from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	import torch

	from transformers.modeling_outputs import BaseModelOutput

	from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

_UpperCAmelCase            =		logging.get_logger(__name__)





@add_end_docstrings(__A  )
class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				def __init__(						self		,     **lowercase ):



						"""simple docstring"""





						super().__init__(**lowercase )

						if self.framework == "tf":
								raise ValueError(F'''The {self.__class__} is only available in PyTorch.''' )

						requires_backends(self		,     'vision' )
						self.check_model_type(lowercase )
				def __call__(						self		,     lowercase		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						if "text_queries" in kwargs:
								A_		:    Dict         =				kwargs.pop('text_queries' )

						if isinstance(lowercase		,     (str, Image.Image) ):
								A_		:    Optional[int]         =				{'image': image, 'candidate_labels': candidate_labels}
						else:
								A_		:    Optional[Any]         =				image
						A_		:    Dict         =				super().__call__(lowercase		,     **lowercase )
						return results
				def   lowerCAmelCase_       (						self		,     **lowercase ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				{}
						if "threshold" in kwargs:
								A_		:    int         =				kwargs['threshold']
						if "top_k" in kwargs:
								A_		:    Any         =				kwargs['top_k']
						return {}, {}, postprocess_params
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Optional[int]         =				load_image(inputs['image'] )
						A_		:    List[Any]         =				inputs['candidate_labels']
						if isinstance(lowercase		,     lowercase ):
								A_		:    Dict         =				candidate_labels.split(',' )

						A_		:    Union[str, Any]         =				torch.tensor([[image.height, image.width]]		,     dtype=torch.intaa )
						for i, candidate_label in enumerate(lowercase ):
								A_		:    Dict         =				self.tokenizer(lowercase		,     return_tensors=self.framework )
								A_		:    List[Any]         =				self.image_processor(lowercase		,     return_tensors=self.framework )
								yield {
								    "is_last": i == len(lowercase ) - 1,
								    "target_size": target_size,
								    "candidate_label": candidate_label,
								    **text_inputs,
								    **image_features,
								}
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    List[Any]         =				model_inputs.pop('target_size' )
						A_		:    str         =				model_inputs.pop('candidate_label' )
						A_		:    Dict         =				model_inputs.pop('is_last' )

						A_		:    Optional[Any]         =				self.model(**lowercase )

						A_		:    Any         =				{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
						return model_outputs
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase=0.1		,     lowercase=None ):



						"""simple docstring"""





						A_		:    Optional[int]         =				[]
						for model_output in model_outputs:
								A_		:    List[str]         =				model_output['candidate_label']
								A_		:    Union[str, Any]         =				BaseModelOutput(lowercase )
								A_		:    Optional[int]         =				self.image_processor.post_process_object_detection(
								    outputs=lowercase		,     threshold=lowercase		,     target_sizes=model_output['target_size'] )[0]

								for index in outputs["scores"].nonzero():
										A_		:    Tuple         =				outputs['scores'][index].item()
										A_		:    List[str]         =				self._get_bounding_box(outputs['boxes'][index][0] )

										A_		:    int         =				{'score': score, 'label': label, 'box': box}
										results.append(lowercase )

						A_		:    str         =				sorted(lowercase		,     key=lambda lowercase : x["score"]		,     reverse=lowercase )
						if top_k:
								A_		:    List[str]         =				results[:top_k]

						return results







				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						if self.framework != "pt":
								raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.' )
						A_       ,      A_       ,      A_       ,      A_		:    List[Any]         =				box.int().tolist()
						A_		:    Tuple         =				{
						    'xmin': xmin,
						    'ymin': ymin,
						    'xmax': xmax,
						    'ymax': ymax,
						}
						return bbox







from __future__ import annotations

import requests

_UpperCAmelCase            =		set(
    """approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			int = 1						,__lowercase						:			str = "new"						,__lowercase						:			list | None = None     ):
		'''simple docstring'''




		A_		:    Tuple         =				wanted_data or []
		if invalid_search_terms := ", ".join(sorted(set(__lowercase     ) - valid_terms     )     ):
				A_		:    int         =				f'''Invalid search term: {invalid_search_terms}'''
				raise ValueError(__lowercase     )
		A_		:    Optional[int]         =				requests.get(
		    f'''https://reddit.com/r/{subreddit}/{age}.json?limit={limit}'''						,headers={'User-agent': 'A random string'}						,)
		if response.status_code == 4_29:
				raise requests.HTTPError

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

		A_		:    Union[str, Any]         =				{}
		for id_ in range(__lowercase     ):
				A_		:    List[str]         =				{
				    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"""]))






