



"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import (
				    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXLMForMultipleChoice,
				    TFXLMForQuestionAnsweringSimple,
				    TFXLMForSequenceClassification,
				    TFXLMForTokenClassification,
				    TFXLMModel,
				    TFXLMWithLMHeadModel,
				    XLMConfig,
				)







class  A_     :



						"""simple docstring"""






						def __init__(   self					:Union[str, Any]  ,    lowercase_					:Union[str, Any]  ,    )							->     int:
												UpperCAmelCase			 =					parent
												UpperCAmelCase			 =					13
												UpperCAmelCase			 =					7
												UpperCAmelCase			 =					True
												UpperCAmelCase			 =					True
												UpperCAmelCase			 =					True
												UpperCAmelCase			 =					True
												UpperCAmelCase			 =					True
												UpperCAmelCase			 =					False
												UpperCAmelCase			 =					False
												UpperCAmelCase			 =					False
												UpperCAmelCase			 =					2
												UpperCAmelCase			 =					99
												UpperCAmelCase			 =					0
												UpperCAmelCase			 =					32
												UpperCAmelCase			 =					2
												UpperCAmelCase			 =					4
												UpperCAmelCase			 =					0.1
												UpperCAmelCase			 =					0.1
												UpperCAmelCase			 =					5_12
												UpperCAmelCase			 =					16
												UpperCAmelCase			 =					2
												UpperCAmelCase			 =					0.02
												UpperCAmelCase			 =					3
												UpperCAmelCase			 =					4
												UpperCAmelCase			 =					'last'
												UpperCAmelCase			 =					True
												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					0






						def       UpperCAmelCase__	(   self					:str   )							->     str:
												UpperCAmelCase			 =					ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size   )
												UpperCAmelCase			 =					random_attention_mask([self.batch_size, self.seq_length]  ,    dtype=tf.floataa   )

												UpperCAmelCase			 =					None
												if self.use_input_lengths:
																		UpperCAmelCase			 =					(
																		    ids_tensor([self.batch_size]  ,    vocab_size=2   ) + self.seq_length - 2
																		)  # small variation of seq_length

												UpperCAmelCase			 =					None
												if self.use_token_type_ids:
																		UpperCAmelCase			 =					ids_tensor([self.batch_size, self.seq_length]  ,    self.n_langs   )

												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None
												if self.use_labels:
																		UpperCAmelCase			 =					ids_tensor([self.batch_size]  ,    self.type_sequence_label_size   )
																		UpperCAmelCase			 =					ids_tensor([self.batch_size, self.seq_length]  ,    self.num_labels   )
																		UpperCAmelCase			 =					ids_tensor([self.batch_size]  ,    2  ,    dtype=tf.floataa   )
																		UpperCAmelCase			 =					ids_tensor([self.batch_size]  ,    self.num_choices   )

												UpperCAmelCase			 =					XLMConfig(
												    vocab_size=self.vocab_size  ,    n_special=self.n_special  ,    emb_dim=self.hidden_size  ,    n_layers=self.num_hidden_layers  ,    n_heads=self.num_attention_heads  ,    dropout=self.hidden_dropout_prob  ,    attention_dropout=self.attention_probs_dropout_prob  ,    gelu_activation=self.gelu_activation  ,    sinusoidal_embeddings=self.sinusoidal_embeddings  ,    asm=self.asm  ,    causal=self.causal  ,    n_langs=self.n_langs  ,    max_position_embeddings=self.max_position_embeddings  ,    initializer_range=self.initializer_range  ,    summary_type=self.summary_type  ,    use_proj=self.use_proj  ,    bos_token_id=self.bos_token_id  ,    )

												return (
												    config,
												    input_ids,
												    token_type_ids,
												    input_lengths,
												    sequence_labels,
												    token_labels,
												    is_impossible_labels,
												    choice_labels,
												    input_mask,
												)






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:Tuple  ,    lowercase_					:Optional[Any]  ,    lowercase_					:int  ,    lowercase_					:Tuple  ,    lowercase_					:str  ,    lowercase_					:int  ,    lowercase_					:Dict  ,    lowercase_					:Any  ,    lowercase_					:str  ,    )							->     str:
												UpperCAmelCase			 =					TFXLMModel(config=lowercase_   )
												UpperCAmelCase			 =					{'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
												UpperCAmelCase			 =					model(lowercase_   )

												UpperCAmelCase			 =					[input_ids, input_mask]
												UpperCAmelCase			 =					model(lowercase_   )
												self.parent.assertEqual(result.last_hidden_state.shape  ,    (self.batch_size, self.seq_length, self.hidden_size)   )






						def       UpperCAmelCase__	(   self					:str  ,    lowercase_					:Optional[int]  ,    lowercase_					:int  ,    lowercase_					:Optional[int]  ,    lowercase_					:Optional[int]  ,    lowercase_					:Optional[Any]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Optional[Any]  ,    lowercase_					:int  ,    )							->     Optional[Any]:
												UpperCAmelCase			 =					TFXLMWithLMHeadModel(lowercase_   )

												UpperCAmelCase			 =					{'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
												UpperCAmelCase			 =					model(lowercase_   )

												UpperCAmelCase			 =					outputs

												self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.seq_length, self.vocab_size)   )






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:List[Any]  ,    lowercase_					:str  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:List[Any]  ,    lowercase_					:Tuple  ,    lowercase_					:Optional[int]  ,    lowercase_					:int  ,    lowercase_					:Dict  ,    lowercase_					:Dict  ,    )							->     Dict:
												UpperCAmelCase			 =					TFXLMForQuestionAnsweringSimple(lowercase_   )

												UpperCAmelCase			 =					{'input_ids': input_ids, 'lengths': input_lengths}

												UpperCAmelCase			 =					model(lowercase_   )

												self.parent.assertEqual(result.start_logits.shape  ,    (self.batch_size, self.seq_length)   )
												self.parent.assertEqual(result.end_logits.shape  ,    (self.batch_size, self.seq_length)   )






						def       UpperCAmelCase__	(   self					:str  ,    lowercase_					:int  ,    lowercase_					:Any  ,    lowercase_					:List[Any]  ,    lowercase_					:List[str]  ,    lowercase_					:Any  ,    lowercase_					:Tuple  ,    lowercase_					:Optional[Any]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Dict  ,    )							->     Dict:
												UpperCAmelCase			 =					TFXLMForSequenceClassification(lowercase_   )

												UpperCAmelCase			 =					{'input_ids': input_ids, 'lengths': input_lengths}

												UpperCAmelCase			 =					model(lowercase_   )

												self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.type_sequence_label_size)   )






						def       UpperCAmelCase__	(   self					:List[str]  ,    lowercase_					:Tuple  ,    lowercase_					:str  ,    lowercase_					:int  ,    lowercase_					:List[str]  ,    lowercase_					:Any  ,    lowercase_					:Optional[int]  ,    lowercase_					:List[Any]  ,    lowercase_					:int  ,    lowercase_					:Union[str, Any]  ,    )							->     Union[str, Any]:
												UpperCAmelCase			 =					self.num_labels
												UpperCAmelCase			 =					TFXLMForTokenClassification(config=lowercase_   )
												UpperCAmelCase			 =					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
												UpperCAmelCase			 =					model(lowercase_   )
												self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.seq_length, self.num_labels)   )






						def       UpperCAmelCase__	(   self					:int  ,    lowercase_					:Optional[Any]  ,    lowercase_					:Tuple  ,    lowercase_					:str  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:str  ,    lowercase_					:Optional[int]  ,    lowercase_					:Dict  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Dict  ,    )							->     Union[str, Any]:
												UpperCAmelCase			 =					self.num_choices
												UpperCAmelCase			 =					TFXLMForMultipleChoice(config=lowercase_   )
												UpperCAmelCase			 =					tf.tile(tf.expand_dims(lowercase_  ,    1   )  ,    (1, self.num_choices, 1)   )
												UpperCAmelCase			 =					tf.tile(tf.expand_dims(lowercase_  ,    1   )  ,    (1, self.num_choices, 1)   )
												UpperCAmelCase			 =					tf.tile(tf.expand_dims(lowercase_  ,    1   )  ,    (1, self.num_choices, 1)   )
												UpperCAmelCase			 =					{
												    'input_ids': multiple_choice_inputs_ids,
												    'attention_mask': multiple_choice_input_mask,
												    'token_type_ids': multiple_choice_token_type_ids,
												}
												UpperCAmelCase			 =					model(lowercase_   )
												self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.num_choices)   )






						def       UpperCAmelCase__	(   self					:Dict   )							->     Dict:
												UpperCAmelCase			 =					self.prepare_config_and_inputs()
												(
												    (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       (
												    UpperCAmelCase
												) ,       
												)			 =					config_and_inputs
												UpperCAmelCase			 =					{
												    'input_ids': input_ids,
												    'token_type_ids': token_type_ids,
												    'langs': token_type_ids,
												    'lengths': input_lengths,
												}
												return config, inputs_dict







@require_tf
class  A_     (	SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_      ,  unittest.TestCase							):



						"""simple docstring"""
						__UpperCamelCase													=				(
						    (
						        TFXLMModel,
						        TFXLMWithLMHeadModel,
						        TFXLMForSequenceClassification,
						        TFXLMForQuestionAnsweringSimple,
						        TFXLMForTokenClassification,
						        TFXLMForMultipleChoice,
						    )
						    if is_tf_available()
						    else ()
						)
						__UpperCamelCase													=				(
						    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
						)  # TODO (PVP): Check other models whether language generation is also applicable
						__UpperCamelCase													=				(
						    {
						        """feature-extraction""": TFXLMModel,
						        """fill-mask""": TFXLMWithLMHeadModel,
						        """question-answering""": TFXLMForQuestionAnsweringSimple,
						        """text-classification""": TFXLMForSequenceClassification,
						        """text-generation""": TFXLMWithLMHeadModel,
						        """token-classification""": TFXLMForTokenClassification,
						        """zero-shot""": TFXLMForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						__UpperCamelCase													=				False
						__UpperCamelCase													=				False






						def       UpperCAmelCase__	(   self					:int  ,    lowercase_					:List[str]  ,    lowercase_					:Optional[int]  ,    lowercase_					:List[str]  ,    lowercase_					:Optional[int]  ,    lowercase_					:Dict   )							->     str:
												if (
												    pipeline_test_casse_name == "QAPipelineTests"
												    and tokenizer_name is not None
												    and not tokenizer_name.endswith('Fast'   )
												):
																		# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
																		# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
																		# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
																		return True

												return False






						def       UpperCAmelCase__	(   self					:Any   )							->     Optional[Any]:
												UpperCAmelCase			 =					TFXLMModelTester(self   )
												UpperCAmelCase			 =					ConfigTester(self  ,    config_class=lowercase_  ,    emb_dim=37   )






						def       UpperCAmelCase__	(   self					:Any   )							->     Tuple:
												self.config_tester.run_common_tests()






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     int:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_xlm_model(*lowercase_   )






						def       UpperCAmelCase__	(   self					:str   )							->     int:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_xlm_lm_head(*lowercase_   )






						def       UpperCAmelCase__	(   self					:Any   )							->     Optional[Any]:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_xlm_qa(*lowercase_   )






						def       UpperCAmelCase__	(   self					:List[Any]   )							->     List[Any]:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_xlm_sequence_classif(*lowercase_   )






						def       UpperCAmelCase__	(   self					:Any   )							->     List[Any]:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_xlm_for_token_classification(*lowercase_   )






						def       UpperCAmelCase__	(   self					:str   )							->     List[Any]:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_xlm_for_multiple_choice(*lowercase_   )






						@slow
						def       UpperCAmelCase__	(   self					:Any   )							->     List[Any]:
												for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		UpperCAmelCase			 =					TFXLMModel.from_pretrained(lowercase_   )
																		self.assertIsNotNone(lowercase_   )







@require_tf
class  A_     (	unittest.TestCase							):



						"""simple docstring"""






						@slow
						def       UpperCAmelCase__	(   self					:Any   )							->     Any:
												UpperCAmelCase			 =					TFXLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'   )
												UpperCAmelCase			 =					tf.convert_to_tensor([[14, 4_47]]  ,    dtype=tf.intaa   )  # the president
												UpperCAmelCase			 =					[
												    14,
												    4_47,
												    14,
												    4_47,
												    14,
												    4_47,
												    14,
												    4_47,
												    14,
												    4_47,
												    14,
												    4_47,
												    14,
												    4_47,
												    14,
												    4_47,
												    14,
												    4_47,
												    14,
												    4_47,
												]  # the president the president the president the president the president the president the president the president the president the president
												# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
												UpperCAmelCase			 =					model.generate(lowercase_  ,    do_sample=lowercase_   )
												self.assertListEqual(output_ids[0].numpy().tolist()  ,    lowercase_   )










"""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  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				CONFIG_NAME






						@register_to_config
						def __init__(   self					:Tuple  ,    lowercase_					:bool = True  ,    lowercase_					:int = 8  ,    lowercase_					:str = "lanczos"  ,    lowercase_					:bool = True  ,    lowercase_					:bool = False  ,    )							->     Union[str, Any]:
												super().__init__()






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:np.ndarray   )							->     PIL.Image.Image:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					(images * 2_55).round().astype('uint8'   )
												if images.shape[-1] == 1:
																		# special case for grayscale (single channel) images
																		UpperCAmelCase			 =					[Image.fromarray(image.squeeze()  ,    mode='L'   ) for image in images]
												else:
																		UpperCAmelCase			 =					[Image.fromarray(lowercase_   ) for image in images]

												return pil_images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Union[List[PIL.Image.Image], PIL.Image.Image]   )							->     np.ndarray:
												if not isinstance(lowercase_  ,    lowercase_   ):
																		UpperCAmelCase			 =					[images]
												UpperCAmelCase			 =					[np.array(lowercase_   ).astype(np.floataa   ) / 255.0 for image in images]
												UpperCAmelCase			 =					np.stack(lowercase_  ,    axis=0   )

												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:np.ndarray   )							->     torch.FloatTensor:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[..., None]

												UpperCAmelCase			 =					torch.from_numpy(images.transpose(0  ,    3  ,    1  ,    2   )   )
												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:torch.FloatTensor   )							->     np.ndarray:
												UpperCAmelCase			 =					images.cpu().permute(0  ,    2  ,    3  ,    1   ).float().numpy()
												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:List[Any]   )							->     Optional[int]:
												return 2.0 * images - 1.0






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:str   )							->     str:
												return (images / 2 + 0.5).clamp(0  ,    1   )






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:PIL.Image.Image   )							->     PIL.Image.Image:
												UpperCAmelCase			 =					image.convert('RGB'   )
												return image






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:PIL.Image.Image  ,    lowercase_					:Optional[int] = None  ,    lowercase_					:Optional[int] = None  ,    )							->     PIL.Image.Image:
												if height is None:
																		UpperCAmelCase			 =					image.height
												if width is None:
																		UpperCAmelCase			 =					image.width

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






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]  ,    lowercase_					:Optional[int] = None  ,    lowercase_					:Optional[int] = None  ,    )							->     torch.Tensor:
												UpperCAmelCase			 =					(PIL.Image.Image, np.ndarray, torch.Tensor)
												if isinstance(lowercase_  ,    lowercase_   ):
																		UpperCAmelCase			 =					[image]
												elif not (isinstance(lowercase_  ,    lowercase_   ) and all(isinstance(lowercase_  ,    lowercase_   ) for i in image   )):
																		raise ValueError(
																		    f"""Input is in incorrect format: {[type(lowercase_   ) for i in image]}. Currently, we only support {', '.join(lowercase_   )}"""   )

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

												elif isinstance(image[0]  ,    np.ndarray   ):
																		UpperCAmelCase			 =					np.concatenate(lowercase_  ,    axis=0   ) if image[0].ndim == 4 else np.stack(lowercase_  ,    axis=0   )
																		UpperCAmelCase			 =					self.numpy_to_pt(lowercase_   )
																		UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					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   ):
																		UpperCAmelCase			 =					torch.cat(lowercase_  ,    axis=0   ) if image[0].ndim == 4 else torch.stack(lowercase_  ,    axis=0   )
																		UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					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]
												UpperCAmelCase			 =					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()}]"""  ,    lowercase_  ,    )
																		UpperCAmelCase			 =					False

												if do_normalize:
																		UpperCAmelCase			 =					self.normalize(lowercase_   )

												return image






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:str = "pil"  ,    lowercase_					:Optional[List[bool]] = None  ,    )							->     List[str]:
												if not isinstance(lowercase_  ,    torch.Tensor   ):
																		raise ValueError(
																		    f"""Input for postprocessing is in incorrect format: {type(lowercase_   )}. We only support pytorch tensor"""   )
												if output_type not in ["latent", "pt", "np", "pil"]:
																		UpperCAmelCase			 =					(
																		    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'  ,    lowercase_  ,    standard_warn=lowercase_   )
																		UpperCAmelCase			 =					'np'

												if output_type == "latent":
																		return image

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

												UpperCAmelCase			 =					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

												UpperCAmelCase			 =					self.pt_to_numpy(lowercase_   )

												if output_type == "np":
																		return image

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







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				CONFIG_NAME






						@register_to_config
						def __init__(   self					:Union[str, Any]  ,    lowercase_					:bool = True  ,    lowercase_					:int = 8  ,    lowercase_					:str = "lanczos"  ,    lowercase_					:bool = True  ,    )							->     List[Any]:
												super().__init__()






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Optional[int]   )							->     Optional[Any]:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					(images * 2_55).round().astype('uint8'   )
												if images.shape[-1] == 1:
																		# special case for grayscale (single channel) images
																		UpperCAmelCase			 =					[Image.fromarray(image.squeeze()  ,    mode='L'   ) for image in images]
												else:
																		UpperCAmelCase			 =					[Image.fromarray(image[:, :, :3]   ) for image in images]

												return pil_images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Any   )							->     Union[str, Any]:
												return image[:, :, 1] * 2**8 + image[:, :, 2]






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:int   )							->     Tuple:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					images[:, :, :, 3:]
												if images.shape[-1] == 6:
																		UpperCAmelCase			 =					(images_depth * 2_55).round().astype('uint8'   )
																		UpperCAmelCase			 =					[
																		    Image.fromarray(self.rgblike_to_depthmap(lowercase_   )  ,    mode='I;16'   ) for image_depth in images_depth
																		]
												elif images.shape[-1] == 4:
																		UpperCAmelCase			 =					(images_depth * 6_5535.0).astype(np.uintaa   )
																		UpperCAmelCase			 =					[Image.fromarray(lowercase_  ,    mode='I;16'   ) for image_depth in images_depth]
												else:
																		raise Exception('Not supported'   )

												return pil_images






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:str = "pil"  ,    lowercase_					:Optional[List[bool]] = None  ,    )							->     Dict:
												if not isinstance(lowercase_  ,    torch.Tensor   ):
																		raise ValueError(
																		    f"""Input for postprocessing is in incorrect format: {type(lowercase_   )}. We only support pytorch tensor"""   )
												if output_type not in ["latent", "pt", "np", "pil"]:
																		UpperCAmelCase			 =					(
																		    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'  ,    lowercase_  ,    standard_warn=lowercase_   )
																		UpperCAmelCase			 =					'np'

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

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

												UpperCAmelCase			 =					self.pt_to_numpy(lowercase_   )

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

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





