



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 						lowerCamelCase   :







					'''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=0.02     ,						lowerCAmelCase=None     ,						):
							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  A__		(      self  ):
							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  A__		(      self  ):
							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     ,						lowerCAmelCase     ,						lowerCAmelCase     ,						lowerCAmelCase  ):
							UpperCAmelCase_						       =					ViTMSNModel(config=__lowerCamelCase  )
							model.to(__lowerCamelCase  )
							model.eval()
							UpperCAmelCase_						       =					model(__lowerCamelCase  )
							self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size)  )



					def  A__		(      self     ,						lowerCAmelCase     ,						lowerCAmelCase     ,						lowerCAmelCase  ):
							UpperCAmelCase_						       =					self.type_sequence_label_size
							UpperCAmelCase_						       =					ViTMSNForImageClassification(__lowerCamelCase  )
							model.to(__lowerCamelCase  )
							model.eval()
							UpperCAmelCase_						       =					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
							UpperCAmelCase_						       =					1
							UpperCAmelCase_						       =					ViTMSNForImageClassification(__lowerCamelCase  )
							model.to(__lowerCamelCase  )
							model.eval()

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



					def  A__		(      self  ):
							UpperCAmelCase_						       =					self.prepare_config_and_inputs()
							UpperCAmelCase_       ,    UpperCAmelCase_       ,    UpperCAmelCase_						       =					config_and_inputs
							UpperCAmelCase_						       =					{"pixel_values": pixel_values}
							return config, inputs_dict





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







					'''simple docstring'''







					lowerCAmelCase_					:     int					       =	(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
					lowerCAmelCase_					:     List[str]					       =	(
					    {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
					    if is_torch_available()
					    else {}
					)

					lowerCAmelCase_					:     Optional[int]					       =	False
					lowerCAmelCase_					:     Dict					       =	False
					lowerCAmelCase_					:     Optional[int]					       =	False
					lowerCAmelCase_					:     Union[str, Any]					       =	False



					def  A__		(      self  ):
							UpperCAmelCase_						       =					ViTMSNModelTester(self  )
							UpperCAmelCase_						       =					ConfigTester(self     ,						config_class=__lowerCamelCase     ,						has_text_modality=__lowerCamelCase     ,						hidden_size=37  )



					def  A__		(      self  ):
							self.config_tester.run_common_tests()



					@unittest.skip(reason="ViTMSN does not use inputs_embeds"  )
					def  A__		(      self  ):
							pass



					def  A__		(      self  ):
							UpperCAmelCase_       ,    UpperCAmelCase_						       =					self.model_tester.prepare_config_and_inputs_for_common()

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



					def  A__		(      self  ):
							UpperCAmelCase_       ,    UpperCAmelCase_						       =					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									UpperCAmelCase_						       =					model_class(__lowerCamelCase  )
									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]     ,						__lowerCamelCase  )



					def  A__		(      self  ):
							UpperCAmelCase_						       =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCamelCase  )



					def  A__		(      self  ):
							UpperCAmelCase_						       =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase  )



					@slow
					def  A__		(      self  ):
							for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCAmelCase_						       =					ViTMSNModel.from_pretrained(__lowerCamelCase  )
									self.assertIsNotNone(__lowerCamelCase  )







def snake_case__	(      )		->				str:
		UpperCAmelCase_						       =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
		return image



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







					'''simple docstring'''







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



					@slow
					def  A__		(      self  ):
							torch.manual_seed(2  )
							UpperCAmelCase_						       =					ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"  ).to(__lowerCamelCase  )

							UpperCAmelCase_						       =					self.default_image_processor
							UpperCAmelCase_						       =					prepare_img()
							UpperCAmelCase_						       =					image_processor(images=__lowerCamelCase     ,						return_tensors="pt"  ).to(__lowerCamelCase  )

							# forward pass
							with torch.no_grad():
									UpperCAmelCase_						       =					model(**__lowerCamelCase  )

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

							UpperCAmelCase_						       =					torch.tensor([-0.0803, -0.4454, -0.2375]  ).to(__lowerCamelCase  )

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




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__A			:					Optional[Any]						=			logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      field(default=__snake_case					,       metadata={"help": "Whether to use SortishSampler or not."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": (
               "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `max_length` value of the model configuration."
           )
       }					,       )
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": (
               "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
               "to the `num_beams` value of the model configuration."
           )
       }					,       )
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
       }					,       )





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   super().to_dict()
       for k, v in d.items():
           if isinstance(__lowerCamelCase     , __lowerCamelCase			):
               SCREAMING_SNAKE_CASE										=   v.to_dict()
       return d