



'''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 	lowerCAmelCase_:
  '''simple docstring'''




  def __init__(   self  ,__UpperCAmelCase  ,)   ->		Optional[int]:
      lowerCAmelCase__			:  Tuple											= parent
      lowerCAmelCase__			:  Dict											= 13
      lowerCAmelCase__			:  Union[str, Any]											= 7
      lowerCAmelCase__			:  Optional[int]											= 30
      lowerCAmelCase__			:  List[Any]											= self.seq_length + self.mem_len
      lowerCAmelCase__			:  str											= 15
      lowerCAmelCase__			:  str											= True
      lowerCAmelCase__			:  Any											= True
      lowerCAmelCase__			:  Optional[Any]											= 99
      lowerCAmelCase__			:  List[Any]											= [10, 50, 80]
      lowerCAmelCase__			:  Optional[int]											= 32
      lowerCAmelCase__			:  Dict											= 32
      lowerCAmelCase__			:  Dict											= 4
      lowerCAmelCase__			:  Tuple											= 8
      lowerCAmelCase__			:  Union[str, Any]											= 128
      lowerCAmelCase__			:  Union[str, Any]											= 2
      lowerCAmelCase__			:  int											= 2
      lowerCAmelCase__			:  Union[str, Any]											= None
      lowerCAmelCase__			:  List[Any]											= 1
      lowerCAmelCase__			:  Any											= 0
      lowerCAmelCase__			:  Dict											= 3
      lowerCAmelCase__			:  Union[str, Any]											= self.vocab_size - 1
      lowerCAmelCase__			:  Optional[Any]											= 0.0_1




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  int											= ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size				)
      lowerCAmelCase__			:  Optional[int]											= ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size				)

      lowerCAmelCase__			:  List[Any]											= None
      if self.use_labels:
          lowerCAmelCase__			:  List[str]											= ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size				)

      lowerCAmelCase__			:  List[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 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      random.seed(self.seed				)
      tf.random.set_seed(self.seed				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		List[str]:
      lowerCAmelCase__			:  Any											= TFTransfoXLModel(__UpperCAmelCase				)

      lowerCAmelCase__				,       lowerCAmelCase__			:  Tuple											= model(__UpperCAmelCase				).to_tuple()

      lowerCAmelCase__			:  List[str]											= {"""input_ids""": input_ids_a, """mems""": mems_a}

      lowerCAmelCase__				,       lowerCAmelCase__			:  Dict											= 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 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Any:
      lowerCAmelCase__			:  Union[str, Any]											= TFTransfoXLLMHeadModel(__UpperCAmelCase				)

      lowerCAmelCase__				,       lowerCAmelCase__			:  List[str]											= model(__UpperCAmelCase				).to_tuple()

      lowerCAmelCase__			:  Optional[int]											= {"""input_ids""": input_ids_a, """labels""": lm_labels}
      lowerCAmelCase__				,       lowerCAmelCase__			:  Union[str, Any]											= model(__UpperCAmelCase				).to_tuple()

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

      lowerCAmelCase__			:  List[str]											= {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

      lowerCAmelCase__				,       lowerCAmelCase__			:  Any											= 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 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Tuple:
      lowerCAmelCase__			:  Any											= TFTransfoXLForSequenceClassification(__UpperCAmelCase				)
      lowerCAmelCase__			:  List[str]											= model(__UpperCAmelCase				)
      self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)				)






  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      lowerCAmelCase__			:  int											= self.prepare_config_and_inputs()
      ((lowerCAmelCase__)				,       (lowerCAmelCase__)				,       (lowerCAmelCase__)				,       (lowerCAmelCase__))			:  Dict											= config_and_inputs
      lowerCAmelCase__			:  Dict											= {"""input_ids""": input_ids_a}
      return config, inputs_dict







@require_tf
class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				(
      (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
  )
  __lowercase							:				List[Any]           =				() if is_tf_available() else ()
  __lowercase							:				Optional[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
  __lowercase							:				Optional[Any]           =				False
  __lowercase							:				List[str]           =				False
  __lowercase							:				Union[str, Any]           =				False
  __lowercase							:				int           =				False




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		List[str]:
      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 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__			:  int											= TFTransfoXLModelTester(self				)
      lowerCAmelCase__			:  Optional[int]											= ConfigTester(self  ,config_class=__UpperCAmelCase  ,d_embed=37				)




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      self.config_tester.run_common_tests()




  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      self.model_tester.set_seed()
      lowerCAmelCase__			:  Optional[Any]											= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_model(*__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		int:
      self.model_tester.set_seed()
      lowerCAmelCase__			:  int											= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_lm_head(*__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__			:  Tuple											= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__				,       lowerCAmelCase__			:  List[str]											= self.model_tester.prepare_config_and_inputs_for_common()
      lowerCAmelCase__			:  Dict											= [TFTransfoXLForSequenceClassification]

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




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




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase__			:  Optional[int]											= 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 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      pass







@require_tf
class 	lowerCAmelCase_(    unittest.TestCase					):
  '''simple docstring'''




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

      lowerCAmelCase__			:  Dict											= model.generate(__UpperCAmelCase  ,max_length=200  ,do_sample=__UpperCAmelCase				)
      self.assertListEqual(output_ids[0].numpy().tolist()  ,__UpperCAmelCase				)







'''simple docstring'''





# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    return 1 / (1 + np.exp(-z      ))
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    return (-y * np.log(UpperCamelCase      ) - (1 - y) * np.log(1 - h      )).mean()
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= np.dot(UpperCamelCase							,   UpperCamelCase      )
    return np.sum(y * scores - np.log(1 + np.exp(UpperCamelCase      )      )      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase=70000      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= np.zeros(x.shape[1]      )

    for iterations in range(UpperCamelCase      ):
        lowerCAmelCase__			:  str											= np.dot(UpperCamelCase							,   UpperCamelCase      )
        lowerCAmelCase__			:  Tuple											= sigmoid_function(UpperCamelCase      )
        lowerCAmelCase__			:  List[str]											= np.dot(x.T							,   h - y      ) / y.size
        lowerCAmelCase__			:  List[Any]											= theta - alpha * gradient  # updating the weights
        lowerCAmelCase__			:  List[Any]											= np.dot(UpperCamelCase							,   UpperCamelCase      )
        lowerCAmelCase__			:  List[Any]											= sigmoid_function(UpperCamelCase      )
        lowerCAmelCase__			:  List[Any]											= cost_function(UpperCamelCase							,   UpperCamelCase      )
        if iterations % 100 == 0:
            print(f"""loss: {j} \t"""      )  # printing the loss after every 100 iterations
    return theta


# In[68]:

if __name__ == "__main__":
 _lowerCAmelCase										=     datasets.load_iris()
 _lowerCAmelCase										=     iris.data[:, :2]
 _lowerCAmelCase										=     (iris.target != 0) * 1

 _lowerCAmelCase										=     0.1
 _lowerCAmelCase										=     logistic_reg(alpha, x, y, max_iterations=7_0000)
 print('''theta: ''', theta)  # printing the theta i.e our weights vector
 def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



     """simple docstring"""





     return sigmoid_function(
         np.dot(UpperCamelCase							,   UpperCamelCase      )      )  # predicting the value of probability from the logistic regression algorithm

 plt.figure(figsize=(10, 6))
 plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''')
 plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''')
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     (x[:, 0].min(), x[:, 0].max())
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     (x[:, 1].min(), x[:, 1].max())
 ((_lowerCAmelCase)						,      (_lowerCAmelCase))										=     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
 _lowerCAmelCase										=     np.c_[xxa.ravel(), xxa.ravel()]
 _lowerCAmelCase										=     predict_prob(grid).reshape(xxa.shape)
 plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

 plt.legend()




 plt.show()


