




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_       :

    '''simple docstring'''






    def __init__(     self :      Optional[int]   ,					UpperCAmelCase__ :      Optional[int]   ,					)		->List[Any]:





           '''simple docstring'''

           A__ 					=       parent
           A__ 					=       13
           A__ 					=       7
           A__ 					=       30
           A__ 					=       self.seq_length + self.mem_len
           A__ 					=       15
           A__ 					=       True
           A__ 					=       True
           A__ 					=       99
           A__ 					=       [10, 50, 80]
           A__ 					=       32
           A__ 					=       32
           A__ 					=       4
           A__ 					=       8
           A__ 					=       128
           A__ 					=       2
           A__ 					=       2
           A__ 					=       None
           A__ 					=       1
           A__ 					=       0
           A__ 					=       3
           A__ 					=       self.vocab_size - 1
           A__ 					=       0.01


    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->Any:





           '''simple docstring'''

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

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

           A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      str)		->Optional[Any]:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      int   ,					UpperCAmelCase__ :      Any   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      Optional[Any]   ,					UpperCAmelCase__ :      List[Any])		->Optional[int]:





           '''simple docstring'''

           A__ 					=       TFTransfoXLModel(UpperCAmelCase__)

           A__					,				A__ 					=       model(UpperCAmelCase__).to_tuple()

           A__ 					=       {'''input_ids''': input_ids_a, '''mems''': mems_a}

           A__					,				A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      Any   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      Any   ,					UpperCAmelCase__ :      Tuple)		->Optional[Any]:





           '''simple docstring'''

           A__ 					=       TFTransfoXLLMHeadModel(UpperCAmelCase__)

           A__					,				A__ 					=       model(UpperCAmelCase__).to_tuple()

           A__ 					=       {'''input_ids''': input_ids_a, '''labels''': lm_labels}
           A__					,				A__ 					=       model(UpperCAmelCase__).to_tuple()

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

           A__ 					=       {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

           A__					,				A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      int   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      Optional[Any]   ,					UpperCAmelCase__ :      Optional[int])		->str:





           '''simple docstring'''

           A__ 					=       TFTransfoXLForSequenceClassification(UpperCAmelCase__)
           A__ 					=       model(UpperCAmelCase__)
           self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels))





    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->Union[str, Any]:





           '''simple docstring'''

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


@require_tf
class 							UpperCamelCase_       (					UpperCAmelCase__						,   UpperCAmelCase__						,   unittest.TestCase	):

    '''simple docstring'''






    UpperCAmelCase__				    = (
        (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
    )
    UpperCAmelCase__				    = () if is_tf_available() else ()
    UpperCAmelCase__				    = (
        {
            '''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__				    = False
    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = False


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int]   ,					UpperCAmelCase__ :      Tuple   ,					UpperCAmelCase__ :      Any   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      Optional[Any])		->Dict:





           '''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 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->List[str]:





           '''simple docstring'''

           A__ 					=       TFTransfoXLModelTester(self)
           A__ 					=       ConfigTester(self   ,					config_class=UpperCAmelCase__   ,					d_embed=37)


    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->Tuple:





           '''simple docstring'''

           self.config_tester.run_common_tests()


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->str:





           '''simple docstring'''

           self.model_tester.set_seed()
           A__ 					=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_model(*UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->Optional[int]:





           '''simple docstring'''

           self.model_tester.set_seed()
           A__ 					=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      str)		->List[Any]:





           '''simple docstring'''

           A__ 					=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->Tuple:





           '''simple docstring'''

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

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->int:





           '''simple docstring'''

           pass


    @slow
    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->Any:





           '''simple docstring'''

           for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      str)		->str:





           '''simple docstring'''

           pass


@require_tf
class 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






    @unittest.skip('''Skip test until #12651 is resolved.''')
    @slow
    def 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->List[Any]:





           '''simple docstring'''

           A__ 					=       TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103''')
           # fmt: off
           A__ 					=       tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]]   ,					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__ 					=       [33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0]  # 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__ 					=       model.generate(UpperCAmelCase__   ,					max_length=200   ,					do_sample=UpperCAmelCase__)
           self.assertListEqual(output_ids[0].numpy().tolist()   ,					UpperCAmelCase__)







from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase    :		int		      =    logging.get_logger(__name__)

_lowerCamelCase    :		Union[str, Any]		      =    {
    """microsoft/xprophetnet-large-wiki100-cased""": (
        """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"""
    ),
}


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		Tuple		=		'xlm-prophetnet'
						UpperCAmelCase			:		int		=		['past_key_values']
						UpperCAmelCase			:		List[Any]		=		{
						    'num_attention_heads': 'num_encoder_attention_heads',
						}







						def __init__(  self    :				List[Any]       ,	snake_case    :				Optional[float] = 0.1       ,	snake_case    :				Optional[Union[str, Callable]] = "gelu"       ,	snake_case    :				Optional[int] = 30522       ,	snake_case    :				Optional[int] = 1024       ,	snake_case    :				Optional[int] = 4096       ,	snake_case    :				Optional[int] = 12       ,	snake_case    :				Optional[int] = 16       ,	snake_case    :				Optional[int] = 4096       ,	snake_case    :				Optional[int] = 12       ,	snake_case    :				Optional[int] = 16       ,	snake_case    :				Optional[float] = 0.1       ,	snake_case    :				Optional[float] = 0.1       ,	snake_case    :				Optional[int] = 512       ,	snake_case    :				Optional[float] = 0.02       ,	snake_case    :				Optional[bool] = True       ,	snake_case    :				Optional[bool] = True       ,	snake_case    :				Optional[int] = 0       ,	snake_case    :				Optional[int] = 2       ,	snake_case    :				Optional[int] = 32       ,	snake_case    :				Optional[int] = 128       ,	snake_case    :				Optional[bool] = False       ,	snake_case    :				Optional[float] = 0.0       ,	snake_case    :				Optional[bool] = True       ,	snake_case    :				Optional[int] = 0       ,	snake_case    :				Optional[int] = 1       ,	snake_case    :				Optional[int] = 2       ,	**snake_case    :				Optional[int]       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				vocab_size
											SCREAMING_SNAKE_CASE							:     int						=				hidden_size
											SCREAMING_SNAKE_CASE							:     Dict						=				encoder_ffn_dim
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				num_encoder_layers
											SCREAMING_SNAKE_CASE							:     str						=				num_encoder_attention_heads
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				decoder_ffn_dim
											SCREAMING_SNAKE_CASE							:     Any						=				num_decoder_layers
											SCREAMING_SNAKE_CASE							:     List[str]						=				num_decoder_attention_heads
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				max_position_embeddings
											SCREAMING_SNAKE_CASE							:     Any						=				init_std  # Normal(0, this parameter)
											SCREAMING_SNAKE_CASE							:     List[str]						=				activation_function

											# parameters for xlmprophetnet
											SCREAMING_SNAKE_CASE							:     Tuple						=				ngram
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				num_buckets
											SCREAMING_SNAKE_CASE							:     Any						=				relative_max_distance
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				disable_ngram_loss
											SCREAMING_SNAKE_CASE							:     List[str]						=				eps

											# 3 Types of Dropout
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				attention_dropout
											SCREAMING_SNAKE_CASE							:     Any						=				activation_dropout
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				dropout

											SCREAMING_SNAKE_CASE							:     Tuple						=				use_cache

											super().__init__(
											    pad_token_id=snake_case       ,	bos_token_id=snake_case       ,	eos_token_id=snake_case       ,	is_encoder_decoder=snake_case       ,	add_cross_attention=snake_case       ,	decoder_start_token_id=snake_case       ,	**snake_case       ,	)







						@property
						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											return self.num_encoder_layers + self.num_decoder_layers







						@num_hidden_layers.setter
						def       lowerCamelCase_				(  self    :				List[Any]       ,	snake_case    :				Optional[int]	):



											'''simple docstring'''


											raise NotImplementedError(
											    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
											    ' `num_decoder_layers`.'	)