



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 SCREAMING_SNAKE_CASE__					:




				'''simple docstring'''






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






				def       _lowerCAmelCase (	self		):
								A			:	List[Any]										=       ids_tensor([self.batch_size, self.seq_length],		self.vocab_size		)
								A			:	Optional[Any]										=       ids_tensor([self.batch_size, self.seq_length],		self.vocab_size		)

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

								A			:	Union[str, 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       _lowerCAmelCase (	self		):
								random.seed(self.seed		)
								tf.random.set_seed(self.seed		)






				def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
								A			:	List[Any]										=       TFTransfoXLModel(lowerCAmelCase__		)

								A			:	Any										=       model(lowerCAmelCase__		).to_tuple()

								A			:	List[str]										=       {"input_ids": input_ids_a, "mems": mems_a}

								A			:	Tuple										=       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,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
								A			:	List[str]										=       TFTransfoXLLMHeadModel(lowerCAmelCase__		)

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

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

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

								A			:	Tuple										=       {"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}

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






				def       _lowerCAmelCase (	self		):
								A			:	Tuple										=       self.prepare_config_and_inputs()
								(A)			:	List[Any]										=       config_and_inputs
								A			:	Any										=       {"input_ids": input_ids_a}
								return config, inputs_dict






@require_tf
class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__			,						unittest.TestCase   ):




				'''simple docstring'''






				__lowerCamelCase      :			int =				(
				    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
				)
				__lowerCamelCase      :			Union[str, Any] =				() if is_tf_available() else ()
				__lowerCamelCase      :			Tuple =				(
				    {
				        '''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      :			Optional[Any] =				False
				__lowerCamelCase      :			Dict =				False
				__lowerCamelCase      :			List[Any] =				False
				__lowerCamelCase      :			Optional[Any] =				False






				def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):




								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		):
								A			:	int										=       TFTransfoXLModelTester(self		)
								A			:	Union[str, Any]										=       ConfigTester(self,		config_class=lowerCAmelCase__,		d_embed=37		)






				def       _lowerCAmelCase (	self		):
								self.config_tester.run_common_tests()






				def       _lowerCAmelCase (	self		):
								self.model_tester.set_seed()
								A			:	int										=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_model(*lowerCAmelCase__		)






				def       _lowerCAmelCase (	self		):
								self.model_tester.set_seed()
								A			:	Any										=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_lm_head(*lowerCAmelCase__		)






				def       _lowerCAmelCase (	self		):
								A			:	List[Any]										=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowerCAmelCase__		)






				def       _lowerCAmelCase (	self		):
								A			:	str										=       self.model_tester.prepare_config_and_inputs_for_common()
								A			:	str										=       [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			:	List[Any]										=       model.get_output_embeddings()
																assert isinstance(lowerCAmelCase__,		tf.keras.layers.Layer		)
																A			:	str										=       model.get_bias()
																assert name is None
												else:
																A			:	Optional[Any]										=       model.get_output_embeddings()
																assert x is None
																A			:	int										=       model.get_bias()
																assert name is None






				def       _lowerCAmelCase (	self		):
								pass






				@slow
				def       _lowerCAmelCase (	self		):




								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       _lowerCAmelCase (	self		):
								pass






@require_tf
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):




				'''simple docstring'''






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

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







from __future__ import annotations




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  list[int]:



    """simple docstring"""






    A			:	Tuple										=       2
    A			:	List[Any]										=       []
    while i * i <= n:
        if n % i:
            i += 1
        else:
            n //= i
            factors.append(_lowerCAmelCase		)
    if n > 1:
        factors.append(_lowerCAmelCase		)
    return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()


