



import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		_lowercase		:				Tuple      		="platform"
		import jax
		import jax.numpy as jnp
		import numpy as np

		from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel

@require_flax
class        snake_case__    :







						"""simple docstring"""




						__lowerCAmelCase					:List[str]      =					PegasusConfig
						__lowerCAmelCase					:Optional[int]      =					{}
						__lowerCAmelCase					:Dict      =					"gelu"

						def __init__(       self ,       __lowercase ,       __lowercase=1_3 ,       __lowercase=7 ,       __lowercase=True ,       __lowercase=False ,       __lowercase=9_9 ,       __lowercase=3_2 ,       __lowercase=5 ,       __lowercase=4 ,       __lowercase=3_7 ,       __lowercase=0.1 ,       __lowercase=0.1 ,       __lowercase=2_0 ,       __lowercase=2 ,       __lowercase=1 ,       __lowercase=0 ,       ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						Optional[int]							=  parent
									a__       :						Optional[int]							=  batch_size
									a__       :						List[Any]							=  seq_length
									a__       :						int							=  is_training
									a__       :						Any							=  use_labels
									a__       :						Tuple							=  vocab_size
									a__       :						int							=  hidden_size
									a__       :						Dict							=  num_hidden_layers
									a__       :						Union[str, Any]							=  num_attention_heads
									a__       :						Tuple							=  intermediate_size

									a__       :						Any							=  hidden_dropout_prob
									a__       :						int							=  attention_probs_dropout_prob
									a__       :						Dict							=  max_position_embeddings
									a__       :						Optional[int]							=  eos_token_id
									a__       :						Any							=  pad_token_id
									a__       :						Dict							=  bos_token_id

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						Dict							=  ids_tensor([self.batch_size, self.seq_length - 1] ,       self.vocab_size  ).clip(3 ,       self.vocab_size  )
									a__       :						int							=  np.expand_dims(np.array([self.eos_token_id] * self.batch_size  ) ,       1  )
									a__       :						Optional[int]							=  np.concatenate([input_ids, eos_tensor] ,       axis=1  )

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

									a__       :						List[str]							=  self.config_cls(
									    vocab_size=self.vocab_size ,       d_model=self.hidden_size ,       encoder_layers=self.num_hidden_layers ,       decoder_layers=self.num_hidden_layers ,       encoder_attention_heads=self.num_attention_heads ,       decoder_attention_heads=self.num_attention_heads ,       encoder_ffn_dim=self.intermediate_size ,       decoder_ffn_dim=self.intermediate_size ,       dropout=self.hidden_dropout_prob ,       attention_dropout=self.attention_probs_dropout_prob ,       max_position_embeddings=self.max_position_embeddings ,       eos_token_ids=[2] ,       bos_token_id=self.bos_token_id ,       pad_token_id=self.pad_token_id ,       decoder_start_token_id=self.pad_token_id ,       **self.config_updates ,       )
									a__       :						Union[str, Any]							=  prepare_pegasus_inputs_dict(__lowercase ,       __lowercase ,       __lowercase  )
									return config, inputs_dict

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				int:
									"""simple docstring"""




									a__       :						Dict							=  2_0
									a__       :						str							=  model_class_name(__lowercase  )

									a__       :						Union[str, Any]							=  model.encode(inputs_dict["""input_ids"""]  )

									a__	,						a__       :						Dict							=  (
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

									a__       :						Dict							=  model.init_cache(decoder_input_ids.shape[0] ,       __lowercase ,       __lowercase  )
									a__       :						List[Any]							=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,       dtype="""i4"""  )

									a__       :						Tuple							=  jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :] ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,       )
									a__       :						List[Any]							=  model.decode(
									    decoder_input_ids[:, :-1] ,       __lowercase ,       decoder_attention_mask=__lowercase ,       past_key_values=__lowercase ,       decoder_position_ids=__lowercase ,       )

									a__       :						Any							=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,       dtype="""i4"""  )
									a__       :						Tuple							=  model.decode(
									    decoder_input_ids[:, -1:] ,       __lowercase ,       decoder_attention_mask=__lowercase ,       past_key_values=outputs_cache.past_key_values ,       decoder_position_ids=__lowercase ,       )

									a__       :						List[str]							=  model.decode(__lowercase ,       __lowercase  )

									a__       :						Tuple							=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
									self.parent.assertTrue(diff < 1E-3 ,       msg=F'''Max diff is {diff}'''  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Dict:
									"""simple docstring"""




									a__       :						Tuple							=  2_0
									a__       :						List[Any]							=  model_class_name(__lowercase  )

									a__       :						Dict							=  model.encode(inputs_dict["""input_ids"""]  )

									a__	,						a__       :						Optional[Any]							=  (
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

									a__       :						Any							=  jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])  ),
									    ] ,       axis=-1 ,       )

									a__       :						Any							=  model.init_cache(decoder_input_ids.shape[0] ,       __lowercase ,       __lowercase  )
									a__       :						List[str]							=  jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :] ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,       )

									a__       :						Tuple							=  model.decode(
									    decoder_input_ids[:, :-1] ,       __lowercase ,       decoder_attention_mask=__lowercase ,       past_key_values=__lowercase ,       decoder_position_ids=__lowercase ,       )
									a__       :						List[str]							=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,       dtype="""i4"""  )
									a__       :						Optional[Any]							=  model.decode(
									    decoder_input_ids[:, -1:] ,       __lowercase ,       past_key_values=outputs_cache.past_key_values ,       decoder_attention_mask=__lowercase ,       decoder_position_ids=__lowercase ,       )

									a__       :						Any							=  model.decode(__lowercase ,       __lowercase ,       decoder_attention_mask=__lowercase  )

									a__       :						int							=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
									self.parent.assertTrue(diff < 1E-3 ,       msg=F'''Max diff is {diff}'''  )




def      lowerCAmelCase_					(  _lowercase    :       Dict						,       _lowercase    :       Optional[int]						,       _lowercase    :       Union[str, Any]						,       _lowercase    :       List[str]=None						,       _lowercase    :       Optional[int]=None						,       )							->	Any:







			"""simple docstring"""
			if attention_mask is None:
						a__       :						Union[str, Any]							=  np.not_equal(_lowercase						,       config.pad_token_id).astype(np.inta)
			if decoder_attention_mask is None:
						a__       :						int							=  np.concatenate(
						    [
						        np.ones(decoder_input_ids[:, :1].shape						,       dtype=np.inta),
						        np.not_equal(decoder_input_ids[:, 1:]						,       config.pad_token_id).astype(np.inta),
						    ]						,       axis=-1						,       )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			}





@require_flax
class        snake_case__    (A__ ,     unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:Tuple      =					(
						    (
						        FlaxPegasusForConditionalGeneration,
						        FlaxPegasusModel,
						    )
						    if is_flax_available()
						    else ()
						)
						__lowerCAmelCase					:Optional[Any]      =					(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
						__lowerCAmelCase					:Union[str, Any]      =					True
						__lowerCAmelCase					:Dict      =					False
						__lowerCAmelCase					:int      =					False
						__lowerCAmelCase					:Dict      =					False

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						List[str]							=  FlaxPegasusModelTester(self  )
									a__       :						List[str]							=  ConfigTester(self ,       config_class=__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									self.config_tester.run_common_tests()

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__	,						a__       :						int							=  self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward(__lowercase ,       __lowercase ,       __lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__	,						a__       :						Optional[Any]							=  self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward_with_attn_mask(__lowercase ,       __lowercase ,       __lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									a__	,						a__       :						Any							=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__  ):
															a__       :						str							=  self._prepare_for_class(__lowercase ,       __lowercase  )
															a__       :						Union[str, Any]							=  model_class(__lowercase  )

															@jax.jit
															def encode_jitted(__lowercase ,       __lowercase=None ,       **__lowercase  ):
																		return model.encode(input_ids=__lowercase ,       attention_mask=__lowercase  )

															with self.subTest("""JIT Enabled"""  ):
																		a__       :						List[str]							=  encode_jitted(**__lowercase  ).to_tuple()

															with self.subTest("""JIT Disabled"""  ):
																		with jax.disable_jit():
																					a__       :						str							=  encode_jitted(**__lowercase  ).to_tuple()

															self.assertEqual(len(__lowercase  ) ,       len(__lowercase  )  )
															for jitted_output, output in zip(__lowercase ,       __lowercase  ):
																		self.assertEqual(jitted_output.shape ,       output.shape  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									a__	,						a__       :						Tuple							=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__  ):
															a__       :						Tuple							=  model_class(__lowercase  )
															a__       :						int							=  model.encode(inputs_dict["""input_ids"""] ,       inputs_dict["""attention_mask"""]  )

															a__       :						Union[str, Any]							=  {
															    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
															    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
															    """encoder_outputs""": encoder_outputs,
															}

															@jax.jit
															def decode_jitted(__lowercase ,       __lowercase ,       __lowercase  ):
																		return model.decode(
																		    decoder_input_ids=__lowercase ,       decoder_attention_mask=__lowercase ,       encoder_outputs=__lowercase ,       )

															with self.subTest("""JIT Enabled"""  ):
																		a__       :						str							=  decode_jitted(**__lowercase  ).to_tuple()

															with self.subTest("""JIT Disabled"""  ):
																		with jax.disable_jit():
																					a__       :						List[str]							=  decode_jitted(**__lowercase  ).to_tuple()

															self.assertEqual(len(__lowercase  ) ,       len(__lowercase  )  )
															for jitted_output, output in zip(__lowercase ,       __lowercase  ):
																		self.assertEqual(jitted_output.shape ,       output.shape  )

						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									for model_class_name in self.all_model_classes:
												a__       :						Union[str, Any]							=  model_class_name.from_pretrained("""google/pegasus-large""" ,       from_pt=__lowercase  )
												a__       :						Optional[int]							=  np.ones((1, 1)  )
												a__       :						Dict							=  model(__lowercase  )
												self.assertIsNotNone(__lowercase  )

						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						Dict							=  FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""  )
									a__       :						Union[str, Any]							=  PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""  )

									a__       :						List[str]							=  [
									    """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
									    """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
									]

									a__       :						Optional[int]							=  [
									    """California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
									    """Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
									]

									a__       :						List[str]							=  tokenizer(__lowercase ,       return_tensors="""np""" ,       truncation=__lowercase ,       max_length=5_1_2 ,       padding=__lowercase  )
									a__       :						Tuple							=  model.generate(**__lowercase ,       num_beams=2  ).sequences
									a__       :						int							=  tokenizer.batch_decode(__lowercase ,       skip_special_tokens=__lowercase  )
									assert tgt_text == decoded









from __future__ import annotations

import requests

_lowercase		:				Dict      		=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      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       int = 1						,       _lowercase    :       str = "new"						,       _lowercase    :       list | None = None)							->	dict:







			"""simple docstring"""
			a__       :						Optional[Any]							=  wanted_data or []
			if invalid_search_terms := ", ".join(sorted(set(_lowercase) - valid_terms)):
						a__       :						Any							=  F'''Invalid search term: {invalid_search_terms}'''
						raise ValueError(_lowercase)
			a__       :						int							=  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

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

			a__       :						int							=  {}
			for id_ in range(_lowercase):
						a__       :						List[Any]							=  {
						    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"]))




