
import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel





class       _snake_case					:







			def __init__(				self						,   _a						,   _a=99						,   _a=13						,   _a=7						,   _a=9						,   _a=True						,   _a=True						,   _a=False						,   _a=32						,   _a=5						,   _a=4						,   _a=37						,   _a=8						,   _a=0.1						,   _a=0.0_02						,   _a=1						,   _a=0						,   _a=0						,   _a=None						,   _a=None						,   ):
				__magic_name__		:		Union[str, Any]	   =     parent
				__magic_name__		:		Optional[Any]	   =     batch_size
				__magic_name__		:		int	   =     encoder_seq_length
				__magic_name__		:		List[Any]	   =     decoder_seq_length
				# For common tests
				__magic_name__		:		str	   =     self.decoder_seq_length
				__magic_name__		:		int	   =     is_training
				__magic_name__		:		Tuple	   =     use_attention_mask
				__magic_name__		:		Any	   =     use_labels
				__magic_name__		:		List[str]	   =     vocab_size
				__magic_name__		:		Dict	   =     hidden_size
				__magic_name__		:		Optional[Any]	   =     num_hidden_layers
				__magic_name__		:		List[Any]	   =     num_attention_heads
				__magic_name__		:		Tuple	   =     d_ff
				__magic_name__		:		List[str]	   =     relative_attention_num_buckets
				__magic_name__		:		Tuple	   =     dropout_rate
				__magic_name__		:		Optional[Any]	   =     initializer_factor
				__magic_name__		:		List[str]	   =     eos_token_id
				__magic_name__		:		Union[str, Any]	   =     pad_token_id
				__magic_name__		:		Any	   =     decoder_start_token_id
				__magic_name__		:		int	   =     None
				__magic_name__		:		Tuple	   =     decoder_layers







			def 			SCREAMING_SNAKE_CASE    (				self		):
				return TaConfig.from_pretrained("google/umt5-base"		)







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





				if attention_mask is None:
					__magic_name__		:		Union[str, Any]	   =     input_ids.ne(config.pad_token_id		)
				if decoder_attention_mask is None:
					__magic_name__		:		Any	   =     decoder_input_ids.ne(config.pad_token_id		)
				if head_mask is None:
					__magic_name__		:		str	   =     torch.ones(config.num_hidden_layers						,   config.num_attention_heads						,   device=_SCREAMING_SNAKE_CASE		)
				if decoder_head_mask is None:
					__magic_name__		:		List[str]	   =     torch.ones(config.num_decoder_layers						,   config.num_attention_heads						,   device=_SCREAMING_SNAKE_CASE		)
				if cross_attn_head_mask is None:
					__magic_name__		:		Union[str, Any]	   =     torch.ones(
					    config.num_decoder_layers						,   config.num_attention_heads						,   device=_SCREAMING_SNAKE_CASE		)
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[Any]	   =     ids_tensor([self.batch_size, self.encoder_seq_length]						,   self.vocab_size		)
				__magic_name__		:		int	   =     ids_tensor([self.batch_size, self.decoder_seq_length]						,   self.vocab_size		)

				# we need to clamp the input ids here to avoid having pad token in between
				# this is because for NllbMoe the position_ids are prepared such that
				# all pad tokens have pos id = 2 and rest are between 2..seq_length
				# and the seq_length here is seq_length - num_pad_tokens
				# but when using past, there is no way of knowing if the past input ids had
				# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
				# position_ids being off by num_pad_tokens in past input
				__magic_name__		:		Tuple	   =     input_ids.clamp(self.pad_token_id + 1		)
				__magic_name__		:		int	   =     decoder_input_ids.clamp(self.pad_token_id + 1		)

				__magic_name__		:		Optional[Any]	   =     self.get_config()
				__magic_name__		:		int	   =     config.num_attention_heads
				__magic_name__		:		Dict	   =     self.prepare_inputs_dict(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE		)
				return config, input_dict







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		str	   =     self.prepare_config_and_inputs()
				return config, inputs_dict







			def 			SCREAMING_SNAKE_CASE    (				self		):
				return TaConfig(
				    vocab_size=166						,   d_model=self.hidden_size						,   d_ff=self.d_ff						,   d_kv=self.hidden_size // self.num_attention_heads						,   num_layers=self.num_hidden_layers						,   num_decoder_layers=self.decoder_layers						,   num_heads=self.num_attention_heads						,   relative_attention_num_buckets=self.relative_attention_num_buckets						,   dropout_rate=self.dropout_rate						,   initializer_factor=self.initializer_factor						,   eos_token_id=self.eos_token_id						,   bos_token_id=self.pad_token_id						,   pad_token_id=self.pad_token_id						,   decoder_start_token_id=self.decoder_start_token_id						,   )







			def 			SCREAMING_SNAKE_CASE    (				self		):
				return TaConfig(
				    vocab_size=self.vocab_size						,   d_model=self.hidden_size						,   d_ff=self.d_ff						,   d_kv=self.hidden_size // self.num_attention_heads						,   num_layers=self.num_hidden_layers						,   num_decoder_layers=self.decoder_layers						,   num_heads=self.num_attention_heads						,   relative_attention_num_buckets=self.relative_attention_num_buckets						,   dropout_rate=self.dropout_rate						,   initializer_factor=self.initializer_factor						,   eos_token_id=self.eos_token_id						,   bos_token_id=self.pad_token_id						,   pad_token_id=self.pad_token_id						,   decoder_start_token_id=self.decoder_start_token_id						,   )







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   ):
				__magic_name__		:		Dict	   =     UMTaModel(config=_SCREAMING_SNAKE_CASE		)
				model.to(_SCREAMING_SNAKE_CASE		)
				model.eval()
				__magic_name__		:		Tuple	   =     model(
				    input_ids=_SCREAMING_SNAKE_CASE						,   decoder_input_ids=_SCREAMING_SNAKE_CASE						,   attention_mask=_SCREAMING_SNAKE_CASE						,   decoder_attention_mask=_SCREAMING_SNAKE_CASE						,   )
				__magic_name__		:		List[Any]	   =     model(input_ids=_SCREAMING_SNAKE_CASE						,   decoder_input_ids=_SCREAMING_SNAKE_CASE		)
				__magic_name__		:		str	   =     result.last_hidden_state
				__magic_name__		:		List[Any]	   =     result.past_key_values
				__magic_name__		:		Any	   =     result.encoder_last_hidden_state

				self.parent.assertEqual(encoder_output.size()						,   (self.batch_size, self.encoder_seq_length, self.hidden_size)		)
				self.parent.assertEqual(decoder_output.size()						,   (self.batch_size, self.decoder_seq_length, self.hidden_size)		)
				# There should be `num_layers` key value embeddings stored in decoder_past
				self.parent.assertEqual(len(_SCREAMING_SNAKE_CASE		)						,   config.num_layers		)
				# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
				self.parent.assertEqual(len(decoder_past[0]		)						,   4		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   ):
				__magic_name__		:		List[Any]	   =     UMTaModel(config=_SCREAMING_SNAKE_CASE		).get_decoder().to(_SCREAMING_SNAKE_CASE		).eval()
				# first forward pass
				__magic_name__		:		Union[str, Any]	   =     model(_SCREAMING_SNAKE_CASE						,   use_cache=_SCREAMING_SNAKE_CASE		)
				__magic_name__		:		List[str]	   =     model(_SCREAMING_SNAKE_CASE		)
				__magic_name__		:		int	   =     model(_SCREAMING_SNAKE_CASE						,   use_cache=_SCREAMING_SNAKE_CASE		)

				self.parent.assertTrue(len(_SCREAMING_SNAKE_CASE		) == len(_SCREAMING_SNAKE_CASE		)		)
				self.parent.assertTrue(len(_SCREAMING_SNAKE_CASE		) == len(_SCREAMING_SNAKE_CASE		) + 1		)

				__magic_name__		:		int	   =     outputs.to_tuple()

				# create hypothetical next token and extent to next_input_ids
				__magic_name__		:		str	   =     ids_tensor((self.batch_size, 1)						,   config.vocab_size		)

				# append to next input_ids and
				__magic_name__		:		Tuple	   =     torch.cat([input_ids, next_tokens]						,   dim=-1		)

				__magic_name__		:		Tuple	   =     model(_SCREAMING_SNAKE_CASE		)['''last_hidden_state''']
				__magic_name__		:		Union[str, Any]	   =     model(_SCREAMING_SNAKE_CASE						,   past_key_values=_SCREAMING_SNAKE_CASE		)['''last_hidden_state''']

				# select random slice
				__magic_name__		:		List[Any]	   =     ids_tensor((1,)						,   output_from_past.shape[-1]		).item()
				__magic_name__		:		Union[str, Any]	   =     output_from_no_past[:, -1, random_slice_idx].detach()
				__magic_name__		:		List[str]	   =     output_from_past[:, 0, random_slice_idx].detach()

				# test that outputs are equal for slice
				self.parent.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						,   atol=1e-3		)		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   ):
				__magic_name__		:		List[str]	   =     UMTaModel(config=_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		).half().eval()
				__magic_name__		:		Tuple	   =     model(**_SCREAMING_SNAKE_CASE		)['''last_hidden_state''']
				self.parent.assertFalse(torch.isnan(_SCREAMING_SNAKE_CASE		).any().item()		)







@require_torch
class       _snake_case					(     snake_case				,						snake_case				,						snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    (
			    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
			)
			UpperCamelCase__          =    (UMTaForConditionalGeneration,) if is_torch_available() else ()
			UpperCamelCase__          =    (
			    {
			        'conversational': UMTaForConditionalGeneration,
			        'feature-extraction': UMTaModel,
			        'summarization': UMTaForConditionalGeneration,
			        'text2text-generation': UMTaForConditionalGeneration,
			        'translation': UMTaForConditionalGeneration,
			        'question-answering': UMTaForQuestionAnswering,
			    }
			    if is_torch_available()
			    else {}
			)
			UpperCamelCase__          =    True
			UpperCamelCase__          =    False
			UpperCamelCase__          =    False
			UpperCamelCase__          =    True
			UpperCamelCase__          =    True
			# The small UMT5 model needs higher percentages for CPU/MP tests
			UpperCamelCase__          =    [0.8, 0.9]







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Union[str, Any]	   =     UMTaModelTester(self		)







			@unittest.skip("Test has a segmentation fault on torch 1.8.0"		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Any	   =     self.model_tester.prepare_config_and_inputs()
				__magic_name__		:		str	   =     UMTaModel(config_and_inputs[0]		).to(_SCREAMING_SNAKE_CASE		)
				with tempfile.TemporaryDirectory() as tmpdirname:
					torch.onnx.export(
					    _SCREAMING_SNAKE_CASE						,   (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])						,   f'''{tmpdirname}/t5_test.onnx'''						,   export_params=_SCREAMING_SNAKE_CASE						,   opset_version=9						,   input_names=["input_ids", "decoder_input_ids"]						,   )







			@unittest.skipIf(torch_device == "cpu"						,   "Cant do half precision"		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model_fpaa_forward(*_SCREAMING_SNAKE_CASE		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
				__magic_name__		:		List[Any]	   =     self.model_tester.prepare_config_and_inputs()
				__magic_name__		:		Union[str, Any]	   =     config_and_inputs[0]
				__magic_name__		:		int	   =     UMTaForConditionalGeneration(_SCREAMING_SNAKE_CASE		).eval()
				model.to(_SCREAMING_SNAKE_CASE		)

				__magic_name__		:		Any	   =     {
				    '''head_mask''': torch.zeros(config.num_layers						,   config.num_heads						,   device=_SCREAMING_SNAKE_CASE		),
				    '''decoder_head_mask''': torch.zeros(config.num_decoder_layers						,   config.num_heads						,   device=_SCREAMING_SNAKE_CASE		),
				    '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers						,   config.num_heads						,   device=_SCREAMING_SNAKE_CASE		),
				}

				for attn_name, (name, mask) in zip(_SCREAMING_SNAKE_CASE						,   head_masking.items()		):
					__magic_name__		:		Optional[int]	   =     {name: mask}
					# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
					if name == "head_mask":
						__magic_name__		:		Dict	   =     torch.ones(
						    config.num_decoder_layers						,   config.num_heads						,   device=_SCREAMING_SNAKE_CASE		)

					__magic_name__		:		Union[str, Any]	   =     model.generate(
					    config_and_inputs[1]["input_ids"]						,   num_beams=1						,   max_length=3						,   output_attentions=_SCREAMING_SNAKE_CASE						,   return_dict_in_generate=_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						,   )
					# We check the state of decoder_attentions and cross_attentions just from the last step
					__magic_name__		:		Tuple	   =     out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
					self.assertEqual(sum([w.sum().item() for w in attn_weights]		)						,   0.0		)







			@unittest.skip("Does not work on the tiny model as we keep hitting edge cases."		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				pass







@require_torch
@require_sentencepiece
@require_tokenizers
class       _snake_case					(     unittest.TestCase				):







			@slow
			@unittest.skip(
			    "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     UMTaForConditionalGeneration.from_pretrained("google/umt5-small"						,   return_dict=_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)
				__magic_name__		:		Union[str, Any]	   =     AutoTokenizer.from_pretrained("google/umt5-small"						,   use_fast=_SCREAMING_SNAKE_CASE						,   legacy=_SCREAMING_SNAKE_CASE		)
				__magic_name__		:		Tuple	   =     [
				    '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
				    '''No se como puedo <extra_id_0>.''',
				    '''This is the reason why we <extra_id_0> them.''',
				    '''The <extra_id_0> walks in <extra_id_1>, seats''',
				    '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
				]
				__magic_name__		:		Union[str, Any]	   =     tokenizer(_SCREAMING_SNAKE_CASE						,   return_tensors="pt"						,   padding=_SCREAMING_SNAKE_CASE		).input_ids
				# fmt: off
				__magic_name__		:		Dict	   =     torch.tensor(
				    [
				        [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
				        [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
				        [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
				    ]		)
				# fmt: on
				torch.testing.assert_allclose(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE		)

				__magic_name__		:		List[Any]	   =     model.generate(input_ids.to(_SCREAMING_SNAKE_CASE		)		)
				__magic_name__		:		str	   =     [
				    '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
				    '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
				    '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
				    '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
				    '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
				]
				__magic_name__		:		str	   =     tokenizer.batch_decode(_SCREAMING_SNAKE_CASE		)
				self.assertEqual(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE		)




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   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=sys.maxsize		)->Any:




         '''simple docstring'''





         A_      :				Dict		     =					'''bilinear'''
         A_      :				Optional[Any]		     =					max_size
         A_      :				Optional[Any]		     =					short_edge_length







       def __call__(      self				,       _SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





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

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

         return img_augs






class   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE		)->Tuple:




         '''simple docstring'''





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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[int]:




         '''simple docstring'''





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

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







       def __call__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->Dict:




         '''simple docstring'''





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

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




def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  boxes[:, 0::2] *= scale_yx[:, 1]
  boxes[:, 1::2] *= scale_yx[:, 0]
  return boxes







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




