

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 :	Optional[int]     ,						A_ :	Any     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    parent
      _lowerCAmelCase   :					List[str]        =    1_3
      _lowerCAmelCase   :					Tuple        =    7
      _lowerCAmelCase   :					Tuple        =    3_0
      _lowerCAmelCase   :					Union[str, Any]        =    self.seq_length + self.mem_len
      _lowerCAmelCase   :					Optional[int]        =    1_5
      _lowerCAmelCase   :					str        =    True
      _lowerCAmelCase   :					List[str]        =    True
      _lowerCAmelCase   :					Optional[Any]        =    9_9
      _lowerCAmelCase   :					int        =    [1_0, 5_0, 8_0]
      _lowerCAmelCase   :					Tuple        =    3_2
      _lowerCAmelCase   :					Dict        =    3_2
      _lowerCAmelCase   :					Optional[int]        =    4
      _lowerCAmelCase   :					str        =    8
      _lowerCAmelCase   :					Dict        =    1_2_8
      _lowerCAmelCase   :					Union[str, Any]        =    2
      _lowerCAmelCase   :					List[str]        =    2
      _lowerCAmelCase   :					Tuple        =    None
      _lowerCAmelCase   :					List[str]        =    1
      _lowerCAmelCase   :					List[Any]        =    0
      _lowerCAmelCase   :					Optional[Any]        =    3
      _lowerCAmelCase   :					Any        =    self.vocab_size - 1
      _lowerCAmelCase   :					Tuple        =    0.01

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size       )
      _lowerCAmelCase   :					Any        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size       )

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

      _lowerCAmelCase   :					Dict        =    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    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	int     ,						A_ :	str     ,						A_ :	Dict     ,						A_ :	int     ,						A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    TFTransfoXLModel(A_       )

      _lowerCAmelCase   :					List[str]        =    model(A_       ).to_tuple()

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

      _lowerCAmelCase   :					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    __magic_name__       (		self :	str     ,						A_ :	Optional[Any]     ,						A_ :	Dict     ,						A_ :	Dict     ,						A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    TFTransfoXLLMHeadModel(A_       )

      _lowerCAmelCase   :					List[Any]        =    model(A_       ).to_tuple()

      _lowerCAmelCase   :					Union[str, Any]        =    {"input_ids": input_ids_a, "labels": lm_labels}
      _lowerCAmelCase   :					Any        =    model(A_       ).to_tuple()

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

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

      _lowerCAmelCase   :					Optional[Any]        =    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    __magic_name__       (		self :	int     ,						A_ :	List[Any]     ,						A_ :	str     ,						A_ :	int     ,						A_ :	int       ):






      '''simple docstring'''




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


 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




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





@require_tf
class A__    ( A  ,		A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Dict      =						(
     (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
 )
 _lowercase     :					Union[str, Any]      =						() if is_tf_available() else ()
 _lowercase     :					Optional[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
 _lowercase     :					Tuple      =						False
 _lowercase     :					List[str]      =						False
 _lowercase     :					Any      =						False
 _lowercase     :					Optional[Any]      =						False

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	Tuple     ,						A_ :	str     ,						A_ :	List[Any]     ,						A_ :	List[str]     ,						A_ :	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    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    TFTransfoXLModelTester(self       )
      _lowerCAmelCase   :					Tuple        =    ConfigTester(self     ,						config_class=A_     ,						d_embed=3_7       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      self.config_tester.run_common_tests()

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      self.model_tester.set_seed()
      _lowerCAmelCase   :					Dict        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_model(*A_       )

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      self.model_tester.set_seed()
      _lowerCAmelCase   :					Optional[Any]        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_lm_head(*A_       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*A_       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    self.model_tester.prepare_config_and_inputs_for_common()
      _lowerCAmelCase   :					Optional[int]        =    [TFTransfoXLForSequenceClassification]

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

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      pass

 @slow
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowerCAmelCase   :					Tuple        =    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    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      pass





@require_tf
class A__    ( unittest.TestCase ):



 """simple docstring"""







 @unittest.skip("Skip test until #12651 is resolved."       )
 @slow
 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"       )
      # fmt: off
      _lowerCAmelCase   :					str        =    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
      _lowerCAmelCase   :					Optional[Any]        =    [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>

      _lowerCAmelCase   :					Union[str, Any]        =    model.generate(A_     ,						max_length=2_0_0     ,						do_sample=A_       )
      self.assertListEqual(output_ids[0].numpy().tolist()     ,						A_       )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


__UpperCAmelCase    				=     {
    'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ErnieForCausalLM',
      'ErnieForMaskedLM',
      'ErnieForMultipleChoice',
      'ErnieForNextSentencePrediction',
      'ErnieForPreTraining',
      'ErnieForQuestionAnswering',
      'ErnieForSequenceClassification',
      'ErnieForTokenClassification',
      'ErnieModel',
      'ErniePreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_ernie import (
        ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
        ErnieForCausalLM,
        ErnieForMaskedLM,
        ErnieForMultipleChoice,
        ErnieForNextSentencePrediction,
        ErnieForPreTraining,
        ErnieForQuestionAnswering,
        ErnieForSequenceClassification,
        ErnieForTokenClassification,
        ErnieModel,
        ErniePreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase    				=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






