'''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 		__A							:






       def __init__(   self					,  UpperCamelCase_					,  ):
              __UpperCAmelCase	:					Union[str, Any]												=		parent
              __UpperCAmelCase	:					str												=		13
              __UpperCAmelCase	:					Union[str, Any]												=		7
              __UpperCAmelCase	:					Optional[int]												=		30
              __UpperCAmelCase	:					Optional[int]												=		self.seq_length + self.mem_len
              __UpperCAmelCase	:					Dict												=		15
              __UpperCAmelCase	:					int												=		True
              __UpperCAmelCase	:					Union[str, Any]												=		True
              __UpperCAmelCase	:					List[Any]												=		99
              __UpperCAmelCase	:					Tuple												=		[10, 50, 80]
              __UpperCAmelCase	:					Tuple												=		32
              __UpperCAmelCase	:					int												=		32
              __UpperCAmelCase	:					Optional[Any]												=		4
              __UpperCAmelCase	:					Optional[Any]												=		8
              __UpperCAmelCase	:					List[Any]												=		1_28
              __UpperCAmelCase	:					Dict												=		2
              __UpperCAmelCase	:					str												=		2
              __UpperCAmelCase	:					Any												=		None
              __UpperCAmelCase	:					Union[str, Any]												=		1
              __UpperCAmelCase	:					int												=		0
              __UpperCAmelCase	:					Tuple												=		3
              __UpperCAmelCase	:					str												=		self.vocab_size - 1
              __UpperCAmelCase	:					str												=		0.0_1






       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					Any												=		ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size   )
              __UpperCAmelCase	:					Union[str, Any]												=		ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size   )

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

              __UpperCAmelCase	:					Optional[Any]												=		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       _snake_case    (   self   ):
              random.seed(self.seed   )
              tf.random.set_seed(self.seed   )






       def       _snake_case    (   self					,  UpperCamelCase_					,  UpperCamelCase_					,  UpperCamelCase_					,  UpperCamelCase_   ):
              __UpperCAmelCase	:					str												=		TFTransfoXLModel(UpperCamelCase_   )

              __UpperCAmelCase	:					Union[str, Any]												=		model(UpperCamelCase_   ).to_tuple()

              __UpperCAmelCase	:					int												=		{"""input_ids""": input_ids_a, """mems""": mems_a}

              __UpperCAmelCase	:					Optional[Any]												=		model(UpperCamelCase_   ).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       _snake_case    (   self					,  UpperCamelCase_					,  UpperCamelCase_					,  UpperCamelCase_					,  UpperCamelCase_   ):
              __UpperCAmelCase	:					str												=		TFTransfoXLLMHeadModel(UpperCamelCase_   )

              __UpperCAmelCase	:					Optional[Any]												=		model(UpperCamelCase_   ).to_tuple()

              __UpperCAmelCase	:					Tuple												=		{"""input_ids""": input_ids_a, """labels""": lm_labels}
              __UpperCAmelCase	:					Tuple												=		model(UpperCamelCase_   ).to_tuple()

              __UpperCAmelCase	:					int												=		model([input_ids_a, mems_a]   ).to_tuple()

              __UpperCAmelCase	:					Optional[Any]												=		{"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

              __UpperCAmelCase	:					Optional[int]												=		model(UpperCamelCase_   ).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       _snake_case    (   self					,  UpperCamelCase_					,  UpperCamelCase_					,  UpperCamelCase_					,  UpperCamelCase_   ):
              __UpperCAmelCase	:					Tuple												=		TFTransfoXLForSequenceClassification(UpperCamelCase_   )
              __UpperCAmelCase	:					Any												=		model(UpperCamelCase_   )
              self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.num_labels)   )







       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					str												=		self.prepare_config_and_inputs()
              (__UpperCAmelCase)	:					int												=		config_and_inputs
              __UpperCAmelCase	:					Optional[Any]												=		{"""input_ids""": input_ids_a}
              return config, inputs_dict






@require_tf
class 		__A							(A_			,       A_			,       unittest.TestCase ):
       snake_case							:Optional[int]					     =       (
           (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
       )
       snake_case							:List[str]					     =       () if is_tf_available() else ()
       snake_case							:List[Any]					     =       (
           {
               "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
       snake_case							:int					     =       False
       snake_case							:Optional[Any]					     =       False
       snake_case							:List[Any]					     =       False
       snake_case							:Any					     =       False






       def       _snake_case    (   self					,  UpperCamelCase_					,  UpperCamelCase_					,  UpperCamelCase_					,  UpperCamelCase_					,  UpperCamelCase_   ):
              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       _snake_case    (   self   ):
              __UpperCAmelCase	:					List[str]												=		TFTransfoXLModelTester(self   )
              __UpperCAmelCase	:					Any												=		ConfigTester(self					,  config_class=UpperCamelCase_					,  d_embed=37   )






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






       def       _snake_case    (   self   ):
              self.model_tester.set_seed()
              __UpperCAmelCase	:					Union[str, Any]												=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase_   )






       def       _snake_case    (   self   ):
              self.model_tester.set_seed()
              __UpperCAmelCase	:					Tuple												=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase_   )






       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					str												=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase_   )






       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					int												=		self.model_tester.prepare_config_and_inputs_for_common()
              __UpperCAmelCase	:					Any												=		[TFTransfoXLForSequenceClassification]

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






       def       _snake_case    (   self   ):
              # TODO JP: Make TransfoXL XLA compliant
              pass






       @slow
       def       _snake_case    (   self   ):
              for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     __UpperCAmelCase	:					Any												=		TFTransfoXLModel.from_pretrained(UpperCamelCase_   )
                     self.assertIsNotNone(UpperCamelCase_   )







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






@require_tf
class 		__A							(unittest.TestCase ):






       @unittest.skip("Skip test until #12651 is resolved."   )
       @slow
       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					Optional[int]												=		TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"   )
              # fmt: off
              __UpperCAmelCase	:					int												=		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
              __UpperCAmelCase	:					List[str]												=		[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>

              __UpperCAmelCase	:					Optional[int]												=		model.generate(UpperCamelCase_					,  max_length=2_00					,  do_sample=UpperCamelCase_   )
              self.assertListEqual(output_ids[0].numpy().tolist()					,  UpperCamelCase_   )










"""simple docstring"""
from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray

def 				_snake_case      (   __snake_case							:      float						, __snake_case							:      float						, __snake_case							:      bool = False   ):




   """simple docstring"""







   if radian_mode:
      return [magnitude * cos(__snake_case   ), magnitude * sin(__snake_case   )]
   return [magnitude * cos(radians(__snake_case   )   ), magnitude * sin(radians(__snake_case   )   )]

def 				_snake_case      (   __snake_case							:      NDArray[floataa]						, __snake_case							:      NDArray[floataa]						, __snake_case							:      float = 10**-1   ):




   """simple docstring"""







   _lowerCamelCase	:					NDArray[floataa]   =		cross(__snake_case						, __snake_case   )
   _lowerCamelCase	:					float   =		sum(__snake_case   )
   return abs(__snake_case   ) < eps


if __name__ == "__main__":
 # Test to check if it works
 UpperCAmelCase				=						array(
     [
         polar_force(718.4, 180 - 30),
         polar_force(879.54, 45),
         polar_force(100, -90),
     ]
 )

 UpperCAmelCase				=						array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem 1 in image_data/2D_problems.jpg
 UpperCAmelCase				=						array(
     [
         polar_force(30 * 9.81, 15),
         polar_force(215, 180 - 45),
         polar_force(264, 90 - 30),
     ]
 )

 UpperCAmelCase				=						array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem in image_data/2D_problems_1.jpg
 UpperCAmelCase				=						array([[0, -2000], [0, -1200], [0, 1_5600], [0, -1_2400]])

 UpperCAmelCase				=						array([[0, 0], [6, 0], [10, 0], [12, 0]])

 assert in_static_equilibrium(forces, location)

 import doctest

 doctest.testmod()

