
'''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 ,       lowercase_ ,       ):
     snake_case_ :    Tuple     			=						parent
     snake_case_ :    List[str]     			=						13
     snake_case_ :    Tuple     			=						7
     snake_case_ :    List[Any]     			=						30
     snake_case_ :    Optional[int]     			=						self.seq_length + self.mem_len
     snake_case_ :    List[str]     			=						15
     snake_case_ :    Union[str, Any]     			=						True
     snake_case_ :    Dict     			=						True
     snake_case_ :    List[Any]     			=						99
     snake_case_ :    List[Any]     			=						[10, 50, 80]
     snake_case_ :    int     			=						32
     snake_case_ :    Tuple     			=						32
     snake_case_ :    List[Any]     			=						4
     snake_case_ :    Any     			=						8
     snake_case_ :    str     			=						1_28
     snake_case_ :    Any     			=						2
     snake_case_ :    List[str]     			=						2
     snake_case_ :    Dict     			=						None
     snake_case_ :    List[Any]     			=						1
     snake_case_ :    List[str]     			=						0
     snake_case_ :    str     			=						3
     snake_case_ :    Union[str, Any]     			=						self.vocab_size - 1
     snake_case_ :    str     			=						0.01





 def        snake_case__		(    self):
     snake_case_ :    int     			=						ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size)
     snake_case_ :    Dict     			=						ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size)

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

     snake_case_ :    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        snake_case__		(    self):
     random.seed(self.seed)
     tf.random.set_seed(self.seed)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_):
     snake_case_ :    List[Any]     			=						TFTransfoXLModel(lowercase_)

     snake_case_			, snake_case_ :    Union[str, Any]     			=						model(lowercase_).to_tuple()

     snake_case_ :    Optional[int]     			=						{"input_ids": input_ids_a, "mems": mems_a}

     snake_case_			, snake_case_ :    Dict     			=						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        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_):
     snake_case_ :    str     			=						TFTransfoXLLMHeadModel(lowercase_)

     snake_case_			, snake_case_ :    int     			=						model(lowercase_).to_tuple()

     snake_case_ :    List[str]     			=						{"input_ids": input_ids_a, "labels": lm_labels}
     snake_case_			, snake_case_ :    List[Any]     			=						model(lowercase_).to_tuple()

     snake_case_			, snake_case_ :    str     			=						model([input_ids_a, mems_a]).to_tuple()

     snake_case_ :    List[Any]     			=						{"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}

     snake_case_			, snake_case_ :    Dict     			=						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        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_):
     snake_case_ :    Tuple     			=						TFTransfoXLForSequenceClassification(lowercase_)
     snake_case_ :    Any     			=						model(lowercase_)
     self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.num_labels))





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						self.prepare_config_and_inputs()
     ((snake_case_)			, (snake_case_)			, (snake_case_)			, (snake_case_)) :    Union[str, Any]     			=						config_and_inputs
     snake_case_ :    int     			=						{"input_ids": input_ids_a}
     return config, inputs_dict






@require_tf
class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						(
     (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
 )
 UpperCAmelCase_		    =						() if is_tf_available() else ()
 UpperCAmelCase_		    =						(
     {
         """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_		    =						False
 UpperCAmelCase_		    =						False
 UpperCAmelCase_		    =						False
 UpperCAmelCase_		    =						False





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_):
     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):
     snake_case_ :    int     			=						TFTransfoXLModelTester(self)
     snake_case_ :    Any     			=						ConfigTester(self ,       config_class=lowercase_ ,       d_embed=37)





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





 def        snake_case__		(    self):
     self.model_tester.set_seed()
     snake_case_ :    Optional[int]     			=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_transfo_xl_model(*lowercase_)





 def        snake_case__		(    self):
     self.model_tester.set_seed()
     snake_case_ :    str     			=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_transfo_xl_lm_head(*lowercase_)





 def        snake_case__		(    self):
     snake_case_ :    Tuple     			=						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowercase_)





 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Optional[Any]     			=						self.model_tester.prepare_config_and_inputs_for_common()
     snake_case_ :    str     			=						[TFTransfoXLForSequenceClassification]

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






@require_tf
class    UpperCAmelCase_				(	unittest.TestCase					):





 @unittest.skip("Skip test until #12651 is resolved.")
 @slow
 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103")
     # fmt: off
     snake_case_ :    Union[str, Any]     			=						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
     snake_case_ :    Union[str, Any]     			=						[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>

     snake_case_ :    Dict     			=						model.generate(lowercase_ ,       max_length=2_00 ,       do_sample=lowercase_)
     self.assertListEqual(output_ids[0].numpy().tolist() ,       lowercase_)






'''simple docstring'''
from __future__ import annotations

from cmath import sqrt

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if a == 0:
        raise ValueError("Coefficient 'a' must not be zero."       )
    snake_case_ :    Union[str, Any]     			=						b * b - 4 * a * c

    snake_case_ :    List[str]     			=						(-b + sqrt(__SCREAMING_SNAKE_CASE       )) / (2 * a)
    snake_case_ :    int     			=						(-b - sqrt(__SCREAMING_SNAKE_CASE       )) / (2 * a)

    return (
        root_a.real if not root_a.imag else root_a,
        root_a.real if not root_a.imag else root_a,
    )

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_			, snake_case_ :    str     			=						quadratic_roots(a=5,		b=6,		c=1       )
    print(f'The solutions are: {solutiona} and {solutiona}'       )


if __name__ == "__main__":
  main()




