
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		:					List[str]    ,						snake_case__		:					str    ,						)		->	int:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      parent
          snake_case       :   List[str]							=      13
          snake_case       :   Tuple							=      7
          snake_case       :   str							=      30
          snake_case       :   str							=      self.seq_length + self.mem_len
          snake_case       :   List[Any]							=      15
          snake_case       :   List[Any]							=      True
          snake_case       :   int							=      True
          snake_case       :   Dict							=      99
          snake_case       :   Optional[Any]							=      [10, 50, 80]
          snake_case       :   Any							=      32
          snake_case       :   List[Any]							=      32
          snake_case       :   Tuple							=      4
          snake_case       :   Union[str, Any]							=      8
          snake_case       :   Optional[int]							=      1_28
          snake_case       :   List[str]							=      2
          snake_case       :   Optional[int]							=      2
          snake_case       :   Optional[Any]							=      None
          snake_case       :   Optional[Any]							=      1
          snake_case       :   str							=      0
          snake_case       :   Any							=      3
          snake_case       :   Union[str, Any]							=      self.vocab_size - 1
          snake_case       :   str							=      0.01



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


          '''simple docstring'''


          snake_case       :   Any							=      ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size )
          snake_case       :   Union[str, Any]							=      ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size )

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

          snake_case       :   str							=      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		:					int )		->	int:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					Any    ,						snake_case__		:					List[str]    ,						snake_case__		:					Tuple    ,						snake_case__		:					Union[str, Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      TFTransfoXLModel(snake_case__ )

          snake_case				,					snake_case       :   List[Any]							=      model(snake_case__ ).to_tuple()

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

          snake_case				,					snake_case       :   str							=      model(snake_case__ ).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		:					List[str]    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					List[Any]    ,						snake_case__		:					int    ,						snake_case__		:					Any )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      TFTransfoXLLMHeadModel(snake_case__ )

          snake_case				,					snake_case       :   Dict							=      model(snake_case__ ).to_tuple()

          snake_case       :   Any							=      {"input_ids": input_ids_a, "labels": lm_labels}
          snake_case				,					snake_case       :   str							=      model(snake_case__ ).to_tuple()

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

          snake_case       :   List[Any]							=      {"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}

          snake_case				,					snake_case       :   Optional[int]							=      model(snake_case__ ).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		:					Optional[Any]    ,						snake_case__		:					Any    ,						snake_case__		:					Tuple    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					str )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   int							=      TFTransfoXLForSequenceClassification(snake_case__ )
          snake_case       :   Union[str, Any]							=      model(snake_case__ )
          self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Dict:


          '''simple docstring'''


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






@require_tf
class   UpperCAmelCase		(						A_       ,A_       ,unittest.TestCase ):
   A__					:		Dict						     =     (
       (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
   )
   A__					:		List[Any]						     =     () if is_tf_available() else ()
   A__					:		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
   A__					:		Optional[Any]						     =     False
   A__					:		List[str]						     =     False
   A__					:		Any						     =     False
   A__					:		List[str]						     =     False



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					int    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Any    ,						snake_case__		:					List[str]    ,						snake_case__		:					Tuple )		->	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 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	int:


          '''simple docstring'''


          snake_case       :   Dict							=      TFTransfoXLModelTester(self )
          snake_case       :   str							=      ConfigTester(self    ,						config_class=snake_case__    ,						d_embed=37 )



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


          '''simple docstring'''


          self.config_tester.run_common_tests()



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Dict:


          '''simple docstring'''


          self.model_tester.set_seed()
          snake_case       :   List[Any]							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_transfo_xl_model(*snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Dict:


          '''simple docstring'''


          self.model_tester.set_seed()
          snake_case       :   Tuple							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_transfo_xl_lm_head(*snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   int							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*snake_case__ )



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


          '''simple docstring'''


          snake_case				,					snake_case       :   Union[str, Any]							=      self.model_tester.prepare_config_and_inputs_for_common()
          snake_case       :   List[Any]							=      [TFTransfoXLForSequenceClassification]

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Dict:


          '''simple docstring'''


          pass



   @slow
   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Union[str, Any]:


          '''simple docstring'''


          for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case       :   Any							=      TFTransfoXLModel.from_pretrained(snake_case__ )
                 self.assertIsNotNone(snake_case__ )



   @unittest.skip(reason="This model doesn't play well with fit() due to not returning a single loss." )
   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Any:


          '''simple docstring'''


          pass






@require_tf
class   UpperCAmelCase		(						unittest.TestCase ):



   @unittest.skip("Skip test until #12651 is resolved." )
   @slow
   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	Tuple:


          '''simple docstring'''


          snake_case       :   Any							=      TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103" )
          # fmt: off
          snake_case       :   List[str]							=      tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]]    ,						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
          snake_case       :   int							=      [33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # 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>

          snake_case       :   Optional[int]							=      model.generate(snake_case__    ,						max_length=2_00    ,						do_sample=snake_case__ )
          self.assertListEqual(output_ids[0].numpy().tolist()    ,						snake_case__ )







from typing import TYPE_CHECKING

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


__lowerCamelCase							=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BioGptForCausalLM""",
       """BioGptForTokenClassification""",
       """BioGptForSequenceClassification""",
       """BioGptModel""",
       """BioGptPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

   __lowerCamelCase							=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





