						:Optional[int]								=						np.stack(a    ,       axis=0   )

										return images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										if images.ndim == 3:
															snake_case_						:int								=						images[..., None]

										snake_case_						:str								=						torch.from_numpy(images.transpose(0    ,       3    ,       1    ,       2   )   )
										return images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										snake_case_						:str								=						images.cpu().permute(0    ,       2    ,       3    ,       1   ).float().numpy()
										return images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										return 2.0 * images - 1.0







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										return (images / 2 + 0.5).clamp(0    ,       1   )







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										snake_case_						:List[str]								=						image.convert("RGB"   )
										return image







					def 		_a (      self    ,       a    ,       a = None    ,       a = None    ,       ):




										"""simple docstring"""





										if height is None:
															snake_case_						:Optional[Any]								=						image.height
										if width is None:
															snake_case_						:Dict								=						image.width

										snake_case_	,  snake_case_						:Optional[Any]								=						(
										    x - x % self.config.vae_scale_factor for x in (width, height)
										)  # resize to integer multiple of vae_scale_factor
										snake_case_						:Any								=						image.resize((width, height)    ,       resample=PIL_INTERPOLATION[self.config.resample]   )
										return image







					def 		_a (      self    ,       a    ,       a = None    ,       a = None    ,       ):




										"""simple docstring"""





										snake_case_						:Optional[Any]								=						(PIL.Image.Image, np.ndarray, torch.Tensor)
										if isinstance(a    ,       a   ):
															snake_case_						:str								=						[image]
										elif not (isinstance(a    ,       a   ) and all(isinstance(a    ,       a   ) for i in image   )):
															raise ValueError(
															    F'''Input is in incorrect format: {[type(a   ) for i in image]}. Currently, we only support {', '.join(a   )}'''   )

										if isinstance(image[0]    ,       PIL.Image.Image   ):
															if self.config.do_convert_rgb:
																				snake_case_						:int								=						[self.convert_to_rgb(a   ) for i in image]
															if self.config.do_resize:
																				snake_case_						:Tuple								=						[self.resize(a    ,       a    ,       a   ) for i in image]
															snake_case_						:Dict								=						self.pil_to_numpy(a   )  # to np
															snake_case_						:int								=						self.numpy_to_pt(a   )  # to pt

										elif isinstance(image[0]    ,       np.ndarray   ):
															snake_case_						:Optional[Any]								=						np.concatenate(a    ,       axis=0   ) if image[0].ndim == 4 else np.stack(a    ,       axis=0   )
															snake_case_						:List[Any]								=						self.numpy_to_pt(a   )
															snake_case_	,  snake_case_	,  snake_case_	,  snake_case_						:Optional[int]								=						image.shape
															if self.config.do_resize and (
															    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
															):
																				raise ValueError(
																				    F'''Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'''
																				    F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''   )

										elif isinstance(image[0]    ,       torch.Tensor   ):
															snake_case_						:Optional[Any]								=						torch.cat(a    ,       axis=0   ) if image[0].ndim == 4 else torch.stack(a    ,       axis=0   )
															snake_case_	,  snake_case_	,  snake_case_	,  snake_case_						:int								=						image.shape

															# don't need any preprocess if the image is latents
															if channel == 4:
																				return image

															if self.config.do_resize and (
															    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
															):
																				raise ValueError(
																				    F'''Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'''
																				    F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''   )

        # expected range [0,1], normalize to [-1,1]
										snake_case_						:Union[str, Any]								=						self.config.do_normalize
										if image.min() < 0:
															warnings.warn(
															    "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
															    F'''when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'''    ,       a    ,       )
															snake_case_						:Optional[int]								=						False

										if do_normalize:
															snake_case_						:Tuple								=						self.normalize(a   )

										return image







					def 		_a (      self    ,       a    ,       a = "pil"    ,       a = None    ,       ):




										"""simple docstring"""





										if not isinstance(a    ,       torch.Tensor   ):
															raise ValueError(
															    F'''Input for postprocessing is in incorrect format: {type(a   )}. We only support pytorch tensor'''   )
										if output_type not in ["latent", "pt", "np", "pil"]:
															snake_case_						:List[Any]								=						(
															    F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
															    "`pil`, `np`, `pt`, `latent`"
															)
															deprecate("Unsupported output_type"    ,       "1.0.0"    ,       a    ,       standard_warn=a   )
															snake_case_						:Dict								=						"np"

										if output_type == "latent":
															return image

										if do_denormalize is None:
															snake_case_						:List[Any]								=						[self.config.do_normalize] * image.shape[0]

										snake_case_						:Any								=						torch.stack(
										    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

										if output_type == "pt":
															return image

										snake_case_						:int								=						self.pt_to_numpy(a   )

										if output_type == "np":
															return image

										if output_type == "pil":
															return self.numpy_to_pil(a   )



class 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					a__        =     CONFIG_NAME







					@register_to_config
					def __init__(      self    ,       a = True    ,       a = 8    ,       a = "lanczos"    ,       a = True    ,       ):




										"""simple docstring"""





										super().__init__()







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										if images.ndim == 3:
															snake_case_						:List[Any]								=						images[None, ...]
										snake_case_						:Optional[int]								=						(images * 2_55).round().astype("uint8"   )
										if images.shape[-1] == 1:
															# special case for grayscale (single channel) images
															snake_case_						:str								=						[Image.fromarray(image.squeeze()    ,       mode="L"   ) for image in images]
										else:
															snake_case_						:Any								=						[Image.fromarray(image[:, :, :3]   ) for image in images]

										return pil_images







					@staticmethod
					def 		_a (      a   ):




										"""simple docstring"""





										return image[:, :, 1] * 2**8 + image[:, :, 2]







					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										if images.ndim == 3:
															snake_case_						:Optional[int]								=						images[None, ...]
										snake_case_						:Union[str, Any]								=						images[:, :, :, 3:]
										if images.shape[-1] == 6:
															snake_case_						:Tuple								=						(images_depth * 2_55).round().astype("uint8"   )
															snake_case_						:Optional[Any]								=						[
															    Image.fromarray(self.rgblike_to_depthmap(a   )    ,       mode="I;16"   ) for image_depth in images_depth
															]
										elif images.shape[-1] == 4:
															snake_case_						:Optional[Any]								=						(images_depth * 65535.0).astype(np.uintaa   )
															snake_case_						:List[Any]								=						[Image.fromarray(a    ,       mode="I;16"   ) for image_depth in images_depth]
										else:
															raise Exception("Not supported"   )

										return pil_images







					def 		_a (      self    ,       a    ,       a = "pil"    ,       a = None    ,       ):




										"""simple docstring"""





										if not isinstance(a    ,       torch.Tensor   ):
															raise ValueError(
															    F'''Input for postprocessing is in incorrect format: {type(a   )}. We only support pytorch tensor'''   )
										if output_type not in ["latent", "pt", "np", "pil"]:
															snake_case_						:Any								=						(
															    F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
															    "`pil`, `np`, `pt`, `latent`"
															)
															deprecate("Unsupported output_type"    ,       "1.0.0"    ,       a    ,       standard_warn=a   )
															snake_case_						:Optional[Any]								=						"np"

										if do_denormalize is None:
															snake_case_						:Any								=						[self.config.do_normalize] * image.shape[0]

										snake_case_						:Tuple								=						torch.stack(
										    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

										snake_case_						:List[str]								=						self.pt_to_numpy(a   )

										if output_type == "np":
															if image.shape[-1] == 6:
																				snake_case_						:Optional[int]								=						np.stack([self.rgblike_to_depthmap(im[:, :, 3:]   ) for im in image]    ,       axis=0   )
															else:
																				snake_case_						:Any								=						image[:, :, :, 3:]
															return image[:, :, :, :3], image_depth

										if output_type == "pil":
															return self.numpy_to_pil(a   ), self.numpy_to_depth(a   )
										else:
															raise Exception(F'''This type {output_type} is not supported'''   )

