
'''simple docstring'''







import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
						from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor


class 			lowerCAmelCase :




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase=13					,						UpperCamelCase=32					,						UpperCamelCase=2					,						UpperCamelCase=3					,						UpperCamelCase=16					,						UpperCamelCase=[1, 2, 1]					,						UpperCamelCase=[2, 2, 4]					,						UpperCamelCase=2					,						UpperCamelCase=2.0					,						UpperCamelCase=True					,						UpperCamelCase=0.0					,						UpperCamelCase=0.0					,						UpperCamelCase=0.1					,						UpperCamelCase="gelu"					,						UpperCamelCase=False					,						UpperCamelCase=True					,						UpperCamelCase=0.02					,						UpperCamelCase=1e-5					,						UpperCamelCase=True					,						UpperCamelCase=None					,						UpperCamelCase=True					,						UpperCamelCase=10					,						UpperCamelCase=8					,						):
								_SCREAMING_SNAKE_CASE				= parent
								_SCREAMING_SNAKE_CASE				= batch_size
								_SCREAMING_SNAKE_CASE				= image_size
								_SCREAMING_SNAKE_CASE				= patch_size
								_SCREAMING_SNAKE_CASE				= num_channels
								_SCREAMING_SNAKE_CASE				= embed_dim
								_SCREAMING_SNAKE_CASE				= depths
								_SCREAMING_SNAKE_CASE				= num_heads
								_SCREAMING_SNAKE_CASE				= window_size
								_SCREAMING_SNAKE_CASE				= mlp_ratio
								_SCREAMING_SNAKE_CASE				= qkv_bias
								_SCREAMING_SNAKE_CASE				= hidden_dropout_prob
								_SCREAMING_SNAKE_CASE				= attention_probs_dropout_prob
								_SCREAMING_SNAKE_CASE				= drop_path_rate
								_SCREAMING_SNAKE_CASE				= hidden_act
								_SCREAMING_SNAKE_CASE				= use_absolute_embeddings
								_SCREAMING_SNAKE_CASE				= patch_norm
								_SCREAMING_SNAKE_CASE				= layer_norm_eps
								_SCREAMING_SNAKE_CASE				= initializer_range
								_SCREAMING_SNAKE_CASE				= is_training
								_SCREAMING_SNAKE_CASE				= scope
								_SCREAMING_SNAKE_CASE				= use_labels
								_SCREAMING_SNAKE_CASE				= type_sequence_label_size
								_SCREAMING_SNAKE_CASE				= encoder_stride




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

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

								_SCREAMING_SNAKE_CASE				= self.get_config()

								return config, pixel_values, labels




		def     lowercase       (				self      ):
								return SwinvaConfig(
								    image_size=self.image_size					,						patch_size=self.patch_size					,						num_channels=self.num_channels					,						embed_dim=self.embed_dim					,						depths=self.depths					,						num_heads=self.num_heads					,						window_size=self.window_size					,						mlp_ratio=self.mlp_ratio					,						qkv_bias=self.qkv_bias					,						hidden_dropout_prob=self.hidden_dropout_prob					,						attention_probs_dropout_prob=self.attention_probs_dropout_prob					,						drop_path_rate=self.drop_path_rate					,						hidden_act=self.hidden_act					,						use_absolute_embeddings=self.use_absolute_embeddings					,						path_norm=self.patch_norm					,						layer_norm_eps=self.layer_norm_eps					,						initializer_range=self.initializer_range					,						encoder_stride=self.encoder_stride					,						)




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= SwinvaModel(config=UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
								_SCREAMING_SNAKE_CASE				= int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

								self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, expected_seq_len, expected_dim)      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= SwinvaForMaskedImageModeling(config=UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase      )
								self.parent.assertEqual(
								    result.logits.shape					,						(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

								# test greyscale images
								_SCREAMING_SNAKE_CASE				= 1
								_SCREAMING_SNAKE_CASE				= SwinvaForMaskedImageModeling(UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()

								_SCREAMING_SNAKE_CASE				= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase      )
								self.parent.assertEqual(result.logits.shape					,						(self.batch_size, 1, self.image_size, self.image_size)      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= self.type_sequence_label_size
								_SCREAMING_SNAKE_CASE				= SwinvaForImageClassification(UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()
								_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						labels=UpperCamelCase      )
								self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.type_sequence_label_size)      )




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


@require_torch
class 			lowerCAmelCase (				__UpperCAmelCase		,     __UpperCAmelCase		,     unittest.TestCase			):
		a							:					Tuple       =					(
		    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
		)
		a							:					Optional[Any]       =					(
		    {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
		    if is_torch_available()
		    else {}
		)

		a							:					Any       =					False
		a							:					Union[str, Any]       =					False
		a							:					List[Any]       =					False
		a							:					List[str]       =					False




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= SwinvaModelTester(self      )
								_SCREAMING_SNAKE_CASE				= ConfigTester(self					,						config_class=UpperCamelCase					,						embed_dim=37      )




		def     lowercase       (				self      ):
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase      )




		@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."      )
		def     lowercase       (				self      ):
								pass




		@unittest.skip(reason="Swinv2 does not use inputs_embeds"      )
		def     lowercase       (				self      ):
								pass




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_common()

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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_common()

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

														_SCREAMING_SNAKE_CASE				= ["pixel_values"]
														self.assertListEqual(arg_names[:1]					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_common()
								_SCREAMING_SNAKE_CASE				= True

								for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE				= True
														_SCREAMING_SNAKE_CASE				= False
														_SCREAMING_SNAKE_CASE				= True
														_SCREAMING_SNAKE_CASE				= model_class(UpperCamelCase      )
														model.to(UpperCamelCase      )
														model.eval()
														with torch.no_grad():
																				_SCREAMING_SNAKE_CASE				= model(**self._prepare_for_class(UpperCamelCase					,						UpperCamelCase      )      )
														_SCREAMING_SNAKE_CASE				= outputs.attentions
														_SCREAMING_SNAKE_CASE				= len(self.model_tester.depths      )
														self.assertEqual(len(UpperCamelCase      )					,						UpperCamelCase      )

														# check that output_attentions also work using config
														del inputs_dict["output_attentions"]
														_SCREAMING_SNAKE_CASE				= True
														_SCREAMING_SNAKE_CASE				= config.window_size**2
														_SCREAMING_SNAKE_CASE				= model_class(UpperCamelCase      )
														model.to(UpperCamelCase      )
														model.eval()
														with torch.no_grad():
																				_SCREAMING_SNAKE_CASE				= model(**self._prepare_for_class(UpperCamelCase					,						UpperCamelCase      )      )
														_SCREAMING_SNAKE_CASE				= outputs.attentions
														self.assertEqual(len(UpperCamelCase      )					,						UpperCamelCase      )

														self.assertListEqual(
														    list(attentions[0].shape[-3:]      )					,						[self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,						)
														_SCREAMING_SNAKE_CASE				= len(UpperCamelCase      )

														# Check attention is always last and order is fine
														_SCREAMING_SNAKE_CASE				= True
														_SCREAMING_SNAKE_CASE				= True
														_SCREAMING_SNAKE_CASE				= model_class(UpperCamelCase      )
														model.to(UpperCamelCase      )
														model.eval()
														with torch.no_grad():
																				_SCREAMING_SNAKE_CASE				= model(**self._prepare_for_class(UpperCamelCase					,						UpperCamelCase      )      )

														if hasattr(self.model_tester					,						"num_hidden_states_types"      ):
																				_SCREAMING_SNAKE_CASE				= self.model_tester.num_hidden_states_types
														else:
																				# also another +1 for reshaped_hidden_states
																				_SCREAMING_SNAKE_CASE				= 2
														self.assertEqual(out_len + added_hidden_states					,						len(UpperCamelCase      )      )

														_SCREAMING_SNAKE_CASE				= outputs.attentions

														self.assertEqual(len(UpperCamelCase      )					,						UpperCamelCase      )

														self.assertListEqual(
														    list(self_attentions[0].shape[-3:]      )					,						[self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,						)




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= model_class(UpperCamelCase      )
								model.to(UpperCamelCase      )
								model.eval()

								with torch.no_grad():
														_SCREAMING_SNAKE_CASE				= model(**self._prepare_for_class(UpperCamelCase					,						UpperCamelCase      )      )

								_SCREAMING_SNAKE_CASE				= outputs.hidden_states

								_SCREAMING_SNAKE_CASE				= getattr(
								    self.model_tester					,						"expected_num_hidden_layers"					,						len(self.model_tester.depths      ) + 1      )
								self.assertEqual(len(UpperCamelCase      )					,						UpperCamelCase      )

								# Swinv2 has a different seq_length
								_SCREAMING_SNAKE_CASE				= (
								    config.patch_size
								    if isinstance(config.patch_size					,						collections.abc.Iterable      )
								    else (config.patch_size, config.patch_size)
								)

								_SCREAMING_SNAKE_CASE				= (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

								self.assertListEqual(
								    list(hidden_states[0].shape[-2:]      )					,						[num_patches, self.model_tester.embed_dim]					,						)

								_SCREAMING_SNAKE_CASE				= outputs.reshaped_hidden_states
								self.assertEqual(len(UpperCamelCase      )					,						UpperCamelCase      )

								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= reshaped_hidden_states[0].shape
								_SCREAMING_SNAKE_CASE				= (
								    reshaped_hidden_states[0].view(UpperCamelCase					,						UpperCamelCase					,						height * width      ).permute(0					,						2					,						1      )
								)
								self.assertListEqual(
								    list(reshaped_hidden_states.shape[-2:]      )					,						[num_patches, self.model_tester.embed_dim]					,						)




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_common()

								_SCREAMING_SNAKE_CASE				= (
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size					,						collections.abc.Iterable      )
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)

								for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE				= True
														self.check_hidden_states_output(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      )

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														_SCREAMING_SNAKE_CASE				= True

														self.check_hidden_states_output(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_common()
								_SCREAMING_SNAKE_CASE				= 3

								_SCREAMING_SNAKE_CASE				= (
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size					,						collections.abc.Iterable      )
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)
								_SCREAMING_SNAKE_CASE				= (
								    config.patch_size
								    if isinstance(config.patch_size					,						collections.abc.Iterable      )
								    else (config.patch_size, config.patch_size)
								)

								_SCREAMING_SNAKE_CASE				= image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
								_SCREAMING_SNAKE_CASE				= image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

								for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE				= True
														self.check_hidden_states_output(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						(padded_height, padded_width)      )

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														_SCREAMING_SNAKE_CASE				= True
														self.check_hidden_states_output(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						(padded_height, padded_width)      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCamelCase      )




		@slow
		def     lowercase       (				self      ):
								for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_SCREAMING_SNAKE_CASE				= SwinvaModel.from_pretrained(UpperCamelCase      )
														self.assertIsNotNone(UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_common()

								_SCREAMING_SNAKE_CASE				= _config_zero_init(UpperCamelCase      )
								for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE				= model_class(config=UpperCamelCase      )
														for name, param in model.named_parameters():
																				if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
																										self.assertIn(
																										    ((param.data.mean() * 1e9).round() / 1e9).item()					,						[0.0, 1.0]					,						msg=F'Parameter {name} of model {model_class} seems not properly initialized'					,						)


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




		@cached_property
		def     lowercase       (				self      ):
								return (
								    AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"      )
								    if is_vision_available()
								    else None
								)




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"      ).to(
								    UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= self.default_image_processor

								_SCREAMING_SNAKE_CASE				= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
								_SCREAMING_SNAKE_CASE				= image_processor(images=UpperCamelCase					,						return_tensors="pt"      ).to(UpperCamelCase      )

								# forward pass
								with torch.no_grad():
														_SCREAMING_SNAKE_CASE				= model(**UpperCamelCase      )

								# verify the logits
								_SCREAMING_SNAKE_CASE				= torch.Size((1, 1_000)      )
								self.assertEqual(outputs.logits.shape					,						UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= torch.tensor([-0.39_47, -0.43_06, 0.00_26]      ).to(UpperCamelCase      )
								self.assertTrue(torch.allclose(outputs.logits[0, :3]					,						UpperCamelCase					,						atol=1e-4      )      )

'''simple docstring'''







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

from ..utils.generic import ModelOutput


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= dataset
								_SCREAMING_SNAKE_CASE				= process
								_SCREAMING_SNAKE_CASE				= params




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




		def __getitem__(				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= self.dataset[i]
								_SCREAMING_SNAKE_CASE				= self.process(UpperCamelCase					,						**self.params      )
								return processed


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=None      ):
								_SCREAMING_SNAKE_CASE				= loader
								_SCREAMING_SNAKE_CASE				= infer
								_SCREAMING_SNAKE_CASE				= params
								if loader_batch_size == 1:
														# Let's spare some time by deactivating altogether
														_SCREAMING_SNAKE_CASE				= None
								_SCREAMING_SNAKE_CASE				= loader_batch_size

								# Internal bookkeeping
								_SCREAMING_SNAKE_CASE				= None
								_SCREAMING_SNAKE_CASE				= None




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




		def __iter__(				self      ):
								_SCREAMING_SNAKE_CASE				= iter(self.loader      )
								return self




		def     lowercase       (				self      ):
								if isinstance(self._loader_batch_data					,						torch.Tensor      ):
														# Batch data is simple tensor, just fetch the slice
														_SCREAMING_SNAKE_CASE				= self._loader_batch_data[self._loader_batch_index]
								else:
														# Batch data is assumed to be BaseModelOutput (or dict)
														_SCREAMING_SNAKE_CASE				= {}
														for k, element in self._loader_batch_data.items():
																				if isinstance(UpperCamelCase					,						UpperCamelCase      ):
																										# Convert ModelOutput to tuple first
																										_SCREAMING_SNAKE_CASE				= element.to_tuple()
																										if isinstance(element[0]					,						torch.Tensor      ):
																																_SCREAMING_SNAKE_CASE				= tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
																										elif isinstance(element[0]					,						np.ndarray      ):
																																_SCREAMING_SNAKE_CASE				= 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      ):
																																_SCREAMING_SNAKE_CASE				= tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
																										elif isinstance(element[0]					,						np.ndarray      ):
																																_SCREAMING_SNAKE_CASE				= 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
																										_SCREAMING_SNAKE_CASE				= 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

																										_SCREAMING_SNAKE_CASE				= 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
																										_SCREAMING_SNAKE_CASE				= np.expand_dims(element[self._loader_batch_index]					,						0      )
																				else:
																										# This is typically a list, so no need to `unsqueeze`.
																										_SCREAMING_SNAKE_CASE				= element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
														_SCREAMING_SNAKE_CASE				= self._loader_batch_data.__class__(UpperCamelCase      )
								self._loader_batch_index += 1
								return result




		def     lowercase       (				self      ):
								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
								_SCREAMING_SNAKE_CASE				= next(self.iterator      )
								_SCREAMING_SNAKE_CASE				= 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      ):
																				_SCREAMING_SNAKE_CASE				= processed
														else:
																				_SCREAMING_SNAKE_CASE				= list(processed.keys()      )[0]
																				_SCREAMING_SNAKE_CASE				= processed[key]
														if isinstance(UpperCamelCase					,						UpperCamelCase      ):
																				_SCREAMING_SNAKE_CASE				= len(UpperCamelCase      )
														else:
																				_SCREAMING_SNAKE_CASE				= 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.
																				_SCREAMING_SNAKE_CASE				= observed_batch_size
														# Setting internal index to unwrap the batch
														_SCREAMING_SNAKE_CASE				= processed
														_SCREAMING_SNAKE_CASE				= 0
														return self.loader_batch_item()
								else:
														# We're not unrolling batches
														return processed


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=None      ):
								super().__init__(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      )




		def __iter__(				self      ):
								_SCREAMING_SNAKE_CASE				= iter(self.loader      )
								_SCREAMING_SNAKE_CASE				= None
								return self




		def     lowercase       (				self      ):
								if self.subiterator is None:
														_SCREAMING_SNAKE_CASE				= self.infer(next(self.iterator      )					,						**self.params      )
								try:
														# Try to return next item
														_SCREAMING_SNAKE_CASE				= 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
														_SCREAMING_SNAKE_CASE				= self.infer(next(self.iterator      )					,						**self.params      )
														_SCREAMING_SNAKE_CASE				= next(self.subiterator      )
								return processed


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __iter__(				self      ):
								_SCREAMING_SNAKE_CASE				= iter(self.loader      )
								return self




		def     lowercase       (				self      ):
								# 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.
								_SCREAMING_SNAKE_CASE				= False
								_SCREAMING_SNAKE_CASE				= []
								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:
																				_SCREAMING_SNAKE_CASE				= self.loader_batch_item()
																				_SCREAMING_SNAKE_CASE				= item.pop("is_last"      )
																				accumulator.append(UpperCamelCase      )
																				if is_last:
																										return accumulator

								while not is_last:
														_SCREAMING_SNAKE_CASE				= self.infer(next(self.iterator      )					,						**self.params      )
														if self.loader_batch_size is not None:
																				if isinstance(UpperCamelCase					,						torch.Tensor      ):
																										_SCREAMING_SNAKE_CASE				= processed
																				else:
																										_SCREAMING_SNAKE_CASE				= list(processed.keys()      )[0]
																										_SCREAMING_SNAKE_CASE				= processed[key]
																				if isinstance(UpperCamelCase					,						UpperCamelCase      ):
																										_SCREAMING_SNAKE_CASE				= len(UpperCamelCase      )
																				else:
																										_SCREAMING_SNAKE_CASE				= 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.
																										_SCREAMING_SNAKE_CASE				= observed_batch_size
																				_SCREAMING_SNAKE_CASE				= processed
																				_SCREAMING_SNAKE_CASE				= 0
																				while self._loader_batch_index < self.loader_batch_size:
																										_SCREAMING_SNAKE_CASE				= self.loader_batch_item()
																										_SCREAMING_SNAKE_CASE				= item.pop("is_last"      )
																										accumulator.append(UpperCamelCase      )
																										if is_last:
																																return accumulator
														else:
																				_SCREAMING_SNAKE_CASE				= processed
																				_SCREAMING_SNAKE_CASE				= item.pop("is_last"      )
																				accumulator.append(UpperCamelCase      )
								return accumulator


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= dataset
								_SCREAMING_SNAKE_CASE				= key




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




		def __getitem__(				self					,						UpperCamelCase      ):
								return self.dataset[i][self.key]


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= dataset
								_SCREAMING_SNAKE_CASE				= keya
								_SCREAMING_SNAKE_CASE				= keya




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




		def __getitem__(				self					,						UpperCamelCase      ):
								return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}