




'''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     A					:







							def __init__(						self :						List[Any]							,    lowerCAmelCase_ :						Optional[int]							,    )    ->							Dict:


														"""simple docstring"""


														_a			  =       parent
														_a			  =       13
														_a			  =       7
														_a			  =       30
														_a			  =       self.seq_length + self.mem_len
														_a			  =       15
														_a			  =       True
														_a			  =       True
														_a			  =       99
														_a			  =       [10, 50, 80]
														_a			  =       32
														_a			  =       32
														_a			  =       4
														_a			  =       8
														_a			  =       1_28
														_a			  =       2
														_a			  =       2
														_a			  =       None
														_a			  =       1
														_a			  =       0
														_a			  =       3
														_a			  =       self.vocab_size - 1
														_a			  =       0.0_1







							def      __lowerCAmelCase    (						self :						int      )    ->							Any:


														"""simple docstring"""


														_a			  =       ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size      )
														_a			  =       ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size      )

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

														_a			  =       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 :						List[str]      )    ->							Dict:


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						Any							,    lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						Optional[int]							,    lowerCAmelCase_ :						Tuple							,    lowerCAmelCase_ :						Union[str, Any]      )    ->							str:


														"""simple docstring"""


														_a			  =       TFTransfoXLModel(lowerCAmelCase_      )

														_a  ,							_a			  =       model(lowerCAmelCase_      ).to_tuple()

														_a			  =       {'''input_ids''': input_ids_a, '''mems''': mems_a}

														_a  ,							_a			  =       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      __lowerCAmelCase    (						self :						Any							,    lowerCAmelCase_ :						Tuple							,    lowerCAmelCase_ :						Union[str, Any]							,    lowerCAmelCase_ :						Tuple							,    lowerCAmelCase_ :						Tuple      )    ->							Any:


														"""simple docstring"""


														_a			  =       TFTransfoXLLMHeadModel(lowerCAmelCase_      )

														_a  ,							_a			  =       model(lowerCAmelCase_      ).to_tuple()

														_a			  =       {'''input_ids''': input_ids_a, '''labels''': lm_labels}
														_a  ,							_a			  =       model(lowerCAmelCase_      ).to_tuple()

														_a  ,							_a			  =       model([input_ids_a, mems_a]      ).to_tuple()

														_a			  =       {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

														_a  ,							_a			  =       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      __lowerCAmelCase    (						self :						Any							,    lowerCAmelCase_ :						str							,    lowerCAmelCase_ :						List[Any]							,    lowerCAmelCase_ :						Optional[Any]							,    lowerCAmelCase_ :						str      )    ->							Dict:


														"""simple docstring"""


														_a			  =       TFTransfoXLForSequenceClassification(lowerCAmelCase_      )
														_a			  =       model(lowerCAmelCase_      )
														self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)      )







							def      __lowerCAmelCase    (						self :						List[str]      )    ->							int:


														"""simple docstring"""


														_a			  =       self.prepare_config_and_inputs()
														((_a)  ,							(_a)  ,							(_a)  ,							(_a))			  =       config_and_inputs
														_a			  =       {'''input_ids''': input_ids_a}
														return config, inputs_dict



@require_tf
class     A					(	_a  ,_a  ,unittest.TestCase					):
							lowercase_           =       (
							    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
							)
							lowercase_           =       () if is_tf_available() else ()
							lowercase_           =       (
							    {
							        '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
							lowercase_           =       False
							lowercase_           =       False
							lowercase_           =       False
							lowercase_           =       False







							def      __lowerCAmelCase    (						self :						Optional[Any]							,    lowerCAmelCase_ :						Any							,    lowerCAmelCase_ :						Any							,    lowerCAmelCase_ :						List[Any]							,    lowerCAmelCase_ :						Optional[int]							,    lowerCAmelCase_ :						Union[str, Any]      )    ->							Optional[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      __lowerCAmelCase    (						self :						Tuple      )    ->							Union[str, Any]:


														"""simple docstring"""


														_a			  =       TFTransfoXLModelTester(self      )
														_a			  =       ConfigTester(self							,    config_class=lowerCAmelCase_							,    d_embed=37      )







							def      __lowerCAmelCase    (						self :						int      )    ->							Dict:


														"""simple docstring"""


														self.config_tester.run_common_tests()







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							Dict:


														"""simple docstring"""


														self.model_tester.set_seed()
														_a			  =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_transfo_xl_model(*lowerCAmelCase_      )







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


														"""simple docstring"""


														self.model_tester.set_seed()
														_a			  =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_transfo_xl_lm_head(*lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Any      )    ->							int:


														"""simple docstring"""


														_a			  =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						str      )    ->							int:


														"""simple docstring"""


														_a  ,							_a			  =       self.model_tester.prepare_config_and_inputs_for_common()
														_a			  =       [TFTransfoXLForSequenceClassification]

														for model_class in self.all_model_classes:
																					_a			  =       model_class(lowerCAmelCase_      )
																					assert isinstance(model.get_input_embeddings()							,    tf.keras.layers.Layer      )
																					if model_class in list_other_models_with_output_ebd:
																												_a			  =       model.get_output_embeddings()
																												assert isinstance(lowerCAmelCase_							,    tf.keras.layers.Layer      )
																												_a			  =       model.get_bias()
																												assert name is None
																					else:
																												_a			  =       model.get_output_embeddings()
																												assert x is None
																												_a			  =       model.get_bias()
																												assert name is None







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


														"""simple docstring"""


														pass







							@slow
							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Tuple:


														"""simple docstring"""


														for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_a			  =       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      __lowerCAmelCase    (						self :						List[Any]      )    ->							int:


														"""simple docstring"""


														pass



@require_tf
class     A					(	unittest.TestCase					):







							@unittest.skip('''Skip test until #12651 is resolved.'''      )
							@slow
							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''      )
														# fmt: off
														_a			  =       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
														_a			  =       [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>

														_a			  =       model.generate(lowerCAmelCase_							,    max_length=2_00							,    do_sample=lowerCAmelCase_      )
														self.assertListEqual(output_ids[0].numpy().tolist()							,    lowerCAmelCase_      )







'''simple docstring'''


_snake_case   :   Any     							=	tuple[float, float, float]
_snake_case   :   Optional[int]     							=	tuple[float, float, float]
def 	snake_case_					(UpperCamelCase :						Pointad				,		UpperCamelCase :						Pointad   ):





							'''simple docstring'''


							_a			  =       end_pointa[0] - end_pointa[0]
							_a			  =       end_pointa[1] - end_pointa[1]
							_a			  =       end_pointa[2] - end_pointa[2]
							return (x, y, z)
def 	snake_case_					(UpperCamelCase :						Vectorad				,		UpperCamelCase :						Vectorad   ):





							'''simple docstring'''


							_a			  =       ab[1] * ac[2] - ab[2] * ac[1]  # *i
							_a			  =       (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
							_a			  =       ab[0] * ac[1] - ab[1] * ac[0]  # *k
							return (x, y, z)
def 	snake_case_					(UpperCamelCase :						Vectorad				,		UpperCamelCase :						int   ):





							'''simple docstring'''


							return tuple(round(UpperCamelCase				,		UpperCamelCase   ) for x in vector   ) == (0, 0, 0)

def 	snake_case_					(UpperCamelCase :						Pointad				,		UpperCamelCase :						Pointad				,		UpperCamelCase :						Pointad				,		UpperCamelCase :						int = 10   ):





							'''simple docstring'''


							_a			  =       create_vector(UpperCamelCase				,		UpperCamelCase   )
							_a			  =       create_vector(UpperCamelCase				,		UpperCamelCase   )
							return is_zero_vector(get_ad_vectors_cross(UpperCamelCase				,		UpperCamelCase   )				,		UpperCamelCase   )

