





"""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    _lowerCAmelCase  :
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase       ,					):



         '''simple docstring'''

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size )
         lowerCAmelCase__     :List[str]											=				ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size )

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

         lowerCAmelCase__     :Optional[int]											=				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 	snake_case		(       self ):



         '''simple docstring'''

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






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				TFTransfoXLModel(__UpperCAmelCase )

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

         lowerCAmelCase__     :str											=				{'input_ids': input_ids_a, 'mems': mems_a}

         lowerCAmelCase__				,							lowerCAmelCase__     :str											=				model(__UpperCAmelCase ).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 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

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

         lowerCAmelCase__				,							lowerCAmelCase__     :List[str]											=				model(__UpperCAmelCase ).to_tuple()

         lowerCAmelCase__     :int											=				{'input_ids': input_ids_a, 'labels': lm_labels}
         lowerCAmelCase__				,							lowerCAmelCase__     :Any											=				model(__UpperCAmelCase ).to_tuple()

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

         lowerCAmelCase__     :int											=				{'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}

         lowerCAmelCase__				,							lowerCAmelCase__     :str											=				model(__UpperCAmelCase ).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 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

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






       def 	snake_case		(       self ):



         '''simple docstring'''

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






@require_tf
class    _lowerCAmelCase  (	a    ,  a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Any						    =   (
           (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
       )
       __magic_name__	:List[str]						    =   () if is_tf_available() else ()
       __magic_name__	:str						    =   (
           {
               """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
       __magic_name__	:Dict						    =   False
       __magic_name__	:List[Any]						    =   False
       __magic_name__	:Any						    =   False
       __magic_name__	:Any						    =   False






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''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 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				TFTransfoXLModelTester(self )
         lowerCAmelCase__     :Optional[Any]											=				ConfigTester(self       ,					config_class=__UpperCAmelCase       ,					d_embed=3_7 )






       def 	snake_case		(       self ):



         '''simple docstring'''

         self.config_tester.run_common_tests()






       def 	snake_case		(       self ):



         '''simple docstring'''

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         self.model_tester.set_seed()
         lowerCAmelCase__     :str											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_lm_head(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__     :Tuple											=				self.model_tester.prepare_config_and_inputs_for_common()
         lowerCAmelCase__     :Optional[Any]											=				[TFTransfoXLForSequenceClassification]

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCAmelCase__     :Optional[Any]											=				TFTransfoXLModel.from_pretrained(__UpperCAmelCase )
           self.assertIsNotNone(__UpperCAmelCase )






       @unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.' )
       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






@require_tf
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       @unittest.skip('Skip test until #12651 is resolved.' )
       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103' )
         # fmt: off
         lowerCAmelCase__     :Dict											=				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__     :Dict											=				[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__     :Optional[Any]											=				model.generate(__UpperCAmelCase       ,					max_length=2_0_0       ,					do_sample=__UpperCAmelCase )
         self.assertListEqual(output_ids[0].numpy().tolist()       ,					__UpperCAmelCase )







"""simple docstring"""





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__A											=						logging.get_logger(__name__)

__A											=						{
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:Union[str, Any]						    =   """deberta-v2"""






       def __init__(       self       ,					__UpperCAmelCase=1_2_8_1_0_0       ,					__UpperCAmelCase=1_5_3_6       ,					__UpperCAmelCase=2_4       ,					__UpperCAmelCase=2_4       ,					__UpperCAmelCase=6_1_4_4       ,					__UpperCAmelCase="gelu"       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=5_1_2       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=0.02       ,					__UpperCAmelCase=1E-7       ,					__UpperCAmelCase=False       ,					__UpperCAmelCase=-1       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase="gelu"       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         super().__init__(**__UpperCAmelCase )

         lowerCAmelCase__     :Optional[int]											=				hidden_size
         lowerCAmelCase__     :Optional[int]											=				num_hidden_layers
         lowerCAmelCase__     :Optional[int]											=				num_attention_heads
         lowerCAmelCase__     :int											=				intermediate_size
         lowerCAmelCase__     :Dict											=				hidden_act
         lowerCAmelCase__     :Tuple											=				hidden_dropout_prob
         lowerCAmelCase__     :Optional[int]											=				attention_probs_dropout_prob
         lowerCAmelCase__     :Optional[Any]											=				max_position_embeddings
         lowerCAmelCase__     :Optional[Any]											=				type_vocab_size
         lowerCAmelCase__     :List[Any]											=				initializer_range
         lowerCAmelCase__     :Dict											=				relative_attention
         lowerCAmelCase__     :Union[str, Any]											=				max_relative_positions
         lowerCAmelCase__     :List[str]											=				pad_token_id
         lowerCAmelCase__     :Optional[int]											=				position_biased_input

         # Backwards compatibility
         if type(__UpperCAmelCase ) == str:
           lowerCAmelCase__     :Dict											=				[x.strip() for x in pos_att_type.lower().split('|' )]

         lowerCAmelCase__     :str											=				pos_att_type
         lowerCAmelCase__     :Dict											=				vocab_size
         lowerCAmelCase__     :Tuple											=				layer_norm_eps

         lowerCAmelCase__     :Optional[Any]											=				kwargs.get('pooler_hidden_size'       ,					__UpperCAmelCase )
         lowerCAmelCase__     :Union[str, Any]											=				pooler_dropout
         lowerCAmelCase__     :int											=				pooler_hidden_act






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         if self.task == "multiple-choice":
           lowerCAmelCase__     :str											=				{0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
           lowerCAmelCase__     :Tuple											=				{0: 'batch', 1: 'sequence'}
         if self._config.type_vocab_size > 0:
           return OrderedDict(
               [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)] )
         else:
           return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)] )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return 1_2






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = -1       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 3       ,					__UpperCAmelCase = 4_0       ,					__UpperCAmelCase = 4_0       ,					__UpperCAmelCase = None       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				super().generate_dummy_inputs(preprocessor=__UpperCAmelCase       ,					framework=__UpperCAmelCase )
         if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
           del dummy_inputs["token_type_ids"]
         return dummy_inputs
