from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import numpy
	import tensorflow as tf

	from transformers import (
	    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
	    BertConfig,
	    DPRConfig,
	    TFDPRContextEncoder,
	    TFDPRQuestionEncoder,
	    TFDPRReader,
	)





class UpperCAmelCase						:


				'''simple docstring'''






				def __init__(						self		,     lowercase		,     lowercase=1_3		,     lowercase=7		,     lowercase=True		,     lowercase=True		,     lowercase=True		,     lowercase=True		,     lowercase=9_9		,     lowercase=3_2		,     lowercase=2		,     lowercase=4		,     lowercase=3_7		,     lowercase="gelu"		,     lowercase=0.1		,     lowercase=0.1		,     lowercase=5_1_2		,     lowercase=1_6		,     lowercase=2		,     lowercase=0.02		,     lowercase=3		,     lowercase=4		,     lowercase=None		,     lowercase=0		,     ):



						"""simple docstring"""





						A_		:    int         =				parent
						A_		:    Tuple         =				batch_size
						A_		:    str         =				seq_length
						A_		:    Dict         =				is_training
						A_		:    Optional[Any]         =				use_input_mask
						A_		:    int         =				use_token_type_ids
						A_		:    Optional[Any]         =				use_labels
						A_		:    int         =				vocab_size
						A_		:    List[str]         =				hidden_size
						A_		:    str         =				num_hidden_layers
						A_		:    Any         =				num_attention_heads
						A_		:    List[Any]         =				intermediate_size
						A_		:    Optional[int]         =				hidden_act
						A_		:    Dict         =				hidden_dropout_prob
						A_		:    Dict         =				attention_probs_dropout_prob
						A_		:    str         =				max_position_embeddings
						A_		:    List[str]         =				type_vocab_size
						A_		:    Union[str, Any]         =				type_sequence_label_size
						A_		:    List[str]         =				initializer_range
						A_		:    int         =				num_labels
						A_		:    int         =				num_choices
						A_		:    List[str]         =				scope
						A_		:    int         =				projection_dim
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size )

						A_		:    str         =				None
						if self.use_input_mask:
								# follow test_modeling_tf_ctrl.py
								A_		:    List[str]         =				random_attention_mask([self.batch_size, self.seq_length] )

						A_		:    Union[str, Any]         =				None
						if self.use_token_type_ids:
								A_		:    str         =				ids_tensor([self.batch_size, self.seq_length]		,     self.type_vocab_size )

						A_		:    Optional[int]         =				None
						A_		:    Union[str, Any]         =				None
						A_		:    Union[str, Any]         =				None
						if self.use_labels:
								A_		:    Dict         =				ids_tensor([self.batch_size]		,     self.type_sequence_label_size )
								A_		:    Optional[Any]         =				ids_tensor([self.batch_size, self.seq_length]		,     self.num_labels )
								A_		:    int         =				ids_tensor([self.batch_size]		,     self.num_choices )

						A_		:    Union[str, Any]         =				BertConfig(
						    vocab_size=self.vocab_size		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     num_attention_heads=self.num_attention_heads		,     intermediate_size=self.intermediate_size		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     max_position_embeddings=self.max_position_embeddings		,     type_vocab_size=self.type_vocab_size		,     is_decoder=lowercase		,     initializer_range=self.initializer_range		,     )
						A_		:    str         =				DPRConfig(projection_dim=self.projection_dim		,     **config.to_dict() )

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase ):



						"""simple docstring"""





						A_		:    int         =				TFDPRContextEncoder(config=lowercase )
						A_		:    List[str]         =				model(lowercase		,     attention_mask=lowercase		,     token_type_ids=lowercase )
						A_		:    int         =				model(lowercase		,     token_type_ids=lowercase )
						A_		:    str         =				model(lowercase )
						self.parent.assertEqual(result.pooler_output.shape		,     (self.batch_size, self.projection_dim or self.hidden_size) )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase ):



						"""simple docstring"""





						A_		:    Dict         =				TFDPRQuestionEncoder(config=lowercase )
						A_		:    Optional[Any]         =				model(lowercase		,     attention_mask=lowercase		,     token_type_ids=lowercase )
						A_		:    Optional[Any]         =				model(lowercase		,     token_type_ids=lowercase )
						A_		:    Optional[int]         =				model(lowercase )
						self.parent.assertEqual(result.pooler_output.shape		,     (self.batch_size, self.projection_dim or self.hidden_size) )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase ):



						"""simple docstring"""





						A_		:    List[str]         =				TFDPRReader(config=lowercase )
						A_		:    Dict         =				model(lowercase		,     attention_mask=lowercase )

						self.parent.assertEqual(result.start_logits.shape		,     (self.batch_size, self.seq_length) )
						self.parent.assertEqual(result.end_logits.shape		,     (self.batch_size, self.seq_length) )
						self.parent.assertEqual(result.relevance_logits.shape		,     (self.batch_size,) )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Any         =				self.prepare_config_and_inputs()
						(
						    (
						    A_
						)       ,      (
						    A_
						)       ,      (
						    A_
						)       ,      (
						    A_
						)       ,      (
						    A_
						)       ,      (
						    A_
						)       ,      (
						    A_
						)       ,      
						)		:    Any         =				config_and_inputs
						A_		:    List[Any]         =				{'input_ids': input_ids}
						return config, inputs_dict





@require_tf
class UpperCAmelCase						( __A ,	__A ,	unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    = (
				    (
				        TFDPRContextEncoder,
				        TFDPRQuestionEncoder,
				        TFDPRReader,
				    )
				    if is_tf_available()
				    else ()
				)
				lowerCamelCase_    = {'''feature-extraction''': TFDPRQuestionEncoder} if is_tf_available() else {}

				lowerCamelCase_    = False
				lowerCamelCase_    = False
				lowerCamelCase_    = False
				lowerCamelCase_    = False
				lowerCamelCase_    = False
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				TFDPRModelTester(self )
						A_		:    Any         =				ConfigTester(self		,     config_class=lowercase		,     hidden_size=3_7 )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						self.config_tester.run_common_tests()
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_dpr_context_encoder(*lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_dpr_question_encoder(*lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_dpr_reader(*lowercase )







				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A_		:    Optional[Any]         =				TFDPRContextEncoder.from_pretrained(lowercase )
								self.assertIsNotNone(lowercase )

						for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A_		:    Dict         =				TFDPRContextEncoder.from_pretrained(lowercase )
								self.assertIsNotNone(lowercase )

						for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A_		:    Optional[Any]         =				TFDPRQuestionEncoder.from_pretrained(lowercase )
								self.assertIsNotNone(lowercase )

						for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A_		:    Union[str, Any]         =				TFDPRReader.from_pretrained(lowercase )
								self.assertIsNotNone(lowercase )





@require_tf
class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				@slow
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    str         =				TFDPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base' )

						A_		:    int         =				tf.constant(
						    [[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]] )  # [CLS] hello, is my dog cute? [SEP]
						A_		:    Optional[Any]         =				model(lowercase )[0]  # embedding shape = (1, 768)
						# compare the actual values for a slice.
						A_		:    str         =				tf.constant(
						    [
						        [
						            0.0323_6253,
						            0.1275_3335,
						            0.1681_8509,
						            0.0027_9786,
						            0.389_6933,
						            0.2426_4945,
						            0.217_8971,
						            -0.0233_5227,
						            -0.0848_1959,
						            -0.1432_4117,
						        ]
						    ] )
						self.assertTrue(numpy.allclose(output[:, :1_0].numpy()		,     expected_slice.numpy()		,     atol=1E-4 ) )







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"""]))






