






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







				def 				__a   ( self     :int     ):
					UpperCamelCase__ :Optional[int]						=      ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size     )
					UpperCamelCase__ :Optional[Any]						=      ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size     )

					UpperCamelCase__ :Union[str, Any]						=      None
					if self.use_labels:
						UpperCamelCase__ :Tuple						=      ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size     )

					UpperCamelCase__ :Dict						=      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     :Union[str, Any]     ):
					random.seed(self.seed     )
					tf.random.set_seed(self.seed     )







				def 				__a   ( self     :Union[str, Any]  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :List[Any]  ,     lowerCamelCase__     :List[str]  ,     lowerCamelCase__     :Tuple     ):
					UpperCamelCase__ :Optional[int]						=      TFTransfoXLModel(lowerCAmelCase__     )

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

					UpperCamelCase__ :List[Any]						=      {"""input_ids""": input_ids_a, """mems""": mems_a}

					UpperCamelCase__				,		UpperCamelCase__ :List[str]						=      model(lowerCAmelCase__     ).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     :Any  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :Optional[int]  ,     lowerCamelCase__     :Any     ):
					UpperCamelCase__ :Optional[Any]						=      TFTransfoXLLMHeadModel(lowerCAmelCase__     )

					UpperCamelCase__				,		UpperCamelCase__ :Optional[Any]						=      model(lowerCAmelCase__     ).to_tuple()

					UpperCamelCase__ :int						=      {"""input_ids""": input_ids_a, """labels""": lm_labels}
					UpperCamelCase__				,		UpperCamelCase__ :Dict						=      model(lowerCAmelCase__     ).to_tuple()

					UpperCamelCase__				,		UpperCamelCase__ :Dict						=      model([input_ids_a, mems_a]     ).to_tuple()

					UpperCamelCase__ :Dict						=      {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

					UpperCamelCase__				,		UpperCamelCase__ :Optional[Any]						=      model(lowerCAmelCase__     ).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     :Optional[int]  ,     lowerCamelCase__     :List[Any]  ,     lowerCamelCase__     :Dict  ,     lowerCamelCase__     :Dict  ,     lowerCamelCase__     :Tuple     ):
					UpperCamelCase__ :Tuple						=      TFTransfoXLForSequenceClassification(lowerCAmelCase__     )
					UpperCamelCase__ :str						=      model(lowerCAmelCase__     )
					self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_labels)     )







				def 				__a   ( self     :Optional[Any]     ):
					UpperCamelCase__ :List[str]						=      self.prepare_config_and_inputs()
					((UpperCamelCase__)				,		(UpperCamelCase__)				,		(UpperCamelCase__)				,		(UpperCamelCase__)) :str						=      config_and_inputs
					UpperCamelCase__ :List[str]						=      {"""input_ids""": input_ids_a}
					return config, inputs_dict

@require_tf
class        lowerCAmelCase_				(      __a						,      __a						,      unittest.TestCase	):







				"""simple docstring"""




				_snake_case				:   Optional[int]        =			(
				    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
				)
				_snake_case				:   Optional[int]        =			() if is_tf_available() else ()
				_snake_case				:   int        =			(
				    {
				        '''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
				_snake_case				:   Tuple        =			False
				_snake_case				:   Tuple        =			False
				_snake_case				:   int        =			False
				_snake_case				:   str        =			False







				def 				__a   ( self     :Union[str, Any]  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :Optional[Any]  ,     lowerCamelCase__     :Any     ):
					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]     ):
					UpperCamelCase__ :List[Any]						=      TFTransfoXLModelTester(self     )
					UpperCamelCase__ :List[Any]						=      ConfigTester(self  ,     config_class=lowerCAmelCase__  ,     d_embed=37     )







				def 				__a   ( self     :List[Any]     ):
					self.config_tester.run_common_tests()







				def 				__a   ( self     :Optional[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(*lowerCAmelCase__     )







				def 				__a   ( self     :str     ):
					self.model_tester.set_seed()
					UpperCamelCase__ :List[Any]						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_transfo_xl_lm_head(*lowerCAmelCase__     )







				def 				__a   ( self     :int     ):
					UpperCamelCase__ :List[Any]						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowerCAmelCase__     )







				def 				__a   ( self     :int     ):
					UpperCamelCase__				,		UpperCamelCase__ :List[Any]						=      self.model_tester.prepare_config_and_inputs_for_common()
					UpperCamelCase__ :Dict						=      [TFTransfoXLForSequenceClassification]

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







				def 				__a   ( self     :List[str]     ):
					# TODO JP: Make TransfoXL XLA compliant
					pass







				@slow
				def 				__a   ( self     :Optional[int]     ):
					for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase__ :str						=      TFTransfoXLModel.from_pretrained(lowerCAmelCase__     )
						self.assertIsNotNone(lowerCAmelCase__     )







				@unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss."""     )
				def 				__a   ( self     :Optional[Any]     ):
					pass

@require_tf
class        lowerCAmelCase_				(      unittest.TestCase	):







				"""simple docstring"""




				@unittest.skip("""Skip test until #12651 is resolved."""     )
				@slow
				def 				__a   ( self     :Any     ):
					UpperCamelCase__ :Dict						=      TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""     )
					# fmt: off
					UpperCamelCase__ :Union[str, Any]						=      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
					UpperCamelCase__ :Any						=      [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>

					UpperCamelCase__ :Union[str, Any]						=      model.generate(lowerCAmelCase__  ,     max_length=2_00  ,     do_sample=lowerCAmelCase__     )
					self.assertListEqual(output_ids[0].numpy().tolist()  ,     lowerCAmelCase__     )

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase         =      set()
				# To detect a back edge, keep track of vertices currently in the recursion stack
				lowercase         =      set()
				return any(
				    node not in visited and depth_first_search(__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						)
				    for node in graph						)

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				visited.add(__magic_name__						)
				rec_stk.add(__magic_name__						)

				for node in graph[vertex]:
								if node not in visited:
												if depth_first_search(__magic_name__		,    __magic_name__		,    __magic_name__		,    __magic_name__						):
																return True
								elif node in rec_stk:
												return True

    # The node needs to be removed from recursion stack before function ends
				rec_stk.remove(__magic_name__						)
				return False


if __name__ == "__main__":
						from doctest import testmod

						testmod()
