





"""simple docstring"""




from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFTransfoXLForSequenceClassification,
		    TFTransfoXLLMHeadModel,
		    TFTransfoXLModel,
		)


class        __UpperCamelCase				:






							def __init__(   self      ,					lowerCAmelCase__      ,					)	-> int:
								a      :		str	=				parent
								a      :		List[str]	=				13
								a      :		Any	=				7
								a      :		List[Any]	=				30
								a      :		str	=				self.seq_length + self.mem_len
								a      :		str	=				15
								a      :		List[str]	=				True
								a      :		int	=				True
								a      :		List[Any]	=				99
								a      :		Optional[int]	=				[10, 50, 80]
								a      :		str	=				32
								a      :		Union[str, Any]	=				32
								a      :		Any	=				4
								a      :		int	=				8
								a      :		Union[str, Any]	=				128
								a      :		Dict	=				2
								a      :		List[Any]	=				2
								a      :		Union[str, Any]	=				None
								a      :		List[str]	=				1
								a      :		Optional[int]	=				0
								a      :		Dict	=				3
								a      :		Any	=				self.vocab_size - 1
								a      :		int	=				0.01






							def     __a   (   self      )	-> List[Any]:
								a      :		Dict	=				ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size      )
								a      :		Tuple	=				ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size      )

								a      :		int	=				None
								if self.use_labels:
									a      :		Tuple	=				ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size      )

								a      :		str	=				TransfoXLConfig(
								    vocab_size=self.vocab_size      ,					mem_len=self.mem_len      ,					clamp_len=self.clamp_len      ,					cutoffs=self.cutoffs      ,					d_model=self.hidden_size      ,					d_embed=self.d_embed      ,					n_head=self.num_attention_heads      ,					d_head=self.d_head      ,					d_inner=self.d_inner      ,					div_val=self.div_val      ,					n_layer=self.num_hidden_layers      ,					eos_token_id=self.eos_token_id      ,					pad_token_id=self.vocab_size - 1      ,					init_range=self.init_range      ,					num_labels=self.num_labels      ,					)

								return (config, input_ids_a, input_ids_a, lm_labels)






							def     __a   (   self      )	-> List[str]:
								random.seed(self.seed      )
								tf.random.set_seed(self.seed      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Any:
								a      :		int	=				TFTransfoXLModel(__lowercase      )

								a      :		Optional[Any]	=				model(__lowercase      ).to_tuple()

								a      :		str	=				{'''input_ids''': input_ids_a, '''mems''': mems_a}

								a      :		Union[str, Any]	=				model(__lowercase      ).to_tuple()

								self.parent.assertEqual(hidden_states_a.shape      ,					(self.batch_size, self.seq_length, self.hidden_size)      )
								self.parent.assertEqual(hidden_states_a.shape      ,					(self.batch_size, self.seq_length, self.hidden_size)      )
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]      ,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,					)
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]      ,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,					)






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Union[str, Any]:
								a      :		Optional[Any]	=				TFTransfoXLLMHeadModel(__lowercase      )

								a      :		List[Any]	=				model(__lowercase      ).to_tuple()

								a      :		int	=				{'''input_ids''': input_ids_a, '''labels''': lm_labels}
								a      :		Any	=				model(__lowercase      ).to_tuple()

								a      :		Any	=				model([input_ids_a, mems_a]      ).to_tuple()

								a      :		List[str]	=				{'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

								a      :		Optional[int]	=				model(__lowercase      ).to_tuple()

								self.parent.assertEqual(lm_logits_a.shape      ,					(self.batch_size, self.seq_length, self.vocab_size)      )
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]      ,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,					)

								self.parent.assertEqual(lm_logits_a.shape      ,					(self.batch_size, self.seq_length, self.vocab_size)      )
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]      ,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers      ,					)






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> str:
								a      :		Dict	=				TFTransfoXLForSequenceClassification(__lowercase      )
								a      :		str	=				model(__lowercase      )
								self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.num_labels)      )






							def     __a   (   self      )	-> str:
								a      :		Dict	=				self.prepare_config_and_inputs()
								(a)      :		Optional[int]	=				config_and_inputs
								a      :		int	=				{'''input_ids''': input_ids_a}
								return config, inputs_dict


@require_tf
class        __UpperCamelCase				(						UpperCAmelCase__		,       UpperCAmelCase__		,       unittest.TestCase						):
							lowerCamelCase   :					Optional[int]      					=(
							    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
							)
							lowerCamelCase   :					int      					=() if is_tf_available() else ()
							lowerCamelCase   :					str      					=(
							    {
							        """feature-extraction""": TFTransfoXLModel,
							        """text-classification""": TFTransfoXLForSequenceClassification,
							        """text-generation""": TFTransfoXLLMHeadModel,
							        """zero-shot""": TFTransfoXLForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
							lowerCamelCase   :					str      					=False
							lowerCamelCase   :					str      					=False
							lowerCamelCase   :					int      					=False
							lowerCamelCase   :					int      					=False






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> int:
								if pipeline_test_casse_name == "TextGenerationPipelineTests":
									# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
									# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
									# tokenizer.
									return True

								return False






							def     __a   (   self      )	-> Union[str, Any]:
								a      :		List[Any]	=				TFTransfoXLModelTester(self      )
								a      :		Optional[Any]	=				ConfigTester(self      ,					config_class=__lowercase      ,					d_embed=37      )






							def     __a   (   self      )	-> Optional[int]:
								self.config_tester.run_common_tests()






							def     __a   (   self      )	-> Optional[Any]:
								self.model_tester.set_seed()
								a      :		Optional[int]	=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_model(*__lowercase      )






							def     __a   (   self      )	-> Tuple:
								self.model_tester.set_seed()
								a      :		int	=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_lm_head(*__lowercase      )






							def     __a   (   self      )	-> Tuple:
								a      :		int	=				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__lowercase      )






							def     __a   (   self      )	-> List[str]:
								a      :		List[str]	=				self.model_tester.prepare_config_and_inputs_for_common()
								a      :		str	=				[TFTransfoXLForSequenceClassification]

								for model_class in self.all_model_classes:
									a      :		int	=				model_class(__lowercase      )
									assert isinstance(model.get_input_embeddings()      ,					tf.keras.layers.Layer      )
									if model_class in list_other_models_with_output_ebd:
										a      :		List[Any]	=				model.get_output_embeddings()
										assert isinstance(__lowercase      ,					tf.keras.layers.Layer      )
										a      :		List[str]	=				model.get_bias()
										assert name is None
									else:
										a      :		str	=				model.get_output_embeddings()
										assert x is None
										a      :		int	=				model.get_bias()
										assert name is None






							def     __a   (   self      )	-> Optional[Any]:
								# TODO JP: Make TransfoXL XLA compliant
								pass






							@slow
							def     __a   (   self      )	-> Any:
								for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a      :		int	=				TFTransfoXLModel.from_pretrained(__lowercase      )
									self.assertIsNotNone(__lowercase      )






							@unittest.skip(reason="This model doesn\'t play well with fit() due to not returning a single loss."      )
							def     __a   (   self      )	-> Dict:
								pass


@require_tf
class        __UpperCamelCase				(						unittest.TestCase						):






							@unittest.skip("Skip test until #12651 is resolved."      )
							@slow
							def     __a   (   self      )	-> List[Any]:
								a      :		Any	=				TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"      )
								# fmt: off
								a      :		Tuple	=				tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]]      ,					dtype=tf.intaa      )  # noqa: E231
								# fmt: on
								#  In 1991 , the remains of Russian Tsar Nicholas II and his family
								#  ( except for Alexei and Maria ) are discovered .
								#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
								#  remainder of the story . 1883 Western Siberia ,
								#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
								#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
								#  father initially slaps him for making such an accusation , Rasputin watches as the
								#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
								#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
								#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

								# fmt: off
								a      :		int	=				[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # noqa: E231
								# fmt: on
								#  In 1991, the remains of Russian Tsar Nicholas II and his family (
								#  except for Alexei and Maria ) are discovered. The voice of young son,
								#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
								#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
								#  and a group of men to perform magic. Rasputin has a vision and
								#  denounces one of the men as a horse thief. Although his father initially
								#  slaps him for making such an accusation, Rasputin watches as the man
								#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
								#  of the Virgin Mary, prompting him to become a priest.
								#  Rasputin quickly becomes famous, with people, even a bishop, begging for
								#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
								# Nicholas II and his family were discovered. The voice of <unk> young son,
								# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

								a      :		Tuple	=				model.generate(__lowercase      ,					max_length=200      ,					do_sample=__lowercase      )
								self.assertListEqual(output_ids[0].numpy().tolist()      ,					__lowercase      )






from __future__ import annotations

import requests



def 	lowerCAmelCase       (						lowerCAmelCase_       )->       dict:
				lowerCAmelCase_       :					List[Any]			 = f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
				return requests.get(lowerCAmelCase_       ).json()



def 	lowerCAmelCase       (						lowerCAmelCase_ = 10       )->       list[dict]:
				lowerCAmelCase_       :					List[Any]			 = '''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
				lowerCAmelCase_       :					Tuple			 = requests.get(lowerCAmelCase_       ).json()[:max_stories]
				return [get_hackernews_story(lowerCAmelCase_       ) for story_id in story_ids]



def 	lowerCAmelCase       (						lowerCAmelCase_ = 10       )->       str:
				lowerCAmelCase_       :					Optional[Any]			 = hackernews_top_stories(lowerCAmelCase_       )
				return "\n".join('''* [{title}]({url})'''.format(**lowerCAmelCase_       ) for story in stories       )


if __name__ == "__main__":
						print(hackernews_top_stories_as_markdown())