


'''simple docstring'''



import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
	import torch

	from transformers import (
	    XLMForMultipleChoice,
	    XLMForQuestionAnswering,
	    XLMForQuestionAnsweringSimple,
	    XLMForSequenceClassification,
	    XLMForTokenClassification,
	    XLMModel,
	    XLMWithLMHeadModel,
	)
	from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST





class 							__UpperCamelCase				:




	def __init__(   self						,				__a						,				__a=13						,				__a=7						,				__a=True						,				__a=True						,				__a=True						,				__a=True						,				__a=True						,				__a=False						,				__a=False						,				__a=False						,				__a=2						,				__a=99						,				__a=0						,				__a=32						,				__a=5						,				__a=4						,				__a=0.1						,				__a=0.1						,				__a=512						,				__a=2						,				__a=0.02						,				__a=2						,				__a=4						,				__a="last"						,				__a=True						,				__a=None						,				__a=0						,				):

						'''simple docstring'''





						__a			:       List[str]					    =  parent
						__a			:       int					    =  batch_size
						__a			:       str					    =  seq_length
						__a			:       Optional[int]					    =  is_training
						__a			:       Any					    =  use_input_lengths
						__a			:       Optional[int]					    =  use_token_type_ids
						__a			:       int					    =  use_labels
						__a			:       Dict					    =  gelu_activation
						__a			:       int					    =  sinusoidal_embeddings
						__a			:       str					    =  causal
						__a			:       Union[str, Any]					    =  asm
						__a			:       Optional[Any]					    =  n_langs
						__a			:       Union[str, Any]					    =  vocab_size
						__a			:       List[str]					    =  n_special
						__a			:       Tuple					    =  hidden_size
						__a			:       Any					    =  num_hidden_layers
						__a			:       Dict					    =  num_attention_heads
						__a			:       Tuple					    =  hidden_dropout_prob
						__a			:       Dict					    =  attention_probs_dropout_prob
						__a			:       Tuple					    =  max_position_embeddings
						__a			:       Optional[Any]					    =  type_sequence_label_size
						__a			:       List[Any]					    =  initializer_range
						__a			:       Tuple					    =  num_labels
						__a			:       List[str]					    =  num_choices
						__a			:       str					    =  summary_type
						__a			:       int					    =  use_proj
						__a			:       Dict					    =  scope
						__a			:       Optional[int]					    =  bos_token_id




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  ids_tensor([self.batch_size, self.seq_length]						,				self.vocab_size							)
						__a			:       List[Any]					    =  random_attention_mask([self.batch_size, self.seq_length]							)

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

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

						__a			:       Optional[Any]					    =  None
						__a			:       Optional[int]					    =  None
						__a			:       List[Any]					    =  None
						if self.use_labels:
											__a			:       Tuple					    =  ids_tensor([self.batch_size]						,				self.type_sequence_label_size							)
											__a			:       List[Any]					    =  ids_tensor([self.batch_size, self.seq_length]						,				self.num_labels							)
											__a			:       List[Any]					    =  ids_tensor([self.batch_size]						,				2							).float()
											__a			:       Any					    =  ids_tensor([self.batch_size]						,				self.num_choices							)

						__a			:       List[str]					    =  self.get_config()

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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return 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						,				num_labels=self.num_labels						,				bos_token_id=self.bos_token_id						,				)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       Any					    =  XLMModel(config=__a							)
						model.to(__a							)
						model.eval()
						__a			:       Optional[Any]					    =  model(__a						,				lengths=__a						,				langs=__a							)
						__a			:       Union[str, Any]					    =  model(__a						,				langs=__a							)
						__a			:       Optional[int]					    =  model(__a							)
						self.parent.assertEqual(result.last_hidden_state.shape						,				(self.batch_size, self.seq_length, self.hidden_size)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  XLMWithLMHeadModel(__a							)
						model.to(__a							)
						model.eval()

						__a			:       Optional[Any]					    =  model(__a						,				token_type_ids=__a						,				labels=__a							)
						self.parent.assertEqual(result.loss.shape						,				()							)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.seq_length, self.vocab_size)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       List[str]					    =  XLMForQuestionAnsweringSimple(__a							)
						model.to(__a							)
						model.eval()

						__a			:       List[str]					    =  model(__a							)

						__a			:       Optional[int]					    =  model(__a						,				start_positions=__a						,				end_positions=__a							)
						__a			:       Optional[Any]					    =  outputs
						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						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       Any					    =  XLMForQuestionAnswering(__a							)
						model.to(__a							)
						model.eval()

						__a			:       Dict					    =  model(__a							)

						__a			:       Optional[Any]					    =  model(
						    __a						,				start_positions=__a						,				end_positions=__a						,				cls_index=__a						,				is_impossible=__a						,				p_mask=__a						,				)

						__a			:       List[Any]					    =  model(
						    __a						,				start_positions=__a						,				end_positions=__a						,				cls_index=__a						,				is_impossible=__a						,				)

						((__a)    ,				)			:       Union[str, Any]					    =  result_with_labels.to_tuple()

						__a			:       Union[str, Any]					    =  model(__a						,				start_positions=__a						,				end_positions=__a							)

						((__a)    ,				)			:       Dict					    =  result_with_labels.to_tuple()

						self.parent.assertEqual(result_with_labels.loss.shape						,				()							)
						self.parent.assertEqual(result.start_top_log_probs.shape						,				(self.batch_size, model.config.start_n_top)							)
						self.parent.assertEqual(result.start_top_index.shape						,				(self.batch_size, model.config.start_n_top)							)
						self.parent.assertEqual(
						    result.end_top_log_probs.shape						,				(self.batch_size, model.config.start_n_top * model.config.end_n_top)							)
						self.parent.assertEqual(
						    result.end_top_index.shape						,				(self.batch_size, model.config.start_n_top * model.config.end_n_top)							)
						self.parent.assertEqual(result.cls_logits.shape						,				(self.batch_size,)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       List[Any]					    =  XLMForSequenceClassification(__a							)
						model.to(__a							)
						model.eval()

						__a			:       Optional[int]					    =  model(__a							)
						__a			:       Union[str, Any]					    =  model(__a						,				labels=__a							)
						self.parent.assertEqual(result.loss.shape						,				()							)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.type_sequence_label_size)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       List[Any]					    =  self.num_labels
						__a			:       Any					    =  XLMForTokenClassification(__a							)
						model.to(__a							)
						model.eval()

						__a			:       Optional[int]					    =  model(__a						,				attention_mask=__a						,				labels=__a							)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.seq_length, self.num_labels)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a						,				):

						'''simple docstring'''





						__a			:       str					    =  self.num_choices
						__a			:       Union[str, Any]					    =  XLMForMultipleChoice(config=__a							)
						model.to(__a							)
						model.eval()
						__a			:       int					    =  input_ids.unsqueeze(1							).expand(-1						,				self.num_choices						,				-1							).contiguous()
						__a			:       Any					    =  token_type_ids.unsqueeze(1							).expand(-1						,				self.num_choices						,				-1							).contiguous()
						__a			:       Optional[Any]					    =  input_mask.unsqueeze(1							).expand(-1						,				self.num_choices						,				-1							).contiguous()
						__a			:       str					    =  model(
						    __a						,				attention_mask=__a						,				token_type_ids=__a						,				labels=__a						,				)
						self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_choices)							)





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[Any]					    =  self.prepare_config_and_inputs()
						(
						    (
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				(
						    __a
						)    ,				
						)			:       Optional[int]					    =  config_and_inputs
						__a			:       Tuple					    =  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
						return config, inputs_dict





@require_torch
class 							__UpperCamelCase				(		lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    (
	    (
	        XLMModel,
	        XLMWithLMHeadModel,
	        XLMForQuestionAnswering,
	        XLMForSequenceClassification,
	        XLMForQuestionAnsweringSimple,
	        XLMForTokenClassification,
	        XLMForMultipleChoice,
	    )
	    if is_torch_available()
	    else ()
	)
	A_				=    (
	    (XLMWithLMHeadModel,) if is_torch_available() else ()
	)  # TODO (PVP): Check other models whether language generation is also applicable
	A_				=    (
	    {
	        "feature-extraction": XLMModel,
	        "fill-mask": XLMWithLMHeadModel,
	        "question-answering": XLMForQuestionAnsweringSimple,
	        "text-classification": XLMForSequenceClassification,
	        "text-generation": XLMWithLMHeadModel,
	        "token-classification": XLMForTokenClassification,
	        "zero-shot": XLMForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a							):

						'''simple docstring'''





						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						,				__a						,				__a						,				__a=False							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  super()._prepare_for_class(__a						,				__a						,				return_labels=__a							)

						if return_labels:
											if model_class.__name__ == "XLMForQuestionAnswering":
																__a			:       List[Any]					    =  torch.zeros(
																    self.model_tester.batch_size						,				dtype=torch.long						,				device=__a							)
																__a			:       Any					    =  torch.zeros(
																    self.model_tester.batch_size						,				dtype=torch.long						,				device=__a							)

						return inputs_dict




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[Any]					    =  XLMModelTester(self							)
						__a			:       Union[str, Any]					    =  ConfigTester(self						,				config_class=__a						,				emb_dim=37							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						self.config_tester.run_common_tests()




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_model(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_lm_head(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_simple_qa(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_qa(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_sequence_classif(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_token_classif(*__a							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_for_multiple_choice(*__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a=False						,				__a=1							):

						'''simple docstring'''





						self.assertIsInstance(__a						,				__a							)
						self.assertListEqual(
						    [isinstance(__a						,				__a							) for iter_attentions in attentions]						,				[True] * len(__a							)							)
						self.assertEqual(len(__a							)						,				(max_length - min_length) * num_beam_groups							)

						for idx, iter_attentions in enumerate(__a							):
											# adds PAD dummy token
											__a			:       Optional[Any]					    =  min_length + idx + 1
											__a			:       List[str]					    =  min_length + idx + 1

											__a			:       List[Any]					    =  (
											    batch_size * num_beam_groups,
											    config.num_attention_heads,
											    tgt_len,
											    src_len,
											)
											# check attn size
											self.assertListEqual(
											    [layer_attention.shape for layer_attention in iter_attentions]						,				[expected_shape] * len(__a							)							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a						,				__a						,				__a=False						,				__a=1							):

						'''simple docstring'''





						self.assertIsInstance(__a						,				__a							)
						self.assertListEqual(
						    [isinstance(__a						,				__a							) for iter_hidden_states in hidden_states]						,				[True] * len(__a							)						,				)
						self.assertEqual(len(__a							)						,				(max_length - min_length) * num_beam_groups							)

						for idx, iter_hidden_states in enumerate(__a							):
											# adds PAD dummy token
											__a			:       List[str]					    =  min_length + idx + 1
											__a			:       Dict					    =  (batch_size * num_beam_groups, seq_len, config.hidden_size)
											# check hidden size
											self.assertListEqual(
											    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]						,				[expected_shape] * len(__a							)						,				)
						pass





	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__a			:       Optional[Any]					    =  XLMModel.from_pretrained(__a							)
											self.assertIsNotNone(__a							)





@require_torch
class 							__UpperCamelCase				(		unittest.TestCase					):




	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'							)
						model.to(__a							)
						__a			:       Union[str, Any]					    =  torch.tensor([[14, 447]]						,				dtype=torch.long						,				device=__a							)  # the president
						__a			:       int					    =  [
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						    14,
						    447,
						]  # 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
						__a			:       Optional[Any]					    =  model.generate(__a						,				do_sample=__a							)
						self.assertListEqual(output_ids[0].cpu().numpy().tolist()						,				__a							)







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       )




