






"""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:     List[Any]				,		__A:     int				,		):

												'''simple docstring'''

												a__          =  parent
												a__          =  13
												a__          =  7
												a__          =  30
												a__          =  self.seq_length + self.mem_len
												a__          =  15
												a__          =  True
												a__          =  True
												a__          =  99
												a__          =  [10, 50, 80]
												a__          =  32
												a__          =  32
												a__          =  4
												a__          =  8
												a__          =  128
												a__          =  2
												a__          =  2
												a__          =  None
												a__          =  1
												a__          =  0
												a__          =  3
												a__          =  self.vocab_size - 1
												a__          =  0.0_1




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

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

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

												a__          =  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  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     List[str]				,		__A:     Dict				,		__A:     Dict				,		__A:     str				,		__A:     List[str]    ):

												'''simple docstring'''

												a__          =  TFTransfoXLModel(__A    )

												a__       ,a__          =  model(__A    ).to_tuple()

												a__          =  {'''input_ids''': input_ids_a, '''mems''': mems_a}

												a__       ,a__          =  model(__A    ).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  lowercase		(   self:     int				,		__A:     Union[str, Any]				,		__A:     Optional[Any]				,		__A:     Any				,		__A:     List[str]    ):

												'''simple docstring'''

												a__          =  TFTransfoXLLMHeadModel(__A    )

												a__       ,a__          =  model(__A    ).to_tuple()

												a__          =  {'''input_ids''': input_ids_a, '''labels''': lm_labels}
												a__       ,a__          =  model(__A    ).to_tuple()

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

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

												a__       ,a__          =  model(__A    ).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  lowercase		(   self:     str				,		__A:     Tuple				,		__A:     List[str]				,		__A:     Union[str, Any]				,		__A:     List[Any]    ):

												'''simple docstring'''

												a__          =  TFTransfoXLForSequenceClassification(__A    )
												a__          =  model(__A    )
												self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels)    )





							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

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







@require_tf
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =(
							    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
							)
							_SCREAMING_SNAKE_CASE    =() if is_tf_available() else ()
							_SCREAMING_SNAKE_CASE    =(
							    {
							        '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
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False




							def  lowercase		(   self:     Union[str, Any]				,		__A:     Tuple				,		__A:     Optional[int]				,		__A:     Tuple				,		__A:     Optional[int]				,		__A:     Union[str, Any]    ):

												'''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  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  TFTransfoXLModelTester(self    )
												a__          =  ConfigTester(self				,		config_class=__A				,		d_embed=37    )




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												self.config_tester.run_common_tests()




							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												self.model_tester.set_seed()
												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_model(*__A    )




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												self.model_tester.set_seed()
												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_lm_head(*__A    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__A    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

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

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




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												pass




							@slow
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

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





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

												'''simple docstring'''

												pass







@require_tf
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@unittest.skip('''Skip test until #12651 is resolved.'''    )
							@slow
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''    )
												# fmt: off
												a__          =  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
												a__          =  [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>

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










"""simple docstring"""







import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
	    FlaxRobertaPreLayerNormForCausalLM,
	    FlaxRobertaPreLayerNormForMaskedLM,
	    FlaxRobertaPreLayerNormForMultipleChoice,
	    FlaxRobertaPreLayerNormForQuestionAnswering,
	    FlaxRobertaPreLayerNormForSequenceClassification,
	    FlaxRobertaPreLayerNormForTokenClassification,
	    FlaxRobertaPreLayerNormModel,
	)







class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def __init__(   self:     List[str]				,		__A:     Optional[Any]				,		__A:     List[Any]=13				,		__A:     Optional[int]=7				,		__A:     Any=True				,		__A:     str=True				,		__A:     Any=True				,		__A:     str=True				,		__A:     Optional[Any]=99				,		__A:     Union[str, Any]=32				,		__A:     str=5				,		__A:     Any=4				,		__A:     List[str]=37				,		__A:     Union[str, Any]="gelu"				,		__A:     str=0.1				,		__A:     Tuple=0.1				,		__A:     Optional[Any]=512				,		__A:     Union[str, Any]=16				,		__A:     str=2				,		__A:     Any=0.0_2				,		__A:     Tuple=4				,		):

												'''simple docstring'''

												a__          =  parent
												a__          =  batch_size
												a__          =  seq_length
												a__          =  is_training
												a__          =  use_attention_mask
												a__          =  use_token_type_ids
												a__          =  use_labels
												a__          =  vocab_size
												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  intermediate_size
												a__          =  hidden_act
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  max_position_embeddings
												a__          =  type_vocab_size
												a__          =  type_sequence_label_size
												a__          =  initializer_range
												a__          =  num_choices




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

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

												a__          =  None
												if self.use_attention_mask:
																	a__          =  random_attention_mask([self.batch_size, self.seq_length]    )

												a__          =  None
												if self.use_token_type_ids:
																	a__          =  ids_tensor([self.batch_size, self.seq_length]				,		self.type_vocab_size    )

												a__          =  RobertaPreLayerNormConfig(
												    vocab_size=self.vocab_size				,		hidden_size=self.hidden_size				,		num_hidden_layers=self.num_hidden_layers				,		num_attention_heads=self.num_attention_heads				,		intermediate_size=self.intermediate_size				,		hidden_act=self.hidden_act				,		hidden_dropout_prob=self.hidden_dropout_prob				,		attention_probs_dropout_prob=self.attention_probs_dropout_prob				,		max_position_embeddings=self.max_position_embeddings				,		type_vocab_size=self.type_vocab_size				,		is_decoder=__A				,		initializer_range=self.initializer_range				,		)

												return config, input_ids, token_type_ids, attention_mask




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												a__       ,a__       ,a__       ,a__          =  config_and_inputs
												a__          =  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
												return config, inputs_dict





							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												a__       ,a__       ,a__       ,a__          =  config_and_inputs

												a__          =  True
												a__          =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
												a__          =  ids_tensor([self.batch_size, self.seq_length]				,		vocab_size=2    )

												return (
												    config,
												    input_ids,
												    token_type_ids,
												    encoder_hidden_states,
												    encoder_attention_mask,
												)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =True

							_SCREAMING_SNAKE_CASE    =(
							    (
							        FlaxRobertaPreLayerNormModel,
							        FlaxRobertaPreLayerNormForCausalLM,
							        FlaxRobertaPreLayerNormForMaskedLM,
							        FlaxRobertaPreLayerNormForSequenceClassification,
							        FlaxRobertaPreLayerNormForTokenClassification,
							        FlaxRobertaPreLayerNormForMultipleChoice,
							        FlaxRobertaPreLayerNormForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormModelTester(self    )





							@slow
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												for model_class_name in self.all_model_classes:
																	a__          =  model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )
																	a__          =  model(np.ones((1, 1)    )    )
																	self.assertIsNotNone(__A    )







@require_flax
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )

												a__          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,		dtype=jnp.intaa    )
												a__          =  model(__A    )[0]
												a__          =  [1, 11, 50265]
												self.assertEqual(list(output.shape    )				,		__A    )
												# compare the actual values for a slice.
												a__          =  np.array(
												    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]				,		dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )





							@slow
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )

												a__          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,		dtype=jnp.intaa    )
												a__          =  model(__A    )[0]
												# compare the actual values for a slice.
												a__          =  np.array(
												    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]				,		dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )


