



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,     SCREAMING_SNAKE_CASE_,     )	->						Dict:
          UpperCamelCase       :    List[Any]		      =      parent
          UpperCamelCase       :    List[Any]		      =      13
          UpperCamelCase       :    Tuple		      =      7
          UpperCamelCase       :    int		      =      30
          UpperCamelCase       :    Any		      =      self.seq_length + self.mem_len
          UpperCamelCase       :    List[Any]		      =      15
          UpperCamelCase       :    Any		      =      True
          UpperCamelCase       :    Any		      =      True
          UpperCamelCase       :    Optional[Any]		      =      99
          UpperCamelCase       :    str		      =      [10, 50, 80]
          UpperCamelCase       :    Tuple		      =      32
          UpperCamelCase       :    Optional[int]		      =      32
          UpperCamelCase       :    Dict		      =      4
          UpperCamelCase       :    Any		      =      8
          UpperCamelCase       :    str		      =      128
          UpperCamelCase       :    Optional[Any]		      =      2
          UpperCamelCase       :    Union[str, Any]		      =      2
          UpperCamelCase       :    Dict		      =      None
          UpperCamelCase       :    Optional[Any]		      =      1
          UpperCamelCase       :    Dict		      =      0
          UpperCamelCase       :    Any		      =      3
          UpperCamelCase       :    int		      =      self.vocab_size - 1
          UpperCamelCase       :    List[str]		      =      0.01
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    Optional[Any]		      =      ids_tensor([self.batch_size, self.seq_length],     self.vocab_size			)
          UpperCamelCase       :    List[str]		      =      ids_tensor([self.batch_size, self.seq_length],     self.vocab_size			)

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

          UpperCamelCase       :    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    snake_case_			(     self			)	->						List[Any]:
          random.seed(self.seed			)
          tf.random.set_seed(self.seed			)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Union[str, Any]:
          UpperCamelCase       :    str		      =      TFTransfoXLModel(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase      ,	UpperCamelCase       :    Optional[int]		      =      model(SCREAMING_SNAKE_CASE_			).to_tuple()

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

          UpperCamelCase      ,	UpperCamelCase       :    int		      =      model(SCREAMING_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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Optional[int]:
          UpperCamelCase       :    Optional[int]		      =      TFTransfoXLLMHeadModel(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase      ,	UpperCamelCase       :    Union[str, Any]		      =      model(SCREAMING_SNAKE_CASE_			).to_tuple()

          UpperCamelCase       :    List[str]		      =      {'input_ids': input_ids_a, 'labels': lm_labels}
          UpperCamelCase      ,	UpperCamelCase       :    List[Any]		      =      model(SCREAMING_SNAKE_CASE_			).to_tuple()

          UpperCamelCase      ,	UpperCamelCase       :    Optional[Any]		      =      model([input_ids_a, mems_a]			).to_tuple()

          UpperCamelCase       :    List[Any]		      =      {'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}

          UpperCamelCase      ,	UpperCamelCase       :    int		      =      model(SCREAMING_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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						List[str]:
          UpperCamelCase       :    Union[str, Any]		      =      TFTransfoXLForSequenceClassification(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[int]		      =      model(SCREAMING_SNAKE_CASE_			)
          self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.num_labels)			)






       def    snake_case_			(     self			)	->						Tuple:
          UpperCamelCase       :    str		      =      self.prepare_config_and_inputs()
          ((UpperCamelCase)      ,	(UpperCamelCase)      ,	(UpperCamelCase)      ,	(UpperCamelCase))       :    Optional[int]		      =      config_and_inputs
          UpperCamelCase       :    Dict		      =      {'input_ids': input_ids_a}
          return config, inputs_dict






@require_tf
class 							lowerCAmelCase_						(    a__			,       a__			,       unittest.TestCase				):
       UpperCAmelCase__							:   int  	=    (
           (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
       )
       UpperCAmelCase__							:   Optional[Any]  	=    () if is_tf_available() else ()
       UpperCAmelCase__							:   Union[str, 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
       UpperCAmelCase__							:   Optional[int]  	=    False
       UpperCAmelCase__							:   List[str]  	=    False
       UpperCAmelCase__							:   int  	=    False
       UpperCAmelCase__							:   Any  	=    False
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						str:
          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			)	->						Union[str, Any]:
          UpperCamelCase       :    Tuple		      =      TFTransfoXLModelTester(self			)
          UpperCamelCase       :    int		      =      ConfigTester(self,     config_class=SCREAMING_SNAKE_CASE_,     d_embed=37			)
       def    snake_case_			(     self			)	->						Dict:
          self.config_tester.run_common_tests()
       def    snake_case_			(     self			)	->						Union[str, Any]:
          self.model_tester.set_seed()
          UpperCamelCase       :    List[str]		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_transfo_xl_model(*SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						List[str]:
          self.model_tester.set_seed()
          UpperCamelCase       :    Optional[int]		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_transfo_xl_lm_head(*SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						Optional[int]:
          UpperCamelCase       :    str		      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						List[str]:
          UpperCamelCase      ,	UpperCamelCase       :    Tuple		      =      self.model_tester.prepare_config_and_inputs_for_common()
          UpperCamelCase       :    str		      =      [TFTransfoXLForSequenceClassification]

          for model_class in self.all_model_classes:
             UpperCamelCase       :    Union[str, Any]		      =      model_class(SCREAMING_SNAKE_CASE_			)
             assert isinstance(model.get_input_embeddings(),     tf.keras.layers.Layer			)
             if model_class in list_other_models_with_output_ebd:
                UpperCamelCase       :    Any		      =      model.get_output_embeddings()
                assert isinstance(SCREAMING_SNAKE_CASE_,     tf.keras.layers.Layer			)
                UpperCamelCase       :    str		      =      model.get_bias()
                assert name is None
             else:
                UpperCamelCase       :    Tuple		      =      model.get_output_embeddings()
                assert x is None
                UpperCamelCase       :    Tuple		      =      model.get_bias()
                assert name is None
       def    snake_case_			(     self			)	->						List[Any]:
          # TODO JP: Make TransfoXL XLA compliant
          pass
       @slow
       def    snake_case_			(     self			)	->						List[Any]:
          for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase       :    List[str]		      =      TFTransfoXLModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)






       @unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.'			)
       def    snake_case_			(     self			)	->						List[str]:
          pass






@require_tf
class 							lowerCAmelCase_						(    unittest.TestCase				):
       @unittest.skip('Skip test until #12651 is resolved.'			)
       @slow
       def    snake_case_			(     self			)	->						int:
          UpperCamelCase       :    Any		      =      TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'			)
          # fmt: off
          UpperCamelCase       :    Optional[int]		      =      tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]],     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
          UpperCamelCase       :    List[str]		      =      [33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # 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>

          UpperCamelCase       :    List[Any]		      =      model.generate(SCREAMING_SNAKE_CASE_,     max_length=200,     do_sample=SCREAMING_SNAKE_CASE_			)
          self.assertListEqual(output_ids[0].numpy().tolist(),     SCREAMING_SNAKE_CASE_			)








'''simple docstring'''

from diffusers.utils.testing_utils import require_onnxruntime






@require_onnxruntime
class 						a_		:
							pass




