"""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 DetrImageProcessor

class       a_      (     unittest.TestCase			):




					def __init__(	self						:     Union[str, Any]  ,						snake_case__						:     List[Any]  ,						snake_case__						:     Tuple=7  ,						snake_case__						:     str=3  ,						snake_case__						:     int=30  ,						snake_case__						:     Dict=400  ,						snake_case__						:     Optional[Any]=True  ,						snake_case__						:     int=None  ,						snake_case__						:     Any=True  ,						snake_case__						:     str=1 / 255  ,						snake_case__						:     Tuple=True  ,						snake_case__						:     int=[0.5, 0.5, 0.5]  ,						snake_case__						:     List[str]=[0.5, 0.5, 0.5]  ,						snake_case__						:     Optional[int]=True  ,						):
							# 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_rescale
							lowerCAmelCase__        =   rescale_factor
							lowerCAmelCase__        =   do_normalize
							lowerCAmelCase__        =   image_mean
							lowerCAmelCase__        =   image_std
							lowerCAmelCase__        =   do_pad




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_pad": self.do_pad,
							}




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Dict=False     ):
							if not batched:
									lowerCAmelCase__        =   image_inputs[0]
									if isinstance(snake_case__  ,						Image.Image     ):
											lowerCAmelCase__   ,					lowerCAmelCase__        =   image.size
									else:
											lowerCAmelCase__   ,					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__   ,					lowerCAmelCase__        =   self.get_expected_values([image]     )
											expected_values.append((expected_height, expected_width)     )
									lowerCAmelCase__        =   max(snake_case__  ,						key=lambda snake_case__     : item[0]     )[0]
									lowerCAmelCase__        =   max(snake_case__  ,						key=lambda snake_case__     : item[1]     )[1]

							return expected_height, expected_width

@require_torch
@require_vision
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	int			=    DetrImageProcessor if is_vision_available() else None




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   DetrImageProcessingTester(self     )




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							return self.image_processor_tester.prepare_image_processor_dict()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   self.image_processing_class(**self.image_processor_dict     )
							self.assertTrue(hasattr(snake_case__  ,						"""image_mean"""     )     )
							self.assertTrue(hasattr(snake_case__  ,						"""image_std"""     )     )
							self.assertTrue(hasattr(snake_case__  ,						"""do_normalize"""     )     )
							self.assertTrue(hasattr(snake_case__  ,						"""do_rescale"""     )     )
							self.assertTrue(hasattr(snake_case__  ,						"""rescale_factor"""     )     )
							self.assertTrue(hasattr(snake_case__  ,						"""do_resize"""     )     )
							self.assertTrue(hasattr(snake_case__  ,						"""size"""     )     )
							self.assertTrue(hasattr(snake_case__  ,						"""do_pad"""     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							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  ,						snake_case__     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							pass




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							# 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=snake_case__     )
							for image in image_inputs:
									self.assertIsInstance(snake_case__  ,						Image.Image     )

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

							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.image_processor_tester.get_expected_values(snake_case__     )

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

							# Test batched
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.image_processor_tester.get_expected_values(snake_case__  ,						batched=snake_case__     )

							lowerCAmelCase__        =   image_processing(snake_case__  ,						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 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							# 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=snake_case__  ,						numpify=snake_case__     )
							for image in image_inputs:
									self.assertIsInstance(snake_case__  ,						np.ndarray     )

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

							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.image_processor_tester.get_expected_values(snake_case__     )

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

							# Test batched
							lowerCAmelCase__        =   image_processing(snake_case__  ,						return_tensors="""pt"""     ).pixel_values

							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.image_processor_tester.get_expected_values(snake_case__  ,						batched=snake_case__     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     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=snake_case__  ,						torchify=snake_case__     )
							for image in image_inputs:
									self.assertIsInstance(snake_case__  ,						torch.Tensor     )

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

							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.image_processor_tester.get_expected_values(snake_case__     )

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

							# Test batched
							lowerCAmelCase__        =   image_processing(snake_case__  ,						return_tensors="""pt"""     ).pixel_values

							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.image_processor_tester.get_expected_values(snake_case__  ,						batched=snake_case__     )

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




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							# 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__        =   DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""     )
							lowerCAmelCase__        =   image_processing(images=snake_case__  ,						annotations=snake_case__  ,						return_tensors="""pt"""     )

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

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

							# verify area
							lowerCAmelCase__        =   torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]  ,						snake_case__     )     )
							# verify boxes
							lowerCAmelCase__        =   torch.Size([6, 4]     )
							self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape  ,						snake_case__     )
							lowerCAmelCase__        =   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]  ,						snake_case__  ,						atol=1E-3     )     )
							# verify image_id
							lowerCAmelCase__        =   torch.tensor([39769]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]  ,						snake_case__     )     )
							# verify is_crowd
							lowerCAmelCase__        =   torch.tensor([0, 0, 0, 0, 0, 0]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]  ,						snake_case__     )     )
							# verify class_labels
							lowerCAmelCase__        =   torch.tensor([75, 75, 63, 65, 17, 17]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]  ,						snake_case__     )     )
							# verify orig_size
							lowerCAmelCase__        =   torch.tensor([480, 640]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]  ,						snake_case__     )     )
							# verify size
							lowerCAmelCase__        =   torch.tensor([800, 1066]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]  ,						snake_case__     )     )




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							# 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__        =   DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""     )
							lowerCAmelCase__        =   image_processing(images=snake_case__  ,						annotations=snake_case__  ,						masks_path=snake_case__  ,						return_tensors="""pt"""     )

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

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

							# verify area
							lowerCAmelCase__        =   torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]  ,						snake_case__     )     )
							# verify boxes
							lowerCAmelCase__        =   torch.Size([6, 4]     )
							self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape  ,						snake_case__     )
							lowerCAmelCase__        =   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]  ,						snake_case__  ,						atol=1E-3     )     )
							# verify image_id
							lowerCAmelCase__        =   torch.tensor([39769]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]  ,						snake_case__     )     )
							# verify is_crowd
							lowerCAmelCase__        =   torch.tensor([0, 0, 0, 0, 0, 0]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]  ,						snake_case__     )     )
							# verify class_labels
							lowerCAmelCase__        =   torch.tensor([17, 17, 63, 75, 75, 93]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]  ,						snake_case__     )     )
							# verify masks
							lowerCAmelCase__        =   822873
							self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()  ,						snake_case__     )
							# verify orig_size
							lowerCAmelCase__        =   torch.tensor([480, 640]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]  ,						snake_case__     )     )
							# verify size
							lowerCAmelCase__        =   torch.tensor([800, 1066]     )
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]  ,						snake_case__     )     )





"""simple docstring"""




import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType

class       a_      :




					def __init__(	self						:     Optional[int]  ,						snake_case__						:     List[Any]=2  ,						snake_case__						:     Any=3  ,						snake_case__						:     Union[str, Any]=64  ,						snake_case__						:     Any=None     ):
							lowerCAmelCase__        =   np.random.default_rng(snake_case__     )
							lowerCAmelCase__        =   length
							lowerCAmelCase__        =   rng.normal(size=(length,)     ).astype(np.floataa     )
							lowerCAmelCase__        =   a * self.x + b + rng.normal(scale=0.1  ,						size=(length,)     ).astype(np.floataa     )




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




					def __getitem__(	self						:     List[str]  ,						snake_case__						:     Optional[int]     ):
							return {"x": self.x[i], "y": self.y[i]}

class       a_      (     torch.nn.Module			):




					def __init__(	self						:     List[str]  ,						snake_case__						:     str=0  ,						snake_case__						:     Dict=0  ,						snake_case__						:     Any=False     ):
							super().__init__()
							lowerCAmelCase__        =   torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase__        =   torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase__        =   True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     Any=None     ):
							if self.first_batch:
									print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
									lowerCAmelCase__        =   False
							return x * self.a[0] + self.b[0]

class       a_      (     torch.nn.Module			):




					def __init__(	self						:     Any  ,						snake_case__						:     Union[str, Any]=0  ,						snake_case__						:     Union[str, Any]=0  ,						snake_case__						:     List[Any]=False     ):
							super().__init__()
							lowerCAmelCase__        =   torch.nn.Parameter(torch.tensor(snake_case__     ).float()     )
							lowerCAmelCase__        =   torch.nn.Parameter(torch.tensor(snake_case__     ).float()     )
							lowerCAmelCase__        =   True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     Optional[Any]=None     ):
							if self.first_batch:
									print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
									lowerCAmelCase__        =   False
							return x * self.a + self.b







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__ = 16     ):





		"""simple docstring"""




		from datasets import load_dataset
		from transformers import AutoTokenizer

		lowerCAmelCase__        =   AutoTokenizer.from_pretrained("""bert-base-cased"""     )
		lowerCAmelCase__        =   {"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
		lowerCAmelCase__        =   load_dataset("""csv"""			,  data_files=lowerCamelCase__     )
		lowerCAmelCase__        =   datasets["""train"""].unique("""label"""     )

		lowerCAmelCase__        =   {v: i for i, v in enumerate(lowerCamelCase__     )}

		def tokenize_function(lowerCamelCase__     ):
				# max_length=None => use the model max length (it's actually the default)
				lowerCAmelCase__        =   tokenizer(
				    examples["""sentence1"""]			,  examples["""sentence2"""]			,  truncation=lowerCamelCase__			,  max_length=lowerCamelCase__			,  padding="""max_length"""     )
				if "label" in examples:
						lowerCAmelCase__        =   [label_to_id[l] for l in examples["""label"""]]
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		lowerCAmelCase__        =   datasets.map(
		    lowerCamelCase__			,  batched=lowerCamelCase__			,  remove_columns=["""sentence1""", """sentence2""", """label"""]			,  )

		def collate_fn(lowerCamelCase__     ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(lowerCamelCase__			,  padding="""max_length"""			,  max_length=128			,  return_tensors="""pt"""     )
				return tokenizer.pad(lowerCamelCase__			,  padding="""longest"""			,  return_tensors="""pt"""     )

		# Instantiate dataloaders.
		lowerCAmelCase__        =   DataLoader(tokenized_datasets["""train"""]			,  shuffle=lowerCamelCase__			,  collate_fn=lowerCamelCase__			,  batch_size=2     )
		lowerCAmelCase__        =   DataLoader(tokenized_datasets["""validation"""]			,  shuffle=lowerCamelCase__			,  collate_fn=lowerCamelCase__			,  batch_size=1     )

		return train_dataloader, eval_dataloader




