


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  snake_case_     :
					def __init__(   self     :Dict    ,__snake_case     :Optional[Any]    ,)      ->					Tuple:
												a__    =  parent
												a__    =  13
												a__    =  7
												a__    =  30
												a__    =  self.seq_length + self.mem_len
												a__    =  15
												a__    =  True
												a__    =  True
												a__    =  99
												a__    =  [10, 50, 80]
												a__    =  32
												a__    =  32
												a__    =  4
												a__    =  8
												a__    =  1_28
												a__    =  2
												a__    =  2
												a__    =  None
												a__    =  1
												a__    =  0
												a__    =  3
												a__    =  self.vocab_size - 1
												a__    =  0.01
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					Any:
												a__    =  ids_tensor([self.batch_size, self.seq_length]    ,self.vocab_size   )
												a__    =  ids_tensor([self.batch_size, self.seq_length]    ,self.vocab_size   )

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

												a__    =  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 				lowerCamelCase__(   self     :Any   )      ->					Tuple:
												random.seed(self.seed   )
												tf.random.set_seed(self.seed   )
					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :int    ,__snake_case     :List[Any]    ,__snake_case     :List[Any]    ,__snake_case     :Optional[int]   )      ->					Optional[int]:
												a__    =  TFTransfoXLModel(__snake_case   )

												a__   ,      a__    =  model(__snake_case   ).to_tuple()

												a__    =  {'input_ids': input_ids_a, 'mems': mems_a}

												a__   ,      a__    =  model(__snake_case   ).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 				lowerCamelCase__(   self     :str    ,__snake_case     :Optional[Any]    ,__snake_case     :Any    ,__snake_case     :List[Any]    ,__snake_case     :Optional[Any]   )      ->					Tuple:
												a__    =  TFTransfoXLLMHeadModel(__snake_case   )

												a__   ,      a__    =  model(__snake_case   ).to_tuple()

												a__    =  {'input_ids': input_ids_a, 'labels': lm_labels}
												a__   ,      a__    =  model(__snake_case   ).to_tuple()

												a__   ,      a__    =  model([input_ids_a, mems_a]   ).to_tuple()

												a__    =  {'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}

												a__   ,      a__    =  model(__snake_case   ).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 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Tuple    ,__snake_case     :Dict    ,__snake_case     :str    ,__snake_case     :List[Any]   )      ->					int:
												a__    =  TFTransfoXLForSequenceClassification(__snake_case   )
												a__    =  model(__snake_case   )
												self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_labels)   )



					def 				lowerCamelCase__(   self     :str   )      ->					Any:
												a__    =  self.prepare_config_and_inputs()
												((a__)   ,      (a__)   ,      (a__)   ,      (a__))    =  config_and_inputs
												a__    =  {'input_ids': input_ids_a}
												return config, inputs_dict







@require_tf
class  snake_case_     (lowerCamelCase_			,		lowerCamelCase_			,		unittest.TestCase   ):
					UpperCAmelCase__      :					Any					   =    (
					    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
					)
					UpperCAmelCase__      :					str					   =    () if is_tf_available() else ()
					UpperCAmelCase__      :					Dict					   =    (
					    {
					        '''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
					UpperCAmelCase__      :					str					   =    False
					UpperCAmelCase__      :					List[str]					   =    False
					UpperCAmelCase__      :					str					   =    False
					UpperCAmelCase__      :					List[Any]					   =    False
					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :Union[str, Any]    ,__snake_case     :List[str]    ,__snake_case     :int    ,__snake_case     :str    ,__snake_case     :List[Any]   )      ->					Optional[Any]:
												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 				lowerCamelCase__(   self     :Any   )      ->					Optional[Any]:
												a__    =  TFTransfoXLModelTester(self   )
												a__    =  ConfigTester(self    ,config_class=__snake_case    ,d_embed=37   )
					def 				lowerCamelCase__(   self     :str   )      ->					List[str]:
												self.config_tester.run_common_tests()
					def 				lowerCamelCase__(   self     :Dict   )      ->					Any:
												self.model_tester.set_seed()
												a__    =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_model(*__snake_case   )
					def 				lowerCamelCase__(   self     :int   )      ->					List[Any]:
												self.model_tester.set_seed()
												a__    =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_lm_head(*__snake_case   )
					def 				lowerCamelCase__(   self     :Any   )      ->					str:
												a__    =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__snake_case   )
					def 				lowerCamelCase__(   self     :Tuple   )      ->					Tuple:
												a__   ,      a__    =  self.model_tester.prepare_config_and_inputs_for_common()
												a__    =  [TFTransfoXLForSequenceClassification]

												for model_class in self.all_model_classes:
																			a__    =  model_class(__snake_case   )
																			assert isinstance(model.get_input_embeddings()    ,tf.keras.layers.Layer   )
																			if model_class in list_other_models_with_output_ebd:
																										a__    =  model.get_output_embeddings()
																										assert isinstance(__snake_case    ,tf.keras.layers.Layer   )
																										a__    =  model.get_bias()
																										assert name is None
																			else:
																										a__    =  model.get_output_embeddings()
																										assert x is None
																										a__    =  model.get_bias()
																										assert name is None
					def 				lowerCamelCase__(   self     :str   )      ->					Tuple:
												# TODO JP: Make TransfoXL XLA compliant
												pass
					@slow
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					List[Any]:
												for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			a__    =  TFTransfoXLModel.from_pretrained(__snake_case   )
																			self.assertIsNotNone(__snake_case   )



					@unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.'   )
					def 				lowerCamelCase__(   self     :str   )      ->					Optional[int]:
												pass







@require_tf
class  snake_case_     (unittest.TestCase   ):
					@unittest.skip('Skip test until #12651 is resolved.'   )
					@slow
					def 				lowerCamelCase__(   self     :Any   )      ->					int:
												a__    =  TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'   )
												# fmt: off
												a__    =  tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]]    ,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__    =  [33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # 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__    =  model.generate(__snake_case    ,max_length=2_00    ,do_sample=__snake_case   )
												self.assertListEqual(output_ids[0].numpy().tolist()    ,__snake_case   )








import unittest

from knapsack import greedy_knapsack as kp







class  snake_case_     (unittest.TestCase   ):
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Union[str, Any]:
												a__    =  [10, 20, 30, 40, 50, 60]
												a__    =  [2, 4, 6, 8, 10, 12]
												a__    =  1_00
												self.assertEqual(kp.calc_profit(__snake_case    ,__snake_case    ,__snake_case   )    ,2_10   )
					def 				lowerCamelCase__(   self     :str   )      ->					Optional[int]:
												self.assertRaisesRegex(__snake_case    ,'max_weight must greater than zero.'   )
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					int:
												self.assertRaisesRegex(__snake_case    ,'Weight can not be negative.'   )
					def 				lowerCamelCase__(   self     :str   )      ->					List[str]:
												self.assertRaisesRegex(__snake_case    ,'Profit can not be negative.'   )
					def 				lowerCamelCase__(   self     :str   )      ->					Optional[Any]:
												self.assertRaisesRegex(__snake_case    ,'max_weight must greater than zero.'   )



					def 				lowerCamelCase__(   self     :int   )      ->					List[Any]:
												self.assertRaisesRegex(
												    __snake_case    ,'The length of profit and weight must be same.'   )


if __name__ == "__main__":
						unittest.main()




