



'''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        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






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







	def   A			(		self    :  Union[str, Any]	)    ->	str:
								UpperCAmelCase				: Dict											=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size	)
								UpperCAmelCase				: List[str]											=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size	)

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

								UpperCAmelCase				: Dict											=	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   A			(		self    :  Optional[Any]	)    ->	Optional[Any]:
								random.seed(self.seed	)
								tf.random.set_seed(self.seed	)







	def   A			(		self    :  int	,    __snake_case    :  int	,    __snake_case    :  Dict	,    __snake_case    :  Dict	,    __snake_case    :  str	)    ->	Tuple:
								UpperCAmelCase				: Union[str, Any]											=	TFTransfoXLModel(UpperCamelCase__	)

								UpperCAmelCase				: Tuple											=	model(UpperCamelCase__	).to_tuple()

								UpperCAmelCase				: Optional[Any]											=	{'''input_ids''': input_ids_a, '''mems''': mems_a}

								UpperCAmelCase				: int											=	model(UpperCamelCase__	).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   A			(		self    :  Optional[Any]	,    __snake_case    :  str	,    __snake_case    :  str	,    __snake_case    :  Tuple	,    __snake_case    :  Tuple	)    ->	List[str]:
								UpperCAmelCase				: Optional[Any]											=	TFTransfoXLLMHeadModel(UpperCamelCase__	)

								UpperCAmelCase				: Optional[int]											=	model(UpperCamelCase__	).to_tuple()

								UpperCAmelCase				: List[Any]											=	{'''input_ids''': input_ids_a, '''labels''': lm_labels}
								UpperCAmelCase				: Tuple											=	model(UpperCamelCase__	).to_tuple()

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

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

								UpperCAmelCase				: List[str]											=	model(UpperCamelCase__	).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   A			(		self    :  Optional[Any]	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Optional[int]	,    __snake_case    :  Any	,    __snake_case    :  List[str]	)    ->	str:
								UpperCAmelCase				: Tuple											=	TFTransfoXLForSequenceClassification(UpperCamelCase__	)
								UpperCAmelCase				: Tuple											=	model(UpperCamelCase__	)
								self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)	)







	def   A			(		self    :  List[Any]	)    ->	Tuple:
								UpperCAmelCase				: int											=	self.prepare_config_and_inputs()
								(UpperCAmelCase)				: Dict											=	config_and_inputs
								UpperCAmelCase				: List[str]											=	{'''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__    				= (
	    (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   A			(		self    :  Any	,    __snake_case    :  str	,    __snake_case    :  Optional[Any]	,    __snake_case    :  List[Any]	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Optional[Any]	)    ->	Union[str, Any]:
								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   A			(		self    :  int	)    ->	Dict:
								UpperCAmelCase				: str											=	TFTransfoXLModelTester(self	)
								UpperCAmelCase				: List[Any]											=	ConfigTester(self	,    config_class=UpperCamelCase__	,    d_embed=37	)







	def   A			(		self    :  str	)    ->	Optional[int]:
								self.config_tester.run_common_tests()







	def   A			(		self    :  Optional[int]	)    ->	Any:
								self.model_tester.set_seed()
								UpperCAmelCase				: str											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase__	)







	def   A			(		self    :  Tuple	)    ->	str:
								self.model_tester.set_seed()
								UpperCAmelCase				: str											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase__	)







	def   A			(		self    :  List[str]	)    ->	int:
								UpperCAmelCase				: str											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase__	)







	def   A			(		self    :  str	)    ->	List[Any]:
								UpperCAmelCase				: List[Any]											=	self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase				: Tuple											=	[TFTransfoXLForSequenceClassification]

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







	def   A			(		self    :  List[str]	)    ->	Optional[int]:
								# TODO JP: Make TransfoXL XLA compliant
								pass







	@slow
	def   A			(		self    :  List[str]	)    ->	str:
								for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase				: Optional[Any]											=	TFTransfoXLModel.from_pretrained(UpperCamelCase__	)
															self.assertIsNotNone(UpperCamelCase__	)







	@unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''	)
	def   A			(		self    :  List[str]	)    ->	Tuple:
								pass



@require_tf
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






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

								UpperCAmelCase				: Union[str, Any]											=	model.generate(UpperCamelCase__	,    max_length=200	,    do_sample=UpperCamelCase__	)
								self.assertListEqual(output_ids[0].numpy().tolist()	,    UpperCamelCase__	)










'''simple docstring'''






import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy


def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Tuple						)   ->     str:
							assert isinstance(_lowerCAmelCase     ,   _lowerCAmelCase						)
							assert dataset.num_rows == 4
							assert dataset.num_columns == 3
							assert dataset.column_names == ["col_1", "col_2", "col_3"]
							for feature, expected_dtype in expected_features.items():
														assert dataset.features[feature].dtype == expected_dtype


@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory'''     ,   [False, True]						)
def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		Any     ,   _lowerCAmelCase						:		Optional[Any]						)   ->     List[str]:
							UpperCAmelCase				: int											=	tmp_path / '''cache'''
							UpperCAmelCase				: Dict											=	{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														UpperCAmelCase				: Any											=	SqlDatasetReader(
														    '''dataset'''     ,   '''sqlite:///''' + sqlite_path     ,   cache_dir=_lowerCAmelCase     ,   keep_in_memory=_lowerCAmelCase						).read()
							_check_sql_dataset(_lowerCAmelCase     ,   _lowerCAmelCase						)


@require_sqlalchemy
@pytest.mark.parametrize(
    '''features'''     ,   [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]     ,   )
def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Any     ,   _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		str						)   ->     Optional[int]:
							UpperCAmelCase				: Union[str, Any]											=	tmp_path / '''cache'''
							UpperCAmelCase				: Union[str, Any]											=	{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
							UpperCAmelCase				: str											=	features.copy() if features else default_expected_features
							UpperCAmelCase				: Optional[Any]											=	(
							    Features({feature: Value(_lowerCAmelCase						) for feature, dtype in features.items()}						) if features is not None else None
							)
							UpperCAmelCase				: Tuple											=	SqlDatasetReader('''dataset'''     ,   '''sqlite:///''' + sqlite_path     ,   features=_lowerCAmelCase     ,   cache_dir=_lowerCAmelCase						).read()
							_check_sql_dataset(_lowerCAmelCase     ,   _lowerCAmelCase						)


def 				snake_case_  (      _lowerCAmelCase						:		List[Any]						)   ->     Dict:
							with contextlib.closing(sqlitea.connect(_lowerCAmelCase						)						) as con:
														UpperCAmelCase				: Any											=	con.cursor()
														cur.execute('''SELECT * FROM dataset'''						)
														for row in cur:
																					yield row


@require_sqlalchemy
def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Any						)   ->     Optional[int]:
							UpperCAmelCase				: Optional[int]											=	tmp_path / '''cache'''
							UpperCAmelCase				: int											=	os.path.join(_lowerCAmelCase     ,   '''tmp.sql'''						)
							UpperCAmelCase				: Any											=	SqlDatasetReader('''dataset'''     ,   '''sqlite:///''' + sqlite_path     ,   cache_dir=_lowerCAmelCase						).read()
							SqlDatasetWriter(_lowerCAmelCase     ,   '''dataset'''     ,   '''sqlite:///''' + output_sqlite_path     ,   num_proc=1						).write()

							UpperCAmelCase				: List[Any]											=	iter_sql_file(_lowerCAmelCase						)
							UpperCAmelCase				: List[Any]											=	iter_sql_file(_lowerCAmelCase						)

							for rowa, rowa in zip(_lowerCAmelCase     ,   _lowerCAmelCase						):
														assert rowa == rowa


@require_sqlalchemy
def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Optional[Any]     ,   _lowerCAmelCase						:		Union[str, Any]						)   ->     int:
							UpperCAmelCase				: Optional[Any]											=	tmp_path / '''cache'''
							UpperCAmelCase				: Any											=	os.path.join(_lowerCAmelCase     ,   '''tmp.sql'''						)
							UpperCAmelCase				: List[str]											=	SqlDatasetReader('''dataset'''     ,   '''sqlite:///''' + sqlite_path     ,   cache_dir=_lowerCAmelCase						).read()
							SqlDatasetWriter(_lowerCAmelCase     ,   '''dataset'''     ,   '''sqlite:///''' + output_sqlite_path     ,   num_proc=2						).write()

							UpperCAmelCase				: List[str]											=	iter_sql_file(_lowerCAmelCase						)
							UpperCAmelCase				: Any											=	iter_sql_file(_lowerCAmelCase						)

							for rowa, rowa in zip(_lowerCAmelCase     ,   _lowerCAmelCase						):
														assert rowa == rowa




@require_sqlalchemy
def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		Any     ,   _lowerCAmelCase						:		Optional[Any]						)   ->     List[Any]:
							UpperCAmelCase				: Union[str, Any]											=	tmp_path / '''cache'''
							UpperCAmelCase				: Tuple											=	os.path.join(_lowerCAmelCase     ,   '''tmp.sql'''						)
							UpperCAmelCase				: Optional[int]											=	SqlDatasetReader('''dataset'''     ,   '''sqlite:///''' + sqlite_path     ,   cache_dir=_lowerCAmelCase						).read()
							with pytest.raises(_lowerCAmelCase						):
														SqlDatasetWriter(_lowerCAmelCase     ,   '''dataset'''     ,   '''sqlite:///''' + output_sqlite_path     ,   num_proc=0						).write()





