




'''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   :  Dict						,						lowercase__   :  Any						,						):


           '''simple docstring'''

           a_     :							List[str]     						=  parent
           a_     :							str     						=  13
           a_     :							List[str]     						=  7
           a_     :							str     						=  30
           a_     :							Optional[Any]     						=  self.seq_length + self.mem_len
           a_     :							List[str]     						=  15
           a_     :							Optional[Any]     						=  True
           a_     :							Any     						=  True
           a_     :							Dict     						=  99
           a_     :							Dict     						=  [10, 50, 80]
           a_     :							Any     						=  32
           a_     :							Optional[Any]     						=  32
           a_     :							int     						=  4
           a_     :							Optional[Any]     						=  8
           a_     :							Optional[int]     						=  128
           a_     :							Dict     						=  2
           a_     :							int     						=  2
           a_     :							List[str]     						=  None
           a_     :							List[str]     						=  1
           a_     :							Dict     						=  0
           a_     :							List[Any]     						=  3
           a_     :							List[str]     						=  self.vocab_size - 1
           a_     :							Dict     						=  0.01






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size       )
           a_     :							str     						=  ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size       )

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

           a_     :							Union[str, 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 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  int						,						lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							List[str]     						=  TFTransfoXLModel(lowerCamelCase__       )

           a_     :							int     						=  model(lowerCamelCase__       ).to_tuple()

           a_     :							Union[str, Any]     						=  {"""input_ids""": input_ids_a, """mems""": mems_a}

           a_     :							List[str]     						=  model(lowerCamelCase__       ).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   :  Optional[Any]						,						lowercase__   :  List[Any]						,						lowercase__   :  Any						,						lowercase__   :  Optional[int]						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Any     						=  TFTransfoXLLMHeadModel(lowerCamelCase__       )

           a_     :							Any     						=  model(lowerCamelCase__       ).to_tuple()

           a_     :							Union[str, Any]     						=  {"""input_ids""": input_ids_a, """labels""": lm_labels}
           a_     :							Any     						=  model(lowerCamelCase__       ).to_tuple()

           a_     :							Any     						=  model([input_ids_a, mems_a]       ).to_tuple()

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

           a_     :							Tuple     						=  model(lowerCamelCase__       ).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   :  Union[str, Any]						,						lowercase__   :  Tuple						,						lowercase__   :  List[Any]						,						lowercase__   :  Optional[int]						,						lowercase__   :  Dict       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							str     						=  self.prepare_config_and_inputs()
           (a_)     :							int     						=  config_and_inputs
           a_     :							int     						=  {"""input_ids""": input_ids_a}
           return config, inputs_dict







@require_tf
class      SCREAMING_SNAKE_CASE    (						__UpperCAmelCase							,      __UpperCAmelCase							,      unittest.TestCase				):
      __magic_name__  :       List[Any]   							=					(
          (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
      )
      __magic_name__  :       Any   							=					() if is_tf_available() else ()
      __magic_name__  :       Tuple   							=					(
          {
              '''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
      __magic_name__  :       List[str]   							=					False
      __magic_name__  :       Optional[Any]   							=					False
      __magic_name__  :       int   							=					False
      __magic_name__  :       int   							=					False






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  Tuple						,						lowercase__   :  Any						,						lowercase__   :  str						,						lowercase__   :  Optional[Any]						,						lowercase__   :  int       ):


           '''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   :  Tuple       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  TFTransfoXLModelTester(self       )
           a_     :							Optional[int]     						=  ConfigTester(self						,						config_class=lowerCamelCase__						,						d_embed=37       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           self.config_tester.run_common_tests()






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           self.model_tester.set_seed()
           a_     :							Optional[int]     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_model(*lowerCamelCase__       )






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           self.model_tester.set_seed()
           a_     :							List[Any]     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_lm_head(*lowerCamelCase__       )






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowerCamelCase__       )






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_     :							int     						=  self.model_tester.prepare_config_and_inputs_for_common()
           a_     :							List[Any]     						=  [TFTransfoXLForSequenceClassification]

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






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           pass






      @slow
      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                a_     :							Optional[Any]     						=  TFTransfoXLModel.from_pretrained(lowerCamelCase__       )
                self.assertIsNotNone(lowerCamelCase__       )






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


           '''simple docstring'''

           pass







@require_tf
class      SCREAMING_SNAKE_CASE    (						unittest.TestCase				):






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


           '''simple docstring'''

           a_     :							Any     						=  TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""       )
           # fmt: off
           a_     :							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
           a_     :							List[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>

           a_     :							Any     						=  model.generate(lowerCamelCase__						,						max_length=200						,						do_sample=lowerCamelCase__       )
           self.assertListEqual(output_ids[0].numpy().tolist()						,						lowerCamelCase__       )




"""simple docstring"""


from ..utils import DummyObject, requires_backends

class 				_SCREAMING_SNAKE_CASE				(							metaclass=__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       Optional[int]									=    ['''note_seq''']


   def __init__(						self ,     *lowerCamelCase__ ,     **lowerCamelCase__						)      ->							Tuple:
    requires_backends(self ,     ["""note_seq"""]						)


   @classmethod
   def 					UpperCAmelCase__(						cls ,     *lowerCamelCase__ ,     **lowerCamelCase__						)      ->							Tuple:
    requires_backends(cls ,     ["""note_seq"""]						)







   @classmethod
   def 					UpperCAmelCase__(						cls ,     *lowerCamelCase__ ,     **lowerCamelCase__						)      ->							Tuple:
    requires_backends(cls ,     ["""note_seq"""]						)