





"""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 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




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




				def        __lowercase	(  self	:List[Any]      ):
											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        __lowercase	(  self	:Optional[Any]    ,     __lowercase	:Union[str, Any]    ,     __lowercase	:Tuple=False      ):
											if not batched:
																		__lowerCamelCase   :   Tuple       =image_inputs[0]
																		if isinstance(__lowercase    ,     Image.Image      ):
																									__lowerCamelCase     ,    __lowerCamelCase   :   int       =image.size
																		else:
																									__lowerCamelCase     ,    __lowerCamelCase   :   str       =image.shape[1], image.shape[2]
																		if w < h:
																									__lowerCamelCase   :   Tuple       =int(self.size['''shortest_edge'''] * h / w      )
																									__lowerCamelCase   :   Optional[Any]       =self.size['''shortest_edge''']
																		elif w > h:
																									__lowerCamelCase   :   List[Any]       =self.size['''shortest_edge''']
																									__lowerCamelCase   :   Optional[Any]       =int(self.size['''shortest_edge'''] * w / h      )
																		else:
																									__lowerCamelCase   :   Any       =self.size['''shortest_edge''']
																									__lowerCamelCase   :   int       =self.size['''shortest_edge''']

											else:
																		__lowerCamelCase   :   str       =[]
																		for image in image_inputs:
																									__lowerCamelCase     ,    __lowerCamelCase   :   Dict       =self.get_expected_values([image]      )
																									expected_values.append((expected_height, expected_width)      )
																		__lowerCamelCase   :   List[Any]       =max(__lowercase    ,     key=lambda __lowercase      : item[0]      )[0]
																		__lowerCamelCase   :   Optional[int]       =max(__lowercase    ,     key=lambda __lowercase      : item[1]      )[1]

											return expected_height, expected_width

@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE_				(     snake_case__							,	unittest.TestCase ):
				"""simple docstring"""
				__snake_case :							Union[str, Any]									=		ConditionalDetrImageProcessor if is_vision_available() else None




				def        __lowercase	(  self	:List[str]      ):
											__lowerCamelCase   :   Dict       =ConditionalDetrImageProcessingTester(self      )




				@property
				def        __lowercase	(  self	:Optional[int]      ):
											return self.image_processor_tester.prepare_image_processor_dict()




				def        __lowercase	(  self	:Tuple      ):
											__lowerCamelCase   :   List[str]       =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        __lowercase	(  self	:Optional[int]      ):
											__lowerCamelCase   :   str       =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   :   Tuple       =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        __lowercase	(  self	:List[Any]      ):
											pass




				def        __lowercase	(  self	:List[Any]      ):
											# Initialize image_processing
											__lowerCamelCase   :   Dict       =self.image_processing_class(**self.image_processor_dict      )
											# create random PIL images
											__lowerCamelCase   :   List[Any]       =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   :   Tuple       =image_processing(image_inputs[0]    ,     return_tensors='''pt'''      ).pixel_values

											__lowerCamelCase     ,    __lowerCamelCase   :   Optional[int]       =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     ,    __lowerCamelCase   :   Any       =self.image_processor_tester.get_expected_values(__lowercase    ,     batched=__lowercase      )

											__lowerCamelCase   :   Dict       =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        __lowercase	(  self	:Dict      ):
											# Initialize image_processing
											__lowerCamelCase   :   str       =self.image_processing_class(**self.image_processor_dict      )
											# create random numpy tensors
											__lowerCamelCase   :   Union[str, Any]       =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   :   List[str]       =image_processing(image_inputs[0]    ,     return_tensors='''pt'''      ).pixel_values

											__lowerCamelCase     ,    __lowerCamelCase   :   str       =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   :   Optional[Any]       =image_processing(__lowercase    ,     return_tensors='''pt'''      ).pixel_values

											__lowerCamelCase     ,    __lowerCamelCase   :   List[Any]       =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        __lowercase	(  self	:Optional[int]      ):
											# Initialize image_processing
											__lowerCamelCase   :   List[str]       =self.image_processing_class(**self.image_processor_dict      )
											# create random PyTorch tensors
											__lowerCamelCase   :   Union[str, Any]       =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   :   Optional[int]       =image_processing(image_inputs[0]    ,     return_tensors='''pt'''      ).pixel_values

											__lowerCamelCase     ,    __lowerCamelCase   :   Dict       =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   :   Union[str, Any]       =image_processing(__lowercase    ,     return_tensors='''pt'''      ).pixel_values

											__lowerCamelCase     ,    __lowerCamelCase   :   int       =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        __lowercase	(  self	:Dict      ):
											# prepare image and target
											__lowerCamelCase   :   List[Any]       =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
											with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''    ,     '''r'''      ) as f:
																		__lowerCamelCase   :   int       =json.loads(f.read()      )

											__lowerCamelCase   :   List[Any]       ={'''image_id''': 3_9769, '''annotations''': target}

											# encode them
											__lowerCamelCase   :   Dict       =ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''      )
											__lowerCamelCase   :   List[str]       =image_processing(images=__lowercase    ,     annotations=__lowercase    ,     return_tensors='''pt'''      )

											# verify pixel values
											__lowerCamelCase   :   Dict       =torch.Size([1, 3, 800, 1066]      )
											self.assertEqual(encoding['''pixel_values'''].shape    ,     __lowercase      )

											__lowerCamelCase   :   Optional[int]       =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   :   int       =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   :   List[Any]       =torch.Size([6, 4]      )
											self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,     __lowercase      )
											__lowerCamelCase   :   str       =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   :   Tuple       =torch.tensor([3_9769]      )
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,     __lowercase      )      )
											# verify is_crowd
											__lowerCamelCase   :   Dict       =torch.tensor([0, 0, 0, 0, 0, 0]      )
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,     __lowercase      )      )
											# verify class_labels
											__lowerCamelCase   :   List[str]       =torch.tensor([75, 75, 63, 65, 17, 17]      )
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,     __lowercase      )      )
											# verify orig_size
											__lowerCamelCase   :   List[Any]       =torch.tensor([480, 640]      )
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,     __lowercase      )      )
											# verify size
											__lowerCamelCase   :   Any       =torch.tensor([800, 1066]      )
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,     __lowercase      )      )




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

											__lowerCamelCase   :   Any       ={'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

											__lowerCamelCase   :   List[str]       =pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''      )

											# encode them
											__lowerCamelCase   :   int       =ConditionalDetrImageProcessor(format='''coco_panoptic'''      )
											__lowerCamelCase   :   Optional[int]       =image_processing(images=__lowercase    ,     annotations=__lowercase    ,     masks_path=__lowercase    ,     return_tensors='''pt'''      )

											# verify pixel values
											__lowerCamelCase   :   Optional[Any]       =torch.Size([1, 3, 800, 1066]      )
											self.assertEqual(encoding['''pixel_values'''].shape    ,     __lowercase      )

											__lowerCamelCase   :   Dict       =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   :   int       =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   :   Any       =torch.Size([6, 4]      )
											self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,     __lowercase      )
											__lowerCamelCase   :   int       =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   :   List[Any]       =torch.tensor([3_9769]      )
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,     __lowercase      )      )
											# verify is_crowd
											__lowerCamelCase   :   Tuple       =torch.tensor([0, 0, 0, 0, 0, 0]      )
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,     __lowercase      )      )
											# verify class_labels
											__lowerCamelCase   :   Any       =torch.tensor([17, 17, 63, 75, 75, 93]      )
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,     __lowercase      )      )
											# verify masks
											__lowerCamelCase   :   Optional[Any]       =82_2873
											self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,     __lowercase      )
											# verify orig_size
											__lowerCamelCase   :   List[str]       =torch.tensor([480, 640]      )
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,     __lowercase      )      )
											# verify size
											__lowerCamelCase   :   Any       =torch.tensor([800, 1066]      )
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,     __lowercase      )      )











"""simple docstring"""





import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
							_UpperCamelCase					      =   pd.read_csv('sample_data.csv', header=None)
							_UpperCamelCase					      =   df.shape[:1][0]
							# If you're using some other dataset input the target column
							_UpperCamelCase					      =   df.iloc[:, 1:2]
							_UpperCamelCase					      =   actual_data.values.reshape(len_data, 1)
							_UpperCamelCase					      =   MinMaxScaler().fit_transform(actual_data)
							_UpperCamelCase					      =   10
							_UpperCamelCase					      =   5
							_UpperCamelCase					      =   20
							_UpperCamelCase					      =   len_data - periods * look_back
							_UpperCamelCase					      =   actual_data[:division]
							_UpperCamelCase					      =   actual_data[division - look_back :]
							_UpperCamelCase      ,     _UpperCamelCase					      =   [], []
							_UpperCamelCase      ,     _UpperCamelCase					      =   [], []

							for i in range(0, len(train_data) - forward_days - look_back + 1):
														train_x.append(train_data[i : i + look_back])
														train_y.append(train_data[i + look_back : i + look_back + forward_days])
							for i in range(0, len(test_data) - forward_days - look_back + 1):
														test_x.append(test_data[i : i + look_back])
														test_y.append(test_data[i + look_back : i + look_back + forward_days])
							_UpperCamelCase					      =   np.array(train_x)
							_UpperCamelCase					      =   np.array(test_x)
							_UpperCamelCase					      =   np.array([list(i.ravel()) for i in train_y])
							_UpperCamelCase					      =   np.array([list(i.ravel()) for i in test_y])

							_UpperCamelCase					      =   Sequential()
							model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
							model.add(LSTM(64, input_shape=(128, 1)))
							model.add(Dense(forward_days))
							model.compile(loss='mean_squared_error', optimizer='adam')
							_UpperCamelCase					      =   model.fit(
							    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
							)
							_UpperCamelCase					      =   model.predict(x_test)




