



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 :




				def __init__(							self						,				_snake_case						,				):



									"""simple docstring"""





									_lowerCAmelCase						=    parent
									_lowerCAmelCase						=    13
									_lowerCAmelCase						=    7
									_lowerCAmelCase						=    30
									_lowerCAmelCase						=    self.seq_length + self.mem_len
									_lowerCAmelCase						=    15
									_lowerCAmelCase						=    True
									_lowerCAmelCase						=    True
									_lowerCAmelCase						=    99
									_lowerCAmelCase						=    [10, 50, 80]
									_lowerCAmelCase						=    32
									_lowerCAmelCase						=    32
									_lowerCAmelCase						=    4
									_lowerCAmelCase						=    8
									_lowerCAmelCase						=    128
									_lowerCAmelCase						=    2
									_lowerCAmelCase						=    2
									_lowerCAmelCase						=    None
									_lowerCAmelCase						=    1
									_lowerCAmelCase						=    0
									_lowerCAmelCase						=    3
									_lowerCAmelCase						=    self.vocab_size - 1
									_lowerCAmelCase						=    0.01




				def 							snake_case   (							self		):



									"""simple docstring"""





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

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

									_lowerCAmelCase						=    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 							snake_case   (							self		):



									"""simple docstring"""





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




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    TFTransfoXLModel(_snake_case		)

									_lowerCAmelCase	,	_lowerCAmelCase						=    model(_snake_case		).to_tuple()

									_lowerCAmelCase						=    {"""input_ids""": input_ids_a, """mems""": mems_a}

									_lowerCAmelCase	,	_lowerCAmelCase						=    model(_snake_case		).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 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    TFTransfoXLLMHeadModel(_snake_case		)

									_lowerCAmelCase	,	_lowerCAmelCase						=    model(_snake_case		).to_tuple()

									_lowerCAmelCase						=    {"""input_ids""": input_ids_a, """labels""": lm_labels}
									_lowerCAmelCase	,	_lowerCAmelCase						=    model(_snake_case		).to_tuple()

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

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

									_lowerCAmelCase	,	_lowerCAmelCase						=    model(_snake_case		).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 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    TFTransfoXLForSequenceClassification(_snake_case		)
									_lowerCAmelCase						=    model(_snake_case		)
									self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_labels)		)




				def 							snake_case   (							self		):



									"""simple docstring"""





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





@require_tf
class 					__lowerCAmelCase (     lowerCamelCase__       ,						lowerCamelCase__       ,						unittest.TestCase       ):
				__lowerCamelCase =				(
				    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
				)
				__lowerCamelCase =				() if is_tf_available() else ()
				__lowerCamelCase =				(
				    {
				        '''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 =				False
				__lowerCamelCase =				False
				__lowerCamelCase =				False
				__lowerCamelCase =				False




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case						,				_snake_case		):



									"""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 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    TFTransfoXLModelTester(self		)
									_lowerCAmelCase						=    ConfigTester(self						,				config_class=_snake_case						,				d_embed=37		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									self.config_tester.run_common_tests()




				def 							snake_case   (							self		):



									"""simple docstring"""





									self.model_tester.set_seed()
									_lowerCAmelCase						=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_model(*_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									self.model_tester.set_seed()
									_lowerCAmelCase						=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_lm_head(*_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





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

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									pass




				@slow
				def 							snake_case   (							self		):



									"""simple docstring"""





									for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_lowerCAmelCase						=    TFTransfoXLModel.from_pretrained(_snake_case		)
														self.assertIsNotNone(_snake_case		)




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



									"""simple docstring"""





									pass





@require_tf
class 					__lowerCAmelCase (     unittest.TestCase       ):




				@unittest.skip("""Skip test until #12651 is resolved."""		)
				@slow
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""		)
									# fmt: off
									_lowerCAmelCase						=    tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]]						,				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
									_lowerCAmelCase						=    [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0]  # 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>

									_lowerCAmelCase						=    model.generate(_snake_case						,				max_length=200						,				do_sample=_snake_case		)
									self.assertListEqual(output_ids[0].numpy().tolist()						,				_snake_case		)







'''simple docstring'''
from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class    lowercase	(							A__					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__(
							    features=UpperCamelCase_    ,   cache_dir=UpperCamelCase_    ,   keep_in_memory=UpperCamelCase_    ,   streaming=UpperCamelCase_    ,   num_proc=UpperCamelCase_    ,   **UpperCamelCase_    ,   )
							UpperCamelCase__   :Any												= Generator(
							    cache_dir=UpperCamelCase_    ,   features=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   gen_kwargs=UpperCamelCase_    ,   **UpperCamelCase_    ,   )




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if self.streaming:
												UpperCamelCase__   :Optional[Any]												= self.builder.as_streaming_dataset(split='''train'''							)
							# Build regular (map-style) dataset
							else:
												UpperCamelCase__   :Optional[int]												= None
												UpperCamelCase__   :int												= None
												UpperCamelCase__   :Any												= None
												UpperCamelCase__   :Any												= None

												self.builder.download_and_prepare(
												    download_config=UpperCamelCase_    ,   download_mode=UpperCamelCase_    ,   verification_mode=UpperCamelCase_    ,   base_path=UpperCamelCase_    ,   num_proc=self.num_proc    ,   )
												UpperCamelCase__   :List[Any]												= self.builder.as_dataset(
												    split='''train'''    ,   verification_mode=UpperCamelCase_    ,   in_memory=self.keep_in_memory							)
							return dataset