





"""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        _lowerCamelCase      :


						def __init__(self   ,							__a   ,							) ->   Tuple:
								UpperCamelCase   =	parent
								UpperCamelCase   =	13
								UpperCamelCase   =	7
								UpperCamelCase   =	30
								UpperCamelCase   =	self.seq_length + self.mem_len
								UpperCamelCase   =	15
								UpperCamelCase   =	True
								UpperCamelCase   =	True
								UpperCamelCase   =	99
								UpperCamelCase   =	[10, 50, 80]
								UpperCamelCase   =	32
								UpperCamelCase   =	32
								UpperCamelCase   =	4
								UpperCamelCase   =	8
								UpperCamelCase   =	1_28
								UpperCamelCase   =	2
								UpperCamelCase   =	2
								UpperCamelCase   =	None
								UpperCamelCase   =	1
								UpperCamelCase   =	0
								UpperCamelCase   =	3
								UpperCamelCase   =	self.vocab_size - 1
								UpperCamelCase   =	0.01


						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)
								UpperCamelCase   =	ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)

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

								UpperCamelCase   =	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		) ->   List[str]:
								random.seed(self.seed		)
								tf.random.set_seed(self.seed		)


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a		) ->   List[Any]:
								UpperCamelCase   =	TFTransfoXLModel(__a		)

								UpperCamelCase   ,				UpperCamelCase   =	model(__a		).to_tuple()

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

								UpperCamelCase   ,				UpperCamelCase   =	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     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a		) ->   Optional[int]:
								UpperCamelCase   =	TFTransfoXLLMHeadModel(__a		)

								UpperCamelCase   ,				UpperCamelCase   =	model(__a		).to_tuple()

								UpperCamelCase   =	{"input_ids": input_ids_a, "labels": lm_labels}
								UpperCamelCase   ,				UpperCamelCase   =	model(__a		).to_tuple()

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

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

								UpperCamelCase   ,				UpperCamelCase   =	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     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a		) ->   Dict:
								UpperCamelCase   =	TFTransfoXLForSequenceClassification(__a		)
								UpperCamelCase   =	model(__a		)
								self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)		)




						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	self.prepare_config_and_inputs()
								((UpperCamelCase)   ,				(UpperCamelCase)   ,				(UpperCamelCase)   ,				(UpperCamelCase))   =	config_and_inputs
								UpperCamelCase   =	{"input_ids": input_ids_a}
								return config, inputs_dict





@require_tf
class        _lowerCamelCase      (       _lowercase  ,			_lowercase  ,			unittest.TestCase ):
						UpperCAmelCase_ =			(
						    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
						)
						UpperCAmelCase_ =			() if is_tf_available() else ()
						UpperCAmelCase_ =			(
						    {
						        "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
						UpperCAmelCase_ =			False
						UpperCAmelCase_ =			False
						UpperCAmelCase_ =			False
						UpperCAmelCase_ =			False


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a   ,							__a   ,							__a		) ->   List[str]:
								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		) ->   int:
								UpperCamelCase   =	TFTransfoXLModelTester(self		)
								UpperCamelCase   =	ConfigTester(self   ,							config_class=__a   ,							d_embed=37		)


						def     snake_case_   (self		) ->   List[Any]:
								self.config_tester.run_common_tests()


						def     snake_case_   (self		) ->   Dict:
								self.model_tester.set_seed()
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_model(*__a		)


						def     snake_case_   (self		) ->   List[Any]:
								self.model_tester.set_seed()
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_lm_head(*__a		)


						def     snake_case_   (self		) ->   List[Any]:
								UpperCamelCase   =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__a		)


						def     snake_case_   (self		) ->   Union[str, Any]:
								UpperCamelCase   ,				UpperCamelCase   =	self.model_tester.prepare_config_and_inputs_for_common()
								UpperCamelCase   =	[TFTransfoXLForSequenceClassification]

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


						def     snake_case_   (self		) ->   int:
								# TODO JP: Make TransfoXL XLA compliant
								pass


						@slow
						def     snake_case_   (self		) ->   Tuple:
								for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										UpperCamelCase   =	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     snake_case_   (self		) ->   List[Any]:
								pass





@require_tf
class        _lowerCamelCase      (       unittest.TestCase ):


						@unittest.skip("Skip test until #12651 is resolved."		)
						@slow
						def     snake_case_   (self		) ->   List[str]:
								UpperCamelCase   =	TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"		)
								# fmt: off
								UpperCamelCase   =	tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]]   ,							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   =	[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # 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   =	model.generate(__a   ,							max_length=2_00   ,							do_sample=__a		)
								self.assertListEqual(output_ids[0].numpy().tolist()   ,							__a		)













"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCAmelCase__     			=			{'''tokenization_bertweet''': ['''BertweetTokenizer''']}


if TYPE_CHECKING:
						from .tokenization_bertweet import BertweetTokenizer

else:
						import sys

						lowerCAmelCase__     			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






