


"""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 						_A  :







    """simple docstring"""




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






    def 	__snake_case							(					self				:    Union[str, Any]):
      a						:		List[Any]      							=			ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size)
      a						:		List[Any]      							=			ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size)

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

      a						:		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				:    List[str]):
      random.seed(self.seed)
      tf.random.set_seed(self.seed)






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    Optional[Any]				, __UpperCAmelCase				:    Any				, __UpperCAmelCase				:    int):
      a						:		List[str]      							=			TFTransfoXLModel(__UpperCAmelCase)

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

      a						:		Optional[Any]      							=			{"input_ids": input_ids_a, "mems": mems_a}

      a							,		a						:		Union[str, Any]      							=			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 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    Union[str, Any]):
      a						:		List[Any]      							=			TFTransfoXLLMHeadModel(__UpperCAmelCase)

      a							,		a						:		Optional[Any]      							=			model(__UpperCAmelCase).to_tuple()

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

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

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

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






    def 	__snake_case							(					self				:    Union[str, Any]):
      a						:		Optional[Any]      							=			self.prepare_config_and_inputs()
      ((a)							,		(a)							,		(a)							,		(a))						:		List[Any]      							=			config_and_inputs
      a						:		List[str]      							=			{"input_ids": input_ids_a}
      return config, inputs_dict






@require_tf
class 						_A  (  _a    ,_a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							Any	=       (
        (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
    )
    UpperCAmelCase       :							List[Any]	=       () if is_tf_available() else ()
    UpperCAmelCase       :							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
    UpperCAmelCase       :							Any	=       False
    UpperCAmelCase       :							int	=       False
    UpperCAmelCase       :							Dict	=       False
    UpperCAmelCase       :							Any	=       False






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    List[Any]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    Union[str, Any]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    Optional[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				:    Any):
      a						:		Any      							=			TFTransfoXLModelTester(self)
      a						:		Any      							=			ConfigTester(self				, config_class=__UpperCAmelCase				, d_embed=37)






    def 	__snake_case							(					self				:    int):
      self.config_tester.run_common_tests()






    def 	__snake_case							(					self				:    int):
      self.model_tester.set_seed()
      a						:		str      							=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_model(*__UpperCAmelCase)






    def 	__snake_case							(					self				:    str):
      self.model_tester.set_seed()
      a						:		Tuple      							=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_lm_head(*__UpperCAmelCase)






    def 	__snake_case							(					self				:    Optional[int]):
      a						:		List[str]      							=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__UpperCAmelCase)






    def 	__snake_case							(					self				:    Tuple):
      a							,		a						:		List[str]      							=			self.model_tester.prepare_config_and_inputs_for_common()
      a						:		Optional[Any]      							=			[TFTransfoXLForSequenceClassification]

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






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






    @slow
    def 	__snake_case							(					self				:    str):
      for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a						:		Tuple      							=			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 	__snake_case							(					self				:    Tuple):
      pass






@require_tf
class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    @unittest.skip("Skip test until #12651 is resolved.")
    @slow
    def 	__snake_case							(					self				:    List[str]):
      a						:		int      							=			TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103")
      # fmt: off
      a						:		Tuple      							=			tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]]				, 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						:		Union[str, Any]      							=			[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0]  # 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						:		Optional[int]      							=			model.generate(__UpperCAmelCase				, max_length=200				, do_sample=__UpperCAmelCase)
      self.assertListEqual(output_ids[0].numpy().tolist()				, __UpperCAmelCase)










"""simple docstring"""







class 						_A  :







    """simple docstring"""




    def __init__(					self				:    int				, __UpperCAmelCase				:    int):
      a						:		Tuple      							=			size
      a						:		Dict      							=			[0] * size
      a						:		Optional[int]      							=			[0] * size






    @staticmethod
    def 	__snake_case							(					__UpperCAmelCase				:    int):
      return index | (index + 1)






    @staticmethod
    def 	__snake_case							(					__UpperCAmelCase				:    int):
      return (index & (index + 1)) - 1






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int):
      a						:		Union[str, Any]      							=			value
      while index < self.size:
        a						:		Dict      							=			self.get_prev(__UpperCAmelCase) + 1
        if current_left_border == index:
          a						:		Optional[int]      							=			value
        else:
          a						:		Any      							=			max(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)
        a						:		Optional[int]      							=			self.get_next(__UpperCAmelCase)






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int):
      right -= 1  # Because of right is exclusive
      a						:		List[str]      							=			0
      while left <= right:
        a						:		Dict      							=			self.get_prev(__UpperCAmelCase)
        if left <= current_left:
          a						:		Optional[int]      							=			max(__UpperCAmelCase				, self.tree[right])
          a						:		Optional[Any]      							=			current_left
        else:
          a						:		List[str]      							=			max(__UpperCAmelCase				, self.arr[right])
          right -= 1
      return result


if __name__ == "__main__":
 import doctest

 doctest.testmod()






