


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 			SCREAMING_SNAKE_CASE__  :
      def __init__(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	int					,							)		->      Any:
             a_ :		List[Any]							=  parent
             a_ :		str							=  1_3
             a_ :		Optional[Any]							=  7
             a_ :		Optional[int]							=  3_0
             a_ :		List[str]							=  self.seq_length + self.mem_len
             a_ :		Tuple							=  1_5
             a_ :		int							=  True
             a_ :		Tuple							=  True
             a_ :		Optional[Any]							=  9_9
             a_ :		Union[str, Any]							=  [1_0, 5_0, 8_0]
             a_ :		List[str]							=  3_2
             a_ :		int							=  3_2
             a_ :		Dict							=  4
             a_ :		Dict							=  8
             a_ :		int							=  1_2_8
             a_ :		int							=  2
             a_ :		Union[str, Any]							=  2
             a_ :		int							=  None
             a_ :		Optional[Any]							=  1
             a_ :		Optional[int]							=  0
             a_ :		Tuple							=  3
             a_ :		Tuple							=  self.vocab_size - 1
             a_ :		Tuple							=  0.01
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Optional[int]:
             a_ :		List[Any]							=  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_ :		Any							=  None
             if self.use_labels:
                    a_ :		Dict							=  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 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      Union[str, Any]:
             random.seed(self.seed					)
             tf.random.set_seed(self.seed					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Dict					,							SCREAMING_SNAKE_CASE__    :	int					)		->      Optional[int]:
             a_ :		List[Any]							=  TFTransfoXLModel(lowerCAmelCase__					)

             a_ :		Any							=  model(lowerCAmelCase__					).to_tuple()

             a_ :		List[str]							=  {"input_ids": input_ids_a, "mems": mems_a}

             a_ :		Tuple							=  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 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	Any					)		->      Optional[Any]:
             a_ :		List[str]							=  TFTransfoXLLMHeadModel(lowerCAmelCase__					)

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

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

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

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

             a_ :		Any							=  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 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Tuple					)		->      str:
             a_ :		Optional[int]							=  TFTransfoXLForSequenceClassification(lowerCAmelCase__					)
             a_ :		List[Any]							=  model(lowerCAmelCase__					)
             self.parent.assertEqual(result.logits.shape					,							(self.batch_size, self.num_labels)					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Optional[int]:
             a_ :		Tuple							=  self.prepare_config_and_inputs()
             (a_) :		List[Any]							=  config_and_inputs
             a_ :		Any							=  {"input_ids": input_ids_a}
             return config, inputs_dict
@require_tf
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   lowercase__						,   unittest.TestCase      ):
      snake_case__    :					int					       =						(
          (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
      )
      snake_case__    :					Union[str, Any]					       =						() if is_tf_available() else ()
      snake_case__    :					Tuple					       =						(
          {
              '''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
      snake_case__    :					Optional[Any]					       =						False
      snake_case__    :					Dict					       =						False
      snake_case__    :					List[Any]					       =						False
      snake_case__    :					Optional[Any]					       =						False
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	int					)		->      Optional[Any]:




             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    :	int					)		->      Any:
             a_ :		int							=  TFTransfoXLModelTester(self					)
             a_ :		Union[str, Any]							=  ConfigTester(self					,							config_class=lowerCAmelCase__					,							d_embed=3_7					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      Any:
             self.config_tester.run_common_tests()
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      Dict:
             self.model_tester.set_seed()
             a_ :		int							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_transfo_xl_model(*lowerCAmelCase__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      Union[str, Any]:
             self.model_tester.set_seed()
             a_ :		Any							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_transfo_xl_lm_head(*lowerCAmelCase__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      Optional[int]:
             a_ :		List[Any]							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowerCAmelCase__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Any:
             a_ :		str							=  self.model_tester.prepare_config_and_inputs_for_common()
             a_ :		str							=  [TFTransfoXLForSequenceClassification]

             for model_class in self.all_model_classes:
                    a_ :		Optional[int]							=  model_class(lowerCAmelCase__					)
                    assert isinstance(model.get_input_embeddings()					,							tf.keras.layers.Layer					)
                    if model_class in list_other_models_with_output_ebd:
                           a_ :		List[Any]							=  model.get_output_embeddings()
                           assert isinstance(lowerCAmelCase__					,							tf.keras.layers.Layer					)
                           a_ :		str							=  model.get_bias()
                           assert name is None
                    else:
                           a_ :		Optional[Any]							=  model.get_output_embeddings()
                           assert x is None
                           a_ :		int							=  model.get_bias()
                           assert name is None
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      List[str]:
             pass
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      List[Any]:




             for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    a_ :		Tuple							=  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 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      Dict:
             pass
@require_tf
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      @unittest.skip('Skip test until #12651 is resolved.'					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      str:
             a_ :		str							=  TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'					)
             # fmt: off
             a_ :		List[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
             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_ :		Optional[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_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			Union[str, Any]   =	['MobileBertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			List[str]   =	[
       'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MobileBertForMaskedLM',
       'MobileBertForMultipleChoice',
       'MobileBertForNextSentencePrediction',
       'MobileBertForPreTraining',
       'MobileBertForQuestionAnswering',
       'MobileBertForSequenceClassification',
       'MobileBertForTokenClassification',
       'MobileBertLayer',
       'MobileBertModel',
       'MobileBertPreTrainedModel',
       'load_tf_weights_in_mobilebert',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			Tuple   =	[
       'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFMobileBertForMaskedLM',
       'TFMobileBertForMultipleChoice',
       'TFMobileBertForNextSentencePrediction',
       'TFMobileBertForPreTraining',
       'TFMobileBertForQuestionAnswering',
       'TFMobileBertForSequenceClassification',
       'TFMobileBertForTokenClassification',
       'TFMobileBertMainLayer',
       'TFMobileBertModel',
       'TFMobileBertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_mobilebert import (
       MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       MobileBertConfig,
       MobileBertOnnxConfig,
   )
   from .tokenization_mobilebert import MobileBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mobilebert_fast import MobileBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mobilebert import (
          MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          MobileBertForMaskedLM,
          MobileBertForMultipleChoice,
          MobileBertForNextSentencePrediction,
          MobileBertForPreTraining,
          MobileBertForQuestionAnswering,
          MobileBertForSequenceClassification,
          MobileBertForTokenClassification,
          MobileBertLayer,
          MobileBertModel,
          MobileBertPreTrainedModel,
          load_tf_weights_in_mobilebert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mobilebert import (
          TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFMobileBertForMaskedLM,
          TFMobileBertForMultipleChoice,
          TFMobileBertForNextSentencePrediction,
          TFMobileBertForPreTraining,
          TFMobileBertForQuestionAnswering,
          TFMobileBertForSequenceClassification,
          TFMobileBertForTokenClassification,
          TFMobileBertMainLayer,
          TFMobileBertModel,
          TFMobileBertPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase_				:			Tuple   =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


