




"""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	, __lowerCAmelCase	, __lowerCAmelCase=sys.maxsize		):







            """simple docstring"""



            lowercase														=			"""bilinear"""
            lowercase														=			max_size
            lowercase														=			short_edge_length







      def __call__(		self	, __lowerCAmelCase		):







            """simple docstring"""



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

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

            return img_augs






class 				_A		:

      def __init__(		self	, __lowerCAmelCase		):







            """simple docstring"""



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

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



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

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







      def __call__(		self	, __lowerCAmelCase	, __lowerCAmelCase=False		):







            """simple docstring"""



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

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






def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :List[str]	)				->					Optional[Any]:
      '''simple docstring'''


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






def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :Tuple[int, int]	)				->					List[Any]:
      '''simple docstring'''


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




"""simple docstring"""






import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           DebertaForMaskedLM,
           DebertaForQuestionAnswering,
           DebertaForSequenceClassification,
           DebertaForTokenClassification,
           DebertaModel,
       )
       from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST





class 				_A		( lowerCAmelCase			):

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase=13	, __lowerCAmelCase=7	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=99	, __lowerCAmelCase=32	, __lowerCAmelCase=5	, __lowerCAmelCase=4	, __lowerCAmelCase=37	, __lowerCAmelCase="gelu"	, __lowerCAmelCase=0.1	, __lowerCAmelCase=0.1	, __lowerCAmelCase=512	, __lowerCAmelCase=16	, __lowerCAmelCase=2	, __lowerCAmelCase=0.0_2	, __lowerCAmelCase=False	, __lowerCAmelCase=True	, __lowerCAmelCase="None"	, __lowerCAmelCase=3	, __lowerCAmelCase=4	, __lowerCAmelCase=None	, ):







            """simple docstring"""



            lowercase														=			parent
            lowercase														=			batch_size
            lowercase														=			seq_length
            lowercase														=			is_training
            lowercase														=			use_input_mask
            lowercase														=			use_token_type_ids
            lowercase														=			use_labels
            lowercase														=			vocab_size
            lowercase														=			hidden_size
            lowercase														=			num_hidden_layers
            lowercase														=			num_attention_heads
            lowercase														=			intermediate_size
            lowercase														=			hidden_act
            lowercase														=			hidden_dropout_prob
            lowercase														=			attention_probs_dropout_prob
            lowercase														=			max_position_embeddings
            lowercase														=			type_vocab_size
            lowercase														=			type_sequence_label_size
            lowercase														=			initializer_range
            lowercase														=			num_labels
            lowercase														=			num_choices
            lowercase														=			relative_attention
            lowercase														=			position_biased_input
            lowercase														=			pos_att_type
            lowercase														=			scope

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, self.vocab_size		)

            lowercase														=			None
            if self.use_input_mask:
                  lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, vocab_size=2		)

            lowercase														=			None
            if self.use_token_type_ids:
                  lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, self.type_vocab_size		)

            lowercase														=			None
            lowercase														=			None
            lowercase														=			None
            if self.use_labels:
                  lowercase														=			ids_tensor([self.batch_size]	, self.type_sequence_label_size		)
                  lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, self.num_labels		)
                  lowercase														=			ids_tensor([self.batch_size]	, self.num_choices		)

            lowercase														=			self.get_config()

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

      def        A__ (		self		):







            """simple docstring"""



            return DebertaConfig(
                vocab_size=self.vocab_size	, hidden_size=self.hidden_size	, num_hidden_layers=self.num_hidden_layers	, num_attention_heads=self.num_attention_heads	, intermediate_size=self.intermediate_size	, hidden_act=self.hidden_act	, hidden_dropout_prob=self.hidden_dropout_prob	, attention_probs_dropout_prob=self.attention_probs_dropout_prob	, max_position_embeddings=self.max_position_embeddings	, type_vocab_size=self.type_vocab_size	, initializer_range=self.initializer_range	, relative_attention=self.relative_attention	, position_biased_input=self.position_biased_input	, pos_att_type=self.pos_att_type	, )

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.get_config()
            lowercase														=			300
            return config

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            self.parent.assertListEqual(list(result.loss.size()		)	, []		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			DebertaModel(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase		)[0]
            lowercase														=			model(__lowerCAmelCase	, token_type_ids=__lowerCAmelCase		)[0]
            lowercase														=			model(__lowerCAmelCase		)[0]

            self.parent.assertListEqual(list(sequence_output.size()		)	, [self.batch_size, self.seq_length, self.hidden_size]		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			DebertaForMaskedLM(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase	, labels=__lowerCAmelCase		)

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

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.num_labels
            lowercase														=			DebertaForSequenceClassification(__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase	, labels=__lowerCAmelCase		)
            self.parent.assertListEqual(list(result.logits.size()		)	, [self.batch_size, self.num_labels]		)
            self.check_loss_output(__lowerCAmelCase		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.num_labels
            lowercase														=			DebertaForTokenClassification(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase	, labels=__lowerCAmelCase		)
            self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.num_labels)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			DebertaForQuestionAnswering(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase														=			model(
                __lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase	, start_positions=__lowerCAmelCase	, end_positions=__lowerCAmelCase	, )
            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        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.prepare_config_and_inputs()
            (
                (
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							(
                lowercase
            )							,							
            )														=			config_and_inputs
            lowercase														=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
            return config, inputs_dict






@require_torch
class 				_A		( lowerCAmelCase						,					lowerCAmelCase						,					unittest.TestCase			):
      snake_case__    :     Any													=			(
          (
              DebertaModel,
              DebertaForMaskedLM,
              DebertaForSequenceClassification,
              DebertaForTokenClassification,
              DebertaForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      snake_case__    :     int													=			(
          {
              'feature-extraction': DebertaModel,
              'fill-mask': DebertaForMaskedLM,
              'question-answering': DebertaForQuestionAnswering,
              'text-classification': DebertaForSequenceClassification,
              'token-classification': DebertaForTokenClassification,
              'zero-shot': DebertaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )

      snake_case__    :     Optional[Any]													=			True
      snake_case__    :     Optional[Any]													=			False
      snake_case__    :     Optional[int]													=			False
      snake_case__    :     Any													=			False
      snake_case__    :     List[Any]													=			False

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			DebertaModelTester(self		)
            lowercase														=			ConfigTester(self	, config_class=__lowerCAmelCase	, hidden_size=37		)

      def        A__ (		self		):







            """simple docstring"""



            self.config_tester.run_common_tests()

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_model(*__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_sequence_classification(*__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_masked_lm(*__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_question_answering(*__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_token_classification(*__lowerCAmelCase		)







      @slow
      def        A__ (		self		):







            """simple docstring"""



            for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase														=			DebertaModel.from_pretrained(__lowerCAmelCase		)
                  self.assertIsNotNone(__lowerCAmelCase		)






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

      @unittest.skip(reason="""Model not available yet"""		)
      def        A__ (		self		):







            """simple docstring"""



            pass







      @slow
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			DebertaModel.from_pretrained("""microsoft/deberta-base"""		)

            lowercase														=			torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]		)
            lowercase														=			torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
            with torch.no_grad():
                  lowercase														=			model(__lowerCAmelCase	, attention_mask=__lowerCAmelCase		)[0]
            # compare the actual values for a slice.
            lowercase														=			torch.tensor(
                [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]		)
            self.assertTrue(torch.allclose(output[:, 1:4, 1:4]	, __lowerCAmelCase	, atol=1E-4		)	, f'{output[:, 1:4, 1:4]}'		)



