





"""simple docstring"""



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 		__snake_case :





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase=13 ,						__lowerCamelCase=30 ,						__lowerCamelCase=2 ,						__lowerCamelCase=3 ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=32 ,						__lowerCamelCase=5 ,						__lowerCamelCase=4 ,						__lowerCamelCase=37 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=10 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=None ,						):



     '''simple docstring'''




     __A   :						Union[str, Any]									=					parent
     __A   :						Optional[Any]									=					batch_size
     __A   :						Any									=					image_size
     __A   :						str									=					patch_size
     __A   :						List[Any]									=					num_channels
     __A   :						Optional[Any]									=					is_training
     __A   :						List[str]									=					use_labels
     __A   :						int									=					hidden_size
     __A   :						Dict									=					num_hidden_layers
     __A   :						List[Any]									=					num_attention_heads
     __A   :						Optional[int]									=					intermediate_size
     __A   :						List[Any]									=					hidden_act
     __A   :						Optional[int]									=					hidden_dropout_prob
     __A   :						Union[str, Any]									=					attention_probs_dropout_prob
     __A   :						int									=					type_sequence_label_size
     __A   :						Optional[Any]									=					initializer_range
     __A   :						str									=					scope

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

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

     __A   :						Optional[Any]									=					None
     if self.use_labels:
         __A   :						Any									=					ids_tensor([self.batch_size] ,						self.type_sequence_label_size  )

     __A   :						str									=					self.get_config()

     return config, pixel_values, labels

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     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 ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					ViTMSNModel(config=__lowerCamelCase  )
     model.to(__lowerCamelCase  )
     model.eval()
     __A   :						Optional[int]									=					model(__lowerCamelCase  )
     self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						str									=					self.type_sequence_label_size
     __A   :						List[Any]									=					ViTMSNForImageClassification(__lowerCamelCase  )
     model.to(__lowerCamelCase  )
     model.eval()
     __A   :						Dict									=					model(__lowerCamelCase ,						labels=__lowerCamelCase  )
     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
     __A   :						str									=					1
     __A   :						Dict									=					ViTMSNForImageClassification(__lowerCamelCase  )
     model.to(__lowerCamelCase  )
     model.eval()

     __A   :						List[str]									=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
     __A   :						Dict									=					model(__lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.type_sequence_label_size)  )







 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					self.prepare_config_and_inputs()
     __A			,	__A			,	__A   :						int									=					config_and_inputs
     __A   :						Union[str, Any]									=					{'''pixel_values''': pixel_values}
     return config, inputs_dict






@require_torch
class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


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

 _lowerCamelCase				=					False
 _lowerCamelCase				=					False
 _lowerCamelCase				=					False
 _lowerCamelCase				=					False

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Tuple									=					ViTMSNModelTester(self  )
     __A   :						Union[str, Any]									=					ConfigTester(self ,						config_class=__lowerCamelCase ,						has_text_modality=__lowerCamelCase ,						hidden_size=37  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     self.config_tester.run_common_tests()

 @unittest.skip(reason='''ViTMSN does not use inputs_embeds'''  )
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     pass

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A			,	__A   :						Dict									=					self.model_tester.prepare_config_and_inputs_for_common()

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

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A			,	__A   :						str									=					self.model_tester.prepare_config_and_inputs_for_common()

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

         __A   :						str									=					['''pixel_values''']
         self.assertListEqual(arg_names[:1] ,						__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						int									=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase  )







 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __A   :						Optional[int]									=					ViTMSNModel.from_pretrained(__lowerCamelCase  )
         self.assertIsNotNone(__lowerCamelCase  )







def 		__lowercase					(       )       ->Tuple:



    '''simple docstring'''







    __A   :						Optional[Any]									=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
    return image






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





 """simple docstring"""


 @cached_property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''  ) if is_vision_available() else None







 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     torch.manual_seed(2  )
     __A   :						Union[str, Any]									=					ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''  ).to(__lowerCamelCase  )

     __A   :						List[str]									=					self.default_image_processor
     __A   :						Dict									=					prepare_img()
     __A   :						Optional[int]									=					image_processor(images=__lowerCamelCase ,						return_tensors='''pt'''  ).to(__lowerCamelCase  )

     # forward pass
     with torch.no_grad():
         __A   :						Union[str, Any]									=					model(**__lowerCamelCase  )

     # verify the logits
     __A   :						Optional[Any]									=					torch.Size((1, 1000)  )
     self.assertEqual(outputs.logits.shape ,						__lowerCamelCase  )

     __A   :						Tuple									=					torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]  ).to(__lowerCamelCase  )

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











"""simple docstring"""



import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					XLMTokenizer
 _lowerCamelCase				=					False

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     __A   :						Tuple									=					[
         '''l''',
         '''o''',
         '''w''',
         '''e''',
         '''r''',
         '''s''',
         '''t''',
         '''i''',
         '''d''',
         '''n''',
         '''w</w>''',
         '''r</w>''',
         '''t</w>''',
         '''lo''',
         '''low''',
         '''er</w>''',
         '''low</w>''',
         '''lowest</w>''',
         '''newer</w>''',
         '''wider</w>''',
         '''<unk>''',
     ]
     __A   :						Dict									=					dict(zip(__lowerCamelCase ,						range(len(__lowerCamelCase  )  )  )  )
     __A   :						Union[str, Any]									=					['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

     __A   :						Optional[Any]									=					os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''vocab_file''']  )
     __A   :						Optional[Any]									=					os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''merges_file''']  )
     with open(self.vocab_file ,						'''w'''  ) as fp:
         fp.write(json.dumps(__lowerCamelCase  )  )
     with open(self.merges_file ,						'''w'''  ) as fp:
         fp.write('''\n'''.join(__lowerCamelCase  )  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					'''lower newer'''
     __A   :						int									=					'''lower newer'''
     return input_text, output_text

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Union[str, Any]									=					XLMTokenizer(self.vocab_file ,						self.merges_file  )

     __A   :						Optional[Any]									=					'''lower'''
     __A   :						Any									=					['''low''', '''er</w>''']
     __A   :						Tuple									=					tokenizer.tokenize(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					tokens + ['''<unk>''']
     __A   :						List[str]									=					[14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase  ) ,						__lowerCamelCase  )







 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''  )

     __A   :						Union[str, Any]									=					tokenizer.encode('''sequence builders''' ,						add_special_tokens=__lowerCamelCase  )
     __A   :						int									=					tokenizer.encode('''multi-sequence build''' ,						add_special_tokens=__lowerCamelCase  )

     __A   :						int									=					tokenizer.build_inputs_with_special_tokens(__lowerCamelCase  )
     __A   :						List[Any]									=					tokenizer.build_inputs_with_special_tokens(__lowerCamelCase ,						__lowerCamelCase  )

     assert encoded_sentence == [0] + text + [1]
     assert encoded_pair == [0] + text + [1] + text_a + [1]




