
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       ,	SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




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

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

   lowercase__	: int     		=   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   lowercase__  (       self):


   '''simple docstring'''




   random.seed(self.seed)
   tf.random.set_seed(self.seed)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: List[str]     		=   TFTransfoXLModel(SCREAMING_SNAKE_CASE_)

   lowercase__		,     lowercase__	: Optional[Any]     		=   model(SCREAMING_SNAKE_CASE_).to_tuple()

   lowercase__	: List[Any]     		=   {"""input_ids""": input_ids_a, """mems""": mems_a}

   lowercase__		,     lowercase__	: Tuple     		=   model(SCREAMING_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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   TFTransfoXLLMHeadModel(SCREAMING_SNAKE_CASE_)

   lowercase__		,     lowercase__	: Dict     		=   model(SCREAMING_SNAKE_CASE_).to_tuple()

   lowercase__	: int     		=   {"""input_ids""": input_ids_a, """labels""": lm_labels}
   lowercase__		,     lowercase__	: str     		=   model(SCREAMING_SNAKE_CASE_).to_tuple()

   lowercase__		,     lowercase__	: Dict     		=   model([input_ids_a, mems_a]).to_tuple()

   lowercase__	: Tuple     		=   {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

   lowercase__		,     lowercase__	: Dict     		=   model(SCREAMING_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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   TFTransfoXLForSequenceClassification(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   model(SCREAMING_SNAKE_CASE_)
   self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels))



 def   lowercase__  (       self):


   '''simple docstring'''




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



@require_tf
class    _snake_case (						UpperCAmelCase_	,    UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  List[Any]	   =      (
     (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
 )
 __lowerCAmelCase	:  Union[str, Any]	   =      () if is_tf_available() else ()
 __lowerCAmelCase	:  Optional[int]	   =      (
     {
         '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
 __lowerCAmelCase	:  Any	   =      False
 __lowerCAmelCase	:  Dict	   =      False
 __lowerCAmelCase	:  Union[str, Any]	   =      False
 __lowerCAmelCase	:  List[Any]	   =      False


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   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   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   TFTransfoXLModelTester(self)
   lowercase__	: int     		=   ConfigTester(self       ,	config_class=SCREAMING_SNAKE_CASE_       ,	d_embed=37)


 def   lowercase__  (       self):


   '''simple docstring'''




   self.config_tester.run_common_tests()


 def   lowercase__  (       self):


   '''simple docstring'''




   self.model_tester.set_seed()
   lowercase__	: Dict     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_transfo_xl_model(*SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   self.model_tester.set_seed()
   lowercase__	: List[str]     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_transfo_xl_lm_head(*SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: Tuple     		=   self.model_tester.prepare_config_and_inputs_for_common()
   lowercase__	: List[str]     		=   [TFTransfoXLForSequenceClassification]

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


 def   lowercase__  (       self):


   '''simple docstring'''




   pass


 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




   for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowercase__	: Union[str, Any]     		=   TFTransfoXLModel.from_pretrained(SCREAMING_SNAKE_CASE_)
     self.assertIsNotNone(SCREAMING_SNAKE_CASE_)



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


   '''simple docstring'''




   pass



@require_tf
class    _snake_case (						unittest.TestCase				):


 @unittest.skip("""Skip test until #12651 is resolved.""")
 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103""")
   # fmt: off
   lowercase__	: 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
   lowercase__	: Optional[int]     		=   [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>

   lowercase__	: List[Any]     		=   model.generate(SCREAMING_SNAKE_CASE_       ,	max_length=2_00       ,	do_sample=SCREAMING_SNAKE_CASE_)
   self.assertListEqual(output_ids[0].numpy().tolist()       ,	SCREAMING_SNAKE_CASE_)








def 		UpperCamelCase						(   lowercase_ )		->						float:


  '''simple docstring'''






  if not nums:  # Makes sure that the list is not empty
    raise ValueError("""List is empty""" )

  lowercase__	: int     		=   sum(lowercase_ ) / len(lowercase_ )  # Calculate the average
  return sum(abs(x - average ) for x in nums ) / len(lowercase_ )


if __name__ == "__main__":
     import doctest

     doctest.testmod()






