



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        snake_case__    :







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       ) ->				Any:
									"""simple docstring"""




									a__       :						int							=  parent
									a__       :						Optional[int]							=  1_3
									a__       :						int							=  7
									a__       :						Dict							=  3_0
									a__       :						List[str]							=  self.seq_length + self.mem_len
									a__       :						Any							=  1_5
									a__       :						Any							=  True
									a__       :						Any							=  True
									a__       :						List[str]							=  9_9
									a__       :						Union[str, Any]							=  [1_0, 5_0, 8_0]
									a__       :						Dict							=  3_2
									a__       :						List[Any]							=  3_2
									a__       :						List[Any]							=  4
									a__       :						Dict							=  8
									a__       :						Union[str, Any]							=  1_2_8
									a__       :						Optional[int]							=  2
									a__       :						Union[str, Any]							=  2
									a__       :						Optional[int]							=  None
									a__       :						List[Any]							=  1
									a__       :						Optional[int]							=  0
									a__       :						Dict							=  3
									a__       :						Union[str, Any]							=  self.vocab_size - 1
									a__       :						Any							=  0.0_1

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						int							=  ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size  )
									a__       :						Dict							=  ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size  )

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

									a__       :						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 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						Tuple							=  TFTransfoXLModel(_lowerCamelCase  )

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

									a__       :						Dict							=  {"""input_ids""": input_ids_a, """mems""": mems_a}

									a__	,						a__       :						Any							=  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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				str:
									"""simple docstring"""




									a__       :						str							=  TFTransfoXLLMHeadModel(_lowerCamelCase  )

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

									a__       :						Union[str, Any]							=  {"""input_ids""": input_ids_a, """labels""": lm_labels}
									a__	,						a__       :						str							=  model(_lowerCamelCase  ).to_tuple()

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

									a__       :						List[Any]							=  {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

									a__	,						a__       :						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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Tuple:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




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







@require_tf
class        snake_case__    (A_ ,     A_ ,     unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:str      =					(
						    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
						)
						__lowerCAmelCase					:List[str]      =					() if is_tf_available() else ()
						__lowerCAmelCase					:Optional[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
						__lowerCAmelCase					:Any      =					False
						__lowerCAmelCase					:int      =					False
						__lowerCAmelCase					:Any      =					False
						__lowerCAmelCase					:Any      =					False

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Tuple:
									"""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 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									a__       :						Any							=  TFTransfoXLModelTester(self  )
									a__       :						Optional[Any]							=  ConfigTester(self ,       config_class=_lowerCamelCase ,       d_embed=3_7  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									self.config_tester.run_common_tests()

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									self.model_tester.set_seed()
									a__       :						Dict							=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_model(*_lowerCamelCase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									self.model_tester.set_seed()
									a__       :						Dict							=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_lm_head(*_lowerCamelCase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									a__       :						List[str]							=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_lowerCamelCase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									a__	,						a__       :						Union[str, Any]							=  self.model_tester.prepare_config_and_inputs_for_common()
									a__       :						Optional[int]							=  [TFTransfoXLForSequenceClassification]

									for model_class in self.all_model_classes:
												a__       :						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:
															a__       :						Tuple							=  model.get_output_embeddings()
															assert isinstance(_lowerCamelCase ,       tf.keras.layers.Layer  )
															a__       :						int							=  model.get_bias()
															assert name is None
												else:
															a__       :						Union[str, Any]							=  model.get_output_embeddings()
															assert x is None
															a__       :						Union[str, Any]							=  model.get_bias()
															assert name is None

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									pass

						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												a__       :						Tuple							=  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 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									pass







@require_tf
class        snake_case__    (unittest.TestCase		):







						"""simple docstring"""




						@unittest.skip("""Skip test until #12651 is resolved."""  )
						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									a__       :						List[Any]							=  TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""  )
									# fmt: off
									a__       :						Optional[int]							=  tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]] ,       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__       :						int							=  [3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # 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__       :						Union[str, Any]							=  model.generate(_lowerCamelCase ,       max_length=2_0_0 ,       do_sample=_lowerCamelCase  )
									self.assertListEqual(output_ids[0].numpy().tolist() ,       _lowerCamelCase  )









from __future__ import annotations

def      lowerCAmelCase_					(  _lowercase    :       float						,       _lowercase    :       float						,       _lowercase    :       float						,       )							->	tuple[str, float]:







			"""simple docstring"""
			if (stress, tangential_force, area).count(0) != 1:
						raise ValueError("""You cannot supply more or less than 2 values""")
			elif stress < 0:
						raise ValueError("""Stress cannot be negative""")
			elif tangential_force < 0:
						raise ValueError("""Tangential Force cannot be negative""")
			elif area < 0:
						raise ValueError("""Area cannot be negative""")
			elif stress == 0:
						return (
						    "stress",
						    tangential_force / area,
						)
			elif tangential_force == 0:
						return (
						    "tangential_force",
						    stress * area,
						)
			else:
						return (
						    "area",
						    tangential_force / stress,
						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




