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      __lowercase					:

						def __init__(	self				:      int							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Optional[Any]=13							,							__lowerCamelCase				:      Optional[Any]=30							,							__lowerCamelCase				:      Any=2							,							__lowerCamelCase				:      List[Any]=3							,							__lowerCamelCase				:      Optional[int]=True							,							__lowerCamelCase				:      str=True							,							__lowerCamelCase				:      int=32							,							__lowerCamelCase				:      List[Any]=5							,							__lowerCamelCase				:      Dict=4							,							__lowerCamelCase				:      Dict=37							,							__lowerCamelCase				:      str="gelu"							,							__lowerCamelCase				:      List[str]=0.1							,							__lowerCamelCase				:      Any=0.1							,							__lowerCamelCase				:      Dict=10							,							__lowerCamelCase				:      Union[str, Any]=0.02							,							__lowerCamelCase				:      Union[str, Any]=None							,							)							->		Dict:


									'''simple docstring'''

									lowercase							=		parent
									lowercase							=		batch_size
									lowercase							=		image_size
									lowercase							=		patch_size
									lowercase							=		num_channels
									lowercase							=		is_training
									lowercase							=		use_labels
									lowercase							=		hidden_size
									lowercase							=		num_hidden_layers
									lowercase							=		num_attention_heads
									lowercase							=		intermediate_size
									lowercase							=		hidden_act
									lowercase							=		hidden_dropout_prob
									lowercase							=		attention_probs_dropout_prob
									lowercase							=		type_sequence_label_size
									lowercase							=		initializer_range
									lowercase							=		scope

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

						def        __a		(	self				:      Any   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

									lowercase							=		self.get_config()

									return config, pixel_values, labels

						def        __a		(	self				:      str   )							->		Optional[Any]:


									'''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        __a		(	self				:      str							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Optional[int]   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		ViTMSNModel(config=__lowerCamelCase   )
									model.to(__lowerCamelCase   )
									model.eval()
									lowercase							=		model(__lowerCamelCase   )
									self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size)   )

						def        __a		(	self				:      str							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      int   )							->		Union[str, Any]:


									'''simple docstring'''

									lowercase							=		self.type_sequence_label_size
									lowercase							=		ViTMSNForImageClassification(__lowerCamelCase   )
									model.to(__lowerCamelCase   )
									model.eval()
									lowercase							=		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
									lowercase							=		1
									lowercase							=		ViTMSNForImageClassification(__lowerCamelCase   )
									model.to(__lowerCamelCase   )
									model.eval()

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



						def        __a		(	self				:      List[str]   )							->		Any:


									'''simple docstring'''

									lowercase							=		self.prepare_config_and_inputs()
									lowercase		,lowercase		,lowercase							=		config_and_inputs
									lowercase							=		{'''pixel_values''': pixel_values}
									return config, inputs_dict






@require_torch
class      __lowercase					(    _A       ,     _A       ,     unittest.TestCase    ):
						lowercase					 =     (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
						lowercase					 =     (
						    {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
						    if is_torch_available()
						    else {}
						)

						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False

						def        __a		(	self				:      Union[str, Any]   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		ViTMSNModelTester(self   )
									lowercase							=		ConfigTester(self							,							config_class=__lowerCamelCase							,							has_text_modality=__lowerCamelCase							,							hidden_size=37   )

						def        __a		(	self				:      str   )							->		Tuple:


									'''simple docstring'''

									self.config_tester.run_common_tests()

						@unittest.skip(reason='''ViTMSN does not use inputs_embeds'''   )
						def        __a		(	self				:      List[Any]   )							->		str:


									'''simple docstring'''

									pass

						def        __a		(	self				:      Union[str, Any]   )							->		Dict:


									'''simple docstring'''

									lowercase		,lowercase							=		self.model_tester.prepare_config_and_inputs_for_common()

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

						def        __a		(	self				:      str   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase		,lowercase							=		self.model_tester.prepare_config_and_inputs_for_common()

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

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

						def        __a		(	self				:      Any   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowerCamelCase   )

						def        __a		(	self				:      Tuple   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase   )



						@slow
						def        __a		(	self				:      int   )							->		Dict:


									'''simple docstring'''

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






def        __UpperCAmelCase    (       )->       int:



			"""simple docstring"""







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






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

						@cached_property
						def        __a		(	self				:      List[Any]   )							->		str:


									'''simple docstring'''

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



						@slow
						def        __a		(	self				:      List[Any]   )							->		List[str]:


									'''simple docstring'''

									torch.manual_seed(2   )
									lowercase							=		ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''   ).to(__lowerCamelCase   )

									lowercase							=		self.default_image_processor
									lowercase							=		prepare_img()
									lowercase							=		image_processor(images=__lowerCamelCase							,							return_tensors='''pt'''   ).to(__lowerCamelCase   )

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

									# verify the logits
									lowercase							=		torch.Size((1, 10_00)   )
									self.assertEqual(outputs.logits.shape							,							__lowerCamelCase   )

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

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






import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam






class      __lowercase					(    datasets.BeamBasedBuilder    ):

						def        __a		(	self				:      Tuple   )							->		Tuple:


									'''simple docstring'''

									return datasets.DatasetInfo(
									    features=datasets.Features({'''content''': datasets.Value('''string'''   )}   )							,							supervised_keys=__lowerCamelCase							,							)

						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Tuple   )							->		int:


									'''simple docstring'''

									return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,							gen_kwargs={'''examples''': get_test_dummy_examples()}   )]



						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Union[str, Any]   )							->		Optional[int]:


									'''simple docstring'''

									import apache_beam as beam

									return pipeline | "Load Examples" >> beam.Create(__lowerCamelCase   )






class      __lowercase					(    datasets.BeamBasedBuilder    ):

						def        __a		(	self				:      int   )							->		Any:


									'''simple docstring'''

									return datasets.DatasetInfo(
									    features=datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string'''   )}   )}   )							,							supervised_keys=__lowerCamelCase							,							)

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Optional[int]   )							->		int:


									'''simple docstring'''

									return [
									    datasets.SplitGenerator(name=datasets.Split.TRAIN							,							gen_kwargs={'''examples''': get_test_nested_examples()}   )
									]



						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Union[str, Any]   )							->		Tuple:


									'''simple docstring'''

									import apache_beam as beam

									return pipeline | "Load Examples" >> beam.Create(__lowerCamelCase   )






def        __UpperCAmelCase    (       )->       Dict:



			"""simple docstring"""







			return [(i, {"content": content}) for i, content in enumerate(['''foo''', '''bar''', '''foobar''']       )]






def        __UpperCAmelCase    (       )->       List[str]:



			"""simple docstring"""







			return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['''foo''', '''bar''', '''foobar''']       )]






class      __lowercase					(    _A    ):

						@require_beam
						def        __a		(	self				:      Optional[int]   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		len(get_test_dummy_examples()   )
									with tempfile.TemporaryDirectory() as tmp_cache_dir:
												lowercase							=		DummyBeamDataset(cache_dir=__lowerCamelCase							,							beam_runner='''DirectRunner'''   )
												builder.download_and_prepare()
												self.assertTrue(
												    os.path.exists(
												        os.path.join(__lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							f'{builder.name}-train.arrow'   )   )   )
												self.assertDictEqual(builder.info.features							,							datasets.Features({'''content''': datasets.Value('''string'''   )}   )   )
												lowercase							=		builder.as_dataset()
												self.assertEqual(dset['''train'''].num_rows							,							__lowerCamelCase   )
												self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples							,							__lowerCamelCase   )
												self.assertDictEqual(dset['''train'''][0]							,							get_test_dummy_examples()[0][1]   )
												self.assertDictEqual(
												    dset['''train'''][expected_num_examples - 1]							,							get_test_dummy_examples()[expected_num_examples - 1][1]   )
												self.assertTrue(
												    os.path.exists(os.path.join(__lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							'''dataset_info.json'''   )   )   )
												del dset

						@require_beam
						def        __a		(	self				:      Union[str, Any]   )							->		List[Any]:


									'''simple docstring'''

									import apache_beam as beam

									lowercase							=		beam.io.parquetio.WriteToParquet

									lowercase							=		len(get_test_dummy_examples()   )
									with tempfile.TemporaryDirectory() as tmp_cache_dir:
												lowercase							=		DummyBeamDataset(cache_dir=__lowerCamelCase							,							beam_runner='''DirectRunner'''   )
												with patch('''apache_beam.io.parquetio.WriteToParquet'''   ) as write_parquet_mock:
															lowercase							=		partial(__lowerCamelCase							,							num_shards=2   )
															builder.download_and_prepare()
												self.assertTrue(
												    os.path.exists(
												        os.path.join(
												            __lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							f'{builder.name}-train-00000-of-00002.arrow'   )   )   )
												self.assertTrue(
												    os.path.exists(
												        os.path.join(
												            __lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							f'{builder.name}-train-00000-of-00002.arrow'   )   )   )
												self.assertDictEqual(builder.info.features							,							datasets.Features({'''content''': datasets.Value('''string'''   )}   )   )
												lowercase							=		builder.as_dataset()
												self.assertEqual(dset['''train'''].num_rows							,							__lowerCamelCase   )
												self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples							,							__lowerCamelCase   )
												# Order is not preserved when sharding, so we just check that all the elements are there
												self.assertListEqual(sorted(dset['''train''']['''content''']   )							,							sorted(['''foo''', '''bar''', '''foobar''']   )   )
												self.assertTrue(
												    os.path.exists(os.path.join(__lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							'''dataset_info.json'''   )   )   )
												del dset

						@require_beam
						def        __a		(	self				:      int   )							->		int:


									'''simple docstring'''

									with tempfile.TemporaryDirectory() as tmp_cache_dir:
												lowercase							=		DummyBeamDataset(cache_dir=__lowerCamelCase   )
												self.assertRaises(datasets.builder.MissingBeamOptions							,							builder.download_and_prepare   )



						@require_beam
						def        __a		(	self				:      str   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		len(get_test_nested_examples()   )
									with tempfile.TemporaryDirectory() as tmp_cache_dir:
												lowercase							=		NestedBeamDataset(cache_dir=__lowerCamelCase							,							beam_runner='''DirectRunner'''   )
												builder.download_and_prepare()
												self.assertTrue(
												    os.path.exists(
												        os.path.join(__lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							f'{builder.name}-train.arrow'   )   )   )
												self.assertDictEqual(
												    builder.info.features							,							datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string'''   )}   )}   )   )
												lowercase							=		builder.as_dataset()
												self.assertEqual(dset['''train'''].num_rows							,							__lowerCamelCase   )
												self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples							,							__lowerCamelCase   )
												self.assertDictEqual(dset['''train'''][0]							,							get_test_nested_examples()[0][1]   )
												self.assertDictEqual(
												    dset['''train'''][expected_num_examples - 1]							,							get_test_nested_examples()[expected_num_examples - 1][1]   )
												self.assertTrue(
												    os.path.exists(os.path.join(__lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							'''dataset_info.json'''   )   )   )
												del dset





