



'''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 		_UpperCamelCase						:


			'''simple docstring'''

			def __init__(							self  :				str							,  lowerCAmelCase__  :				List[str]							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	parent
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	1_3
					__SCREAMING_SNAKE_CASE :	str					 =	7
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	3_0
					__SCREAMING_SNAKE_CASE :	int					 =	self.seq_length + self.mem_len
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	1_5
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	True
					__SCREAMING_SNAKE_CASE :	str					 =	True
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	9_9
					__SCREAMING_SNAKE_CASE :	str					 =	[1_0, 5_0, 8_0]
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	3_2
					__SCREAMING_SNAKE_CASE :	Any					 =	3_2
					__SCREAMING_SNAKE_CASE :	Tuple					 =	4
					__SCREAMING_SNAKE_CASE :	Dict					 =	8
					__SCREAMING_SNAKE_CASE :	Tuple					 =	1_2_8
					__SCREAMING_SNAKE_CASE :	int					 =	2
					__SCREAMING_SNAKE_CASE :	Any					 =	2
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	None
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	1
					__SCREAMING_SNAKE_CASE :	List[str]					 =	0
					__SCREAMING_SNAKE_CASE :	int					 =	3
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.vocab_size - 1
					__SCREAMING_SNAKE_CASE :	str					 =	0.01

			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size		)
					__SCREAMING_SNAKE_CASE :	str					 =	ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size		)

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

					__SCREAMING_SNAKE_CASE :	Optional[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 UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				Tuple							,  lowerCAmelCase__  :				List[Any]							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	TFTransfoXLModel(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	model(lowerCAmelCase__		).to_tuple()

					__SCREAMING_SNAKE_CASE :	str					 =	{"""input_ids""": input_ids_a, """mems""": mems_a}

					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	List[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 UpperCamelCase__      (							self  :				Optional[int]							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				Optional[int]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	TFTransfoXLLMHeadModel(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	int					 =	model(lowerCAmelCase__		).to_tuple()

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

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

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	{"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Tuple					 =	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 UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				Tuple							,  lowerCAmelCase__  :				Optional[int]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	TFTransfoXLForSequenceClassification(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	model(lowerCAmelCase__		)
					self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_labels)		)



			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.prepare_config_and_inputs()
					((__SCREAMING_SNAKE_CASE) ,		(__SCREAMING_SNAKE_CASE) ,		(__SCREAMING_SNAKE_CASE) ,		(__SCREAMING_SNAKE_CASE)) :	List[str]					 =	config_and_inputs
					__SCREAMING_SNAKE_CASE :	Dict					 =	{"""input_ids""": input_ids_a}
					return config, inputs_dict






@require_tf
class 		_UpperCamelCase						(							lowerCamelCase__					,      lowerCamelCase__					,      unittest.TestCase					):


			'''simple docstring'''

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

			def UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				List[str]		):





					"""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 UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	TFTransfoXLModelTester(self		)
					__SCREAMING_SNAKE_CASE :	str					 =	ConfigTester(self							,  config_class=lowerCAmelCase__							,  d_embed=3_7		)

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					self.config_tester.run_common_tests()

			def UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					self.model_tester.set_seed()
					__SCREAMING_SNAKE_CASE :	Any					 =	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_transfo_xl_model(*lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					self.model_tester.set_seed()
					__SCREAMING_SNAKE_CASE :	int					 =	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_transfo_xl_lm_head(*lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Tuple					 =	self.model_tester.prepare_config_and_inputs_for_common()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	[TFTransfoXLForSequenceClassification]

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

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					pass

			@slow
			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	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 UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					pass






@require_tf
class 		_UpperCamelCase						(							unittest.TestCase					):


			'''simple docstring'''

			@unittest.skip("""Skip test until #12651 is resolved."""		)
			@slow
			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""		)
					# fmt: off
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	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
					__SCREAMING_SNAKE_CASE :	Optional[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>

					__SCREAMING_SNAKE_CASE :	int					 =	model.generate(lowerCAmelCase__							,  max_length=2_0_0							,  do_sample=lowerCAmelCase__		)
					self.assertListEqual(output_ids[0].numpy().tolist()							,  lowerCAmelCase__		)




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__ :			List[Any]												=				{
    '''configuration_time_series_transformer''': [
        '''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''TimeSeriesTransformerConfig''',
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :			List[Any]												=				[
						    '''TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TimeSeriesTransformerForPrediction''',
						    '''TimeSeriesTransformerModel''',
						    '''TimeSeriesTransformerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_time_series_transformer import (
						    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TimeSeriesTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_time_series_transformer import (
												    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TimeSeriesTransformerForPrediction,
												    TimeSeriesTransformerModel,
												    TimeSeriesTransformerPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__ :			Dict												=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)