



import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
					import jax
					import jax.numpy as jnp

					from transformers.modeling_flax_pytorch_utils import (
					    convert_pytorch_state_dict_to_flax,
					    load_flax_weights_in_pytorch_model,
					)
					from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
					import torch

class 		UpperCAmelCase__      :




			'''simple docstring'''






			def __init__(       self							:			Any	,	a_							:			Dict	,	a_							:			int=14	,	a_							:			List[Any]=7	,	a_							:			int=True	,	a_							:			Optional[Any]=True	,	a_							:			Any=False	,	a_							:			List[Any]=True	,	a_							:			Tuple=99	,	a_							:			List[str]=32	,	a_							:			str=5	,	a_							:			Union[str, Any]=4	,	a_							:			List[Any]=37	,	a_							:			Dict="gelu"	,	a_							:			List[Any]=0.1	,	a_							:			List[str]=0.1	,	a_							:			str=5_12	,	a_							:			Dict=0.0_2	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     parent
					__UpperCAmelCase   :					Dict													=     batch_size
					__UpperCAmelCase   :					List[str]													=     seq_length
					__UpperCAmelCase   :					List[Any]													=     is_training
					__UpperCAmelCase   :					str													=     use_input_mask
					__UpperCAmelCase   :					List[str]													=     use_token_type_ids
					__UpperCAmelCase   :					List[Any]													=     use_labels
					__UpperCAmelCase   :					int													=     vocab_size
					__UpperCAmelCase   :					Optional[Any]													=     hidden_size
					__UpperCAmelCase   :					Optional[int]													=     num_hidden_layers
					__UpperCAmelCase   :					Optional[int]													=     num_attention_heads
					__UpperCAmelCase   :					Tuple													=     intermediate_size
					__UpperCAmelCase   :					Any													=     hidden_act
					__UpperCAmelCase   :					Tuple													=     hidden_dropout_prob
					__UpperCAmelCase   :					str													=     attention_probs_dropout_prob
					__UpperCAmelCase   :					str													=     max_position_embeddings
					__UpperCAmelCase   :					List[Any]													=     initializer_range
					__UpperCAmelCase   :					Optional[Any]													=     None
					__UpperCAmelCase   :					Optional[Any]													=     vocab_size - 1
					__UpperCAmelCase   :					Union[str, Any]													=     vocab_size - 1
					__UpperCAmelCase   :					int													=     vocab_size - 1




			def 			snake_case__					(       self							:			int			):
					'''simple docstring'''





					__UpperCAmelCase   :					Any													=     ids_tensor([self.batch_size, self.seq_length]	,	self.vocab_size			)

					__UpperCAmelCase   :					Dict													=     None
					if self.use_input_mask:
							__UpperCAmelCase   :					Tuple													=     random_attention_mask([self.batch_size, self.seq_length]			)

					__UpperCAmelCase   :					Optional[int]													=     GPTaConfig(
					    vocab_size=self.vocab_size	,	n_embd=self.hidden_size	,	n_layer=self.num_hidden_layers	,	n_head=self.num_attention_heads	,	n_positions=self.max_position_embeddings	,	use_cache=__UpperCamelCase	,	bos_token_id=self.bos_token_id	,	eos_token_id=self.eos_token_id	,	pad_token_id=self.pad_token_id	,	)

					return (config, input_ids, input_mask)




			def 			snake_case__					(       self							:			List[str]			):
					'''simple docstring'''





					__UpperCAmelCase   :					int													=     self.prepare_config_and_inputs()
					__UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase   :					Any													=     config_and_inputs
					__UpperCAmelCase   :					str													=     {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
					return config, inputs_dict




			def 			snake_case__					(       self							:			int			):
					'''simple docstring'''





					__UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase   :					Dict													=     self.prepare_config_and_inputs()

					__UpperCAmelCase   :					Optional[int]													=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
					__UpperCAmelCase   :					Optional[int]													=     ids_tensor([self.batch_size, self.seq_length]	,	vocab_size=2			)

					return (
					    config,
					    input_ids,
					    attention_mask,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)




			def 			snake_case__					(       self							:			Any	,	a_							:			Union[str, Any]	,	a_							:			Tuple	,	a_							:			Optional[Any]	,	a_							:			List[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Union[str, Any]													=     20
					__UpperCAmelCase   :					List[Any]													=     model_class_name(__UpperCamelCase			)

					__UpperCAmelCase   :					Any													=     model.init_cache(input_ids.shape[0]	,	__UpperCamelCase			)
					__UpperCAmelCase   :					Tuple													=     jnp.ones((input_ids.shape[0], max_decoder_length)	,	dtype='''i4'''			)

					__UpperCAmelCase   :					Optional[Any]													=     jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1			)[None, :]	,	(input_ids.shape[0], input_ids.shape[-1] - 1)			)
					__UpperCAmelCase   :					int													=     model(
					    input_ids[:, :-1]	,	attention_mask=__UpperCamelCase	,	past_key_values=__UpperCamelCase	,	position_ids=__UpperCamelCase	,	)

					__UpperCAmelCase   :					Optional[Any]													=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,	dtype='''i4'''			)
					__UpperCAmelCase   :					List[Any]													=     model(
					    input_ids[:, -1:]	,	attention_mask=__UpperCamelCase	,	past_key_values=outputs_cache.past_key_values	,	position_ids=__UpperCamelCase	,	)

					__UpperCAmelCase   :					List[Any]													=     model(__UpperCamelCase			)

					__UpperCAmelCase   :					str													=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
					self.parent.assertTrue(diff < 1e-3	,	msg=F'Max diff is {diff}'			)




			def 			snake_case__					(       self							:			Union[str, Any]	,	a_							:			Union[str, Any]	,	a_							:			List[str]	,	a_							:			Optional[Any]	,	a_							:			str			):
					'''simple docstring'''





					__UpperCAmelCase   :					Union[str, Any]													=     20
					__UpperCAmelCase   :					Optional[int]													=     model_class_name(__UpperCamelCase			)

					__UpperCAmelCase   :					Tuple													=     jnp.concatenate(
					    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])			)]	,	axis=-1	,	)

					__UpperCAmelCase   :					Tuple													=     model.init_cache(input_ids.shape[0]	,	__UpperCamelCase			)
					__UpperCAmelCase   :					Tuple													=     jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1			)[None, :]	,	(input_ids.shape[0], input_ids.shape[-1] - 1)			)

					__UpperCAmelCase   :					Tuple													=     model(
					    input_ids[:, :-1]	,	attention_mask=__UpperCamelCase	,	past_key_values=__UpperCamelCase	,	position_ids=__UpperCamelCase	,	)
					__UpperCAmelCase   :					Optional[int]													=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,	dtype='''i4'''			)
					__UpperCAmelCase   :					str													=     model(
					    input_ids[:, -1:]	,	past_key_values=outputs_cache.past_key_values	,	attention_mask=__UpperCamelCase	,	position_ids=__UpperCamelCase	,	)

					__UpperCAmelCase   :					List[str]													=     model(__UpperCamelCase	,	attention_mask=__UpperCamelCase			)

					__UpperCAmelCase   :					List[Any]													=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
					self.parent.assertTrue(diff < 1e-3	,	msg=F'Max diff is {diff}'			)

@require_flax
class 		UpperCAmelCase__      (      UpperCAmelCase_						,UpperCAmelCase_						,unittest.TestCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
			UpperCamelCase      						=	(FlaxGPTaLMHeadModel,) if is_flax_available() else ()




			def 			snake_case__					(       self							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     FlaxGPTaModelTester(self			)




			def 			snake_case__					(       self							:			Optional[int]			):
					'''simple docstring'''





					for model_class_name in self.all_model_classes:
							__UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase   :					str													=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)




			def 			snake_case__					(       self							:			Optional[Any]			):
					'''simple docstring'''





					for model_class_name in self.all_model_classes:
							__UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase   :					Optional[int]													=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward_with_attn_mask(
							    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)




			@slow
			def 			snake_case__					(       self							:			List[str]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     GPTaTokenizer.from_pretrained('''gpt2'''	,	pad_token='''</s>'''	,	padding_side='''left'''			)
					__UpperCAmelCase   :					Tuple													=     tokenizer(['''Hello this is a long string''', '''Hey''']	,	return_tensors='''np'''	,	padding=__UpperCamelCase	,	truncation=__UpperCamelCase			)

					__UpperCAmelCase   :					Union[str, Any]													=     FlaxGPTaLMHeadModel.from_pretrained('''gpt2'''			)
					__UpperCAmelCase   :					str													=     False
					__UpperCAmelCase   :					List[Any]													=     model.config.eos_token_id

					__UpperCAmelCase   :					Optional[Any]													=     jax.jit(model.generate			)

					__UpperCAmelCase   :					Union[str, Any]													=     jit_generate(inputs['''input_ids''']	,	attention_mask=inputs['''attention_mask''']			).sequences

					__UpperCAmelCase   :					List[str]													=     tokenizer.batch_decode(__UpperCamelCase	,	skip_special_tokens=__UpperCamelCase			)

					__UpperCAmelCase   :					Any													=     [
					    '''Hello this is a long string of words. I\'m going to start with the first one.\n''',
					    '''Hey, I\'m not sure if I\'m going to be able to do''',
					]

					self.assertListEqual(__UpperCamelCase	,	__UpperCamelCase			)




			@is_pt_flax_cross_test
			def 			snake_case__					(       self							:			Dict			):
					'''simple docstring'''





					__UpperCAmelCase							, __UpperCAmelCase   :					Union[str, Any]													=     self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__			):
									# prepare inputs
									__UpperCAmelCase   :					Any													=     self._prepare_for_class(__UpperCamelCase	,	__UpperCamelCase			)
									__UpperCAmelCase   :					Any													=     {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

									# load corresponding PyTorch class
									__UpperCAmelCase   :					str													=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
									__UpperCAmelCase   :					List[Any]													=     getattr(__UpperCamelCase	,	__UpperCamelCase			)

									__UpperCAmelCase							, __UpperCAmelCase   :					Tuple													=     pt_inputs['''input_ids'''].shape
									__UpperCAmelCase   :					Union[str, Any]													=     np.random.randint(0	,	seq_length - 1	,	size=(batch_size,)			)
									for batch_idx, start_index in enumerate(__UpperCamelCase			):
											__UpperCAmelCase   :					Optional[Any]													=     0
											__UpperCAmelCase   :					List[Any]													=     1
											__UpperCAmelCase   :					Union[str, Any]													=     0
											__UpperCAmelCase   :					Optional[Any]													=     1
									__UpperCAmelCase   :					Any													=     pt_model_class(__UpperCamelCase			).eval()
									__UpperCAmelCase   :					Tuple													=     model_class(__UpperCamelCase	,	dtype=jnp.floataa			)

									__UpperCAmelCase   :					Tuple													=     convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,	__UpperCamelCase			)
									__UpperCAmelCase   :					Tuple													=     fx_state

									with torch.no_grad():
											__UpperCAmelCase   :					Union[str, Any]													=     pt_model(**__UpperCamelCase			).to_tuple()

									__UpperCAmelCase   :					Dict													=     fx_model(**__UpperCamelCase			).to_tuple()
									self.assertEqual(len(__UpperCamelCase			)	,	len(__UpperCamelCase			)	,	'''Output lengths differ between Flax and PyTorch'''			)
									for fx_output, pt_output in zip(__UpperCamelCase	,	__UpperCamelCase			):
											self.assert_almost_equals(fx_output[:, -1]	,	pt_output[:, -1].numpy()	,	4e-2			)

									with tempfile.TemporaryDirectory() as tmpdirname:
											pt_model.save_pretrained(__UpperCamelCase			)
											__UpperCAmelCase   :					List[Any]													=     model_class.from_pretrained(__UpperCamelCase	,	from_pt=__UpperCamelCase			)

									__UpperCAmelCase   :					Optional[Any]													=     fx_model_loaded(**__UpperCamelCase			).to_tuple()
									self.assertEqual(
									    len(__UpperCamelCase			)	,	len(__UpperCamelCase			)	,	'''Output lengths differ between Flax and PyTorch'''			)
									for fx_output_loaded, pt_output in zip(__UpperCamelCase	,	__UpperCamelCase			):
											self.assert_almost_equals(fx_output_loaded[:, -1]	,	pt_output[:, -1].numpy()	,	4e-2			)




			@is_pt_flax_cross_test
			def 			snake_case__					(       self							:			Union[str, Any]			):
					'''simple docstring'''





					__UpperCAmelCase							, __UpperCAmelCase   :					Tuple													=     self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__			):
									# prepare inputs
									__UpperCAmelCase   :					Union[str, Any]													=     self._prepare_for_class(__UpperCamelCase	,	__UpperCamelCase			)
									__UpperCAmelCase   :					Any													=     {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

									# load corresponding PyTorch class
									__UpperCAmelCase   :					str													=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
									__UpperCAmelCase   :					int													=     getattr(__UpperCamelCase	,	__UpperCamelCase			)

									__UpperCAmelCase   :					Optional[int]													=     pt_model_class(__UpperCamelCase			).eval()
									__UpperCAmelCase   :					str													=     model_class(__UpperCamelCase	,	dtype=jnp.floataa			)

									__UpperCAmelCase   :					List[str]													=     load_flax_weights_in_pytorch_model(__UpperCamelCase	,	fx_model.params			)
									__UpperCAmelCase							, __UpperCAmelCase   :					Tuple													=     pt_inputs['''input_ids'''].shape
									__UpperCAmelCase   :					Union[str, Any]													=     np.random.randint(0	,	seq_length - 1	,	size=(batch_size,)			)
									for batch_idx, start_index in enumerate(__UpperCamelCase			):
											__UpperCAmelCase   :					int													=     0
											__UpperCAmelCase   :					Dict													=     1
											__UpperCAmelCase   :					List[str]													=     0
											__UpperCAmelCase   :					List[str]													=     1

									# make sure weights are tied in PyTorch
									pt_model.tie_weights()

									with torch.no_grad():
											__UpperCAmelCase   :					Optional[int]													=     pt_model(**__UpperCamelCase			).to_tuple()

									__UpperCAmelCase   :					Optional[int]													=     fx_model(**__UpperCamelCase			).to_tuple()
									self.assertEqual(len(__UpperCamelCase			)	,	len(__UpperCamelCase			)	,	'''Output lengths differ between Flax and PyTorch'''			)
									for fx_output, pt_output in zip(__UpperCamelCase	,	__UpperCamelCase			):
											self.assert_almost_equals(fx_output[:, -1]	,	pt_output[:, -1].numpy()	,	4e-2			)

									with tempfile.TemporaryDirectory() as tmpdirname:
											fx_model.save_pretrained(__UpperCamelCase			)
											__UpperCAmelCase   :					Any													=     pt_model_class.from_pretrained(__UpperCamelCase	,	from_flax=__UpperCamelCase			)

									with torch.no_grad():
											__UpperCAmelCase   :					int													=     pt_model_loaded(**__UpperCamelCase			).to_tuple()

									self.assertEqual(
									    len(__UpperCamelCase			)	,	len(__UpperCamelCase			)	,	'''Output lengths differ between Flax and PyTorch'''			)
									for fx_output, pt_output in zip(__UpperCamelCase	,	__UpperCamelCase			):
											self.assert_almost_equals(fx_output[:, -1]	,	pt_output[:, -1].numpy()	,	4e-2			)




			@slow
			def 			snake_case__					(       self							:			List[str]			):
					'''simple docstring'''





					for model_class_name in self.all_model_classes:
							__UpperCAmelCase   :					Optional[int]													=     model_class_name.from_pretrained('''gpt2'''	,	from_pt=__UpperCamelCase			)
							__UpperCAmelCase   :					str													=     model(np.ones((1, 1)			)			)
							self.assertIsNotNone(__UpperCamelCase			)










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 		UpperCAmelCase__      :




			'''simple docstring'''






			def __init__(       self							:			int	,	a_							:			str	,	a_							:			List[str]=sys.maxsize			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     '''bilinear'''
					__UpperCAmelCase   :					str													=     max_size
					__UpperCAmelCase   :					Dict													=     short_edge_length




			def __call__(       self							:			str	,	a_							:			Union[str, Any]			):
					'''simple docstring'''





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

							if img.dtype == np.uinta:
									__UpperCAmelCase   :					Any													=     Image.fromarray(a_			)
									__UpperCAmelCase   :					Any													=     pil_image.resize((neww, newh)	,	PILImageResampling.BILINEAR			)
									__UpperCAmelCase   :					List[Any]													=     np.asarray(a_			)
							else:
									__UpperCAmelCase   :					Union[str, Any]													=     img.permute(2	,	0	,	1			).unsqueeze(0			)  # 3, 0, 1)  # hw(c) -> nchw
									__UpperCAmelCase   :					Optional[int]													=     nn.functional.interpolate(
									    a_	,	(newh, neww)	,	mode=self.interp_method	,	align_corners=a_			).squeeze(0			)
							img_augs.append(a_			)

					return img_augs

class 		UpperCAmelCase__      :




			'''simple docstring'''






			def __init__(       self							:			Tuple	,	a_							:			List[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]	,	cfg.INPUT.MAX_SIZE_TEST			)
					__UpperCAmelCase   :					List[str]													=     cfg.INPUT.FORMAT
					__UpperCAmelCase   :					int													=     cfg.SIZE_DIVISIBILITY
					__UpperCAmelCase   :					int													=     cfg.PAD_VALUE
					__UpperCAmelCase   :					Any													=     cfg.INPUT.MAX_SIZE_TEST
					__UpperCAmelCase   :					Tuple													=     cfg.MODEL.DEVICE
					__UpperCAmelCase   :					Union[str, Any]													=     torch.tensor(cfg.MODEL.PIXEL_STD			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)	,	1	,	1			)
					__UpperCAmelCase   :					List[Any]													=     torch.tensor(cfg.MODEL.PIXEL_MEAN			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)	,	1	,	1			)
					__UpperCAmelCase   :					Union[str, Any]													=     lambda a_			: (x - self.pixel_mean) / self.pixel_std




			def 			snake_case__					(       self							:			int	,	a_							:			Tuple			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     tuple(max(a_			) for s in zip(*[img.shape for img in images]			)			)
					__UpperCAmelCase   :					Any													=     [im.shape[-2:] for im in images]
					__UpperCAmelCase   :					Union[str, Any]													=     [
					    nn.functional.pad(
					        a_	,	[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]	,	value=self.pad_value	,	)
					    for size, im in zip(a_	,	a_			)
					]

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




			def __call__(       self							:			List[str]	,	a_							:			int	,	a_							:			int=False			):
					'''simple docstring'''





					with torch.no_grad():
							if not isinstance(a_	,	a_			):
									__UpperCAmelCase   :					Optional[Any]													=     [images]
							if single_image:
									assert len(a_			) == 1
							for i in range(len(a_			)			):
									if isinstance(images[i]	,	torch.Tensor			):
											images.insert(a_	,	images.pop(a_			).to(self.device			).float()			)
									elif not isinstance(images[i]	,	torch.Tensor			):
											images.insert(
											    a_	,	torch.as_tensor(img_tensorize(images.pop(a_			)	,	input_format=self.input_format			)			)
											    .to(self.device			)
											    .float()	,	)
            # resize smallest edge
							__UpperCAmelCase   :					Optional[Any]													=     torch.tensor([im.shape[:2] for im in images]			)
							__UpperCAmelCase   :					Dict													=     self.aug(a_			)
							# 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
							__UpperCAmelCase   :					Optional[int]													=     [self.normalizer(a_			) for x in images]
							# now pad them to do the following operations
							__UpperCAmelCase							, __UpperCAmelCase   :					Optional[int]													=     self.pad(a_			)
							# Normalize

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

def 	a						(				_UpperCAmelCase							: str			,       _UpperCAmelCase							: Optional[int]						):







		'''simple docstring'''





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

def 	a						(				_UpperCAmelCase							: Any			,       _UpperCAmelCase							: Tuple[int, int]						):







		'''simple docstring'''





		assert torch.isfinite(_UpperCAmelCase						).all(), "Box tensor contains infinite or NaN!"
		__UpperCAmelCase							, __UpperCAmelCase   :					List[str]													=     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						)





