






'''simple docstring'''







import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
			from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor

class _lowercase			:




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

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




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			List[Any] 						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

														lowerCamelCase__      :			List[str] 						= None
														if self.use_labels:
																					lowerCamelCase__      :			Union[str, Any] 						= ids_tensor([self.batch_size]    ,							self.type_sequence_label_size  )

														lowerCamelCase__      :			str 						= self.get_config()

														return config, pixel_values, labels




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														return ViTConfig(
														    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    ,							is_decoder=UpperCamelCase__    ,							initializer_range=self.initializer_range    ,							encoder_stride=self.encoder_stride    ,							)




							def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       Union[str, Any]  ):
														lowerCamelCase__      :			str 						= ViTModel(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Union[str, Any] 						= model(UpperCamelCase__  )
														self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size)  )




							def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       int  ):
														lowerCamelCase__      :			int 						= ViTForMaskedImageModeling(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Union[str, Any] 						= model(UpperCamelCase__  )
														self.parent.assertEqual(
														    result.reconstruction.shape    ,							(self.batch_size, self.num_channels, self.image_size, self.image_size)  )

														# test greyscale images
														lowerCamelCase__      :			List[Any] 						= 1
														lowerCamelCase__      :			str 						= ViTForMaskedImageModeling(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()

														lowerCamelCase__      :			str 						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
														lowerCamelCase__      :			Union[str, Any] 						= model(UpperCamelCase__  )
														self.parent.assertEqual(result.reconstruction.shape    ,							(self.batch_size, 1, self.image_size, self.image_size)  )




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Optional[Any]  ):
														lowerCamelCase__      :			Union[str, Any] 						= self.type_sequence_label_size
														lowerCamelCase__      :			List[str] 						= ViTForImageClassification(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			str 						= model(UpperCamelCase__    ,							labels=UpperCamelCase__  )
														self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.type_sequence_label_size)  )

														# test greyscale images
														lowerCamelCase__      :			List[str] 						= 1
														lowerCamelCase__      :			List[Any] 						= ViTForImageClassification(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()

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




							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__      :			Tuple 						= self.prepare_config_and_inputs()
														(
														    (
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	(
														    lowerCamelCase__
														)						,	
														)      :			Optional[Any] 						= config_and_inputs
														lowerCamelCase__      :			str 						= {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_torch
class _lowercase			(       _lowercase       ,   _lowercase       ,   unittest.TestCase	):
							a          = (
							    (
							        ViTModel,
							        ViTForImageClassification,
							        ViTForMaskedImageModeling,
							    )
							    if is_torch_available()
							    else ()
							)
							a          = (
							    {"""feature-extraction""": ViTModel, """image-classification""": ViTForImageClassification}
							    if is_torch_available()
							    else {}
							)
							a          = True

							a          = False
							a          = False
							a          = False




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			List[Any] 						= ViTModelTester(self  )
														lowerCamelCase__      :			List[Any] 						= ConfigTester(self    ,							config_class=UpperCamelCase__    ,							has_text_modality=UpperCamelCase__    ,							hidden_size=37  )




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""ViT does not use inputs_embeds"""  )
							def 	lowerCamelCase_       (							self:       List[Any]  ):
														pass




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__						,	lowerCamelCase__      :			str 						= self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			Optional[Any] 						= model_class(UpperCamelCase__  )
																					self.assertIsInstance(model.get_input_embeddings()    ,							(nn.Module)  )
																					lowerCamelCase__      :			str 						= model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(UpperCamelCase__    ,							nn.Linear  )  )




							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__						,	lowerCamelCase__      :			str 						= self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCamelCase__      :			List[str] 						= ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			Optional[int] 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			List[str] 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Optional[int] 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__  )




							@slow
							def 	lowerCamelCase_       (							self:       Any  ):
														for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__      :			Optional[int] 						= ViTModel.from_pretrained(UpperCamelCase__  )
																					self.assertIsNotNone(UpperCamelCase__  )





def 				SCREAMING_SNAKE_CASE_ ()   -> Union[str, Any]:
							lowerCamelCase__      :			Tuple 						= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
							return image



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




							@cached_property
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224"""  ) if is_vision_available() else None




							@slow
							def 	lowerCamelCase_       (							self:       List[Any]  ):
														lowerCamelCase__      :			int 						= ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""  ).to(UpperCamelCase__  )

														lowerCamelCase__      :			Optional[int] 						= self.default_image_processor
														lowerCamelCase__      :			Any 						= prepare_img()
														lowerCamelCase__      :			str 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  ).to(UpperCamelCase__  )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__      :			int 						= model(**UpperCamelCase__  )

														# verify the logits
														lowerCamelCase__      :			Union[str, Any] 						= torch.Size((1, 1_000)  )
														self.assertEqual(outputs.logits.shape    ,							UpperCamelCase__  )

														lowerCamelCase__      :			List[Any] 						= torch.tensor([-0.2_744, 0.8_215, -0.0_836]  ).to(UpperCamelCase__  )

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




							@slow
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														# ViT models have an `interpolate_pos_encoding` argument in their forward method,
														# allowing to interpolate the pre-trained position embeddings in order to use
														# the model on higher resolutions. The DINO model by Facebook AI leverages this
														# to visualize self-attention on higher resolution images.
														lowerCamelCase__      :			Optional[Any] 						= ViTModel.from_pretrained("""facebook/dino-vits8"""  ).to(UpperCamelCase__  )

														lowerCamelCase__      :			int 						= ViTImageProcessor.from_pretrained("""facebook/dino-vits8"""    ,							size=480  )
														lowerCamelCase__      :			List[str] 						= prepare_img()
														lowerCamelCase__      :			List[Any] 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  )
														lowerCamelCase__      :			Optional[int] 						= inputs.pixel_values.to(UpperCamelCase__  )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__      :			int 						= model(UpperCamelCase__    ,							interpolate_pos_encoding=UpperCamelCase__  )

														# verify the logits
														lowerCamelCase__      :			Dict 						= torch.Size((1, 3_601, 384)  )
														self.assertEqual(outputs.last_hidden_state.shape    ,							UpperCamelCase__  )

														lowerCamelCase__      :			int 						= torch.tensor(
														    [[4.2_340, 4.3_906, -6.6_692], [4.5_463, 1.8_928, -6.7_257], [4.4_429, 0.8_496, -5.8_585]]  ).to(UpperCamelCase__  )

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




							@slow
							@require_accelerate
							@require_torch_gpu
							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			Dict 						= ViTModel.from_pretrained("""facebook/dino-vits8"""    ,							torch_dtype=torch.floataa    ,							device_map="""auto"""  )
														lowerCamelCase__      :			Tuple 						= self.default_image_processor

														lowerCamelCase__      :			str 						= prepare_img()
														lowerCamelCase__      :			Dict 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  )
														lowerCamelCase__      :			Dict 						= inputs.pixel_values.to(UpperCamelCase__  )

														# forward pass to make sure inference works in fp16
														with torch.no_grad():
																					lowerCamelCase__      :			str 						= model(UpperCamelCase__  )









'''simple docstring'''







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

from ..utils.generic import ModelOutput

class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Optional[int]  ):
														lowerCamelCase__      :			Optional[int] 						= dataset
														lowerCamelCase__      :			Optional[int] 						= process
														lowerCamelCase__      :			List[str] 						= params




							def __len__(							self:       List[str]  ):
														return len(self.dataset  )




							def __getitem__(							self:       Any    ,							UpperCamelCase__:       int  ):
														lowerCamelCase__      :			Dict 						= self.dataset[i]
														lowerCamelCase__      :			Union[str, Any] 						= self.process(UpperCamelCase__    ,							**self.params  )
														return processed



class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Any=None  ):
														lowerCamelCase__      :			int 						= loader
														lowerCamelCase__      :			str 						= infer
														lowerCamelCase__      :			Optional[int] 						= params
														if loader_batch_size == 1:
																					# Let's spare some time by deactivating altogether
																					lowerCamelCase__      :			Optional[int] 						= None
														lowerCamelCase__      :			int 						= loader_batch_size

														# Internal bookkeeping
														lowerCamelCase__      :			Optional[Any] 						= None
														lowerCamelCase__      :			Optional[Any] 						= None




							def __len__(							self:       Dict  ):
														return len(self.loader  )




							def __iter__(							self:       Optional[int]  ):
														lowerCamelCase__      :			List[Any] 						= iter(self.loader  )
														return self




							def 	lowerCamelCase_       (							self:       Any  ):
														if isinstance(self._loader_batch_data    ,							torch.Tensor  ):
																					# Batch data is simple tensor, just fetch the slice
																					lowerCamelCase__      :			str 						= self._loader_batch_data[self._loader_batch_index]
														else:
																					# Batch data is assumed to be BaseModelOutput (or dict)
																					lowerCamelCase__      :			int 						= {}
																					for k, element in self._loader_batch_data.items():
																												if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			# Convert ModelOutput to tuple first
																																			lowerCamelCase__      :			str 						= element.to_tuple()
																																			if isinstance(element[0]    ,							torch.Tensor  ):
																																										lowerCamelCase__      :			Tuple 						= tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
																																			elif isinstance(element[0]    ,							np.ndarray  ):
																																										lowerCamelCase__      :			str 						= 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(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			# Those are stored as lists of tensors so need specific unbatching.
																																			if isinstance(element[0]    ,							torch.Tensor  ):
																																										lowerCamelCase__      :			List[str] 						= tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
																																			elif isinstance(element[0]    ,							np.ndarray  ):
																																										lowerCamelCase__      :			int 						= 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__      :			List[str] 						= 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__      :			Optional[Any] 						= 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__      :			int 						= np.expand_dims(element[self._loader_batch_index]    ,							0  )
																												else:
																																			# This is typically a list, so no need to `unsqueeze`.
																																			lowerCamelCase__      :			str 						= element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																					lowerCamelCase__      :			Optional[int] 						= self._loader_batch_data.__class__(UpperCamelCase__  )
														self._loader_batch_index += 1
														return result




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														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__      :			Optional[Any] 						= next(self.iterator  )
														lowerCamelCase__      :			List[str] 						= self.infer(UpperCamelCase__    ,							**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(UpperCamelCase__    ,							torch.Tensor  ):
																												lowerCamelCase__      :			Optional[Any] 						= processed
																					else:
																												lowerCamelCase__      :			Union[str, Any] 						= list(processed.keys()  )[0]
																												lowerCamelCase__      :			Any 						= processed[key]
																					if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																												lowerCamelCase__      :			Any 						= len(UpperCamelCase__  )
																					else:
																												lowerCamelCase__      :			List[str] 						= 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__      :			Union[str, Any] 						= observed_batch_size
																					# Setting internal index to unwrap the batch
																					lowerCamelCase__      :			List[Any] 						= processed
																					lowerCamelCase__      :			List[Any] 						= 0
																					return self.loader_batch_item()
														else:
																					# We're not unrolling batches
																					return processed



class _lowercase			(       _lowercase	):




							def __init__(							self:       List[str]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       List[Any]=None  ):
														super().__init__(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




							def __iter__(							self:       Union[str, Any]  ):
														lowerCamelCase__      :			str 						= iter(self.loader  )
														lowerCamelCase__      :			int 						= None
														return self




							def 	lowerCamelCase_       (							self:       str  ):
														if self.subiterator is None:
																					lowerCamelCase__      :			Optional[Any] 						= self.infer(next(self.iterator  )    ,							**self.params  )
														try:
																					# Try to return next item
																					lowerCamelCase__      :			Tuple 						= 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__      :			Any 						= self.infer(next(self.iterator  )    ,							**self.params  )
																					lowerCamelCase__      :			Union[str, Any] 						= next(self.subiterator  )
														return processed



class _lowercase			(       _lowercase	):




							def __iter__(							self:       List[Any]  ):
														lowerCamelCase__      :			int 						= iter(self.loader  )
														return self




							def 	lowerCamelCase_       (							self:       Tuple  ):
														# 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__      :			List[str] 						= False
														lowerCamelCase__      :			Union[str, Any] 						= []
														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__      :			Any 						= self.loader_batch_item()
																												lowerCamelCase__      :			Tuple 						= item.pop("""is_last"""  )
																												accumulator.append(UpperCamelCase__  )
																												if is_last:
																																			return accumulator

														while not is_last:
																					lowerCamelCase__      :			str 						= self.infer(next(self.iterator  )    ,							**self.params  )
																					if self.loader_batch_size is not None:
																												if isinstance(UpperCamelCase__    ,							torch.Tensor  ):
																																			lowerCamelCase__      :			Dict 						= processed
																												else:
																																			lowerCamelCase__      :			Dict 						= list(processed.keys()  )[0]
																																			lowerCamelCase__      :			Dict 						= processed[key]
																												if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			lowerCamelCase__      :			List[Any] 						= len(UpperCamelCase__  )
																												else:
																																			lowerCamelCase__      :			Dict 						= 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__      :			str 						= observed_batch_size
																												lowerCamelCase__      :			str 						= processed
																												lowerCamelCase__      :			Optional[int] 						= 0
																												while self._loader_batch_index < self.loader_batch_size:
																																			lowerCamelCase__      :			List[Any] 						= self.loader_batch_item()
																																			lowerCamelCase__      :			str 						= item.pop("""is_last"""  )
																																			accumulator.append(UpperCamelCase__  )
																																			if is_last:
																																										return accumulator
																					else:
																												lowerCamelCase__      :			Optional[Any] 						= processed
																												lowerCamelCase__      :			Optional[int] 						= item.pop("""is_last"""  )
																												accumulator.append(UpperCamelCase__  )
														return accumulator



class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Dataset    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			Union[str, Any] 						= dataset
														lowerCamelCase__      :			str 						= key




							def __len__(							self:       Optional[Any]  ):
														return len(self.dataset  )




							def __getitem__(							self:       List[str]    ,							UpperCamelCase__:       Any  ):
														return self.dataset[i][self.key]



class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Dataset    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			str 						= dataset
														lowerCamelCase__      :			Dict 						= keya
														lowerCamelCase__      :			List[str] 						= keya




							def __len__(							self:       str  ):
														return len(self.dataset  )




							def __getitem__(							self:       List[str]    ,							UpperCamelCase__:       Union[str, Any]  ):
														return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}

