
'''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  : int			,							a__  : Union[str, Any]			,							):
  '''simple docstring'''

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


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

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

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

  lowerCAmelCase__     :   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    _A	(    self  : str    ):
  '''simple docstring'''

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


 def    _A	(    self  : int			,							a__  : Tuple			,							a__  : int			,							a__  : List[str]			,							a__  : int    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	TFTransfoXLModel(a__    )

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

  lowerCAmelCase__     :   Any     		=	{"input_ids": input_ids_a, "mems": mems_a}

  lowerCAmelCase__		,     lowerCAmelCase__     :   List[Any]     		=	model(a__    ).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    _A	(    self  : int			,							a__  : Any			,							a__  : List[Any]			,							a__  : List[str]			,							a__  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	TFTransfoXLLMHeadModel(a__    )

  lowerCAmelCase__		,     lowerCAmelCase__     :   Tuple     		=	model(a__    ).to_tuple()

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

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

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

  lowerCAmelCase__		,     lowerCAmelCase__     :   Union[str, Any]     		=	model(a__    ).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    _A	(    self  : Tuple			,							a__  : str			,							a__  : Tuple			,							a__  : List[Any]			,							a__  : int    ):
  '''simple docstring'''

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


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

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




@require_tf
class    lowerCAmelCase       (     UpperCamelCase_    ,  UpperCamelCase_    ,  unittest.TestCase  ):
 A_				:				Any	=			(
     (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
 )
 A_				:				List[str]	=			() if is_tf_available() else ()
 A_				:				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
 A_				:				str	=			False
 A_				:				Any	=			False
 A_				:				List[str]	=			False
 A_				:				Union[str, Any]	=			False


 def    _A	(    self  : Optional[int]			,							a__  : int			,							a__  : Dict			,							a__  : Dict			,							a__  : Optional[Any]			,							a__  : Union[str, 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    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	TFTransfoXLModelTester(self    )
  lowerCAmelCase__     :   Optional[Any]     		=	ConfigTester(self			,							config_class=a__			,							d_embed=37    )


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  self.config_tester.run_common_tests()


 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  self.model_tester.set_seed()
  lowerCAmelCase__     :   Optional[Any]     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_transfo_xl_model(*a__    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  self.model_tester.set_seed()
  lowerCAmelCase__     :   Tuple     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_transfo_xl_lm_head(*a__    )


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*a__    )


 def    _A	(    self  : Dict    ):
  '''simple docstring'''

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

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


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  pass


 @slow
 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

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


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

  pass




@require_tf
class    lowerCAmelCase       (     unittest.TestCase  ):


 @unittest.skip("Skip test until #12651 is resolved."    )
 @slow
 def    _A	(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"    )
  # fmt: off
  lowerCAmelCase__     :   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
  lowerCAmelCase__     :   Optional[Any]     		=	[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>

  lowerCAmelCase__     :   str     		=	model.generate(a__			,							max_length=200			,							do_sample=a__    )
  self.assertListEqual(output_ids[0].numpy().tolist()			,							a__    )







'''simple docstring'''

from ....configuration_utils import PretrainedConfig
from ....utils import logging


snake_case									=    logging.get_logger(__name__)

snake_case									=    {
    """Visual-Attention-Network/van-base""": (
        """https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
    ),
}




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Tuple	=			"""van"""


 def __init__(    self  : List[Any]			,							a__  : Dict=224			,							a__  : Dict=3			,							a__  : Union[str, Any]=[7, 3, 3, 3]			,							a__  : Optional[Any]=[4, 2, 2, 2]			,							a__  : Optional[Any]=[64, 128, 320, 512]			,							a__  : List[str]=[3, 3, 12, 3]			,							a__  : Any=[8, 8, 4, 4]			,							a__  : Optional[int]="gelu"			,							a__  : List[Any]=0.02			,							a__  : Tuple=1e-6			,							a__  : List[str]=1e-2			,							a__  : List[str]=0.0			,							a__  : List[Any]=0.0			,							**a__  : Tuple			,							):
  '''simple docstring'''

  super().__init__(**a__    )
  lowerCAmelCase__     :   Any     		=	image_size
  lowerCAmelCase__     :   Tuple     		=	num_channels
  lowerCAmelCase__     :   List[Any]     		=	patch_sizes
  lowerCAmelCase__     :   Dict     		=	strides
  lowerCAmelCase__     :   List[str]     		=	hidden_sizes
  lowerCAmelCase__     :   Union[str, Any]     		=	depths
  lowerCAmelCase__     :   Tuple     		=	mlp_ratios
  lowerCAmelCase__     :   Optional[Any]     		=	hidden_act
  lowerCAmelCase__     :   Dict     		=	initializer_range
  lowerCAmelCase__     :   int     		=	layer_norm_eps
  lowerCAmelCase__     :   Optional[Any]     		=	layer_scale_init_value
  lowerCAmelCase__     :   List[str]     		=	drop_path_rate
  lowerCAmelCase__     :   Any     		=	dropout_rate





