



'''simple docstring'''





import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_UpperCamelCase											=		{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class    lowerCamelCase_       (							nn.Module	):
  """simple docstring"""




  def __init__(					self :      Tuple     ,      _a :      Optional[Any]  )   ->   int:
    super().__init__()
    __lowerCamelCase			:			Optional[int]       			=					torchvision.models.resnetaaa(pretrained=_a  )
    __lowerCamelCase			:			Tuple       			=					list(model.children()  )[:-2]
    __lowerCamelCase			:			List[str]       			=					nn.Sequential(*_a  )
    __lowerCamelCase			:			Optional[int]       			=					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )




  def 		_lowercase	(					self :      List[Any]     ,      _a :      Optional[int]  )   ->   Union[str, Any]:
    # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
    __lowerCamelCase			:			Optional[Any]       			=					self.pool(self.model(_a  )  )
    __lowerCamelCase			:			Optional[Any]       			=					torch.flatten(_a     ,      start_dim=2  )
    __lowerCamelCase			:			Union[str, Any]       			=					out.transpose(1     ,      2  ).contiguous()
    return out  # BxNx2048






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      List[Any]     ,      _a :      List[str]     ,      _a :      List[str]     ,      _a :      str     ,      _a :      Any     ,      _a :      List[Any]  )   ->   Optional[Any]:
    __lowerCamelCase			:			Tuple       			=					[json.loads(_a  ) for l in open(_a  )]
    __lowerCamelCase			:			Any       			=					os.path.dirname(_a  )
    __lowerCamelCase			:			Tuple       			=					tokenizer
    __lowerCamelCase			:			int       			=					labels
    __lowerCamelCase			:			str       			=					len(_a  )
    __lowerCamelCase			:			List[Any]       			=					max_seq_length

    __lowerCamelCase			:			Dict       			=					transforms




  def __len__(					self :      str  )   ->   str:
    return len(self.data  )




  def __getitem__(					self :      Any     ,      _a :      Optional[int]  )   ->   Optional[int]:
    __lowerCamelCase			:			Dict       			=					torch.LongTensor(self.tokenizer.encode(self.data[index]['text']     ,      add_special_tokens=_a  )  )
    __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Any       			=					sentence[0], sentence[1:-1], sentence[-1]
    __lowerCamelCase			:			Tuple       			=					sentence[: self.max_seq_length]

    __lowerCamelCase			:			Optional[int]       			=					torch.zeros(self.n_classes  )
    __lowerCamelCase			:			Optional[int]       			=					1

    __lowerCamelCase			:			List[Any]       			=					Image.open(os.path.join(self.data_dir     ,      self.data[index]['img']  )  ).convert('RGB'  )
    __lowerCamelCase			:			int       			=					self.transforms(_a  )

    return {
        "image_start_token": start_token,
        "image_end_token": end_token,
        "sentence": sentence,
        "image": image,
        "label": label,
    }




  def 		_lowercase	(					self :      Dict  )   ->   Tuple:
    __lowerCamelCase			:			Any       			=					Counter()
    for row in self.data:
      label_freqs.update(row['label']  )
    return label_freqs




def 			a_	(				_lowerCAmelCase			) -> Optional[int]:
  __lowerCamelCase			:			Tuple       			=					[len(row['sentence']			) for row in batch]
  __lowerCamelCase			,__lowerCamelCase			:			Optional[int]       			=					len(_lowerCAmelCase			), max(_lowerCAmelCase			)

  __lowerCamelCase			:			Any       			=					torch.zeros(_lowerCAmelCase			,_lowerCAmelCase			,dtype=torch.long			)
  __lowerCamelCase			:			Union[str, Any]       			=					torch.zeros(_lowerCAmelCase			,_lowerCAmelCase			,dtype=torch.long			)

  for i_batch, (input_row, length) in enumerate(zip(_lowerCAmelCase			,_lowerCAmelCase			)			):
    __lowerCamelCase			:			List[str]       			=					input_row['sentence']
    __lowerCamelCase			:			str       			=					1

  __lowerCamelCase			:			Tuple       			=					torch.stack([row['image'] for row in batch]			)
  __lowerCamelCase			:			Optional[int]       			=					torch.stack([row['label'] for row in batch]			)
  __lowerCamelCase			:			Optional[Any]       			=					torch.stack([row['image_start_token'] for row in batch]			)
  __lowerCamelCase			:			str       			=					torch.stack([row['image_end_token'] for row in batch]			)

  return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor

def 			a_	(				) -> Dict:
  return [
      "Crime",
      "Drama",
      "Thriller",
      "Action",
      "Comedy",
      "Romance",
      "Documentary",
      "Short",
      "Mystery",
      "History",
      "Family",
      "Adventure",
      "Fantasy",
      "Sci-Fi",
      "Western",
      "Horror",
      "Sport",
      "War",
      "Music",
      "Musical",
      "Animation",
      "Biography",
      "Film-Noir",
  ]


def 			a_	(				) -> Any:
  return transforms.Compose(
      [
          transforms.Resize(256			),
          transforms.CenterCrop(224			),
          transforms.ToTensor(),
          transforms.Normalize(
              mean=[0.46777044, 0.44531429, 0.40661017]			,std=[0.12221994, 0.12145835, 0.14380469]			,),
      ]			)











'''simple docstring'''





import unittest

from transformers import DebertaVaConfig, 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 (
       DebertaVaForMaskedLM,
       DebertaVaForMultipleChoice,
       DebertaVaForQuestionAnswering,
       DebertaVaForSequenceClassification,
       DebertaVaForTokenClassification,
       DebertaVaModel,
   )
   from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      Dict     ,      _a :      int     ,      _a :      Any=13     ,      _a :      Union[str, Any]=7     ,      _a :      int=True     ,      _a :      Optional[int]=True     ,      _a :      Union[str, Any]=True     ,      _a :      Dict=True     ,      _a :      Optional[Any]=99     ,      _a :      List[str]=32     ,      _a :      Union[str, Any]=5     ,      _a :      Optional[Any]=4     ,      _a :      Dict=37     ,      _a :      Dict="gelu"     ,      _a :      Optional[int]=0.1     ,      _a :      Tuple=0.1     ,      _a :      Dict=512     ,      _a :      Tuple=16     ,      _a :      List[str]=2     ,      _a :      Union[str, Any]=0.02     ,      _a :      List[Any]=False     ,      _a :      str=True     ,      _a :      List[Any]="None"     ,      _a :      Dict=3     ,      _a :      Union[str, Any]=4     ,      _a :      Union[str, Any]=None     ,      )   ->   List[str]:
    __lowerCamelCase			:			Any       			=					parent
    __lowerCamelCase			:			List[Any]       			=					batch_size
    __lowerCamelCase			:			str       			=					seq_length
    __lowerCamelCase			:			Dict       			=					is_training
    __lowerCamelCase			:			str       			=					use_input_mask
    __lowerCamelCase			:			Any       			=					use_token_type_ids
    __lowerCamelCase			:			Dict       			=					use_labels
    __lowerCamelCase			:			str       			=					vocab_size
    __lowerCamelCase			:			Optional[Any]       			=					hidden_size
    __lowerCamelCase			:			Union[str, Any]       			=					num_hidden_layers
    __lowerCamelCase			:			Union[str, Any]       			=					num_attention_heads
    __lowerCamelCase			:			str       			=					intermediate_size
    __lowerCamelCase			:			Union[str, Any]       			=					hidden_act
    __lowerCamelCase			:			Tuple       			=					hidden_dropout_prob
    __lowerCamelCase			:			Union[str, Any]       			=					attention_probs_dropout_prob
    __lowerCamelCase			:			Optional[Any]       			=					max_position_embeddings
    __lowerCamelCase			:			Tuple       			=					type_vocab_size
    __lowerCamelCase			:			Dict       			=					type_sequence_label_size
    __lowerCamelCase			:			str       			=					initializer_range
    __lowerCamelCase			:			Tuple       			=					num_labels
    __lowerCamelCase			:			Optional[Any]       			=					num_choices
    __lowerCamelCase			:			Any       			=					relative_attention
    __lowerCamelCase			:			Any       			=					position_biased_input
    __lowerCamelCase			:			Dict       			=					pos_att_type
    __lowerCamelCase			:			Any       			=					scope




  def 		_lowercase	(					self :      Dict  )   ->   str:
    __lowerCamelCase			:			List[str]       			=					ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size  )

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

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

    __lowerCamelCase			:			Any       			=					None
    __lowerCamelCase			:			Optional[int]       			=					None
    __lowerCamelCase			:			Tuple       			=					None
    if self.use_labels:
      __lowerCamelCase			:			Union[str, Any]       			=					ids_tensor([self.batch_size]     ,      self.type_sequence_label_size  )
      __lowerCamelCase			:			Optional[Any]       			=					ids_tensor([self.batch_size, self.seq_length]     ,      self.num_labels  )
      __lowerCamelCase			:			str       			=					ids_tensor([self.batch_size]     ,      self.num_choices  )

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

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




  def 		_lowercase	(					self :      List[str]  )   ->   Optional[int]:
    return DebertaVaConfig(
        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 		_lowercase	(					self :      Any     ,      _a :      Optional[Any]  )   ->   Any:
    self.parent.assertListEqual(list(result.loss.size()  )     ,      []  )




  def 		_lowercase	(					self :      List[str]     ,      _a :      Any     ,      _a :      List[str]     ,      _a :      List[Any]     ,      _a :      Dict     ,      _a :      Union[str, Any]     ,      _a :      Tuple     ,      _a :      List[Any]  )   ->   List[str]:
    __lowerCamelCase			:			str       			=					DebertaVaModel(config=_a  )
    model.to(_a  )
    model.eval()
    __lowerCamelCase			:			Optional[Any]       			=					model(_a     ,      attention_mask=_a     ,      token_type_ids=_a  )[0]
    __lowerCamelCase			:			List[Any]       			=					model(_a     ,      token_type_ids=_a  )[0]
    __lowerCamelCase			:			int       			=					model(_a  )[0]

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




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      str     ,      _a :      Optional[int]     ,      _a :      Dict     ,      _a :      Optional[int]     ,      _a :      Dict     ,      _a :      Optional[Any]     ,      _a :      Optional[Any]  )   ->   str:
    __lowerCamelCase			:			List[Any]       			=					DebertaVaForMaskedLM(config=_a  )
    model.to(_a  )
    model.eval()
    __lowerCamelCase			:			Optional[int]       			=					model(_a     ,      attention_mask=_a     ,      token_type_ids=_a     ,      labels=_a  )

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




  def 		_lowercase	(					self :      Optional[Any]     ,      _a :      List[str]     ,      _a :      str     ,      _a :      Any     ,      _a :      Dict     ,      _a :      Optional[Any]     ,      _a :      Optional[Any]     ,      _a :      str  )   ->   List[Any]:
    __lowerCamelCase			:			List[str]       			=					self.num_labels
    __lowerCamelCase			:			List[str]       			=					DebertaVaForSequenceClassification(_a  )
    model.to(_a  )
    model.eval()
    __lowerCamelCase			:			Tuple       			=					model(_a     ,      attention_mask=_a     ,      token_type_ids=_a     ,      labels=_a  )
    self.parent.assertListEqual(list(result.logits.size()  )     ,      [self.batch_size, self.num_labels]  )
    self.check_loss_output(_a  )




  def 		_lowercase	(					self :      int     ,      _a :      List[str]     ,      _a :      Union[str, Any]     ,      _a :      Any     ,      _a :      int     ,      _a :      Optional[int]     ,      _a :      Tuple     ,      _a :      Tuple  )   ->   Union[str, Any]:
    __lowerCamelCase			:			Union[str, Any]       			=					self.num_labels
    __lowerCamelCase			:			Union[str, Any]       			=					DebertaVaForTokenClassification(config=_a  )
    model.to(_a  )
    model.eval()
    __lowerCamelCase			:			str       			=					model(_a     ,      attention_mask=_a     ,      token_type_ids=_a     ,      labels=_a  )
    self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.num_labels)  )




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      Optional[int]     ,      _a :      List[Any]     ,      _a :      Optional[Any]     ,      _a :      List[str]     ,      _a :      Optional[int]     ,      _a :      Union[str, Any]     ,      _a :      Any  )   ->   Tuple:
    __lowerCamelCase			:			int       			=					DebertaVaForQuestionAnswering(config=_a  )
    model.to(_a  )
    model.eval()
    __lowerCamelCase			:			List[str]       			=					model(
        _a     ,      attention_mask=_a     ,      token_type_ids=_a     ,      start_positions=_a     ,      end_positions=_a     ,      )
    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 		_lowercase	(					self :      Union[str, Any]     ,      _a :      Any     ,      _a :      List[str]     ,      _a :      str     ,      _a :      str     ,      _a :      List[Any]     ,      _a :      Tuple     ,      _a :      str  )   ->   Optional[int]:
    __lowerCamelCase			:			Union[str, Any]       			=					DebertaVaForMultipleChoice(config=_a  )
    model.to(_a  )
    model.eval()
    __lowerCamelCase			:			List[Any]       			=					input_ids.unsqueeze(1  ).expand(-1     ,      self.num_choices     ,      -1  ).contiguous()
    __lowerCamelCase			:			List[str]       			=					token_type_ids.unsqueeze(1  ).expand(-1     ,      self.num_choices     ,      -1  ).contiguous()
    __lowerCamelCase			:			Union[str, Any]       			=					input_mask.unsqueeze(1  ).expand(-1     ,      self.num_choices     ,      -1  ).contiguous()
    __lowerCamelCase			:			Optional[int]       			=					model(
        _a     ,      attention_mask=_a     ,      token_type_ids=_a     ,      labels=_a     ,      )
    self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_choices)  )




  def 		_lowercase	(					self :      Any  )   ->   Any:
    __lowerCamelCase			:			Tuple       			=					self.prepare_config_and_inputs()
    (
        (
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,
    )			:			Optional[int]       			=					config_and_inputs
    __lowerCamelCase			:			List[Any]       			=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
    return config, inputs_dict






@require_torch
class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  unittest.TestCase	):
  """simple docstring"""




  a_				   =(
      (
          DebertaVaModel,
          DebertaVaForMaskedLM,
          DebertaVaForSequenceClassification,
          DebertaVaForTokenClassification,
          DebertaVaForQuestionAnswering,
          DebertaVaForMultipleChoice,
      )
      if is_torch_available()
      else ()
  )
  a_				   =(
      {
          """feature-extraction""": DebertaVaModel,
          """fill-mask""": DebertaVaForMaskedLM,
          """question-answering""": DebertaVaForQuestionAnswering,
          """text-classification""": DebertaVaForSequenceClassification,
          """token-classification""": DebertaVaForTokenClassification,
          """zero-shot""": DebertaVaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  a_				   =True
  a_				   =False
  a_				   =False
  a_				   =False
  a_				   =False




  def 		_lowercase	(					self :      Tuple  )   ->   Dict:
    __lowerCamelCase			:			str       			=					DebertaVaModelTester(self  )
    __lowerCamelCase			:			Dict       			=					ConfigTester(self     ,      config_class=_a     ,      hidden_size=37  )




  def 		_lowercase	(					self :      int  )   ->   List[str]:
    self.config_tester.run_common_tests()




  def 		_lowercase	(					self :      Any  )   ->   int:
    __lowerCamelCase			:			Any       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_model(*_a  )




  def 		_lowercase	(					self :      Dict  )   ->   Tuple:
    __lowerCamelCase			:			List[str]       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_for_sequence_classification(*_a  )




  def 		_lowercase	(					self :      Tuple  )   ->   Optional[Any]:
    __lowerCamelCase			:			Tuple       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_for_masked_lm(*_a  )




  def 		_lowercase	(					self :      Union[str, Any]  )   ->   Optional[Any]:
    __lowerCamelCase			:			Union[str, Any]       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_for_question_answering(*_a  )




  def 		_lowercase	(					self :      Union[str, Any]  )   ->   List[Any]:
    __lowerCamelCase			:			int       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_for_token_classification(*_a  )




  def 		_lowercase	(					self :      Optional[Any]  )   ->   List[Any]:
    __lowerCamelCase			:			Tuple       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_for_multiple_choice(*_a  )




  @slow
  def 		_lowercase	(					self :      Optional[int]  )   ->   int:
    for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      __lowerCamelCase			:			Any       			=					DebertaVaModel.from_pretrained(_a  )
      self.assertIsNotNone(_a  )






@require_torch
@require_sentencepiece
@require_tokenizers
class    lowerCamelCase_       (							unittest.TestCase	):
  """simple docstring"""




  @unittest.skip(reason='Model not available yet'  )
  def 		_lowercase	(					self :      List[str]  )   ->   Dict:
    pass




  @slow
  def 		_lowercase	(					self :      List[str]  )   ->   List[Any]:
    __lowerCamelCase			:			List[Any]       			=					DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'  )

    __lowerCamelCase			:			Optional[Any]       			=					torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]  )
    __lowerCamelCase			:			List[str]       			=					torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
    with torch.no_grad():
      __lowerCamelCase			:			List[str]       			=					model(_a     ,      attention_mask=_a  )[0]
    # compare the actual values for a slice.
    __lowerCamelCase			:			Tuple       			=					torch.tensor(
        [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]  )
    self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,      _a     ,      atol=1e-4  )     ,      f'{output[:, 1:4, 1:4]}'  )






