




'''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 						a				:


				def __init__(		self    :					Any       ,				lowercase_    :					str       ,				lowercase_    :					str=13       ,				lowercase_    :					Dict=30       ,				lowercase_    :					Tuple=2       ,				lowercase_    :					str=3       ,				lowercase_    :					Optional[int]=True       ,				lowercase_    :					Optional[int]=True       ,				lowercase_    :					Dict=32       ,				lowercase_    :					List[str]=5       ,				lowercase_    :					Union[str, Any]=4       ,				lowercase_    :					Tuple=37       ,				lowercase_    :					Union[str, Any]="gelu"       ,				lowercase_    :					List[str]=0.1       ,				lowercase_    :					Optional[Any]=0.1       ,				lowercase_    :					str=10       ,				lowercase_    :					List[str]=0.02       ,				lowercase_    :					str=None       ,				):
									snake_case_									=				parent
									snake_case_									=				batch_size
									snake_case_									=				image_size
									snake_case_									=				patch_size
									snake_case_									=				num_channels
									snake_case_									=				is_training
									snake_case_									=				use_labels
									snake_case_									=				hidden_size
									snake_case_									=				num_hidden_layers
									snake_case_									=				num_attention_heads
									snake_case_									=				intermediate_size
									snake_case_									=				hidden_act
									snake_case_									=				hidden_dropout_prob
									snake_case_									=				attention_probs_dropout_prob
									snake_case_									=				type_sequence_label_size
									snake_case_									=				initializer_range
									snake_case_									=				scope

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


				def   A_ (		self    :					str					):
									snake_case_									=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

									snake_case_									=				self.get_config()

									return config, pixel_values, labels


				def   A_ (		self    :					Union[str, Any]					):
									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   A_ (		self    :					Optional[int]       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					int       ,				lowercase_    :					Dict					):
									snake_case_									=				ViTMSNModel(config=lowercase_					)
									model.to(lowercase_					)
									model.eval()
									snake_case_									=				model(lowercase_					)
									self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)					)


				def   A_ (		self    :					Tuple       ,				lowercase_    :					Dict       ,				lowercase_    :					Any       ,				lowercase_    :					List[Any]					):
									snake_case_									=				self.type_sequence_label_size
									snake_case_									=				ViTMSNForImageClassification(lowercase_					)
									model.to(lowercase_					)
									model.eval()
									snake_case_									=				model(lowercase_       ,				labels=lowercase_					)
									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
									snake_case_									=				1
									snake_case_									=				ViTMSNForImageClassification(lowercase_					)
									model.to(lowercase_					)
									model.eval()

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


				def   A_ (		self    :					Dict					):
									snake_case_									=				self.prepare_config_and_inputs()
									snake_case_			,snake_case_			,snake_case_									=				config_and_inputs
									snake_case_									=				{'''pixel_values''': pixel_values}
									return config, inputs_dict







@require_torch
class 						a				(   _lowerCamelCase   ,     _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				snake_case_      =		(
				    {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				snake_case_      =		False
				snake_case_      =		False
				snake_case_      =		False
				snake_case_      =		False


				def   A_ (		self    :					int					):
									snake_case_									=				ViTMSNModelTester(self					)
									snake_case_									=				ConfigTester(self       ,				config_class=lowercase_       ,				has_text_modality=lowercase_       ,				hidden_size=37					)


				def   A_ (		self    :					Dict					):
									self.config_tester.run_common_tests()


				@unittest.skip(reason='''ViTMSN does not use inputs_embeds'''					)
				def   A_ (		self    :					List[Any]					):
									pass


				def   A_ (		self    :					Tuple					):
									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()

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


				def   A_ (		self    :					List[str]					):
									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()

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

														snake_case_									=				['''pixel_values''']
														self.assertListEqual(arg_names[:1]       ,				lowercase_					)


				def   A_ (		self    :					Tuple					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase_					)


				def   A_ (		self    :					int					):
									snake_case_									=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowercase_					)


				@slow
				def   A_ (		self    :					Dict					):
									for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_									=				ViTMSNModel.from_pretrained(lowercase_					)
														self.assertIsNotNone(lowercase_					)







def 		__magic_name__    (	)		->					Optional[Any]:

					'''simple docstring'''

					snake_case_									=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
					return image






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


				@cached_property
				def   A_ (		self    :					Any					):
									return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''					) if is_vision_available() else None


				@slow
				def   A_ (		self    :					str					):
									torch.manual_seed(2					)
									snake_case_									=				ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''					).to(lowercase_					)

									snake_case_									=				self.default_image_processor
									snake_case_									=				prepare_img()
									snake_case_									=				image_processor(images=lowercase_       ,				return_tensors='''pt'''					).to(lowercase_					)

									# forward pass
									with torch.no_grad():
														snake_case_									=				model(**lowercase_					)

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

									snake_case_									=				torch.tensor([-0.0803, -0.4454, -0.2375]					).to(lowercase_					)

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





"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case   			=    logging.get_logger(__name__)

__snake_case   			=    {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       Optional[Any]							  =  '''xmod'''







   def __init__(	self						,	UpperCamelCase__=3_0522						,	UpperCamelCase__=768						,	UpperCamelCase__=12						,	UpperCamelCase__=12						,	UpperCamelCase__=3072						,	UpperCamelCase__="gelu"						,	UpperCamelCase__=0.1						,	UpperCamelCase__=0.1						,	UpperCamelCase__=512						,	UpperCamelCase__=2						,	UpperCamelCase__=0.02						,	UpperCamelCase__=1e-12						,	UpperCamelCase__=1						,	UpperCamelCase__=0						,	UpperCamelCase__=2						,	UpperCamelCase__="absolute"						,	UpperCamelCase__=True						,	UpperCamelCase__=None						,	UpperCamelCase__=False						,	UpperCamelCase__=2						,	UpperCamelCase__=False						,	UpperCamelCase__=True						,	UpperCamelCase__=True						,	UpperCamelCase__=("en_XX",)						,	UpperCamelCase__=None						,	**UpperCamelCase__						,	)	->		int:

       '''simple docstring'''




       super().__init__(pad_token_id=UpperCamelCase__						,	bos_token_id=UpperCamelCase__						,	eos_token_id=UpperCamelCase__						,	**UpperCamelCase__						)

       snake_case    :      List[Any]      					=    vocab_size
       snake_case    :      List[Any]      					=    hidden_size
       snake_case    :      Union[str, Any]      					=    num_hidden_layers
       snake_case    :      Any      					=    num_attention_heads
       snake_case    :      List[str]      					=    hidden_act
       snake_case    :      Union[str, Any]      					=    intermediate_size
       snake_case    :      int      					=    hidden_dropout_prob
       snake_case    :      Any      					=    attention_probs_dropout_prob
       snake_case    :      Optional[int]      					=    max_position_embeddings
       snake_case    :      Tuple      					=    type_vocab_size
       snake_case    :      List[str]      					=    initializer_range
       snake_case    :      int      					=    layer_norm_eps
       snake_case    :      Optional[Any]      					=    position_embedding_type
       snake_case    :      int      					=    use_cache
       snake_case    :      Dict      					=    classifier_dropout
       snake_case    :      Dict      					=    pre_norm
       snake_case    :      Union[str, Any]      					=    adapter_reduction_factor
       snake_case    :      Any      					=    adapter_layer_norm
       snake_case    :      Optional[int]      					=    adapter_reuse_layer_norm
       snake_case    :      List[Any]      					=    ln_before_adapter
       snake_case    :      str      					=    list(UpperCamelCase__						)
       snake_case    :      int      					=    default_language







class _lowerCAmelCase				(      snake_case_	):







   @property
   def  lowerCamelCase		(	self						)	->		Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task == "multiple-choice":
           snake_case    :      Optional[int]      					=    {0: "batch", 1: "choice", 2: "sequence"}
       else:
           snake_case    :      Optional[Any]      					=    {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]						)





