
'''simple docstring'''

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
				import tensorflow as tf

				from transformers.models.swin.modeling_tf_swin import (
				    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSwinForImageClassification,
				    TFSwinForMaskedImageModeling,
				    TFSwinModel,
				)


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor






class 				A       :





	def __init__(   self     :    List[str] ,							__magic_name__     :    Union[str, Any] ,							__magic_name__     :    Dict=13 ,							__magic_name__     :    Optional[Any]=32 ,							__magic_name__     :    Any=2 ,							__magic_name__     :    Optional[int]=3 ,							__magic_name__     :    Optional[Any]=16 ,							__magic_name__     :    Tuple=[1, 2, 1] ,							__magic_name__     :    Optional[Any]=[2, 2, 4] ,							__magic_name__     :    Optional[Any]=2 ,							__magic_name__     :    List[Any]=2.0 ,							__magic_name__     :    int=True ,							__magic_name__     :    List[Any]=0.0 ,							__magic_name__     :    Any=0.0 ,							__magic_name__     :    List[Any]=0.1 ,							__magic_name__     :    List[Any]="gelu" ,							__magic_name__     :    Any=False ,							__magic_name__     :    int=True ,							__magic_name__     :    int=0.02 ,							__magic_name__     :    Dict=1E-5 ,							__magic_name__     :    Dict=True ,							__magic_name__     :    Optional[int]=None ,							__magic_name__     :    List[str]=True ,							__magic_name__     :    str=10 ,							__magic_name__     :    Optional[Any]=8 ,							):


				"""simple docstring"""

				lowerCAmelCase__									=							parent
				lowerCAmelCase__									=							batch_size
				lowerCAmelCase__									=							image_size
				lowerCAmelCase__									=							patch_size
				lowerCAmelCase__									=							num_channels
				lowerCAmelCase__									=							embed_dim
				lowerCAmelCase__									=							depths
				lowerCAmelCase__									=							num_heads
				lowerCAmelCase__									=							window_size
				lowerCAmelCase__									=							mlp_ratio
				lowerCAmelCase__									=							qkv_bias
				lowerCAmelCase__									=							hidden_dropout_prob
				lowerCAmelCase__									=							attention_probs_dropout_prob
				lowerCAmelCase__									=							drop_path_rate
				lowerCAmelCase__									=							hidden_act
				lowerCAmelCase__									=							use_absolute_embeddings
				lowerCAmelCase__									=							patch_norm
				lowerCAmelCase__									=							layer_norm_eps
				lowerCAmelCase__									=							initializer_range
				lowerCAmelCase__									=							is_training
				lowerCAmelCase__									=							scope
				lowerCAmelCase__									=							use_labels
				lowerCAmelCase__									=							type_sequence_label_size
				lowerCAmelCase__									=							encoder_stride





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__									=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

				lowerCAmelCase__									=							None
				if self.use_labels:
							lowerCAmelCase__									=							ids_tensor([self.batch_size] ,							self.type_sequence_label_size							)

				lowerCAmelCase__									=							self.get_config()

				return config, pixel_values, labels





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				return SwinConfig(
				    image_size=self.image_size ,							patch_size=self.patch_size ,							num_channels=self.num_channels ,							embed_dim=self.embed_dim ,							depths=self.depths ,							num_heads=self.num_heads ,							window_size=self.window_size ,							mlp_ratio=self.mlp_ratio ,							qkv_bias=self.qkv_bias ,							hidden_dropout_prob=self.hidden_dropout_prob ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob ,							drop_path_rate=self.drop_path_rate ,							hidden_act=self.hidden_act ,							use_absolute_embeddings=self.use_absolute_embeddings ,							path_norm=self.patch_norm ,							layer_norm_eps=self.layer_norm_eps ,							initializer_range=self.initializer_range ,							encoder_stride=self.encoder_stride ,							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any] ,							__magic_name__     :    List[str] ,							__magic_name__     :    List[Any] ,							__magic_name__     :    str							):


				"""simple docstring"""

				lowerCAmelCase__									=							TFSwinModel(config=lowercase_							)
				lowerCAmelCase__									=							model(lowercase_							)

				lowerCAmelCase__									=							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
				lowerCAmelCase__									=							int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

				self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, expected_seq_len, expected_dim)							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str ,							__magic_name__     :    int ,							__magic_name__     :    List[str] ,							__magic_name__     :    Dict							):


				"""simple docstring"""

				lowerCAmelCase__									=							TFSwinForMaskedImageModeling(config=lowercase_							)
				lowerCAmelCase__									=							model(lowercase_							)
				self.parent.assertEqual(
				    result.logits.shape ,							(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

				# test greyscale images
				lowerCAmelCase__									=							1
				lowerCAmelCase__									=							TFSwinForMaskedImageModeling(lowercase_							)

				lowerCAmelCase__									=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
				lowerCAmelCase__									=							model(lowercase_							)
				self.parent.assertEqual(result.logits.shape ,							(self.batch_size, 1, self.image_size, self.image_size)							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any ,							__magic_name__     :    str ,							__magic_name__     :    List[Any] ,							__magic_name__     :    Tuple							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.type_sequence_label_size
				lowerCAmelCase__									=							TFSwinForImageClassification(lowercase_							)
				lowerCAmelCase__									=							model(lowercase_ ,							labels=lowercase_							)
				self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.type_sequence_label_size)							)

				# test greyscale images
				lowerCAmelCase__									=							1
				lowerCAmelCase__									=							TFSwinForImageClassification(lowercase_							)
				lowerCAmelCase__									=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
				lowerCAmelCase__									=							model(lowercase_							)
				self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.type_sequence_label_size)							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.prepare_config_and_inputs()
				lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__									=							config_and_inputs
				lowerCAmelCase__									=							{"pixel_values": pixel_values}
				return config, inputs_dict






@require_tf
class 				A       (		SCREAMING_SNAKE_CASE_					, SCREAMING_SNAKE_CASE_					, unittest.TestCase      ):
	snake_case__ :List[Any]				       =       (
	    (
	        TFSwinModel,
	        TFSwinForImageClassification,
	        TFSwinForMaskedImageModeling,
	    )
	    if is_tf_available()
	    else ()
	)
	snake_case__ :Optional[Any]				       =       (
	    {"feature-extraction": TFSwinModel, "image-classification": TFSwinForImageClassification}
	    if is_tf_available()
	    else {}
	)

	snake_case__ :List[str]				       =       False
	snake_case__ :List[str]				       =       False
	snake_case__ :Optional[Any]				       =       False
	snake_case__ :Tuple				       =       False





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Dict							):


				"""simple docstring"""

				lowerCAmelCase__									=							TFSwinModelTester(self							)
				lowerCAmelCase__									=							ConfigTester(self ,							config_class=lowercase_ ,							embed_dim=37							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any]							):


				"""simple docstring"""

				self.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				return





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase_							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*lowercase_							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase_							)





	@unittest.skip(reason="Swin does not use inputs_embeds"							)
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int]							):


				"""simple docstring"""

				pass





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__		,lowerCAmelCase__									=							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							lowerCAmelCase__									=							model_class(lowercase_							)
							self.assertIsInstance(model.get_input_embeddings() ,							tf.keras.layers.Layer							)
							lowerCAmelCase__									=							model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(lowercase_ ,							tf.keras.layers.Dense							)							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Dict							):


				"""simple docstring"""

				lowerCAmelCase__		,lowerCAmelCase__									=							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							lowerCAmelCase__									=							model_class(lowercase_							)
							lowerCAmelCase__									=							inspect.signature(model.call							)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							lowerCAmelCase__									=							[*signature.parameters.keys()]

							lowerCAmelCase__									=							["pixel_values"]
							self.assertListEqual(arg_names[:1] ,							lowercase_							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				lowerCAmelCase__		,lowerCAmelCase__									=							self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase__									=							True

				for model_class in self.all_model_classes:
							lowerCAmelCase__									=							True
							lowerCAmelCase__									=							False
							lowerCAmelCase__									=							True
							lowerCAmelCase__									=							model_class(lowercase_							)
							lowerCAmelCase__									=							model(**self._prepare_for_class(lowercase_ ,							lowercase_							)							)
							lowerCAmelCase__									=							outputs.attentions
							lowerCAmelCase__									=							len(self.model_tester.depths							)
							self.assertEqual(len(lowercase_							) ,							lowercase_							)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							lowerCAmelCase__									=							True
							lowerCAmelCase__									=							config.window_size**2
							lowerCAmelCase__									=							model_class(lowercase_							)
							lowerCAmelCase__									=							model(**self._prepare_for_class(lowercase_ ,							lowercase_							)							)
							lowerCAmelCase__									=							outputs.attentions
							self.assertEqual(len(lowercase_							) ,							lowercase_							)

							self.assertListEqual(
							    list(attentions[0].shape[-3:]							) ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,							)
							lowerCAmelCase__									=							len(lowercase_							)

							# Check attention is always last and order is fine
							lowerCAmelCase__									=							True
							lowerCAmelCase__									=							True
							lowerCAmelCase__									=							model_class(lowercase_							)
							lowerCAmelCase__									=							model(**self._prepare_for_class(lowercase_ ,							lowercase_							)							)

							if hasattr(self.model_tester ,							"num_hidden_states_types"							):
										lowerCAmelCase__									=							self.model_tester.num_hidden_states_types
							else:
										# also another +1 for reshaped_hidden_states
										lowerCAmelCase__									=							2
							self.assertEqual(out_len + added_hidden_states ,							len(lowercase_							)							)

							lowerCAmelCase__									=							outputs.attentions

							self.assertEqual(len(lowercase_							) ,							lowercase_							)

							self.assertListEqual(
							    list(self_attentions[0].shape[-3:]							) ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str] ,							__magic_name__     :    Dict ,							__magic_name__     :    List[str] ,							__magic_name__     :    int ,							__magic_name__     :    List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							model_class(lowercase_							)
				lowerCAmelCase__									=							model(**self._prepare_for_class(lowercase_ ,							lowercase_							)							)
				lowerCAmelCase__									=							outputs.hidden_states

				lowerCAmelCase__									=							getattr(
				    self.model_tester ,							"expected_num_hidden_layers" ,							len(self.model_tester.depths							) + 1							)
				self.assertEqual(len(lowercase_							) ,							lowercase_							)

				# Swin has a different seq_length
				lowerCAmelCase__									=							to_atuple(config.patch_size							)

				lowerCAmelCase__									=							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

				self.assertListEqual(
				    list(hidden_states[0].shape[-2:]							) ,							[num_patches, self.model_tester.embed_dim] ,							)

				lowerCAmelCase__									=							outputs.reshaped_hidden_states
				self.assertEqual(len(lowercase_							) ,							lowercase_							)

				lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__									=							reshaped_hidden_states[0].shape

				lowerCAmelCase__									=							tf.reshape(reshaped_hidden_states[0] ,							(batch_size, num_channels, height * width)							)
				lowerCAmelCase__									=							tf.transpose(lowercase_ ,							(0, 2, 1)							)

				self.assertListEqual(
				    list(reshaped_hidden_states.shape[-2:]							) ,							[num_patches, self.model_tester.embed_dim] ,							)





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


				"""simple docstring"""

				lowerCAmelCase__		,lowerCAmelCase__									=							self.model_tester.prepare_config_and_inputs_for_common()

				lowerCAmelCase__									=							to_atuple(self.model_tester.image_size							)

				for model_class in self.all_model_classes:
							lowerCAmelCase__									=							True
							self.check_hidden_states_output(lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_							)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							lowerCAmelCase__									=							True

							self.check_hidden_states_output(lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Dict							):


				"""simple docstring"""

				lowerCAmelCase__		,lowerCAmelCase__									=							self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase__									=							3

				lowerCAmelCase__									=							to_atuple(self.model_tester.image_size							)
				lowerCAmelCase__									=							to_atuple(config.patch_size							)

				lowerCAmelCase__									=							image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
				lowerCAmelCase__									=							image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

				for model_class in self.all_model_classes:
							lowerCAmelCase__									=							True
							self.check_hidden_states_output(lowercase_ ,							lowercase_ ,							lowercase_ ,							(padded_height, padded_width)							)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							lowerCAmelCase__									=							True
							self.check_hidden_states_output(lowercase_ ,							lowercase_ ,							lowercase_ ,							(padded_height, padded_width)							)







	@slow
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

				for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCAmelCase__									=							TFSwinModel.from_pretrained(lowercase_							)
							self.assertIsNotNone(lowercase_							)






@require_vision
@require_tf
class 				A       (		unittest.TestCase      ):





	@cached_property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any]							):


				"""simple docstring"""

				return (
				    AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"							)
				    if is_vision_available()
				    else None
				)







	@slow
	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__									=							TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"							)
				lowerCAmelCase__									=							self.default_image_processor

				lowerCAmelCase__									=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
				lowerCAmelCase__									=							image_processor(images=lowercase_ ,							return_tensors="tf"							)

				# forward pass
				lowerCAmelCase__									=							model(lowercase_							)

				# verify the logits
				lowerCAmelCase__									=							tf.TensorShape((1, 1000)							)
				self.assertEqual(outputs.logits.shape ,							lowercase_							)
				lowerCAmelCase__									=							tf.constant([-0.0948, -0.6454, -0.0921]							)
				self.assertTrue(np.allclose(outputs.logits[0, :3] ,							lowercase_ ,							atol=1E-4							)							)







'''simple docstring'''

import sys
from typing import Tuple

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

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class 				A       :





	def __init__(   self     :    Union[str, Any] ,							__magic_name__     :    Union[str, Any] ,							__magic_name__     :    Optional[Any]=sys.maxsize							):


				"""simple docstring"""

				lowerCAmelCase__									=							"bilinear"
				lowerCAmelCase__									=							max_size
				lowerCAmelCase__									=							short_edge_length







	def __call__(   self     :    List[str] ,							__magic_name__     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							[]
				for img in imgs:
							lowerCAmelCase__		,lowerCAmelCase__									=							img.shape[:2]
							# later: provide list and randomly choose index for resize
							lowerCAmelCase__									=							np.random.randint(self.short_edge_length[0] ,							self.short_edge_length[1] + 1							)
							if size == 0:
										return img
							lowerCAmelCase__									=							size * 1.0 / min(__magic_name__ ,							__magic_name__							)
							if h < w:
										lowerCAmelCase__		,lowerCAmelCase__									=							size, scale * w
							else:
										lowerCAmelCase__		,lowerCAmelCase__									=							scale * h, size
							if max(__magic_name__ ,							__magic_name__							) > self.max_size:
										lowerCAmelCase__									=							self.max_size * 1.0 / max(__magic_name__ ,							__magic_name__							)
										lowerCAmelCase__									=							newh * scale
										lowerCAmelCase__									=							neww * scale
							lowerCAmelCase__									=							int(neww + 0.5							)
							lowerCAmelCase__									=							int(newh + 0.5							)

							if img.dtype == np.uinta:
										lowerCAmelCase__									=							Image.fromarray(__magic_name__							)
										lowerCAmelCase__									=							pil_image.resize((neww, newh) ,							PILImageResampling.BILINEAR							)
										lowerCAmelCase__									=							np.asarray(__magic_name__							)
							else:
										lowerCAmelCase__									=							img.permute(2 ,							0 ,							1							).unsqueeze(0							)  # 3, 0, 1)  # hw(c) -> nchw
										lowerCAmelCase__									=							nn.functional.interpolate(
										    __magic_name__ ,							(newh, neww) ,							mode=self.interp_method ,							align_corners=__magic_name__							).squeeze(0							)
							img_augs.append(__magic_name__							)

				return img_augs






class 				A       :





	def __init__(   self     :    str ,							__magic_name__     :    Union[str, Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] ,							cfg.INPUT.MAX_SIZE_TEST							)
				lowerCAmelCase__									=							cfg.INPUT.FORMAT
				lowerCAmelCase__									=							cfg.SIZE_DIVISIBILITY
				lowerCAmelCase__									=							cfg.PAD_VALUE
				lowerCAmelCase__									=							cfg.INPUT.MAX_SIZE_TEST
				lowerCAmelCase__									=							cfg.MODEL.DEVICE
				lowerCAmelCase__									=							torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							) ,							1 ,							1							)
				lowerCAmelCase__									=							torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							) ,							1 ,							1							)
				lowerCAmelCase__									=							lambda __magic_name__							: (x - self.pixel_mean) / self.pixel_std





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							tuple(max(__magic_name__							) for s in zip(*[img.shape for img in images]							)							)
				lowerCAmelCase__									=							[im.shape[-2:] for im in images]
				lowerCAmelCase__									=							[
				    nn.functional.pad(
				        __magic_name__ ,							[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] ,							value=self.pad_value ,							)
				    for size, im in zip(__magic_name__ ,							__magic_name__							)
				]

				return torch.stack(__magic_name__							), torch.tensor(__magic_name__							)







	def __call__(   self     :    List[str] ,							__magic_name__     :    int ,							__magic_name__     :    Optional[int]=False							):


				"""simple docstring"""

				with torch.no_grad():
							if not isinstance(__magic_name__ ,							__magic_name__							):
										lowerCAmelCase__									=							[images]
							if single_image:
										assert len(__magic_name__							) == 1
							for i in range(len(__magic_name__							)							):
										if isinstance(images[i] ,							torch.Tensor							):
													images.insert(__magic_name__ ,							images.pop(__magic_name__							).to(self.device							).float()							)
										elif not isinstance(images[i] ,							torch.Tensor							):
													images.insert(
													    __magic_name__ ,							torch.as_tensor(img_tensorize(images.pop(__magic_name__							) ,							input_format=self.input_format							)							)
													    .to(self.device							)
													    .float() ,							)
            # resize smallest edge
							lowerCAmelCase__									=							torch.tensor([im.shape[:2] for im in images]							)
							lowerCAmelCase__									=							self.aug(__magic_name__							)
							# transpose images and convert to torch tensors
							# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
							# now normalize before pad to avoid useless arithmetic
							lowerCAmelCase__									=							[self.normalizer(__magic_name__							) for x in images]
							# now pad them to do the following operations
							lowerCAmelCase__		,lowerCAmelCase__									=							self.pad(__magic_name__							)
							# Normalize

							if self.size_divisibility > 0:
										raise NotImplementedError()
							# pad
							lowerCAmelCase__									=							torch.true_divide(__magic_name__ ,							__magic_name__							)
							if single_image:
										return images[0], sizes[0], scales_yx[0]
							else:
										return images, sizes, scales_yx







def 					A       ( UpperCamelCase_   :       int  ,		UpperCamelCase_   :       Optional[Any] )   ->    List[str]:







			'''simple docstring'''



			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes







def 					A       ( UpperCamelCase_   :       Optional[Any]  ,		UpperCamelCase_   :       Tuple[int, int] )   ->    Tuple:







			'''simple docstring'''



			assert torch.isfinite(UpperCamelCase_ ).all(), "Box tensor contains infinite or NaN!"
			lowerCAmelCase__		,lowerCAmelCase__									=							box_size
			tensor[:, 0].clamp_(min=0  ,		max=UpperCamelCase_ )
			tensor[:, 1].clamp_(min=0  ,		max=UpperCamelCase_ )
			tensor[:, 2].clamp_(min=0  ,		max=UpperCamelCase_ )
			tensor[:, 3].clamp_(min=0  ,		max=UpperCamelCase_ )





