






'''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 							_UpperCAmelCase    :




    def __init__(      self	:							Optional[Any]	,  a	:							Union[str, Any]	,  ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     int							     =  ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size      )
       lowercase_						:     List[Any]							     =  ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size      )

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

       lowercase_						:     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 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]	,  a	:							Any	,  a	:							Any	,  a	:							str	,  a	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  TFTransfoXLModel(a      )

       lowercase_     ,		lowercase_						:     Dict							     =  model(a      ).to_tuple()

       lowercase_						:     Optional[int]							     =  {"input_ids": input_ids_a, "mems": mems_a}

       lowercase_     ,		lowercase_						:     Optional[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 					lowerCAmelCase__					(      self	:							Optional[int]	,  a	:							int	,  a	:							Optional[int]	,  a	:							Optional[int]	,  a	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  TFTransfoXLLMHeadModel(a      )

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

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

       lowercase_     ,		lowercase_						:     Union[str, Any]							     =  model([input_ids_a, mems_a]      ).to_tuple()

       lowercase_						:     Union[str, Any]							     =  {"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}

       lowercase_     ,		lowercase_						:     List[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 					lowerCAmelCase__					(      self	:							Union[str, Any]	,  a	:							int	,  a	:							int	,  a	:							int	,  a	:							int      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  TFTransfoXLForSequenceClassification(a      )
       lowercase_						:     int							     =  model(a      )
       self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_labels)      )





    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






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


@require_tf
class 							_UpperCAmelCase    (		snake_case					,       snake_case					,       unittest.TestCase      ):
    __lowerCamelCase: Union[str, Any]									=     (
        (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
    )
    __lowerCamelCase: Union[str, Any]									=     () if is_tf_available() else ()
    __lowerCamelCase: List[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
    __lowerCamelCase: List[str]									=     False
    __lowerCamelCase: Dict									=     False
    __lowerCamelCase: Any									=     False
    __lowerCamelCase: Optional[Any]									=     False




    def 					lowerCAmelCase__					(      self	:							Any	,  a	:							Optional[int]	,  a	:							List[Any]	,  a	:							int	,  a	:							Dict	,  a	:							List[str]      ):






       '''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 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     Tuple							     =  TFTransfoXLModelTester(self      )
       lowercase_						:     Dict							     =  ConfigTester(self	,  config_class=a	,  d_embed=3_7      )




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       self.config_tester.run_common_tests()




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       self.model_tester.set_seed()
       lowercase_						:     List[str]							     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_model(*a      )




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       self.model_tester.set_seed()
       lowercase_						:     Optional[int]							     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_lm_head(*a      )




    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     int							     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*a      )




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






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

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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       pass




    @slow
    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase_						:     Optional[int]							     =  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 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       pass


@require_tf
class 							_UpperCAmelCase    (		unittest.TestCase      ):




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






       '''simple docstring'''






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

       lowercase_						:     List[str]							     =  model.generate(a	,  max_length=2_0_0	,  do_sample=a      )
       self.assertListEqual(output_ids[0].numpy().tolist()	,  a      )












'''simple docstring'''
from typing import TYPE_CHECKING

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


UpperCamelCase__			=      {
    'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
    'convert_funnel_original_tf_checkpoint_to_pytorch': [],
    'tokenization_funnel': ['FunnelTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      ['FunnelTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'FunnelBaseModel',
     'FunnelForMaskedLM',
     'FunnelForMultipleChoice',
     'FunnelForPreTraining',
     'FunnelForQuestionAnswering',
     'FunnelForSequenceClassification',
     'FunnelForTokenClassification',
     'FunnelModel',
     'FunnelPreTrainedModel',
     'load_tf_weights_in_funnel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFFunnelBaseModel',
     'TFFunnelForMaskedLM',
     'TFFunnelForMultipleChoice',
     'TFFunnelForPreTraining',
     'TFFunnelForQuestionAnswering',
     'TFFunnelForSequenceClassification',
     'TFFunnelForTokenClassification',
     'TFFunnelModel',
     'TFFunnelPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
 from .tokenization_funnel import FunnelTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_funnel_fast import FunnelTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_funnel import (
      FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
      FunnelBaseModel,
      FunnelForMaskedLM,
      FunnelForMultipleChoice,
      FunnelForPreTraining,
      FunnelForQuestionAnswering,
      FunnelForSequenceClassification,
      FunnelForTokenClassification,
      FunnelModel,
      FunnelPreTrainedModel,
      load_tf_weights_in_funnel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_funnel import (
      TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFFunnelBaseModel,
      TFFunnelForMaskedLM,
      TFFunnelForMultipleChoice,
      TFFunnelForPreTraining,
      TFFunnelForQuestionAnswering,
      TFFunnelForSequenceClassification,
      TFFunnelForTokenClassification,
      TFFunnelModel,
      TFFunnelPreTrainedModel,
  )

else:
 import sys

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




