


"""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							:







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

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




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

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

       snake_case       : List[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 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						int:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       TFTransfoXLModel(UpperCamelCase__		)

       snake_case   ,snake_case       : int    =       model(UpperCamelCase__		).to_tuple()

       snake_case       : int    =       {"input_ids": input_ids_a, "mems": mems_a}

       snake_case   ,snake_case       : Dict    =       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 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Union[str, Any]:

       '''simple docstring'''




       snake_case       : List[Any]    =       TFTransfoXLLMHeadModel(UpperCamelCase__		)

       snake_case   ,snake_case       : Any    =       model(UpperCamelCase__		).to_tuple()

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

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

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

       snake_case   ,snake_case       : Dict    =       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 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Optional[Any]:

       '''simple docstring'''




       snake_case       : Tuple    =       TFTransfoXLForSequenceClassification(UpperCamelCase__		)
       snake_case       : Optional[int]    =       model(UpperCamelCase__		)
       self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels)		)







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

       '''simple docstring'''




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







@require_tf
class _lowerCAmelCase							( snake_case_       ,				snake_case_       ,				unittest.TestCase      ):
   __UpperCAmelCase					:			Dict				    =    (
       (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
   )
   __UpperCAmelCase					:			int				    =    () if is_tf_available() else ()
   __UpperCAmelCase					:			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
   __UpperCAmelCase					:			Optional[int]				    =    False
   __UpperCAmelCase					:			Optional[Any]				    =    False
   __UpperCAmelCase					:			Dict				    =    False
   __UpperCAmelCase					:			Optional[int]				    =    False







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						str:

       '''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 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       snake_case       : List[Any]    =       TFTransfoXLModelTester(self		)
       snake_case       : Tuple    =       ConfigTester(self		,      config_class=UpperCamelCase__		,      d_embed=37		)







   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       self.config_tester.run_common_tests()







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       self.model_tester.set_seed()
       snake_case       : str    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       self.model_tester.set_seed()
       snake_case       : Dict    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       snake_case       : Optional[int]    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




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

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







   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       pass







   @slow
   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




       for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           snake_case       : Tuple    =       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 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       pass







@require_tf
class _lowerCAmelCase							( unittest.TestCase      ):







   @unittest.skip("Skip test until #12651 is resolved."		)
   @slow
   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : Any    =       TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"		)
       # fmt: off
       snake_case       : 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
       snake_case       : int    =       [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>

       snake_case       : Optional[Any]    =       model.generate(UpperCamelCase__		,      max_length=200		,      do_sample=UpperCamelCase__		)
       self.assertListEqual(output_ids[0].numpy().tolist()		,      UpperCamelCase__		)









"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


__snake_case       =       logging.get_logger(__name__)







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      *UpperCamelCase__		,      **UpperCamelCase__		)      ->						None:

       '''simple docstring'''




       warnings.warn(
           "The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
           " Please use SegformerImageProcessor instead."		,      UpperCamelCase__		,      )
       super().__init__(*UpperCamelCase__		,      **UpperCamelCase__		)





