



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,    __a,    ):

              '''simple docstring'''

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







       def      snake_case__   (     self):

              '''simple docstring'''

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

              _lowerCAmelCase  :							List[Any]				     =					None
              if self.use_labels:
                     _lowerCAmelCase  :							Optional[int]				     =					ids_tensor([self.batch_size, self.seq_length],    self.vocab_size)

              _lowerCAmelCase  :							Tuple				     =					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):

              '''simple docstring'''

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







       def      snake_case__   (     self,    __a,    __a,    __a,    __a):

              '''simple docstring'''

              _lowerCAmelCase  :							List[str]				     =					TFTransfoXLModel(__a)

              _lowerCAmelCase  :							Any				     =					model(__a).to_tuple()

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

              _lowerCAmelCase  :							Union[str, Any]				     =					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,    __a,    __a,    __a,    __a):

              '''simple docstring'''

              _lowerCAmelCase  :							List[Any]				     =					TFTransfoXLLMHeadModel(__a)

              _lowerCAmelCase  :							Tuple				     =					model(__a).to_tuple()

              _lowerCAmelCase  :							str				     =					{"input_ids": input_ids_a, "labels": lm_labels}
              _lowerCAmelCase  :							Tuple				     =					model(__a).to_tuple()

              _lowerCAmelCase  :							int				     =					model([input_ids_a, mems_a]).to_tuple()

              _lowerCAmelCase  :							Union[str, Any]				     =					{"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}

              _lowerCAmelCase  :							str				     =					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,    __a,    __a,    __a,    __a):

              '''simple docstring'''

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







       def      snake_case__   (     self):

              '''simple docstring'''

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



@require_tf
class 						UpperCAmelCase_					( _UpperCamelCase     ,      _UpperCamelCase     ,      unittest.TestCase):
       lowerCamelCase__         =	(
           (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
       )
       lowerCamelCase__         =	() if is_tf_available() else ()
       lowerCamelCase__         =	(
           {
               '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__         =	False
       lowerCamelCase__         =	False
       lowerCamelCase__         =	False
       lowerCamelCase__         =	False







       def      snake_case__   (     self,    __a,    __a,    __a,    __a,    __a):

              '''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      snake_case__   (     self):

              '''simple docstring'''

              _lowerCAmelCase  :							Any				     =					TFTransfoXLModelTester(self)
              _lowerCAmelCase  :							Optional[Any]				     =					ConfigTester(self,    config_class=__a,    d_embed=37)







       def      snake_case__   (     self):

              '''simple docstring'''

              self.config_tester.run_common_tests()







       def      snake_case__   (     self):

              '''simple docstring'''

              self.model_tester.set_seed()
              _lowerCAmelCase  :							Any				     =					self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_transfo_xl_model(*__a)







       def      snake_case__   (     self):

              '''simple docstring'''

              self.model_tester.set_seed()
              _lowerCAmelCase  :							List[Any]				     =					self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_transfo_xl_lm_head(*__a)







       def      snake_case__   (     self):

              '''simple docstring'''

              _lowerCAmelCase  :							List[Any]				     =					self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__a)







       def      snake_case__   (     self):

              '''simple docstring'''

              _lowerCAmelCase  :							List[Any]				     =					self.model_tester.prepare_config_and_inputs_for_common()
              _lowerCAmelCase  :							Optional[Any]				     =					[TFTransfoXLForSequenceClassification]

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







       def      snake_case__   (     self):

              '''simple docstring'''

              pass







       @slow
       def      snake_case__   (     self):

              '''simple docstring'''

              for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     _lowerCAmelCase  :							Union[str, Any]				     =					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):

              '''simple docstring'''

              pass



@require_tf
class 						UpperCAmelCase_					( unittest.TestCase):







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

              '''simple docstring'''

              _lowerCAmelCase  :							Any				     =					TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103")
              # fmt: off
              _lowerCAmelCase  :							int				     =					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  :							str				     =					[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(__a,    max_length=200,    do_sample=__a)
              self.assertListEqual(output_ids[0].numpy().tolist(),    __a)





def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							return price * (1 + tax_rate)


if __name__ == "__main__":
						print(f'''{price_plus_tax(100, 0.25) = }''')
						print(f'''{price_plus_tax(125.50, 0.05) = }''')
