





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 				__lowerCAmelCase       :







      def __init__(    self		,      lowerCAmelCase__		,      )   ->						Optional[Any]:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




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

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

        a__	:					Union[str, Any]										=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    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						str:



        '''simple docstring'''




        a__	:					List[Any]										=TFTransfoXLModel(lowerCAmelCase__							)

        a__					,							a__	:					Dict										=model(lowerCAmelCase__							).to_tuple()

        a__	:					List[Any]										={"input_ids": input_ids_a, "mems": mems_a}

        a__					,							a__	:					str										=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    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					Union[str, Any]										=TFTransfoXLLMHeadModel(lowerCAmelCase__							)

        a__					,							a__	:					Optional[Any]										=model(lowerCAmelCase__							).to_tuple()

        a__	:					Optional[int]										={"input_ids": input_ids_a, "labels": lm_labels}
        a__					,							a__	:					Optional[int]										=model(lowerCAmelCase__							).to_tuple()

        a__					,							a__	:					int										=model([input_ids_a, mems_a]							).to_tuple()

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

        a__					,							a__	:					str										=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    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Optional[Any]										=TFTransfoXLForSequenceClassification(lowerCAmelCase__							)
        a__	:					int										=model(lowerCAmelCase__							)
        self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels)							)







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        a__	:					int										=self.prepare_config_and_inputs()
        ((a__)					,							(a__)					,							(a__)					,							(a__))	:					List[Any]										=config_and_inputs
        a__	:					List[str]										={"input_ids": input_ids_a}
        return config, inputs_dict







@require_tf
class 				__lowerCAmelCase       (    UpperCamelCase__						,				UpperCamelCase__						,				unittest.TestCase):
      _lowercase				:					Union[str, Any]						=			(
          (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
      )
      _lowercase				:					Optional[Any]						=			() if is_tf_available() else ()
      _lowercase				:					Optional[int]						=			(
          {
              """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				:					Dict						=			False
      _lowercase				:					Optional[Any]						=			False
      _lowercase				:					List[str]						=			False
      _lowercase				:					str						=			False







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      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    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					Tuple										=TFTransfoXLModelTester(self							)
        a__	:					List[Any]										=ConfigTester(self		,      config_class=lowerCAmelCase__		,      d_embed=3_7							)







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



        '''simple docstring'''




        self.config_tester.run_common_tests()







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        self.model_tester.set_seed()
        a__	:					List[Any]										=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_model(*lowerCAmelCase__							)







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



        '''simple docstring'''




        self.model_tester.set_seed()
        a__	:					Union[str, Any]										=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_lm_head(*lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        a__	:					int										=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__					,							a__	:					List[str]										=self.model_tester.prepare_config_and_inputs_for_common()
        a__	:					str										=[TFTransfoXLForSequenceClassification]

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







      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        pass







      @slow
      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__	:					str										=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    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        pass







@require_tf
class 				__lowerCAmelCase       (    unittest.TestCase):







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



        '''simple docstring'''




        a__	:					str										=TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"							)
        # fmt: off
        a__	:					Tuple										=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
        a__	:					Union[str, 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>

        a__	:					List[Any]										=model.generate(lowerCAmelCase__		,      max_length=2_0_0		,      do_sample=lowerCAmelCase__							)
        self.assertListEqual(output_ids[0].numpy().tolist()		,      lowerCAmelCase__							)













from typing import TYPE_CHECKING

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


UpperCAmelCase	:	Any		     =       {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	List[Any]		     =       ["""ConvBertTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	List[str]		     =       [
         """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """ConvBertForMaskedLM""",
         """ConvBertForMultipleChoice""",
         """ConvBertForQuestionAnswering""",
         """ConvBertForSequenceClassification""",
         """ConvBertForTokenClassification""",
         """ConvBertLayer""",
         """ConvBertModel""",
         """ConvBertPreTrainedModel""",
         """load_tf_weights_in_convbert""",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase	:	Union[str, Any]		     =       [
         """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFConvBertForMaskedLM""",
         """TFConvBertForMultipleChoice""",
         """TFConvBertForQuestionAnswering""",
         """TFConvBertForSequenceClassification""",
         """TFConvBertForTokenClassification""",
         """TFConvBertLayer""",
         """TFConvBertModel""",
         """TFConvBertPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
     from .tokenization_convbert import ConvBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_convbert_fast import ConvBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convbert import (
              CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvBertForMaskedLM,
              ConvBertForMultipleChoice,
              ConvBertForQuestionAnswering,
              ConvBertForSequenceClassification,
              ConvBertForTokenClassification,
              ConvBertLayer,
              ConvBertModel,
              ConvBertPreTrainedModel,
              load_tf_weights_in_convbert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_convbert import (
              TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFConvBertForMaskedLM,
              TFConvBertForMultipleChoice,
              TFConvBertForQuestionAnswering,
              TFConvBertForSequenceClassification,
              TFConvBertForTokenClassification,
              TFConvBertLayer,
              TFConvBertModel,
              TFConvBertPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase	:	Union[str, Any]		     =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






