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        lowercase_   (					unittest.TestCase		):



							def __init__(						self				,    lowercase_				,    lowercase_=7				,    lowercase_=3				,    lowercase_=30				,    lowercase_=400				,    lowercase_=True				,    lowercase_=None				,    lowercase_=True				,    lowercase_=1 / 255				,    lowercase_=True				,    lowercase_=[0.5, 0.5, 0.5]				,    lowercase_=[0.5, 0.5, 0.5]				,    lowercase_=True				,    ):
													# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
													_snake_case						:					Any    =   size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
													_snake_case						:					Union[str, Any]    =   parent
													_snake_case						:					Optional[int]    =   batch_size
													_snake_case						:					Optional[Any]    =   num_channels
													_snake_case						:					str    =   min_resolution
													_snake_case						:					Dict    =   max_resolution
													_snake_case						:					Optional[Any]    =   do_resize
													_snake_case						:					List[str]    =   size
													_snake_case						:					Union[str, Any]    =   do_rescale
													_snake_case						:					Optional[Any]    =   rescale_factor
													_snake_case						:					str    =   do_normalize
													_snake_case						:					Optional[int]    =   image_mean
													_snake_case						:					Optional[int]    =   image_std
													_snake_case						:					Tuple    =   do_pad



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

													else:
																			_snake_case						:					Optional[int]    =   []
																			for image in image_inputs:
																									_snake_case    ,_snake_case						:					Union[str, Any]    =   self.get_expected_values([image]    )
																									expected_values.append((expected_height, expected_width)    )
																			_snake_case						:					int    =   max(lowercase_				,    key=lambda lowercase_    : item[0]    )[0]
																			_snake_case						:					Tuple    =   max(lowercase_				,    key=lambda lowercase_    : item[1]    )[1]

													return expected_height, expected_width




@require_torch
@require_vision
class        lowercase_   (					__snake_case						,			unittest.TestCase		):
							_lowerCamelCase						=		DetrImageProcessor if is_vision_available() else None



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   DetrImageProcessingTester(self    )



							@property
							def 	UpperCamelCase	(						self    ):
													return self.image_processor_tester.prepare_image_processor_dict()



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   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_rescale"    )    )
													self.assertTrue(hasattr(lowercase_				,    "rescale_factor"    )    )
													self.assertTrue(hasattr(lowercase_				,    "do_resize"    )    )
													self.assertTrue(hasattr(lowercase_				,    "size"    )    )
													self.assertTrue(hasattr(lowercase_				,    "do_pad"    )    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   self.image_processing_class.from_dict(self.image_processor_dict    )
													self.assertEqual(image_processor.size				,    {"shortest_edge": 18, "longest_edge": 1_333}    )
													self.assertEqual(image_processor.do_pad				,    lowercase_    )

													_snake_case						:					List[Any]    =   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 	UpperCamelCase	(						self    ):
													pass



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

													# Test not batched input
													_snake_case						:					List[Any]    =   image_processing(image_inputs[0]				,    return_tensors="pt"    ).pixel_values

													_snake_case    ,_snake_case						:					Optional[Any]    =   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
													_snake_case    ,_snake_case						:					int    =   self.image_processor_tester.get_expected_values(lowercase_				,    batched=lowercase_    )

													_snake_case						:					Tuple    =   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 	UpperCamelCase	(						self    ):
													# Initialize image_processing
													_snake_case						:					Union[str, Any]    =   self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													_snake_case						:					int    =   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
													_snake_case						:					Optional[int]    =   image_processing(image_inputs[0]				,    return_tensors="pt"    ).pixel_values

													_snake_case    ,_snake_case						:					Union[str, Any]    =   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
													_snake_case						:					Dict    =   image_processing(lowercase_				,    return_tensors="pt"    ).pixel_values

													_snake_case    ,_snake_case						:					Union[str, 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 	UpperCamelCase	(						self    ):
													# Initialize image_processing
													_snake_case						:					Tuple    =   self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													_snake_case						:					List[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
													_snake_case						:					Tuple    =   image_processing(image_inputs[0]				,    return_tensors="pt"    ).pixel_values

													_snake_case    ,_snake_case						:					Optional[Any]    =   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
													_snake_case						:					Union[str, Any]    =   image_processing(lowercase_				,    return_tensors="pt"    ).pixel_values

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

													_snake_case						:					List[str]    =   {"image_id": 39_769, "annotations": target}

													# encode them
													_snake_case						:					Optional[int]    =   DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"    )
													_snake_case						:					Dict    =   image_processing(images=lowercase_				,    annotations=lowercase_				,    return_tensors="pt"    )

													# verify pixel values
													_snake_case						:					Union[str, Any]    =   torch.Size([1, 3, 800, 1_066]    )
													self.assertEqual(encoding["pixel_values"].shape				,    lowercase_    )

													_snake_case						:					Dict    =   torch.tensor([0.2_796, 0.3_138, 0.3_481]    )
													self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,    lowercase_				,    atol=1e-4    )    )

													# verify area
													_snake_case						:					Optional[Any]    =   torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,    lowercase_    )    )
													# verify boxes
													_snake_case						:					List[Any]    =   torch.Size([6, 4]    )
													self.assertEqual(encoding["labels"][0]["boxes"].shape				,    lowercase_    )
													_snake_case						:					Dict    =   torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,    lowercase_				,    atol=1e-3    )    )
													# verify image_id
													_snake_case						:					int    =   torch.tensor([39_769]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,    lowercase_    )    )
													# verify is_crowd
													_snake_case						:					Tuple    =   torch.tensor([0, 0, 0, 0, 0, 0]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,    lowercase_    )    )
													# verify class_labels
													_snake_case						:					Tuple    =   torch.tensor([75, 75, 63, 65, 17, 17]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,    lowercase_    )    )
													# verify orig_size
													_snake_case						:					str    =   torch.tensor([480, 640]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,    lowercase_    )    )
													# verify size
													_snake_case						:					List[str]    =   torch.tensor([800, 1_066]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,    lowercase_    )    )



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

													_snake_case						:					Union[str, Any]    =   {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

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

													# encode them
													_snake_case						:					Any    =   DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"    )
													_snake_case						:					Optional[Any]    =   image_processing(images=lowercase_				,    annotations=lowercase_				,    masks_path=lowercase_				,    return_tensors="pt"    )

													# verify pixel values
													_snake_case						:					List[Any]    =   torch.Size([1, 3, 800, 1_066]    )
													self.assertEqual(encoding["pixel_values"].shape				,    lowercase_    )

													_snake_case						:					Dict    =   torch.tensor([0.2_796, 0.3_138, 0.3_481]    )
													self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,    lowercase_				,    atol=1e-4    )    )

													# verify area
													_snake_case						:					Dict    =   torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,    lowercase_    )    )
													# verify boxes
													_snake_case						:					Optional[Any]    =   torch.Size([6, 4]    )
													self.assertEqual(encoding["labels"][0]["boxes"].shape				,    lowercase_    )
													_snake_case						:					Optional[Any]    =   torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,    lowercase_				,    atol=1e-3    )    )
													# verify image_id
													_snake_case						:					str    =   torch.tensor([39_769]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,    lowercase_    )    )
													# verify is_crowd
													_snake_case						:					Any    =   torch.tensor([0, 0, 0, 0, 0, 0]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,    lowercase_    )    )
													# verify class_labels
													_snake_case						:					Dict    =   torch.tensor([17, 17, 63, 75, 75, 93]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,    lowercase_    )    )
													# verify masks
													_snake_case						:					int    =   822_873
													self.assertEqual(encoding["labels"][0]["masks"].sum().item()				,    lowercase_    )
													# verify orig_size
													_snake_case						:					Optional[int]    =   torch.tensor([480, 640]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,    lowercase_    )    )
													# verify size
													_snake_case						:					Dict    =   torch.tensor([800, 1_066]    )
													self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,    lowercase_    )    )
from __future__ import annotations

import requests

__SCREAMING_SNAKE_CASE						:   Tuple         =			set(
    'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports'.split()
)
def  snake_case						(__lowercase ,      __lowercase = 1 ,      __lowercase = "new" ,      __lowercase = None						)	->	dict:

						'''simple docstring'''



						_snake_case						:					Union[str, Any]    =   wanted_data or []
						if invalid_search_terms := ", ".join(sorted(set(__lowercase						) - valid_terms						)						):
												_snake_case						:					List[str]    =   F"""Invalid search term: {invalid_search_terms}"""
												raise ValueError(__lowercase						)
						_snake_case						:					Any    =   requests.get(
						    F"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}""" ,      headers={"User-agent": "A random string"} ,      )
						if response.status_code == 429:
												raise requests.HTTPError

						_snake_case						:					Optional[int]    =   response.json()
						if not wanted_data:
												return {id_: data["data"]["children"][id_] for id_ in range(__lowercase						)}

						_snake_case						:					Union[str, Any]    =   {}
						for id_ in range(__lowercase						):
												_snake_case						:					Dict    =   {
												    item: data["data"]["children"][id_]["data"][item] for item in wanted_data
												}
						return data_dict


if __name__ == "__main__":
				# If you get Error 429, that means you are rate limited.Try after some time
				print(get_subreddit_data('learnpython', wanted_data=['title', 'url', 'selftext']))