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      __lowercase					:

						def __init__(	self				:      Optional[Any]							,							__lowerCamelCase				:      Optional[int]							,							)							->		Union[str, Any]:


									'''simple docstring'''

									lowercase							=		parent
									lowercase							=		13
									lowercase							=		7
									lowercase							=		30
									lowercase							=		self.seq_length + self.mem_len
									lowercase							=		15
									lowercase							=		True
									lowercase							=		True
									lowercase							=		99
									lowercase							=		[10, 50, 80]
									lowercase							=		32
									lowercase							=		32
									lowercase							=		4
									lowercase							=		8
									lowercase							=		1_28
									lowercase							=		2
									lowercase							=		2
									lowercase							=		None
									lowercase							=		1
									lowercase							=		0
									lowercase							=		3
									lowercase							=		self.vocab_size - 1
									lowercase							=		0.01

						def        __a		(	self				:      Tuple   )							->		List[Any]:


									'''simple docstring'''

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

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

									lowercase							=		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[int]:


									'''simple docstring'''

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

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Tuple   )							->		int:


									'''simple docstring'''

									lowercase							=		TFTransfoXLModel(__lowerCamelCase   )

									lowercase		,lowercase							=		model(__lowerCamelCase   ).to_tuple()

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

									lowercase		,lowercase							=		model(__lowerCamelCase   ).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[int]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Any   )							->		Dict:


									'''simple docstring'''

									lowercase							=		TFTransfoXLLMHeadModel(__lowerCamelCase   )

									lowercase		,lowercase							=		model(__lowerCamelCase   ).to_tuple()

									lowercase							=		{'''input_ids''': input_ids_a, '''labels''': lm_labels}
									lowercase		,lowercase							=		model(__lowerCamelCase   ).to_tuple()

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

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

									lowercase		,lowercase							=		model(__lowerCamelCase   ).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				:      List[Any]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Tuple   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		TFTransfoXLForSequenceClassification(__lowerCamelCase   )
									lowercase							=		model(__lowerCamelCase   )
									self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)   )



						def        __a		(	self				:      Optional[Any]   )							->		Dict:


									'''simple docstring'''

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






@require_tf
class      __lowercase					(    _A       ,     _A       ,     unittest.TestCase    ):
						lowercase					 =     (
						    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
						)
						lowercase					 =     () if is_tf_available() else ()
						lowercase					 =     (
						    {
						        '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
						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False

						def        __a		(	self				:      int							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      int   )							->		int:


									'''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        __a		(	self				:      str   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		TFTransfoXLModelTester(self   )
									lowercase							=		ConfigTester(self							,							config_class=__lowerCamelCase							,							d_embed=37   )

						def        __a		(	self				:      Any   )							->		List[str]:


									'''simple docstring'''

									self.config_tester.run_common_tests()

						def        __a		(	self				:      List[Any]   )							->		Dict:


									'''simple docstring'''

									self.model_tester.set_seed()
									lowercase							=		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_model(*__lowerCamelCase   )

						def        __a		(	self				:      Dict   )							->		str:


									'''simple docstring'''

									self.model_tester.set_seed()
									lowercase							=		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_lm_head(*__lowerCamelCase   )

						def        __a		(	self				:      List[Any]   )							->		Any:


									'''simple docstring'''

									lowercase							=		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__lowerCamelCase   )

						def        __a		(	self				:      Tuple   )							->		Any:


									'''simple docstring'''

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

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

						def        __a		(	self				:      List[str]   )							->		str:


									'''simple docstring'''

									pass

						@slow
						def        __a		(	self				:      Union[str, Any]   )							->		Tuple:


									'''simple docstring'''

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



						@unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''   )
						def        __a		(	self				:      Optional[int]   )							->		Union[str, Any]:


									'''simple docstring'''

									pass






@require_tf
class      __lowercase					(    unittest.TestCase    ):

						@unittest.skip('''Skip test until #12651 is resolved.'''   )
						@slow
						def        __a		(	self				:      int   )							->		List[str]:


									'''simple docstring'''

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

									lowercase							=		model.generate(__lowerCamelCase							,							max_length=2_00							,							do_sample=__lowerCamelCase   )
									self.assertListEqual(output_ids[0].numpy().tolist()							,							__lowerCamelCase   )






# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


A_      =      re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")






@total_ordering
@dataclass
class      __lowercase					:
						lowercase					 =     42
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None

						def        __a		(	self				:      int   )							->		str:


									'''simple docstring'''

									lowercase		,lowercase		,lowercase							=		_str_to_version_tuple(self.version_str   )

						def __repr__(	self				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'

						@property
						def        __a		(	self				:      Dict   )							->		Union[str, Any]:


									'''simple docstring'''

									return self.major, self.minor, self.patch

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Any   )							->		Dict:


									'''simple docstring'''

									if isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												return Version(__lowerCamelCase   )
									elif isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												return other
									raise TypeError(f'{other} (type {type(__lowerCamelCase   )}) cannot be compared to version.'   )

						def __eq__(	self				:      Tuple							,							__lowerCamelCase				:      Tuple   )							->		List[Any]:


									'''simple docstring'''

									try:
												lowercase							=		self._validate_operand(__lowerCamelCase   )
									except (TypeError, ValueError):
												return False
									else:
												return self.tuple == other.tuple

						def __lt__(	self				:      Any							,							__lowerCamelCase				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									lowercase							=		self._validate_operand(__lowerCamelCase   )
									return self.tuple < other.tuple

						def __hash__(	self				:      int   )							->		str:


									'''simple docstring'''

									return hash(_version_tuple_to_str(self.tuple   )   )

						@classmethod
						def        __a		(	cls				:      List[str]							,							__lowerCamelCase				:      Any   )							->		int:


									'''simple docstring'''

									lowercase							=		{f.name for f in dataclasses.fields(cls   )}
									return cls(**{k: v for k, v in dic.items() if k in field_names}   )



						def        __a		(	self				:      Union[str, Any]   )							->		str:


									'''simple docstring'''

									return self.version_str






def        __UpperCAmelCase    (       UpperCAmelCase       )->       List[str]:



			"""simple docstring"""







			lowercase							=		_VERSION_REG.match(UpperCAmelCase       )
			if not res:
						raise ValueError(f'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'       )
			return tuple(int(UpperCAmelCase       ) for v in [res.group('''major'''       ), res.group('''minor'''       ), res.group('''patch'''       )]       )






def        __UpperCAmelCase    (       UpperCAmelCase       )->       int:



			"""simple docstring"""







			return ".".join(str(UpperCAmelCase       ) for v in version_tuple       )





