



"""simple docstring"""






import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
				from tokenizers import Tokenizer
				from tokenizers.models import WordPiece

class 	__lowerCAmelCase							(unittest.TestCase  ):


					'''simple docstring'''


					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										snake_case_						:Dict								=						list(tokenizer_class.max_model_input_sizes.keys()   )
										for model_name in sa_models[:1]:
															snake_case_						:Dict								=						tokenizer_class.from_pretrained(a   )
															self.assertIsNotNone(a   )
															self.assertIsInstance(a    ,       a   )
															self.assertIsInstance(a    ,       a   )

															for special_tok in tokenizer.all_special_tokens:
																				self.assertIsInstance(a    ,       a   )
																				snake_case_						:Optional[int]								=						tokenizer.convert_tokens_to_ids(a   )
																				self.assertIsInstance(a    ,       a   )







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




										"""simple docstring"""





										snake_case_						:Optional[int]								=						pickle.dumps(a   )
										self.assertIsNotNone(a   )

										snake_case_						:Any								=						pickle.loads(a   )

										# Ensure is_fast is correctly restored
										self.assertEqual(be_restored.is_fast    ,       be_original.is_fast   )

										# Ensure encodings are potentially correctly restored
										if be_original.is_fast:
															self.assertIsNotNone(be_restored.encodings   )
										else:
															self.assertIsNone(be_restored.encodings   )

										# Ensure the keys are the same
										for original_v, restored_v in zip(be_original.values()    ,       be_restored.values()   ):
															if equal_op:
																				self.assertTrue(equal_op(a    ,       a   )   )
															else:
																				self.assertEqual(a    ,       a   )







					@slow
					def 		_a (      self   ):




										"""simple docstring"""





										self.check_tokenizer_from_pretrained(a   )







					def 		_a (      self   ):




										"""simple docstring"""





										self.assertEqual(TensorType("tf"   )    ,       TensorType.TENSORFLOW   )
										self.assertEqual(TensorType("pt"   )    ,       TensorType.PYTORCH   )
										self.assertEqual(TensorType("np"   )    ,       TensorType.NUMPY   )







					@require_tokenizers
					def 		_a (      self   ):




										"""simple docstring"""





										import numpy as np

										snake_case_						:Union[str, Any]								=						BertTokenizer.from_pretrained("bert-base-cased"   )
										snake_case_						:Optional[int]								=						BertTokenizerFast.from_pretrained("bert-base-cased"   )

										# Python no tensor
										with self.subTest("BatchEncoding (Python, return_tensors=None)"   ):
															self.assert_dump_and_restore(tokenizer_p("Small example to encode"   )   )

										with self.subTest("BatchEncoding (Python, return_tensors=NUMPY)"   ):
															self.assert_dump_and_restore(
															    tokenizer_p("Small example to encode"    ,       return_tensors=TensorType.NUMPY   )    ,       np.array_equal   )

										with self.subTest("BatchEncoding (Rust, return_tensors=None)"   ):
															self.assert_dump_and_restore(tokenizer_r("Small example to encode"   )   )

										with self.subTest("BatchEncoding (Rust, return_tensors=NUMPY)"   ):
															self.assert_dump_and_restore(
															    tokenizer_r("Small example to encode"    ,       return_tensors=TensorType.NUMPY   )    ,       np.array_equal   )







					@require_tf
					@require_tokenizers
					def 		_a (      self   ):




										"""simple docstring"""





										import tensorflow as tf

										def tf_array_equals(a    ,       a   ):
															return tf.reduce_all(tf.equal(a    ,       a   )   )

										snake_case_						:Dict								=						BertTokenizer.from_pretrained("bert-base-cased"   )
										snake_case_						:Tuple								=						BertTokenizerFast.from_pretrained("bert-base-cased"   )

										with self.subTest("BatchEncoding (Python, return_tensors=TENSORFLOW)"   ):
															self.assert_dump_and_restore(
															    tokenizer_p("Small example to encode"    ,       return_tensors=TensorType.TENSORFLOW   )    ,       a   )

										with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"   ):
															self.assert_dump_and_restore(
															    tokenizer_r("Small example to encode"    ,       return_tensors=TensorType.TENSORFLOW   )    ,       a   )







					@require_torch
					@require_tokenizers
					def 		_a (      self   ):




										"""simple docstring"""





										import torch

										snake_case_						:Tuple								=						BertTokenizer.from_pretrained("bert-base-cased"   )
										snake_case_						:Optional[int]								=						BertTokenizerFast.from_pretrained("bert-base-cased"   )

										with self.subTest("BatchEncoding (Python, return_tensors=PYTORCH)"   ):
															self.assert_dump_and_restore(
															    tokenizer_p("Small example to encode"    ,       return_tensors=TensorType.PYTORCH   )    ,       torch.equal   )

										with self.subTest("BatchEncoding (Rust, return_tensors=PYTORCH)"   ):
															self.assert_dump_and_restore(
															    tokenizer_r("Small example to encode"    ,       return_tensors=TensorType.PYTORCH   )    ,       torch.equal   )







					@require_tokenizers
					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Union[str, Any]								=						BertTokenizer.from_pretrained("bert-base-cased"   )
										snake_case_						:int								=						BertTokenizerFast.from_pretrained("bert-base-cased"   )

										with self.subTest("Python Tokenizer"   ):
															self.assertFalse(tokenizer_p("Small example to_encode"   ).is_fast   )

										with self.subTest("Rust Tokenizer"   ):
															self.assertTrue(tokenizer_r("Small example to_encode"   ).is_fast   )







					@require_tokenizers
					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Optional[Any]								=						BertTokenizerFast.from_pretrained("bert-base-cased"   )
										snake_case_						:Optional[int]								=						tokenizer_r(["Test", "\xad", "test"]    ,       is_split_into_words=a   )

										self.assertEqual(encoded.word_to_tokens(0   )    ,       TokenSpan(start=1    ,       end=2   )   )
										self.assertEqual(encoded.word_to_tokens(1   )    ,       a   )
										self.assertEqual(encoded.word_to_tokens(2   )    ,       TokenSpan(start=2    ,       end=3   )   )







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:List[Any]								=						BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}   )
										snake_case_						:Tuple								=						batch.convert_to_tensors(tensor_type="np"   )
										self.assertEqual(tensor_batch["inputs"].shape    ,       (2, 3)   )
										self.assertEqual(tensor_batch["labels"].shape    ,       (2,)   )
										# test converting the converted
										with CaptureStderr() as cs:
															snake_case_						:Union[str, Any]								=						batch.convert_to_tensors(tensor_type="np"   )
										self.assertFalse(len(cs.err   )    ,       msg=F'''should have no warning, but got {cs.err}'''   )

										snake_case_						:str								=						BatchEncoding({"inputs": [1, 2, 3], "labels": 0}   )
										snake_case_						:Optional[Any]								=						batch.convert_to_tensors(tensor_type="np"    ,       prepend_batch_axis=a   )
										self.assertEqual(tensor_batch["inputs"].shape    ,       (1, 3)   )
										self.assertEqual(tensor_batch["labels"].shape    ,       (1,)   )







					@require_torch
					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:int								=						BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}   )
										snake_case_						:int								=						batch.convert_to_tensors(tensor_type="pt"   )
										self.assertEqual(tensor_batch["inputs"].shape    ,       (2, 3)   )
										self.assertEqual(tensor_batch["labels"].shape    ,       (2,)   )
										# test converting the converted
										with CaptureStderr() as cs:
															snake_case_						:Union[str, Any]								=						batch.convert_to_tensors(tensor_type="pt"   )
										self.assertFalse(len(cs.err   )    ,       msg=F'''should have no warning, but got {cs.err}'''   )

										snake_case_						:Tuple								=						BatchEncoding({"inputs": [1, 2, 3], "labels": 0}   )
										snake_case_						:str								=						batch.convert_to_tensors(tensor_type="pt"    ,       prepend_batch_axis=a   )
										self.assertEqual(tensor_batch["inputs"].shape    ,       (1, 3)   )
										self.assertEqual(tensor_batch["labels"].shape    ,       (1,)   )







					@require_tf
					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Optional[Any]								=						BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}   )
										snake_case_						:Optional[Any]								=						batch.convert_to_tensors(tensor_type="tf"   )
										self.assertEqual(tensor_batch["inputs"].shape    ,       (2, 3)   )
										self.assertEqual(tensor_batch["labels"].shape    ,       (2,)   )
										# test converting the converted
										with CaptureStderr() as cs:
															snake_case_						:str								=						batch.convert_to_tensors(tensor_type="tf"   )
										self.assertFalse(len(cs.err   )    ,       msg=F'''should have no warning, but got {cs.err}'''   )

										snake_case_						:Tuple								=						BatchEncoding({"inputs": [1, 2, 3], "labels": 0}   )
										snake_case_						:int								=						batch.convert_to_tensors(tensor_type="tf"    ,       prepend_batch_axis=a   )
										self.assertEqual(tensor_batch["inputs"].shape    ,       (1, 3)   )
										self.assertEqual(tensor_batch["labels"].shape    ,       (1,)   )







					@require_flax
					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:int								=						BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}   )
										snake_case_						:Optional[Any]								=						batch.convert_to_tensors(tensor_type="jax"   )
										self.assertEqual(tensor_batch["inputs"].shape    ,       (2, 3)   )
										self.assertEqual(tensor_batch["labels"].shape    ,       (2,)   )
										# test converting the converted
										with CaptureStderr() as cs:
															snake_case_						:Union[str, Any]								=						batch.convert_to_tensors(tensor_type="jax"   )
										self.assertFalse(len(cs.err   )    ,       msg=F'''should have no warning, but got {cs.err}'''   )

										snake_case_						:Optional[Any]								=						BatchEncoding({"inputs": [1, 2, 3], "labels": 0}   )
										snake_case_						:int								=						batch.convert_to_tensors(tensor_type="jax"    ,       prepend_batch_axis=a   )
										self.assertEqual(tensor_batch["inputs"].shape    ,       (1, 3)   )
										self.assertEqual(tensor_batch["labels"].shape    ,       (1,)   )







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Optional[Any]								=						[{"input_ids": np.array([0, 1, 2]   )}, {"input_ids": np.array([0, 1, 2, 3]   )}]
										snake_case_						:str								=						BertTokenizer.from_pretrained("bert-base-cased"   )

										snake_case_						:Dict								=						tokenizer.pad(a    ,       padding=a   )
										self.assertTrue(isinstance(batch["input_ids"]    ,       np.ndarray   )   )
										self.assertEqual(batch["input_ids"].tolist()    ,       [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
										snake_case_						:List[str]								=						tokenizer.pad(a    ,       padding=a    ,       return_tensors="np"   )
										self.assertTrue(isinstance(batch["input_ids"]    ,       np.ndarray   )   )
										self.assertEqual(batch["input_ids"].tolist()    ,       [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )







					@require_torch
					def 		_a (      self   ):




										"""simple docstring"""





										import torch

										snake_case_						:Optional[Any]								=						[{"input_ids": torch.tensor([0, 1, 2]   )}, {"input_ids": torch.tensor([0, 1, 2, 3]   )}]
										snake_case_						:str								=						BertTokenizer.from_pretrained("bert-base-cased"   )

										snake_case_						:Optional[int]								=						tokenizer.pad(a    ,       padding=a   )
										self.assertTrue(isinstance(batch["input_ids"]    ,       torch.Tensor   )   )
										self.assertEqual(batch["input_ids"].tolist()    ,       [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
										snake_case_						:Union[str, Any]								=						tokenizer.pad(a    ,       padding=a    ,       return_tensors="pt"   )
										self.assertTrue(isinstance(batch["input_ids"]    ,       torch.Tensor   )   )
										self.assertEqual(batch["input_ids"].tolist()    ,       [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )







					@require_tf
					def 		_a (      self   ):




										"""simple docstring"""





										import tensorflow as tf

										snake_case_						:List[str]								=						[{"input_ids": tf.constant([0, 1, 2]   )}, {"input_ids": tf.constant([0, 1, 2, 3]   )}]
										snake_case_						:Tuple								=						BertTokenizer.from_pretrained("bert-base-cased"   )

										snake_case_						:List[str]								=						tokenizer.pad(a    ,       padding=a   )
										self.assertTrue(isinstance(batch["input_ids"]    ,       tf.Tensor   )   )
										self.assertEqual(batch["input_ids"].numpy().tolist()    ,       [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
										snake_case_						:Dict								=						tokenizer.pad(a    ,       padding=a    ,       return_tensors="tf"   )
										self.assertTrue(isinstance(batch["input_ids"]    ,       tf.Tensor   )   )
										self.assertEqual(batch["input_ids"].numpy().tolist()    ,       [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )







					@require_tokenizers
					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Tuple								=						Tokenizer(WordPiece(unk_token="[UNK]"   )   )
										PreTrainedTokenizerFast(tokenizer_object=a   )







					@require_tokenizers
					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Any								=						Tokenizer(WordPiece(unk_token="[UNK]"   )   )
										with tempfile.TemporaryDirectory() as tmpdirname:
															bert_tokenizer.save(os.path.join(a    ,       "tokenizer.json"   )   )
															PreTrainedTokenizerFast(tokenizer_file=os.path.join(a    ,       "tokenizer.json"   )   )






"""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'''   )

