



"""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			:				Any ,  lowerCAmelCase_			:				int ,  ):







      """simple docstring"""







      lowercase_								= parent
      lowercase_								= 1_3
      lowercase_								= 7
      lowercase_								= 3_0
      lowercase_								= self.seq_length + self.mem_len
      lowercase_								= 1_5
      lowercase_								= True
      lowercase_								= True
      lowercase_								= 9_9
      lowercase_								= [1_0, 5_0, 8_0]
      lowercase_								= 3_2
      lowercase_								= 3_2
      lowercase_								= 4
      lowercase_								= 8
      lowercase_								= 1_2_8
      lowercase_								= 2
      lowercase_								= 2
      lowercase_								= None
      lowercase_								= 1
      lowercase_								= 0
      lowercase_								= 3
      lowercase_								= self.vocab_size - 1
      lowercase_								= 0.01

    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      lowercase_								= ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)
      lowercase_								= ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)

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

      lowercase_								= 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			:				Dict):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				List[Any] ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      lowercase_								= TFTransfoXLModel(lowerCAmelCase_)

      lowercase_					,    lowercase_								= model(lowerCAmelCase_).to_tuple()

      lowercase_								= {"""input_ids""": input_ids_a, """mems""": mems_a}

      lowercase_					,    lowercase_								= 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 					_UpperCAmelCase	(				self			:				int ,  lowerCAmelCase_			:				Optional[Any] ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				int):







      """simple docstring"""







      lowercase_								= TFTransfoXLLMHeadModel(lowerCAmelCase_)

      lowercase_					,    lowercase_								= model(lowerCAmelCase_).to_tuple()

      lowercase_								= {"""input_ids""": input_ids_a, """labels""": lm_labels}
      lowercase_					,    lowercase_								= model(lowerCAmelCase_).to_tuple()

      lowercase_					,    lowercase_								= model([input_ids_a, mems_a]).to_tuple()

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

      lowercase_					,    lowercase_								= 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 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				Dict ,  lowerCAmelCase_			:				Optional[Any] ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      lowercase_								= TFTransfoXLForSequenceClassification(lowerCAmelCase_)
      lowercase_								= model(lowerCAmelCase_)
      self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels))




    def 					_UpperCAmelCase	(				self			:				Tuple):







      """simple docstring"""







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







@require_tf
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  __UpperCAmelCase      ,  unittest.TestCase   ):
    lowercase__      =   (
        (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
    )
    lowercase__      =   () if is_tf_available() else ()
    lowercase__      =   (
        {
            "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__      =   False
    lowercase__      =   False
    lowercase__      =   False
    lowercase__      =   False

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				Optional[Any] ,  lowerCAmelCase_			:				Union[str, Any]):







      """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 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      lowercase_								= TFTransfoXLModelTester(self)
      lowercase_								= ConfigTester(self ,  config_class=lowerCAmelCase_ ,  d_embed=3_7)

    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      self.config_tester.run_common_tests()

    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      self.model_tester.set_seed()
      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_model(*lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      self.model_tester.set_seed()
      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_lm_head(*lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







      lowercase_								= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				List[str]):







      """simple docstring"""







      lowercase_					,    lowercase_								= self.model_tester.prepare_config_and_inputs_for_common()
      lowercase_								= [TFTransfoXLForSequenceClassification]

      for model_class in self.all_model_classes:
        lowercase_								= model_class(lowerCAmelCase_)
        assert isinstance(model.get_input_embeddings() ,  tf.keras.layers.Layer)
        if model_class in list_other_models_with_output_ebd:
          lowercase_								= model.get_output_embeddings()
          assert isinstance(lowerCAmelCase_ ,  tf.keras.layers.Layer)
          lowercase_								= model.get_bias()
          assert name is None
        else:
          lowercase_								= model.get_output_embeddings()
          assert x is None
          lowercase_								= model.get_bias()
          assert name is None

    def 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







      pass

    @slow
    def 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







      for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase_								= 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 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







      pass







@require_tf
class    SCREAMING_SNAKE_CASE__ (							unittest.TestCase   ):

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







      """simple docstring"""







      lowercase_								= TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103""")
      # fmt: off
      lowercase_								= tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]] ,  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_								= [3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # 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_								= model.generate(lowerCAmelCase_ ,  max_length=2_0_0 ,  do_sample=lowerCAmelCase_)
      self.assertListEqual(output_ids[0].numpy().tolist() ,  lowerCAmelCase_)






"""simple docstring"""
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						list[list[int]]:







  '''simple docstring'''


  lowercase_								= []
  if len(__lowerCAmelCase							) == 1:
    return [nums.copy()]
  for _ in range(len(__lowerCAmelCase							)							):
    lowercase_								= nums.pop(0							)
    lowercase_								= permute(__lowerCAmelCase							)
    for perm in permutations:
      perm.append(__lowerCAmelCase							)
    result.extend(__lowerCAmelCase							)
    nums.append(__lowerCAmelCase							)
  return result


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Tuple:







  '''simple docstring'''


  def backtrack(__lowerCAmelCase							):
    if start == len(__lowerCAmelCase							) - 1:
      output.append(nums[:]							)
    else:
      for i in range(__lowerCAmelCase ,  len(__lowerCAmelCase							)							):
        lowercase_					,    lowercase_								= nums[i], nums[start]
        backtrack(start + 1							)
        lowercase_					,    lowercase_								= nums[i], nums[start]  # backtrack

  lowercase_								= []
  backtrack(0							)
  return output


if __name__ == "__main__":
       import doctest

       # use res to print the data in permute2 function
       UpperCAmelCase						:		Optional[Any]			= permutea([1, 2, 3])
       print(res)
       doctest.testmod()

