

'''simple docstring'''




from __future__ import annotations




def 						UpperCamelCase	(			a		,						a			)				->     tuple[int, int]:






     '''simple docstring'''
     if b == 0:
          return (1, 0)
     ((__magic_name__)       ,  (__magic_name__))						       =    extended_euclid(a		,						a % b			)
     __magic_name__						       =    a // b
     return (y, x - k * y)




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     int:






     '''simple docstring'''
     ((__magic_name__)       ,  (__magic_name__))						       =    extended_euclid(a		,						a			)
     __magic_name__						       =    na * na
     __magic_name__						       =    ra * x * na + ra * y * na
     return (n % m + m) % m




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     ((__magic_name__)       ,  (__magic_name__))						       =    extended_euclid(a		,						a			)
     if b < 0:
          __magic_name__						       =    (b % n + n) % n
     return b




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     int:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    invert_modulo(a		,						a			), invert_modulo(a		,						a			)
     __magic_name__						       =    na * na
     __magic_name__						       =    ra * x * na + ra * y * na
     return (n % m + m) % m


if __name__ == "__main__":
      from doctest import testmod

      testmod(name="chinese_remainder_theorem", verbose=True)
      testmod(name="chinese_remainder_theorem2", verbose=True)
      testmod(name="invert_modulo", verbose=True)
      testmod(name="extended_euclid", verbose=True)









'''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   _SCREAMING_SNAKE_CASE   :



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



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

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

      __magic_name__						       =    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		:   str      ):
      random.seed(self.seed      )
      tf.random.set_seed(self.seed      )



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   Tuple			,				a__		:   Any			,				a__		:   Union[str, Any]			,				a__		:   int      ):
      __magic_name__						       =    TFTransfoXLModel(a__      )

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

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

      __magic_name__       ,  __magic_name__						       =    model(a__      ).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		:   List[Any]			,				a__		:   int			,				a__		:   List[str]			,				a__		:   List[str]			,				a__		:   Dict      ):
      __magic_name__						       =    TFTransfoXLLMHeadModel(a__      )

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

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

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

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

      __magic_name__       ,  __magic_name__						       =    model(a__      ).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		:   List[Any]			,				a__		:   str			,				a__		:   Optional[int]			,				a__		:   Dict			,				a__		:   List[Any]      ):
      __magic_name__						       =    TFTransfoXLForSequenceClassification(a__      )
      __magic_name__						       =    model(a__      )
      self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)      )



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    self.prepare_config_and_inputs()
      ((__magic_name__)       ,  (__magic_name__)       ,  (__magic_name__)       ,  (__magic_name__))						       =    config_and_inputs
      __magic_name__						       =    {'''input_ids''': input_ids_a}
      return config, inputs_dict






@require_tf
class   _SCREAMING_SNAKE_CASE   (   __a		,__a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					(
     (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
 )
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					() if is_tf_available() else ()
 __SCREAMING_SNAKE_CASE					: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
 __SCREAMING_SNAKE_CASE					:List[str]      	=					False
 __SCREAMING_SNAKE_CASE					:str      	=					False
 __SCREAMING_SNAKE_CASE					:str      	=					False
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					False



 def 			snake_case__  (   self		:   int			,				a__		:   int			,				a__		:   List[str]			,				a__		:   Any			,				a__		:   Optional[Any]			,				a__		:   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 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    TFTransfoXLModelTester(self      )
      __magic_name__						       =    ConfigTester(self			,				config_class=a__			,				d_embed=37      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      self.config_tester.run_common_tests()



 def 			snake_case__  (   self		:   int      ):
      self.model_tester.set_seed()
      __magic_name__						       =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_model(*a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      self.model_tester.set_seed()
      __magic_name__						       =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_lm_head(*a__      )



 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*a__      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__       ,  __magic_name__						       =    self.model_tester.prepare_config_and_inputs_for_common()
      __magic_name__						       =    [TFTransfoXLForSequenceClassification]

      for model_class in self.all_model_classes:
           __magic_name__						       =    model_class(a__      )
           assert isinstance(model.get_input_embeddings()			,				tf.keras.layers.Layer      )
           if model_class in list_other_models_with_output_ebd:
                __magic_name__						       =    model.get_output_embeddings()
                assert isinstance(a__			,				tf.keras.layers.Layer      )
                __magic_name__						       =    model.get_bias()
                assert name is None
           else:
                __magic_name__						       =    model.get_output_embeddings()
                assert x is None
                __magic_name__						       =    model.get_bias()
                assert name is None



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



 @slow
 def 			snake_case__  (   self		:   List[Any]      ):
      for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __magic_name__						       =    TFTransfoXLModel.from_pretrained(a__      )
           self.assertIsNotNone(a__      )



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






@require_tf
class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 @unittest.skip('''Skip test until #12651 is resolved.'''      )
 @slow
 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''      )
      # fmt: off
      __magic_name__						       =    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
      __magic_name__						       =    [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>

      __magic_name__						       =    model.generate(a__			,				max_length=200			,				do_sample=a__      )
      self.assertListEqual(output_ids[0].numpy().tolist()			,				a__      )






