




"""simple docstring"""




import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					dataset
							lowerCamelCase			      =					process
							lowerCamelCase			      =					params





				def __len__(       self ):





							"""simple docstring"""
							return len(self.dataset )





				def __getitem__(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.dataset[i]
							lowerCamelCase			      =					self.process(_a		, **self.params )
							return processed






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a		, _a=None ):





							"""simple docstring"""
							lowerCamelCase			      =					loader
							lowerCamelCase			      =					infer
							lowerCamelCase			      =					params
							if loader_batch_size == 1:
										# Let's spare some time by deactivating altogether
										lowerCamelCase			      =					None
							lowerCamelCase			      =					loader_batch_size

							# Internal bookkeeping
							lowerCamelCase			      =					None
							lowerCamelCase			      =					None





				def __len__(       self ):





							"""simple docstring"""
							return len(self.loader )





				def __iter__(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					iter(self.loader )
							return self





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							if isinstance(self._loader_batch_data		, torch.Tensor ):
										# Batch data is simple tensor, just fetch the slice
										lowerCamelCase			      =					self._loader_batch_data[self._loader_batch_index]
							else:
										# Batch data is assumed to be BaseModelOutput (or dict)
										lowerCamelCase			      =					{}
										for k, element in self._loader_batch_data.items():
													if isinstance(_a		, _a ):
																# Convert ModelOutput to tuple first
																lowerCamelCase			      =					element.to_tuple()
																if isinstance(element[0]		, torch.Tensor ):
																			lowerCamelCase			      =					tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
																elif isinstance(element[0]		, np.ndarray ):
																			lowerCamelCase			      =					tuple(np.expand_dims(el[self._loader_batch_index]		, 0 ) for el in element )
																continue
													if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_a		, _a ):
																# Those are stored as lists of tensors so need specific unbatching.
																if isinstance(element[0]		, torch.Tensor ):
																			lowerCamelCase			      =					tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
																elif isinstance(element[0]		, np.ndarray ):
																			lowerCamelCase			      =					tuple(np.expand_dims(el[self._loader_batch_index]		, 0 ) for el in element )
																continue
													if element is None:
																# This can happen for optional data that get passed around
																lowerCamelCase			      =					None
													elif isinstance(element[self._loader_batch_index]		, torch.Tensor ):
																# Take correct batch data, but make it looked like batch_size=1
																# For compatibility with other methods within transformers

																lowerCamelCase			      =					element[self._loader_batch_index].unsqueeze(0 )
													elif isinstance(element[self._loader_batch_index]		, np.ndarray ):
																# Take correct batch data, but make it looked like batch_size=1
																# For compatibility with other methods within transformers
																lowerCamelCase			      =					np.expand_dims(element[self._loader_batch_index]		, 0 )
													else:
																# This is typically a list, so no need to `unsqueeze`.
																lowerCamelCase			      =					element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
										lowerCamelCase			      =					self._loader_batch_data.__class__(_a )
							self._loader_batch_index += 1
							return result





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
										# We are currently unrolling a batch so we just need to return
										# the current item within a batch
										return self.loader_batch_item()

							# We're out of items within a batch
							lowerCamelCase			      =					next(self.iterator )
							lowerCamelCase			      =					self.infer(_a		, **self.params )
							# We now have a batch of "inferred things".
							if self.loader_batch_size is not None:
										# Try to infer the size of the batch
										if isinstance(_a		, torch.Tensor ):
													lowerCamelCase			      =					processed
										else:
													lowerCamelCase			      =					list(processed.keys() )[0]
													lowerCamelCase			      =					processed[key]
										if isinstance(_a		, _a ):
													lowerCamelCase			      =					len(_a )
										else:
													lowerCamelCase			      =					first_tensor.shape[0]
										if 0 < observed_batch_size < self.loader_batch_size:
													# could be last batch so we can't unroll as many
													# elements.
													lowerCamelCase			      =					observed_batch_size
										# Setting internal index to unwrap the batch
										lowerCamelCase			      =					processed
										lowerCamelCase			      =					0
										return self.loader_batch_item()
							else:
										# We're not unrolling batches
										return processed






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a		, _a=None ):





							"""simple docstring"""
							super().__init__(_a		, _a		, _a )





				def __iter__(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					iter(self.loader )
							lowerCamelCase			      =					None
							return self





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							if self.subiterator is None:
										lowerCamelCase			      =					self.infer(next(self.iterator )		, **self.params )
							try:
										# Try to return next item
										lowerCamelCase			      =					next(self.subiterator )
							except StopIteration:
										# When a preprocess iterator ends, we can start lookig at the next item
										# ChunkIterator will keep feeding until ALL elements of iterator
										# all have created their subiterator and have been iterating against.
										#
										# Another way to look at it, is we're basically flattening lists of lists
										# into a single list, but with generators
										lowerCamelCase			      =					self.infer(next(self.iterator )		, **self.params )
										lowerCamelCase			      =					next(self.subiterator )
							return processed






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __iter__(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					iter(self.loader )
							return self





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							# Extremely similar to PipelineIterator in its unpacking mechanism
							# BUT, we have an extra required item which is the presence of `is_last`
							# That is because everything is flattened by `PipelineChunkIterator` we
							# need to keep track of how to regroup here in the original `process`
							# boundaries so that `process` and `postprocess` see the same data.

							# This iterator accumulates items (possibly while unbatching) until it
							# its a `is_last` and then just passes it on to the caller.
							lowerCamelCase			      =					False
							lowerCamelCase			      =					[]
							if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
										while self._loader_batch_index < self.loader_batch_size:
													lowerCamelCase			      =					self.loader_batch_item()
													lowerCamelCase			      =					item.pop("""is_last""" )
													accumulator.append(_a )
													if is_last:
																return accumulator

							while not is_last:
										lowerCamelCase			      =					self.infer(next(self.iterator )		, **self.params )
										if self.loader_batch_size is not None:
													if isinstance(_a		, torch.Tensor ):
																lowerCamelCase			      =					processed
													else:
																lowerCamelCase			      =					list(processed.keys() )[0]
																lowerCamelCase			      =					processed[key]
													if isinstance(_a		, _a ):
																lowerCamelCase			      =					len(_a )
													else:
																lowerCamelCase			      =					first_tensor.shape[0]
													if 0 < observed_batch_size < self.loader_batch_size:
																# could be last batch so we can't unroll as many
																# elements.
																lowerCamelCase			      =					observed_batch_size
													lowerCamelCase			      =					processed
													lowerCamelCase			      =					0
													while self._loader_batch_index < self.loader_batch_size:
																lowerCamelCase			      =					self.loader_batch_item()
																lowerCamelCase			      =					item.pop("""is_last""" )
																accumulator.append(_a )
																if is_last:
																			return accumulator
										else:
													lowerCamelCase			      =					processed
													lowerCamelCase			      =					item.pop("""is_last""" )
													accumulator.append(_a )
							return accumulator






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					dataset
							lowerCamelCase			      =					key





				def __len__(       self ):





							"""simple docstring"""
							return len(self.dataset )





				def __getitem__(       self		, _a ):





							"""simple docstring"""
							return self.dataset[i][self.key]






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					dataset
							lowerCamelCase			      =					keya
							lowerCamelCase			      =					keya





				def __len__(       self ):





							"""simple docstring"""
							return len(self.dataset )





				def __getitem__(       self		, _a ):





							"""simple docstring"""
							return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}









"""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     __magic_name__							:






				'''simple docstring'''





				def __init__(       self		, _a		, _a=13		, _a=30		, _a=2		, _a=3		, _a=True		, _a=True		, _a=32		, _a=5		, _a=4		, _a=37		, _a="gelu"		, _a=0.1		, _a=0.1		, _a=10		, _a=0.02		, _a=None		, ):





							"""simple docstring"""
							lowerCamelCase			      =					parent
							lowerCamelCase			      =					batch_size
							lowerCamelCase			      =					image_size
							lowerCamelCase			      =					patch_size
							lowerCamelCase			      =					num_channels
							lowerCamelCase			      =					is_training
							lowerCamelCase			      =					use_labels
							lowerCamelCase			      =					hidden_size
							lowerCamelCase			      =					num_hidden_layers
							lowerCamelCase			      =					num_attention_heads
							lowerCamelCase			      =					intermediate_size
							lowerCamelCase			      =					hidden_act
							lowerCamelCase			      =					hidden_dropout_prob
							lowerCamelCase			      =					attention_probs_dropout_prob
							lowerCamelCase			      =					type_sequence_label_size
							lowerCamelCase			      =					initializer_range
							lowerCamelCase			      =					scope

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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

							lowerCamelCase			      =					self.get_config()

							return config, pixel_values, labels





				def    _lowerCAmelCase				(       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    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					ViTMSNModel(config=_a )
							model.to(_a )
							model.eval()
							lowerCamelCase			      =					model(_a )
							self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size) )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.type_sequence_label_size
							lowerCamelCase			      =					ViTMSNForImageClassification(_a )
							model.to(_a )
							model.eval()
							lowerCamelCase			      =					model(_a		, labels=_a )
							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
							lowerCamelCase			      =					1
							lowerCamelCase			      =					ViTMSNForImageClassification(_a )
							model.to(_a )
							model.eval()

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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.prepare_config_and_inputs()
							lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					config_and_inputs
							lowerCamelCase			      =					{"""pixel_values""": pixel_values}
							return config, inputs_dict






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






				'''simple docstring'''
				__UpperCamelCase			  =						(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				__UpperCamelCase			  =						(
				    {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					ViTMSNModelTester(self )
							lowerCamelCase			      =					ConfigTester(self		, config_class=_a		, has_text_modality=_a		, hidden_size=37 )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							self.config_tester.run_common_tests()





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





							"""simple docstring"""
							pass





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase      ,   lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs_for_common()

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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase      ,   lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs_for_common()

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

										lowerCamelCase			      =					["""pixel_values"""]
										self.assertListEqual(arg_names[:1]		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_a )





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCamelCase			      =					ViTMSNModel.from_pretrained(_a )
										self.assertIsNotNone(_a )




def     a__ (				)						->					Any:
			lowerCamelCase			      =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
			return image






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






				'''simple docstring'''





				@cached_property
				def    _lowerCAmelCase				(       self ):





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





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							torch.manual_seed(2 )
							lowerCamelCase			      =					ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small""" ).to(_a )

							lowerCamelCase			      =					self.default_image_processor
							lowerCamelCase			      =					prepare_img()
							lowerCamelCase			      =					image_processor(images=_a		, return_tensors="""pt""" ).to(_a )

							# forward pass
							with torch.no_grad():
										lowerCamelCase			      =					model(**_a )

							# verify the logits
							lowerCamelCase			      =					torch.Size((1, 1_000) )
							self.assertEqual(outputs.logits.shape		, _a )

							lowerCamelCase			      =					torch.tensor([-0.0_803, -0.4_454, -0.2_375] ).to(_a )

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



