






import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
 from transformers.models.rag.configuration_rag import RagConfig
 from transformers.models.rag.tokenization_rag import RagTokenizer


@require_faiss
@require_torch
class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     tempfile.mkdtemp()
     UpperCamelCase_									=     8

     # DPR tok
     UpperCamelCase_									=     [
         '[UNK]',
         '[CLS]',
         '[SEP]',
         '[PAD]',
         '[MASK]',
         'want',
         '##want',
         '##ed',
         'wa',
         'un',
         'runn',
         '##ing',
         ',',
         'low',
         'lowest',
     ]
     UpperCamelCase_									=     os.path.join(self.tmpdirname		,		'dpr_tokenizer' )
     os.makedirs(_UpperCAmelCase		,		exist_ok=_UpperCAmelCase )
     UpperCamelCase_									=     os.path.join(_UpperCAmelCase		,		DPR_VOCAB_FILES_NAMES['vocab_file'] )
     with open(self.vocab_file		,		'w'		,		encoding='utf-8' ) as vocab_writer:
        vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )

     # BART tok
     UpperCamelCase_									=     [
         'l',
         'o',
         'w',
         'e',
         'r',
         's',
         't',
         'i',
         'd',
         'n',
         '\u0120',
         '\u0120l',
         '\u0120n',
         '\u0120lo',
         '\u0120low',
         'er',
         '\u0120lowest',
         '\u0120newer',
         '\u0120wider',
         '<unk>',
     ]
     UpperCamelCase_									=     dict(zip(_UpperCAmelCase		,		range(len(_UpperCAmelCase ) ) ) )
     UpperCamelCase_									=     ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
     UpperCamelCase_									=     {'unk_token': '<unk>'}

     UpperCamelCase_									=     os.path.join(self.tmpdirname		,		'bart_tokenizer' )
     os.makedirs(_UpperCAmelCase		,		exist_ok=_UpperCAmelCase )
     UpperCamelCase_									=     os.path.join(_UpperCAmelCase		,		BART_VOCAB_FILES_NAMES['vocab_file'] )
     UpperCamelCase_									=     os.path.join(_UpperCAmelCase		,		BART_VOCAB_FILES_NAMES['merges_file'] )
     with open(self.vocab_file		,		'w'		,		encoding='utf-8' ) as fp:
        fp.write(json.dumps(_UpperCAmelCase ) + '\n' )
     with open(self.merges_file		,		'w'		,		encoding='utf-8' ) as fp:
        fp.write('\n'.join(_UpperCAmelCase ) )




  def    _UpperCAmelCase     (     self )			-> DPRQuestionEncoderTokenizer:
     return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,		'dpr_tokenizer' ) )




  def    _UpperCAmelCase     (     self )			-> BartTokenizer:
     return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		,		'bart_tokenizer' ) )




  def    _UpperCAmelCase     (     self )			-> List[str]:
     shutil.rmtree(self.tmpdirname )




  @require_tokenizers
  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     os.path.join(self.tmpdirname		,		'rag_tokenizer' )
     UpperCamelCase_									=     RagConfig(question_encoder=DPRConfig().to_dict()		,		generator=BartConfig().to_dict() )
     UpperCamelCase_									=     RagTokenizer(question_encoder=self.get_dpr_tokenizer()		,		generator=self.get_bart_tokenizer() )
     rag_config.save_pretrained(_UpperCAmelCase )
     rag_tokenizer.save_pretrained(_UpperCAmelCase )
     UpperCamelCase_									=     RagTokenizer.from_pretrained(_UpperCAmelCase		,		config=_UpperCAmelCase )
     self.assertIsInstance(new_rag_tokenizer.question_encoder		,		_UpperCAmelCase )
     self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()		,		rag_tokenizer.question_encoder.get_vocab() )
     self.assertIsInstance(new_rag_tokenizer.generator		,		_UpperCAmelCase )
     self.assertEqual(new_rag_tokenizer.generator.get_vocab()		,		rag_tokenizer.generator.get_vocab() )




  @slow
  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     RagTokenizer.from_pretrained('facebook/rag-token-nq' )
     UpperCamelCase_									=     [
         'who got the first nobel prize in physics',
         'when is the next deadpool movie being released',
         'which mode is used for short wave broadcast service',
         'who is the owner of reading football club',
         'when is the next scandal episode coming out',
         'when is the last time the philadelphia won the superbowl',
         'what is the most current adobe flash player version',
         'how many episodes are there in dragon ball z',
         'what is the first step in the evolution of the eye',
         'where is gall bladder situated in human body',
         'what is the main mineral in lithium batteries',
         'who is the president of usa right now',
         'where do the greasers live in the outsiders',
         'panda is a national animal of which country',
         'what is the name of manchester united stadium',
     ]
     UpperCamelCase_									=     tokenizer(_UpperCAmelCase )
     self.assertIsNotNone(_UpperCAmelCase )




  @slow
  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     RagTokenizer.from_pretrained('facebook/rag-sequence-nq' )
     UpperCamelCase_									=     [
         'who got the first nobel prize in physics',
         'when is the next deadpool movie being released',
         'which mode is used for short wave broadcast service',
         'who is the owner of reading football club',
         'when is the next scandal episode coming out',
         'when is the last time the philadelphia won the superbowl',
         'what is the most current adobe flash player version',
         'how many episodes are there in dragon ball z',
         'what is the first step in the evolution of the eye',
         'where is gall bladder situated in human body',
         'what is the main mineral in lithium batteries',
         'who is the president of usa right now',
         'where do the greasers live in the outsiders',
         'panda is a national animal of which country',
         'what is the name of manchester united stadium',
     ]
     UpperCamelCase_									=     tokenizer(_UpperCAmelCase )
     self.assertIsNotNone(_UpperCAmelCase )










import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ViTMSNForImageClassification, ViTMSNModel
 from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor


class 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase=13		,		_UpperCAmelCase=30		,		_UpperCAmelCase=2		,		_UpperCAmelCase=3		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=32		,		_UpperCAmelCase=5		,		_UpperCAmelCase=4		,		_UpperCAmelCase=37		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=10		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=None		,		)			-> Optional[Any]:
     UpperCamelCase_									=     parent
     UpperCamelCase_									=     batch_size
     UpperCamelCase_									=     image_size
     UpperCamelCase_									=     patch_size
     UpperCamelCase_									=     num_channels
     UpperCamelCase_									=     is_training
     UpperCamelCase_									=     use_labels
     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     intermediate_size
     UpperCamelCase_									=     hidden_act
     UpperCamelCase_									=     hidden_dropout_prob
     UpperCamelCase_									=     attention_probs_dropout_prob
     UpperCamelCase_									=     type_sequence_label_size
     UpperCamelCase_									=     initializer_range
     UpperCamelCase_									=     scope

     # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
     UpperCamelCase_									=     (image_size // patch_size) ** 2
     UpperCamelCase_									=     num_patches + 1




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

     UpperCamelCase_									=     None
     if self.use_labels:
        UpperCamelCase_									=     ids_tensor([self.batch_size]		,		self.type_sequence_label_size )

     UpperCamelCase_									=     self.get_config()

     return config, pixel_values, labels




  def    _UpperCAmelCase     (     self )			-> int:
     return ViTMSNConfig(
         image_size=self.image_size		,		patch_size=self.patch_size		,		num_channels=self.num_channels		,		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		,		initializer_range=self.initializer_range		,		)




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Any:
     UpperCamelCase_									=     ViTMSNModel(config=_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     model(_UpperCAmelCase )
     self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.seq_length, self.hidden_size) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> str:
     UpperCamelCase_									=     self.type_sequence_label_size
     UpperCamelCase_									=     ViTMSNForImageClassification(_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     model(_UpperCAmelCase		,		labels=_UpperCAmelCase )
     print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}' )
     print('Labels: {labels}' )
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.type_sequence_label_size) )

     # test greyscale images
     UpperCamelCase_									=     1
     UpperCamelCase_									=     ViTMSNForImageClassification(_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()

     UpperCamelCase_									=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
     UpperCamelCase_									=     model(_UpperCAmelCase )
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.type_sequence_label_size) )




  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     self.prepare_config_and_inputs()
     UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_									=     config_and_inputs
     UpperCamelCase_									=     {'pixel_values': pixel_values}
     return config, inputs_dict




@require_torch
class 						_a       (  UpperCAmelCase__						,       UpperCAmelCase__						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
  A_						=   (
      {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
      if is_torch_available()
      else {}
  )

  A_						=   False
  A_						=   False
  A_						=   False
  A_						=   False




  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     ViTMSNModelTester(self )
     UpperCamelCase_									=     ConfigTester(self		,		config_class=_UpperCAmelCase		,		has_text_modality=_UpperCAmelCase		,		hidden_size=37 )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     self.config_tester.run_common_tests()




  @unittest.skip(reason='ViTMSN does not use inputs_embeds' )
  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     pass




  def    _UpperCAmelCase     (     self )			-> List[str]:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCamelCase_									=     model_class(_UpperCAmelCase )
        self.assertIsInstance(model.get_input_embeddings()		,		(nn.Module) )
        UpperCamelCase_									=     model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(_UpperCAmelCase		,		nn.Linear ) )




  def    _UpperCAmelCase     (     self )			-> List[str]:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCamelCase_									=     model_class(_UpperCAmelCase )
        UpperCamelCase_									=     inspect.signature(model.forward )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        UpperCamelCase_									=     [*signature.parameters.keys()]

        UpperCamelCase_									=     ['pixel_values']
        self.assertListEqual(arg_names[:1]		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )




  @slow
  def    _UpperCAmelCase     (     self )			-> Dict:
     for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase_									=     ViTMSNModel.from_pretrained(_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )






def     _snake_case    ():
   UpperCamelCase_									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
   return image


@require_torch
@require_vision
class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  @cached_property
  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     return ViTImageProcessor.from_pretrained('facebook/vit-msn-small' ) if is_vision_available() else None




  @slow
  def    _UpperCAmelCase     (     self )			-> List[str]:
     torch.manual_seed(2 )
     UpperCamelCase_									=     ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small' ).to(_UpperCAmelCase )

     UpperCamelCase_									=     self.default_image_processor
     UpperCamelCase_									=     prepare_img()
     UpperCamelCase_									=     image_processor(images=_UpperCAmelCase		,		return_tensors='pt' ).to(_UpperCAmelCase )

     # forward pass
     with torch.no_grad():
        UpperCamelCase_									=     model(**_UpperCAmelCase )

     # verify the logits
     UpperCamelCase_									=     torch.Size((1, 1000) )
     self.assertEqual(outputs.logits.shape		,		_UpperCAmelCase )

     UpperCamelCase_									=     torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5] ).to(_UpperCAmelCase )

     self.assertTrue(torch.allclose(outputs.logits[0, :3]		,		_UpperCAmelCase		,		atol=1e-4 ) )





