





'''simple docstring'''


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ConditionalDetrImageProcessor

class       lowerCAmelCase_						(						unittest.TestCase						):




 def __init__(		self	,   _lowerCAmelCase	,   _lowerCAmelCase=7	,   _lowerCAmelCase=3	,   _lowerCAmelCase=30	,   _lowerCAmelCase=400	,   _lowerCAmelCase=True	,   _lowerCAmelCase=None	,   _lowerCAmelCase=True	,   _lowerCAmelCase=[0.5, 0.5, 0.5]	,   _lowerCAmelCase=[0.5, 0.5, 0.5]	,   _lowerCAmelCase=True	,   _lowerCAmelCase=1 / 255	,   _lowerCAmelCase=True	,   )			->							Optional[Any]:
     # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
     _lowerCAmelCase			=					size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
     _lowerCAmelCase			=					parent
     _lowerCAmelCase			=					batch_size
     _lowerCAmelCase			=					num_channels
     _lowerCAmelCase			=					min_resolution
     _lowerCAmelCase			=					max_resolution
     _lowerCAmelCase			=					do_resize
     _lowerCAmelCase			=					size
     _lowerCAmelCase			=					do_normalize
     _lowerCAmelCase			=					image_mean
     _lowerCAmelCase			=					image_std
     _lowerCAmelCase			=					do_rescale
     _lowerCAmelCase			=					rescale_factor
     _lowerCAmelCase			=					do_pad




 def 							_snake_case      (		self						)			->							int:
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_pad": self.do_pad,
     }





 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase=False						)			->							str:
     if not batched:
         _lowerCAmelCase			=					image_inputs[0]
         if isinstance(lowercase_	,   Image.Image						):
             _lowerCAmelCase			=					image.size
         else:
             _lowerCAmelCase			=					image.shape[1], image.shape[2]
         if w < h:
             _lowerCAmelCase			=					int(self.size["shortest_edge"] * h / w						)
             _lowerCAmelCase			=					self.size['''shortest_edge''']
         elif w > h:
             _lowerCAmelCase			=					self.size['''shortest_edge''']
             _lowerCAmelCase			=					int(self.size["shortest_edge"] * w / h						)
         else:
             _lowerCAmelCase			=					self.size['''shortest_edge''']
             _lowerCAmelCase			=					self.size['''shortest_edge''']

     else:
         _lowerCAmelCase			=					[]
         for image in image_inputs:
             _lowerCAmelCase			=					self.get_expected_values([image]						)
             expected_values.append((expected_height, expected_width)						)
         _lowerCAmelCase			=					max(lowercase_	,   key=lambda _lowerCAmelCase						: item[0]						)[0]
         _lowerCAmelCase			=					max(lowercase_	,   key=lambda _lowerCAmelCase						: item[1]						)[1]

     return expected_height, expected_width







@require_torch
@require_vision
class       lowerCAmelCase_						(						a_  ,unittest.TestCase						):
 __lowerCamelCase  :							Optional[int]				     =    ConditionalDetrImageProcessor if is_vision_available() else None




 def 							_snake_case      (		self						)			->							Union[str, Any]:
     _lowerCAmelCase			=					ConditionalDetrImageProcessingTester(self						)




 @property
 def 							_snake_case      (		self						)			->							Dict:
     return self.image_processor_tester.prepare_image_processor_dict()




 def 							_snake_case      (		self						)			->							List[Any]:
     _lowerCAmelCase			=					self.image_processing_class(**self.image_processor_dict						)
     self.assertTrue(hasattr(lowercase_	,   "image_mean"						)						)
     self.assertTrue(hasattr(lowercase_	,   "image_std"						)						)
     self.assertTrue(hasattr(lowercase_	,   "do_normalize"						)						)
     self.assertTrue(hasattr(lowercase_	,   "do_resize"						)						)
     self.assertTrue(hasattr(lowercase_	,   "size"						)						)




 def 							_snake_case      (		self						)			->							Any:
     _lowerCAmelCase			=					self.image_processing_class.from_dict(self.image_processor_dict						)
     self.assertEqual(image_processor.size	,   {"shortest_edge": 18, "longest_edge": 1333}						)
     self.assertEqual(image_processor.do_pad	,   lowercase_						)

     _lowerCAmelCase			=					self.image_processing_class.from_dict(
         self.image_processor_dict	,   size=42	,   max_size=84	,   pad_and_return_pixel_mask=lowercase_						)
     self.assertEqual(image_processor.size	,   {"shortest_edge": 42, "longest_edge": 84}						)
     self.assertEqual(image_processor.do_pad	,   lowercase_						)




 def 							_snake_case      (		self						)			->							Dict:
     pass




 def 							_snake_case      (		self						)			->							str:
     # Initialize image_processing
     _lowerCAmelCase			=					self.image_processing_class(**self.image_processor_dict						)
     # create random PIL images
     _lowerCAmelCase			=					prepare_image_inputs(self.image_processor_tester	,   equal_resolution=lowercase_						)
     for image in image_inputs:
         self.assertIsInstance(lowercase_	,   Image.Image						)

     # Test not batched input
     _lowerCAmelCase			=					image_processing(image_inputs[0]	,   return_tensors="pt"						).pixel_values

     _lowerCAmelCase			=					self.image_processor_tester.get_expected_values(lowercase_						)

     self.assertEqual(
         encoded_images.shape	,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,   )

     # Test batched
     _lowerCAmelCase			=					self.image_processor_tester.get_expected_values(lowercase_	,   batched=lowercase_						)

     _lowerCAmelCase			=					image_processing(lowercase_	,   return_tensors="pt"						).pixel_values
     self.assertEqual(
         encoded_images.shape	,   (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )	,   )




 def 							_snake_case      (		self						)			->							Optional[int]:
     # Initialize image_processing
     _lowerCAmelCase			=					self.image_processing_class(**self.image_processor_dict						)
     # create random numpy tensors
     _lowerCAmelCase			=					prepare_image_inputs(self.image_processor_tester	,   equal_resolution=lowercase_	,   numpify=lowercase_						)
     for image in image_inputs:
         self.assertIsInstance(lowercase_	,   np.ndarray						)

     # Test not batched input
     _lowerCAmelCase			=					image_processing(image_inputs[0]	,   return_tensors="pt"						).pixel_values

     _lowerCAmelCase			=					self.image_processor_tester.get_expected_values(lowercase_						)

     self.assertEqual(
         encoded_images.shape	,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,   )

     # Test batched
     _lowerCAmelCase			=					image_processing(lowercase_	,   return_tensors="pt"						).pixel_values

     _lowerCAmelCase			=					self.image_processor_tester.get_expected_values(lowercase_	,   batched=lowercase_						)

     self.assertEqual(
         encoded_images.shape	,   (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )	,   )




 def 							_snake_case      (		self						)			->							Optional[int]:
     # Initialize image_processing
     _lowerCAmelCase			=					self.image_processing_class(**self.image_processor_dict						)
     # create random PyTorch tensors
     _lowerCAmelCase			=					prepare_image_inputs(self.image_processor_tester	,   equal_resolution=lowercase_	,   torchify=lowercase_						)
     for image in image_inputs:
         self.assertIsInstance(lowercase_	,   torch.Tensor						)

     # Test not batched input
     _lowerCAmelCase			=					image_processing(image_inputs[0]	,   return_tensors="pt"						).pixel_values

     _lowerCAmelCase			=					self.image_processor_tester.get_expected_values(lowercase_						)

     self.assertEqual(
         encoded_images.shape	,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,   )

     # Test batched
     _lowerCAmelCase			=					image_processing(lowercase_	,   return_tensors="pt"						).pixel_values

     _lowerCAmelCase			=					self.image_processor_tester.get_expected_values(lowercase_	,   batched=lowercase_						)

     self.assertEqual(
         encoded_images.shape	,   (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )	,   )




 @slow
 def 							_snake_case      (		self						)			->							List[str]:
     # prepare image and target
     _lowerCAmelCase			=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
     with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"	,   "r"						) as f:
         _lowerCAmelCase			=					json.loads(f.read()						)

     _lowerCAmelCase			=					{'''image_id''': 39769, '''annotations''': target}

     # encode them
     _lowerCAmelCase			=					ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"						)
     _lowerCAmelCase			=					image_processing(images=lowercase_	,   annotations=lowercase_	,   return_tensors="pt"						)

     # verify pixel values
     _lowerCAmelCase			=					torch.Size([1, 3, 800, 1066]						)
     self.assertEqual(encoding["pixel_values"].shape	,   lowercase_						)

     _lowerCAmelCase			=					torch.tensor([0.2796, 0.3138, 0.3481]						)
     self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]	,   lowercase_	,   atol=1E-4						)						)

     # verify area
     _lowerCAmelCase			=					torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]						)
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,   lowercase_						)						)
     # verify boxes
     _lowerCAmelCase			=					torch.Size([6, 4]						)
     self.assertEqual(encoding["labels"][0]["boxes"].shape	,   lowercase_						)
     _lowerCAmelCase			=					torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,   lowercase_	,   atol=1E-3						)						)
     # verify image_id
     _lowerCAmelCase			=					torch.tensor([39769]						)
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,   lowercase_						)						)
     # verify is_crowd
     _lowerCAmelCase			=					torch.tensor([0, 0, 0, 0, 0, 0]						)
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,   lowercase_						)						)
     # verify class_labels
     _lowerCAmelCase			=					torch.tensor([75, 75, 63, 65, 17, 17]						)
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,   lowercase_						)						)
     # verify orig_size
     _lowerCAmelCase			=					torch.tensor([480, 640]						)
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,   lowercase_						)						)
     # verify size
     _lowerCAmelCase			=					torch.tensor([800, 1066]						)
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,   lowercase_						)						)





 @slow
 def 							_snake_case      (		self						)			->							Union[str, Any]:
     # prepare image, target and masks_path
     _lowerCAmelCase			=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
     with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"	,   "r"						) as f:
         _lowerCAmelCase			=					json.loads(f.read()						)

     _lowerCAmelCase			=					{'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

     _lowerCAmelCase			=					pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"						)

     # encode them
     _lowerCAmelCase			=					ConditionalDetrImageProcessor(format="coco_panoptic"						)
     _lowerCAmelCase			=					image_processing(images=lowercase_	,   annotations=lowercase_	,   masks_path=lowercase_	,   return_tensors="pt"						)

     # verify pixel values
     _lowerCAmelCase			=					torch.Size([1, 3, 800, 1066]						)
     self.assertEqual(encoding["pixel_values"].shape	,   lowercase_						)

     _lowerCAmelCase			=					torch.tensor([0.2796, 0.3138, 0.3481]						)
     self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]	,   lowercase_	,   atol=1E-4						)						)

     # verify area
     _lowerCAmelCase			=					torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]						)
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,   lowercase_						)						)
     # verify boxes
     _lowerCAmelCase			=					torch.Size([6, 4]						)
     self.assertEqual(encoding["labels"][0]["boxes"].shape	,   lowercase_						)
     _lowerCAmelCase			=					torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,   lowercase_	,   atol=1E-3						)						)
     # verify image_id
     _lowerCAmelCase			=					torch.tensor([39769]						)
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,   lowercase_						)						)
     # verify is_crowd
     _lowerCAmelCase			=					torch.tensor([0, 0, 0, 0, 0, 0]						)
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,   lowercase_						)						)
     # verify class_labels
     _lowerCAmelCase			=					torch.tensor([17, 17, 63, 75, 75, 93]						)
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,   lowercase_						)						)
     # verify masks
     _lowerCAmelCase			=					822873
     self.assertEqual(encoding["labels"][0]["masks"].sum().item()	,   lowercase_						)
     # verify orig_size
     _lowerCAmelCase			=					torch.tensor([480, 640]						)
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,   lowercase_						)						)
     # verify size
     _lowerCAmelCase			=					torch.tensor([800, 1066]						)
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,   lowercase_						)						)





"""simple docstring"""





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

from ..utils.generic import ModelOutput







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				List[Any]   ,lowercase_			:				Tuple   ,lowercase_			:				Dict   ,lowercase_			:				str		):
						lowerCAmelCase__	:						int  				=	dataset
						lowerCAmelCase__	:						List[str]  				=	process
						lowerCAmelCase__	:						Dict  				=	params







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







					def __getitem__(       self			:				Union[str, Any]   ,lowercase_			:				List[Any]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.dataset[i]
						lowerCAmelCase__	:						Optional[Any]  				=	self.process(lowercase_   ,**self.params		)
						return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



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

						# Internal bookkeeping
						lowerCAmelCase__	:						Optional[Any]  				=	None
						lowerCAmelCase__	:						Optional[int]  				=	None







					def __len__(       self			:				Union[str, Any]		):
						return len(self.loader		)







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







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







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







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				int   ,lowercase_			:				str   ,lowercase_			:				str   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				int=None		):
						super().__init__(lowercase_   ,lowercase_   ,lowercase_		)







					def __iter__(       self			:				List[Any]		):
						lowerCAmelCase__	:						Dict  				=	iter(self.loader		)
						lowerCAmelCase__	:						Tuple  				=	None
						return self







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						if self.subiterator is None:
							lowerCAmelCase__	:						List[Any]  				=	self.infer(next(self.iterator		)   ,**self.params		)
						try:
							# Try to return next item
							lowerCAmelCase__	:						Optional[int]  				=	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__	:						int  				=	next(self.subiterator		)
						return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __iter__(       self			:				Tuple		):
						lowerCAmelCase__	:						int  				=	iter(self.loader		)
						return self







					def        __lowerCAmelCase (       self			:				List[Any]		):
						# 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__	:						Tuple  				=	False
						lowerCAmelCase__	:						str  				=	[]
						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__	:						Dict  				=	self.loader_batch_item()
								lowerCAmelCase__	:						Optional[Any]  				=	item.pop('''is_last'''		)
								accumulator.append(lowercase_		)
								if is_last:
									return accumulator

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







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				int   ,lowercase_			:				Dataset   ,lowercase_			:				str		):
						lowerCAmelCase__	:						List[Any]  				=	dataset
						lowerCAmelCase__	:						List[Any]  				=	key







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







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







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Dict   ,lowercase_			:				Dataset   ,lowercase_			:				str   ,lowercase_			:				str		):
						lowerCAmelCase__	:						str  				=	dataset
						lowerCAmelCase__	:						List[str]  				=	keya
						lowerCAmelCase__	:						Optional[Any]  				=	keya







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







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




