"""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      :



				def __init__(self    :				Union[str, Any]     ,  _A    :				List[str]     ,  )   ->     Union[str, Any]:
					__snake_case      :							List[Any]						=			parent
					__snake_case      :							Any						=			13
					__snake_case      :							Any						=			7
					__snake_case      :							List[Any]						=			30
					__snake_case      :							Union[str, Any]						=			self.seq_length + self.mem_len
					__snake_case      :							Optional[Any]						=			15
					__snake_case      :							int						=			True
					__snake_case      :							Any						=			True
					__snake_case      :							List[Any]						=			99
					__snake_case      :							Optional[int]						=			[10, 50, 80]
					__snake_case      :							List[Any]						=			32
					__snake_case      :							List[str]						=			32
					__snake_case      :							Optional[int]						=			4
					__snake_case      :							Any						=			8
					__snake_case      :							Optional[int]						=			1_28
					__snake_case      :							List[str]						=			2
					__snake_case      :							List[Any]						=			2
					__snake_case      :							int						=			None
					__snake_case      :							Optional[int]						=			1
					__snake_case      :							str						=			0
					__snake_case      :							Optional[int]						=			3
					__snake_case      :							Any						=			self.vocab_size - 1
					__snake_case      :							Tuple						=			0.01



				def  _lowercase					(self    :				List[str])   ->     Dict:
					__snake_case      :							int						=			ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size)
					__snake_case      :							List[Any]						=			ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size)

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

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



				def  _lowercase					(self    :				List[Any]     ,  _A    :				Dict     ,  _A    :				List[str]     ,  _A    :				List[str]     ,  _A    :				str)   ->     str:
					__snake_case      :							Optional[int]						=			TFTransfoXLModel(_A)

					__snake_case      :							Optional[Any]						=			model(_A).to_tuple()

					__snake_case      :							str						=			{'input_ids': input_ids_a, 'mems': mems_a}

					__snake_case      :							Union[str, Any]						=			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    :				Optional[Any]     ,  _A    :				Dict     ,  _A    :				List[Any]     ,  _A    :				Union[str, Any]     ,  _A    :				Tuple)   ->     int:
					__snake_case      :							Union[str, Any]						=			TFTransfoXLLMHeadModel(_A)

					__snake_case      :							Tuple						=			model(_A).to_tuple()

					__snake_case      :							Tuple						=			{'input_ids': input_ids_a, 'labels': lm_labels}
					__snake_case      :							Optional[Any]						=			model(_A).to_tuple()

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

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

					__snake_case      :							Optional[int]						=			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    :				Tuple     ,  _A    :				Union[str, Any]     ,  _A    :				Dict     ,  _A    :				Optional[Any]     ,  _A    :				Optional[int])   ->     Union[str, Any]:
					__snake_case      :							Optional[int]						=			TFTransfoXLForSequenceClassification(_A)
					__snake_case      :							Tuple						=			model(_A)
					self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels))






				def  _lowercase					(self    :				int)   ->     Optional[Any]:
					__snake_case      :							List[str]						=			self.prepare_config_and_inputs()
					(__snake_case)      :							int						=			config_and_inputs
					__snake_case      :							Dict						=			{'input_ids': input_ids_a}
					return config, inputs_dict







@require_tf
class 		UpperCamelCase      (   lowercase				,     lowercase				,     unittest.TestCase						):
				UpperCAmelCase    :				Dict   =		(
				    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
				)
				UpperCAmelCase    :				Union[str, Any]   =		() if is_tf_available() else ()
				UpperCAmelCase    :				Tuple   =		(
				    {
				        """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    :				Optional[int]   =		False
				UpperCAmelCase    :				Optional[Any]   =		False
				UpperCAmelCase    :				Tuple   =		False
				UpperCAmelCase    :				Optional[int]   =		False



				def  _lowercase					(self    :				str     ,  _A    :				Union[str, Any]     ,  _A    :				List[str]     ,  _A    :				Optional[int]     ,  _A    :				str     ,  _A    :				Union[str, Any])   ->     Tuple:
					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    :				Optional[int])   ->     int:
					__snake_case      :							Optional[int]						=			TFTransfoXLModelTester(self)
					__snake_case      :							Union[str, Any]						=			ConfigTester(self     ,  config_class=_A     ,  d_embed=37)



				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[Any]:
					self.config_tester.run_common_tests()



				def  _lowercase					(self    :				str)   ->     Dict:
					self.model_tester.set_seed()
					__snake_case      :							int						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_transfo_xl_model(*_A)



				def  _lowercase					(self    :				Any)   ->     Union[str, Any]:
					self.model_tester.set_seed()
					__snake_case      :							int						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_transfo_xl_lm_head(*_A)



				def  _lowercase					(self    :				Any)   ->     Optional[int]:
					__snake_case      :							List[str]						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_A)



				def  _lowercase					(self    :				List[str])   ->     List[Any]:
					__snake_case      :							Any						=			self.model_tester.prepare_config_and_inputs_for_common()
					__snake_case      :							int						=			[TFTransfoXLForSequenceClassification]

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



				def  _lowercase					(self    :				Any)   ->     Dict:
					# TODO JP: Make TransfoXL XLA compliant
					pass



				@slow
				def  _lowercase					(self    :				int)   ->     Optional[int]:
					for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case      :							Any						=			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    :				List[Any])   ->     List[Any]:
					pass







@require_tf
class 		UpperCamelCase      (   unittest.TestCase						):



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

					__snake_case      :							Dict						=			model.generate(_A     ,  max_length=2_00     ,  do_sample=_A)
					self.assertListEqual(output_ids[0].numpy().tolist()     ,  _A)






"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_a					:					Tuple=			{
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				str   =		"""albert"""



				def __init__(self    :				Union[str, Any]     ,  _A    :				int=3_00_00     ,  _A    :				Any=1_28     ,  _A    :				Tuple=40_96     ,  _A    :				int=12     ,  _A    :				Tuple=1     ,  _A    :				int=64     ,  _A    :				Optional[Any]=1_63_84     ,  _A    :				Optional[Any]=1     ,  _A    :				List[str]="gelu_new"     ,  _A    :				Any=0     ,  _A    :				Optional[Any]=0     ,  _A    :				List[Any]=5_12     ,  _A    :				List[Any]=2     ,  _A    :				Dict=0.02     ,  _A    :				Union[str, Any]=1E-12     ,  _A    :				Tuple=0.1     ,  _A    :				int="absolute"     ,  _A    :				List[Any]=0     ,  _A    :				str=2     ,  _A    :				int=3     ,  **_A    :				Tuple     ,  )   ->     Union[str, Any]:
					super().__init__(pad_token_id=_A     ,  bos_token_id=_A     ,  eos_token_id=_A     ,  **_A)

					__snake_case      :							str						=			vocab_size
					__snake_case      :							List[Any]						=			embedding_size
					__snake_case      :							Tuple						=			hidden_size
					__snake_case      :							Optional[Any]						=			num_hidden_layers
					__snake_case      :							List[str]						=			num_hidden_groups
					__snake_case      :							Dict						=			num_attention_heads
					__snake_case      :							Any						=			inner_group_num
					__snake_case      :							Union[str, Any]						=			hidden_act
					__snake_case      :							List[Any]						=			intermediate_size
					__snake_case      :							List[str]						=			hidden_dropout_prob
					__snake_case      :							int						=			attention_probs_dropout_prob
					__snake_case      :							Any						=			max_position_embeddings
					__snake_case      :							Optional[int]						=			type_vocab_size
					__snake_case      :							Optional[Any]						=			initializer_range
					__snake_case      :							List[Any]						=			layer_norm_eps
					__snake_case      :							Optional[Any]						=			classifier_dropout_prob
					__snake_case      :							List[str]						=			position_embedding_type







class 		UpperCamelCase      (   lowercase						):



				@property
				def  _lowercase					(self    :				Tuple)   ->     Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
						__snake_case      :							int						=			{0: 'batch', 1: 'choice', 2: 'sequence'}
					else:
						__snake_case      :							Optional[Any]						=			{0: 'batch', 1: 'sequence'}
					return OrderedDict(
					    [
					        ('input_ids', dynamic_axis),
					        ('attention_mask', dynamic_axis),
					        ('token_type_ids', dynamic_axis),
					    ])





