




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      snake_case	:


   '''simple docstring'''







   def __init__(       self  :						Tuple    ,     lowerCamelCase_  :						Optional[Any]    ,     )       ->Optional[int]:
     '''simple docstring'''







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




   def 						UpperCAmelCase							(       self  :						Any      )       ->Union[str, Any]:
     '''simple docstring'''







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

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

     UpperCAmelCase__         = 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  :						List[str]      )       ->Tuple:
     '''simple docstring'''







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




   def 						UpperCAmelCase							(       self  :						Dict    ,     lowerCamelCase_  :						List[Any]    ,     lowerCamelCase_  :						str    ,     lowerCamelCase_  :						Any    ,     lowerCamelCase_  :						Optional[Any]      )       ->List[Any]:
     '''simple docstring'''







     UpperCAmelCase__         = TFTransfoXLModel(a__      )

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

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

     UpperCAmelCase__    ,       UpperCAmelCase__         = 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 						UpperCAmelCase							(       self  :						int    ,     lowerCamelCase_  :						Optional[Any]    ,     lowerCamelCase_  :						Union[str, Any]    ,     lowerCamelCase_  :						Tuple    ,     lowerCamelCase_  :						Dict      )       ->List[str]:
     '''simple docstring'''







     UpperCAmelCase__         = TFTransfoXLLMHeadModel(a__      )

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

     UpperCAmelCase__         = {"""input_ids""": input_ids_a, """labels""": lm_labels}
     UpperCAmelCase__    ,       UpperCAmelCase__         = model(a__      ).to_tuple()

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

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

     UpperCAmelCase__    ,       UpperCAmelCase__         = 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 						UpperCAmelCase							(       self  :						str    ,     lowerCamelCase_  :						Dict    ,     lowerCamelCase_  :						List[Any]    ,     lowerCamelCase_  :						Optional[int]    ,     lowerCamelCase_  :						int      )       ->int:
     '''simple docstring'''







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







   def 						UpperCAmelCase							(       self  :						Optional[int]      )       ->Optional[Any]:
     '''simple docstring'''







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





@require_tf
class      snake_case	(						__snake_case			,	__snake_case			,	unittest.TestCase				):


   '''simple docstring'''







   UpperCamelCase__						:							int 	=   (
       (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
   )
   UpperCamelCase__						:							Dict 	=   () if is_tf_available() else ()
   UpperCamelCase__						:							Union[str, Any] 	=   (
       {
           "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__						:							List[Any] 	=   False
   UpperCamelCase__						:							Optional[Any] 	=   False




   def 						UpperCAmelCase							(       self  :						Any    ,     lowerCamelCase_  :						str    ,     lowerCamelCase_  :						int    ,     lowerCamelCase_  :						int    ,     lowerCamelCase_  :						str    ,     lowerCamelCase_  :						List[str]      )       ->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  :						Tuple      )       ->Optional[Any]:
     '''simple docstring'''







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




   def 						UpperCAmelCase							(       self  :						Union[str, Any]      )       ->int:
     '''simple docstring'''







     self.config_tester.run_common_tests()




   def 						UpperCAmelCase							(       self  :						Any      )       ->Dict:
     '''simple docstring'''







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




   def 						UpperCAmelCase							(       self  :						Tuple      )       ->Tuple:
     '''simple docstring'''







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




   def 						UpperCAmelCase							(       self  :						Tuple      )       ->Any:
     '''simple docstring'''







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




   def 						UpperCAmelCase							(       self  :						Optional[int]      )       ->Dict:
     '''simple docstring'''







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

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




   def 						UpperCAmelCase							(       self  :						Dict      )       ->Tuple:
     '''simple docstring'''







     pass




   @slow
   def 						UpperCAmelCase							(       self  :						Tuple      )       ->str:
     '''simple docstring'''







     for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase__         = 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 						UpperCAmelCase							(       self  :						Optional[int]      )       ->str:
     '''simple docstring'''







     pass





@require_tf
class      snake_case	(						unittest.TestCase				):


   '''simple docstring'''







   @unittest.skip("""Skip test until #12651 is resolved."""      )
   @slow
   def 						UpperCAmelCase							(       self  :						Any      )       ->List[str]:
     '''simple docstring'''







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

     UpperCAmelCase__         = model.generate(a__    ,     max_length=200    ,     do_sample=a__      )
     self.assertListEqual(output_ids[0].numpy().tolist()    ,     a__      )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase							:   int						=						{
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   List[Any]						=						[
				    "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "RobertaPreLayerNormForCausalLM",
				    "RobertaPreLayerNormForMaskedLM",
				    "RobertaPreLayerNormForMultipleChoice",
				    "RobertaPreLayerNormForQuestionAnswering",
				    "RobertaPreLayerNormForSequenceClassification",
				    "RobertaPreLayerNormForTokenClassification",
				    "RobertaPreLayerNormModel",
				    "RobertaPreLayerNormPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   Optional[int]						=						[
				    "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFRobertaPreLayerNormForCausalLM",
				    "TFRobertaPreLayerNormForMaskedLM",
				    "TFRobertaPreLayerNormForMultipleChoice",
				    "TFRobertaPreLayerNormForQuestionAnswering",
				    "TFRobertaPreLayerNormForSequenceClassification",
				    "TFRobertaPreLayerNormForTokenClassification",
				    "TFRobertaPreLayerNormMainLayer",
				    "TFRobertaPreLayerNormModel",
				    "TFRobertaPreLayerNormPreTrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   str						=						[
				    "FlaxRobertaPreLayerNormForCausalLM",
				    "FlaxRobertaPreLayerNormForMaskedLM",
				    "FlaxRobertaPreLayerNormForMultipleChoice",
				    "FlaxRobertaPreLayerNormForQuestionAnswering",
				    "FlaxRobertaPreLayerNormForSequenceClassification",
				    "FlaxRobertaPreLayerNormForTokenClassification",
				    "FlaxRobertaPreLayerNormModel",
				    "FlaxRobertaPreLayerNormPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_roberta_prelayernorm import (
				    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    RobertaPreLayerNormConfig,
				    RobertaPreLayerNormOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roberta_prelayernorm import (
								    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RobertaPreLayerNormForCausalLM,
								    RobertaPreLayerNormForMaskedLM,
								    RobertaPreLayerNormForMultipleChoice,
								    RobertaPreLayerNormForQuestionAnswering,
								    RobertaPreLayerNormForSequenceClassification,
								    RobertaPreLayerNormForTokenClassification,
								    RobertaPreLayerNormModel,
								    RobertaPreLayerNormPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roberta_prelayernorm import (
								    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRobertaPreLayerNormForCausalLM,
								    TFRobertaPreLayerNormForMaskedLM,
								    TFRobertaPreLayerNormForMultipleChoice,
								    TFRobertaPreLayerNormForQuestionAnswering,
								    TFRobertaPreLayerNormForSequenceClassification,
								    TFRobertaPreLayerNormForTokenClassification,
								    TFRobertaPreLayerNormMainLayer,
								    TFRobertaPreLayerNormModel,
								    TFRobertaPreLayerNormPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roberta_prelayernorm import (
								    FlaxRobertaPreLayerNormForCausalLM,
								    FlaxRobertaPreLayerNormForMaskedLM,
								    FlaxRobertaPreLayerNormForMultipleChoice,
								    FlaxRobertaPreLayerNormForQuestionAnswering,
								    FlaxRobertaPreLayerNormForSequenceClassification,
								    FlaxRobertaPreLayerNormForTokenClassification,
								    FlaxRobertaPreLayerNormModel,
								    FlaxRobertaPreLayerNormPreTrainedModel,
								)

else:
				import sys

				_lowercase							:   List[Any]						=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



